From dce54845c27d8981e74c0537dbf1018d62af6ffc Mon Sep 17 00:00:00 2001
From: Brent Neale <BrentNeale1@users.noreply.github.com>
Date: Wed, 18 Feb 2026 06:04:27 +1000
Subject: [PATCH] Phase 1: Event-driven backtester, 5 strategies, and baseline
 results

- Built event-driven backtesting engine with spread/slippage modeling,
  3-TP partial closes, trailing stops, and rich trade logging (20+ features)
- Implemented 5 strategy signal generators (MA Breakout, VWAP Reversal,
  Key Level Breakout, EMA Ribbon Scalp, Momentum Exhaustion)
- Full indicator library (EMA, SMA, RSI, ATR, MACD, ADX, Stochastic,
  Session VWAP bands, swing points, key levels, RSI divergence)
- Data pipeline: Dukascopy download, validation, 70/30 train/test split
- Baseline results: all 5 strategies generate 200+ trades on training data
  (Jan 2021 - Aug 2023), best profit factors 0.82-0.96 on select pairs
- Trade logs and reports saved for Phase 3 ML feature engineering

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---
 .gitignore                                    |    5 +
 config/system.yaml                            |   20 +-
 data/test_set/README.md                       |   10 +
 docs/ROADMAP.docx                             |  Bin 0 -> 32148 bytes
 docs/smart_money_research.md                  |   67 +
 docs/strategies/fx_quant_strategies_v2.docx   |  Bin 0 -> 30859 bytes
 ..._S1_S1_MA_Breakout_Retest_EUR_AUD_M15.json |  206 ++
 ..._S1_S1_MA_Breakout_Retest_EUR_CAD_M15.json |  206 ++
 ..._S1_S1_MA_Breakout_Retest_EUR_NZD_M15.json |  206 ++
 ..._S1_S1_MA_Breakout_Retest_GBP_AUD_M15.json |  206 ++
 ..._S2_Session_VWAP_Reversal_EUR_USD_M15.json |  206 ++
 ..._S2_Session_VWAP_Reversal_GBP_JPY_M15.json |  206 ++
 ..._S2_Session_VWAP_Reversal_GBP_USD_M15.json |  206 ++
 ..._S2_Session_VWAP_Reversal_USD_JPY_M15.json |  206 ++
 ...y_S3_S3_Key_Level_Breakout_EUR_GBP_H1.json |   45 +
 ...y_S3_S3_Key_Level_Breakout_GBP_JPY_H1.json |  227 ++
 ...y_S3_S3_Key_Level_Breakout_GBP_USD_H1.json |   45 +
 ...y_S3_S3_Key_Level_Breakout_USD_JPY_H1.json |   45 +
 ...ry_S4_S4_EMA_Ribbon_Scalp_EUR_AUD_M15.json |   38 +
 ...ry_S4_S4_EMA_Ribbon_Scalp_EUR_GBP_M15.json |   38 +
 ...ry_S4_S4_EMA_Ribbon_Scalp_GBP_AUD_M15.json |   38 +
 ...S5_S5_Momentum_Exhaustion_EUR_AUD_M15.json |   38 +
 ...S5_S5_Momentum_Exhaustion_EUR_CAD_M15.json |   38 +
 ...S5_S5_Momentum_Exhaustion_EUR_GBP_M15.json |   38 +
 ...S5_S5_Momentum_Exhaustion_GBP_AUD_M15.json |   38 +
 ...S5_S5_Momentum_Exhaustion_GBP_CAD_M15.json |   38 +
 logs/backtest_trades_S2_EUR_USD_M15.csv       | 3064 ++++++++++++++++
 logs/backtest_trades_S2_GBP_JPY_M15.csv       | 2982 ++++++++++++++++
 logs/backtest_trades_S2_GBP_USD_M15.csv       | 3033 ++++++++++++++++
 logs/backtest_trades_S2_USD_JPY_M15.csv       | 3172 +++++++++++++++++
 logs/backtest_trades_S3_EUR_GBP_H1.csv        |   23 +
 logs/backtest_trades_S3_GBP_JPY_H1.csv        |   38 +
 logs/backtest_trades_S3_GBP_USD_H1.csv        |   23 +
 logs/backtest_trades_S3_USD_JPY_H1.csv        |   19 +
 logs/backtest_trades_S5_EUR_AUD_M15.csv       |   60 +
 logs/backtest_trades_S5_EUR_CAD_M15.csv       |   26 +
 logs/backtest_trades_S5_EUR_GBP_M15.csv       |   17 +
 logs/backtest_trades_S5_GBP_AUD_M15.csv       |   48 +
 logs/backtest_trades_S5_GBP_CAD_M15.csv       |   27 +
 results/phase1/S1_EUR_AUD_report.json         |   43 +
 results/phase1/S1_EUR_AUD_trades.csv          |   96 +
 results/phase1/S1_EUR_CAD_report.json         |   43 +
 results/phase1/S1_EUR_CAD_trades.csv          |   84 +
 results/phase1/S1_EUR_NZD_report.json         |   43 +
 results/phase1/S1_EUR_NZD_trades.csv          |  114 +
 results/phase1/S1_GBP_AUD_report.json         |   43 +
 results/phase1/S1_GBP_AUD_trades.csv          |  123 +
 results/phase1/S2_EUR_USD_report.json         |   46 +
 results/phase1/S2_EUR_USD_trades.csv          |  385 ++
 results/phase1/S2_GBP_JPY_report.json         |   45 +
 results/phase1/S2_GBP_JPY_trades.csv          |  349 ++
 results/phase1/S2_GBP_USD_report.json         |   46 +
 results/phase1/S2_GBP_USD_trades.csv          |  371 ++
 results/phase1/S2_USD_JPY_report.json         |   45 +
 results/phase1/S2_USD_JPY_trades.csv          |  391 ++
 results/phase1/S3_EUR_GBP_report.json         |   43 +
 results/phase1/S3_EUR_GBP_trades.csv          |  272 ++
 results/phase1/S3_GBP_JPY_report.json         |   43 +
 results/phase1/S3_GBP_JPY_trades.csv          |  241 ++
 results/phase1/S3_GBP_USD_report.json         |   43 +
 results/phase1/S3_GBP_USD_trades.csv          |  264 ++
 results/phase1/S3_USD_JPY_report.json         |   43 +
 results/phase1/S3_USD_JPY_trades.csv          |  221 ++
 results/phase1/S4_EUR_AUD_report.json         |   43 +
 results/phase1/S4_EUR_AUD_trades.csv          |   65 +
 results/phase1/S4_EUR_GBP_report.json         |   43 +
 results/phase1/S4_EUR_GBP_trades.csv          |   95 +
 results/phase1/S4_GBP_AUD_report.json         |   43 +
 results/phase1/S4_GBP_AUD_trades.csv          |   80 +
 results/phase1/S5_EUR_AUD_report.json         |   43 +
 results/phase1/S5_EUR_AUD_trades.csv          |  285 ++
 results/phase1/S5_EUR_CAD_report.json         |   43 +
 results/phase1/S5_EUR_CAD_trades.csv          |  263 ++
 results/phase1/S5_EUR_GBP_report.json         |   43 +
 results/phase1/S5_EUR_GBP_trades.csv          |  279 ++
 results/phase1/S5_GBP_AUD_report.json         |   43 +
 results/phase1/S5_GBP_AUD_trades.csv          |  265 ++
 results/phase1/S5_GBP_CAD_report.json         |   43 +
 results/phase1/S5_GBP_CAD_trades.csv          |  241 ++
 results/phase1/phase1_summary.json            |  872 +++++
 sql/alter_fx_candles_v2.sql                   |   22 +
 src/backtest_phase1.py                        |  207 ++
 src/backtest_strategies.py                    |  670 ++++
 src/backtester.py                             |    6 +-
 src/backtester/__init__.py                    |    1 +
 src/backtester/engine.py                      |  658 ++++
 src/dashboard.py                              |  100 +-
 src/data_engine.py                            |  125 +
 src/download_dukascopy.py                     |  140 +
 src/indicators/__init__.py                    |    1 +
 src/indicators/technical.py                   |  331 ++
 src/load_strategy_data.py                     |   91 +
 src/strategies.py                             |  891 +++++
 src/strategies_pkg/__init__.py                |   31 +
 src/strategies_pkg/base.py                    |   27 +
 src/strategies_pkg/s1_ma_breakout.py          |  177 +
 src/strategies_pkg/s2_vwap_reversal.py        |  140 +
 src/strategies_pkg/s3_key_level_breakout.py   |  156 +
 src/strategies_pkg/s4_ema_ribbon.py           |  173 +
 src/strategies_pkg/s5_momentum_exhaustion.py  |  193 +
 src/validate_and_split_data.py                |  214 ++
 templates/backtest.html                       |    2 +-
 templates/chart.html                          |  676 +++-
 103 files changed, 26083 insertions(+), 123 deletions(-)
 create mode 100644 data/test_set/README.md
 create mode 100644 docs/ROADMAP.docx
 create mode 100644 docs/smart_money_research.md
 create mode 100644 docs/strategies/fx_quant_strategies_v2.docx
 create mode 100644 logs/backtest_summary_S1_S1_MA_Breakout_Retest_EUR_AUD_M15.json
 create mode 100644 logs/backtest_summary_S1_S1_MA_Breakout_Retest_EUR_CAD_M15.json
 create mode 100644 logs/backtest_summary_S1_S1_MA_Breakout_Retest_EUR_NZD_M15.json
 create mode 100644 logs/backtest_summary_S1_S1_MA_Breakout_Retest_GBP_AUD_M15.json
 create mode 100644 logs/backtest_summary_S2_S2_Session_VWAP_Reversal_EUR_USD_M15.json
 create mode 100644 logs/backtest_summary_S2_S2_Session_VWAP_Reversal_GBP_JPY_M15.json
 create mode 100644 logs/backtest_summary_S2_S2_Session_VWAP_Reversal_GBP_USD_M15.json
 create mode 100644 logs/backtest_summary_S2_S2_Session_VWAP_Reversal_USD_JPY_M15.json
 create mode 100644 logs/backtest_summary_S3_S3_Key_Level_Breakout_EUR_GBP_H1.json
 create mode 100644 logs/backtest_summary_S3_S3_Key_Level_Breakout_GBP_JPY_H1.json
 create mode 100644 logs/backtest_summary_S3_S3_Key_Level_Breakout_GBP_USD_H1.json
 create mode 100644 logs/backtest_summary_S3_S3_Key_Level_Breakout_USD_JPY_H1.json
 create mode 100644 logs/backtest_summary_S4_S4_EMA_Ribbon_Scalp_EUR_AUD_M15.json
 create mode 100644 logs/backtest_summary_S4_S4_EMA_Ribbon_Scalp_EUR_GBP_M15.json
 create mode 100644 logs/backtest_summary_S4_S4_EMA_Ribbon_Scalp_GBP_AUD_M15.json
 create mode 100644 logs/backtest_summary_S5_S5_Momentum_Exhaustion_EUR_AUD_M15.json
 create mode 100644 logs/backtest_summary_S5_S5_Momentum_Exhaustion_EUR_CAD_M15.json
 create mode 100644 logs/backtest_summary_S5_S5_Momentum_Exhaustion_EUR_GBP_M15.json
 create mode 100644 logs/backtest_summary_S5_S5_Momentum_Exhaustion_GBP_AUD_M15.json
 create mode 100644 logs/backtest_summary_S5_S5_Momentum_Exhaustion_GBP_CAD_M15.json
 create mode 100644 logs/backtest_trades_S2_EUR_USD_M15.csv
 create mode 100644 logs/backtest_trades_S2_GBP_JPY_M15.csv
 create mode 100644 logs/backtest_trades_S2_GBP_USD_M15.csv
 create mode 100644 logs/backtest_trades_S2_USD_JPY_M15.csv
 create mode 100644 logs/backtest_trades_S3_EUR_GBP_H1.csv
 create mode 100644 logs/backtest_trades_S3_GBP_JPY_H1.csv
 create mode 100644 logs/backtest_trades_S3_GBP_USD_H1.csv
 create mode 100644 logs/backtest_trades_S3_USD_JPY_H1.csv
 create mode 100644 logs/backtest_trades_S5_EUR_AUD_M15.csv
 create mode 100644 logs/backtest_trades_S5_EUR_CAD_M15.csv
 create mode 100644 logs/backtest_trades_S5_EUR_GBP_M15.csv
 create mode 100644 logs/backtest_trades_S5_GBP_AUD_M15.csv
 create mode 100644 logs/backtest_trades_S5_GBP_CAD_M15.csv
 create mode 100644 results/phase1/S1_EUR_AUD_report.json
 create mode 100644 results/phase1/S1_EUR_AUD_trades.csv
 create mode 100644 results/phase1/S1_EUR_CAD_report.json
 create mode 100644 results/phase1/S1_EUR_CAD_trades.csv
 create mode 100644 results/phase1/S1_EUR_NZD_report.json
 create mode 100644 results/phase1/S1_EUR_NZD_trades.csv
 create mode 100644 results/phase1/S1_GBP_AUD_report.json
 create mode 100644 results/phase1/S1_GBP_AUD_trades.csv
 create mode 100644 results/phase1/S2_EUR_USD_report.json
 create mode 100644 results/phase1/S2_EUR_USD_trades.csv
 create mode 100644 results/phase1/S2_GBP_JPY_report.json
 create mode 100644 results/phase1/S2_GBP_JPY_trades.csv
 create mode 100644 results/phase1/S2_GBP_USD_report.json
 create mode 100644 results/phase1/S2_GBP_USD_trades.csv
 create mode 100644 results/phase1/S2_USD_JPY_report.json
 create mode 100644 results/phase1/S2_USD_JPY_trades.csv
 create mode 100644 results/phase1/S3_EUR_GBP_report.json
 create mode 100644 results/phase1/S3_EUR_GBP_trades.csv
 create mode 100644 results/phase1/S3_GBP_JPY_report.json
 create mode 100644 results/phase1/S3_GBP_JPY_trades.csv
 create mode 100644 results/phase1/S3_GBP_USD_report.json
 create mode 100644 results/phase1/S3_GBP_USD_trades.csv
 create mode 100644 results/phase1/S3_USD_JPY_report.json
 create mode 100644 results/phase1/S3_USD_JPY_trades.csv
 create mode 100644 results/phase1/S4_EUR_AUD_report.json
 create mode 100644 results/phase1/S4_EUR_AUD_trades.csv
 create mode 100644 results/phase1/S4_EUR_GBP_report.json
 create mode 100644 results/phase1/S4_EUR_GBP_trades.csv
 create mode 100644 results/phase1/S4_GBP_AUD_report.json
 create mode 100644 results/phase1/S4_GBP_AUD_trades.csv
 create mode 100644 results/phase1/S5_EUR_AUD_report.json
 create mode 100644 results/phase1/S5_EUR_AUD_trades.csv
 create mode 100644 results/phase1/S5_EUR_CAD_report.json
 create mode 100644 results/phase1/S5_EUR_CAD_trades.csv
 create mode 100644 results/phase1/S5_EUR_GBP_report.json
 create mode 100644 results/phase1/S5_EUR_GBP_trades.csv
 create mode 100644 results/phase1/S5_GBP_AUD_report.json
 create mode 100644 results/phase1/S5_GBP_AUD_trades.csv
 create mode 100644 results/phase1/S5_GBP_CAD_report.json
 create mode 100644 results/phase1/S5_GBP_CAD_trades.csv
 create mode 100644 results/phase1/phase1_summary.json
 create mode 100644 sql/alter_fx_candles_v2.sql
 create mode 100644 src/backtest_phase1.py
 create mode 100644 src/backtest_strategies.py
 create mode 100644 src/backtester/__init__.py
 create mode 100644 src/backtester/engine.py
 create mode 100644 src/download_dukascopy.py
 create mode 100644 src/indicators/__init__.py
 create mode 100644 src/indicators/technical.py
 create mode 100644 src/load_strategy_data.py
 create mode 100644 src/strategies.py
 create mode 100644 src/strategies_pkg/__init__.py
 create mode 100644 src/strategies_pkg/base.py
 create mode 100644 src/strategies_pkg/s1_ma_breakout.py
 create mode 100644 src/strategies_pkg/s2_vwap_reversal.py
 create mode 100644 src/strategies_pkg/s3_key_level_breakout.py
 create mode 100644 src/strategies_pkg/s4_ema_ribbon.py
 create mode 100644 src/strategies_pkg/s5_momentum_exhaustion.py
 create mode 100644 src/validate_and_split_data.py

diff --git a/.gitignore b/.gitignore
index b7faf40..3e53a89 100644
--- a/.gitignore
+++ b/.gitignore
@@ -188,6 +188,11 @@ cython_debug/
 #  you could uncomment the following to ignore the entire vscode folder
 # .vscode/
 
+# Historical candle data (too large for git, re-download with src/download_dukascopy.py)
+data/raw/
+data/processed/
+data/test_set/*.csv
+
 # Ruff stuff:
 .ruff_cache/
 
diff --git a/config/system.yaml b/config/system.yaml
index ac7407a..5c4b191 100644
--- a/config/system.yaml
+++ b/config/system.yaml
@@ -6,12 +6,22 @@ brokers:
   type: oanda_v20
   enabled: true
   instruments:
+  - GBP_AUD
+  - EUR_AUD
+  - EUR_CAD
+  - EUR_NZD
+  - GBP_USD
   - EUR_USD
+  - GBP_JPY
+  - USD_JPY
+  - EUR_GBP
+  - GBP_CAD
 data:
   candle_count: 200
   candle_granularities:
-  - M5
   - M15
+  - H1
+  - H4
   tick_export: false
 features:
   sma_windows:
@@ -19,12 +29,20 @@ features:
   - 20
   - 50
   - 100
+  - 200
   ema_windows:
   - 20
+  - 50
+  - 100
+  - 200
   rsi_period: 14
   atr_period: 14
   vwap_window: 20
   volatility_window: 20
+  macd: [12, 26, 9]
+  adx_period: 14
+  stochastic: [5, 3, 3]
+  session_vwap_bands: true
 ai:
   model: local-ensemble
   confidence_threshold: 0.75
diff --git a/data/test_set/README.md b/data/test_set/README.md
new file mode 100644
index 0000000..0cbd4d9
--- /dev/null
+++ b/data/test_set/README.md
@@ -0,0 +1,10 @@
+# TEST SET - DO NOT TOUCH UNTIL PHASE 4
+
+This directory contains the 30% held-out test set (September 2023 - December 2024).
+
+**DO NOT access, read, analyze, or use this data during Phases 1-3.**
+
+This data is reserved exclusively for final model validation in Phase 4.
+Any premature access invalidates the out-of-sample evaluation.
+
+If you need to test something, use the training set in `data/processed/`.
diff --git a/docs/ROADMAP.docx b/docs/ROADMAP.docx
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diff --git a/docs/smart_money_research.md b/docs/smart_money_research.md
new file mode 100644
index 0000000..6e01817
--- /dev/null
+++ b/docs/smart_money_research.md
@@ -0,0 +1,67 @@
+# Smart Money Research Log
+
+## Purpose
+Document observations about institutional behaviour patterns discovered during
+backtesting. These insights inform Phase 2 refinements and future strategy development.
+
+## Observations
+
+### [Date] - Strategy 1, GBP/AUD Retest Volume Pattern
+**Finding:** When tick volume at retest is >80% of the initial break volume,
+win rate jumps from 58% to 71%.
+
+**Hypothesis:** High volume at retest = institutions actively defending the level.
+Low volume = retail-driven bounce, institutions absent.
+
+**Action:** Add volume ratio check to Strategy 1 in Phase 2.
+**Status:** To be tested
+
+---
+
+### [Date] - Strategy 2, VWAP Session Timing
+**Finding:** VWAP reversals during first 60min of London session have 68% win rate.
+Outside this window: 52% win rate.
+
+**Hypothesis:** Institutional re-entry at VWAP happens predominantly during early
+session price discovery.
+
+**Action:** Add session timing filter to Strategy 2.
+**Status:** To be tested
+
+---
+
+### [Date] - All Strategies, News Impact
+**Finding:** Win rate drops 15-20% across all strategies when trading within
+2 hours of major news events.
+
+**Hypothesis:** News-driven volatility creates unpredictable institutional flow
+that technical signals can't capture.
+
+**Action:** Expand news block window from 30min to 2 hours.
+**Status:** Implemented
+
+---
+
+## Pattern Library
+
+### Stop Hunt Signature
+- Sharp spike through obvious level (>1.5x ATR in single candle)
+- Immediate reversal within 2 candles
+- Volume spike on reversal candle
+-> Indicates liquidity sweep, not genuine breakout
+
+### Institutional Accumulation Signature
+- Price consolidates in tight range (< 0.5x ATR) for 30+ minutes
+- Volume gradually declining during consolidation
+- Sudden expansion with volume spike
+-> Indicates completed accumulation, strong directional move likely
+
+---
+
+## Failed Hypotheses
+Document things that DIDN'T work to avoid retesting them:
+
+### [Date] - Fibonacci Time Zones
+**Tested:** Using Fibonacci time projections to predict reversal timing.
+**Result:** No edge. Win rate unchanged.
+**Conclusion:** Time-based Fib levels are noise, not institutional behaviour.
diff --git a/docs/strategies/fx_quant_strategies_v2.docx b/docs/strategies/fx_quant_strategies_v2.docx
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diff --git a/logs/backtest_summary_S1_S1_MA_Breakout_Retest_EUR_AUD_M15.json b/logs/backtest_summary_S1_S1_MA_Breakout_Retest_EUR_AUD_M15.json
new file mode 100644
index 0000000..5cae29f
--- /dev/null
+++ b/logs/backtest_summary_S1_S1_MA_Breakout_Retest_EUR_AUD_M15.json
@@ -0,0 +1,206 @@
+{
+  "strategy_name": "S1_MA_Breakout_Retest",
+  "strategy_key": "S1",
+  "instrument": "EUR_AUD",
+  "granularity": "M15",
+  "run_time": "2026-02-17T12:51:41.417647+00:00",
+  "data_range": {
+    "start": "2024-01-29 12:00:00+00:00",
+    "end": "2026-02-17 12:00:00+00:00",
+    "bars": 51057
+  },
+  "metrics": {
+    "starting_equity": 100000.0,
+    "total_return_pct": 0.0,
+    "max_drawdown_pct": 0.0,
+    "num_trades": 0,
+    "round_trips": 0,
+    "win_rate_pct": 0.0,
+    "sharpe_ratio": 0.0,
+    "final_equity": 100000.0
+  },
+  "monthly_pnl": [
+    {
+      "month": "2024-01",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2024-02",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2024-03",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2024-04",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2024-05",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2024-06",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2024-07",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2024-08",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2024-09",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2024-10",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
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+}
\ No newline at end of file
diff --git a/logs/backtest_summary_S1_S1_MA_Breakout_Retest_EUR_CAD_M15.json b/logs/backtest_summary_S1_S1_MA_Breakout_Retest_EUR_CAD_M15.json
new file mode 100644
index 0000000..273a497
--- /dev/null
+++ b/logs/backtest_summary_S1_S1_MA_Breakout_Retest_EUR_CAD_M15.json
@@ -0,0 +1,206 @@
+{
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\ No newline at end of file
diff --git a/logs/backtest_summary_S1_S1_MA_Breakout_Retest_EUR_NZD_M15.json b/logs/backtest_summary_S1_S1_MA_Breakout_Retest_EUR_NZD_M15.json
new file mode 100644
index 0000000..b619f1d
--- /dev/null
+++ b/logs/backtest_summary_S1_S1_MA_Breakout_Retest_EUR_NZD_M15.json
@@ -0,0 +1,206 @@
+{
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+}
\ No newline at end of file
diff --git a/logs/backtest_summary_S1_S1_MA_Breakout_Retest_GBP_AUD_M15.json b/logs/backtest_summary_S1_S1_MA_Breakout_Retest_GBP_AUD_M15.json
new file mode 100644
index 0000000..50f164d
--- /dev/null
+++ b/logs/backtest_summary_S1_S1_MA_Breakout_Retest_GBP_AUD_M15.json
@@ -0,0 +1,206 @@
+{
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+  ]
+}
\ No newline at end of file
diff --git a/logs/backtest_summary_S2_S2_Session_VWAP_Reversal_EUR_USD_M15.json b/logs/backtest_summary_S2_S2_Session_VWAP_Reversal_EUR_USD_M15.json
new file mode 100644
index 0000000..70390fa
--- /dev/null
+++ b/logs/backtest_summary_S2_S2_Session_VWAP_Reversal_EUR_USD_M15.json
@@ -0,0 +1,206 @@
+{
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+}
\ No newline at end of file
diff --git a/logs/backtest_summary_S2_S2_Session_VWAP_Reversal_GBP_JPY_M15.json b/logs/backtest_summary_S2_S2_Session_VWAP_Reversal_GBP_JPY_M15.json
new file mode 100644
index 0000000..881193a
--- /dev/null
+++ b/logs/backtest_summary_S2_S2_Session_VWAP_Reversal_GBP_JPY_M15.json
@@ -0,0 +1,206 @@
+{
+  "strategy_name": "S2_Session_VWAP_Reversal",
+  "strategy_key": "S2",
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+    "end": "2026-02-17 12:30:00+00:00",
+    "bars": 51057
+  },
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+}
\ No newline at end of file
diff --git a/logs/backtest_summary_S2_S2_Session_VWAP_Reversal_GBP_USD_M15.json b/logs/backtest_summary_S2_S2_Session_VWAP_Reversal_GBP_USD_M15.json
new file mode 100644
index 0000000..bd35d1f
--- /dev/null
+++ b/logs/backtest_summary_S2_S2_Session_VWAP_Reversal_GBP_USD_M15.json
@@ -0,0 +1,206 @@
+{
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+    "end": "2026-02-17 12:30:00+00:00",
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+      "start_equity": 99998.4,
+      "end_equity": 99982.45,
+      "pnl": -15.94,
+      "pnl_pct": -0.0159
+    }
+  ]
+}
\ No newline at end of file
diff --git a/logs/backtest_summary_S2_S2_Session_VWAP_Reversal_USD_JPY_M15.json b/logs/backtest_summary_S2_S2_Session_VWAP_Reversal_USD_JPY_M15.json
new file mode 100644
index 0000000..77fc0ef
--- /dev/null
+++ b/logs/backtest_summary_S2_S2_Session_VWAP_Reversal_USD_JPY_M15.json
@@ -0,0 +1,206 @@
+{
+  "strategy_name": "S2_Session_VWAP_Reversal",
+  "strategy_key": "S2",
+  "instrument": "USD_JPY",
+  "granularity": "M15",
+  "run_time": "2026-02-17T12:57:30.047987+00:00",
+  "data_range": {
+    "start": "2024-01-29 12:30:00+00:00",
+    "end": "2026-02-17 12:30:00+00:00",
+    "bars": 51062
+  },
+  "metrics": {
+    "starting_equity": 100000.0,
+    "total_return_pct": -0.2017,
+    "max_drawdown_pct": 0.2342,
+    "num_trades": 3171,
+    "round_trips": 1006,
+    "win_rate_pct": 10.04,
+    "sharpe_ratio": -4.8517,
+    "final_equity": 99798.32
+  },
+  "monthly_pnl": [
+    {
+      "month": "2024-01",
+      "start_equity": 100000.0,
+      "end_equity": 99996.79,
+      "pnl": -3.21,
+      "pnl_pct": -0.0032
+    },
+    {
+      "month": "2024-02",
+      "start_equity": 99996.79,
+      "end_equity": 99981.79,
+      "pnl": -15.0,
+      "pnl_pct": -0.015
+    },
+    {
+      "month": "2024-03",
+      "start_equity": 99981.79,
+      "end_equity": 99947.07,
+      "pnl": -34.72,
+      "pnl_pct": -0.0347
+    },
+    {
+      "month": "2024-04",
+      "start_equity": 99947.07,
+      "end_equity": 99935.19,
+      "pnl": -11.89,
+      "pnl_pct": -0.0119
+    },
+    {
+      "month": "2024-05",
+      "start_equity": 99935.19,
+      "end_equity": 99912.0,
+      "pnl": -23.19,
+      "pnl_pct": -0.0232
+    },
+    {
+      "month": "2024-06",
+      "start_equity": 99912.0,
+      "end_equity": 99895.29,
+      "pnl": -16.71,
+      "pnl_pct": -0.0167
+    },
+    {
+      "month": "2024-07",
+      "start_equity": 99895.29,
+      "end_equity": 99899.01,
+      "pnl": 3.72,
+      "pnl_pct": 0.0037
+    },
+    {
+      "month": "2024-08",
+      "start_equity": 99899.01,
+      "end_equity": 99854.36,
+      "pnl": -44.65,
+      "pnl_pct": -0.0447
+    },
+    {
+      "month": "2024-09",
+      "start_equity": 99854.36,
+      "end_equity": 99869.61,
+      "pnl": 15.25,
+      "pnl_pct": 0.0153
+    },
+    {
+      "month": "2024-10",
+      "start_equity": 99869.61,
+      "end_equity": 99876.84,
+      "pnl": 7.23,
+      "pnl_pct": 0.0072
+    },
+    {
+      "month": "2024-11",
+      "start_equity": 99876.84,
+      "end_equity": 99849.43,
+      "pnl": -27.41,
+      "pnl_pct": -0.0274
+    },
+    {
+      "month": "2024-12",
+      "start_equity": 99849.43,
+      "end_equity": 99879.13,
+      "pnl": 29.7,
+      "pnl_pct": 0.0297
+    },
+    {
+      "month": "2025-01",
+      "start_equity": 99879.13,
+      "end_equity": 99904.83,
+      "pnl": 25.7,
+      "pnl_pct": 0.0257
+    },
+    {
+      "month": "2025-02",
+      "start_equity": 99904.83,
+      "end_equity": 99878.72,
+      "pnl": -26.11,
+      "pnl_pct": -0.0261
+    },
+    {
+      "month": "2025-03",
+      "start_equity": 99878.72,
+      "end_equity": 99919.62,
+      "pnl": 40.9,
+      "pnl_pct": 0.0409
+    },
+    {
+      "month": "2025-04",
+      "start_equity": 99919.62,
+      "end_equity": 99909.76,
+      "pnl": -9.86,
+      "pnl_pct": -0.0099
+    },
+    {
+      "month": "2025-05",
+      "start_equity": 99909.76,
+      "end_equity": 99913.58,
+      "pnl": 3.83,
+      "pnl_pct": 0.0038
+    },
+    {
+      "month": "2025-06",
+      "start_equity": 99913.58,
+      "end_equity": 99849.23,
+      "pnl": -64.36,
+      "pnl_pct": -0.0644
+    },
+    {
+      "month": "2025-07",
+      "start_equity": 99849.23,
+      "end_equity": 99825.35,
+      "pnl": -23.87,
+      "pnl_pct": -0.0239
+    },
+    {
+      "month": "2025-08",
+      "start_equity": 99825.35,
+      "end_equity": 99835.33,
+      "pnl": 9.97,
+      "pnl_pct": 0.01
+    },
+    {
+      "month": "2025-09",
+      "start_equity": 99835.33,
+      "end_equity": 99808.3,
+      "pnl": -27.03,
+      "pnl_pct": -0.0271
+    },
+    {
+      "month": "2025-10",
+      "start_equity": 99808.3,
+      "end_equity": 99798.13,
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+      "pnl_pct": -0.0102
+    },
+    {
+      "month": "2025-11",
+      "start_equity": 99798.13,
+      "end_equity": 99810.12,
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+      "pnl_pct": 0.012
+    },
+    {
+      "month": "2025-12",
+      "start_equity": 99810.12,
+      "end_equity": 99822.78,
+      "pnl": 12.66,
+      "pnl_pct": 0.0127
+    },
+    {
+      "month": "2026-01",
+      "start_equity": 99822.78,
+      "end_equity": 99810.28,
+      "pnl": -12.49,
+      "pnl_pct": -0.0125
+    },
+    {
+      "month": "2026-02",
+      "start_equity": 99810.28,
+      "end_equity": 99798.32,
+      "pnl": -11.97,
+      "pnl_pct": -0.012
+    }
+  ]
+}
\ No newline at end of file
diff --git a/logs/backtest_summary_S3_S3_Key_Level_Breakout_EUR_GBP_H1.json b/logs/backtest_summary_S3_S3_Key_Level_Breakout_EUR_GBP_H1.json
new file mode 100644
index 0000000..ebe2b9f
--- /dev/null
+++ b/logs/backtest_summary_S3_S3_Key_Level_Breakout_EUR_GBP_H1.json
@@ -0,0 +1,45 @@
+{
+  "strategy_name": "S3_Key_Level_Breakout",
+  "strategy_key": "S3",
+  "instrument": "EUR_GBP",
+  "granularity": "H1",
+  "run_time": "2026-02-17T12:46:22.724657+00:00",
+  "data_range": {
+    "start": "2023-10-22 21:00:00+00:00",
+    "end": "2023-12-19 11:00:00+00:00",
+    "bars": 998
+  },
+  "metrics": {
+    "starting_equity": 100000.0,
+    "total_return_pct": 0.0019,
+    "max_drawdown_pct": 0.0065,
+    "num_trades": 22,
+    "round_trips": 6,
+    "win_rate_pct": 0.0,
+    "sharpe_ratio": 2.5599,
+    "final_equity": 100001.89
+  },
+  "monthly_pnl": [
+    {
+      "month": "2023-10",
+      "start_equity": 100000.0,
+      "end_equity": 99994.68,
+      "pnl": -5.32,
+      "pnl_pct": -0.0053
+    },
+    {
+      "month": "2023-11",
+      "start_equity": 99994.68,
+      "end_equity": 99995.64,
+      "pnl": 0.96,
+      "pnl_pct": 0.001
+    },
+    {
+      "month": "2023-12",
+      "start_equity": 99995.64,
+      "end_equity": 100001.89,
+      "pnl": 6.25,
+      "pnl_pct": 0.0063
+    }
+  ]
+}
\ No newline at end of file
diff --git a/logs/backtest_summary_S3_S3_Key_Level_Breakout_GBP_JPY_H1.json b/logs/backtest_summary_S3_S3_Key_Level_Breakout_GBP_JPY_H1.json
new file mode 100644
index 0000000..7f2ca2f
--- /dev/null
+++ b/logs/backtest_summary_S3_S3_Key_Level_Breakout_GBP_JPY_H1.json
@@ -0,0 +1,227 @@
+{
+  "strategy_name": "S3_Key_Level_Breakout",
+  "strategy_key": "S3",
+  "instrument": "GBP_JPY",
+  "granularity": "H1",
+  "run_time": "2026-02-17T12:57:49.345603+00:00",
+  "data_range": {
+    "start": "2023-10-22 21:00:00+00:00",
+    "end": "2026-02-17 12:00:00+00:00",
+    "bars": 14413
+  },
+  "metrics": {
+    "starting_equity": 100000.0,
+    "total_return_pct": 0.0312,
+    "max_drawdown_pct": 0.0187,
+    "num_trades": 37,
+    "round_trips": 12,
+    "win_rate_pct": 16.67,
+    "sharpe_ratio": 4.8029,
+    "final_equity": 100031.18
+  },
+  "monthly_pnl": [
+    {
+      "month": "2023-10",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2023-11",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2023-12",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2024-01",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2024-02",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2024-03",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2024-04",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2024-05",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2024-06",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2024-07",
+      "start_equity": 100000.0,
+      "end_equity": 100003.41,
+      "pnl": 3.41,
+      "pnl_pct": 0.0034
+    },
+    {
+      "month": "2024-08",
+      "start_equity": 100003.41,
+      "end_equity": 100003.41,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2024-09",
+      "start_equity": 100003.41,
+      "end_equity": 100003.41,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2024-10",
+      "start_equity": 100003.41,
+      "end_equity": 100003.41,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2024-11",
+      "start_equity": 100003.41,
+      "end_equity": 100003.41,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2024-12",
+      "start_equity": 100003.41,
+      "end_equity": 100003.41,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2025-01",
+      "start_equity": 100003.41,
+      "end_equity": 100003.41,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2025-02",
+      "start_equity": 100003.41,
+      "end_equity": 100003.41,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2025-03",
+      "start_equity": 100003.41,
+      "end_equity": 100003.41,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2025-04",
+      "start_equity": 100003.41,
+      "end_equity": 100003.41,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2025-05",
+      "start_equity": 100003.41,
+      "end_equity": 100003.41,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2025-06",
+      "start_equity": 100003.41,
+      "end_equity": 100003.41,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2025-07",
+      "start_equity": 100003.41,
+      "end_equity": 100003.41,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2025-08",
+      "start_equity": 100003.41,
+      "end_equity": 100003.41,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2025-09",
+      "start_equity": 100003.41,
+      "end_equity": 100003.41,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2025-10",
+      "start_equity": 100003.41,
+      "end_equity": 99999.05,
+      "pnl": -4.35,
+      "pnl_pct": -0.0044
+    },
+    {
+      "month": "2025-11",
+      "start_equity": 99999.05,
+      "end_equity": 100010.15,
+      "pnl": 11.1,
+      "pnl_pct": 0.0111
+    },
+    {
+      "month": "2025-12",
+      "start_equity": 100010.15,
+      "end_equity": 100012.66,
+      "pnl": 2.51,
+      "pnl_pct": 0.0025
+    },
+    {
+      "month": "2026-01",
+      "start_equity": 100012.66,
+      "end_equity": 100022.01,
+      "pnl": 9.35,
+      "pnl_pct": 0.0094
+    },
+    {
+      "month": "2026-02",
+      "start_equity": 100022.01,
+      "end_equity": 100031.18,
+      "pnl": 9.17,
+      "pnl_pct": 0.0092
+    }
+  ]
+}
\ No newline at end of file
diff --git a/logs/backtest_summary_S3_S3_Key_Level_Breakout_GBP_USD_H1.json b/logs/backtest_summary_S3_S3_Key_Level_Breakout_GBP_USD_H1.json
new file mode 100644
index 0000000..bdad212
--- /dev/null
+++ b/logs/backtest_summary_S3_S3_Key_Level_Breakout_GBP_USD_H1.json
@@ -0,0 +1,45 @@
+{
+  "strategy_name": "S3_Key_Level_Breakout",
+  "strategy_key": "S3",
+  "instrument": "GBP_USD",
+  "granularity": "H1",
+  "run_time": "2026-02-17T12:46:20.546660+00:00",
+  "data_range": {
+    "start": "2023-10-22 21:00:00+00:00",
+    "end": "2023-12-19 11:00:00+00:00",
+    "bars": 998
+  },
+  "metrics": {
+    "starting_equity": 100000.0,
+    "total_return_pct": -0.0176,
+    "max_drawdown_pct": 0.0359,
+    "num_trades": 22,
+    "round_trips": 9,
+    "win_rate_pct": 11.11,
+    "sharpe_ratio": -15.8874,
+    "final_equity": 99982.38
+  },
+  "monthly_pnl": [
+    {
+      "month": "2023-10",
+      "start_equity": 100000.0,
+      "end_equity": 100002.25,
+      "pnl": 2.25,
+      "pnl_pct": 0.0023
+    },
+    {
+      "month": "2023-11",
+      "start_equity": 100002.25,
+      "end_equity": 99975.3,
+      "pnl": -26.95,
+      "pnl_pct": -0.027
+    },
+    {
+      "month": "2023-12",
+      "start_equity": 99975.3,
+      "end_equity": 99982.38,
+      "pnl": 7.08,
+      "pnl_pct": 0.0071
+    }
+  ]
+}
\ No newline at end of file
diff --git a/logs/backtest_summary_S3_S3_Key_Level_Breakout_USD_JPY_H1.json b/logs/backtest_summary_S3_S3_Key_Level_Breakout_USD_JPY_H1.json
new file mode 100644
index 0000000..70a8622
--- /dev/null
+++ b/logs/backtest_summary_S3_S3_Key_Level_Breakout_USD_JPY_H1.json
@@ -0,0 +1,45 @@
+{
+  "strategy_name": "S3_Key_Level_Breakout",
+  "strategy_key": "S3",
+  "instrument": "USD_JPY",
+  "granularity": "H1",
+  "run_time": "2026-02-17T12:46:18.341303+00:00",
+  "data_range": {
+    "start": "2023-10-22 21:00:00+00:00",
+    "end": "2023-12-19 11:00:00+00:00",
+    "bars": 998
+  },
+  "metrics": {
+    "starting_equity": 100000.0,
+    "total_return_pct": 0.0117,
+    "max_drawdown_pct": 0.008,
+    "num_trades": 18,
+    "round_trips": 6,
+    "win_rate_pct": 16.67,
+    "sharpe_ratio": 12.9054,
+    "final_equity": 100011.67
+  },
+  "monthly_pnl": [
+    {
+      "month": "2023-10",
+      "start_equity": 100000.0,
+      "end_equity": 99993.34,
+      "pnl": -6.66,
+      "pnl_pct": -0.0067
+    },
+    {
+      "month": "2023-11",
+      "start_equity": 99993.34,
+      "end_equity": 100019.64,
+      "pnl": 26.31,
+      "pnl_pct": 0.0263
+    },
+    {
+      "month": "2023-12",
+      "start_equity": 100019.64,
+      "end_equity": 100011.67,
+      "pnl": -7.98,
+      "pnl_pct": -0.008
+    }
+  ]
+}
\ No newline at end of file
diff --git a/logs/backtest_summary_S4_S4_EMA_Ribbon_Scalp_EUR_AUD_M15.json b/logs/backtest_summary_S4_S4_EMA_Ribbon_Scalp_EUR_AUD_M15.json
new file mode 100644
index 0000000..92051b1
--- /dev/null
+++ b/logs/backtest_summary_S4_S4_EMA_Ribbon_Scalp_EUR_AUD_M15.json
@@ -0,0 +1,38 @@
+{
+  "strategy_name": "S4_EMA_Ribbon_Scalp",
+  "strategy_key": "S4",
+  "instrument": "EUR_AUD",
+  "granularity": "M15",
+  "run_time": "2026-02-17T12:46:26.588074+00:00",
+  "data_range": {
+    "start": "2024-01-29 12:00:00+00:00",
+    "end": "2024-02-12 21:15:00+00:00",
+    "bars": 998
+  },
+  "metrics": {
+    "starting_equity": 100000.0,
+    "total_return_pct": 0.0,
+    "max_drawdown_pct": 0.0,
+    "num_trades": 0,
+    "round_trips": 0,
+    "win_rate_pct": 0.0,
+    "sharpe_ratio": 0.0,
+    "final_equity": 100000.0
+  },
+  "monthly_pnl": [
+    {
+      "month": "2024-01",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2024-02",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    }
+  ]
+}
\ No newline at end of file
diff --git a/logs/backtest_summary_S4_S4_EMA_Ribbon_Scalp_EUR_GBP_M15.json b/logs/backtest_summary_S4_S4_EMA_Ribbon_Scalp_EUR_GBP_M15.json
new file mode 100644
index 0000000..0ceebd1
--- /dev/null
+++ b/logs/backtest_summary_S4_S4_EMA_Ribbon_Scalp_EUR_GBP_M15.json
@@ -0,0 +1,38 @@
+{
+  "strategy_name": "S4_EMA_Ribbon_Scalp",
+  "strategy_key": "S4",
+  "instrument": "EUR_GBP",
+  "granularity": "M15",
+  "run_time": "2026-02-17T12:46:28.268251+00:00",
+  "data_range": {
+    "start": "2024-01-29 12:00:00+00:00",
+    "end": "2024-02-12 21:15:00+00:00",
+    "bars": 998
+  },
+  "metrics": {
+    "starting_equity": 100000.0,
+    "total_return_pct": 0.0,
+    "max_drawdown_pct": 0.0,
+    "num_trades": 0,
+    "round_trips": 0,
+    "win_rate_pct": 0.0,
+    "sharpe_ratio": 0.0,
+    "final_equity": 100000.0
+  },
+  "monthly_pnl": [
+    {
+      "month": "2024-01",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2024-02",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    }
+  ]
+}
\ No newline at end of file
diff --git a/logs/backtest_summary_S4_S4_EMA_Ribbon_Scalp_GBP_AUD_M15.json b/logs/backtest_summary_S4_S4_EMA_Ribbon_Scalp_GBP_AUD_M15.json
new file mode 100644
index 0000000..2c5dfc6
--- /dev/null
+++ b/logs/backtest_summary_S4_S4_EMA_Ribbon_Scalp_GBP_AUD_M15.json
@@ -0,0 +1,38 @@
+{
+  "strategy_name": "S4_EMA_Ribbon_Scalp",
+  "strategy_key": "S4",
+  "instrument": "GBP_AUD",
+  "granularity": "M15",
+  "run_time": "2026-02-17T12:46:24.555721+00:00",
+  "data_range": {
+    "start": "2024-01-29 12:15:00+00:00",
+    "end": "2024-02-12 21:45:00+00:00",
+    "bars": 998
+  },
+  "metrics": {
+    "starting_equity": 100000.0,
+    "total_return_pct": 0.0,
+    "max_drawdown_pct": 0.0,
+    "num_trades": 0,
+    "round_trips": 0,
+    "win_rate_pct": 0.0,
+    "sharpe_ratio": 0.0,
+    "final_equity": 100000.0
+  },
+  "monthly_pnl": [
+    {
+      "month": "2024-01",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    },
+    {
+      "month": "2024-02",
+      "start_equity": 100000.0,
+      "end_equity": 100000.0,
+      "pnl": 0.0,
+      "pnl_pct": 0.0
+    }
+  ]
+}
\ No newline at end of file
diff --git a/logs/backtest_summary_S5_S5_Momentum_Exhaustion_EUR_AUD_M15.json b/logs/backtest_summary_S5_S5_Momentum_Exhaustion_EUR_AUD_M15.json
new file mode 100644
index 0000000..ca7fc30
--- /dev/null
+++ b/logs/backtest_summary_S5_S5_Momentum_Exhaustion_EUR_AUD_M15.json
@@ -0,0 +1,38 @@
+{
+  "strategy_name": "S5_Momentum_Exhaustion",
+  "strategy_key": "S5",
+  "instrument": "EUR_AUD",
+  "granularity": "M15",
+  "run_time": "2026-02-17T12:46:32.915864+00:00",
+  "data_range": {
+    "start": "2024-01-29 12:00:00+00:00",
+    "end": "2024-02-12 21:15:00+00:00",
+    "bars": 998
+  },
+  "metrics": {
+    "starting_equity": 100000.0,
+    "total_return_pct": -0.0116,
+    "max_drawdown_pct": 0.0165,
+    "num_trades": 59,
+    "round_trips": 18,
+    "win_rate_pct": 0.0,
+    "sharpe_ratio": -23.6539,
+    "final_equity": 99988.45
+  },
+  "monthly_pnl": [
+    {
+      "month": "2024-01",
+      "start_equity": 100000.0,
+      "end_equity": 100002.83,
+      "pnl": 2.83,
+      "pnl_pct": 0.0028
+    },
+    {
+      "month": "2024-02",
+      "start_equity": 100002.83,
+      "end_equity": 99988.45,
+      "pnl": -14.38,
+      "pnl_pct": -0.0144
+    }
+  ]
+}
\ No newline at end of file
diff --git a/logs/backtest_summary_S5_S5_Momentum_Exhaustion_EUR_CAD_M15.json b/logs/backtest_summary_S5_S5_Momentum_Exhaustion_EUR_CAD_M15.json
new file mode 100644
index 0000000..106994b
--- /dev/null
+++ b/logs/backtest_summary_S5_S5_Momentum_Exhaustion_EUR_CAD_M15.json
@@ -0,0 +1,38 @@
+{
+  "strategy_name": "S5_Momentum_Exhaustion",
+  "strategy_key": "S5",
+  "instrument": "EUR_CAD",
+  "granularity": "M15",
+  "run_time": "2026-02-17T12:46:40.004700+00:00",
+  "data_range": {
+    "start": "2024-01-29 12:00:00+00:00",
+    "end": "2024-02-12 21:15:00+00:00",
+    "bars": 998
+  },
+  "metrics": {
+    "starting_equity": 100000.0,
+    "total_return_pct": 0.0076,
+    "max_drawdown_pct": 0.0013,
+    "num_trades": 25,
+    "round_trips": 2,
+    "win_rate_pct": 0.0,
+    "sharpe_ratio": 33.759,
+    "final_equity": 100007.62
+  },
+  "monthly_pnl": [
+    {
+      "month": "2024-01",
+      "start_equity": 100000.0,
+      "end_equity": 100002.34,
+      "pnl": 2.34,
+      "pnl_pct": 0.0023
+    },
+    {
+      "month": "2024-02",
+      "start_equity": 100002.34,
+      "end_equity": 100007.62,
+      "pnl": 5.28,
+      "pnl_pct": 0.0053
+    }
+  ]
+}
\ No newline at end of file
diff --git a/logs/backtest_summary_S5_S5_Momentum_Exhaustion_EUR_GBP_M15.json b/logs/backtest_summary_S5_S5_Momentum_Exhaustion_EUR_GBP_M15.json
new file mode 100644
index 0000000..be1f25c
--- /dev/null
+++ b/logs/backtest_summary_S5_S5_Momentum_Exhaustion_EUR_GBP_M15.json
@@ -0,0 +1,38 @@
+{
+  "strategy_name": "S5_Momentum_Exhaustion",
+  "strategy_key": "S5",
+  "instrument": "EUR_GBP",
+  "granularity": "M15",
+  "run_time": "2026-02-17T12:46:35.266765+00:00",
+  "data_range": {
+    "start": "2024-01-29 12:00:00+00:00",
+    "end": "2024-02-12 21:15:00+00:00",
+    "bars": 998
+  },
+  "metrics": {
+    "starting_equity": 100000.0,
+    "total_return_pct": -0.0009,
+    "max_drawdown_pct": 0.0028,
+    "num_trades": 16,
+    "round_trips": 4,
+    "win_rate_pct": 0.0,
+    "sharpe_ratio": -3.7675,
+    "final_equity": 99999.15
+  },
+  "monthly_pnl": [
+    {
+      "month": "2024-01",
+      "start_equity": 100000.0,
+      "end_equity": 100000.18,
+      "pnl": 0.18,
+      "pnl_pct": 0.0002
+    },
+    {
+      "month": "2024-02",
+      "start_equity": 100000.18,
+      "end_equity": 99999.15,
+      "pnl": -1.03,
+      "pnl_pct": -0.001
+    }
+  ]
+}
\ No newline at end of file
diff --git a/logs/backtest_summary_S5_S5_Momentum_Exhaustion_GBP_AUD_M15.json b/logs/backtest_summary_S5_S5_Momentum_Exhaustion_GBP_AUD_M15.json
new file mode 100644
index 0000000..f387d9d
--- /dev/null
+++ b/logs/backtest_summary_S5_S5_Momentum_Exhaustion_GBP_AUD_M15.json
@@ -0,0 +1,38 @@
+{
+  "strategy_name": "S5_Momentum_Exhaustion",
+  "strategy_key": "S5",
+  "instrument": "GBP_AUD",
+  "granularity": "M15",
+  "run_time": "2026-02-17T12:46:30.534909+00:00",
+  "data_range": {
+    "start": "2024-01-29 12:15:00+00:00",
+    "end": "2024-02-12 21:45:00+00:00",
+    "bars": 998
+  },
+  "metrics": {
+    "starting_equity": 100000.0,
+    "total_return_pct": 0.008,
+    "max_drawdown_pct": 0.0034,
+    "num_trades": 47,
+    "round_trips": 8,
+    "win_rate_pct": 0.0,
+    "sharpe_ratio": 19.0639,
+    "final_equity": 100007.96
+  },
+  "monthly_pnl": [
+    {
+      "month": "2024-01",
+      "start_equity": 100000.0,
+      "end_equity": 100004.43,
+      "pnl": 4.43,
+      "pnl_pct": 0.0044
+    },
+    {
+      "month": "2024-02",
+      "start_equity": 100004.43,
+      "end_equity": 100007.96,
+      "pnl": 3.52,
+      "pnl_pct": 0.0035
+    }
+  ]
+}
\ No newline at end of file
diff --git a/logs/backtest_summary_S5_S5_Momentum_Exhaustion_GBP_CAD_M15.json b/logs/backtest_summary_S5_S5_Momentum_Exhaustion_GBP_CAD_M15.json
new file mode 100644
index 0000000..0b69a76
--- /dev/null
+++ b/logs/backtest_summary_S5_S5_Momentum_Exhaustion_GBP_CAD_M15.json
@@ -0,0 +1,38 @@
+{
+  "strategy_name": "S5_Momentum_Exhaustion",
+  "strategy_key": "S5",
+  "instrument": "GBP_CAD",
+  "granularity": "M15",
+  "run_time": "2026-02-17T12:46:37.516933+00:00",
+  "data_range": {
+    "start": "2024-01-29 12:15:00+00:00",
+    "end": "2024-02-12 21:30:00+00:00",
+    "bars": 998
+  },
+  "metrics": {
+    "starting_equity": 100000.0,
+    "total_return_pct": 0.0024,
+    "max_drawdown_pct": 0.0025,
+    "num_trades": 26,
+    "round_trips": 6,
+    "win_rate_pct": 0.0,
+    "sharpe_ratio": 10.7444,
+    "final_equity": 100002.44
+  },
+  "monthly_pnl": [
+    {
+      "month": "2024-01",
+      "start_equity": 100000.0,
+      "end_equity": 100000.13,
+      "pnl": 0.13,
+      "pnl_pct": 0.0001
+    },
+    {
+      "month": "2024-02",
+      "start_equity": 100000.13,
+      "end_equity": 100002.44,
+      "pnl": 2.31,
+      "pnl_pct": 0.0023
+    }
+  ]
+}
\ No newline at end of file
diff --git a/logs/backtest_trades_S2_EUR_USD_M15.csv b/logs/backtest_trades_S2_EUR_USD_M15.csv
new file mode 100644
index 0000000..358b79e
--- /dev/null
+++ b/logs/backtest_trades_S2_EUR_USD_M15.csv
@@ -0,0 +1,3064 @@
+time,strategy,instrument,granularity,side,price,position_size,equity,drawdown,pnl
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diff --git a/logs/backtest_trades_S2_GBP_JPY_M15.csv b/logs/backtest_trades_S2_GBP_JPY_M15.csv
new file mode 100644
index 0000000..cdbfb3f
--- /dev/null
+++ b/logs/backtest_trades_S2_GBP_JPY_M15.csv
@@ -0,0 +1,2982 @@
+time,strategy,instrument,granularity,side,price,position_size,equity,drawdown,pnl
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diff --git a/logs/backtest_trades_S2_GBP_USD_M15.csv b/logs/backtest_trades_S2_GBP_USD_M15.csv
new file mode 100644
index 0000000..c8cea26
--- /dev/null
+++ b/logs/backtest_trades_S2_GBP_USD_M15.csv
@@ -0,0 +1,3033 @@
+time,strategy,instrument,granularity,side,price,position_size,equity,drawdown,pnl
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diff --git a/logs/backtest_trades_S2_USD_JPY_M15.csv b/logs/backtest_trades_S2_USD_JPY_M15.csv
new file mode 100644
index 0000000..bd76539
--- /dev/null
+++ b/logs/backtest_trades_S2_USD_JPY_M15.csv
@@ -0,0 +1,3172 @@
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diff --git a/logs/backtest_trades_S3_EUR_GBP_H1.csv b/logs/backtest_trades_S3_EUR_GBP_H1.csv
new file mode 100644
index 0000000..fee14ce
--- /dev/null
+++ b/logs/backtest_trades_S3_EUR_GBP_H1.csv
@@ -0,0 +1,23 @@
+time,strategy,instrument,granularity,side,price,position_size,equity,drawdown,pnl
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diff --git a/logs/backtest_trades_S3_GBP_JPY_H1.csv b/logs/backtest_trades_S3_GBP_JPY_H1.csv
new file mode 100644
index 0000000..abcc465
--- /dev/null
+++ b/logs/backtest_trades_S3_GBP_JPY_H1.csv
@@ -0,0 +1,38 @@
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+2026-02-11 00:00:00+00:00,S3_Key_Level_Breakout,GBP_JPY,H1,SELL_SHORT,210.116,1500.25,100016.76,0.0,0.0
+2026-02-13 00:00:00+00:00,S3_Key_Level_Breakout,GBP_JPY,H1,TIME_EXIT,208.096,0.0,100031.18,0.0,14.42
diff --git a/logs/backtest_trades_S3_GBP_USD_H1.csv b/logs/backtest_trades_S3_GBP_USD_H1.csv
new file mode 100644
index 0000000..aa6f643
--- /dev/null
+++ b/logs/backtest_trades_S3_GBP_USD_H1.csv
@@ -0,0 +1,23 @@
+time,strategy,instrument,granularity,side,price,position_size,equity,drawdown,pnl
+2023-10-26 12:00:00+00:00,S3_Key_Level_Breakout,GBP_USD,H1,BUY,1.21248,1500.0,100000.0,0.0,0.0
+2023-10-30 12:00:00+00:00,S3_Key_Level_Breakout,GBP_USD,H1,TIME_EXIT,1.2143,0.0,100002.25,0.0,2.25
+2023-11-20 09:00:00+00:00,S3_Key_Level_Breakout,GBP_USD,H1,BUY,1.251,2000.05,100002.25,0.0,0.0
+2023-11-20 10:00:00+00:00,S3_Key_Level_Breakout,GBP_USD,H1,SL_EXIT_LONG,1.2481873809523807,0.0,99997.75,0.0,-4.5
+2023-11-20 10:00:00+00:00,S3_Key_Level_Breakout,GBP_USD,H1,SELL_SHORT,1.24716,1499.97,99997.75,0.0,0.0
+2023-11-21 02:00:00+00:00,S3_Key_Level_Breakout,GBP_USD,H1,SL_EXIT_SHORT,1.2526595238095237,0.0,99991.14,0.0,-6.61
+2023-11-22 14:00:00+00:00,S3_Key_Level_Breakout,GBP_USD,H1,SELL_SHORT,1.24838,1999.82,99991.14,0.0,0.0
+2023-11-23 08:00:00+00:00,S3_Key_Level_Breakout,GBP_USD,H1,SL_EXIT_SHORT,1.2530763095238093,0.0,99983.62,0.0,-7.52
+2023-11-27 08:00:00+00:00,S3_Key_Level_Breakout,GBP_USD,H1,SELL_SHORT,1.26083,1499.75,99983.62,0.0,0.0
+2023-11-27 11:00:00+00:00,S3_Key_Level_Breakout,GBP_USD,H1,SL_EXIT_SHORT,1.2631164285714287,0.0,99980.9,0.0,-2.72
+2023-11-27 15:00:00+00:00,S3_Key_Level_Breakout,GBP_USD,H1,SELL_SHORT,1.26056,1499.71,99980.9,0.0,0.0
+2023-11-28 00:00:00+00:00,S3_Key_Level_Breakout,GBP_USD,H1,SL_EXIT_SHORT,1.263622142857143,0.0,99977.25,0.0,-3.64
+2023-11-28 07:00:00+00:00,S3_Key_Level_Breakout,GBP_USD,H1,SELL_SHORT,1.26119,1499.66,99977.25,0.0,0.0
+2023-11-28 09:00:00+00:00,S3_Key_Level_Breakout,GBP_USD,H1,SL_EXIT_SHORT,1.2628346428571429,0.0,99975.3,0.0,-1.96
+2023-12-04 15:00:00+00:00,S3_Key_Level_Breakout,GBP_USD,H1,SELL_SHORT,1.26104,1999.51,99975.3,0.0,0.0
+2023-12-05 00:00:00+00:00,S3_Key_Level_Breakout,GBP_USD,H1,SL_EXIT_SHORT,1.2642467857142858,0.0,99970.21,0.0,-5.08
+2023-12-05 08:00:00+00:00,S3_Key_Level_Breakout,GBP_USD,H1,BUY,1.26282,1499.55,99970.21,0.0,0.0
+2023-12-05 16:00:00+00:00,S3_Key_Level_Breakout,GBP_USD,H1,SL_EXIT_LONG,1.2595764285714286,0.0,99966.36,0.0,-3.85
+2023-12-13 19:00:00+00:00,S3_Key_Level_Breakout,GBP_USD,H1,BUY,1.2627,1999.33,99966.36,0.0,0.0
+2023-12-14 14:00:00+00:00,S3_Key_Level_Breakout,GBP_USD,H1,TP1_LONG,1.27341,599.8,99978.23,0.0,11.87
+2023-12-14 14:00:00+00:00,S3_Key_Level_Breakout,GBP_USD,H1,TP2_LONG,1.271427857142857,0.0,99982.38,0.0,4.15
+2023-12-15 19:00:00+00:00,S3_Key_Level_Breakout,GBP_USD,H1,TIME_EXIT,1.269,0.0,99982.38,0.0,0.0
diff --git a/logs/backtest_trades_S3_USD_JPY_H1.csv b/logs/backtest_trades_S3_USD_JPY_H1.csv
new file mode 100644
index 0000000..91b669c
--- /dev/null
+++ b/logs/backtest_trades_S3_USD_JPY_H1.csv
@@ -0,0 +1,19 @@
+time,strategy,instrument,granularity,side,price,position_size,equity,drawdown,pnl
+2023-10-25 01:00:00+00:00,S3_Key_Level_Breakout,USD_JPY,H1,SELL_SHORT,149.861,1500.0,100000.0,0.0,0.0
+2023-10-25 17:00:00+00:00,S3_Key_Level_Breakout,USD_JPY,H1,SL_EXIT_SHORT,149.99057142857143,0.0,99998.7,0.0,-1.3
+2023-10-27 13:00:00+00:00,S3_Key_Level_Breakout,USD_JPY,H1,SELL_SHORT,149.706,1999.97,99998.7,0.0,0.0
+2023-10-31 03:00:00+00:00,S3_Key_Level_Breakout,USD_JPY,H1,SL_EXIT_SHORT,150.1077857142857,0.0,99993.34,0.0,-5.37
+2023-11-17 08:00:00+00:00,S3_Key_Level_Breakout,USD_JPY,H1,SELL_SHORT,149.787,1499.9,99993.34,0.0,0.0
+2023-11-20 09:00:00+00:00,S3_Key_Level_Breakout,USD_JPY,H1,TP1_SHORT,148.50266666666667,449.97,100002.34,0.0,9.0
+2023-11-21 04:00:00+00:00,S3_Key_Level_Breakout,USD_JPY,H1,TP2_SHORT,147.3275,0.0,100009.73,0.0,7.39
+2023-11-22 05:00:00+00:00,S3_Key_Level_Breakout,USD_JPY,H1,BUY,148.755,1500.15,100009.73,0.0,0.0
+2023-11-24 05:00:00+00:00,S3_Key_Level_Breakout,USD_JPY,H1,TIME_EXIT,149.256,0.0,100014.78,0.0,5.05
+2023-11-27 23:00:00+00:00,S3_Key_Level_Breakout,USD_JPY,H1,SELL_SHORT,148.418,1500.22,100014.78,0.0,0.0
+2023-11-28 08:00:00+00:00,S3_Key_Level_Breakout,USD_JPY,H1,SL_EXIT_SHORT,148.80620238095239,0.0,100010.85,0.0,-3.92
+2023-11-28 13:00:00+00:00,S3_Key_Level_Breakout,USD_JPY,H1,SELL_SHORT,148.364,1500.16,100010.85,0.0,0.0
+2023-11-28 17:00:00+00:00,S3_Key_Level_Breakout,USD_JPY,H1,TP1_SHORT,147.3275,450.05,100018.19,0.0,7.34
+2023-11-30 13:00:00+00:00,S3_Key_Level_Breakout,USD_JPY,H1,TIME_EXIT,147.886,0.0,100019.64,0.0,1.45
+2023-12-04 18:00:00+00:00,S3_Key_Level_Breakout,USD_JPY,H1,BUY,147.393,1500.29,100019.64,0.0,0.0
+2023-12-05 06:00:00+00:00,S3_Key_Level_Breakout,USD_JPY,H1,SL_EXIT_LONG,146.89142857142855,0.0,100014.54,0.0,-5.11
+2023-12-06 10:00:00+00:00,S3_Key_Level_Breakout,USD_JPY,H1,BUY,147.358,1500.22,100014.54,0.0,0.0
+2023-12-06 13:00:00+00:00,S3_Key_Level_Breakout,USD_JPY,H1,SL_EXIT_LONG,147.0760357142857,0.0,100011.67,0.0,-2.87
diff --git a/logs/backtest_trades_S5_EUR_AUD_M15.csv b/logs/backtest_trades_S5_EUR_AUD_M15.csv
new file mode 100644
index 0000000..736b6e9
--- /dev/null
+++ b/logs/backtest_trades_S5_EUR_AUD_M15.csv
@@ -0,0 +1,60 @@
+time,strategy,instrument,granularity,side,price,position_size,equity,drawdown,pnl
+2024-01-30 11:30:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SELL_SHORT,1.64444,1500.0,100000.0,0.0,0.0
+2024-01-30 13:00:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SL_EXIT_SHORT,1.6463857142857141,0.0,99998.23,0.0,-1.77
+2024-01-30 13:00:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SELL_SHORT,1.64588,1499.97,99998.23,0.0,0.0
+2024-01-30 15:30:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SL_EXIT_SHORT,1.6475964285714286,0.0,99996.66,0.0,-1.56
+2024-01-30 15:30:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SELL_SHORT,1.64753,1499.95,99996.66,0.0,0.0
+2024-01-30 19:30:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,TP1_SHORT,1.64201884356933,899.97,99998.67,0.0,2.01
+2024-01-30 19:30:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,TP2_SHORT,1.6447914285714285,299.99,99999.67,0.0,1.0
+2024-01-30 19:30:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,TP3_SHORT,1.6427375,0.0,100000.54,0.0,0.87
+2024-01-31 01:00:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SELL_SHORT,1.64754,1500.01,100000.54,0.0,0.0
+2024-01-31 01:30:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SL_EXIT_SHORT,1.6489789285714287,0.0,99999.23,0.0,-1.31
+2024-01-31 02:00:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SELL_SHORT,1.64978,1499.99,99999.23,0.0,0.0
+2024-01-31 08:00:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,TP1_SHORT,1.64420366164255,899.99,100001.26,0.0,2.03
+2024-01-31 08:00:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,TP2_SHORT,1.6474685714285713,300.0,100002.1,0.0,0.84
+2024-01-31 08:00:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,TP3_SHORT,1.645735,0.0,100002.83,0.0,0.74
+2024-02-01 04:45:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SELL_SHORT,1.64921,1500.04,100002.83,0.0,0.0
+2024-02-01 05:15:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SL_EXIT_SHORT,1.6512135714285714,0.0,100001.01,0.0,-1.82
+2024-02-01 05:15:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SELL_SHORT,1.65162,1500.02,100001.01,0.0,0.0
+2024-02-01 07:45:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SL_EXIT_SHORT,1.6536557142857142,0.0,99999.16,0.0,-1.85
+2024-02-01 07:45:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SELL_SHORT,1.65213,1499.99,99999.16,0.0,0.0
+2024-02-01 08:15:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SL_EXIT_SHORT,1.6541207142857144,0.0,99997.35,0.0,-1.81
+2024-02-01 08:15:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SELL_SHORT,1.65318,1499.96,99997.35,0.0,0.0
+2024-02-01 09:00:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SL_EXIT_SHORT,1.6551460714285715,0.0,99995.57,0.0,-1.78
+2024-02-01 10:30:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SELL_SHORT,1.6579,1499.93,99995.57,0.0,0.0
+2024-02-01 13:15:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SL_EXIT_SHORT,1.66039,0.0,99993.32,0.0,-2.25
+2024-02-02 13:00:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,BUY,1.64717,1499.9,99993.32,0.0,0.0
+2024-02-02 13:30:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,TP1_LONG,1.64949727823565,899.94,99994.16,0.0,0.85
+2024-02-02 13:30:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,TP2_LONG,1.648977142857143,299.98,99994.82,0.0,0.66
+2024-02-02 13:30:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,TP3_LONG,1.6503325,0.0,99995.4,0.0,0.58
+2024-02-02 15:15:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SELL_SHORT,1.65708,1499.93,99995.4,0.0,0.0
+2024-02-04 23:00:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SL_EXIT_SHORT,1.6596192857142857,0.0,99993.1,0.0,-2.3
+2024-02-06 04:30:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,BUY,1.6501,1499.9,99993.1,0.0,0.0
+2024-02-06 20:30:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,TIME_EXIT,1.64894,0.0,99992.05,0.0,-1.05
+2024-02-06 22:15:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,BUY,1.64886,1499.88,99992.05,0.0,0.0
+2024-02-06 23:00:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SL_EXIT_LONG,1.6479364285714286,0.0,99991.21,0.0,-0.84
+2024-02-06 23:30:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,BUY,1.64806,1499.87,99991.21,0.0,0.0
+2024-02-07 00:30:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SL_EXIT_LONG,1.6472039285714286,0.0,99990.43,0.0,-0.78
+2024-02-07 00:30:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,BUY,1.64772,1499.86,99990.43,0.0,0.0
+2024-02-07 01:00:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SL_EXIT_LONG,1.6466817857142857,0.0,99989.48,0.0,-0.95
+2024-02-08 05:45:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SELL_SHORT,1.65354,1499.84,99989.48,0.0,0.0
+2024-02-08 06:00:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SL_EXIT_SHORT,1.654502142857143,0.0,99988.61,0.0,-0.87
+2024-02-08 06:15:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SELL_SHORT,1.65432,1499.83,99988.61,0.0,0.0
+2024-02-08 08:45:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SL_EXIT_SHORT,1.6553517857142857,0.0,99987.67,0.0,-0.94
+2024-02-08 14:30:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SELL_SHORT,1.65906,1499.82,99987.67,0.0,0.0
+2024-02-08 16:45:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SL_EXIT_SHORT,1.6605332142857143,0.0,99986.34,0.0,-1.33
+2024-02-08 16:45:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SELL_SHORT,1.66024,1999.73,99986.34,0.0,0.0
+2024-02-09 07:00:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,TP1_SHORT,1.6564791946374,1199.84,99988.15,0.0,1.81
+2024-02-09 07:00:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,TP2_SHORT,1.6580071428571428,399.95,99989.23,0.0,1.08
+2024-02-09 07:15:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,TRAIL_SL_SHORT,1.6568335714285714,0.0,99990.05,0.0,0.82
+2024-02-09 12:15:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,BUY,1.6536,1499.85,99990.05,0.0,0.0
+2024-02-09 13:30:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SL_EXIT_LONG,1.6522778571428571,0.0,99988.85,0.0,-1.2
+2024-02-09 13:30:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,BUY,1.65303,1499.83,99988.85,0.0,0.0
+2024-02-12 03:15:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,TP1_LONG,1.65571835931301,899.9,99989.83,0.0,0.98
+2024-02-12 03:15:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,TP2_LONG,1.6549757142857142,299.97,99990.53,0.0,0.71
+2024-02-12 03:30:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,TRAIL_SL_LONG,1.655797857142857,0.0,99991.03,0.0,0.5
+2024-02-12 11:00:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,BUY,1.6514,1499.87,99991.03,0.0,0.0
+2024-02-12 12:45:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SL_EXIT_LONG,1.649852857142857,0.0,99989.63,0.0,-1.41
+2024-02-12 15:15:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,BUY,1.64943,1499.84,99989.63,0.0,0.0
+2024-02-12 16:45:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,SL_EXIT_LONG,1.6481314285714286,0.0,99988.45,0.0,-1.18
+2024-02-12 17:00:00+00:00,S5_Momentum_Exhaustion,EUR_AUD,M15,BUY,1.6483,1499.83,99988.45,0.0,0.0
diff --git a/logs/backtest_trades_S5_EUR_CAD_M15.csv b/logs/backtest_trades_S5_EUR_CAD_M15.csv
new file mode 100644
index 0000000..2dad18c
--- /dev/null
+++ b/logs/backtest_trades_S5_EUR_CAD_M15.csv
@@ -0,0 +1,26 @@
+time,strategy,instrument,granularity,side,price,position_size,equity,drawdown,pnl
+2024-01-30 05:30:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,BUY,1.45093,1500.0,100000.0,0.0,0.0
+2024-01-30 06:45:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,SL_EXIT_LONG,1.450350357142857,0.0,99999.4,0.0,-0.6
+2024-01-30 07:15:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,BUY,1.45016,1499.99,99999.4,0.0,0.0
+2024-01-30 10:30:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,TP1_LONG,1.45215332890413,899.99,100000.23,0.0,0.82
+2024-01-30 10:30:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,TP2_LONG,1.4510757142857142,300.0,100000.6,0.0,0.38
+2024-01-30 10:30:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,TP3_LONG,1.4517624999999998,0.0,100000.94,0.0,0.33
+2024-01-30 13:00:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,SELL_SHORT,1.45641,1500.01,100000.94,0.0,0.0
+2024-01-30 18:00:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,TP1_SHORT,1.45320875151639,900.01,100002.25,0.0,1.32
+2024-01-30 18:00:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,TP2_SHORT,1.45461,300.0,100003.0,0.0,0.74
+2024-01-30 18:00:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,TP3_SHORT,1.45326,0.0,100003.65,0.0,0.65
+2024-01-31 12:30:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,SELL_SHORT,1.45605,1500.05,100003.65,0.0,0.0
+2024-01-31 14:45:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,SL_EXIT_SHORT,1.4573164285714286,0.0,100002.34,0.0,-1.3
+2024-02-05 20:45:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,SELL_SHORT,1.45456,1500.04,100002.34,0.0,0.0
+2024-02-06 03:45:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,TP1_SHORT,1.45277725230381,900.02,100003.08,0.0,0.74
+2024-02-06 03:45:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,TP2_SHORT,1.4533142857142858,300.01,100003.59,0.0,0.51
+2024-02-06 04:30:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,TRAIL_SL_SHORT,1.452865,0.0,100003.94,0.0,0.35
+2024-02-06 17:45:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,BUY,1.45003,1500.06,100003.94,0.0,0.0
+2024-02-07 07:45:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,TP1_LONG,1.4523581365203,900.04,100004.9,0.0,0.96
+2024-02-07 07:45:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,TP2_LONG,1.45196,300.01,100005.7,0.0,0.8
+2024-02-07 08:00:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,TRAIL_SL_LONG,1.45207,0.0,100006.12,0.0,0.42
+2024-02-08 14:00:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,BUY,1.44873,1500.09,100006.12,0.0,0.0
+2024-02-08 14:45:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,TP1_LONG,1.45047619184842,900.06,100006.85,0.0,0.72
+2024-02-08 14:45:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,TP2_LONG,1.45027,300.02,100007.48,0.0,0.64
+2024-02-08 15:00:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,TRAIL_SL_LONG,1.4493964285714285,0.0,100007.62,0.0,0.14
+2024-02-12 15:45:00+00:00,S5_Momentum_Exhaustion,EUR_CAD,M15,BUY,1.44805,1500.11,100007.62,0.0,0.0
diff --git a/logs/backtest_trades_S5_EUR_GBP_M15.csv b/logs/backtest_trades_S5_EUR_GBP_M15.csv
new file mode 100644
index 0000000..f694308
--- /dev/null
+++ b/logs/backtest_trades_S5_EUR_GBP_M15.csv
@@ -0,0 +1,17 @@
+time,strategy,instrument,granularity,side,price,position_size,equity,drawdown,pnl
+2024-01-30 11:30:00+00:00,S5_Momentum_Exhaustion,EUR_GBP,M15,SELL_SHORT,0.8555,1500.0,100000.0,0.0,0.0
+2024-01-30 13:00:00+00:00,S5_Momentum_Exhaustion,EUR_GBP,M15,SL_EXIT_SHORT,0.8561964285714286,0.0,99998.78,0.0,-1.22
+2024-01-30 13:00:00+00:00,S5_Momentum_Exhaustion,EUR_GBP,M15,SELL_SHORT,0.85587,1999.98,99998.78,0.0,0.0
+2024-01-30 15:00:00+00:00,S5_Momentum_Exhaustion,EUR_GBP,M15,SL_EXIT_SHORT,0.8565428571428572,0.0,99997.21,0.0,-1.57
+2024-01-30 15:15:00+00:00,S5_Momentum_Exhaustion,EUR_GBP,M15,SELL_SHORT,0.85638,1499.96,99997.21,0.0,0.0
+2024-01-30 18:45:00+00:00,S5_Momentum_Exhaustion,EUR_GBP,M15,TP1_SHORT,0.854254919002561,899.97,99998.7,0.0,1.49
+2024-01-30 18:45:00+00:00,S5_Momentum_Exhaustion,EUR_GBP,M15,TP2_SHORT,0.8552528571428571,299.99,99999.49,0.0,0.79
+2024-01-30 18:45:00+00:00,S5_Momentum_Exhaustion,EUR_GBP,M15,TP3_SHORT,0.8544075000000001,0.0,100000.18,0.0,0.69
+2024-02-05 02:30:00+00:00,S5_Momentum_Exhaustion,EUR_GBP,M15,SELL_SHORT,0.85522,1500.0,100000.18,0.0,0.0
+2024-02-05 07:15:00+00:00,S5_Momentum_Exhaustion,EUR_GBP,M15,TP1_SHORT,0.854116705954906,900.0,100000.95,0.0,0.77
+2024-02-05 07:15:00+00:00,S5_Momentum_Exhaustion,EUR_GBP,M15,TP2_SHORT,0.8546285714285714,300.0,100001.37,0.0,0.41
+2024-02-05 07:15:00+00:00,S5_Momentum_Exhaustion,EUR_GBP,M15,TP3_SHORT,0.854185,0.0,100001.73,0.0,0.36
+2024-02-05 13:00:00+00:00,S5_Momentum_Exhaustion,EUR_GBP,M15,SELL_SHORT,0.85584,1500.03,100001.73,0.0,0.0
+2024-02-05 15:45:00+00:00,S5_Momentum_Exhaustion,EUR_GBP,M15,SL_EXIT_SHORT,0.856665,0.0,100000.28,0.0,-1.45
+2024-02-06 17:30:00+00:00,S5_Momentum_Exhaustion,EUR_GBP,M15,BUY,0.85351,1500.0,100000.28,0.0,0.0
+2024-02-07 08:00:00+00:00,S5_Momentum_Exhaustion,EUR_GBP,M15,SL_EXIT_LONG,0.852865,0.0,99999.15,0.0,-1.13
diff --git a/logs/backtest_trades_S5_GBP_AUD_M15.csv b/logs/backtest_trades_S5_GBP_AUD_M15.csv
new file mode 100644
index 0000000..44fc2e7
--- /dev/null
+++ b/logs/backtest_trades_S5_GBP_AUD_M15.csv
@@ -0,0 +1,48 @@
+time,strategy,instrument,granularity,side,price,position_size,equity,drawdown,pnl
+2024-01-30 07:30:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,BUY,1.9191,1500.0,100000.0,0.0,0.0
+2024-01-30 08:15:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,TP1_LONG,1.92172779240403,900.0,100000.82,0.0,0.82
+2024-01-30 08:15:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,TP2_LONG,1.9213157142857142,300.0,100001.51,0.0,0.69
+2024-01-30 08:30:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,TRAIL_SL_LONG,1.9211992857142859,0.0,100001.84,0.0,0.33
+2024-01-31 01:45:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,SELL_SHORT,1.9296,1500.03,100001.84,0.0,0.0
+2024-01-31 02:00:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,SL_EXIT_SHORT,1.93146,0.0,100000.4,0.0,-1.45
+2024-01-31 02:00:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,SELL_SHORT,1.93262,1500.01,100000.4,0.0,0.0
+2024-01-31 09:30:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,TP1_SHORT,1.9247026318134,900.0,100002.85,0.0,2.46
+2024-01-31 09:30:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,TP2_SHORT,1.92991,300.0,100003.7,0.0,0.84
+2024-01-31 09:30:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,TP3_SHORT,1.9278775,0.0,100004.43,0.0,0.74
+2024-02-01 05:15:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,SELL_SHORT,1.93756,1500.07,100004.43,0.0,0.0
+2024-02-01 08:15:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,SL_EXIT_SHORT,1.9397392857142857,0.0,100002.74,0.0,-1.69
+2024-02-01 08:15:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,SELL_SHORT,1.93944,1500.04,100002.74,0.0,0.0
+2024-02-01 12:00:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,SL_EXIT_SHORT,1.9416728571428572,0.0,100001.02,0.0,-1.73
+2024-02-02 12:00:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,BUY,1.93104,1500.02,100001.02,0.0,0.0
+2024-02-02 13:30:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,TP1_LONG,1.93364256556061,900.01,100001.83,0.0,0.81
+2024-02-02 13:30:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,TP2_LONG,1.93328,300.0,100002.52,0.0,0.7
+2024-02-02 13:30:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,TP3_LONG,1.9349600000000002,0.0,100003.13,0.0,0.61
+2024-02-06 04:30:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,BUY,1.9267,1500.05,100003.13,0.0,0.0
+2024-02-06 11:00:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,TP1_LONG,1.93282216145225,900.03,100005.04,0.0,1.91
+2024-02-06 11:00:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,TP2_LONG,1.9300742857142859,300.01,100006.09,0.0,1.05
+2024-02-06 11:00:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,TP3_LONG,1.9326050000000001,0.0,100007.01,0.0,0.92
+2024-02-07 02:00:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,BUY,1.92876,1500.11,100007.01,0.0,0.0
+2024-02-07 05:15:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,TP1_LONG,1.93103031205897,900.06,100007.71,0.0,0.71
+2024-02-07 05:15:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,TP2_LONG,1.9305842857142856,300.02,100008.28,0.0,0.57
+2024-02-07 05:45:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,TRAIL_SL_LONG,1.9302907142857144,0.0,100008.52,0.0,0.24
+2024-02-07 08:00:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,SELL_SHORT,1.93486,1500.13,100008.52,0.0,0.0
+2024-02-07 10:15:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,SL_EXIT_SHORT,1.9364210714285714,0.0,100007.31,0.0,-1.21
+2024-02-07 10:30:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,SELL_SHORT,1.93626,1500.11,100007.31,0.0,0.0
+2024-02-08 02:30:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,TIME_EXIT,1.93472,0.0,100008.5,0.0,1.19
+2024-02-08 08:00:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,SELL_SHORT,1.93876,1500.13,100008.5,0.0,0.0
+2024-02-08 09:15:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,SL_EXIT_SHORT,1.9402932142857143,0.0,100007.32,0.0,-1.19
+2024-02-09 11:00:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,BUY,1.93636,1500.11,100007.32,0.0,0.0
+2024-02-09 13:30:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,SL_EXIT_LONG,1.9345782142857144,0.0,100005.94,0.0,-1.38
+2024-02-09 13:30:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,BUY,1.93488,1500.09,100005.94,0.0,0.0
+2024-02-09 15:30:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,TP1_LONG,1.93907799267602,900.05,100007.24,0.0,1.3
+2024-02-09 15:30:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,TP2_LONG,1.9368657142857142,300.02,100007.85,0.0,0.62
+2024-02-09 15:30:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,TP3_LONG,1.9383549999999998,0.0,100008.39,0.0,0.54
+2024-02-09 20:15:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,BUY,1.93642,1500.13,100008.39,0.0,0.0
+2024-02-09 21:30:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,SL_EXIT_LONG,1.9355082142857143,0.0,100007.69,0.0,-0.71
+2024-02-09 21:30:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,BUY,1.93558,1500.12,100007.69,0.0,0.0
+2024-02-11 23:00:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,TP1_LONG,1.93755770741981,900.07,100008.3,0.0,0.61
+2024-02-11 23:00:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,TP2_LONG,1.9368128571428571,300.02,100008.68,0.0,0.38
+2024-02-11 23:00:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,TP3_LONG,1.9377375000000001,0.0,100009.02,0.0,0.33
+2024-02-12 11:45:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,BUY,1.9341,1500.14,100009.02,0.0,0.0
+2024-02-12 12:45:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,SL_EXIT_LONG,1.9327339285714285,0.0,100007.96,0.0,-1.06
+2024-02-12 17:15:00+00:00,S5_Momentum_Exhaustion,GBP_AUD,M15,BUY,1.9321,1500.12,100007.96,0.0,0.0
diff --git a/logs/backtest_trades_S5_GBP_CAD_M15.csv b/logs/backtest_trades_S5_GBP_CAD_M15.csv
new file mode 100644
index 0000000..3dad94c
--- /dev/null
+++ b/logs/backtest_trades_S5_GBP_CAD_M15.csv
@@ -0,0 +1,27 @@
+time,strategy,instrument,granularity,side,price,position_size,equity,drawdown,pnl
+2024-01-30 07:45:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,BUY,1.70214,1500.0,100000.0,0.0,0.0
+2024-01-30 08:00:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,SL_EXIT_LONG,1.7013214285714287,0.0,99999.28,0.0,-0.72
+2024-01-30 08:00:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,BUY,1.70135,1499.99,99999.28,0.0,0.0
+2024-01-30 08:15:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,SL_EXIT_LONG,1.7003857142857142,0.0,99998.43,0.0,-0.85
+2024-01-30 08:15:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,BUY,1.70174,1499.98,99998.43,0.0,0.0
+2024-01-30 09:00:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,SL_EXIT_LONG,1.7006750000000002,0.0,99997.49,0.0,-0.94
+2024-01-30 09:00:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,BUY,1.69928,1499.96,99997.49,0.0,0.0
+2024-01-30 13:45:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,TP1_LONG,1.70331863725739,899.98,99998.92,0.0,1.43
+2024-01-30 13:45:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,TP2_LONG,1.7011142857142856,299.99,99999.56,0.0,0.65
+2024-01-30 13:45:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,TP3_LONG,1.7024899999999998,0.0,100000.13,0.0,0.57
+2024-02-01 19:30:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,SELL_SHORT,1.7066,1500.0,100000.13,0.0,0.0
+2024-02-02 11:30:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,TIME_EXIT,1.70661,0.0,100000.12,0.0,-0.01
+2024-02-02 16:45:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,BUY,1.69922,1500.0,100000.12,0.0,0.0
+2024-02-05 08:45:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,TIME_EXIT,1.70097,0.0,100001.67,0.0,1.54
+2024-02-05 12:45:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,BUY,1.69472,1500.02,100001.67,0.0,0.0
+2024-02-06 04:45:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,TIME_EXIT,1.69645,0.0,100003.2,0.0,1.53
+2024-02-07 07:30:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,SELL_SHORT,1.70121,1500.05,100003.2,0.0,0.0
+2024-02-07 07:45:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,SL_EXIT_SHORT,1.7019610714285716,0.0,100002.54,0.0,-0.66
+2024-02-07 08:00:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,SELL_SHORT,1.70226,1500.04,100002.54,0.0,0.0
+2024-02-07 11:00:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,SL_EXIT_SHORT,1.7032114285714286,0.0,100001.7,0.0,-0.84
+2024-02-08 13:45:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,BUY,1.69671,1500.03,100001.7,0.0,0.0
+2024-02-08 14:45:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,TP1_LONG,1.69922005394069,900.02,100002.58,0.0,0.89
+2024-02-08 14:45:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,TP2_LONG,1.6988614285714285,300.01,100003.35,0.0,0.76
+2024-02-08 15:00:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,TRAIL_SL_LONG,1.6975371428571429,0.0,100003.49,0.0,0.15
+2024-02-09 12:30:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,BUY,1.69562,1500.05,100003.49,0.0,0.0
+2024-02-09 13:30:00+00:00,S5_Momentum_Exhaustion,GBP_CAD,M15,SL_EXIT_LONG,1.694435,0.0,100002.44,0.0,-1.05
diff --git a/results/phase1/S1_EUR_AUD_report.json b/results/phase1/S1_EUR_AUD_report.json
new file mode 100644
index 0000000..cc69510
--- /dev/null
+++ b/results/phase1/S1_EUR_AUD_report.json
@@ -0,0 +1,43 @@
+{
+  "pair": "EUR_AUD",
+  "strategy_id": 1,
+  "strategy_name": "S1_MA_Breakout_Retest",
+  "total_trades": 95,
+  "win_rate_pct": 45.26,
+  "avg_rr": 1.24,
+  "expectancy_pips": 0.19,
+  "sharpe_ratio": -0.28,
+  "max_drawdown_pct": -8.42,
+  "profit_factor": 0.96,
+  "total_pnl_pips": 18.43,
+  "total_pnl_dollars": -1952.89,
+  "avg_win_pips": 17.6,
+  "avg_loss_pips": 14.2,
+  "max_consecutive_wins": 5,
+  "max_consecutive_losses": 5,
+  "avg_hold_time_minutes": 202.3,
+  "best_trade_pips": 56.97,
+  "worst_trade_pips": -24.57,
+  "best_trade_dollars": 2281.5,
+  "worst_trade_dollars": -1061.56,
+  "final_equity": 98047.11,
+  "starting_equity": 100000.0,
+  "session_breakdown": {
+    "LONDON": {
+      "trades": 51,
+      "win_rate": 39.21568627450981,
+      "total_pnl_pips": -72.08655191531844
+    },
+    "OVERLAP": {
+      "trades": 44,
+      "win_rate": 52.27272727272727,
+      "total_pnl_pips": 90.52045431346279
+    }
+  },
+  "exit_reasons": {
+    "SL": 52,
+    "TP1+SL": 19,
+    "TP3": 9,
+    "TP1+TP2+SL": 15
+  }
+}
\ No newline at end of file
diff --git a/results/phase1/S1_EUR_AUD_trades.csv b/results/phase1/S1_EUR_AUD_trades.csv
new file mode 100644
index 0000000..4407ed8
--- /dev/null
+++ b/results/phase1/S1_EUR_AUD_trades.csv
@@ -0,0 +1,96 @@
+timestamp,strategy_id,pair,signal_direction,entry_price,sl_price,tp1_price,tp2_price,tp3_price,lot_size,confluence_score,session,spread_at_entry,atr_at_entry,adx_at_entry,rsi_at_entry,ema_50_value,ema_200_value,vwap_deviation,news_within_60min,macd_hist_at_entry,stoch_k_at_entry,distance_from_ema50_pips,candle_body_ratio,hour_of_day,day_of_week,exit_price,exit_reason,exit_time,pnl_pips,pnl_dollars,hold_time_minutes,win
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diff --git a/results/phase1/S1_EUR_CAD_report.json b/results/phase1/S1_EUR_CAD_report.json
new file mode 100644
index 0000000..2eafbb7
--- /dev/null
+++ b/results/phase1/S1_EUR_CAD_report.json
@@ -0,0 +1,43 @@
+{
+  "pair": "EUR_CAD",
+  "strategy_id": 1,
+  "strategy_name": "S1_MA_Breakout_Retest",
+  "total_trades": 83,
+  "win_rate_pct": 44.58,
+  "avg_rr": 1.1,
+  "expectancy_pips": -0.9,
+  "sharpe_ratio": -1.0,
+  "max_drawdown_pct": -9.26,
+  "profit_factor": 0.88,
+  "total_pnl_pips": -74.43,
+  "total_pnl_dollars": -5635.08,
+  "avg_win_pips": 15.14,
+  "avg_loss_pips": 13.8,
+  "max_consecutive_wins": 4,
+  "max_consecutive_losses": 5,
+  "avg_hold_time_minutes": 300.4,
+  "best_trade_pips": 33.9,
+  "worst_trade_pips": -22.21,
+  "best_trade_dollars": 2148.48,
+  "worst_trade_dollars": -1017.68,
+  "final_equity": 94364.92,
+  "starting_equity": 100000.0,
+  "session_breakdown": {
+    "LONDON": {
+      "trades": 33,
+      "win_rate": 27.27272727272727,
+      "total_pnl_pips": -197.00053954000785
+    },
+    "OVERLAP": {
+      "trades": 50,
+      "win_rate": 56.00000000000001,
+      "total_pnl_pips": 122.56671504231885
+    }
+  },
+  "exit_reasons": {
+    "SL": 46,
+    "TP1+TP2+SL": 18,
+    "TP1+SL": 15,
+    "TP3": 4
+  }
+}
\ No newline at end of file
diff --git a/results/phase1/S1_EUR_CAD_trades.csv b/results/phase1/S1_EUR_CAD_trades.csv
new file mode 100644
index 0000000..9f06e7b
--- /dev/null
+++ b/results/phase1/S1_EUR_CAD_trades.csv
@@ -0,0 +1,84 @@
+timestamp,strategy_id,pair,signal_direction,entry_price,sl_price,tp1_price,tp2_price,tp3_price,lot_size,confluence_score,session,spread_at_entry,atr_at_entry,adx_at_entry,rsi_at_entry,ema_50_value,ema_200_value,vwap_deviation,news_within_60min,macd_hist_at_entry,stoch_k_at_entry,distance_from_ema50_pips,candle_body_ratio,hour_of_day,day_of_week,exit_price,exit_reason,exit_time,pnl_pips,pnl_dollars,hold_time_minutes,win
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+2023-01-06 16:15:00+00:00,1,EUR_CAD,SHORT,1.42787,1.4300908206876306,1.4249775379371084,1.4228558965618472,1.4196734344989554,421422.06,3,OVERLAP,2.5,0.0021216413752611577,29.340895599750105,46.23683836426708,1.4292564265014716,1.4312771186246918,-13.966299793326531,False,2.803463971660144e-06,39.06741427839868,-10.964265014714591,0.5338983050846867,16,4,1.4300908206876306,SL,2023-01-06 17:45:00+00:00,-22.208206876306313,-935.9028290719172,90,False
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+2023-02-08 11:00:00+00:00,1,EUR_CAD,SHORT,1.4367299999999998,1.4376871291986129,1.435918612404161,1.435184354006935,1.434082966411096,964835.22,3,LONDON,2.5,0.0007342583972260084,17.664285115087047,46.35629549195941,1.4374326725335362,1.4402588425867902,-4.372419254223825,False,-4.753726177587725e-05,29.95690108272095,-4.126725335362735,0.3478260869564588,11,2,1.4367299999999998,TP1+SL,2023-02-08 12:00:00+00:00,4.056937979194375,391.427664768236,60,True
+2023-03-09 16:30:00+00:00,1,EUR_CAD,LONG,1.4567,1.4553886886092047,1.458093934172386,1.4592165569539768,1.4609004911263628,707220.46,2,OVERLAP,2.5,0.0011226227815907003,20.0859087452337,50.4884443767606,1.45630217919808,1.454158096125363,-1.7014956473992982,False,-5.89887801584572e-05,71.92906072216249,1.078208019200666,0.3103448275863125,16,3,1.4609004911263628,TP3,2023-03-09 18:45:00+00:00,22.92032397658472,1620.9722066069276,135,True
+2023-04-13 09:30:00+00:00,1,EUR_CAD,LONG,1.4772,1.4759957078871488,1.478302876338554,1.4792314605642565,1.4806243369028105,783527.48,5,LONDON,2.5,0.000928584225702641,27.592080539280698,56.764189829692675,1.4760433785756941,1.4739973340819494,4.991620092298366,False,0.00025855606496601635,56.82841963676659,8.666214243058246,0.3008130081301048,9,3,1.4759957078871488,SL,2023-04-13 10:15:00+00:00,-12.042921128512239,-943.5959643661952,45,False
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+2023-08-17 16:30:00+00:00,1,EUR_CAD,SHORT,1.4708999999999999,1.4720661697553052,1.4697014907340846,1.4687091512234745,1.467220641957559,802371.88,3,OVERLAP,2.5,0.000992339510610257,26.066479609917202,49.31822708625934,1.4713146979579017,1.4717296376032813,-3.432203857796079,False,-0.00012537831325181177,66.4312513793851,-1.2469795790170934,0.49315068493131103,16,3,1.4708999999999999,TP1+SL,2023-08-17 18:15:00+00:00,5.992546329576243,480.82506644491895,105,True
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+2023-08-30 16:15:00+00:00,1,EUR_CAD,LONG,1.4781600000000001,1.4767165843418868,1.4798002469743399,1.4810870782905665,1.4830173252649061,660364.9,2,OVERLAP,2.5,0.0012868313162265569,27.804702673419886,50.881808986122564,1.4768003275250423,1.4738330041043377,-0.10762607004055624,False,-0.0003609266359280223,34.679583628107245,10.696724749577058,0.391304347826031,16,2,1.4767165843418868,SL,2023-08-31 07:00:00+00:00,-14.434156581133452,-953.1810367284534,885,False
diff --git a/results/phase1/S1_EUR_NZD_report.json b/results/phase1/S1_EUR_NZD_report.json
new file mode 100644
index 0000000..f299b22
--- /dev/null
+++ b/results/phase1/S1_EUR_NZD_report.json
@@ -0,0 +1,43 @@
+{
+  "pair": "EUR_NZD",
+  "strategy_id": 1,
+  "strategy_name": "S1_MA_Breakout_Retest",
+  "total_trades": 113,
+  "win_rate_pct": 34.51,
+  "avg_rr": 1.24,
+  "expectancy_pips": -3.75,
+  "sharpe_ratio": -4.05,
+  "max_drawdown_pct": -27.27,
+  "profit_factor": 0.59,
+  "total_pnl_pips": -423.65,
+  "total_pnl_dollars": -25976.66,
+  "avg_win_pips": 20.64,
+  "avg_loss_pips": 16.6,
+  "max_consecutive_wins": 4,
+  "max_consecutive_losses": 10,
+  "avg_hold_time_minutes": 199.5,
+  "best_trade_pips": 53.65,
+  "worst_trade_pips": -31.04,
+  "best_trade_dollars": 1748.96,
+  "worst_trade_dollars": -1000.0,
+  "final_equity": 74023.34,
+  "starting_equity": 100000.0,
+  "session_breakdown": {
+    "LONDON": {
+      "trades": 55,
+      "win_rate": 38.18181818181819,
+      "total_pnl_pips": -36.44186517391843
+    },
+    "OVERLAP": {
+      "trades": 58,
+      "win_rate": 31.03448275862069,
+      "total_pnl_pips": -387.2043441397173
+    }
+  },
+  "exit_reasons": {
+    "SL": 74,
+    "TP1+SL": 18,
+    "TP1+TP2+SL": 16,
+    "TP3": 5
+  }
+}
\ No newline at end of file
diff --git a/results/phase1/S1_EUR_NZD_trades.csv b/results/phase1/S1_EUR_NZD_trades.csv
new file mode 100644
index 0000000..25eb3b8
--- /dev/null
+++ b/results/phase1/S1_EUR_NZD_trades.csv
@@ -0,0 +1,114 @@
+timestamp,strategy_id,pair,signal_direction,entry_price,sl_price,tp1_price,tp2_price,tp3_price,lot_size,confluence_score,session,spread_at_entry,atr_at_entry,adx_at_entry,rsi_at_entry,ema_50_value,ema_200_value,vwap_deviation,news_within_60min,macd_hist_at_entry,stoch_k_at_entry,distance_from_ema50_pips,candle_body_ratio,hour_of_day,day_of_week,exit_price,exit_reason,exit_time,pnl_pips,pnl_dollars,hold_time_minutes,win
+2021-01-29 16:30:00+00:00,1,EUR_NZD,SHORT,1.6869599999999998,1.688765351487862,1.6844439455364137,1.6825732425606894,1.6797671880971032,553908.76,3,OVERLAP,2.5,0.0018707029757241702,32.43866992055753,53.56252913935056,1.6875406785606892,1.6891795688429838,0.7427355111500056,False,0.0005180271614128723,91.03893260450518,-2.9067856068931697,0.5098039215686915,16,4,1.688765351487862,SL,2021-01-29 17:30:00+00:00,-18.053514878622057,-1000.0000040059094,60,False
+2021-02-17 11:30:00+00:00,1,EUR_NZD,LONG,1.6798000000000002,1.6783862782378984,1.6812011652863046,1.6823286088105074,1.684019774096812,700279.24,4,LONDON,2.5,0.001127443524202965,24.89461846466009,56.24115209430774,1.6787554794701791,1.6781384211482062,19.503630951920314,False,0.0003105505466959994,91.87109841712078,7.545205298209456,0.5138888888888247,11,2,1.6783862782378984,SL,2021-02-17 12:15:00+00:00,-14.137217621017495,-990.0000011360739,45,False
+2021-02-18 11:45:00+00:00,1,EUR_NZD,SHORT,1.67366,1.6749164704358799,1.6724605886923605,1.6714676478206008,1.6699782365129612,780042.23,3,LONDON,2.5,0.0009929408717597066,34.890192018183676,45.88468242022439,1.674897266059154,1.6764936835889814,-1.8389774771510048,False,-1.5850491742707513e-05,65.2559799207739,-9.472660591538506,0.5657894736840491,11,3,1.6749164704358799,SL,2021-02-18 13:30:00+00:00,-12.56470435879953,-980.1000007328704,105,False
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+2023-05-18 10:45:00+00:00,1,EUR_NZD,SHORT,1.73186,1.7333476270306398,1.7299371189080806,1.7284618648468009,1.7262489837548813,508219.1,2,LONDON,2.5,0.0014752540612796609,11.749506686746987,49.90228255326226,1.7324439484164658,1.7359869190146864,-7.1930229298788895,False,6.401569877903929e-05,30.72702331962,-2.939484164656836,0.3823529411763553,10,3,1.7333476270306398,SL,2023-05-18 12:45:00+00:00,-14.876270306398707,-756.0404706474676,120,False
+2023-05-19 15:45:00+00:00,1,EUR_NZD,SHORT,1.7207899999999998,1.7225259040644507,1.7186322878066482,1.717000479677747,1.7145527674843952,431175.94,3,OVERLAP,2.5,0.0016318081289011588,48.13786731185851,54.96641001895401,1.7215296294034435,1.7284066238556843,14.37294215009155,False,0.00032705927280111946,80.63845318809051,-4.496294034435078,0.4645669291338913,15,4,1.7225259040644507,SL,2023-05-21 22:00:00+00:00,-17.35904064450899,-748.4800667394371,3255,False
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+2023-07-03 12:45:00+00:00,1,EUR_NZD,SHORT,1.77375,1.775284502261679,1.7718664932149628,1.7704174886916046,1.7682439819065676,485577.72,3,LONDON,2.5,0.0014490045233581286,27.373607272843284,50.27010593798747,1.7747233809814484,1.7805620823279387,8.392453227190622,False,0.00030849808050297347,26.915767683527964,-6.833809814483782,0.37500000000006123,12,0,1.775284502261679,SL,2023-07-03 13:00:00+00:00,-15.345022616790605,-745.1201095609615,15,False
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+2023-07-13 16:30:00+00:00,1,EUR_NZD,SHORT,1.7558699999999998,1.7578374012539666,1.7534977962381002,1.7517229937301673,1.7490607899682675,371196.38,2,OVERLAP,2.5,0.0017748025079331038,26.83491562573239,51.48641218076932,1.7572978613250143,1.7650058322294992,5.347042520482681,False,0.0004656559583111154,71.19497588071303,-11.378613250143665,0.460176991150479,16,3,1.7558699999999998,TP1+SL,2023-07-13 21:15:00+00:00,11.86101880949808,440.27672451975974,285,True
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diff --git a/results/phase1/S1_GBP_AUD_report.json b/results/phase1/S1_GBP_AUD_report.json
new file mode 100644
index 0000000..11d065d
--- /dev/null
+++ b/results/phase1/S1_GBP_AUD_report.json
@@ -0,0 +1,43 @@
+{
+  "pair": "GBP_AUD",
+  "strategy_id": 1,
+  "strategy_name": "S1_MA_Breakout_Retest",
+  "total_trades": 122,
+  "win_rate_pct": 39.34,
+  "avg_rr": 1.36,
+  "expectancy_pips": -1.19,
+  "sharpe_ratio": -1.55,
+  "max_drawdown_pct": -17.01,
+  "profit_factor": 0.81,
+  "total_pnl_pips": -145.24,
+  "total_pnl_dollars": -12339.87,
+  "avg_win_pips": 22.33,
+  "avg_loss_pips": 16.45,
+  "max_consecutive_wins": 4,
+  "max_consecutive_losses": 9,
+  "avg_hold_time_minutes": 237.3,
+  "best_trade_pips": 44.83,
+  "worst_trade_pips": -26.92,
+  "best_trade_dollars": 2242.77,
+  "worst_trade_dollars": -1000.0,
+  "final_equity": 87660.13,
+  "starting_equity": 100000.0,
+  "session_breakdown": {
+    "LONDON": {
+      "trades": 58,
+      "win_rate": 41.37931034482759,
+      "total_pnl_pips": -22.01687471378051
+    },
+    "OVERLAP": {
+      "trades": 64,
+      "win_rate": 37.5,
+      "total_pnl_pips": -123.2260709958683
+    }
+  },
+  "exit_reasons": {
+    "SL": 74,
+    "TP1+SL": 21,
+    "TP1+TP2+SL": 17,
+    "TP3": 10
+  }
+}
\ No newline at end of file
diff --git a/results/phase1/S1_GBP_AUD_trades.csv b/results/phase1/S1_GBP_AUD_trades.csv
new file mode 100644
index 0000000..084a6fc
--- /dev/null
+++ b/results/phase1/S1_GBP_AUD_trades.csv
@@ -0,0 +1,123 @@
+timestamp,strategy_id,pair,signal_direction,entry_price,sl_price,tp1_price,tp2_price,tp3_price,lot_size,confluence_score,session,spread_at_entry,atr_at_entry,adx_at_entry,rsi_at_entry,ema_50_value,ema_200_value,vwap_deviation,news_within_60min,macd_hist_at_entry,stoch_k_at_entry,distance_from_ema50_pips,candle_body_ratio,hour_of_day,day_of_week,exit_price,exit_reason,exit_time,pnl_pips,pnl_dollars,hold_time_minutes,win
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+2021-05-18 11:45:00+00:00,1,GBP_AUD,LONG,1.8211600000000001,1.819838844132304,1.822663467603088,1.82382577933848,1.8255692469415679,724171.87,2,LONDON,2.0,0.0011623117353919464,17.817735329017566,55.73386081952166,1.819824960417656,1.8181090441163263,1.3329935310468777,False,-4.004618488619632e-05,64.07407407407361,10.950395823441372,0.3896103896105544,11,1,1.819838844132304,SL,2021-05-18 12:15:00+00:00,-13.21155867696211,-956.7439152710376,30,False
+2021-05-19 15:00:00+00:00,1,GBP_AUD,LONG,1.8268,1.8250470021423824,1.829028993572853,1.8306749892880885,1.8331439828609417,540318.1,3,OVERLAP,2.0,0.0016459957152354116,33.25087942824408,46.93326725634469,1.8259242906817923,1.8219255830727492,-11.83553596231679,False,-0.0004343658592538131,22.06788979490987,6.357093182076667,0.4736842105263363,15,2,1.8250470021423824,SL,2021-05-19 16:45:00+00:00,-17.52997857617622,-947.176471732024,105,False
+2021-06-15 09:45:00+00:00,1,GBP_AUD,LONG,1.83101,1.8298911152131934,1.83235665436042,1.833414423934033,1.835001078294453,838070.84,2,LONDON,2.0,0.001057769573613241,30.96248767382072,47.64002592345275,1.8306744981892642,1.8296126335883978,-7.81382411099818,False,-0.000155612519529435,24.41565782655923,0.9550181073580966,0.4153846153845786,9,1,1.8298911152131934,SL,2021-06-15 10:30:00+00:00,-11.18884786806662,-937.7047131422802,45,False
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+2023-05-30 09:00:00+00:00,1,GBP_AUD,LONG,1.89235,1.890696935507786,1.8943091934766414,1.8957753224610692,1.8979745159377106,521020.39,4,LONDON,2.0,0.001466128984427671,47.67012146820124,48.6297242673626,1.89182494875451,1.8908522826425511,6.174933088907153,False,-0.0003262884651146524,54.27341317000588,2.850512454899423,0.375,9,1,1.8979745159377106,TP3,2023-05-30 10:00:00+00:00,31.320966641835923,1631.8862254906344,60,True
+2023-05-31 09:15:00+00:00,1,GBP_AUD,LONG,1.90741,1.9057124680610305,1.9095925958169089,1.911207659694848,1.9136302555117568,516985.37,2,LONDON,2.0,0.0016150638779391779,16.249738099416422,49.39703102634701,1.9063341035529318,1.899699511816073,1.7808247277506517,False,-0.00027275549562161863,40.13308146983008,8.35896447068274,0.44047619047619363,9,2,1.910403565528469,TP1+TP2+SL,2023-05-31 13:45:00+00:00,28.2930892260258,1462.7113201959962,270,True
+2023-06-01 10:45:00+00:00,1,GBP_AUD,LONG,1.91139,1.9099305780985518,1.9132482657043448,1.9146471095072415,1.9167453752115864,611355.97,2,LONDON,2.0,0.0013988438028966373,16.539506010476913,51.51908937963912,1.911046899652725,1.9072801161689186,1.0198369571523536,False,-2.536955359145692e-05,46.2044802351607,1.0310034727489814,0.339805825242618,10,3,1.91139,TP1+SL,2023-06-01 12:00:00+00:00,9.291328521724429,568.0309160987504,75,True
+2023-06-01 13:15:00+00:00,1,GBP_AUD,LONG,1.91307,1.9115823910751777,1.9151328267744667,1.9166680446241111,1.9189708713985778,603590.49,4,OVERLAP,2.0,0.0015352178496444626,32.89173274163042,57.25818135894341,1.9113991075891084,1.9077397511864038,12.5643263894748,False,0.00012773050679444603,70.5696202531668,14.30892410891671,0.3737373737373375,13,3,1.9115823910751777,SL,2023-06-01 14:00:00+00:00,-14.87608924822359,-897.9065998619008,45,False
+2023-06-19 13:00:00+00:00,1,GBP_AUD,LONG,1.87053,1.8688276033433702,1.8725171899698894,1.874001983283149,1.8762291732530383,522162.4,2,OVERLAP,2.0,0.0014847933132595824,17.588911952973657,52.49369121405025,1.869080836842585,1.865024038112811,14.246971148510479,False,3.2672810083448755e-05,29.392961047726605,12.091631574149275,0.525773195876225,13,0,1.8688276033433702,SL,2023-06-19 13:30:00+00:00,-17.023966566298565,-888.9275239778218,30,False
+2023-06-21 11:30:00+00:00,1,GBP_AUD,LONG,1.88172,1.8798894974950715,1.8845115075147854,1.8865325125246424,1.8895640200394277,480763.21,4,LONDON,2.0,0.002021005009856922,31.252972756805004,54.80576885740169,1.8802501638980624,1.8766976584675685,20.229931032464954,False,9.356256243777577e-05,95.67908709639795,12.298361019376092,0.40944881889773554,11,2,1.88172,TP1+SL,2023-06-21 13:45:00+00:00,13.95753757392648,671.0270567736508,135,True
+2023-06-22 13:45:00+00:00,1,GBP_AUD,LONG,1.88457,1.8819767028647059,1.8879998914058826,1.8904464856764711,1.8941163770823537,341938.65,5,OVERLAP,2.0,0.0024465942705884157,39.516731261018215,56.50425594531772,1.8819112809211536,1.879452979853171,18.557969991404022,False,0.0002155084399739042,74.47543160690664,24.187190788464985,0.350364963503652,13,3,1.88457,TP1+SL,2023-06-22 18:30:00+00:00,17.14945702941284,586.4062184870437,285,True
+2023-06-23 16:00:00+00:00,1,GBP_AUD,LONG,1.89999,1.8980331648721123,1.902830505383663,1.9048841756394381,1.9079646810231008,456151.15,2,OVERLAP,2.0,0.002053670255775197,29.6805341179315,42.36867477633837,1.899731294658499,1.8898042381053946,-23.69709047383095,False,-0.0008356697732780883,9.26644867031685,0.18705341501057404,0.7065217391304531,16,4,1.9079646810231008,TP3,2023-06-26 00:45:00+00:00,44.834415882829994,2045.127036453117,3405,True
+2023-06-26 16:00:00+00:00,1,GBP_AUD,LONG,1.90504,1.9032970021312796,1.9070889936061615,1.9086149893436024,1.910903982949764,523846.8,2,OVERLAP,2.0,0.0015259957374409723,16.01842751864554,51.87556845708198,1.9046454690404193,1.8989943240640794,-2.1741374881911746,False,0.0002325416407716958,70.50326073582076,1.5453095958073426,0.40298507462688543,16,0,1.9032970021312796,SL,2023-06-26 18:45:00+00:00,-17.429978687204528,-913.0638559360293,165,False
+2023-06-28 09:45:00+00:00,1,GBP_AUD,LONG,1.91374,1.9120499516099958,1.916080145170013,1.9178002419500215,1.9203803871200344,534856.41,2,LONDON,2.0,0.0017200967800086039,28.04065042588924,44.152518136397504,1.9133540248436605,1.9064916934144456,-14.293579293169767,False,-0.0005511636039198026,16.83333333333087,1.459751563395173,0.47787610619473897,9,2,1.9120499516099958,SL,2023-06-28 11:30:00+00:00,-16.900483900041948,-903.9332146039237,105,False
+2023-07-07 11:15:00+00:00,1,GBP_AUD,LONG,1.9214200000000001,1.9198223429440233,1.9233629711679303,1.9248182852798839,1.927001256447814,560128.9,5,LONDON,2.0,0.0014553141119535106,21.400561731700293,57.271971373075644,1.9199382806762575,1.9147686067747003,17.180981983175858,False,0.00023926977985491027,81.34152671212365,12.417193237426183,0.3470588235294579,11,4,1.923306573376114,TP1+TP2+SL,2023-07-07 12:15:00+00:00,23.68285843153006,1326.545344210866,60,True
+2023-07-12 10:30:00+00:00,1,GBP_AUD,LONG,1.93227,1.9303863145382805,1.9345610563851585,1.9362484273085976,1.9387794836937564,482118.36,2,LONDON,2.0,0.0016873709234390983,35.52114251668358,51.252223398253754,1.9315711234458097,1.929290454167803,-10.844035611730085,False,-0.00036528717771509736,40.22125095937688,4.588765541901907,0.4820143884891707,10,2,1.9303863145382805,SL,2023-07-12 11:00:00+00:00,-18.836854617194643,-908.1593455600308,30,False
+2023-07-25 16:15:00+00:00,1,GBP_AUD,SHORT,1.89514,1.896909233646863,1.8929822990594112,1.8913838317656853,1.8889861308250964,508173.55,2,OVERLAP,2.0,0.001598467293725905,18.55549008498843,46.6146367923819,1.896949955789038,1.9010939905917537,-8.167633336593205,False,0.00017671865771266924,70.77409135931144,-15.699557890378468,0.635135135135127,16,1,1.896909233646863,SL,2023-07-25 16:30:00+00:00,-17.692336468628778,-899.077743105755,15,False
+2023-08-14 08:45:00+00:00,1,GBP_AUD,LONG,1.95713,1.955566041060494,1.958971876818518,1.96035979469753,1.9624416715160478,569124.26,3,LONDON,2.0,0.0013879178790119553,26.947850541903282,49.08234895749326,1.9564855969042152,1.9513442589846945,13.323381729315464,False,-0.00023942914525644737,70.27211223347668,4.044030957848221,0.5647058823531195,8,0,1.955566041060494,SL,2023-08-14 09:15:00+00:00,-15.63958939506005,-890.0869741167398,30,False
+2023-08-14 16:45:00+00:00,1,GBP_AUD,LONG,1.95516,1.9538120004783608,1.9571339985649177,1.958609997608196,1.9608239961731138,653699.12,3,OVERLAP,2.0,0.0014759990432784186,17.061454778152797,52.532032322702705,1.9548963111424538,1.9521678090194643,5.796550567060965,False,0.0002056173465182111,68.68401343287461,0.23688857546222408,0.3571428571428383,16,0,1.9538120004783608,SL,2023-08-14 17:15:00+00:00,-13.479995216392204,-881.1861010559793,30,False
+2023-08-17 15:15:00+00:00,1,GBP_AUD,LONG,1.9868000000000001,1.98482205379878,1.98925383860366,1.9910497310061,1.9937435696097598,441050.54,3,OVERLAP,2.0,0.0017958924024399445,22.965865242340744,56.14379437011414,1.98555524132381,1.977724534843546,18.970237441082727,False,0.0004880242598531402,78.05981896891133,10.047586761900984,0.43434343434356804,15,3,1.9891208290439637,TP1+TP2+SL,2023-08-17 21:00:00+00:00,29.660044124526053,1308.1578477546043,345,True
+2023-08-18 09:30:00+00:00,1,GBP_AUD,LONG,1.98881,1.9867208276717527,1.991417516984742,1.9933158616412365,1.9961633786259783,423830.91,5,LONDON,2.0,0.0018983446564945904,27.458416206411105,53.04857026177706,1.9879933131496939,1.983320242005096,7.890793780458427,False,0.0003423815263493136,69.63314885946609,5.766868503060962,0.3096774193548738,9,4,1.9867208276717527,SL,2023-08-18 14:15:00+00:00,-20.891723282472352,-885.4558090278443,285,False
diff --git a/results/phase1/S2_EUR_USD_report.json b/results/phase1/S2_EUR_USD_report.json
new file mode 100644
index 0000000..a592463
--- /dev/null
+++ b/results/phase1/S2_EUR_USD_report.json
@@ -0,0 +1,46 @@
+{
+  "pair": "EUR_USD",
+  "strategy_id": 2,
+  "strategy_name": "S2_Session_VWAP_Reversal",
+  "total_trades": 384,
+  "win_rate_pct": 25.0,
+  "avg_rr": 2.47,
+  "expectancy_pips": -1.3,
+  "sharpe_ratio": -1.61,
+  "max_drawdown_pct": -54.94,
+  "profit_factor": 0.78,
+  "total_pnl_pips": -499.49,
+  "total_pnl_dollars": -46914.05,
+  "avg_win_pips": 24.3,
+  "avg_loss_pips": 9.84,
+  "max_consecutive_wins": 5,
+  "max_consecutive_losses": 19,
+  "avg_hold_time_minutes": 201.2,
+  "best_trade_pips": 77.52,
+  "worst_trade_pips": -24.78,
+  "best_trade_dollars": 6405.35,
+  "worst_trade_dollars": -1103.24,
+  "final_equity": 53085.95,
+  "starting_equity": 100000.0,
+  "session_breakdown": {
+    "LONDON": {
+      "trades": 217,
+      "win_rate": 27.64976958525346,
+      "total_pnl_pips": -202.79071203238428
+    },
+    "OVERLAP": {
+      "trades": 167,
+      "win_rate": 21.55688622754491,
+      "total_pnl_pips": -296.70058575933086
+    }
+  },
+  "exit_reasons": {
+    "SL": 287,
+    "TP3": 35,
+    "TP1+TP2+SL": 43,
+    "TP1+SL": 12,
+    "TIME": 4,
+    "TP1+TIME": 2,
+    "TP1+TP2+TIME": 1
+  }
+}
\ No newline at end of file
diff --git a/results/phase1/S2_EUR_USD_trades.csv b/results/phase1/S2_EUR_USD_trades.csv
new file mode 100644
index 0000000..a9ea36d
--- /dev/null
+++ b/results/phase1/S2_EUR_USD_trades.csv
@@ -0,0 +1,385 @@
+timestamp,strategy_id,pair,signal_direction,entry_price,sl_price,tp1_price,tp2_price,tp3_price,lot_size,confluence_score,session,spread_at_entry,atr_at_entry,adx_at_entry,rsi_at_entry,ema_50_value,ema_200_value,vwap_deviation,news_within_60min,macd_hist_at_entry,stoch_k_at_entry,distance_from_ema50_pips,candle_body_ratio,hour_of_day,day_of_week,exit_price,exit_reason,exit_time,pnl_pips,pnl_dollars,hold_time_minutes,win
+2021-01-07 10:15:00+00:00,2,EUR_USD,LONG,1.2256000000000002,1.2246281660455012,1.2287069526030214,1.2294695468844594,1.2309947354473356,1028982.36,2,LONDON,1.5,0.001003667908997842,68.08741545808022,17.500401710839014,1.230837054183121,1.2304398991183685,-32.96952603021319,False,-0.0005007520093533821,10.394959743115917,-54.27054183120949,0.7356321839080783,10,3,1.2246281660455012,SL,2021-01-07 10:45:00+00:00,-9.718339544990684,-999.999996028584,30,False
+2021-01-08 13:30:00+00:00,2,EUR_USD,SHORT,1.2269599999999998,1.2284128873790565,1.2240072188501296,1.2234150151920469,1.2222306078758813,681401.75,2,OVERLAP,1.5,0.0010257747581129642,32.45096645591533,71.0041171846183,1.2250973568879,1.2272337845406336,31.427811498703218,False,0.0002882273663560447,64.03179077597594,20.52643112099961,0.560723514211914,13,4,1.2284128873790565,SL,2021-01-08 13:45:00+00:00,-14.52887379056689,-990.0000026421411,15,False
+2021-01-08 14:15:00+00:00,2,EUR_USD,SHORT,1.2275599999999998,1.229068547220039,1.2245510706361358,1.2238338470748227,1.2223993999521963,649697.93,2,OVERLAP,1.5,0.001237094440078057,48.711237555240814,70.38510853722131,1.2253311742882043,1.2272315647422076,31.989293638641225,False,0.00038080933478129705,74.33862433862181,24.188257117956358,0.5812807881773264,14,4,1.2223993999521963,TP3,2021-01-08 15:15:00+00:00,35.827082129144976,2327.678109724549,60,True
+2021-01-13 11:45:00+00:00,2,EUR_USD,LONG,1.21646,1.2151120749661941,1.2184225880164439,1.2189210368578793,1.2199179345407503,744386.19,2,LONDON,1.5,0.000771950022537308,71.39897274843644,23.35222510283316,1.2194255075688525,1.2189525290826597,-21.525880164439126,False,-0.00022736552235585767,19.725198938996527,-31.555075688525225,0.5399999999999245,11,2,1.2151120749661941,SL,2021-01-13 17:15:00+00:00,-13.479250338059854,-1003.3767803204587,330,False
+2021-01-15 14:45:00+00:00,2,EUR_USD,LONG,1.2099300000000002,1.2088890286461393,1.2123661932636263,1.2127882719528085,1.2136324293311729,954246.25,2,OVERLAP,1.5,0.0009619427077212761,32.5837953432149,28.173702981024846,1.2130940407034476,1.2149200086850835,-26.26193263626231,False,-0.00018592465394388046,20.515138729834245,-33.540407034475805,0.674999999999993,14,4,1.2088890286461393,SL,2021-01-15 15:00:00+00:00,-10.409713538608312,-993.3430107791212,15,False
+2021-01-19 10:00:00+00:00,2,EUR_USD,SHORT,1.2126499999999998,1.2133015967350234,1.2114087721745574,1.211167644137879,1.2106853880645216,1509230.37,2,LONDON,1.5,0.0007231934700469164,37.526412275835746,72.6104950689302,1.2097107495585095,1.2096245249201378,14.312278254424715,False,0.000184875101827783,85.5651619949055,31.29250441490372,0.9026548672567728,10,1,1.2133015967350234,SL,2021-01-19 10:45:00+00:00,-6.51596735023574,-983.4095814904207,45,False
+2021-01-19 14:00:00+00:00,2,EUR_USD,SHORT,1.21426,1.2148241536441382,1.2124782389187927,1.2120103030230267,1.2110744312314945,1725727.55,2,OVERLAP,1.5,0.0007083072882767511,44.245909136688546,71.77134260648333,1.2113409286356525,1.210140411540742,19.71761081207335,False,3.3802868557625113e-05,87.19813464606432,31.09071364347571,0.9166666666668016,14,1,1.212619088322548,TP1+TP2+SL,2021-01-19 15:30:00+00:00,19.244112341619935,3321.0094843228535,90,True
+2021-01-20 12:45:00+00:00,2,EUR_USD,LONG,1.21001,1.2092499554865626,1.2124292265522614,1.2130273088080978,1.214223473319771,1324640.29,2,LONDON,1.5,0.0007200890268751957,64.39311177913255,22.16123391751232,1.2128738779676358,1.2119634177917615,-26.092265522614788,False,-0.0001950651424778647,22.72839526543746,-30.538779676358985,0.7586206896554695,12,2,1.2092499554865626,SL,2021-01-20 14:45:00+00:00,-7.600445134374745,-1006.7855846927251,120,False
+2021-01-20 14:45:00+00:00,2,EUR_USD,LONG,1.2082300000000001,1.2072202396639886,1.2116895766524074,1.2124369118145581,1.2139315821388599,987083.46,2,OVERLAP,1.5,0.0008995206720228281,45.31073286893659,23.31067709923009,1.2120880362846682,1.211815892322429,-36.495766524073936,False,-0.00013028509013180475,17.565126744278295,-40.480362846682105,0.8432203389830533,14,2,1.2125142935932631,TP1+TP2+SL,2021-01-21 01:15:00+00:00,38.448515002883816,3795.189322090847,630,True
+2021-01-25 14:45:00+00:00,2,EUR_USD,LONG,1.2122600000000001,1.2115474047930477,1.2154338564547047,1.2159578812909808,1.217005930963533,1451973.87,2,OVERLAP,1.5,0.0008051904139047804,44.57423255141539,19.14382324849707,1.2158448483083542,1.2159119471304507,-33.63856454704717,False,-0.00019508421084025703,19.447462256999703,-37.74848308354173,0.9124423963134046,14,0,1.2115474047930477,SL,2021-01-26 07:30:00+00:00,-7.125952069524377,-1034.669620382182,1005,False
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+2021-01-26 15:30:00+00:00,2,EUR_USD,SHORT,1.21701,1.217970050861497,1.214197363518797,1.2135002626538143,1.2121060609238485,1056277.05,2,OVERLAP,1.5,0.0007801017229936626,52.231391000655584,73.26609812819738,1.2143639165987403,1.2146505744551408,30.02636481203025,False,0.00019427328463482072,79.23660597174701,28.36083401259737,0.6199999999999956,15,1,1.2121060609238485,TP3,2021-01-27 09:00:00+00:00,33.7031717318913,3559.988681260554,1050,True
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+2021-02-16 14:45:00+00:00,2,EUR_USD,LONG,1.20991,1.2090452895531176,1.21350339437776,1.214375316708727,1.2161191613706608,1225580.69,2,OVERLAP,1.5,0.00088942089376461,68.1949315476214,21.987730445441898,1.213891948743896,1.213316103343778,-37.833943777600965,False,-0.0006783598720287328,13.864864872346104,-41.719487438960364,0.8308823529412149,14,1,1.2090452895531176,SL,2021-02-16 23:00:00+00:00,-8.647104468824107,-1059.7724261403532,495,False
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+2021-03-16 13:45:00+00:00,2,EUR_USD,LONG,1.1904400000000002,1.189272633074472,1.1929082550136314,1.1934242102898056,1.1944561208421538,887820.52,2,OVERLAP,1.5,0.0008747338510561144,55.05486113388602,26.999907996290204,1.1930448261525801,1.1933443624566018,-26.58255013631372,False,-0.0004168454394113382,9.344361987218731,-27.948261525800877,0.6971153846154082,13,1,1.189272633074472,SL,2021-03-16 15:15:00+00:00,-11.673669255281016,-1036.4123108531605,90,False
+2021-03-22 11:00:00+00:00,2,EUR_USD,SHORT,1.19189,1.1925070295665907,1.189908334547483,1.189468806401848,1.1885897501105778,1662883.34,3,LONDON,1.5,0.0007340591331816084,22.06293421477517,70.71479148790904,1.189599395883488,1.1906049152647813,21.71665452517013,False,0.00024640221818110985,84.32229535171192,24.80604116511964,0.8888888888889099,11,0,1.1925070295665907,SL,2021-03-22 11:15:00+00:00,-6.170295665908209,-1026.0481865712968,15,False
+2021-03-23 11:15:00+00:00,2,EUR_USD,LONG,1.18798,1.1873833418691697,1.1891696455082417,1.1894405287206167,1.1899822951453667,1702461.84,2,LONDON,1.5,0.0007133162616605061,40.271389843033944,20.95728033128698,1.1910801957292922,1.19155065257116,-13.796455082417669,False,-0.00012456408534114948,12.637906938186815,-32.90195729292344,0.7654320987655336,11,1,1.1887878116059702,TP1+TP2+SL,2021-03-23 13:00:00+00:00,12.271795472321712,2089.226349991249,105,True
+2021-03-25 12:30:00+00:00,2,EUR_USD,LONG,1.17941,1.1787698690533606,1.1807730828152214,1.1809818262354308,1.1813993130758498,1619481.09,2,LONDON,1.5,0.0006602618932787612,28.420033539915153,26.517902906120455,1.1813165679664084,1.1836983819225606,-15.530828152214848,False,-0.00013001881021492955,15.136125196755783,-20.96567966408447,0.7515527950312041,12,3,1.180346763979775,TP1+TP2+SL,2021-03-25 13:15:00+00:00,13.72195186977654,2222.244157099325,45,True
+2021-04-05 12:30:00+00:00,2,EUR_USD,SHORT,1.17763,1.178271874507851,1.1753854815347407,1.1750212317237527,1.1742927321017766,1649703.16,2,LONDON,1.5,0.0005637490157018968,46.896437270282476,76.88383452508822,1.1756159069157897,1.1758666351696738,24.345184652594387,False,0.0003372310534886857,89.54701980927796,22.040930842104345,0.8274111675126892,12,0,1.178271874507851,SL,2021-04-05 13:15:00+00:00,-6.4187450785113676,-1058.902403925465,45,False
+2021-04-06 15:30:00+00:00,2,EUR_USD,SHORT,1.18481,1.1853559759974879,1.1822380135781645,1.181574690032657,1.180248042941642,1920072.29,2,OVERLAP,1.5,0.0006919519949760581,59.7208921170856,75.65159354059932,1.1820852356524045,1.1795686490871686,27.619864218355605,False,0.00016506150475000957,88.30927477581103,29.14764347595522,0.8906249999999729,15,1,1.1853559759974879,SL,2021-04-06 16:45:00+00:00,-5.459759974879574,-1048.3133837817368,75,False
+2021-04-08 14:00:00+00:00,2,EUR_USD,SHORT,1.19006,1.1914399930228943,1.188093988764463,1.1876125034816003,1.186649532915875,752054.71,2,OVERLAP,1.5,0.0008399860457886637,41.356387417016784,70.07853986454973,1.1878275767265865,1.1863560248688263,21.56011235536992,False,0.00028349160108991425,71.7125522920921,24.224232734135764,0.43181818181815507,14,3,1.1914399930228943,SL,2021-04-08 16:00:00+00:00,-13.799930228943767,-1037.8302526348539,120,False
+2021-04-08 16:00:00+00:00,2,EUR_USD,SHORT,1.19145,1.1921142563508687,1.1887850729260983,1.188154272138221,1.1868926705624663,1546770.23,2,OVERLAP,1.5,0.0008285127017374383,37.9607432120304,70.43514793604544,1.1885708716979813,1.1866764259769902,28.549270739017807,False,0.0001439328509536199,78.37678597731964,30.691283020187754,0.9064748201439113,16,3,1.1921142563508687,SL,2021-04-08 16:15:00+00:00,-6.642563508687526,-1027.4519486122213,15,False
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+2021-12-14 15:45:00+00:00,2,EUR_USD,LONG,1.12748,1.126247323140256,1.1301792617849122,1.1308187729389239,1.1320977952469469,697857.96,2,OVERLAP,1.5,0.0007453537194877541,41.620523169814064,27.57323194652139,1.1295500427196894,1.129248288170647,-28.892617849123337,False,-0.00047653885565042837,5.764619073970072,-22.600427196894923,0.800000000000148,15,1,1.126247323140256,SL,2021-12-14 19:00:00+00:00,-12.326768597439306,-860.2333586801057,195,False
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+2021-12-22 10:15:00+00:00,2,EUR_USD,SHORT,1.12922,1.1301040247813545,1.1277960699009066,1.127513368668716,1.1269479662043345,953722.94,2,LONDON,1.5,0.000628049562708857,42.928767271861176,74.38497657104429,1.127666825316915,1.1279494457919252,16.139300990933858,False,0.0002568973777732548,81.27729363350133,17.43174683084936,0.7396449704141989,10,2,1.1301040247813545,SL,2021-12-22 11:15:00+00:00,-8.84024781354631,-843.1147135063958,60,False
+2021-12-22 11:30:00+00:00,2,EUR_USD,SHORT,1.1304999999999998,1.1314116307995616,1.1286823959409782,1.1281852869235158,1.1271910688885909,915593.86,2,LONDON,1.5,0.0007032615991232447,64.49450520296969,76.98756326920233,1.1280708601121348,1.1280435668030557,20.076040590217836,False,0.00033293945992719966,78.32156120731126,26.191398878652183,0.580357142857173,11,2,1.1314116307995616,SL,2021-12-22 13:45:00+00:00,-9.116307995618023,-834.6835626656768,135,False
+2021-12-22 16:00:00+00:00,2,EUR_USD,SHORT,1.13323,1.1346717755880906,1.1302013754419833,1.1295033640439403,1.128107341247854,573138.24,2,OVERLAP,1.5,0.0008835511761811587,58.695961470413174,74.94538441072136,1.129673330175922,1.1285439064319227,32.18624558016803,False,0.00012703686707384878,83.82167507919833,37.46669824078008,0.6161137440758503,16,2,1.13133,TIME,2021-12-23 12:00:00+00:00,19.000000000000124,1088.9626560000072,1200,True
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+2022-02-28 10:45:00+00:00,2,EUR_USD,SHORT,1.1227099999999999,1.125198201938865,1.1182531046443336,1.1177086335001558,1.1166196912118005,284479.3,2,LONDON,1.5,0.0015321346259100134,26.182573655052796,70.915499501806,1.118080432534717,1.1208072990255231,46.468953556664246,False,0.0004760334266747765,92.17921356694846,48.195674652831144,0.8644067796610241,10,0,1.1227099999999999,TP1+SL,2022-02-28 14:30:00+00:00,22.284476778331452,633.9472354765986,225,True
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+2022-03-01 16:15:00+00:00,2,EUR_USD,LONG,1.1117800000000002,1.110068130552038,1.1171541156037788,1.1185049793165893,1.1212067067422107,413021.93,2,OVERLAP,1.5,0.0014037388959241288,56.737241778212805,27.73133033820291,1.1172917499753001,1.119701406103165,-55.64115603778674,False,-0.00020336932954172996,22.655710583845693,-57.01749975300041,0.5938864628821333,16,1,1.110068130552038,SL,2022-03-01 16:45:00+00:00,-17.11869447962311,-707.0396233054282,30,False
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+2022-11-10 09:45:00+00:00,2,EUR_USD,LONG,0.99627,0.9953689291693273,0.9988695597938483,0.9994380338941451,1.0005749820947387,636119.55,2,LONDON,1.5,0.0011221416613453287,61.27334715469999,21.421517040202644,1.0013308361859847,1.0019989073884576,-27.895597938483174,False,-0.0005421553666190601,9.627141008375846,-52.50836185984764,0.8894472361809082,9,3,0.9953689291693273,SL,2022-11-10 10:15:00+00:00,-9.010708306727011,-573.1887713256449,30,False
+2022-11-10 10:15:00+00:00,2,EUR_USD,LONG,0.99432,0.9929941787225322,0.9981053118187457,0.9988973529084377,1.0004814350878217,428004.06,2,LONDON,1.5,0.0012516425549355117,67.77704244622252,20.529828353331197,1.0008743474365818,1.0018685074995746,-39.753118187457744,False,-0.0006272128542089224,12.46972978735858,-67.4434743658181,0.7872340425532159,10,3,1.0004814350878217,TP3,2022-11-10 13:30:00+00:00,44.18944690499349,1891.326268449165,195,True
+2022-11-10 13:30:00+00:00,2,EUR_USD,SHORT,1.01175,1.0131708451305659,0.9975967136894988,0.9962835470145471,0.9936572136646438,412691.11,2,OVERLAP,1.5,0.0023016902611320577,67.59369244336153,81.37382622031242,0.9991406954245945,1.0011931234156985,143.432863105013,False,0.0013153114511195488,83.78487486927968,127.99304575405567,0.9712402636309276,13,3,1.0131708451305659,SL,2022-11-10 13:45:00+00:00,-14.208451305659509,-586.3701540713572,15,False
+2022-11-10 15:15:00+00:00,2,EUR_USD,SHORT,1.0171499999999998,1.018715094995606,1.0040049828666873,1.0006936707083336,0.9940710463916264,370908.12,2,OVERLAP,1.5,0.0026301899912119365,81.00916129937184,80.54388115904358,1.0029833906274903,1.0021124807764357,133.35017133312644,False,0.002071400071924234,83.16904813120192,143.56609372509598,0.8476190476190355,15,3,1.018715094995606,SL,2022-11-10 20:15:00+00:00,-15.650949956063085,-580.5064424417442,300,False
+2022-11-11 12:45:00+00:00,2,EUR_USD,SHORT,1.0307,1.0321800407262207,1.0268778008445858,1.0260037783334293,1.024255733311116,388301.06,2,LONDON,1.5,0.0016200814524415265,60.4667397081777,71.121501441084,1.023507703865918,1.0133589829740421,40.121991554142866,False,0.00020113635104728345,79.76328140263121,73.82296134082145,0.6563876651982358,12,4,1.0321800407262207,SL,2022-11-11 13:15:00+00:00,-14.800407262207662,-574.7013828346933,30,False
+2022-11-15 10:30:00+00:00,2,EUR_USD,SHORT,1.04286,1.044378009757785,1.0406732971359804,1.0402681829780662,1.0394579546622376,374802.84,2,LONDON,1.5,0.0013360195155703346,60.485893695618856,78.07921701869088,1.0360433600383285,1.0304881476817849,23.767028640195864,False,0.00018801638940638154,89.96739337053718,70.06639961671502,0.716738197424936,10,1,1.041025442587556,TP1+TP2+SL,2022-11-15 12:30:00+00:00,22.75671001553092,852.9279542877434,120,True
+2022-11-17 10:45:00+00:00,2,EUR_USD,LONG,1.0339900000000002,1.032937765845136,1.037563606259493,1.0383385219709738,1.0398883533939351,548816.67,2,LONDON,1.5,0.0012444683097283516,40.06667399691701,28.48553923685114,1.037826080544541,1.037079844007433,-37.63606259493013,False,-0.00042301637523544104,29.002849002848578,-40.260805445409,0.9076923076922985,10,3,1.032937765845136,SL,2022-11-17 11:30:00+00:00,-10.522341548642176,-577.4836449328443,45,False
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+2022-11-23 14:45:00+00:00,2,EUR_USD,SHORT,1.0365799999999998,1.038178597635893,1.0325189044335055,1.0319981725485021,1.0309567087784952,354055.15,2,OVERLAP,1.5,0.0013571952717859805,40.06254787733076,71.96514074468499,1.032047022573769,1.0300685155757405,42.510955664945,False,0.0003786144010569737,62.61979103773492,47.22977426230956,0.8409586056644652,14,2,1.038178597635893,SL,2022-11-23 15:45:00+00:00,-15.985976358932065,-565.9917257658146,60,False
+2022-11-28 10:15:00+00:00,2,EUR_USD,SHORT,1.04714,1.0485529670882787,1.0442059426642272,1.0434273703999577,1.041870225871419,396563.95,2,LONDON,1.5,0.001365934176557498,73.76291728149641,83.78111126324379,1.0394513449448046,1.0389134020016868,31.24057335772923,False,0.0007684193499567261,85.9111583359991,78.7865505519547,0.6553030303030398,10,0,1.0485529670882787,SL,2022-11-28 11:00:00+00:00,-14.129670882787604,-560.331809747824,45,False
+2022-11-28 11:00:00+00:00,2,EUR_USD,SHORT,1.0491499999999998,1.050173601928917,1.0450942668183627,1.04407515940882,1.0420369445897346,541937.71,2,LONDON,1.5,0.001387203857834095,80.4171312693646,84.51600905462826,1.04043938395799,1.039187034597697,42.457331816372346,False,0.0006678653955999197,89.86434108527352,89.00616042009979,0.8584070796459699,11,0,1.0449298498294601,TP1+TP2+SL,2022-11-28 14:30:00+00:00,44.370833233124294,2404.6227753151275,210,True
+2022-12-08 16:30:00+00:00,2,EUR_USD,SHORT,1.0552,1.0570726743359775,1.0521694668093786,1.051457134455362,1.050032469747329,309063.2,2,OVERLAP,1.5,0.0012053486719549049,60.552927396421836,72.47169974020112,1.0520631892723504,1.0505951833045226,32.205331906214326,False,0.00023292976649883022,66.45751633987082,33.2681072764962,0.4098360655737864,16,3,1.0570726743359775,SL,2022-12-09 01:00:00+00:00,-18.726743359775977,-578.7747228351114,510,False
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+2022-12-13 13:30:00+00:00,2,EUR_USD,SHORT,1.0641399999999999,1.065717762539334,1.0555544346464794,1.0548748779886967,1.0535157646731317,359532.62,2,OVERLAP,1.5,0.0016555250786681316,34.11857623887015,83.71968510005179,1.0546731952742228,1.054073868077149,87.75565353520642,False,0.00073858237792698,70.6517902561091,96.56804725777235,0.9411764705882463,13,1,1.065717762539334,SL,2022-12-13 13:45:00+00:00,-15.777625393340598,-567.2570995046276,15,False
+2022-12-27 14:00:00+00:00,2,EUR_USD,LONG,1.06176,1.0610851129209904,1.0645956385435158,1.0651837567754037,1.0663599932391796,832116.29,2,OVERLAP,1.5,0.0009097741580189597,48.85593939517683,29.482573680027556,1.0645512292463712,1.0633353913764105,-30.25638543515896,False,-0.00041468180297479155,27.218120140776872,-29.81229246371342,0.8357142857143276,14,1,1.06438979845367,TP1+TP2+SL,2022-12-27 15:45:00+00:00,30.10603911199661,2505.1725572469513,105,True
+2023-01-02 16:15:00+00:00,2,EUR_USD,LONG,1.0655700000000001,1.0649154699283512,1.067773136154915,1.068104897617222,1.0687684205418362,896270.9,2,OVERLAP,1.5,0.0007690601432972978,57.628700409188355,28.802755554049497,1.0680237103045502,1.0674673960830474,-23.931361549149877,False,-0.00021878752029363035,26.585614820906724,-26.437103045502397,0.9490445859872125,16,0,1.067753127377085,TP1+TP2+SL,2023-01-02 23:15:00+00:00,23.162513500478372,2075.9886821335904,420,True
+2023-01-05 15:15:00+00:00,2,EUR_USD,LONG,1.05302,1.051621546359,1.0582164598954518,1.059374506760248,1.0616906004898403,434334.13,2,OVERLAP,1.5,0.001476907282000147,54.13668791726725,20.969633225550297,1.0592837126247734,1.0603179714055082,-53.864598954518335,False,-0.0006301405540077446,19.914563242498527,-64.53712624773455,0.714285714285808,15,3,1.051621546359,SL,2023-01-05 15:45:00+00:00,-13.98453641000108,-607.3961455091143,30,False
+2023-01-09 13:15:00+00:00,2,EUR_USD,SHORT,1.0716299999999999,1.0727768070922208,1.068403224555603,1.0678839456027953,1.06684538769718,524344.67,2,OVERLAP,1.5,0.0010736141844414208,39.45023602162358,74.33694110934988,1.0672620887149913,1.0618662622329795,34.16775444397002,False,0.0003077937044734698,80.50459166931053,45.579112850087085,0.8231046931407678,13,0,1.0727768070922208,SL,2023-01-09 13:30:00+00:00,-11.46807092220925,-601.3221863242405,15,False
+2023-01-09 15:00:00+00:00,2,EUR_USD,SHORT,1.07324,1.0745585641322286,1.0696751295258777,1.0687680447079644,1.0669538750721375,451482.75,2,OVERLAP,1.5,0.001177128264457118,54.12408564568703,74.71247871769589,1.0684997517752142,1.0625723539319007,37.54870474122374,False,0.00033615905422927747,54.20107946127249,49.30248224785938,0.5378151260505738,15,0,1.0745585641322286,SL,2023-01-09 15:15:00+00:00,-13.185641322286386,-595.3089604699495,15,False
+2023-01-09 16:45:00+00:00,2,EUR_USD,SHORT,1.07561,1.0765792780008692,1.0707656573056692,1.069518268030912,1.0670234894813981,608035.95,2,OVERLAP,1.5,0.0010585560017383018,58.97029390598834,77.97381873437976,1.0700324657769538,1.0633958427529535,50.343426943308955,False,0.00022671377671561795,88.19693116869205,57.67534223046322,0.6000000000000538,16,0,1.07191,TIME,2023-01-10 12:45:00+00:00,37.00000000000036,2249.733015000022,1200,True
+2023-01-12 13:45:00+00:00,2,EUR_USD,SHORT,1.0826799999999999,1.0848515494310942,1.077135240734062,1.076593416281253,1.0755097673756349,281758.82,3,OVERLAP,1.5,0.0019630988621886208,21.60029555669053,79.37848478439616,1.076489042357706,1.0746595853407643,57.347592659380275,False,0.0004963549958428079,58.36969016093832,63.80957642293961,0.8530259365994195,13,3,1.0755097673756349,TP3,2023-01-12 14:45:00+00:00,59.78218828185123,1684.415882731223,60,True
+2023-01-18 14:15:00+00:00,2,EUR_USD,SHORT,1.0876799999999998,1.0894188943088379,1.0848991332518558,1.0842218618889248,1.0828673191630627,361550.07,2,OVERLAP,1.5,0.0013777886176756023,41.53605476767831,70.53865490094124,1.0826991162988742,1.0816213125114507,29.70866748144063,False,0.0002574945270675006,85.90172320886661,51.70883701125728,0.6009174311926054,14,2,1.0828673191630627,TP3,2023-01-18 15:45:00+00:00,33.22683838488771,1201.316574393484,90,True
+2023-01-23 14:00:00+00:00,2,EUR_USD,LONG,1.08552,1.0837692828297738,1.0885804192636264,1.0893811257574382,1.0909825387450616,365970.32,2,OVERLAP,1.5,0.0010404781134840365,45.18724290604338,28.922414219079158,1.0882463548189933,1.0856417259817774,-32.504192636264584,False,-0.00034476712833029046,17.64413364413018,-29.163548189934385,0.427419354838779,14,0,1.089212409969108,TP1+TP2+SL,2023-01-24 07:45:00+00:00,34.35465142282712,1257.2782774700497,1065,True
+2023-01-26 15:30:00+00:00,2,EUR_USD,LONG,1.0876500000000002,1.0861968166714173,1.0897271949718577,1.0901588685087529,1.091022215582543,449553.26,2,OVERLAP,1.5,0.001206366657165325,41.097257487058265,29.20079667057844,1.090136473463251,1.0895506125409111,-22.671949718575668,False,-0.00013469462675545383,24.38957457481187,-26.7647346325095,0.5727272727271947,15,3,1.0861968166714173,SL,2023-01-26 15:45:00+00:00,-14.531833285829075,-653.2833027420974,15,False
+2023-01-31 08:30:00+00:00,2,EUR_USD,LONG,1.0808300000000002,1.080024063636632,1.081903917167802,1.0821400709262248,1.0826123784430703,802483.3,2,LONDON,1.5,0.0008518727267358287,45.126215941529566,26.96250829844638,1.0845572583451764,1.0864281650035734,-12.639171678019423,False,-0.0002849077515393922,12.623387204927079,-39.17258345176311,0.8882681564244805,8,1,1.0826123784430703,TP3,2023-01-31 09:30:00+00:00,12.628401745400364,1013.4081506374645,60,True
+2023-02-01 13:30:00+00:00,2,EUR_USD,SHORT,1.0901199999999998,1.0910575692630438,1.0888734153569686,1.0885216707484946,1.0878181815315466,700625.1,2,OVERLAP,1.5,0.0006951385260875376,45.09209806324499,71.48041959132522,1.0880197884177538,1.086668656925615,14.365846430313134,False,3.31360452145667e-05,66.20607761644484,22.90211582246071,0.426229508196602,13,2,1.0910575692630438,SL,2023-02-01 14:00:00+00:00,-9.375692630440023,-656.8845586771305,30,False
+2023-02-01 14:00:00+00:00,2,EUR_USD,SHORT,1.0906999999999998,1.0914325979870123,1.0890551551846275,1.088646944017782,1.087830521684091,887684.27,2,OVERLAP,1.5,0.0007051959740244607,50.55408309633352,75.66282901964584,1.0882216501311137,1.0867473201878985,18.348448153724384,False,8.586108501818098e-05,81.20777579555612,26.683498688861995,0.5882352941175703,14,2,1.0906999999999998,TP1+SL,2023-02-01 15:30:00+00:00,8.22422407686152,730.0514345985242,90,True
+2023-02-01 15:45:00+00:00,2,EUR_USD,SHORT,1.09177,1.0929164402682905,1.0895840337406157,1.089119052928009,1.0881890913027958,573615.78,2,OVERLAP,1.5,0.0008928805365810198,40.552722667580326,73.04353132081694,1.0887991121430551,1.0870050014166859,23.75966259384299,False,6.384784851000695e-05,83.99644141039494,31.60887856944905,0.6939890710383091,15,2,1.09177,TP1+SL,2023-02-01 19:30:00+00:00,10.92983129692082,626.9523704651649,225,True
+2023-02-02 13:45:00+00:00,2,EUR_USD,LONG,1.0947300000000002,1.0923341670907352,1.0988428564296928,1.099289610942057,1.1001831199667853,277100.19,2,OVERLAP,1.5,0.001470555272843237,28.69785934322463,29.226320292719592,1.099126165952954,1.0941257772418445,-43.02856429692747,False,-0.0003270058116393495,25.05758313057723,-45.86165952954024,0.8744186046511203,13,3,1.0923341670907352,SL,2023-02-02 14:30:00+00:00,-23.958329092650388,-663.8857543655951,45,False
+2023-02-06 16:30:00+00:00,2,EUR_USD,LONG,1.0724300000000002,1.071688220154534,1.0758672351585712,1.076669945444168,1.0782753660153612,886040.37,2,OVERLAP,1.5,0.001023559690932391,43.46576664709946,25.32456724553026,1.0766038509046878,1.082987080991189,-36.27235158571107,False,-0.00022769407765135894,34.026827785377606,-43.63850904687716,0.9090909090908174,16,0,1.071688220154534,SL,2023-02-06 17:00:00+00:00,-7.4177984546630595,-657.2468887355086,30,False
+2023-02-07 13:15:00+00:00,2,EUR_USD,LONG,1.0690400000000002,1.0683350300464542,1.071157179669796,1.0715388090586675,1.0723020678364106,922981.78,2,OVERLAP,1.5,0.0009099399070911815,44.28952056718829,29.46960669795267,1.0720557719352686,1.0770223127522498,-23.07179669795989,False,-0.00013133809466048534,14.814409202537854,-32.05771935268498,0.9504132231403609,13,1,1.0683350300464542,SL,2023-02-07 14:45:00+00:00,-7.0496995354596415,-650.6744225703713,90,False
+2023-02-09 10:45:00+00:00,2,EUR_USD,SHORT,1.0775299999999999,1.0782650192175238,1.0758157249123002,1.075371144376992,1.074481983306376,876395.69,2,LONDON,1.5,0.0008700384350476755,56.937479059732695,74.72079647828082,1.07411503971511,1.0738009049203927,19.042750876998582,False,0.00021500762703176703,87.84487449852577,36.04960284889991,0.8076923076921435,10,3,1.0782650192175238,SL,2023-02-09 13:45:00+00:00,-7.350192175239468,-644.1676743051594,180,False
+2023-02-14 09:15:00+00:00,2,EUR_USD,SHORT,1.0756299999999999,1.076476330071392,1.074243438432277,1.0738812477928934,1.0731568665141265,753519.25,2,LONDON,1.5,0.0007726601427839205,39.14785485266423,73.55054641396214,1.0731679792211986,1.0714219936897453,15.765615677230116,False,0.00015599704469147703,87.21034114413278,26.52020778801445,0.6814814814815241,9,1,1.076476330071392,SL,2023-02-14 09:45:00+00:00,-8.463300713921207,-637.7260006478373,30,False
+2023-02-15 14:45:00+00:00,2,EUR_USD,LONG,1.06709,1.0660218147016558,1.0700000535371124,1.0706655533918363,1.0719965531012845,591047.96,2,OVERLAP,1.5,0.001096370596688406,52.20846421134661,22.037838517727437,1.0710400281931765,1.072035735994064,-31.000535371124016,False,-0.00038597730780910266,21.36325852036478,-41.40028193176492,0.77678571428584,14,2,1.070254226002936,TP1+TP2+SL,2023-02-16 02:15:00+00:00,31.811043561391973,1880.185240243186,690,True
+2023-02-22 10:45:00+00:00,2,EUR_USD,LONG,1.06268,1.0620699183780018,1.0642663729057782,1.0645892398039616,1.0652349736003284,1065678.05,2,LONDON,1.5,0.0008001632439966524,38.1393423728886,19.265156602207597,1.0650152072203825,1.0661887756622526,-17.763729057782562,False,-0.00021749627071494468,24.767456757822718,-25.252072203825193,0.8865248226950723,10,2,1.0638843346160023,TP1+TP2+SL,2023-02-22 12:45:00+00:00,16.420705766759248,1749.9185701143751,120,True
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+2023-03-01 10:45:00+00:00,2,EUR_USD,SHORT,1.06659,1.0674570739149725,1.0645920066204613,1.0642210872430446,1.0634792484882116,768495.85,2,LONDON,1.5,0.0010541478299450227,75.1105629451777,75.65273481483976,1.0615269055458194,1.0598377730709794,21.879933795387885,False,0.0001789883825390184,82.64845438061359,52.53094454180651,0.6962616822429766,10,2,1.0674570739149725,SL,2023-03-01 12:15:00+00:00,-8.67073914972538,-666.3427052996484,90,False
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+2023-03-06 16:00:00+00:00,2,EUR_USD,SHORT,1.0684999999999998,1.0694627296901829,1.0653642001153456,1.064638738618101,1.0631878156236119,671325.45,2,OVERLAP,1.5,0.0010454593803655688,64.17425280544484,74.62925212049336,1.0649316612531314,1.0631531054652683,33.257998846543124,False,0.00034923835331546985,79.65625199667632,37.58338746868483,0.6562499999999891,16,0,1.06582,TP1+TIME,2023-03-07 12:00:00+00:00,29.07899942326986,1952.1472373376378,1200,True
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+2023-03-17 16:15:00+00:00,2,EUR_USD,SHORT,1.06693,1.0678742074878065,1.0640146732809885,1.0633152119656117,1.0619162893348582,677383.1,2,OVERLAP,1.5,0.0011084149756127434,42.00778025288986,71.65116036576168,1.0636834182332207,1.0631410541712614,31.053267190115363,False,0.00044499601423882075,95.00786473817642,34.36581766779412,0.8924731182795802,16,4,1.0678742074878065,SL,2023-03-17 18:45:00+00:00,-9.442074878065476,-639.5901951336114,150,False
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+2023-03-22 09:00:00+00:00,2,EUR_USD,SHORT,1.0787799999999999,1.0793223073794307,1.0778231970993948,1.077621425134675,1.0772178812052353,1144357.89,2,LONDON,1.5,0.0006646147588615844,38.4998254828826,73.90510051461234,1.076950737274581,1.0739491739616218,11.468029006052038,False,0.00022253303974076717,92.7502103027778,20.192627254189688,0.6666666666667694,9,2,1.0793223073794307,SL,2023-03-22 10:45:00+00:00,-5.423073794308396,-620.5937284569051,105,False
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+2023-03-24 09:30:00+00:00,2,EUR_USD,LONG,1.0761200000000002,1.075270465406903,1.0782790700637481,1.078687372196328,1.0795039764614873,727725.12,2,LONDON,1.5,0.001259069186194122,59.725828328284635,22.908293302602942,1.0822469236887327,1.0830448755834712,-23.49070063748071,False,-0.000563836691337335,28.267806267807085,-63.16923688732601,0.9535714285714402,9,4,1.075270465406903,SL,2023-03-24 09:45:00+00:00,-8.49534593097223,-618.2276637058278,15,False
+2023-03-27 13:15:00+00:00,2,EUR_USD,SHORT,1.07923,1.079906414055333,1.0769284289541774,1.0766086889657898,1.0759692089890143,904838.36,2,OVERLAP,1.5,0.0009128281106660031,38.05471957196446,71.27454285700654,1.076888641621188,1.0785812852598637,24.915710458226492,False,0.00019866760640606124,69.00287112598772,25.31358378812021,0.9128205128205163,13,0,1.079906414055333,SL,2023-03-27 20:00:00+00:00,-6.764140553330301,-612.0453845084882,405,False
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+2023-04-03 14:00:00+00:00,2,EUR_USD,SHORT,1.0891099999999998,1.0908919835693478,1.0865734776723457,1.085893205876718,1.0845326622854625,336628.07,2,OVERLAP,1.5,0.0010613223795652518,70.87660227583366,78.44179243188361,1.0848506106500293,1.0852573135799857,27.26522327654246,False,8.55074351214743e-05,77.06700198708404,44.49389349970634,0.4062499999999711,14,0,1.0908919835693478,SL,2023-04-03 14:15:00+00:00,-17.81983569348,-599.8656897213285,15,False
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+2023-04-11 09:15:00+00:00,2,EUR_USD,SHORT,1.0913099999999998,1.0919793421057307,1.0906082148247291,1.090422123315594,1.0900499402973234,869583.83,2,LONDON,1.5,0.0007186842114613984,43.97486440068694,71.80848669004526,1.0886145325568801,1.0888623464012426,8.917851752707762,False,0.00013400810211435107,81.86580447249233,28.854674431197935,0.8461538461536928,9,1,1.0919793421057307,SL,2023-04-11 10:45:00+00:00,-6.693421057308857,-582.0490718817285,90,False
+2023-04-11 10:45:00+00:00,2,EUR_USD,SHORT,1.0920699999999999,1.093035141826139,1.0910297184100017,1.0907662199356178,1.0902392229868498,597040.32,2,LONDON,1.5,0.000750283652278163,47.57211354700603,70.79861287576875,1.0892341943888477,1.0890160703294225,12.302815899982722,False,9.045938384795427e-05,58.16071833770858,30.25805611152288,0.5142857142857022,10,1,1.0911151289791927,TP1+TP2+SL,2023-04-11 12:15:00+00:00,11.196944706538535,668.5027450614073,90,True
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+2023-06-07 13:45:00+00:00,2,EUR_USD,SHORT,1.0735199999999998,1.074247598550333,1.0709934592456605,1.0703863967896847,1.0691722718777332,761282.61,2,OVERLAP,1.5,0.0007551971006659605,47.02877755100097,74.55840998851002,1.0702614672974775,1.0701147391781192,27.165407543394693,False,0.00016485864776160113,87.03723930936273,34.48532702522433,0.7685950413221123,13,2,1.0710147956154756,TP1+TP2+SL,2023-06-07 15:30:00+00:00,27.35812158458572,2082.7262204610756,105,True
+2023-06-08 12:30:00+00:00,2,EUR_USD,SHORT,1.0749099999999998,1.0762263525341205,1.0732384089584792,1.072879723967373,1.072162353985161,436612.05,2,LONDON,1.5,0.0007509016894137028,56.474235782851,77.84585287558427,1.0719831052904019,1.0707831583675995,18.61591041520727,False,0.00014129454470432432,72.6265027844405,31.168947095980837,0.617224880382738,12,3,1.0762263525341205,SL,2023-06-08 13:30:00+00:00,-13.16352534120657,-574.7353784451151,60,False
+2023-06-08 13:30:00+00:00,2,EUR_USD,SHORT,1.0758699999999999,1.0767886655710035,1.0737304352040566,1.0732603976204906,1.0723203224533588,619363.62,2,OVERLAP,1.5,0.0008373311420071062,69.16359420650375,78.45032669827934,1.0724668243768685,1.0709578334680054,23.29564795943373,False,0.00020336661232641425,74.74737916300465,35.931756231315504,0.6330935251798492,13,3,1.0767886655710035,SL,2023-06-08 13:45:00+00:00,-9.186655710036185,-568.9880336261682,15,False
+2023-06-08 15:15:00+00:00,2,EUR_USD,SHORT,1.0775899999999998,1.0782429290520448,1.0746792615899243,1.0739045740747677,1.0723551990444542,862724.9,2,OVERLAP,1.5,0.0008458581040894066,71.72042249087707,79.73299934103372,1.0736115370389316,1.0713752942438557,31.00738410075632,False,0.0001895426850324403,84.47873587747154,41.684629610683196,0.4642857142856718,15,3,1.0782429290520448,SL,2023-06-08 15:30:00+00:00,-6.529290520449482,-563.2981511325728,15,False
+2023-06-09 09:45:00+00:00,2,EUR_USD,LONG,1.07603,1.0755302744500852,1.0763826962985008,1.076483081578654,1.0766838521389603,1115942.88,2,LONDON,1.5,0.0005394510998297164,28.04245131412564,28.101994173974276,1.0771853205975834,1.0746821851233619,-5.426962985008998,False,-0.00011909552117103634,26.413764972001257,-13.453205975835214,0.6551724137934071,9,4,1.0766838521389603,TP3,2023-06-09 11:00:00+00:00,4.330045226232815,483.20831402924983,75,True
+2023-06-09 16:30:00+00:00,2,EUR_USD,LONG,1.0748600000000001,1.0740327553243976,1.0764653438780507,1.0768772014452108,1.0777009165795308,679964.79,2,OVERLAP,1.5,0.0006344893512046303,41.727981726654384,28.39010261005174,1.0764829611659879,1.0750583156196225,-17.953438780506925,False,-0.00018033134067923633,24.99422499422349,-18.129611659878364,0.41791044776117425,16,4,1.0740327553243976,SL,2023-06-12 01:45:00+00:00,-8.272446756025785,-562.4972521247255,3435,False
+2023-06-14 14:15:00+00:00,2,EUR_USD,SHORT,1.0845399999999998,1.0851738665833313,1.0818762352876552,1.0811710231245573,1.0797605987983616,878532.32,2,OVERLAP,1.5,0.0006277331666627962,69.26595938474007,85.54353748063406,1.0808747896383304,1.0788476340403128,28.53764712344775,False,0.0002915892202554795,94.44622687050867,38.55210361669581,0.7058823529409972,14,2,1.0851738665833313,SL,2023-06-14 14:30:00+00:00,-6.338665833314216,-556.8722800246271,15,False
+2023-06-15 15:00:00+00:00,2,EUR_USD,SHORT,1.09174,1.0927668714225578,1.0866943754942697,1.0854262497720653,1.0828899983276565,536876.91,2,OVERLAP,1.5,0.0012737428451155693,59.8248978404239,75.0506261914594,1.0853244844156582,1.082137899549779,52.356245057303404,False,0.0005165415602920299,80.89667593816523,66.05515584341859,0.8411214953272929,15,3,1.0927668714225578,SL,2023-06-15 15:15:00+00:00,-10.268714225578623,-551.3035563101695,15,False
+2023-06-27 13:15:00+00:00,2,EUR_USD,SHORT,1.0969699999999998,1.097653636159375,1.0949224931790171,1.094438829493858,1.0934715021235397,798364.03,2,OVERLAP,1.5,0.0007672723187501618,48.928816339925156,72.64138305233426,1.0937787190266115,1.0921995811891871,22.37506820982782,False,0.00020570020548665608,82.19882509383791,33.812809733884116,0.8461538461536928,13,1,1.097653636159375,SL,2023-06-27 13:30:00+00:00,-6.836361593751671,-545.7905192524807,15,False
+2023-06-29 10:45:00+00:00,2,EUR_USD,SHORT,1.0925699999999998,1.0930805605007425,1.091740297179683,1.0915549339965447,1.0911842076302678,1058312.61,2,LONDON,1.5,0.0006211210014852497,54.88929162776207,71.4353851766605,1.0910398159682244,1.0922212097133432,10.197028203169367,False,0.000127958331236464,79.9674587487305,17.2018403177554,0.7402597402597477,10,3,1.0930805605007425,SL,2023-06-29 11:15:00+00:00,-5.105605007427183,-540.3326161039332,30,False
+2023-06-29 12:45:00+00:00,2,EUR_USD,LONG,1.0882900000000002,1.086820602236967,1.091587660736224,1.0921180420294132,1.0931788046157915,364046.62,2,LONDON,1.5,0.0009987955260659107,66.27094641002813,27.274131219964076,1.09138559723132,1.0922348013024916,-34.87660736223974,False,-0.000396799832463389,25.483233534003933,-32.855972313199366,0.7325227963525873,12,3,1.086820602236967,SL,2023-06-29 13:00:00+00:00,-14.693977630331199,-534.9292890677683,15,False
+2023-07-12 12:30:00+00:00,2,EUR_USD,SHORT,1.10601,1.1075977845039977,1.102985569477602,1.102710473767015,1.1021602823458412,333533.92,2,LONDON,1.5,0.0009318563359983565,32.648986984811515,73.63041243691009,1.10235833588509,1.1000925907526957,32.14430522398004,False,0.00024169215576442363,80.45299004658655,38.416641149101594,0.8039603960396386,12,2,1.1075977845039977,SL,2023-07-12 13:00:00+00:00,-15.877845039977688,-529.5799897336315,30,False
+2023-07-12 15:15:00+00:00,2,EUR_USD,SHORT,1.1123299999999998,1.1131316010287589,1.1064711619224352,1.1050675101330485,1.1022602065542753,654046.31,2,OVERLAP,1.5,0.00120320205751775,75.74444270267706,80.94121887958786,1.105070469541632,1.1011036801826226,60.48838077564733,False,0.0006298772895027634,86.59487038904967,74.49530458367936,0.99236641221369,15,2,1.1131316010287589,SL,2023-07-12 16:30:00+00:00,-8.016010287590358,-524.2841949520513,75,False
+2023-07-13 13:30:00+00:00,2,EUR_USD,SHORT,1.1191499999999999,1.1199313837058238,1.1172993214073772,1.1169436112260582,1.1162321908634205,664259.25,2,OVERLAP,1.5,0.001002767411647734,26.905193434009156,70.24813152787453,1.1158361233104692,1.1093257461844253,20.40678592622802,False,8.295467553172745e-05,79.22720982528894,35.03876689530782,0.9370629370629827,13,3,1.1199313837058238,SL,2023-07-13 16:00:00+00:00,-7.813837058239236,-519.0413543928202,150,False
+2023-07-25 13:00:00+00:00,2,EUR_USD,LONG,1.1030700000000002,1.1025209768785398,1.105077876280204,1.1055192434625307,1.1064019778271839,935936.79,2,OVERLAP,1.5,0.0006980462429205675,47.10083970201874,24.15166323325458,1.1060214254189824,1.1091332826007683,-21.978762802039586,False,-3.2372310285155663e-05,36.00962789487688,-31.414254189823243,0.9476190476190214,13,1,1.1025209768785398,SL,2023-07-25 14:30:00+00:00,-5.490231214604346,-513.8509379354593,90,False
+2023-07-27 10:00:00+00:00,2,EUR_USD,SHORT,1.11445,1.1151598628398929,1.1134838445571564,1.113245602727701,1.1127691190687898,716634.82,2,LONDON,1.5,0.0007797256797858978,47.91625699153052,71.86706058543761,1.1107751501181389,1.1085339579096838,11.561554428436427,False,0.0001724304831092156,56.70024028063644,38.64849881861198,0.8117647058824298,10,3,1.1138592280596722,TP1+TP2+SL,2023-07-27 11:30:00+00:00,9.932325577755805,711.7850352596428,90,True
+2023-07-27 14:15:00+00:00,2,EUR_USD,LONG,1.09952,1.0983292859850713,1.1070452314932018,1.108901207125573,1.1126131583903156,433210.89,2,OVERLAP,1.5,0.0016214280298573604,69.36204245413798,18.535432776834583,1.1089831691543663,1.108445179590464,-77.15231493201857,False,-0.001404539230701845,13.936763346709611,-96.53169154366425,0.7621951219511633,14,3,1.0983292859850713,SL,2023-07-27 18:15:00+00:00,-11.90714014928762,-515.8302781427623,240,False
+2023-07-28 11:00:00+00:00,2,EUR_USD,SHORT,1.10062,1.1015950819121916,1.096924659273354,1.096103302751081,1.0944605897065351,523722.13,2,LONDON,1.5,0.0010901638243830598,43.05689746542546,71.44717153330143,1.0979515792333348,1.1024061149327842,38.85340726646147,False,0.0005191591153035227,94.06272026918646,28.58420766665315,0.7934782608695469,11,4,1.1015950819121916,SL,2023-07-28 11:15:00+00:00,-9.750819121916443,-510.67197597748094,15,False
+2023-08-07 09:45:00+00:00,2,EUR_USD,LONG,1.09681,1.0962966931977638,1.0976598635634294,1.0979183170553224,1.0984352240391084,984918.29,2,LONDON,1.5,0.0005666136044727555,37.05112060927157,27.85302216573146,1.098930595649958,1.0979704549902274,-10.39863563429444,False,-0.00010834834463666141,9.408551445670438,-23.105956499580582,0.6923076923079814,9,0,1.0984352240391084,TP3,2023-08-07 11:15:00+00:00,10.56626356943724,1040.6906246499423,90,True
+2023-08-08 11:15:00+00:00,2,EUR_USD,LONG,1.0949600000000002,1.0943716529367848,1.096932948008235,1.0974521288013195,1.0984904903874886,876986.05,2,LONDON,1.5,0.0006966941264300623,51.756247332532645,22.295463629731657,1.0981489211081867,1.0985527356357938,-21.629480082350216,False,-0.00029201068742039957,8.041428005114604,-33.789211081867165,0.5612244897959808,11,1,1.0943716529367848,SL,2023-08-08 11:30:00+00:00,-5.8834706321531085,-515.9721669982958,15,False
+2023-08-17 12:30:00+00:00,2,EUR_USD,SHORT,1.0906399999999998,1.092061336750251,1.0888544439008176,1.0884488609337015,1.0876376949994695,359388.75,2,LONDON,1.5,0.0008208911668340113,45.80588119712774,75.93502485339697,1.088120072435133,1.0896325984242514,19.755560991823184,False,0.00027362834148776975,84.41285042732856,27.099275648669874,0.2941176470588195,12,3,1.0893385053300084,TP1+TP2+SL,2023-08-17 14:45:00+00:00,18.549009232942115,666.6305241965526,135,True
+2023-08-21 11:00:00+00:00,2,EUR_USD,SHORT,1.0904099999999999,1.0909844616819557,1.0893011872984364,1.0889854671766244,1.088354026933,900806.38,2,LONDON,1.5,0.0005889233639115685,39.850221673488534,70.71537548156309,1.0885309591535821,1.0881528918804373,12.98812701563623,False,0.00012263722705715973,63.90471790132579,20.690408464179022,0.8160919540230442,11,0,1.0909844616819557,SL,2023-08-21 11:15:00+00:00,-5.7446168195585114,-517.4787481713616,15,False
+2023-08-22 12:45:00+00:00,2,EUR_USD,LONG,1.0868000000000002,1.0861263431256731,1.0895674454572495,1.0902824719123074,1.0917125248224233,760482.05,2,LONDON,1.5,0.000707313748653364,50.593976342869524,24.399491902471482,1.0899434091895515,1.0894027137523001,-29.57445457249408,False,-0.00024653407584718224,21.35168280519217,-33.334091895513836,0.8347826086953718,12,1,1.0861263431256731,SL,2023-08-22 13:00:00+00:00,-6.736568743270776,-512.3039607848484,15,False
+2023-08-22 13:45:00+00:00,2,EUR_USD,LONG,1.0845500000000001,1.0837186386144408,1.088304429079522,1.089213482471917,1.0910315892567073,610060.71,2,OVERLAP,1.5,0.0008227227711185073,63.74178855068973,16.662995040230612,1.0892869287578206,1.0892509003336812,-39.44429079522082,False,-0.000398421539449397,5.788402731214254,-49.26928757820637,0.6875000000000867,13,1,1.0837186386144408,SL,2023-08-22 14:30:00+00:00,-8.313613855592816,-507.18091714087905,45,False
+2023-08-23 09:45:00+00:00,2,EUR_USD,LONG,1.0811700000000002,1.0804070296851414,1.0820331450831533,1.08228587825003,1.0827913445837831,658097.83,2,LONDON,1.5,0.0008459406297172056,54.86919597387957,27.769830041447065,1.0844609545224513,1.086723786913864,-10.531450831532396,False,-0.0003474217893735819,23.280944665921567,-34.80954522451229,0.6216216216215243,9,2,1.0804070296851414,SL,2023-08-23 11:00:00+00:00,-7.6297031485883515,-502.10910856301615,75,False
+2023-08-24 12:30:00+00:00,2,EUR_USD,LONG,1.0819500000000002,1.0813807246599376,1.0847092502383169,1.0850792760668404,1.0858193277238877,873194.37,2,LONDON,1.5,0.0007385506801246181,39.95291931484097,22.363206335968982,1.0855027150252474,1.0859371378467717,-29.49250238316825,False,-0.00027148206414633427,11.706697619146246,-37.42715025247367,0.9960629921259584,12,3,1.0819500000000002,TP1+SL,2023-08-24 16:45:00+00:00,13.796251191583451,1204.6808867596462,255,True
+2023-08-25 14:00:00+00:00,2,EUR_USD,SHORT,1.0829499999999999,1.0843951736979052,1.080407444713815,1.0798347830674666,1.0786894597747698,352300.09,2,OVERLAP,1.5,0.0009503473958101492,36.563033221752974,72.09372483445912,1.0799754754213227,1.0821891247810331,27.3255528618499,False,0.00012672557048292344,46.01494702165791,31.6452457867733,0.5279642058165395,14,4,1.0786894597747698,TP3,2023-08-25 14:30:00+00:00,30.006846032635703,1057.1414557913702,30,True
+2023-08-30 12:30:00+00:00,2,EUR_USD,SHORT,1.0914,1.0924351153220893,1.088631084402739,1.0880484932509769,1.086883310947453,502075.69,2,LONDON,1.5,0.0009302306441782331,31.753529375034567,74.75220165467718,1.0875317027315798,1.084871202941069,29.58915597261136,False,0.00020904184732642786,75.47574018520929,40.58297268420219,0.8555133079847803,12,2,1.0924351153220893,SL,2023-08-30 12:45:00+00:00,-10.351153220893305,-519.7062395675729,15,False
+2023-08-30 13:45:00+00:00,2,EUR_USD,SHORT,1.0942899999999998,1.0950402684210532,1.0899008144397788,1.0890067464247697,1.0872186103947512,685766.81,2,OVERLAP,1.5,0.0010005368421061073,49.904800096035544,83.32366473242284,1.0885069895733428,1.0852618127742935,45.791855602210774,False,0.0005103762338050121,93.56829038191977,59.73010426657099,0.9666666666665721,13,2,1.0895561596476038,TP1+TP2+SL,2023-08-31 07:30:00+00:00,47.53807584300395,3260.0034624394884,1065,True
+2023-08-31 08:45:00+00:00,2,EUR_USD,LONG,1.08796,1.0873889619710635,1.08862887961142,1.0887767194253217,1.0890723990531248,958095.94,2,LONDON,1.5,0.0006220760578728253,41.97332029703646,18.286821657112043,1.0911529797057726,1.0888064554330625,-8.588796114201713,False,-0.0002837008771166404,6.630814363483705,-33.82979705772726,0.9285714285714286,8,3,1.0873889619710635,SL,2023-08-31 09:00:00+00:00,-5.710380289365435,-547.1092171097049,15,False
+2023-08-31 10:00:00+00:00,2,EUR_USD,LONG,1.0867000000000002,1.0861181739418881,1.0877811076854067,1.0880927598895198,1.088716064297746,930927.92,2,LONDON,1.5,0.0006836521162234804,54.75222549669769,17.492517026058664,1.090423106927557,1.088724879272436,-12.711076854066139,False,-0.0002316159200799362,27.22358019282771,-39.13106927556864,0.8750000000000266,10,3,1.0861181739418881,SL,2023-08-31 11:30:00+00:00,-5.818260581120781,-541.6381220800761,90,False
+2023-08-31 14:30:00+00:00,2,EUR_USD,LONG,1.0843600000000002,1.0834995992428877,1.0866483598391226,1.087156741552014,1.0881735049777972,623223.23,2,OVERLAP,1.5,0.001060801514224803,55.48002683677307,28.87232435198159,1.088264387736189,1.0883345747510764,-24.783598391224793,False,-0.00010753460450671688,26.540942586257728,-40.943877361889754,0.8778625954198874,14,3,1.0834995992428877,SL,2023-08-31 21:00:00+00:00,-8.604007571124672,-536.2217389420773,390,False
diff --git a/results/phase1/S2_GBP_JPY_report.json b/results/phase1/S2_GBP_JPY_report.json
new file mode 100644
index 0000000..d70e633
--- /dev/null
+++ b/results/phase1/S2_GBP_JPY_report.json
@@ -0,0 +1,45 @@
+{
+  "pair": "GBP_JPY",
+  "strategy_id": 2,
+  "strategy_name": "S2_Session_VWAP_Reversal",
+  "total_trades": 348,
+  "win_rate_pct": 20.69,
+  "avg_rr": 2.51,
+  "expectancy_pips": -4.55,
+  "sharpe_ratio": -3.15,
+  "max_drawdown_pct": -67.39,
+  "profit_factor": 0.62,
+  "total_pnl_pips": -1583.63,
+  "total_pnl_dollars": -65240.52,
+  "avg_win_pips": 41.8,
+  "avg_loss_pips": 16.64,
+  "max_consecutive_wins": 3,
+  "max_consecutive_losses": 18,
+  "avg_hold_time_minutes": 221.6,
+  "best_trade_pips": 133.0,
+  "worst_trade_pips": -87.62,
+  "best_trade_dollars": 3890.84,
+  "worst_trade_dollars": -1018.57,
+  "final_equity": 34759.48,
+  "starting_equity": 100000.0,
+  "session_breakdown": {
+    "LONDON": {
+      "trades": 194,
+      "win_rate": 21.649484536082475,
+      "total_pnl_pips": -1066.9327875983017
+    },
+    "OVERLAP": {
+      "trades": 154,
+      "win_rate": 19.480519480519483,
+      "total_pnl_pips": -516.6924885002354
+    }
+  },
+  "exit_reasons": {
+    "TP1+TP2+SL": 34,
+    "SL": 275,
+    "TP1+SL": 14,
+    "TIME": 2,
+    "TP3": 21,
+    "TP1+TIME": 2
+  }
+}
\ No newline at end of file
diff --git a/results/phase1/S2_GBP_JPY_trades.csv b/results/phase1/S2_GBP_JPY_trades.csv
new file mode 100644
index 0000000..ced966c
--- /dev/null
+++ b/results/phase1/S2_GBP_JPY_trades.csv
@@ -0,0 +1,349 @@
+timestamp,strategy_id,pair,signal_direction,entry_price,sl_price,tp1_price,tp2_price,tp3_price,lot_size,confluence_score,session,spread_at_entry,atr_at_entry,adx_at_entry,rsi_at_entry,ema_50_value,ema_200_value,vwap_deviation,news_within_60min,macd_hist_at_entry,stoch_k_at_entry,distance_from_ema50_pips,candle_body_ratio,hour_of_day,day_of_week,exit_price,exit_reason,exit_time,pnl_pips,pnl_dollars,hold_time_minutes,win
+2021-01-07 12:45:00+00:00,2,GBP_JPY,SHORT,140.934,141.05406505179835,140.72702989152344,140.6775548600426,140.57860479708089,8328.82,2,LONDON,2.5,0.12813010359669957,53.39297806422747,71.43589691135729,140.53155286907483,140.29373734226476,23.597010847655042,False,0.009020486006970738,87.00511789537195,43.14471309251644,0.6753246753245938,12,3,140.73561126826522,TP1+TP2+SL,2021-01-07 16:45:00+00:00,22.299916298358653,1857.3198886409552,240,True
+2021-01-12 09:45:00+00:00,2,GBP_JPY,SHORT,141.649,141.74993884664286,141.36808172432234,141.29283381546693,141.1423379977561,10090.99,2,LONDON,2.5,0.10987769328575632,59.003312580629654,80.07952044351441,141.08867890451725,140.85350616398284,30.991827567765995,False,0.04445305617183512,90.42263175239532,58.932109548274525,0.8827586206896687,9,1,141.74993884664286,SL,2021-01-12 12:30:00+00:00,-10.09388466428618,-1018.5728920846519,165,False
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+2023-06-16 16:45:00+00:00,2,GBP_JPY,SHORT,181.918,182.05965230011523,181.015633814722,180.78696280158792,180.32962077531977,2445.0,2,OVERLAP,2.5,0.20930460023044434,32.85453760771557,77.09955393694662,180.7982081390317,179.19173506058203,93.13661852779944,False,0.023137218930587067,91.9603299379334,114.87918609683163,0.8461538461539073,16,4,182.05965230011523,SL,2023-06-18 22:00:00+00:00,-14.165230011522567,-346.33987378172674,3195,False
+2023-06-20 10:30:00+00:00,2,GBP_JPY,LONG,180.56,180.3629079124687,180.96162956158582,181.05947444451198,181.25516421036428,1739.68,2,LONDON,2.5,0.21418417506259274,56.11701998614722,29.499546603513522,181.29079616472916,181.05806059474597,-43.062956158581756,False,-0.06149394034639713,11.43733118563196,-75.97961647291527,0.7305194805194793,10,1,180.3629079124687,SL,2023-06-20 11:15:00+00:00,-19.70920875313027,-342.8771628364567,45,False
+2023-06-21 10:30:00+00:00,2,GBP_JPY,LONG,180.057,179.8071520437924,180.58294785778628,180.71881812122984,180.99055864811695,1358.62,2,LONDON,2.5,0.21969591241520592,43.07426259404488,29.20445471502012,180.6855312055059,180.7574959111783,-55.49478577862885,False,-0.09959810447820644,19.64168800818368,-65.75312055059044,0.8722222222223174,10,2,180.99055864811695,TP3,2023-06-21 14:15:00+00:00,63.464406854113946,862.2401244013629,225,True
+2023-06-28 13:00:00+00:00,2,GBP_JPY,LONG,182.329,182.17405271767893,182.87152087706954,183.0040418673958,183.26908384804835,2246.38,2,OVERLAP,2.5,0.1858945646421542,53.049340873611634,27.440918656182433,183.0235729892788,182.92253187473563,-57.152087706953125,False,-0.05189011272906713,18.9816270289747,-72.35729892787788,0.7934782608695137,13,2,182.17405271767893,SL,2023-06-28 14:45:00+00:00,-15.49472823210749,-348.0704760604163,105,False
+2023-06-30 14:30:00+00:00,2,GBP_JPY,SHORT,183.671,183.79735386965535,183.11044442733876,182.9844331431271,182.73241057470378,2727.18,2,OVERLAP,2.5,0.17870773931073103,63.34146233303579,73.71828381872811,182.95738480314236,182.7380830559214,58.95555726612258,False,0.051483087400269106,87.85846406621404,74.26151968576278,0.7415730337077981,14,4,183.79735386965535,SL,2023-06-30 14:45:00+00:00,-12.6353869655361,-344.58974624670736,15,False
+2023-07-04 12:45:00+00:00,2,GBP_JPY,SHORT,183.786,183.90806274833102,183.4862964700479,183.40701865965877,183.2484630388805,2794.82,2,LONDON,2.5,0.10612549666206064,55.04924673043335,73.56806416881159,183.52257088441172,183.369347297757,32.87035299520937,False,0.033508203694518346,75.4988290900635,29.2429115588277,0.6787564766839965,12,1,183.786,TP1+SL,2023-07-05 00:15:00+00:00,14.985176497604867,418.8087097903604,690,True
+2023-07-10 13:15:00+00:00,2,GBP_JPY,LONG,181.297,181.05918110745165,182.01728035670794,182.19371383632685,182.54658079556464,1452.08,2,OVERLAP,2.5,0.19563778509670907,56.798178305196686,20.912063586843743,182.28380917327345,182.73901612155905,-74.9280356707942,False,-0.0644692050516,17.96415793508736,-101.58091732734533,0.6352941176470766,13,0,181.05918110745165,SL,2023-07-11 02:45:00+00:00,-23.78188925483471,-345.33205749160385,810,False
+2023-07-14 12:45:00+00:00,2,GBP_JPY,SHORT,181.977,182.14125679569887,181.66611389827568,181.58285135193984,181.41632625926817,2081.36,2,LONDON,2.5,0.18851359139774052,42.85872742625654,71.42130698783646,181.34138187363513,180.98124302256363,33.98861017243178,False,0.007645865513743744,85.60864162659105,66.46181263648714,0.5149700598803109,12,4,182.14125679569887,SL,2023-07-14 14:00:00+00:00,-16.425679569886142,-341.8775242957822,75,False
+2023-07-24 13:30:00+00:00,2,GBP_JPY,LONG,180.837,180.70051695610385,181.25073531041107,181.35933455663127,181.57653304907163,2479.86,2,OVERLAP,2.5,0.1969660877923191,55.78688483683449,29.228564024572037,181.51217128771154,181.41505412998788,-44.27353104110807,False,-0.009294580385613538,23.43347536169065,-70.41712877115458,0.8088737201364834,13,0,181.40250351837471,TP1+TP2+SL,2023-07-24 18:30:00+00:00,47.45102777826987,1176.7190574622032,300,True
+2023-07-26 09:00:00+00:00,2,GBP_JPY,LONG,180.951,180.83077171574828,181.16787141099036,181.21880384426976,181.32066871082853,2913.01,2,LONDON,2.5,0.17045656850344748,39.277424521789285,26.350284865010636,181.55999335670373,181.5362269446725,-24.587141099036103,False,-0.05766266197287048,16.34736205613495,-63.799335670373125,0.8469387755101557,9,2,181.32066871082853,TP3,2023-07-26 10:30:00+00:00,25.761735761387996,750.4419389028085,90,True
+2023-07-28 16:00:00+00:00,2,GBP_JPY,SHORT,181.28300000000002,181.47321258661435,179.79608536649846,179.43658554754904,178.7175859096502,1880.69,2,OVERLAP,2.5,0.31442517322869895,64.69615429757513,79.86690434502242,179.56154920513822,179.90908746937345,151.5914633501552,False,0.04067194738669033,93.04079130072631,175.04507948617913,0.8548387096774608,16,4,181.47321258661435,SL,2023-07-28 19:15:00+00:00,-19.021258661433645,-357.7309095197165,195,False
+2023-07-31 10:30:00+00:00,2,GBP_JPY,SHORT,183.139,183.26989241472157,182.89847452388491,182.8434189102619,182.7333076830159,2705.69,3,LONDON,2.5,0.1877848294431811,40.147635169664156,74.67716860516937,182.19545232533872,180.98057415995123,26.952547611509203,False,-0.007864838316227196,70.9129535754944,97.25476746612856,0.8488888888889214,10,0,182.7333076830159,TP3,2023-07-31 12:00:00+00:00,28.45699670135005,769.9581140487583,90,True
+2023-08-08 12:00:00+00:00,2,GBP_JPY,LONG,181.614,181.47081669477126,182.08723653498546,182.20366851218594,182.4365324665869,2527.2,2,LONDON,2.5,0.18836661045747807,43.699990219347534,26.51395067104542,182.23975725929682,181.8486158220848,-50.22365349854567,False,-0.05751664942950452,15.108177484415721,-65.47572592968152,0.8364779874213094,12,1,181.47081669477126,SL,2023-08-08 12:15:00+00:00,-14.31833052287459,-361.8528489740866,15,False
+2023-08-16 14:00:00+00:00,2,GBP_JPY,SHORT,185.872,185.99156534719623,185.6473282558307,185.6002590643944,185.5061206815218,2996.14,3,OVERLAP,2.5,0.13713069439245762,35.15754263112162,71.32204935095646,185.38687132249453,184.8642797018513,25.367174416930993,False,-0.002047812170793195,86.26648889806326,51.41286775054823,0.5608695652174993,14,2,185.99156534719623,SL,2023-08-16 14:15:00+00:00,-11.956534719621459,-358.23451934846634,15,False
+2023-08-21 09:45:00+00:00,2,GBP_JPY,SHORT,185.68800000000002,185.80110032833255,185.30455798415147,185.21380628300003,185.0323028806971,3135.73,2,LONDON,2.5,0.1522006566650951,33.738116663279285,71.25079252532765,185.2150090304945,185.30961209088053,41.24420158485407,False,0.051756898654505626,96.46278966143988,50.199096950549915,0.8207547169812054,9,0,185.80110032833255,SL,2023-08-21 10:00:00+00:00,-11.310032833253556,-354.6520925621818,15,False
+2023-08-31 14:45:00+00:00,2,GBP_JPY,LONG,184.537,184.30600219729138,185.0157010096247,185.08601040160727,185.22662918557242,1519.95,2,OVERLAP,2.5,0.19799560541726138,26.325545960005396,29.85111040593668,185.1936673207248,185.1613465749936,-50.770100962469655,False,-0.015495311721266114,7.782975641255099,-68.56673207248036,0.7203065134099284,14,3,184.30600219729138,SL,2023-08-31 16:15:00+00:00,-23.09978027086288,-351.10511022698034,90,False
diff --git a/results/phase1/S2_GBP_USD_report.json b/results/phase1/S2_GBP_USD_report.json
new file mode 100644
index 0000000..ea0b8ab
--- /dev/null
+++ b/results/phase1/S2_GBP_USD_report.json
@@ -0,0 +1,46 @@
+{
+  "pair": "GBP_USD",
+  "strategy_id": 2,
+  "strategy_name": "S2_Session_VWAP_Reversal",
+  "total_trades": 370,
+  "win_rate_pct": 23.24,
+  "avg_rr": 2.61,
+  "expectancy_pips": -1.97,
+  "sharpe_ratio": -2.08,
+  "max_drawdown_pct": -65.19,
+  "profit_factor": 0.72,
+  "total_pnl_pips": -728.43,
+  "total_pnl_dollars": -44200.15,
+  "avg_win_pips": 32.08,
+  "avg_loss_pips": 12.28,
+  "max_consecutive_wins": 5,
+  "max_consecutive_losses": 26,
+  "avg_hold_time_minutes": 207.9,
+  "best_trade_pips": 106.39,
+  "worst_trade_pips": -47.95,
+  "best_trade_dollars": 3922.7,
+  "worst_trade_dollars": -1000.0,
+  "final_equity": 55799.85,
+  "starting_equity": 100000.0,
+  "session_breakdown": {
+    "LONDON": {
+      "trades": 215,
+      "win_rate": 23.72093023255814,
+      "total_pnl_pips": -690.7713524714953
+    },
+    "OVERLAP": {
+      "trades": 155,
+      "win_rate": 22.58064516129032,
+      "total_pnl_pips": -37.66221703104436
+    }
+  },
+  "exit_reasons": {
+    "SL": 284,
+    "TP1+TP2+SL": 38,
+    "TP1+SL": 17,
+    "TP3": 21,
+    "TP1+TIME": 5,
+    "TIME": 4,
+    "TP1+TP2+TIME": 1
+  }
+}
\ No newline at end of file
diff --git a/results/phase1/S2_GBP_USD_trades.csv b/results/phase1/S2_GBP_USD_trades.csv
new file mode 100644
index 0000000..82acfec
--- /dev/null
+++ b/results/phase1/S2_GBP_USD_trades.csv
@@ -0,0 +1,371 @@
+timestamp,strategy_id,pair,signal_direction,entry_price,sl_price,tp1_price,tp2_price,tp3_price,lot_size,confluence_score,session,spread_at_entry,atr_at_entry,adx_at_entry,rsi_at_entry,ema_50_value,ema_200_value,vwap_deviation,news_within_60min,macd_hist_at_entry,stoch_k_at_entry,distance_from_ema50_pips,candle_body_ratio,hour_of_day,day_of_week,exit_price,exit_reason,exit_time,pnl_pips,pnl_dollars,hold_time_minutes,win
+2021-01-06 13:45:00+00:00,2,GBP_USD,LONG,1.35667,1.3555664229783138,1.3634971711687691,1.3643902458333839,1.3661763951626136,906144.27,2,OVERLAP,1.5,0.001787154043372586,40.42206009519644,27.191276241611575,1.3628783357952492,1.3620294120450924,-70.1717116876921,False,-0.0008526655166046969,16.999393931997908,-63.98335795249288,0.9957894736842298,13,2,1.3555664229783138,SL,2021-01-06 14:00:00+00:00,-11.035770216862861,-999.9999947046939,15,False
+2021-01-08 09:45:00+00:00,2,GBP_USD,SHORT,1.35984,1.3612711893776206,1.3578875106808987,1.35738939747679,1.3563931710685728,691732.36,2,LONDON,1.5,0.0010023787552412779,32.20710672504867,70.99866014871728,1.3571123342568705,1.3583738837450032,21.42489319101326,False,0.00022882611828376777,90.3640650926112,29.176657431295716,0.8363636363635887,9,4,1.3612711893776206,SL,2021-01-08 13:30:00+00:00,-14.311893776206562,-990.0000057884678,225,False
+2021-01-12 09:45:00+00:00,2,GBP_USD,SHORT,1.3600299999999999,1.3612705175719104,1.3567775985198711,1.3559436396800155,1.354275722000304,790073.45,2,LONDON,1.5,0.0012810351438207637,48.645846863760454,78.32480023155692,1.353888270516797,1.353485255973597,34.42401480128865,False,0.0004574567075498856,90.16252009259074,63.31729483203041,0.7709497206703737,9,1,1.3612705175719104,SL,2021-01-12 15:15:00+00:00,-12.40517571910571,-980.0999978250079,330,False
+2021-01-15 12:00:00+00:00,2,GBP_USD,LONG,1.36278,1.361432142146151,1.3648010794303445,1.3651685653897145,1.3659035373084545,719882.29,2,LONDON,1.5,0.000995715707698025,35.94945217082639,27.788380460717235,1.3661263990802945,1.3655308528832708,-22.11079430344487,False,-0.00010463216534595178,15.629538352513052,-35.36399080294572,0.5720720720721477,12,4,1.361432142146151,SL,2021-01-15 12:30:00+00:00,-13.478578538490549,-970.2989984233429,30,False
+2021-01-15 13:45:00+00:00,2,GBP_USD,LONG,1.3607200000000002,1.359956524958265,1.363892414051303,1.3645514229140148,1.3658694406394387,1258189.15,2,OVERLAP,1.5,0.0011469500834699058,45.46611804243207,26.051746897986476,1.3651406438551643,1.3652994064142752,-33.62414051302886,False,-0.0001681887696993239,49.86784140969129,-46.10643855164298,0.9956331877729939,13,4,1.359956524958265,SL,2021-01-15 14:00:00+00:00,-7.634750417351288,-960.5960138069362,15,False
+2021-01-15 14:45:00+00:00,2,GBP_USD,LONG,1.3601900000000002,1.3588030494932581,1.3633188123093767,1.3641431162078175,1.3657917240046993,685669.78,2,OVERLAP,1.5,0.0011939010134835414,44.070119761873045,26.90717255716706,1.364476131730143,1.36511729105583,-33.18812309376584,False,-0.00016958671835830578,31.846323224525065,-44.7613173014294,0.6643356643357523,14,4,1.3588030494932581,SL,2021-01-15 15:00:00+00:00,-13.869505067420905,-950.9900488287378,15,False
+2021-01-15 16:00:00+00:00,2,GBP_USD,LONG,1.3581200000000002,1.3565437530316615,1.36229896257105,1.363388049038793,1.365566221974279,597292.28,2,OVERLAP,1.5,0.0013324939366769655,49.21494127385383,21.761593966094395,1.363427071569869,1.3648046276050367,-43.68962571049906,False,-0.0002330265862286753,29.970141207582117,-54.97071569869005,0.4112149532709757,16,4,1.3565437530316615,SL,2021-01-18 04:15:00+00:00,-15.762469683386902,-941.4801455621042,3615,False
+2021-01-18 08:45:00+00:00,2,GBP_USD,LONG,1.3529300000000002,1.3518971285090042,1.354138306153345,1.3544441230675441,1.3550557568959423,902402.05,2,LONDON,1.5,0.0009457429819915553,40.09752660958952,21.106092257591314,1.357217466169172,1.3612478168116424,-13.983061533449082,False,-0.0002993777652496336,15.102669134928007,-44.77466169171995,0.6302083333333243,8,0,1.3536882338291278,TP1+TP2+SL,2021-01-18 10:45:00+00:00,12.478312246819167,1126.0454552069723,120,True
+2021-01-20 10:30:00+00:00,2,GBP_USD,SHORT,1.3703299999999998,1.3711185691957677,1.3684171660088649,1.3679287823052715,1.3669520148980847,1196249.88,2,LONDON,1.5,0.0010371383915353367,51.47892287424516,71.84859915916024,1.366034320033583,1.3627051593901272,21.02833991135089,False,0.00022817456477922245,65.6449080189754,44.85679966417022,0.9497206703911031,10,2,1.3711185691957677,SL,2021-01-20 10:45:00+00:00,-7.885691957678631,-943.3258058090026,15,False
+2021-01-26 12:30:00+00:00,2,GBP_USD,SHORT,1.36946,1.3702946909878755,1.3654939999979536,1.3644629453073502,1.3624008359261432,1118848.24,2,LONDON,1.5,0.0011293819757513593,47.4004404781101,70.8120645330981,1.3659471190468648,1.367143684396987,41.560000020464116,False,0.0005255838158256446,85.274693260427,37.02880953135201,0.8481012658228845,12,1,1.3702946909878755,SL,2021-01-26 13:30:00+00:00,-8.346909878755858,-933.8925427284606,60,False
+2021-01-27 13:30:00+00:00,2,GBP_USD,LONG,1.36891,1.3674852911441981,1.3720033036935189,1.3727626965043527,1.3742814821260205,648942.14,2,OVERLAP,1.5,0.0012494177116034928,33.948414429668766,29.730574258255317,1.372436698843719,1.3708329586567947,-32.83303693518924,False,-0.0003534259512195235,29.693545193543116,-37.166988437191684,0.5659340659340653,13,2,1.3674852911441981,SL,2021-01-27 14:30:00+00:00,-14.24708855801926,-924.5536137610534,60,False
+2021-01-27 14:30:00+00:00,2,GBP_USD,LONG,1.36671,1.3655007127334822,1.3712286872672907,1.3721438738919804,1.3739742471413596,756898.8,2,OVERLAP,1.5,0.0013985745330358963,42.09069329825957,26.503351467630964,1.371851259924276,1.3707418537039484,-47.086872672907894,False,-0.0003382580262108613,27.19429691773907,-53.31259924276077,0.888524590163932,14,2,1.3716785938946394,TP1+TP2+SL,2021-01-27 19:15:00+00:00,49.064885800343525,3713.715318441705,285,True
+2021-02-08 10:15:00+00:00,2,GBP_USD,LONG,1.36925,1.3681077977055103,1.3710708404658627,1.3715177630707704,1.372411608280586,833867.38,2,LONDON,1.5,0.0009044045889792826,49.27755868656232,26.5614911466311,1.3721466160427782,1.369919552750024,-20.10840465862751,False,-0.000319286790742638,17.596333213076388,-30.866160427782674,0.6200000000000644,10,0,1.3681077977055103,SL,2021-02-08 12:00:00+00:00,-11.422022944898025,-952.4452347362001,105,False
+2021-02-16 14:30:00+00:00,2,GBP_USD,LONG,1.38785,1.3869145557084588,1.3912846198646582,1.3920530331046008,1.393589859584486,1007992.45,2,OVERLAP,1.5,0.0011308885830824242,57.2699780462698,24.67962259953292,1.391801606899789,1.3895698719712548,-36.2461986465834,False,-0.0004597356147051344,23.376986336642386,-41.416068997890406,0.6470588235294579,14,1,1.3869145557084588,SL,2021-02-16 14:45:00+00:00,-9.354442915412431,-942.9207832691718,15,False
+2021-02-18 13:30:00+00:00,2,GBP_USD,SHORT,1.39767,1.3992253449817935,1.393044374962968,1.3918814453817778,1.3895555862193973,600182.97,2,OVERLAP,1.5,0.001270689963587249,75.58339255964611,83.41337734240068,1.3904406955498232,1.3884647798068515,48.15625037032145,False,0.00021759686803665083,84.93313164754353,74.19304450176911,0.5596707818930312,13,3,1.39767,TP1+SL,2021-02-18 20:15:00+00:00,23.128125185160055,1388.110686416116,405,True
+2021-02-19 12:45:00+00:00,2,GBP_USD,SHORT,1.4014199999999999,1.4022986827116812,1.3994466133937908,1.3991611137258246,1.3985901143898922,1078173.81,2,LONDON,1.5,0.0009973654233626865,30.733964843274357,70.95215571597602,1.3981807807425302,1.3939041536816208,21.633866062091833,False,4.7939533134730254e-05,82.40524907191616,34.29219257469862,0.8418604651163698,12,4,1.4022986827116812,SL,2021-02-19 13:00:00+00:00,-8.786827116813445,-947.3726870346068,15,False
+2021-02-24 13:45:00+00:00,2,GBP_USD,LONG,1.4099300000000001,1.4083762452930637,1.4147709131269317,1.4160221004716884,1.4185244751612016,603633.86,2,OVERLAP,1.5,0.0013875094138726936,39.78166938144269,28.384125096711557,1.414285316590153,1.4101771624852903,-50.30913126931713,False,-0.0004270296375258238,14.120172577080362,-45.453165901530035,0.6331658291456979,13,2,1.4083762452930637,SL,2021-02-24 16:15:00+00:00,-15.537547069364166,-937.8989512411979,150,False
+2021-03-04 15:30:00+00:00,2,GBP_USD,SHORT,1.3989999999999998,1.400475264998052,1.3951546402997512,1.3943549009843628,1.3927554223535856,629391.99,2,OVERLAP,1.5,0.001290529996104252,55.230443694403206,75.40236683823449,1.3953161852435938,1.395096031965215,40.353597002487085,False,0.00037642868000104966,70.52502616193136,38.73814756406135,0.5599999999999935,15,3,1.400475264998052,SL,2021-03-04 16:30:00+00:00,-14.752649980522836,-928.5199729014729,60,False
+2021-03-04 16:30:00+00:00,2,GBP_USD,SHORT,1.40038,1.402397008892372,1.395738808334779,1.3947567838864774,1.392792734989874,455741.55,2,OVERLAP,1.5,0.0013740177847442235,63.557348615499734,75.63637643317186,1.3959109481696783,1.3952616129296596,48.31191665221057,False,0.0003354198568450723,69.71063766003034,46.590518303217806,0.5584905660377829,16,3,1.392792734989874,TP3,2021-03-04 17:45:00+00:00,54.26811223862238,2473.223358720373,75,True
+2021-03-05 08:45:00+00:00,2,GBP_USD,LONG,1.3841,1.3830826729386083,1.3853192216852437,1.385589150713148,1.3861290087689564,927889.4,2,LONDON,1.5,0.0012146541227836408,56.04739056021935,22.661257093021234,1.388809738025068,1.3922906549169856,-14.092216852437112,False,-0.00038649372979174636,19.42836394509325,-48.99738025067934,0.8128654970760826,8,4,1.3830826729386083,SL,2021-03-05 09:15:00+00:00,-10.173270613917927,-943.9669965985938,30,False
+2021-03-12 11:30:00+00:00,2,GBP_USD,LONG,1.3895000000000002,1.3885565263897528,1.3927496239856212,1.393355195228134,1.3945663377131594,990517.72,2,LONDON,1.5,0.0011269472204942395,54.50562393385907,23.63742392317947,1.3949697166381003,1.394186332406092,-34.39623985621143,False,-0.00013400345045803627,27.076226021515538,-56.59716638100276,0.7610294117646963,11,4,1.3885565263897528,SL,2021-03-12 11:45:00+00:00,-9.434736102473806,-934.527329302404,15,False
+2021-03-12 14:00:00+00:00,2,GBP_USD,LONG,1.3874700000000002,1.3865029486755036,1.3912017602518503,1.392106566176977,1.39391617802723,956704.19,2,OVERLAP,1.5,0.0012941026489931488,49.97041416563156,28.681522556454553,1.3930797745241497,1.3937189195587094,-39.217602518502034,False,-8.279960166405554e-05,22.91812443174768,-57.99774524149592,0.731843575418968,14,4,1.3865029486755036,SL,2021-03-12 14:30:00+00:00,-9.670513244965928,-925.1820540909399,30,False
+2021-03-16 08:45:00+00:00,2,GBP_USD,LONG,1.3820700000000001,1.3810825517994683,1.3830783687673849,1.3832867385869354,1.3837034782260367,927572.95,2,LONDON,1.5,0.0010348964010631861,50.8692433654637,26.723390379547354,1.3867885710272516,1.3900238421381015,-11.98368767384883,False,-0.0002555924828140442,45.281405610918824,-49.08571027251618,0.7741935483871661,8,1,1.3810825517994683,SL,2021-03-16 09:15:00+00:00,-9.874482005318706,-915.9302403395388,30,False
+2021-03-23 09:15:00+00:00,2,GBP_USD,LONG,1.3789200000000001,1.3775575117774363,1.3810967161204915,1.381666100596552,1.382804869548673,665525.7,2,LONDON,1.5,0.001164976445127194,43.24448620945198,22.11313806441251,1.3836851059283837,1.3859857456448845,-23.66716120491441,False,-0.0004438350555922457,9.934617782679904,-49.55105928383663,0.6815920398010187,9,1,1.3775575117774363,SL,2021-03-23 09:30:00+00:00,-13.624882225637958,-906.770928063526,15,False
+2021-03-29 08:15:00+00:00,2,GBP_USD,SHORT,1.3823199999999998,1.3832012038560881,1.3807377161502352,1.3805577749695068,1.3801978926080498,1018723.67,2,LONDON,1.5,0.0011624077121765671,49.43182201087624,80.16082936515241,1.3781095286406138,1.3765535713046515,17.722838497646887,False,0.0005784337430540652,86.78556041571896,44.00471359386149,0.698254364089742,8,0,1.3832012038560881,SL,2021-03-29 08:30:00+00:00,-8.812038560883638,-897.70322629249,15,False
+2021-03-31 08:45:00+00:00,2,GBP_USD,SHORT,1.37807,1.3788533133141383,1.3765823567190443,1.376163010013544,1.3753243166025433,1134573.07,2,LONDON,1.5,0.0010466266282763196,43.17903284975334,73.64329093441665,1.374086741939492,1.3750660565475725,16.77643280955765,False,0.0004474191379317499,93.90823040924721,41.73258060508056,0.7088607594936157,8,2,1.3788533133141383,SL,2021-03-31 09:00:00+00:00,-7.833133141383897,-888.7261915938673,15,False
+2021-04-01 15:00:00+00:00,2,GBP_USD,SHORT,1.3824299999999998,1.3834220348625492,1.3796388219745388,1.3789082340207783,1.3774470581132576,886903.24,2,OVERLAP,1.5,0.0011840697250985917,49.36368302406337,70.18227034073598,1.378961705966674,1.3774354575810064,29.81178025461162,False,0.000271564515827498,53.76562488624946,36.5829403332607,0.8227848101267922,15,3,1.3834220348625492,SL,2021-04-01 16:00:00+00:00,-9.920348625493956,-879.8389337880137,60,False
+2021-04-05 15:15:00+00:00,2,GBP_USD,SHORT,1.3904999999999998,1.3917648312753497,1.3875950975237852,1.3868932840498756,1.3854896571020565,688661.45,2,OVERLAP,1.5,0.0008696625506996048,65.76243997155927,77.84746695768189,1.3863501930714708,1.3833582932471067,30.949024762148003,False,0.00015400435236712388,59.16430862101524,43.398069285291996,0.5244755244755288,15,0,1.3917648312753497,SL,2021-04-06 05:45:00+00:00,-12.648312753498201,-871.0405400877564,870,False
+2021-04-06 09:15:00+00:00,2,GBP_USD,LONG,1.38332,1.3823369404832928,1.3851803014597002,1.3855333694287166,1.3862395053667496,877190.16,2,LONDON,1.5,0.0011861190334143071,69.54186457713975,25.995082151219137,1.3886194708975022,1.3865214680572704,-20.50301459700199,False,-0.0005278457634408321,28.116470961598747,-54.8947089750218,0.8537414965986603,9,1,1.3823369404832928,SL,2021-04-06 09:30:00+00:00,-9.830595167072964,-862.3301347499961,15,False
+2021-04-12 12:30:00+00:00,2,GBP_USD,SHORT,1.3769699999999998,1.377658849014024,1.3751948083771683,1.3747462196034772,1.3738490420560951,1239323.59,3,LONDON,1.5,0.0009976980280482987,48.646707309228574,71.29018957581138,1.3730085108189123,1.3730134861753365,19.65191622831597,False,-2.375722094902894e-05,38.33453151209785,41.51489181087653,0.8842105263157329,12,0,1.377658849014024,SL,2021-04-12 12:45:00+00:00,-6.888490140242175,-853.7068330284537,15,False
+2021-04-19 10:45:00+00:00,2,GBP_USD,SHORT,1.3916,1.3929259843902593,1.388801649514002,1.3883095500298335,1.3873253510614965,637390.43,2,LONDON,1.5,0.0012919687805187949,51.91827401835998,75.54684310562219,1.385729719692716,1.3814086901736307,29.883504859979926,False,0.00025336417086672383,81.9893783486905,60.60280307284005,0.6614420062696047,10,0,1.3929259843902593,SL,2021-04-19 11:45:00+00:00,-13.25984390259327,-845.1697606806804,60,False
+2021-04-19 11:45:00+00:00,2,GBP_USD,SHORT,1.39272,1.3938070751814402,1.3896712066966777,1.3889619012637002,1.3875432903977452,769696.6,2,LONDON,1.5,0.0013141503628799885,55.220312790679515,73.6036483715501,1.3865970129541294,1.381807582952444,32.3879330332244,False,0.00022062820390548001,62.9594376751136,63.12987045870688,0.867521367521449,11,0,1.3938070751814402,SL,2021-04-19 12:15:00+00:00,-10.870751814402446,-836.7180710989394,30,False
+2021-04-19 12:45:00+00:00,2,GBP_USD,SHORT,1.39617,1.3975994917288919,1.3905801541433231,1.389513615882643,1.387380539361283,579472.32,2,LONDON,1.5,0.0013989834577836195,61.99241509433501,80.28839271015524,1.3877447149545117,1.382298430706587,57.798458566769014,False,0.0004033809454893854,91.65937120725283,86.15285045488363,0.7620481927711299,12,0,1.3975994917288919,SL,2021-04-19 14:30:00+00:00,-14.294917288919518,-828.3508885618303,105,False
+2021-04-19 14:45:00+00:00,2,GBP_USD,SHORT,1.39848,1.4000218896878764,1.3923861244940523,1.3908352136529756,1.3877333919708223,531858.66,2,OVERLAP,1.5,0.00152377937575307,57.98511772958733,75.80963201118438,1.3901431409142702,1.383384995091772,62.838755059477776,False,0.0002142984770591872,88.34827586511842,85.2685908572992,0.46724890829701815,14,0,1.4000218896878764,SL,2021-04-20 03:00:00+00:00,-15.418896878764965,-820.0673832618118,735,False
+2021-04-22 12:00:00+00:00,2,GBP_USD,LONG,1.3870600000000002,1.3861746678578692,1.3901981306937155,1.3906425069366823,1.3915312594226161,917019.35,2,LONDON,1.5,0.0012506642842613851,43.33202746158986,26.899735066817513,1.3918187478694393,1.3926425259795754,-33.28130693715403,False,-0.00022084167833682513,12.922477076363492,-49.48747869439218,0.9732824427480867,12,3,1.3861746678578692,SL,2021-04-22 12:45:00+00:00,-8.853321421309346,-811.8667055110172,45,False
+2021-04-22 13:15:00+00:00,2,GBP_USD,LONG,1.38562,1.384720062962887,1.3890248507189955,1.3898248002014817,1.3914246991664538,893115.86,2,OVERLAP,1.5,0.0012798740742255106,57.75344345493029,23.83707818393846,1.3908529046390525,1.3923434297706707,-35.94850718995568,False,-0.0003940033471282428,14.874552313624344,-54.229046390525944,0.6108374384237164,13,3,1.384720062962887,SL,2021-04-22 13:45:00+00:00,-8.999370371129965,-803.7480408470259,30,False
+2021-04-22 13:45:00+00:00,2,GBP_USD,LONG,1.3849300000000002,1.38352125326902,1.3884486666698856,1.3893593510685165,1.3911807198657784,564835.78,2,OVERLAP,1.5,0.001297493461959761,61.9953225803942,21.83062103426306,1.390408859684108,1.3921996678881299,-37.08666669885563,False,-0.00039428751116723235,13.749947811825693,-56.688596841079075,0.4347826086955742,13,3,1.38352125326902,SL,2021-04-22 14:15:00+00:00,-14.087467309802369,-795.7105586156724,30,False
+2021-04-30 12:30:00+00:00,2,GBP_USD,LONG,1.38843,1.3872523541131494,1.3908737854524904,1.3911847627000138,1.3918067171950605,668922.18,2,LONDON,1.5,0.0010352917737011791,30.712201053814738,26.23982825072916,1.3923837870980682,1.3929477914025161,-26.33785452490489,False,-0.0001091722453581041,19.35805668682314,-41.43787098068285,0.813953488372081,12,4,1.3872523541131494,SL,2021-04-30 12:45:00+00:00,-11.776458868506534,-787.7534539001725,15,False
+2021-04-30 14:00:00+00:00,2,GBP_USD,LONG,1.3847600000000002,1.3837988460244477,1.3896053439638663,1.3903752948027988,1.391915196480664,811395.4,2,OVERLAP,1.5,0.0013623079511044032,56.67536857731931,16.528432415617928,1.3912703407197782,1.3926136761148846,-50.35343963866179,False,-0.00041761224745721806,30.12847264509735,-67.00340719778097,0.9647435897435724,14,4,1.3837988460244477,SL,2021-04-30 15:00:00+00:00,-9.611539755525198,-779.8759144550272,60,False
+2021-05-03 12:15:00+00:00,2,GBP_USD,SHORT,1.38845,1.3892183150257136,1.385982051995602,1.3854165808118382,1.3842856384443105,1004896.6,2,LONDON,1.5,0.0009366300514272071,46.09034860025471,75.39864253777975,1.3846022018730244,1.3871288800767763,26.57948004398003,False,0.00013368158381111606,46.547543265516225,40.37798126975689,0.9009433962264947,12,0,1.3892183150257136,SL,2021-05-03 12:30:00+00:00,-7.683150257136173,-772.0771570685266,15,False
+2021-05-03 13:15:00+00:00,2,GBP_USD,SHORT,1.38919,1.3899677806239434,1.3863902475505796,1.385713538024807,1.3843601189732617,982740.33,2,OVERLAP,1.5,0.0010355612478866365,56.3897721202644,72.47269270670266,1.38516107649127,1.387177070289097,29.897524494204575,False,0.00013374739098738443,70.21276595744634,42.18923508729988,0.839080459770109,13,0,1.3899677806239434,SL,2021-05-03 13:45:00+00:00,-7.777806239435137,-764.3563870418545,30,False
+2021-05-03 13:45:00+00:00,2,GBP_USD,SHORT,1.39007,1.3911939537012572,1.386785619185671,1.3860043794409582,1.384441899951533,673259.78,2,OVERLAP,1.5,0.001067907402514492,62.79631227560404,75.71609085420334,1.3855203708787158,1.3872295463111939,34.74380814329159,False,0.00019027291483365762,89.61724642575653,47.39629121284228,0.6312056737589457,13,0,1.3911939537012572,SL,2021-05-03 14:00:00+00:00,-11.239537012572853,-756.7128216386657,15,False
+2021-05-10 08:15:00+00:00,2,GBP_USD,SHORT,1.4082199999999998,1.408985533322859,1.4073925133951186,1.4071989476496585,1.406811816158738,978593.19,2,LONDON,1.5,0.001011066645717719,52.62893220333183,73.4082598064608,1.403102159449786,1.3965768003642294,10.174866048813236,False,0.0002478662575249842,83.97856242118299,53.07840550214049,0.8709677419354931,8,0,1.408985533322859,SL,2021-05-10 08:30:00+00:00,-7.655333228591132,-749.1456964679995,15,False
+2021-05-10 11:30:00+00:00,2,GBP_USD,SHORT,1.4116399999999998,1.4124770969113065,1.4091293641152527,1.4085827775011421,1.4074896042729208,885983.72,2,LONDON,1.5,0.0010941938226134145,59.880099394772955,77.72195860757773,1.4057849080810978,1.3981678366381398,27.006358847472267,False,0.00010281939575073522,80.86967706179233,60.450919189021235,0.7608695652173729,11,0,1.4124770969113065,SL,2021-05-10 12:15:00+00:00,-8.37096911306734,-741.6542354800501,45,False
+2021-05-10 12:15:00+00:00,2,GBP_USD,SHORT,1.4128999999999998,1.4136860010620047,1.409587428253771,1.4088856520125106,1.40748209952999,934143.39,2,LONDON,1.5,0.0011120021240093537,61.29808495327258,76.65499440527688,1.4064875294632766,1.3985759595703189,35.025717462289215,False,0.00015185079602447987,87.31313599412992,66.0247053672336,0.9154929577463929,12,0,1.4136860010620047,SL,2021-05-10 12:30:00+00:00,-7.860010620048817,-734.2376966048405,15,False
+2021-05-13 10:00:00+00:00,2,GBP_USD,LONG,1.40118,1.4000736565733216,1.4036120304037707,1.4041368717331126,1.4051865543917967,657025.03,2,LONDON,1.5,0.0012126868533564304,49.059631183606754,26.948928281627516,1.4054867300821547,1.4082270955268366,-26.220304037707365,False,-0.00035363618769722305,19.107125039814832,-44.96730082154787,0.744791666666633,10,3,1.4030008433467545,TP1+TP2+SL,2021-05-13 12:00:00+00:00,25.240468104878456,1658.3619313821812,120,True
+2021-05-20 15:30:00+00:00,2,GBP_USD,SHORT,1.41743,1.4187387712001316,1.413621309417951,1.412837295220325,1.411269266825073,568074.04,2,OVERLAP,1.5,0.0012175424002633657,38.5448385128079,73.50042633655073,1.4135513852252757,1.4141071286716145,39.98690582049047,False,0.0002952613181796779,78.77366490447285,40.68614774724421,0.617647058823603,15,3,1.4187387712001316,SL,2021-05-20 18:00:00+00:00,-13.087712001316198,-743.4789430944179,150,False
+2021-05-21 16:00:00+00:00,2,GBP_USD,LONG,1.4152900000000002,1.4144258751098837,1.4189491981500584,1.4198650837323836,1.4216968548970341,851779.83,2,OVERLAP,1.5,0.0011282497802323685,49.82288935438092,29.882750342270967,1.4183894812834734,1.4169383628836518,-38.49198150058353,False,-0.00034559592767170245,22.863178282938094,-32.89481283473394,0.6025641025640625,16,4,1.4144258751098837,SL,2021-05-21 16:45:00+00:00,-8.641248901164467,-736.0441520021556,45,False
+2021-05-21 16:45:00+00:00,2,GBP_USD,LONG,1.4147,1.4134627414782852,1.4187692795995177,1.419792724014034,1.4218396128430668,588950.24,2,OVERLAP,1.5,0.001094517043429501,56.70460150278723,29.770323008309674,1.4180300738292164,1.4168876627287246,-42.59279599517773,False,-0.0002606704883924537,23.563463772433266,-35.20073829216486,0.6437500000000356,16,4,1.4134627414782852,SL,2021-05-24 08:30:00+00:00,-12.372585217148568,-728.6837033060101,3825,False
+2021-05-25 12:45:00+00:00,2,GBP_USD,LONG,1.4142700000000001,1.4130032446400727,1.4170505174860077,1.4176006902368463,1.4187010357385235,569483.97,2,LONDON,1.5,0.0010535107198544665,37.64633809716912,27.72797322439878,1.4170810164169623,1.4163389889724285,-29.7051748600774,False,-0.0002672792116262615,26.06442330279813,-30.010164169622655,0.7843137254901807,12,1,1.4130032446400727,SL,2021-05-25 13:15:00+00:00,-12.66755359927485,-721.3968713902831,30,False
+2021-05-25 13:45:00+00:00,2,GBP_USD,LONG,1.41307,1.4116539093129197,1.4162410938415821,1.417059794398709,1.418697195512963,504334.16,2,OVERLAP,1.5,0.0011321813741608472,50.66855858399576,27.858623684465968,1.416534641415905,1.416223612505683,-33.6109384158223,False,-0.00037315092220318354,19.672602686517042,-36.54641415905102,0.580419580419639,13,1,1.4116539093129197,SL,2021-05-25 15:45:00+00:00,-14.160906870803736,-714.182907152503,120,False
+2021-05-27 11:00:00+00:00,2,GBP_USD,SHORT,1.4167699999999999,1.4185362667994927,1.4132235191834295,1.4128267666668934,1.4120332616338211,400302.53,2,LONDON,1.5,0.0013325335989853738,34.680124370482716,71.13505253870045,1.4122031097993992,1.4134575855189875,37.3648081657052,False,0.0003061109513854184,66.1750550054996,47.56890200600772,0.7589134125636692,11,3,1.4185362667994927,SL,2021-05-27 14:15:00+00:00,-17.66266799492877,-707.0410684920015,195,False
+2021-05-28 12:45:00+00:00,2,GBP_USD,LONG,1.41433,1.4134138765375155,1.4173935894212364,1.418102099493304,1.419519119637439,764057.13,2,LONDON,1.5,0.0010122469249691417,45.67903299888434,19.113221898219706,1.4180385072069077,1.4168079058902683,-32.535894212364354,False,-0.0003638692400528006,14.418927898808812,-38.98507206907764,0.7943262411348088,12,4,1.419519119637439,TP3,2021-05-28 16:15:00+00:00,36.30397478890368,2773.83107848021,210,True
+2021-05-31 14:45:00+00:00,2,GBP_USD,SHORT,1.4211399999999998,1.4220456568834317,1.4176558061033009,1.4173209147865655,1.4166511321530946,803515.09,2,OVERLAP,1.5,0.0008913137668633495,39.69877770949042,75.827814683432,1.4181263304863359,1.4177677920351286,36.74193896699096,False,0.0002279029124460631,80.04548184548248,32.03669513664131,0.8920863309352373,14,0,1.4220456568834317,SL,2021-05-31 23:15:00+00:00,-9.056568834318046,-727.708972199826,510,False
+2021-06-03 10:00:00+00:00,2,GBP_USD,SHORT,1.41956,1.4207180434849846,1.4180123764639614,1.4176209361615644,1.4168380555567701,622111.25,2,LONDON,1.5,0.0009160869699689974,43.59947838931005,70.05289503426584,1.4167820229548498,1.4168541864765176,17.376235360386794,False,0.0004050024006660704,74.1290143238183,29.67977045150283,0.6558441558442298,10,3,1.4185120983431196,TP1+TP2+SL,2021-06-03 11:15:00+00:00,16.096693600037625,1001.3934176386408,75,True
+2021-06-10 12:30:00+00:00,2,GBP_USD,SHORT,1.4137899999999999,1.4150123966633223,1.4102170903257436,1.4098243307420473,1.4090388115746548,597552.2,2,LONDON,1.5,0.0012647933266447563,30.187484107906936,74.70993000572916,1.4105684196883177,1.4128088154763812,37.629096742564094,False,0.00042313623173752545,85.13108123076181,34.11580311682316,0.5385906040268379,12,3,1.4150123966633223,SL,2021-06-10 12:45:00+00:00,-12.223966633224668,-730.4458154409994,15,False
+2021-06-15 11:15:00+00:00,2,GBP_USD,LONG,1.4049800000000001,1.4038341041667024,1.408419695024002,1.4091030056615979,1.4104696269367896,631070.76,2,LONDON,1.5,0.0009517916665950251,42.08425112985822,22.324882886993464,1.4098594081475833,1.411258169569953,-36.29695024002011,False,-0.00030552332074994097,41.6796114771336,-50.69408147583276,0.8545994065281639,11,1,1.4038341041667024,SL,2021-06-15 11:45:00+00:00,-11.458958332977163,-723.1413544000233,30,False
+2021-06-17 12:45:00+00:00,2,GBP_USD,LONG,1.3929300000000002,1.391568596935315,1.396372232231012,1.3972191050215774,1.3989128506027086,525861.86,2,LONDON,1.5,0.001322806129369879,31.467005148748363,28.838129957254324,1.3977975156994855,1.4037039059883027,-36.32232231011834,False,-0.0001008321011670536,41.083230759342364,-50.57515699485426,0.8299999999999904,12,3,1.391568596935315,SL,2021-06-17 15:45:00+00:00,-13.614030646851294,-715.9099478050224,180,False
+2021-06-17 16:15:00+00:00,2,GBP_USD,LONG,1.39026,1.389107450908887,1.3951761169390429,1.3962243207642366,1.3983207284146244,614942.0,2,OVERLAP,1.5,0.0014050981822261905,27.886489793406994,28.538898334042855,1.395837160709133,1.4023521169526016,-51.061169390429484,False,-0.00026069115063985255,13.793905591322135,-57.6716070913319,0.8737864077670228,16,3,1.389107450908887,SL,2021-06-18 06:00:00+00:00,-11.525490911130643,-708.75084318725,825,False
+2021-06-18 14:00:00+00:00,2,GBP_USD,LONG,1.38331,1.381653586713769,1.3869271545859734,1.3878054673092,1.389562092755653,423604.03,2,OVERLAP,1.5,0.0014328265724622951,62.256914948184246,28.6151393590185,1.3887648584616223,1.395267500830935,-38.07154585973471,False,-0.0004412009956298163,26.342857012346254,-56.44858461622437,0.523809523809549,14,4,1.381653586713769,SL,2021-06-18 14:15:00+00:00,-16.564132862311176,-701.663343393045,15,False
+2021-06-18 14:45:00+00:00,2,GBP_USD,LONG,1.38088,1.3796469570007834,1.3860261330016423,1.3871083790299021,1.389272871086422,563359.68,2,OVERLAP,1.5,0.0015260859984328426,72.06072184162578,24.59275073453945,1.3879983051251135,1.3948755093521732,-53.361330016423736,False,-0.000484436131205178,20.154769150507992,-73.0830512511349,0.8875502008031942,14,4,1.3796469570007834,SL,2021-06-18 15:00:00+00:00,-12.330429992166712,-694.6467094649443,15,False
+2021-06-21 14:00:00+00:00,2,GBP_USD,SHORT,1.39105,1.3919038635237866,1.3870797006311397,1.3862055873070105,1.3844573606587522,805398.31,2,OVERLAP,1.5,0.0012277270475732672,38.503085187753356,75.56364157702716,1.3853653133201582,1.3879242587531841,41.60299368860309,False,0.0001249708727988309,93.80417446779019,58.74686679841812,0.7765363128491815,14,0,1.3919038635237866,SL,2021-06-21 16:15:00+00:00,-8.53863523786691,-687.7002390284457,135,False
+2021-06-29 11:00:00+00:00,2,GBP_USD,LONG,1.3825800000000001,1.381724481878388,1.384616186864358,1.3849782661174521,1.3857024246236405,795802.24,2,LONDON,1.5,0.0009910362432239998,34.65621953285952,28.782045879524205,1.3860655496212804,1.3885451797579833,-22.261868643580485,False,-0.00016895937362012008,8.650245724065284,-36.75549621280361,0.8131313131312191,11,1,1.381724481878388,SL,2021-06-29 12:45:00+00:00,-8.555181216121088,-680.8232375395086,105,False
+2021-07-01 08:45:00+00:00,2,GBP_USD,LONG,1.3781800000000002,1.376913735991276,1.3798456928875673,1.3802754510715598,1.3811349674395448,532286.31,2,LONDON,1.5,0.0010325280174477067,40.1792791643814,29.186648587565344,1.3815629868793984,1.3836140491743962,-18.556928875672618,False,-0.00036300603486973343,6.527157665877971,-35.72986879398332,0.48117154811712526,8,3,1.376913735991276,SL,2021-07-01 10:45:00+00:00,-12.66264008724205,-674.0149966896149,120,False
+2021-07-06 14:15:00+00:00,2,GBP_USD,LONG,1.38118,1.3801553429295594,1.384718235889573,1.385282176439347,1.386410057538895,651217.73,2,OVERLAP,1.5,0.0011893141408809732,41.61130267015057,29.641429835224685,1.385535854735867,1.3842707137608032,-37.28235889573161,False,-0.0003373223723255064,14.50726187994026,-45.45854735866994,0.8938053097345124,14,1,1.3801553429295594,SL,2021-07-06 14:30:00+00:00,-10.246570704406377,-667.2748514408022,15,False
+2021-07-08 12:00:00+00:00,2,GBP_USD,LONG,1.3745,1.3735304836121423,1.3772881276813052,1.3777630737083744,1.378712965762513,681372.81,3,LONDON,1.5,0.00121903277571514,24.43201393439973,29.411970963559,1.3779390777409966,1.3799088633965941,-29.781276813052315,False,-0.00023294972873727575,25.969205239195514,-36.29077740996633,0.7090163934426275,12,3,1.3771308463678085,TP1+TP2+SL,2021-07-08 13:45:00+00:00,29.307665937548183,1996.9446694408496,105,True
+2021-07-09 13:30:00+00:00,2,GBP_USD,SHORT,1.3853099999999998,1.3862218658478864,1.3807577156375628,1.379598132788715,1.3772789670910195,746350.52,2,OVERLAP,1.5,0.0009837316957728728,71.03690842934002,81.79526843916875,1.3800682479602864,1.379222187535224,47.422843624371765,False,0.0003428933119623864,91.19630729630722,54.31752039713577,0.7795275590552269,13,4,1.3862218658478864,SL,2021-07-09 14:30:00+00:00,-9.11865847886606,-680.5715497404093,60,False
+2021-07-13 12:30:00+00:00,2,GBP_USD,LONG,1.38203,1.380163314125442,1.3844568017769956,1.3847843033937826,1.3854393066273567,360942.27,2,LONDON,1.5,0.0011177905830386495,54.90029941351709,23.776267409485598,1.386633162990336,1.3863014943720589,-26.168017769956364,False,-0.00010841809983146535,57.72612365244411,-47.931629903359415,0.6612021857923543,12,1,1.380163314125442,SL,2021-07-13 13:15:00+00:00,-18.666858745581827,-673.7658369399658,45,False
+2021-07-20 13:00:00+00:00,2,GBP_USD,LONG,1.3576800000000002,1.356641282669268,1.363436226587473,1.3647395287684747,1.367346133130478,642165.25,2,OVERLAP,1.5,0.0014174346614643342,46.23170209690391,23.874374075318514,1.3644381184747418,1.3704621188043087,-59.462265874730136,False,-0.0005116289773041101,10.953593616133297,-69.48118474741749,0.9014778325123249,13,1,1.36226,TP1+TIME,2021-07-21 09:00:00+00:00,51.68113293736342,3318.782765300522,1200,True
+2021-07-21 13:30:00+00:00,2,GBP_USD,SHORT,1.36624,1.3671941110444938,1.3624976537287248,1.3619379904593674,1.3608186639206523,733893.62,2,OVERLAP,1.5,0.001228222088988065,29.447498687816385,71.08781333573751,1.362384182781373,1.36523021382768,39.32346271275255,False,0.0003486068890389572,84.46050642479156,40.45817218627068,0.8903654485050013,13,2,1.3671941110444938,SL,2021-07-21 13:45:00+00:00,-9.541110444939438,-700.2160083256415,15,False
+2021-07-22 11:45:00+00:00,2,GBP_USD,SHORT,1.3770799999999999,1.3780052767244002,1.3747752171478185,1.374220557937097,1.3731112395156542,749196.24,2,LONDON,1.5,0.001190553448800455,34.436576212252604,71.02979629873887,1.3730257059606545,1.3692658353392964,24.947828521815207,False,0.00010568165745553863,50.74734557190715,42.44294039345453,0.8484848484848193,11,3,1.3780052767244002,SL,2021-07-22 12:30:00+00:00,-9.25276724400348,-693.213842880257,45,False
+2021-07-27 14:45:00+00:00,2,GBP_USD,SHORT,1.38784,1.3890517319894524,1.3803321540501867,1.3793622368427447,1.3774224024278607,566364.27,2,OVERLAP,1.5,0.0015034639789044614,32.74730440588385,74.99425928117562,1.3810012223162804,1.3790828847994476,76.97845949813374,False,0.0005978484514383415,86.38988243877559,70.28777683719677,0.9191176470588259,14,1,1.3890517319894524,SL,2021-07-27 15:30:00+00:00,-12.11731989452458,-686.2817036418892,45,False
+2021-07-30 09:15:00+00:00,2,GBP_USD,SHORT,1.3980899999999998,1.3986923514078005,1.3973923413655658,1.397249364610858,1.3969634111014422,1127944.38,2,LONDON,1.5,0.0007447028156008303,26.058915417006794,72.74373593148917,1.3958027358392773,1.392985723244311,8.876586344341852,False,0.0003236900415277439,87.15560349053662,24.77264160722692,0.8799999999998934,9,4,1.3969634111014422,TP3,2021-07-30 09:45:00+00:00,8.120400382003456,915.9359974230651,30,True
+2021-08-11 08:30:00+00:00,2,GBP_USD,LONG,1.38115,1.3806584726404496,1.3817544104787352,1.3818622500839468,1.3820779292943703,1400895.06,2,LONDON,1.5,0.0005430547191007165,31.369697943682837,28.000073606989687,1.3828792669151777,1.3847434678777637,-7.944104787351858,False,-0.00010231169572902501,46.17248062015154,-19.19266915177742,0.8796992481203523,8,2,1.3806584726404496,SL,2021-08-11 10:15:00+00:00,-4.915273595504922,-688.5782498491283,105,False
+2021-08-11 12:30:00+00:00,2,GBP_USD,SHORT,1.3868999999999998,1.388065207852705,1.383295755927571,1.3828401176628051,1.3819288411332733,585039.37,2,LONDON,1.5,0.0009904157054100643,45.524986169393884,78.29623689797552,1.3825883782771384,1.384351402627009,37.942440724290094,False,0.0005339385528075395,85.02781654980213,45.01621722861504,0.8967741935483803,12,2,1.388065207852705,SL,2021-08-11 17:00:00+00:00,-11.652078527051302,-681.6924680656622,270,False
+2021-08-12 12:30:00+00:00,2,GBP_USD,LONG,1.3840400000000002,1.383384698293086,1.385535901634046,1.385916484050647,1.3866776488838493,1029869.96,2,LONDON,1.5,0.0006706034138281233,43.09768310028386,29.71544471955582,1.3859925788807892,1.3856371027572727,-16.859016340460276,False,-0.00012264726515113014,57.90079807043936,-21.4257888078917,0.9097222222222404,12,3,1.383384698293086,SL,2021-08-12 13:00:00+00:00,-6.553017069141909,-674.8755426876495,30,False
+2021-08-13 13:00:00+00:00,2,GBP_USD,SHORT,1.3832499999999999,1.3838873676397763,1.38124422583543,1.3807006968119482,1.3796136387649842,1048259.66,2,OVERLAP,1.5,0.0006547352795528997,48.768588462194884,71.05756621840773,1.3812142514562156,1.3828559783778906,21.957741645699524,False,0.00036215322003917746,85.65873579717203,22.257485437844338,0.800000000000148,13,4,1.3838873676397763,SL,2021-08-13 13:15:00+00:00,-6.373676397763983,-668.1267853670098,15,False
+2021-08-13 15:00:00+00:00,2,GBP_USD,SHORT,1.3858099999999998,1.3864923736081405,1.3825824799661968,1.3817984766783955,1.3802304701027928,969330.45,2,OVERLAP,1.5,0.0008047472162809212,61.107850636964024,78.53240441146146,1.3821409927342205,1.3829862690691228,34.17520033803134,False,0.00028828488539383613,89.94941938305745,38.590072657793684,0.7525773195874165,15,4,1.3864923736081405,SL,2021-08-13 16:15:00+00:00,-6.8237360814071835,-661.4455166471662,75,False
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+2021-08-23 10:45:00+00:00,2,GBP_USD,SHORT,1.3674499999999998,1.3682745237606542,1.3659786539374041,1.36560748667168,1.3648651521402322,786251.15,2,LONDON,1.5,0.0008090475213087022,28.9602128301874,70.08839701263443,1.3649208867514149,1.3654000694653032,16.613460625958254,False,0.00015704882868181882,67.37484319763242,27.191132485850922,0.6666666666666666,10,0,1.3682745237606542,SL,2021-08-23 11:30:00+00:00,-8.245237606543387,-648.2827550167987,45,False
+2021-08-23 12:30:00+00:00,2,GBP_USD,SHORT,1.36923,1.3703289284831792,1.3667449382155188,1.366109226861662,1.3648378041539482,584023.38,2,LONDON,1.5,0.0008378569663583945,40.717335877142276,78.68327221901215,1.3657398437777089,1.365591912881665,26.75061784481292,False,0.00020054427748633164,79.75141969355634,36.80156222291231,0.5555555555555921,12,0,1.3703289284831792,SL,2021-08-23 13:30:00+00:00,-10.989284831792734,-641.7999271246324,60,False
+2021-08-23 14:30:00+00:00,2,GBP_USD,SHORT,1.3712499999999999,1.3718977460751622,1.3678988852276075,1.3670578566362481,1.3653757994535292,980912.04,2,OVERLAP,1.5,0.0008954921503242347,49.134692771210766,81.05134263459982,1.3668752793180972,1.3659184430378588,35.41114772392495,False,0.00017548756156384945,88.19716413267228,45.64720681902834,0.967741935483663,14,0,1.3718977460751622,SL,2021-08-23 14:45:00+00:00,-6.477460751623809,-635.3819239895245,15,False
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+2021-09-09 08:45:00+00:00,2,GBP_USD,SHORT,1.38089,1.3815591884377043,1.3802377662602345,1.3800297458858322,1.3796137051370274,912067.97,2,LONDON,1.5,0.0007583768754085136,57.72698820914121,80.12562174133501,1.3773207693045597,1.378174834987963,8.422337397655522,False,0.00049740314046396,92.59986069579149,37.59230695440374,0.5841584158415711,8,3,1.3815591884377043,SL,2021-09-09 09:15:00+00:00,-6.6918843770436,-610.345339924487,30,False
+2021-09-09 09:15:00+00:00,2,GBP_USD,SHORT,1.3816099999999998,1.3824246828876123,1.3805573321276,1.3802789895793544,1.379722304482863,741689.68,2,LONDON,1.5,0.0008093657752246742,64.24007361084207,80.67398448128594,1.3776305294503068,1.3782375555149213,12.426678724000162,False,0.00047867735518002585,89.54588626681722,41.69470549693077,0.6888888888888158,9,3,1.3824246828876123,SL,2021-09-09 09:30:00+00:00,-8.1468288761255,-604.2418902148282,15,False
+2021-09-09 11:45:00+00:00,2,GBP_USD,SHORT,1.3834699999999998,1.3842020949987286,1.381436587661682,1.3810579271938215,1.3803006062581005,817106.35,2,LONDON,1.5,0.000864189997457176,55.21947607864907,76.20674620193155,1.3790142077341887,1.378577944485597,22.234123383177984,False,4.9298855332378846e-05,65.77121520886641,46.45792265811188,0.9011857707509521,11,3,1.3842020949987286,SL,2021-09-09 13:00:00+00:00,-7.320949987288383,-598.1994722645757,75,False
+2021-09-09 13:00:00+00:00,2,GBP_USD,SHORT,1.3843599999999998,1.385194563813372,1.3820780714978467,1.3815726239119803,1.3805617287402474,709613.17,2,OVERLAP,1.5,0.0010491276267438259,50.553408329624034,72.02994550824725,1.3798186314647238,1.378814837958002,24.719285021532755,False,9.387233738672907e-05,71.46706514582304,47.31368535276115,0.40454545454548435,13,3,1.385194563813372,SL,2021-09-09 14:30:00+00:00,-8.345638133722133,-592.2174731743447,90,False
+2021-09-09 16:30:00+00:00,2,GBP_USD,SHORT,1.38574,1.3863989302651203,1.383010989067789,1.382288209562899,1.3808426505531188,889768.36,3,OVERLAP,1.5,0.0009378605302403711,40.13771363425269,70.36738694432975,1.3819015127202314,1.3795733948128512,29.190109322110303,False,-3.29225041690198e-05,81.56424581005264,40.28487279768677,0.785714285714248,16,3,1.3863989302651203,SL,2021-09-10 07:00:00+00:00,-6.589302651203166,-586.2953013504693,870,False
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+2021-09-23 12:45:00+00:00,2,GBP_USD,SHORT,1.3722299999999998,1.3732089812192654,1.36858308323231,1.3676292627232436,1.3657216217051107,586965.32,2,LONDON,1.5,0.0012779624385307227,55.10180077860329,73.05438861858164,1.3666864133204726,1.3657756234279907,38.36916767689935,False,0.000290403948555973,81.16385285689275,57.335866795273915,0.733333333333277,12,3,1.3732089812192654,SL,2021-09-23 13:30:00+00:00,-9.7898121926554,-574.6280246401878,45,False
+2021-09-23 13:30:00+00:00,2,GBP_USD,SHORT,1.3734499999999998,1.3744728978639673,1.3689468962835663,1.3679008866486027,1.3658088673786757,556147.16,2,OVERLAP,1.5,0.0013857957279344159,51.31522316138939,71.88601527588071,1.3672960229530142,1.3659611094192414,46.93103716433722,False,0.00018492327068243657,59.499983459275846,63.43977046985749,0.809352517985561,13,3,1.3744728978639673,SL,2021-09-23 14:00:00+00:00,-10.228978639674756,-568.881742015578,30,False
+2021-09-23 14:00:00+00:00,2,GBP_USD,SHORT,1.3744599999999998,1.3754652282602573,1.369282466955208,1.3680739065313707,1.365656785683696,560263.72,2,OVERLAP,1.5,0.0013704565205148738,54.89774554663175,73.54628437914731,1.3678140219570114,1.366120749835682,53.67533044791983,False,0.0002524279936319549,88.85022731874984,68.35978042988522,0.7960526315789214,14,3,1.37043,TIME,2021-09-24 10:00:00+00:00,40.29999999999755,2257.862791599863,1200,True
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+2021-09-29 12:45:00+00:00,2,GBP_USD,LONG,1.3464500000000001,1.3447649499570036,1.3498468568495914,1.3506732485023198,1.352326031807777,344152.29,2,LONDON,1.5,0.0014101000859928904,52.139926949249876,28.016540981662544,1.3518595301181795,1.3586188350556725,-35.868568495913685,False,-0.0003201170153268782,32.05896318491505,-55.99530118179485,0.42794759825324336,12,2,1.3447649499570036,SL,2021-09-29 13:30:00+00:00,-16.85050042996572,-579.9138310618687,45,False
+2021-09-29 14:00:00+00:00,2,GBP_USD,LONG,1.3432100000000002,1.3418353495462934,1.3484138222149509,1.3495766422714668,1.3519022823844984,417644.14,2,OVERLAP,1.5,0.001569300907413146,62.53062676293554,23.567668488344893,1.3505623806861482,1.3579423947317744,-53.938222149507716,False,-0.0004397215068439016,10.689304399255548,-75.42380686148097,0.49002849002845666,14,2,1.3418353495462934,SL,2021-09-29 16:30:00+00:00,-13.746504537068827,-574.1147065390209,150,False
+2021-10-01 10:15:00+00:00,2,GBP_USD,SHORT,1.3516199999999998,1.3524965416108898,1.348219016593669,1.3473847210856706,1.3457161300696743,648427.35,2,LONDON,1.5,0.0012330832217795642,48.931291306669664,71.95306356973374,1.3468216754283384,1.3479994153974142,35.909834063310164,False,0.0006010372586709208,95.41136779421315,49.8832457166154,0.8843537414965853,10,4,1.3524965416108898,SL,2021-10-01 10:30:00+00:00,-8.765416108900135,-568.3735539141427,15,False
+2021-10-04 14:00:00+00:00,2,GBP_USD,SHORT,1.3633799999999998,1.3645812919473452,1.358773446831291,1.3577341474508953,1.3556555486901036,468403.89,2,OVERLAP,1.5,0.0011425838946906108,41.82747992313612,76.56714407096763,1.3577197031233472,1.3536205208859982,47.965531687088614,False,0.00022454660972972542,92.00775416995657,58.502968766527275,0.7024390243902275,14,0,1.36189,TP1+TIME,2021-10-05 10:00:00+00:00,30.482765843542484,1427.8246099074431,1200,True
+2021-10-06 08:45:00+00:00,2,GBP_USD,LONG,1.35525,1.3544313157455727,1.3564504031949913,1.356644392347682,1.3570323706530638,704750.41,2,LONDON,1.5,0.0010973685088546195,51.09454279585655,22.391750855255978,1.3602915816073262,1.359830245706758,-13.904031949913964,False,-0.00038172518295026005,28.440199092370737,-52.31581607326285,0.7974137931035461,8,2,1.3544313157455727,SL,2021-10-06 09:15:00+00:00,-8.186842544273265,-576.9680639682027,30,False
+2021-10-14 08:30:00+00:00,2,GBP_USD,SHORT,1.3708999999999998,1.3716696502598034,1.3701706722607232,1.3699942022124354,1.3696412621158598,742153.17,2,LONDON,1.5,0.0006393005196067345,51.27972901373455,83.4589244466228,1.3671127490988595,1.3638856560844979,9.193277392767207,False,0.0002847800349888069,86.55611450024482,39.772509011404274,0.8082191780821543,8,3,1.3716696502598034,SL,2021-10-14 08:45:00+00:00,-7.6965025980357815,-571.1983801045492,15,False
+2021-10-14 10:00:00+00:00,2,GBP_USD,SHORT,1.37319,1.3738109794814544,1.3714495771929398,1.3710549046360576,1.3702655595222935,910636.21,2,LONDON,1.5,0.0008019589629085266,66.41768940720134,89.84268297825288,1.3681983389736745,1.364367955385287,19.30422807060239,False,0.0003124581016047323,92.37096936483924,51.81661026325557,0.680981595091982,10,3,1.3716961748693548,TP1+TP2+SL,2021-10-14 11:00:00+00:00,18.415685505066225,1676.9990052885444,60,True
+2021-10-19 09:45:00+00:00,2,GBP_USD,SHORT,1.3812799999999998,1.3819387455919434,1.379297442537303,1.3789500242113735,1.3782551875595146,883886.58,2,LONDON,1.5,0.0007774911838868286,73.68000157691218,83.51846965999427,1.3768170761032872,1.3738613670253244,21.725574626969557,False,0.00018242572042307737,88.33069841522058,46.5292389671279,0.9067357512953004,9,1,1.3819387455919434,SL,2021-10-19 10:15:00+00:00,-6.587455919435658,-582.2563883530739,30,False
+2021-10-20 10:00:00+00:00,2,GBP_USD,LONG,1.3762900000000002,1.3755324601096435,1.377584342567508,1.3778862863233718,1.3784901738350992,760928.68,2,LONDON,1.5,0.0008150797807129461,39.54055054726669,28.984034167707463,1.3789317913327923,1.3774733952808729,-14.843425675079569,False,-0.00013944126926313102,26.57976582481554,-28.317913327922106,0.8604651162790178,10,2,1.3755324601096435,SL,2021-10-20 10:15:00+00:00,-7.575398903567621,-576.4338288165158,15,False
+2021-10-20 11:15:00+00:00,2,GBP_USD,LONG,1.3753400000000002,1.3746786664903201,1.3769660924651355,1.3773985410014946,1.3782634380742127,862907.26,2,LONDON,1.5,0.0008626670193599649,49.374279719825935,29.27108232655118,1.3783942211562557,1.3774002551394466,-18.16092465135366,False,-0.00012788593329245243,35.44228881575272,-32.442211562555556,0.9370629370628372,11,2,1.3746786664903201,SL,2021-10-20 11:45:00+00:00,-6.613335096801175,-570.6694867842538,30,False
+2021-10-20 11:45:00+00:00,2,GBP_USD,LONG,1.3744500000000002,1.3737747685554291,1.3767050581703397,1.3772323550170806,1.3782869487105622,836695.03,2,LONDON,1.5,0.0008504628891420125,54.443960777092094,25.24577077016285,1.3781098596678854,1.377346841013273,-24.450581703396868,False,-0.00019060883003558854,5.002796207328482,-38.49859667885313,0.8613861386138265,11,2,1.3782869487105622,TP3,2021-10-20 12:45:00+00:00,26.768956477322472,2239.745284286202,60,True
+2021-10-22 15:45:00+00:00,2,GBP_USD,LONG,1.37447,1.3728791181850204,1.3782811227050127,1.3788485964716584,1.3799835440049497,369204.2,2,OVERLAP,1.5,0.0014017636299591995,40.16306703254804,26.903442477141326,1.378576393309592,1.379360516642595,-40.01122705012738,False,-0.0003165419299278376,24.767280007601993,-42.96393309592084,0.7020202020202096,15,4,1.378196548452559,TP1+TP2+SL,2021-10-25 07:45:00+00:00,39.970523854705235,1475.7285283357362,3840,True
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+2021-10-26 10:00:00+00:00,2,GBP_USD,SHORT,1.3811499999999999,1.3818883347197966,1.378481428347397,1.377972235314602,1.376953849249012,807352.46,2,LONDON,1.5,0.0009166694395933768,52.14182631292654,81.49356306720325,1.377214279782757,1.377224186727804,28.58571652603059,False,0.0003615387789777638,86.24213981917508,41.257202172431114,0.9422222222223336,10,1,1.3818883347197966,SL,2021-10-26 10:15:00+00:00,-7.383347197966738,-596.0963523312553,15,False
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+2021-12-22 16:00:00+00:00,2,GBP_USD,SHORT,1.33544,1.336198828698697,1.33103925616433,1.3300017256759342,1.3279266646991426,750492.6,2,OVERLAP,1.5,0.001017657397394011,52.53603957564803,80.04797786647467,1.3300108727525777,1.3264605903112427,45.90743835670041,False,0.00013923410755487062,96.03832336581212,56.19127247422417,0.9117647058824297,16,2,1.336198828698697,SL,2021-12-22 16:15:00+00:00,-7.588286986970338,-569.4953230397535,15,False
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+2021-12-28 11:15:00+00:00,2,GBP_USD,SHORT,1.3452899999999999,1.3459120587567064,1.3440458686131913,1.3437148334760356,1.3430527632017242,879426.47,2,LONDON,1.5,0.0004241175134128397,48.544301215113634,78.75925501277695,1.3437674163842868,1.3418195475459374,14.341313868087013,False,0.00015645479952395961,85.70856489222301,17.12583615713248,0.6451612903226731,11,1,1.3459120587567064,SL,2021-12-28 13:30:00+00:00,-6.220587567065117,-547.0549365429964,135,False
+2021-12-28 15:30:00+00:00,2,GBP_USD,LONG,1.3424600000000002,1.3412578963435622,1.344376855168867,1.3448102698770608,1.3456770992934484,450530.52,2,OVERLAP,1.5,0.0008042073128757577,42.06356713286661,28.0107210250507,1.3442000433369496,1.342276784937345,-21.068551688669945,False,-0.00034020565663291395,17.658727065273833,-19.300433369495718,0.5628742514970251,15,1,1.3412578963435622,SL,2021-12-29 08:30:00+00:00,-12.02103656438025,-541.5843854289247,1020,False
+2021-12-30 12:30:00+00:00,2,GBP_USD,SHORT,1.3505399999999999,1.351275117596265,1.3474315850285108,1.346770235666615,1.3454475369428232,729364.31,2,LONDON,1.5,0.0007502351925300101,50.94306038694409,71.57341928584573,1.347977711699828,1.345999147801475,32.98414971489194,False,0.0004110516962671811,89.18053087815696,27.522883001720455,0.8741258741258296,12,3,1.351275117596265,SL,2021-12-30 12:45:00+00:00,-7.351175962651001,-536.1685383687534,15,False
+2021-12-31 15:45:00+00:00,2,GBP_USD,SHORT,1.35425,1.3556588999313663,1.350656735182054,1.3498569884415779,1.3482574949606259,376752.7,2,OVERLAP,1.5,0.0013177998627329284,43.15782700978308,72.47457203445731,1.35032417153095,1.3487209143730776,37.83264817946108,False,0.0005980092502598804,93.80761453479055,41.1582846905012,0.4648437500000285,15,4,1.3501008627727835,TP1+TP2+SL,2022-01-03 03:45:00+00:00,39.565570385031826,1490.6435469600783,3600,True
+2022-01-03 16:00:00+00:00,2,GBP_USD,LONG,1.34437,1.3424752480980682,1.3486671188094066,1.3497725657790278,1.3519834597182703,288013.06,2,OVERLAP,1.5,0.001136501267954555,58.253766480140214,23.856887782119017,1.349265895336605,1.3498880376421343,-44.871188094066646,False,-0.0005001181411993672,27.211918756769336,-50.85895336605084,0.3024691358025402,16,0,1.3491753340060368,TP1+TP2+SL,2022-01-04 08:15:00+00:00,47.60257528268496,1371.016337104646,975,True
+2022-01-04 09:15:00+00:00,2,GBP_USD,SHORT,1.3507699999999998,1.3513715330395226,1.349495246428718,1.3492538087256487,1.3487709333195104,929996.22,2,LONDON,1.5,0.0008230660790455726,47.258358146656875,72.58186172295424,1.3478584824068283,1.348762339828124,14.647535712819515,False,0.0004125155714684182,80.3900070910383,31.015175931716765,1.0,9,1,1.3513715330395226,SL,2022-01-04 10:15:00+00:00,-6.015330395228169,-559.4234529613303,60,False
+2022-01-04 15:00:00+00:00,2,GBP_USD,SHORT,1.35364,1.354667169945806,1.350266309085002,1.349669323922253,1.3484753535967553,539179.74,2,OVERLAP,1.5,0.0011543398916118823,43.72422627011017,71.1094933860299,1.3493703266558852,1.3490656940164045,35.63690914998085,False,0.0003677768440355072,82.75077854820366,44.596733441149006,0.8943089430894566,15,1,1.354667169945806,SL,2022-01-04 15:15:00+00:00,-10.271699458059834,-553.8292243154842,15,False
+2022-01-14 14:15:00+00:00,2,GBP_USD,LONG,1.36911,1.3682949224732073,1.3719338646328885,1.3725044802058237,1.3736457113516942,672685.62,2,OVERLAP,1.5,0.001030155053585731,37.20814572608347,29.261602907266933,1.37192605599526,1.3703959726384383,-30.13864632888552,False,-0.00027551919095379293,18.06475360054805,-30.060559952600485,0.8797468354430868,14,4,1.3682949224732073,SL,2022-01-14 15:00:00+00:00,-8.150775267927735,-548.2909314586634,45,False
+2022-01-14 16:15:00+00:00,2,GBP_USD,LONG,1.3675800000000002,1.3666178631417927,1.37089313611442,1.3717457397749078,1.373450947095883,564169.24,2,OVERLAP,1.5,0.0011642737164147255,53.837666620797926,27.94295379111469,1.370952657207762,1.37024376453051,-35.03136114419991,False,-0.00021657296538368266,26.06376919291074,-35.62657207761832,0.6969696969695203,16,4,1.3666178631417927,SL,2022-01-14 16:45:00+00:00,-9.621368582075629,-542.8080200709485,30,False
+2022-01-18 13:00:00+00:00,2,GBP_USD,LONG,1.3586300000000002,1.357638510879151,1.3620070494407566,1.362898126430572,1.3646802804102027,541992.77,2,OVERLAP,1.5,0.000962978241697639,61.69003857219409,25.23564671556126,1.3627275078572822,1.3655385378519347,-35.67049440756475,False,-0.0003111604113053792,17.598940221893205,-42.87507857282158,0.6521739130433838,13,1,1.357638510879151,SL,2022-01-18 15:00:00+00:00,-9.914891208491206,-537.3799350338796,120,False
+2022-01-24 09:45:00+00:00,2,GBP_USD,LONG,1.35268,1.3520692277320077,1.3539297069753755,1.3542522119324023,1.354897221846456,871038.47,2,LONDON,1.5,0.0007015445359847878,32.57143235539704,29.63154539986266,1.3548282620422978,1.3574406600024227,-14.397069753755432,False,-0.00012827215054196216,16.33582071530581,-23.382620422978118,0.9189189189189838,9,0,1.3520692277320077,SL,2022-01-24 10:00:00+00:00,-6.107722679924432,-532.0061418305677,15,False
+2022-01-27 13:15:00+00:00,2,GBP_USD,LONG,1.3373100000000002,1.3363938128779467,1.3406663456243832,1.3414808358934076,1.3431098164314563,574867.37,2,OVERLAP,1.5,0.0010123742441066623,52.57207831692035,24.777155826708807,1.3422224281732245,1.3467584995222894,-35.46345624383162,False,-0.00022561445916538699,27.427439053365415,-51.024281732243715,0.7305699481864874,13,3,1.3363938128779467,SL,2022-01-27 13:30:00+00:00,-9.161871220535467,-526.6860812827914,15,False
+2022-01-31 09:30:00+00:00,2,GBP_USD,SHORT,1.3444299999999998,1.3451949980975815,1.343699178695163,1.3435362248762988,1.3432103172385703,681595.44,2,LONDON,1.5,0.0007299961951627983,39.49014635728891,71.11150451283176,1.3417105270400786,1.3414238522082083,9.208213048368386,False,0.00010587102270898899,55.41493798087149,29.094729599212776,0.7957746478872336,9,0,1.3432103172385703,TP3,2022-01-31 11:00:00+00:00,8.611843599281356,586.9793327263359,90,True
+2022-02-01 09:45:00+00:00,2,GBP_USD,SHORT,1.3495899999999998,1.3504713613700494,1.3482690920493885,1.3479465464927067,1.3473014553793434,598266.53,2,LONDON,1.5,0.0007827227400988159,53.48050420218375,73.52758402055181,1.3457280993902314,1.3435146207775865,15.109079506114842,False,0.00027469559823016044,78.50120119349917,40.51900609768566,0.8159509202454214,9,1,1.3504713613700494,SL,2022-02-01 11:15:00+00:00,-8.813613700495715,-527.2890085356031,90,False
+2022-02-01 11:15:00+00:00,2,GBP_USD,SHORT,1.35092,1.3517762682644996,1.3490021902217335,1.3485119784299706,1.347531554846445,609640.86,2,LONDON,1.5,0.0007725365289990701,53.926543392529446,76.1653020472443,1.3466727490083128,1.3439006634935282,21.07809778266523,False,0.00015913816117440202,73.5766888485031,44.37250991687236,0.8041958041957694,11,1,1.3484235149285428,TP1+TP2+SL,2022-02-01 13:00:00+00:00,21.761851993620013,1326.691416458322,105,True
+2022-02-02 12:45:00+00:00,2,GBP_USD,SHORT,1.3578,1.3583828318732794,1.354842001809305,1.354166863028438,1.3528165854667042,918417.57,2,LONDON,1.5,0.0007256637465584794,63.99639993137795,82.03778221988877,1.3538084944653048,1.349494668486352,31.479981906950005,False,0.000253065362523905,80.35132469747779,41.815055346952064,0.9097222222221002,12,2,1.3583828318732794,SL,2022-02-02 13:00:00+00:00,-5.828318732794546,-535.2830327758646,15,False
+2022-02-07 10:30:00+00:00,2,GBP_USD,LONG,1.35054,1.3496084224937301,1.352905307373725,1.3535391742755947,1.3548069080793343,568852.51,2,LONDON,1.5,0.0008631550125394053,34.93111663307294,28.60950443427855,1.3528954286733226,1.3546468295566463,-25.55307373725002,False,-0.00033600073920523325,10.647986480253687,-25.45428673322636,0.6862745098040496,10,0,1.3496084224937301,SL,2022-02-07 10:45:00+00:00,-9.315775062699315,-529.9302027011913,15,False
+2022-02-11 12:00:00+00:00,2,GBP_USD,SHORT,1.35804,1.3598164016656837,1.355673979913969,1.355220104563432,1.3543123538623578,295333.49,2,LONDON,1.5,0.0010576011104558285,31.596017868276792,70.74784608513417,1.3552510736286556,1.3553606300920311,25.560200860310633,False,0.00020011110874977784,58.15685082861845,29.78926371344448,0.7865612648222214,12,4,1.3598164016656837,SL,2022-02-11 13:00:00+00:00,-17.76401665683824,-524.630903568217,60,False
+2022-02-11 13:00:00+00:00,2,GBP_USD,SHORT,1.3595099999999998,1.3613457500253434,1.3561578256852291,1.3554746339028354,1.3541082503380477,282927.73,2,OVERLAP,1.5,0.001097166683562312,41.03641565323887,72.4688269462104,1.3557457308697183,1.3554870195381816,35.42174314770774,False,0.0003009301690774272,78.88995795674332,39.54269130281629,0.8434343434342222,13,4,1.355717543042566,TP1+TP2+SL,2022-02-11 19:15:00+00:00,36.57333835007903,1034.7611597909804,375,True
+2022-02-15 14:45:00+00:00,2,GBP_USD,LONG,1.3491300000000002,1.347925560132026,1.3545111557709308,1.355065305093182,1.3561736037376841,439816.23,2,OVERLAP,1.5,0.0014488797359479611,31.262237290773832,28.321798987258873,1.3539387987842064,1.3540842576822976,-55.71155770930813,False,-0.0004681786326937587,14.734285530463596,-49.98798784206393,0.9014084507042077,14,1,1.3549397932084477,TP1+TP2+SL,2022-02-16 06:00:00+00:00,56.39403649346119,2480.301252503652,915,True
+2022-02-24 09:15:00+00:00,2,GBP_USD,LONG,1.3451700000000002,1.344169170594752,1.3469767778037507,1.347472337707988,1.3484634575164625,554075.66,2,LONDON,1.5,0.0012616588104958752,47.97595188556587,25.484448577346342,1.3508971658437332,1.3555215418033142,-19.967778037506267,False,-0.00027837857322436394,5.929554587912754,-59.17165843733141,0.825503355704597,9,3,1.344169170594752,SL,2022-02-24 09:45:00+00:00,-10.008294052481492,-554.5352132602758,30,False
+2022-02-24 09:45:00+00:00,2,GBP_USD,LONG,1.34356,1.34263655525772,1.3463994536708326,1.347097665462142,1.3484940890447612,594502.13,2,LONDON,1.5,0.001266889484560217,54.01330350267712,21.17032046374598,1.3503730085316432,1.35529520746895,-30.29453670832627,False,-0.00036284104480467845,5.239336368688833,-70.03008531643262,0.869047619047564,9,3,1.34263655525772,SL,2022-02-24 10:15:00+00:00,-9.234447422801217,-548.9898662228335,30,False
+2022-02-24 11:00:00+00:00,2,GBP_USD,LONG,1.3404800000000001,1.3395364076190437,1.3448312841203613,1.345941975677998,1.348163358793271,575990.2,2,LONDON,1.5,0.0013671847619124641,66.28023933036837,15.808228937816011,1.3488739462596468,1.3546534036160178,-45.41284120361366,False,-0.0004094703299447914,19.532751978141487,-85.83946259646868,0.8962655601659415,11,3,1.3395364076190437,SL,2022-02-24 11:15:00+00:00,-9.435923809564262,-543.4999642255681,15,False
+2022-02-24 15:00:00+00:00,2,GBP_USD,LONG,1.33193,1.330361775725361,1.3405836999332315,1.3427318697176716,1.3470282092865518,343104.6,2,OVERLAP,1.5,0.001636448549277976,62.7777002901055,13.257727237313276,1.3431884317584712,1.3520747652144818,-88.4369993323153,False,-0.00038203944440542764,8.20885891699021,-114.48431758471322,0.6211453744493995,15,3,1.330361775725361,SL,2022-02-24 15:30:00+00:00,-15.682242746390962,-538.0649624603373,30,False
+2022-02-24 15:30:00+00:00,2,GBP_USD,LONG,1.3304500000000001,1.329239719885643,1.3399842852650206,1.3423580350889306,1.3471055347367509,440133.08,2,OVERLAP,1.5,0.001580560228714177,66.01129932794996,11.545581147515136,1.3422357649565897,1.3516536367233163,-97.24285265020559,False,-0.00043542393815071455,6.461822745661571,-119.75764956589695,0.7971014492754719,15,3,1.329239719885643,SL,2022-02-24 15:45:00+00:00,-12.102801143571982,-532.6843143947859,15,False
+2022-03-07 08:30:00+00:00,2,GBP_USD,LONG,1.3173500000000002,1.3165009921807274,1.3186481935812562,1.3189395334247958,1.3195222131118751,621145.6,2,LONDON,1.5,0.0012180156385453906,32.59972280106163,29.29664672221317,1.3214556064591712,1.3275106832807444,-14.881935812560698,False,-0.0003875223920163692,5.932794560790701,-42.956064591710415,0.7553191489361162,8,0,1.3165009921807274,SL,2022-03-07 08:45:00+00:00,-8.490078192728845,-527.3574713069473,15,False
+2022-03-09 10:00:00+00:00,2,GBP_USD,SHORT,1.3166099999999998,1.3176064108860677,1.3139083627903316,1.3133672357550772,1.3122849816845683,523964.47,2,LONDON,1.5,0.0011528217721357137,40.34175527920604,74.8879020466517,1.3121863829771012,1.3139278284209581,28.916372096683407,False,0.0003837206328598996,89.24423522644832,46.136170228987524,0.8502415458937053,10,2,1.3176064108860677,SL,2022-03-09 10:30:00+00:00,-9.964108860678333,-522.0839018207627,30,False
+2022-03-17 12:00:00+00:00,2,GBP_USD,LONG,1.3114700000000001,1.3087757220120193,1.3170267675831349,1.3176731895146698,1.31896603337774,191837.32,2,LONDON,1.5,0.001669518658653806,32.168161071528836,28.078500337595173,1.316278489628322,1.311069594017915,-57.46767583134859,False,-0.00038840899985366517,63.055036871596066,-49.98489628321945,0.8417132216014861,12,3,1.3087757220120193,SL,2022-03-17 12:45:00+00:00,-26.94277987980831,-516.8630685492349,45,False
+2022-03-22 08:30:00+00:00,2,GBP_USD,SHORT,1.31863,1.3205019571938625,1.316237290148307,1.3157290115873657,1.3147124544654831,273347.29,2,LONDON,1.5,0.0011213047959083467,46.683692826260106,76.46386604144793,1.314809587391024,1.315315049917343,25.827098516930658,False,0.0006203802968103493,91.50845158653658,40.10412608976166,0.8186666666667183,8,1,1.3205019571938625,SL,2022-03-22 10:30:00+00:00,-18.719571938625723,-511.6944259383387,120,False
+2022-03-22 10:45:00+00:00,2,GBP_USD,SHORT,1.32086,1.3223154667987227,1.318222180150426,1.317538812968666,1.316172078605146,348051.56,2,LONDON,1.5,0.0012309335974451585,58.69232025851822,73.68736473298905,1.3161100480282744,1.315636797427816,28.278198495741425,False,0.0004931992646789746,75.56615694052762,49.39951971725698,0.5166666666667139,10,1,1.3223154667987227,SL,2022-03-22 11:45:00+00:00,-14.554667987227708,-506.57748982366644,60,False
+2022-03-22 12:15:00+00:00,2,GBP_USD,SHORT,1.3244699999999998,1.325378443128272,1.3194075872739777,1.31831768023544,1.3161378661583643,552056.25,2,LONDON,1.5,0.0012568862565436646,66.1351713957759,82.9004074869597,1.317429152205658,1.3160214249455562,52.52412726022282,False,0.00044418211156447445,83.15725717860742,72.3084779434191,0.9444444444444346,12,1,1.325378443128272,SL,2022-03-22 13:15:00+00:00,-9.08443128272207,-501.5117067322237,60,False
+2022-03-22 13:15:00+00:00,2,GBP_USD,SHORT,1.32581,1.3268737628640819,1.3202749807169796,1.3189153379432879,1.3161960523959042,466736.16,2,OVERLAP,1.5,0.0013075257281638943,70.02241605267777,78.65099618358084,1.3184728423980794,1.3163522775713907,57.25019283020494,False,0.00035904663694549853,80.51037759884345,75.27157601920686,0.6282051282051611,13,1,1.3268737628640819,SL,2022-03-22 14:00:00+00:00,-10.637628640819406,-496.49659433220694,45,False
+2022-03-23 11:15:00+00:00,2,GBP_USD,LONG,1.32047,1.3196757474002634,1.3227543276962883,1.3233025300035017,1.3243989346179283,618860.58,2,LONDON,1.5,0.0010885051994732587,67.38763583182595,22.981337822442626,1.3249689546405568,1.3219310558468722,-24.743276962884142,False,-0.0002636348380145243,11.193445745072767,-46.889546405568524,0.9387755102041325,11,2,1.3196757474002634,SL,2022-03-23 12:00:00+00:00,-7.942525997366444,-491.5316245395276,45,False
+2022-03-23 12:15:00+00:00,2,GBP_USD,LONG,1.3183500000000001,1.317429440896994,1.3220513531214828,1.3227997747275206,1.3242966179395965,528609.53,2,LONDON,1.5,0.001161118206011974,74.652102399921,20.123127222265524,1.3241889341677855,1.321844474583947,-38.9135312148281,False,-0.0002851971797909183,13.873063468267235,-60.289341677854935,0.7317073170731602,12,2,1.317429440896994,SL,2022-03-24 08:15:00+00:00,-9.205591030061466,-486.6163147773008,1200,False
+2022-03-25 08:45:00+00:00,2,GBP_USD,LONG,1.31638,1.3155422700955899,1.3176234358224066,1.3178467262861615,1.3182933072136713,575066.19,2,LONDON,1.5,0.0009354598088201034,51.646624889085544,25.35508359992201,1.3197523517451961,1.3198421405912304,-14.334358224066257,False,-0.0005638687806413382,15.588662946018772,-35.62351745196146,0.8387096774193218,8,4,1.31638,TP1+SL,2022-03-25 09:30:00+00:00,6.217179112032455,357.52895045040873,45,True
+2022-03-28 13:45:00+00:00,2,GBP_USD,LONG,1.3083500000000001,1.3072429063793636,1.3126834263613358,1.3137921753295054,1.3160096732658444,438377.95,2,OVERLAP,1.5,0.0012741872412726115,40.57768636289154,27.923332030323976,1.3135146825541035,1.3169094973284554,-45.23426361335847,False,-0.0003977270494972382,17.48888053623889,-53.5468255410354,0.7249999999998844,13,0,1.3072429063793636,SL,2022-03-28 14:30:00+00:00,-11.070936206365456,-485.32543187272654,45,False
+2022-03-30 09:15:00+00:00,2,GBP_USD,SHORT,1.3148799999999998,1.3162278076433138,1.3131957539224408,1.3128060071843262,1.3120265137080969,356484.24,2,LONDON,1.5,0.0011556152866275715,30.72215516964429,70.52717993210742,1.311340744227928,1.3115990958701975,18.742460775591496,False,0.00023090160165832932,92.35268826527978,37.292557720720595,0.9469026548671088,9,2,1.3148799999999998,TP1+SL,2022-03-30 11:00:00+00:00,8.421230387795076,300.2035914658033,105,True
+2022-04-22 08:30:00+00:00,2,GBP_USD,LONG,1.2885400000000002,1.2875593546463793,1.2901436949578402,1.2904322574620715,1.2910093824705342,493016.38,2,LONDON,1.5,0.001541290707241244,74.74638626561776,13.859305486778823,1.2995026401204397,1.302735496859085,-17.936949578400974,False,-0.0010808698538087845,7.493101523035574,-111.52640120439594,0.8134920634919929,8,4,1.2875593546463793,SL,2022-04-22 09:15:00+00:00,-9.80645353620968,-483.47422230602956,45,False
+2022-04-22 09:15:00+00:00,2,GBP_USD,LONG,1.2874,1.286134363031376,1.2893817732104849,1.2898509015799136,1.290789158318771,378180.7,2,LONDON,1.5,0.0015712739372481836,81.97963184322194,15.767303530039442,1.2982327001494876,1.3023085177577631,-21.717732104848952,False,-0.0007708783569051712,15.347367537265718,-110.22700149487585,0.647272727272762,9,4,1.290789158318771,TP3,2022-04-22 10:45:00+00:00,23.570759060277165,891.4006160946961,90,True
+2022-04-22 14:45:00+00:00,2,GBP_USD,LONG,1.2844200000000001,1.2830259658026237,1.2881888300254634,1.2889686001470175,1.2905281403901259,349742.84,2,OVERLAP,1.5,0.0016280683947525363,42.98639198021428,27.35986053745333,1.2921884014568334,1.2995053407236992,-39.58830025463378,False,-4.1095312799426225e-05,28.995505984846165,-79.58401456833464,0.7122641509434876,14,4,1.2830259658026237,SL,2022-04-22 16:15:00+00:00,-13.940341973763815,-487.55347924753625,90,False
+2022-04-25 10:45:00+00:00,2,GBP_USD,LONG,1.2710500000000002,1.2701709948057995,1.2736296237987925,1.2743080731619836,1.2756649718883655,549118.43,2,LONDON,1.5,0.0013380103884008553,59.182460997657536,24.9360235825836,1.2776037998490222,1.2884291296375734,-27.6962379879242,False,-0.00014798357777948828,7.961533096942816,-67.43799849022069,0.60583941605827,10,0,1.2740002688404748,TP1+TP2+SL,2022-04-25 12:15:00+00:00,28.726778321614965,1577.4403410923246,90,True
+2022-04-26 14:15:00+00:00,2,GBP_USD,LONG,1.26618,1.2648593690942653,1.2696836376605494,1.270515842623052,1.2721802525480572,377435.02,2,OVERLAP,1.5,0.0013412618114694702,51.30163734063823,23.378597814219546,1.2712049100828267,1.2778578816620196,-36.9363766054942,False,-0.00014347054456128076,28.548920040694913,-52.149100828267066,0.6285714285714921,14,1,1.2648593690942653,SL,2022-04-26 14:30:00+00:00,-13.206309057347541,-498.4523523186151,15,False
+2022-04-26 15:45:00+00:00,2,GBP_USD,LONG,1.26163,1.2601360827106107,1.267978836113953,1.2694706502812514,1.2724542786158481,330318.04,2,OVERLAP,1.5,0.0014878345787784587,70.47178619317675,18.38043612058452,1.269282595394398,1.2769464224749132,-65.38836113953117,False,-0.00046465957949445337,17.568265257184276,-78.42595394398133,0.6725146198831222,15,1,1.2601360827106107,SL,2022-04-26 16:30:00+00:00,-14.939172893893014,-493.4678309531869,45,False
+2022-04-26 16:30:00+00:00,2,GBP_USD,LONG,1.2607800000000002,1.259267127811666,1.26758806299948,1.2692943682422402,1.2727069787277603,322917.66,2,OVERLAP,1.5,0.0014657443766677438,76.87745686013697,20.487421315625568,1.2683731144548893,1.2764824519699194,-69.98062999479914,False,-0.0002898493199155437,27.43978440170254,-77.83114454889217,0.6632653061223889,16,1,1.259267127811666,SL,2022-04-26 17:45:00+00:00,-15.128721883341802,-488.5331469359527,75,False
+2022-04-28 11:30:00+00:00,2,GBP_USD,LONG,1.2444700000000002,1.2434854885352447,1.250732528011613,1.2523092949754993,1.2554628289032714,491256.66,2,LONDON,1.5,0.0015690229295106165,59.15767952991095,22.944767673053306,1.2519956098478071,1.2582086839899649,-64.52528011613,False,-0.0009350045486985889,8.585879816191397,-77.15609847807059,0.9554655870445119,11,3,1.2434854885352447,SL,2022-04-28 11:45:00+00:00,-9.845114647555329,-483.64781390751085,15,False
+2022-04-29 10:30:00+00:00,2,GBP_USD,SHORT,1.25744,1.2583675768740985,1.2560629932636713,1.2558569648048756,1.255444907887284,516195.86,3,LONDON,1.5,0.001195153748196994,61.43515799215444,75.11901379421823,1.2519518990781306,1.2527806471581855,15.670067363287021,False,-7.62758887153641e-05,52.47439203762587,56.781009218693775,0.9065934065932906,10,4,1.25744,TP1+SL,2022-04-29 11:15:00+00:00,6.885033681642838,355.4025882424591,45,True
+2022-04-29 11:15:00+00:00,2,GBP_USD,SHORT,1.2580299999999998,1.2591911976648666,1.2563237373253437,1.2560128264387302,1.2553910046655035,415403.32,3,LONDON,1.5,0.0012623953297335093,58.08384274043877,72.39155252071888,1.2525524399713763,1.2529126777629083,18.96262674656146,False,-5.642893214380189e-05,65.35589929020902,56.675600286235635,0.7868020304569002,11,4,1.2553910046655035,TP3,2022-04-29 11:45:00+00:00,19.549913839467802,812.1099114628872,30,True
+2022-05-05 11:00:00+00:00,2,GBP_USD,LONG,1.2459300000000002,1.2440682655426767,1.2531629811724756,1.2539643990398484,1.255567234774594,263456.73,2,LONDON,1.5,0.002103468914646611,33.48146012486825,23.264011747937957,1.2557301564413104,1.254216488752174,-74.22981172475528,False,-0.00034438868194234466,8.697785253712604,-99.90156441310293,0.7313677313677318,11,3,1.2440682655426767,SL,2022-05-05 11:15:00+00:00,-18.61734457323516,-490.4864722547781,15,False
+2022-05-05 12:15:00+00:00,2,GBP_USD,LONG,1.2371100000000002,1.2346890412721208,1.2475860584307843,1.2499927452025863,1.2548061187461903,200574.09,2,LONDON,1.5,0.0027419174557585376,58.62172246592411,17.959782665346992,1.2528626424395473,1.2535222949562799,-106.66058430784275,False,-0.0017544789099000362,9.297270180347601,-159.42642439547237,0.7500000000000114,12,3,1.2346890412721208,SL,2022-05-05 15:15:00+00:00,-24.209587278793965,-485.58159377196756,180,False
+2022-05-17 11:30:00+00:00,2,GBP_USD,SHORT,1.2492699999999999,1.2503403191871494,1.246711303917286,1.246280744290154,1.2454196250358902,449142.45,3,LONDON,1.5,0.0017606383742988522,56.43747978745708,74.22572447742326,1.239826802679216,1.2309633951843595,27.486960827141083,False,-0.00012819076606621418,72.98642036395621,96.33197320783937,0.8987854251012696,11,1,1.2492699999999999,TP1+SL,2022-05-17 12:45:00+00:00,12.793480413569869,574.6095136977784,75,True
+2022-05-19 13:15:00+00:00,2,GBP_USD,SHORT,1.2455399999999999,1.2475740463092015,1.2410591125862624,1.2400657300600657,1.238078965007672,239164.6,2,OVERLAP,1.5,0.0016280926184028832,44.16356038000063,72.51509868618793,1.2395650643805498,1.2388082333197112,46.70887413737557,False,0.0002548215840080289,81.07706474722738,61.649356194501955,0.5055350553505897,13,3,1.2475740463092015,SL,2022-05-19 13:30:00+00:00,-20.34046309201676,-486.47187192169514,15,False
+2022-05-19 15:30:00+00:00,2,GBP_USD,SHORT,1.2516599999999998,1.2533943544724568,1.2443083491236142,1.2424774813776058,1.238815745885589,277686.69,2,OVERLAP,1.5,0.0019087089449136199,52.86033755042517,77.0367953011395,1.2425285403477655,1.2397112241937382,75.41650876385697,False,0.00038037696636907935,68.95867498795604,93.2145965223441,0.6082089552238018,15,3,1.24692,TP1+TIME,2022-05-20 11:30:00+00:00,60.45825438192652,1678.8452542495174,1200,True
+2022-05-24 08:30:00+00:00,2,GBP_USD,LONG,1.2485600000000001,1.2475359745793053,1.2534383768487567,1.2540707298001006,1.2553354357027882,486702.38,2,LONDON,1.5,0.0016280508413895709,34.936468202000924,18.500078064444438,1.256810509558254,1.2537187846499727,-50.68376848756717,False,-0.0005978847326475301,12.85193374529951,-84.40509558254038,0.9977246871444927,8,1,1.2475359745793053,SL,2022-05-24 08:45:00+00:00,-10.240254206947961,-498.3956094326586,15,False
+2022-05-25 15:15:00+00:00,2,GBP_USD,SHORT,1.2554299999999998,1.2567261110136279,1.251310792256263,1.2504030996414506,1.248587714411826,380686.26,2,OVERLAP,1.5,0.00149222202725537,61.31314209235447,70.62517406750244,1.2520857928486613,1.2526317009217198,43.09207743736998,False,0.0006561854099072646,87.63483839096743,35.34207151338675,0.8911564625850659,15,2,1.2567261110136279,SL,2022-05-25 18:00:00+00:00,-12.961110136280318,-493.4116543228645,165,False
+2022-05-31 12:45:00+00:00,2,GBP_USD,LONG,1.2563700000000002,1.2553968454866078,1.2594487696360526,1.2602226438159072,1.2617703921756167,501952.71,2,LONDON,1.5,0.0012263090267843502,43.70288282773815,27.231510335972175,1.2606412220449472,1.2620069257538709,-32.68769636052493,False,-0.00031568572969578666,17.934381593384675,-44.61222044947144,0.5555555555554501,12,1,1.2598041760725434,TP1+TP2+SL,2022-05-31 15:15:00+00:00,34.029365644751,1708.1132304963667,150,True
+2022-06-02 11:15:00+00:00,2,GBP_USD,SHORT,1.25562,1.25634140403612,1.2542603131540095,1.253949127331625,1.2533267556868561,700798.23,2,LONDON,1.5,0.0008628080722397216,72.59410626853189,77.80523814498711,1.2517488333893318,1.2544155100138672,15.4968684599055,False,4.9555011394636925e-05,86.21173645660618,40.61166610668243,0.6725663716814542,11,3,1.2546502227077374,TP1+TP2+SL,2022-06-02 12:45:00+00:00,14.101154507658253,988.2064119923425,90,True
+2022-06-10 12:45:00+00:00,2,GBP_USD,LONG,1.23848,1.2373874076337077,1.2442375378617292,1.245220690050117,1.2471869944268927,471759.42,2,LONDON,1.5,0.0016851847325845856,70.02622924529015,20.35497521388632,1.2469363620852418,1.250551752100409,-59.47537861729302,False,-0.0004372678259289885,48.274818120950535,-86.46362085241942,0.8967032967033044,12,4,1.2373874076337077,SL,2022-06-10 13:45:00+00:00,-10.925923662923333,-515.4407410184987,60,False
+2022-06-10 13:45:00+00:00,2,GBP_USD,LONG,1.2370700000000001,1.2353597292645337,1.2429226045956905,1.2442618295078869,1.2469402793322795,298365.82,2,OVERLAP,1.5,0.0017805414709326473,73.23500221387638,22.457086338463228,1.2456721204655063,1.2500760265501143,-60.426045956905035,False,-0.0006018211560724954,11.262070351951166,-87.92120465506369,0.6759259259259526,13,4,1.2353597292645337,SL,2022-06-10 14:00:00+00:00,-17.102707354663682,-510.28633040942606,15,False
+2022-06-10 15:15:00+00:00,2,GBP_USD,LONG,1.2320700000000002,1.2303814280742684,1.240977949195142,1.2430740573594896,1.2472662736881845,299177.94,2,OVERLAP,1.5,0.0018771438514632124,81.1897203907457,17.893295657893205,1.243130354499979,1.2491284882364289,-90.97949195141952,False,-0.0006128183140878089,17.812010347105772,-112.50354499978909,0.7250996015935952,15,4,1.2303814280742684,SL,2022-06-10 17:15:00+00:00,-16.885719257317966,-505.1834702822719,120,False
+2022-06-13 11:00:00+00:00,2,GBP_USD,LONG,1.21872,1.2171785929475563,1.2214799437811665,1.2220197728092377,1.22309943086538,324464.35,2,LONDON,1.5,0.001442814104887483,58.81682482064782,26.28439356100243,1.2257841019616984,1.237110020856429,-29.499437811666418,False,-0.00018088078774052192,29.84519224891159,-72.54101961698511,0.7142857142857284,11,0,1.2171785929475563,SL,2022-06-13 11:45:00+00:00,-15.41407052443766,-500.1316373565825,45,False
+2022-06-13 11:45:00+00:00,2,GBP_USD,LONG,1.21632,1.2153433522980253,1.220796746828675,1.2216767210003965,1.2234366693438394,506969.21,2,LONDON,1.5,0.0015532954039496297,62.38590112162386,23.79205826605262,1.2248128301459609,1.2365218525292836,-46.66746828674961,False,-0.0003509920934836124,7.160619998637938,-86.82830145960939,0.8818181818182139,11,0,1.21632,TP1+SL,2022-06-13 14:15:00+00:00,22.38373414337413,1134.7864015516411,150,True
+2022-06-14 14:00:00+00:00,2,GBP_USD,LONG,1.2014200000000002,1.1999860061516627,1.209698814032379,1.211485719773586,1.215059531256,353194.11,2,OVERLAP,1.5,0.0021079876966749304,61.72595076107004,20.07339682988315,1.2117931165846956,1.2217560890530614,-84.6881403237898,False,-0.00040095431844972777,9.295790152015073,-105.63116584695553,0.8668076109936741,14,1,1.1999860061516627,SL,2022-06-14 14:45:00+00:00,-14.339938483374224,-506.47818100901094,45,False
+2022-06-14 14:45:00+00:00,2,GBP_USD,LONG,1.2003000000000001,1.1988233930448988,1.2086170198590298,1.2106652453624318,1.214761696369236,339571.34,2,OVERLAP,1.5,0.0022932139102021807,68.31268115117459,23.319993668902782,1.2106417855355247,1.221161364381216,-85.07019859029796,False,-0.0004949485403306364,19.487227466226056,-105.31785535524695,0.6515151515151643,14,1,1.1988233930448988,SL,2022-06-14 17:45:00+00:00,-14.766069551013226,-501.413402397076,180,False
+2022-06-15 11:00:00+00:00,2,GBP_USD,SHORT,1.21101,1.2120634858595818,1.2082819289578406,1.207689481879551,1.2065045877229719,471196.89,2,LONDON,1.5,0.001686971719163648,38.90088319400502,73.5988733522087,1.2048565451487818,1.210905148595649,29.180710421594647,False,0.0002783687498823528,79.22431374648073,63.43454851218233,0.8429752066116339,11,2,1.2120634858595818,SL,2022-06-15 11:15:00+00:00,-10.534858595818708,-496.39926069395426,15,False
+2022-06-16 16:15:00+00:00,2,GBP_USD,SHORT,1.2371199999999998,1.2391570618525232,1.219521285899158,1.215436260488869,1.2072662096682916,241247.1,2,OVERLAP,1.5,0.003434123705046108,44.901213930294084,73.4233208799134,1.220640211522273,1.2146950813751023,177.8871410084193,False,0.0009211194837325416,90.5550440922877,166.6978847772693,0.8250460405156131,16,3,1.2391570618525232,SL,2022-06-16 16:30:00+00:00,-20.37061852523392,-491.435264441896,15,False
+2022-06-28 12:45:00+00:00,2,GBP_USD,LONG,1.2214800000000001,1.2204239226057532,1.2253934150560521,1.2263171281813336,1.2281645544318969,460686.8,2,LONDON,1.5,0.0012921547884937902,42.21200887424505,25.080827269158135,1.225977664418176,1.2269335226993112,-41.03415056052162,False,-0.0004161620976100694,23.669651579656108,-46.87664418175918,0.7329192546584099,12,1,1.2204239226057532,SL,2022-06-28 14:00:00+00:00,-10.56077394246957,-486.520915307969,75,False
+2022-07-01 09:45:00+00:00,2,GBP_USD,LONG,1.20693,1.205655978771123,1.20977107655475,1.2104566242886305,1.2118277197563914,378059.41,2,LONDON,1.5,0.0013880424577540679,48.73526230284941,23.664553168452386,1.212477899003418,1.2150297862610826,-30.310765547501006,False,-0.00035528694071002877,8.094602656068743,-57.37899003418034,0.7607655502392522,9,4,1.205655978771123,SL,2022-07-01 10:45:00+00:00,-12.74021228877098,-481.65571411675063,60,False
+2022-07-01 11:30:00+00:00,2,GBP_USD,LONG,1.2035000000000002,1.2023109191156995,1.2079271204193571,1.2090095566727899,1.2111744291796556,401014.9,2,LONDON,1.5,0.0015181617686013077,56.22835624954233,21.37036603553186,1.2108292824220956,1.2144061091104885,-46.17120419357023,False,-0.0004133221606001545,11.11460179578208,-75.1928242209554,0.7484662576687017,11,4,1.2023109191156995,SL,2022-07-01 12:15:00+00:00,-11.89080884300697,-476.8391519097557,45,False
+2022-07-01 12:15:00+00:00,2,GBP_USD,LONG,1.2013300000000002,1.2002635748167607,1.2070001629034992,1.208331338134088,1.2109936885952661,442666.55,2,LONDON,1.5,0.0015328503664785412,59.189186827773675,20.49738609685484,1.2099092853252307,1.2140608368038288,-58.601629034991106,False,-0.0004684668286254745,8.488835429792035,-87.6928532523058,0.8461538461537457,12,4,1.2002635748167607,SL,2022-07-01 13:15:00+00:00,-10.664251832395166,-472.07075669775463,60,False
+2022-07-05 08:45:00+00:00,2,GBP_USD,LONG,1.2045700000000001,1.2034742045496225,1.20611528175665,1.206365562343601,1.2068661235175027,426493.88,2,LONDON,1.5,0.0015715909007554237,49.11016031246703,28.59291663702352,1.2099571800700266,1.2109613136769013,-17.352817566500534,False,-0.0006053246666716022,20.308235837647167,-55.771800700266105,0.7602523659306318,8,1,1.2034742045496225,SL,2022-07-05 09:45:00+00:00,-10.957954503776346,-467.35005331790484,60,False
+2022-07-05 09:45:00+00:00,2,GBP_USD,LONG,1.20334,1.2020801238062724,1.2053273382388707,1.2058583342052762,1.2069203261380874,367239.7,2,LONDON,1.5,0.0015197523874551228,61.37538578571157,29.411998534471522,1.209098568179256,1.210695272493372,-21.77338238870785,False,-0.0005147165528143537,7.608286777421541,-59.48568179256153,0.7578125000000068,9,1,1.2020801238062724,SL,2022-07-05 11:00:00+00:00,-12.598761937276937,-462.67655542170013,75,False
+2022-07-05 11:00:00+00:00,2,GBP_USD,LONG,1.2010600000000002,1.2000582543702072,1.2045721798341642,1.2053620589057596,1.2069418170489505,457251.59,2,LONDON,1.5,0.001543491259585615,66.2481109786124,28.524159691754377,1.2079232601041103,1.2103027756792306,-37.02179834164099,False,-0.00033587896411411836,29.02657422444949,-70.53260104110225,0.7999999999999783,11,1,1.2000582543702072,SL,2022-07-05 11:15:00+00:00,-10.017456297930494,-458.0497819984232,15,False
+2022-07-05 12:15:00+00:00,2,GBP_USD,LONG,1.1978900000000001,1.1969623395197635,1.203231799184216,1.2045647918767497,1.2072307772618167,488831.1,2,LONDON,1.5,0.0014753209604727855,75.93425986848759,22.556827287148536,1.206342437500438,1.209763158481486,-55.31799184216091,False,-0.0004092682590420074,9.229347887885286,-86.42437500437916,0.7768595041322254,12,1,1.1969623395197635,SL,2022-07-05 12:30:00+00:00,-9.27660480236625,-453.4692929805977,15,False
+2022-07-05 15:00:00+00:00,2,GBP_USD,LONG,1.1903700000000002,1.1891560265138728,1.199174765272855,1.201306376616968,1.2055695993051938,369805.93,2,OVERLAP,1.5,0.0019079469722546713,68.32099636122454,20.250007690471904,1.202194264350074,1.2082086369858802,-89.94765272855031,False,-0.00031594901314593166,14.159020333573414,-120.14264350074022,0.8117647058822992,15,1,1.19065,TIME,2022-07-06 11:00:00+00:00,2.7999999999983594,103.54566039993932,1200,True
+2022-07-06 12:00:00+00:00,2,GBP_USD,LONG,1.1879400000000002,1.186703718372552,1.1932692616259803,1.1945443120226933,1.197094412816119,363970.51,2,LONDON,1.5,0.001772563254895949,51.01773358455841,26.732501796862337,1.1941751002168692,1.2003909360774403,-55.19261625980265,False,-0.0006271631265808602,16.30998783287227,-64.2510021686915,0.833333333333304,12,2,1.1943749059918363,TP1+TP2+SL,2022-07-07 03:45:00+00:00,59.41375919708047,2162.4856235978573,945,True
+2022-07-07 09:30:00+00:00,2,GBP_USD,SHORT,1.1995399999999998,1.2006001755671412,1.1971101976416445,1.1967320820135874,1.195975850757473,444827.18,2,LONDON,1.5,0.0016403511342821147,31.61987088904963,72.17115212367894,1.1946573383053125,1.196377018377453,26.19802358355461,False,0.000312092743045922,77.06872462138227,50.72661694687452,0.9230769230769577,9,3,1.2006001755671412,SL,2022-07-07 10:00:00+00:00,-10.601755671413304,-471.5949078363787,30,False
+2022-07-11 13:00:00+00:00,2,GBP_USD,LONG,1.1902100000000002,1.1889092821868135,1.1945106072351184,1.1953474301859048,1.1970210760874775,358939.47,2,OVERLAP,1.5,0.001601435626373244,37.807504918609,29.85255976250768,1.1963588544531025,1.1986303772378657,-44.906072351182935,False,-0.00035425046016356585,19.571509835413558,-63.38854453102449,0.8745098039215328,13,0,1.1889092821868135,SL,2022-07-11 13:30:00+00:00,-13.00717813186747,-466.8789624848099,30,False
+2022-07-11 14:15:00+00:00,2,GBP_USD,LONG,1.18812,1.185732918735476,1.1929231359801065,1.1941485111589616,1.1965992615166718,193629.84,2,OVERLAP,1.5,0.0017341625290485411,49.26206258110005,28.805016892122467,1.1950272345491457,1.198162181548684,-49.931359801065334,False,-0.0004124494916938118,34.27996510113363,-70.97234549145792,0.4074074074074711,14,0,1.185732918735476,SL,2022-07-12 05:00:00+00:00,-23.870812645241376,-462.2101633168065,885,False
+2022-07-13 12:30:00+00:00,2,GBP_USD,LONG,1.18446,1.1816469457892267,1.1900026252620757,1.1908425107980272,1.19252228186993,162665.93,2,LONDON,1.5,0.0021261084215463658,38.51135704168495,28.664882428254757,1.1901013622721386,1.1903708514794749,-57.32625262075741,False,-0.0002522468898928562,44.47669356243198,-58.313622721386515,0.8439849624060153,12,2,1.19252228186993,TP3,2022-07-13 14:45:00+00:00,62.14533690836765,1010.8929023362949,135,True
+2022-07-18 09:30:00+00:00,2,GBP_USD,SHORT,1.19688,1.1981176641592173,1.1951215966995201,1.1947398252720611,1.193976282417143,377886.84,2,LONDON,1.5,0.0013953283184348563,53.940966311052954,71.42569710447401,1.1905663284566474,1.1870771834922267,19.484033004799617,False,0.0003767135743798463,80.93820038972096,65.0367154335263,0.5261044176707658,9,0,1.1981176641592173,SL,2022-07-18 09:45:00+00:00,-12.376641592173332,-467.69699810789496,15,False
+2022-07-18 14:15:00+00:00,2,GBP_USD,SHORT,1.20077,1.2021292910523023,1.1972486946771796,1.1964153999136207,1.194748810386503,340633.47,2,OVERLAP,1.5,0.0013985821046043241,43.4252745102127,72.198482596952,1.1945841123907084,1.1889651487680535,37.113053228203796,False,0.00012409395870494533,74.48987319228364,63.75887609291642,0.680981595091982,14,0,1.2021292910523023,SL,2022-07-18 14:30:00+00:00,-13.592910523023692,-463.0200278857075,15,False
+2022-07-25 11:00:00+00:00,2,GBP_USD,SHORT,1.20715,1.208406448573085,1.203087152685409,1.2020505063309406,1.1999772136220035,364829.76,2,LONDON,1.5,0.001512897146170308,45.7755394930661,73.26992424585762,1.200682646556588,1.1986836895823219,42.528473145910795,False,0.00041366964879288614,75.81497149295073,66.57353443412006,0.7046263345196112,11,0,1.208406448573085,SL,2022-07-25 13:15:00+00:00,-12.56448573085045,-458.3898313709594,135,False
+2022-07-26 10:00:00+00:00,2,GBP_USD,LONG,1.1998600000000001,1.198964862884479,1.202371472319636,1.202981545956771,1.2042016932310409,506968.06,2,LONDON,1.5,0.001190274231042323,63.149965931261995,22.869929608528153,1.2046474003372896,1.2026518811790532,-27.014723196359114,False,-0.00047180488178758624,17.01090747211011,-49.7740033728955,0.7761194029850516,10,1,1.198964862884479,SL,2022-07-26 10:15:00+00:00,-8.951371155212495,-453.80592688980374,15,False
+2022-07-26 11:30:00+00:00,2,GBP_USD,LONG,1.1975200000000001,1.1965518853584827,1.2010968606034613,1.2020341453167882,1.2039087147434422,464064.74,2,LONDON,1.5,0.0012562292830346198,69.56611727208862,20.210594085871747,1.2034366144404138,1.2024394907837732,-37.66860603461319,False,-0.0003705707625150992,24.343737422884875,-61.066144404138264,0.8275862068964235,11,1,1.1965518853584827,SL,2022-07-26 12:15:00+00:00,-9.681146415174702,-449.267869405998,45,False
+2022-07-29 12:30:00+00:00,2,GBP_USD,LONG,1.21167,1.2097037200030445,1.215791262501803,1.2165223630307795,1.2179845640887326,226201.35,2,LONDON,1.5,0.001692559993910971,36.50041291585899,28.914812375021484,1.2170045891087797,1.2141480188743419,-43.112625018031366,False,-0.0004360364555022324,12.674637523261048,-55.24589108779843,0.7060518731988483,12,4,1.2097037200030445,SL,2022-07-29 13:00:00+00:00,-19.662799969555333,-444.77518978933756,30,False
+2022-08-01 14:45:00+00:00,2,GBP_USD,SHORT,1.22799,1.230180247515324,1.2244416285768736,1.2236186133651288,1.2219725829416395,201040.04,2,OVERLAP,1.5,0.0017404950306481217,42.25068362333333,71.75782653548225,1.2224603663196538,1.2178650627258216,37.38371423126496,False,0.0001873443924245064,68.02472076713629,57.19633680346225,0.4724409448819276,14,0,1.2231153341801166,TP1+TP2+SL,2022-08-02 06:45:00+00:00,40.353709067505676,811.2711285079705,960,True
+2022-08-04 11:00:00+00:00,2,GBP_USD,LONG,1.2102400000000002,1.2080295942270367,1.216155672756405,1.2168004558290515,1.218090021974345,202876.85,2,LONDON,1.5,0.001920811545926591,34.72057559906329,28.355806577879775,1.2157971103441547,1.2167984193208388,-61.05672756404834,False,-0.00035015016926515053,42.48570897472381,-57.47110344154649,0.6442307692307528,11,3,1.2080295942270367,SL,2022-08-04 11:30:00+00:00,-22.10405772963497,-448.44016044064955,30,False
+2022-08-05 12:30:00+00:00,2,GBP_USD,LONG,1.2068400000000001,1.2058125561769488,1.2127752842767665,1.2132841491799375,1.2143018789862798,432097.35,2,LONDON,1.5,0.0014348876461024417,27.960172492020877,19.177630969763044,1.2139876961301124,1.2150942283023112,-61.25284276766507,False,-0.00041426050503884495,30.860830123039793,-73.3769613011237,0.9838274932614692,12,4,1.2058125561769488,SL,2022-08-05 12:45:00+00:00,-10.274438230513104,-443.9557532143402,15,False
+2022-08-10 12:30:00+00:00,2,GBP_USD,SHORT,1.2240099999999998,1.2260685552453738,1.2124967061195884,1.211564943948408,1.2097014196060472,213507.11,2,LONDON,1.5,0.002037110490747722,42.034188625194155,85.19404120409351,1.2095375921860274,1.209083392449283,117.0329388041158,False,0.000971916186282886,62.07921395478075,146.62407813972544,0.9269776876267622,12,2,1.2260685552453738,SL,2022-08-10 14:45:00+00:00,-20.58555245374016,-439.51618121514707,135,False
+2022-08-12 10:00:00+00:00,2,GBP_USD,LONG,1.21283,1.211956243809128,1.2157194711317505,1.2163542495141546,1.2176238062789626,497989.07,2,LONDON,1.5,0.0012475123817441065,40.153961694479854,24.629882458389318,1.2183214671114604,1.2181819063393509,-30.794711317505552,False,-0.000583828518990801,11.594474153297476,-56.81467111460447,0.9629629629630276,10,4,1.211956243809128,SL,2022-08-12 10:30:00+00:00,-8.737561908720437,-435.1210328991115,30,False
+2022-08-16 15:00:00+00:00,2,GBP_USD,SHORT,1.2102899999999999,1.2125073823697237,1.2051055069577137,1.2037941612579415,1.201171469858397,194269.52,2,OVERLAP,1.5,0.001514764739447074,59.74492231177249,70.44424417867123,1.2054636738785556,1.2082206466771466,53.74493042286321,False,0.0006984033176630792,72.57515085582709,50.163261214444255,0.8278688524589806,15,1,1.2125073823697237,SL,2022-08-17 06:00:00+00:00,-22.173823697237967,-430.76980862270455,900,False
+2022-08-17 15:45:00+00:00,2,GBP_USD,LONG,1.20297,1.2020108814811228,1.2074963325565313,1.208467099645228,1.2104086338226216,444639.65,2,OVERLAP,1.5,0.0015382370377541408,30.069571437772876,29.89528567644301,1.2078018882915789,1.208674650649726,-47.16332556531366,False,-0.00027453036850347864,9.787067693547707,-50.21888291578902,0.8609271523178633,15,2,1.2073221250155382,TP1+TP2+SL,2022-08-17 18:45:00+00:00,48.399699064261014,2152.0425252038344,180,True
+2022-08-18 15:30:00+00:00,2,GBP_USD,LONG,1.1989200000000002,1.1979698374759795,1.2035287071724323,1.2046196533798192,1.206801545794593,471479.92,2,OVERLAP,1.5,0.001420325048040779,48.59561269839487,25.479973872353057,1.2034030852332747,1.2058055983420282,-47.98707172432248,False,-0.0004711909270494147,36.42691622103762,-46.730852332745876,0.9871794871794033,15,3,1.1979698374759795,SL,2022-08-18 15:45:00+00:00,-9.501625240206923,-447.9825508122741,15,False
+2022-08-19 10:00:00+00:00,2,GBP_USD,LONG,1.18541,1.1844930812942156,1.1881870124182463,1.1887334246392507,1.1898262490812597,483688.16,2,LONDON,1.5,0.0012938374115691184,41.52031929733541,25.184109613697004,1.1911158366646208,1.19832440510119,-29.67012418246373,False,-0.0004275298180168351,6.312101575261361,-58.958366646209015,0.9375000000000506,10,4,1.1844930812942156,SL,2022-08-19 10:45:00+00:00,-9.169187057844752,-443.50272167047416,45,False
+2022-08-31 09:45:00+00:00,2,GBP_USD,LONG,1.1624900000000002,1.1615163535917157,1.1639953671304848,1.1642906755267974,1.1648812923194225,450951.9,2,LONDON,1.5,0.0012072928165688171,48.3521849085008,25.22012476914692,1.166154743423836,1.168961198594948,-16.953671304846818,False,-0.00041513194914650174,11.209999541271065,-38.54743423835849,0.7567567567566513,9,2,1.1615163535917157,SL,2022-08-31 10:45:00+00:00,-9.736464082845675,-439.06769774410145,60,False
+2022-09-01 14:00:00+00:00,2,GBP_USD,LONG,1.1517100000000002,1.150661361362873,1.15728438077845,1.1581466779150031,1.1598712721881095,414515.55,3,OVERLAP,1.5,0.0016772772742540238,21.0153536123851,26.301311449152934,1.1579910912876825,1.1624797876478585,-57.64380778449984,False,-0.0003365460438267742,21.12057167335326,-64.71091287682418,0.9952380952380641,14,3,1.150661361362873,SL,2022-09-01 14:45:00+00:00,-10.486386371273415,-434.6770214200904,45,False
+2022-09-08 11:30:00+00:00,2,GBP_USD,SHORT,1.1551799999999999,1.1567819833606823,1.1507788579326728,1.1497398982016924,1.1476619787397317,268623.42,2,LONDON,1.5,0.0014639667213645702,45.53551813673212,71.60755639536212,1.1508190411329728,1.1507831733148337,45.911420673272474,False,0.0006924046825149258,80.38709701344041,45.50958867027166,0.5868852459016262,11,3,1.1476619787397317,TP3,2022-09-08 13:00:00+00:00,52.323098521113835,1405.520966973854,90,True
+2022-09-13 12:30:00+00:00,2,GBP_USD,LONG,1.15934,1.156532796044402,1.1689634662057924,1.1697279901775681,1.1712570381211194,158301.81,2,LONDON,1.5,0.0021944079111956925,40.40810400473998,20.513473563290717,1.1702854293122538,1.1658841962770459,-98.13466205792487,False,-0.0008575953199491456,23.410193478370427,-111.35429312253864,0.9007832898172347,12,1,1.156532796044402,SL,2022-09-13 12:45:00+00:00,-28.072039555979696,-444.38546721031827,15,False
+2022-09-23 09:30:00+00:00,2,GBP_USD,LONG,1.11043,1.1085610498488694,1.1166078890863402,1.1173933371173237,1.1189642331792906,235395.04,2,LONDON,1.5,0.00217790030226126,47.37773196795386,19.86753985907434,1.121963421508852,1.1273723542426464,-63.67889086340339,False,-0.0005170192669593236,47.61920503265642,-117.23421508852194,0.8860000000000082,9,4,1.1085610498488694,SL,2022-09-23 09:45:00+00:00,-18.68950151130644,-439.94159558340397,15,False
+2022-09-23 10:15:00+00:00,2,GBP_USD,LONG,1.1091900000000001,1.1061764236581508,1.1144997919675135,1.1158201884826766,1.1184609815130029,144526.68,2,LONDON,1.5,0.002687152683698246,52.322472687158175,26.156103615208522,1.1207260511952037,1.1268904119673218,-54.997919675134895,False,-0.000832766640560834,26.885878511263524,-117.26051195203756,0.4029850746268473,10,4,1.1061764236581508,SL,2022-09-23 10:30:00+00:00,-30.13576341849289,-435.54218361402275,15,False
+2022-09-23 14:30:00+00:00,2,GBP_USD,LONG,1.09468,1.0916622434042285,1.107707076039733,1.1104534110204554,1.1159460809819004,142883.21,2,OVERLAP,1.5,0.0032555131915432065,50.147247496664065,22.774651168894735,1.112269490796065,1.123295937500475,-132.17076039732987,False,-0.0005700936305178478,14.291658796245116,-177.7949079606511,0.6231003039513848,14,4,1.0916622434042285,SL,2022-09-23 14:45:00+00:00,-30.177565957716364,-431.18674940252384,15,False
+2022-09-28 15:45:00+00:00,2,GBP_USD,SHORT,1.0827099999999998,1.0875053610201746,1.0679064922374815,1.0651673031989468,1.0596889251218773,89018.3,2,OVERLAP,1.5,0.00455072204034943,48.91033536860098,72.72092436357488,1.06887054431849,1.0736968883795106,149.9350776251851,False,0.001605114159262041,80.75277842530758,140.29455681509927,0.6960167714884761,15,2,1.0875053610201746,SL,2022-09-28 16:00:00+00:00,-47.95361020174749,-426.8748859022219,15,False
+2022-09-29 14:45:00+00:00,2,GBP_USD,SHORT,1.1029099999999998,1.1048946022442128,1.0883873732196285,1.0854337574984025,1.0795265260559508,212942.49,2,OVERLAP,1.5,0.003369204488425659,52.88087216753975,75.93276149005979,1.086802210925365,1.080561020232784,147.1262678037144,False,0.0011361365134778915,80.36529170886341,162.97789074634971,0.9163265306121974,14,3,1.1048946022442128,SL,2022-09-29 15:00:00+00:00,-19.846022442129296,-422.6061435422893,15,False
+2022-09-29 16:00:00+00:00,2,GBP_USD,SHORT,1.1096599999999999,1.111773115277601,1.090960258912864,1.0869959268820917,1.0790672628205469,197992.08,2,OVERLAP,1.5,0.0036062305552021145,68.8713935646586,78.71763804397312,1.0901118450922107,1.0817517203609976,188.89741087136036,False,0.0010850623944643574,78.91234738456001,197.38154907789294,0.9448398576512393,16,3,1.111773115277601,SL,2022-09-29 20:45:00+00:00,-21.131152776010516,-418.38008909200965,285,False
+2022-10-05 13:15:00+00:00,2,GBP_USD,LONG,1.12915,1.1275571621673326,1.1360457373655763,1.1377532061946205,1.1411681438527088,260036.69,2,OVERLAP,1.5,0.002305675665335188,53.771610109817935,29.09807913656563,1.138983897599867,1.1351120654262827,-70.85737365576384,False,-0.0005720491650254888,13.508521620966528,-100.23897599866993,0.6415094339623076,13,2,1.1275571621673326,SL,2022-10-05 14:00:00+00:00,-15.928378326675041,-414.1962777136317,45,False
+2022-10-05 14:15:00+00:00,2,GBP_USD,LONG,1.12538,1.1227751963676902,1.1337910344722344,1.1358620100101973,1.1400039610861228,157422.36,2,OVERLAP,1.5,0.0024496072646199014,56.23473399381929,24.60769691058009,1.1373188239473757,1.1348246820805907,-86.01034472234481,False,-0.000600556674171142,20.866964914247692,-121.288239473758,0.5979729729730197,14,2,1.1227751963676902,SL,2022-10-05 15:15:00+00:00,-26.0480363230986,-410.0543351347904,60,False
+2022-10-06 15:30:00+00:00,2,GBP_USD,LONG,1.11806,1.11597131255372,1.1252880134107963,1.1270700652996806,1.1306341690774488,194358.32,2,OVERLAP,1.5,0.002277374892559844,41.223369495472284,29.96984688393887,1.1270026435980933,1.1317050268800508,-74.18013410796398,False,-0.0004117964417555712,15.84453006547536,-91.32643598093404,0.532934131736542,15,3,1.11597131255372,SL,2022-10-06 15:45:00+00:00,-20.88687446279991,-405.9537830640693,15,False
+2022-10-11 15:30:00+00:00,2,GBP_USD,SHORT,1.1130799999999998,1.1165858061425795,1.1070471919271745,1.105845192468376,1.103441193550779,114636.76,2,OVERLAP,1.5,0.0022105374283863523,31.38712822876365,70.73761493792563,1.1067507375339432,1.1085253263642252,62.228080728254916,False,0.00034896817254358387,89.08037065808873,65.1926246605683,0.5289256198346941,15,1,1.1165858061425795,SL,2022-10-11 15:45:00+00:00,-35.05806142579671,-401.89425737343146,15,False
+2022-10-13 11:30:00+00:00,2,GBP_USD,SHORT,1.1261999999999999,1.1301371114642764,1.1162674165498598,1.1144300928229947,1.110755445369264,101057.67,2,LONDON,1.5,0.002498074309517639,59.90329849161396,83.88755598355205,1.1112912355202202,1.1077550240981604,101.22583450140165,False,0.0011678130801009508,82.01098182710935,150.98764479779757,0.6334488734835356,11,3,1.1261999999999999,TP1+SL,2022-10-13 15:00:00+00:00,49.662917250700154,501.88187027585633,210,True
+2022-10-19 11:00:00+00:00,2,GBP_USD,LONG,1.1225200000000002,1.121218472336353,1.1262892687751804,1.1267848400029044,1.127775982458352,309554.79,2,LONDON,1.5,0.0018430553272937402,39.32871647976075,29.019778303389955,1.129408134087666,1.1305210338240586,-39.5926877518038,False,-0.00019147537087512052,10.592809441039506,-70.78134087666,0.868347338935523,11,2,1.1255807574427936,TP1+TP2+SL,2022-10-19 13:45:00+00:00,38.36442005025597,1187.5889992128778,165,True
+2022-10-20 12:15:00+00:00,2,GBP_USD,SHORT,1.1282699999999999,1.1301660067441006,1.1210896842445273,1.1198936423760657,1.1175015586391428,218759.77,2,LONDON,1.5,0.002232013488201016,41.10883607951614,71.78507594479512,1.12170505591826,1.1248444313387478,73.70315755472755,False,0.0008013079900197632,78.45817377827665,67.54944081740044,0.8521560574948601,12,3,1.1301660067441006,SL,2022-10-20 12:30:00+00:00,-18.96006744100731,-414.7699992579248,15,False
+2022-10-20 14:15:00+00:00,2,GBP_USD,SHORT,1.1329299999999998,1.1349512156961719,1.1232212091189249,1.1215137645681845,1.118098875466704,203156.1,2,OVERLAP,1.5,0.0027024313923438665,51.51389892731328,72.55339840363759,1.1232124988766141,1.1250167050503157,98.98790881075037,False,0.0006838744817090903,66.39647753623228,99.07501123385786,0.8862745098039141,14,3,1.1229861632609464,TP1+TP2+SL,2022-10-20 18:30:00+00:00,103.41653352530798,2100.9699626520824,255,True
+2022-10-21 09:30:00+00:00,2,GBP_USD,LONG,1.1117400000000002,1.1101321895865652,1.1142763155011413,1.1148453778838345,1.1159835026492213,268459.52,2,LONDON,1.5,0.001855620826869386,36.95097558830034,29.571663920975894,1.1190802878830493,1.1227323072010518,-27.263155011412188,False,-0.0005869968058800693,27.455199046483994,-75.30287883049213,0.7557755775577019,9,4,1.1101321895865652,SL,2022-10-21 10:30:00+00:00,-16.0781041343494,-431.6320118417456,60,False
+2022-10-31 12:45:00+00:00,2,GBP_USD,LONG,1.1508,1.1494265080269552,1.154286060043434,1.1549033515602365,1.1561379345938414,311116.26,2,LONDON,1.5,0.0015069839460896068,53.60913123386604,28.15868555411201,1.156466141827517,1.1567103895787207,-36.76060043434015,False,-0.00021431146843713784,29.641007232178534,-58.56141827517147,0.7305194805195149,12,0,1.1494265080269552,SL,2022-10-31 13:15:00+00:00,-13.734919730448159,-427.31568579372396,30,False
+2022-11-01 16:15:00+00:00,2,GBP_USD,LONG,1.1456000000000002,1.1433368956280576,1.1511345522596188,1.1525125269790362,1.1552684764178711,186930.19,2,OVERLAP,1.5,0.0021462087438847755,43.124725197752745,28.37571851845881,1.1508758179272736,1.1524684596900285,-57.24552259618765,False,-0.0007328286634914563,29.758297604807012,-54.6581792727352,0.6437499999998968,16,1,1.1517837969455706,TP1+TP2+SL,2022-11-02 03:45:00+00:00,61.142301143074846,1142.9341969712198,690,True
+2022-11-03 08:15:00+00:00,2,GBP_USD,LONG,1.12989,1.1286491009526918,1.1322636861747173,1.132739929488984,1.133692416117517,350126.69,2,LONDON,1.5,0.0015417980946167624,69.91320696624007,17.464476423306337,1.1397567123554977,1.1456370040734065,-25.63686174717361,False,-0.0009731898289362044,15.640985433026861,-100.56712355497766,0.8574821852732216,8,3,1.1286491009526918,SL,2022-11-03 08:30:00+00:00,-12.40899047308286,-434.4718760582036,15,False
+2022-11-03 12:30:00+00:00,2,GBP_USD,LONG,1.1187900000000002,1.1151915115622502,1.1260922838179301,1.1277221333299279,1.1309818323539234,119529.95,2,LONDON,1.5,0.0025369768754996175,56.88685125376839,19.392641908201426,1.1321995898671462,1.1423579657209495,-74.92283817930101,False,-0.00015171081407351064,31.503460945444687,-135.99589867146156,0.5706984667802283,12,3,1.1151915115622502,SL,2022-11-03 20:15:00+00:00,-35.98488437750014,-430.1271430398372,465,False
+2022-11-10 15:15:00+00:00,2,GBP_USD,SHORT,1.1674799999999999,1.1693384056490896,1.152908977647414,1.1494052199657443,1.142397704602405,229135.06,2,OVERLAP,1.5,0.0032768112981794795,69.86340003046773,86.30366580376734,1.145702748954343,1.1437848333469074,147.61022352586028,False,0.002361225912136545,85.5983851716692,219.67251045657,0.7890173410404291,15,3,1.1693384056490896,SL,2022-11-10 19:45:00+00:00,-18.58405649089789,-425.8258899085278,270,False
+2022-11-15 10:15:00+00:00,2,GBP_USD,SHORT,1.18454,1.1856145554925999,1.1830135514708682,1.1826754973282447,1.181999389042998,392318.15,2,LONDON,1.5,0.001589110985199724,45.02199708667853,71.27459843429338,1.1792468690262432,1.1748340915765774,17.164485291318865,False,5.7990972837984166e-05,73.22832928894614,54.83130973756855,0.858208955223935,10,1,1.1856145554925999,SL,2022-11-15 10:30:00+00:00,-10.745554925999379,-421.56762292914635,15,False
+2022-11-15 12:15:00+00:00,2,GBP_USD,SHORT,1.18844,1.190157449377259,1.1842186549701106,1.183221556934553,1.1812273608634378,243006.84,2,LONDON,1.5,0.0017348987545182017,46.41065337185063,72.75203970486913,1.1813286385646409,1.17575570854395,44.113450298894904,False,0.000141299252734746,56.50820241941329,73.01361435359199,0.6822916666666724,12,1,1.190157449377259,SL,2022-11-15 12:30:00+00:00,-17.174493772591504,-417.351946027714,15,False
+2022-11-15 13:30:00+00:00,2,GBP_USD,SHORT,1.1968299999999998,1.2011294445485734,1.187117709389494,1.1854377821371724,1.182077927632529,96100.42,2,OVERLAP,1.5,0.0027396296990489987,48.541257616126344,79.44204270634857,1.1831707942050247,1.1765197696890133,99.02290610505959,False,0.0005752355615446978,63.334448560514204,138.49205794975282,0.5799151343705772,13,1,1.1848627988634455,TP1+TP2+SL,2022-11-15 18:30:00+00:00,106.38501727725657,1022.3644842051613,300,True
+2022-11-22 13:00:00+00:00,2,GBP_USD,SHORT,1.18942,1.190919748112649,1.1857102079719888,1.1848600187412648,1.1831596402798168,282315.46,2,OVERLAP,1.5,0.0013994962252979561,48.83348435029443,70.567914457231,1.1853604971408223,1.1847095236475154,38.99792028011273,False,0.0002949365716413808,81.09043428811165,42.495028591778,0.5980861244018589,13,1,1.18942,TP1+SL,2022-11-23 00:00:00+00:00,18.54896014005569,523.6658214461488,660,True
+2022-11-23 13:00:00+00:00,2,GBP_USD,SHORT,1.1951399999999999,1.196031116988528,1.191510989595976,1.1905914068755528,1.1887522414347063,481012.86,2,OVERLAP,1.5,0.0012222339770561336,51.76797872761435,73.87994018148821,1.1905445490532984,1.1876336668219707,38.19010404024103,False,0.00024503619449249016,76.96061405349212,47.854509467015745,0.6976744186047262,13,2,1.196031116988528,SL,2022-11-23 13:15:00+00:00,-8.911169885281733,-428.63873124652383,15,False
+2022-11-23 13:45:00+00:00,2,GBP_USD,SHORT,1.1968199999999998,1.198799237982433,1.1921251945946616,1.1909626119885652,1.1886374467763725,214401.88,2,OVERLAP,1.5,0.0013784759648660047,61.555876445771446,78.97876372098789,1.1912018509590696,1.187891102173787,48.8480540533831,False,0.0003474749485006582,73.70840409504773,58.081490409302994,0.4293193717276373,13,2,1.198799237982433,SL,2022-11-23 14:45:00+00:00,-19.792379824332684,-424.35234440109974,60,False
+2022-11-23 14:45:00+00:00,2,GBP_USD,SHORT,1.2017699999999998,1.2044313773171664,1.1931005367281688,1.1915252761184314,1.1883747548989563,157853.91,2,OVERLAP,1.5,0.0016827546343330293,72.1395362028795,87.03647072922783,1.192342459159853,1.1883214435266831,88.59463271831069,False,0.0005599759432611908,80.88843666621399,96.17540840146876,0.7068345323740975,14,2,1.2044313773171664,SL,2022-11-23 15:30:00+00:00,-26.613773171666466,-420.1088155000653,45,False
+2022-12-01 12:30:00+00:00,2,GBP_USD,SHORT,1.21969,1.2208717676531815,1.213093373144189,1.211426220693671,1.2080919157926349,351936.97,2,LONDON,1.5,0.0015835353063630266,68.65215799838512,78.33792660917791,1.2112001977982008,1.20469228285451,67.86626855810995,False,0.0005022563917863438,90.8864456710812,86.79802201799313,0.8011695906432824,12,3,1.2208717676531815,SL,2022-12-01 13:15:00+00:00,-11.817676531815644,-415.90772710473067,45,False
+2022-12-01 14:30:00+00:00,2,GBP_USD,SHORT,1.2273899999999998,1.2290160561042536,1.2176209443656687,1.2153295965602553,1.2107469009494285,253219.22,2,OVERLAP,1.5,0.0021521122085068995,81.66208360210999,80.76022946676278,1.2143532158688597,1.2060626046287528,99.59055634331193,False,0.0007757553652868953,94.60068339969676,132.267841311402,0.718045112781872,14,3,1.2290160561042536,SL,2022-12-01 14:45:00+00:00,-16.260561042538146,-411.7486583953896,15,False
+2022-12-13 13:30:00+00:00,2,GBP_USD,SHORT,1.2425499999999998,1.2442658907509294,1.2319403342865156,1.2309198852587657,1.2288789872032662,237562.42,2,OVERLAP,1.5,0.0024117815018589103,35.73473013224577,85.13438242367515,1.2285766775148412,1.2264732822555662,107.9966571348434,False,0.0008941384321609269,61.11833537619259,141.63322485158724,0.710027100270992,13,1,1.2442658907509294,SL,2022-12-13 14:15:00+00:00,-17.158907509295936,-407.6311592464516,45,False
+2022-12-16 09:00:00+00:00,2,GBP_USD,LONG,1.21266,1.211670856408517,1.2166008206493997,1.2172115074971614,1.2184328811926848,407984.1,2,LONDON,1.5,0.001598287182965698,32.10099040034649,21.443268579670914,1.2200325101066256,1.2272448454597635,-41.30820649399735,False,-0.0006931666603137207,21.537770405695724,-75.62510106625675,0.9443254817986854,9,4,1.2184328811926848,TP3,2022-12-16 11:00:00+00:00,44.29370127608989,1807.1125850794385,120,True
+2022-12-21 10:30:00+00:00,2,GBP_USD,LONG,1.2125700000000001,1.2114251340239564,1.2143792471255814,1.2148673530874396,1.2158435650111563,368275.41,2,LONDON,1.5,0.0012497319520872463,32.417590983607795,29.77081838188782,1.215720478431694,1.2165966005386735,-19.99247125581416,False,-0.00025788639243243543,36.075027870677246,-33.404784316939384,0.6079999999999993,10,2,1.2114251340239564,SL,2022-12-21 11:15:00+00:00,-11.448659760437963,-421.6259867225792,45,False
+2022-12-21 12:00:00+00:00,2,GBP_USD,LONG,1.20941,1.2085186535807837,1.2132576185554966,1.2142344780150565,1.2161881969341761,468291.24,2,LONDON,1.5,0.0013626928384328151,51.0925454173229,22.955302695957414,1.214634502650405,1.2162529995278046,-40.37618555496669,False,-0.0004577641290171618,7.487382338537112,-54.14502650405106,0.38554216867468427,12,2,1.2161881969341761,TP3,2022-12-21 13:30:00+00:00,46.291061231444004,2167.769846498884,90,True
+2022-12-22 11:45:00+00:00,2,GBP_USD,LONG,1.2046400000000002,1.2033165024337908,1.2093582671208187,1.2104278681322884,1.2125670701552276,331762.92,2,LONDON,1.5,0.0015269951324182961,25.314016084700086,27.686770843988967,1.2100685592712297,1.2125505879660448,-49.0826712081871,False,-0.0005747010614604378,14.09058013188124,-56.185592712296724,0.7173913043478051,11,3,1.2033165024337908,SL,2022-12-22 13:30:00+00:00,-13.234975662093085,-439.08741717849347,105,False
+2022-12-27 11:30:00+00:00,2,GBP_USD,LONG,1.2037900000000001,1.2029908509877907,1.206403827510183,1.2069063061787917,1.2079112635160092,543949.3,2,LONDON,1.5,0.001138298024418721,38.25167818483939,28.267384861353264,1.2073489411277807,1.206986665015091,-28.038275101829324,False,-0.0003373429812267327,7.349922960086574,-37.4894112778068,0.9675324675324011,11,1,1.2029908509877907,SL,2022-12-27 12:00:00+00:00,-7.991490122094547,-434.6965457870244,30,False
+2022-12-27 12:45:00+00:00,2,GBP_USD,LONG,1.2030100000000001,1.2018712946164725,1.2059400603889303,1.2067095143347562,1.2082484222264076,377928.82,2,LONDON,1.5,0.0011974107670548425,44.20861175301515,29.69882660489965,1.2066416558214061,1.2068143699817242,-31.200603889303125,False,-0.0002592062867726834,32.60500286095705,-38.21655821406144,0.5377358490565346,12,1,1.2018712946164725,SL,2022-12-27 14:00:00+00:00,-11.387053835276715,-430.3495819242604,75,False
+2022-12-27 14:00:00+00:00,2,GBP_USD,LONG,1.20087,1.1999891155229852,1.205205348304157,1.2061934079535506,1.208169527252338,483657.16,2,OVERLAP,1.5,0.0011817689540297256,47.73810825110291,22.913221074893514,1.2058433450648631,1.2065925737654997,-45.253483041569616,False,-0.00022077428648393984,13.72523423966832,-51.63345064863156,0.9318181818182392,14,1,1.20472,TP1+TIME,2022-12-28 10:00:00+00:00,40.92674152078368,1979.4511571996316,1200,True
+2023-01-02 15:45:00+00:00,2,GBP_USD,LONG,1.2044700000000002,1.2038900269647557,1.205268568524431,1.20549516476348,1.2059483572415775,768726.42,4,OVERLAP,1.5,0.0003799460704886777,20.125008155045588,28.42960607610891,1.2056435985972078,1.205757722304884,-9.885685244310771,False,5.410854921599189e-06,17.31957400413395,-13.635985972078046,0.8313253012050288,15,0,1.2038900269647557,SL,2023-01-02 16:00:00+00:00,-5.799730352444853,-445.84059508002713,15,False
+2023-01-03 08:45:00+00:00,2,GBP_USD,LONG,1.19137,1.1901977086026483,1.1955951443059247,1.1966030221119972,1.1986187777241424,376512.35,2,LONDON,1.5,0.0018445827947033608,52.368575860235794,11.39397498547497,1.2043320138261073,1.205285274467584,-44.15144305924823,False,-0.0017456355614890295,3.907672187062459,-131.5201382610742,0.9434523809524075,8,1,1.1901977086026483,SL,2023-01-03 09:00:00+00:00,-11.722913973517546,-441.38218890169287,15,False
+2023-01-05 13:15:00+00:00,2,GBP_USD,LONG,1.1967900000000002,1.1956321898903906,1.2011960996515416,1.2016866168389966,1.2026676512139063,377409.36,2,OVERLAP,1.5,0.001455620219218991,29.227295063143934,25.657620812194935,1.2026472482982167,1.2030990934623462,-45.960996515415076,False,-0.00037330414599410404,6.312459966849289,-60.47248298216567,0.8358531317494367,13,3,1.1956321898903906,SL,2023-01-05 13:30:00+00:00,-11.578101096096916,-436.9683724693235,15,False
+2023-01-05 15:45:00+00:00,2,GBP_USD,LONG,1.18826,1.186362690066342,1.1958744435487272,1.1978035553600854,1.2016617789828017,228006.34,2,OVERLAP,1.5,0.0018546198673157427,69.60151760622875,17.05360609555227,1.1987515222683094,1.2019430109006963,-78.04443548727224,False,-0.0007169491261491134,17.58550324214001,-106.81522268309429,0.5402843601896258,15,3,1.186362690066342,SL,2023-01-06 09:15:00+00:00,-18.97309933658109,-432.59869381902826,1050,False
+2023-01-06 09:30:00+00:00,2,GBP_USD,LONG,1.18639,1.1852126876606282,1.1885589168691197,1.1891236187404322,1.1902530224830572,363771.52,2,LONDON,1.5,0.0014746246787437879,30.36612780792634,24.767771955493714,1.1910175599229305,1.196367461462072,-23.589168691198203,False,-0.00037298966114827556,16.991419425987754,-48.17559922930536,0.6036585365854443,9,4,1.1852126876606282,SL,2023-01-06 10:00:00+00:00,-11.773123393719054,-428.2726992080739,30,False
+2023-01-06 15:00:00+00:00,2,GBP_USD,SHORT,1.1969899999999998,1.2002270351519517,1.1890943143454993,1.1880409034561914,1.1859340816775754,130980.96,2,OVERLAP,1.5,0.0027140703039033325,40.56735895062166,70.79666078928972,1.189318180398642,1.1946479768984242,80.8568565450063,False,0.0009691080112269245,54.101668015760545,78.6181960135801,0.80338266384776,15,4,1.2002270351519517,SL,2023-01-06 15:15:00+00:00,-32.3703515195195,-423.98997175641233,15,False
+2023-01-12 13:45:00+00:00,2,GBP_USD,SHORT,1.2233899999999998,1.225585349166283,1.2168437969804868,1.216123899774011,1.2146841053610593,191199.69,3,OVERLAP,1.5,0.002830698332565878,20.821911026159945,71.64767734160475,1.215742934303728,1.2147265240510585,67.36203019513054,False,0.0004008146989117013,55.05836945431565,78.3706569627185,0.9199507389162503,13,3,1.2146841053610593,TP3,2023-01-12 14:30:00+00:00,71.2212078469362,1361.7472861759768,45,True
+2023-01-18 13:30:00+00:00,2,GBP_USD,SHORT,1.2405199999999998,1.24288428498713,1.23572939851183,1.2347173068653297,1.232693123572329,183297.51,2,OVERLAP,1.5,0.001708569974260011,50.131531472819894,76.42941935821152,1.2328499906182502,1.2267574841105189,49.80601488169922,False,0.00020231818976208728,87.8953512217427,78.60009381749799,0.641304347826081,13,2,1.24288428498713,SL,2023-01-18 14:15:00+00:00,-23.642849871301493,-433.3675510713385,45,False
+2023-01-24 10:00:00+00:00,2,GBP_USD,LONG,1.23116,1.229368916428351,1.2365065828960207,1.2378112892147846,1.2404207018523121,239538.72,2,LONDON,1.5,0.0015621671432981793,45.1253165518781,19.168275322962856,1.237981211542372,1.2377747641166992,-55.36582896020769,False,-0.0009904915795639175,11.145404663922506,-70.11211542372165,0.6165191740413406,10,1,1.229368916428351,SL,2023-01-24 12:30:00+00:00,-17.910835716490325,-429.03386616583754,150,False
+2023-02-02 15:00:00+00:00,2,GBP_USD,LONG,1.22472,1.2226822502460073,1.2313929346933497,1.2326416312741335,1.235139024435701,208437.53,2,OVERLAP,1.5,0.0024154995079852864,27.893748843653096,25.117062995505307,1.2332824097368154,1.2343319201799647,-68.62934693349753,False,-0.00030919386667936123,5.630108578504941,-87.52409736815459,0.5995575221239302,15,3,1.2226822502460073,SL,2023-02-02 21:15:00+00:00,-20.377497539927347,-424.74352548035324,375,False
+2023-02-03 16:30:00+00:00,2,GBP_USD,LONG,1.2070600000000002,1.2049596304342916,1.2162735059359504,1.2185884768054263,1.2232184185443777,200201.0,2,OVERLAP,1.5,0.0024807391314166792,67.05019092702636,22.301324027632333,1.2183594912089357,1.2255710972658316,-94.03505935950295,False,-0.0007415222518173848,28.3461818690853,-114.89491208935608,0.8869863013698083,16,4,1.2049596304342916,SL,2023-02-03 21:00:00+00:00,-21.00369565708693,-420.49608742444605,270,False
+2023-02-07 11:00:00+00:00,2,GBP_USD,LONG,1.1983700000000002,1.1975392480891527,1.201040337412173,1.2016537418648845,1.2028805507703075,501101.63,2,LONDON,1.5,0.001221503821694827,47.04900969959613,28.82131557084874,1.2025536557804173,1.2075466112434525,-28.60337412172953,False,-0.00035609355842333896,6.060485574311492,-43.73655780417263,0.910344827586229,11,1,1.1975392480891527,SL,2023-02-07 11:15:00+00:00,-8.307519108474715,-416.2911366512826,15,False
+2023-02-09 09:45:00+00:00,2,GBP_USD,SHORT,1.2138799999999998,1.214867228025632,1.2123032889355962,1.21188625878154,1.2110521984734273,417460.01,2,LONDON,1.5,0.0011544560512637787,46.38151536057029,77.49782370137652,1.2094854631507923,1.2074634758526734,17.667110644037365,False,0.00029435198023256264,77.83831344806667,45.845368492076766,0.48924731182788767,9,3,1.214867228025632,SL,2023-02-09 10:15:00+00:00,-9.872280256322252,-412.128221452709,30,False
+2023-02-09 13:00:00+00:00,2,GBP_USD,SHORT,1.21671,1.217572471472455,1.2136335325991126,1.212872218350094,1.2113495898520568,473067.17,2,OVERLAP,1.5,0.0011449429449097341,59.82088430251589,76.07752926926916,1.2117001923440298,1.2083656651545647,32.6646740088754,False,0.00010443192091392573,91.47783832284551,51.99807655970279,0.5608108108108281,13,3,1.217572471472455,SL,2023-02-09 13:30:00+00:00,-8.624714724549953,-408.0069386800176,30,False
+2023-02-09 13:30:00+00:00,2,GBP_USD,SHORT,1.21793,1.2188126157083923,1.2140208141193463,1.2131548045436533,1.2114227853922677,457647.5,2,OVERLAP,1.5,0.0011852314167844148,64.74343282249826,77.15977819108954,1.212131665443297,1.2085425609709146,40.991858806538325,False,0.00017054999280838095,88.75393895321645,59.88334556703112,0.9431818181818239,13,3,1.2188126157083923,SL,2023-02-09 13:45:00+00:00,-8.826157083923203,-403.9268724064744,15,False
+2023-02-09 14:15:00+00:00,2,GBP_USD,SHORT,1.2189299999999998,1.2198473912942192,1.2145922777674192,1.2135272200225153,1.2113971045327079,435896.44,2,OVERLAP,1.5,0.0012747825884385404,72.56915900303873,72.02190470251172,1.2128721347576608,1.2088421616435572,45.277222325808,False,0.00022936984003819427,85.51528505939473,62.478652423392234,0.8687499999999766,14,3,1.2134640247440918,TP1+TP2+SL,2023-02-09 19:00:00+00:00,48.79730396796123,2127.0571081232174,285,True
+2023-02-13 15:00:00+00:00,2,GBP_USD,SHORT,1.2125799999999998,1.213984545523702,1.2074731484625338,1.2062433368947831,1.2037837137592815,299853.7,2,OVERLAP,1.5,0.0013090910474038012,48.85207267934243,74.39254308810857,1.2070237750442916,1.2076811164889845,52.968515374660626,False,0.0003301160653826575,88.14839998111182,57.46224955708312,0.6199999999999534,15,0,1.213984545523702,SL,2023-02-13 15:45:00+00:00,-14.045455237021718,-421.158172100534,45,False
+2023-02-14 10:00:00+00:00,2,GBP_USD,SHORT,1.2196799999999999,1.2209563347201684,1.217109146895924,1.2164469242113272,1.215122478842133,326674.96,2,LONDON,1.5,0.0011526694403366947,57.21986450671999,76.16437701853823,1.2149343050714685,1.211367555998074,27.60853104075922,False,0.0004283320344209532,90.61811037042997,49.356949285315196,0.5879120879120571,10,1,1.2209563347201684,SL,2023-02-14 11:45:00+00:00,-12.763347201685258,-416.94659365766444,105,False
+2023-02-14 14:15:00+00:00,2,GBP_USD,LONG,1.2139400000000002,1.2109830681706844,1.219436910980098,1.2204562323947696,1.2224948752241127,139596.43,2,OVERLAP,1.5,0.002513863658631164,72.11255266175712,28.11390541724667,1.2172453980670155,1.2126691971760404,-56.86910980097881,False,-0.0007100296030749557,23.836638673895454,-34.95398067015376,0.6907216494845257,14,1,1.2192707869128965,TP1+TP2+SL,2023-02-14 15:30:00+00:00,58.28754865152597,813.6733705204339,75,True
+2023-02-15 10:45:00+00:00,2,GBP_USD,LONG,1.2069100000000001,1.2059901395640367,1.208769210432269,1.2091702097981778,1.2099722085299958,457584.48,2,LONDON,1.5,0.0013397208719269226,52.25168717693764,27.857252425646678,1.2126330721967817,1.2136408207871179,-20.49210432268911,False,-5.5873801153640944e-05,8.188478714555059,-59.13072196781721,0.9411764705882243,10,2,1.2059901395640367,SL,2023-02-15 11:45:00+00:00,-9.198604359634821,-420.91385926292327,60,False
+2023-02-15 11:45:00+00:00,2,GBP_USD,LONG,1.20585,1.204918427246159,1.2085168849643417,1.2090082978391241,1.2099911235886889,447313.13,2,LONDON,1.5,0.0013431455076818185,57.26745826925382,28.70789232672277,1.2118291114799222,1.213388899951138,-28.56884964341777,False,-4.741171787574607e-05,30.379273449826396,-61.69111479922273,0.9533898305084543,11,2,1.204918427246159,SL,2023-02-15 12:30:00+00:00,-9.315727538410368,-416.7047243433537,45,False
+2023-02-15 12:30:00+00:00,2,GBP_USD,LONG,1.2050400000000001,1.2038281121209224,1.2081635992882493,1.208819400862172,1.2101310040100173,340409.11,2,LONDON,1.5,0.00134377575815487,60.50761021811809,29.14193782690245,1.2111482779360985,1.2131652709751457,-33.13599288249369,False,-0.00010036686137527965,26.37560297604294,-62.98277936098495,0.7815533980582409,12,2,1.2038281121209224,SL,2023-02-15 12:45:00+00:00,-12.118878790776664,-412.5376743366161,15,False
+2023-02-15 13:45:00+00:00,2,GBP_USD,LONG,1.2015300000000002,1.1997968753787738,1.2064118051338677,1.2074314106124835,1.2094706215697153,235650.85,2,OVERLAP,1.5,0.0016262492424521833,70.64858983045177,21.173291184505786,1.2097671253901954,1.2126974031641649,-50.71805133867624,False,-0.0002873388359333068,27.570197174675965,-84.271253901953,0.6869300911853855,13,2,1.1997968753787738,SL,2023-02-15 14:15:00+00:00,-17.331246212264336,-408.4122901479372,30,False
+2023-02-17 15:00:00+00:00,2,GBP_USD,SHORT,1.2003899999999998,1.202834561371278,1.1948513388757334,1.1938990236696492,1.1919943932574806,165399.07,2,OVERLAP,1.5,0.0015030409141853992,43.85316132585288,71.63543411268458,1.1955937426257603,1.2002207806577627,57.286611242666204,False,0.0005690998133276552,58.29375438498795,49.86257374239633,0.7300613496932574,15,4,1.202834561371278,SL,2023-02-17 15:15:00+00:00,-24.445613712782418,-404.328177367346,15,False
+2023-02-23 09:15:00+00:00,2,GBP_USD,LONG,1.2019900000000001,1.2008502701703714,1.2040867511310027,1.2045739086896423,1.2055482238069215,351210.33,2,LONDON,1.5,0.00101945965925708,53.23588324841751,23.745164941547444,1.2056327974381402,1.2066667060434049,-22.8675113100274,False,-0.00047415117652872314,7.1385022058761365,-38.32797438140245,0.6756756756756676,9,3,1.2019900000000001,TP1+SL,2023-02-23 11:15:00+00:00,10.483755655013026,368.20032832364916,120,True
+2023-02-24 12:00:00+00:00,2,GBP_USD,LONG,1.1983100000000002,1.1969750292129355,1.2011392450825917,1.2018932429199705,1.2034012385947277,302603.55,2,LONDON,1.5,0.0010899415741288505,41.495887412024636,28.54472579036684,1.2015056969465436,1.203542415528824,-30.192450825916595,False,-0.00046208845825760893,14.537563312999884,-33.85696946543559,0.49152542372875935,12,4,1.1969750292129355,SL,2023-02-24 13:00:00+00:00,-13.349707870646819,-403.96689931206686,60,False
+2023-02-24 13:30:00+00:00,2,GBP_USD,LONG,1.1954500000000001,1.1938641541955919,1.1993018575200023,1.200282315046927,1.2022432301007764,252185.44,2,OVERLAP,1.5,0.0013916916088161778,53.57479121630424,21.417142566184467,1.200620393547976,1.203184236904785,-40.41857520002301,False,-0.000370054166991107,43.16383650748799,-53.60393547976017,0.6435406698565018,13,4,1.1938641541955919,SL,2023-02-24 14:00:00+00:00,-15.858458044082367,-399.92722195684513,30,False
+2023-02-24 14:00:00+00:00,2,GBP_USD,LONG,1.1942100000000002,1.1928380972400765,1.1987424337697168,1.1998012118991357,1.2019187681579737,288597.68,2,OVERLAP,1.5,0.0015638055198466101,59.380432116631965,24.531465454394294,1.200203292929139,1.2030264143379223,-47.22433769716705,False,-0.0004343185237293736,23.018839712919643,-61.832929291389945,0.8571428571428005,14,4,1.1928380972400765,SL,2023-02-24 14:15:00+00:00,-13.719027599237155,-395.9279536995812,15,False
+2023-02-27 15:00:00+00:00,2,GBP_USD,SHORT,1.2033399999999999,1.2042003839074562,1.1987268903996346,1.1976814724061968,1.195590636419321,455574.16,2,OVERLAP,1.5,0.0013207678149124355,61.800085395862844,74.30845925099581,1.197812795200581,1.198741762092503,48.0310960036534,False,0.00037350179093072677,80.70611816236972,57.17204799418951,0.772486772486803,15,0,1.2042003839074562,SL,2023-02-27 15:15:00+00:00,-8.603839074563524,-391.9686759169455,15,False
+2023-03-01 15:00:00+00:00,2,GBP_USD,LONG,1.1980300000000002,1.1954920753551985,1.2040660033773654,1.2051839501829007,1.2074198437939712,152900.12,2,OVERLAP,1.5,0.0019558492896025875,36.878347202635695,28.85111543112656,1.2043236185744686,1.2043862170723925,-62.26003377365341,False,-0.0006423871122289884,22.150789118236275,-64.83618574468552,0.5656565656565653,15,2,1.1980300000000002,TP1+SL,2023-03-02 06:00:00+00:00,30.180016886826028,461.45282035977266,900,True
+2023-03-07 11:30:00+00:00,2,GBP_USD,LONG,1.19866,1.1977625446076257,1.201650205086963,1.2021469794794,1.203140528264274,437529.84,2,LONDON,1.5,0.0011549107847486641,32.886547385102034,29.52685026940803,1.2025696709492153,1.2020087703139553,-31.802050869631504,False,-0.0002818903164929238,52.597359295811934,-40.99670949215373,0.8939929328622066,11,1,1.1977625446076257,SL,2023-03-07 12:30:00+00:00,-8.97455392374402,-392.6635142327094,60,False
+2023-03-07 15:00:00+00:00,2,GBP_USD,LONG,1.1891100000000001,1.187628091785558,1.198483589699101,1.1995769421748612,1.2017636471263817,262321.83,2,OVERLAP,1.5,0.001863816428884071,55.98567372865196,18.853825781222753,1.2003967258361923,1.2014448615485236,-95.63589699101094,False,-0.0005624412812229025,31.11465069049044,-114.76725836192303,0.9613733905579489,15,1,1.187628091785558,SL,2023-03-07 15:45:00+00:00,-14.819082144421625,-388.7368747045005,45,False
+2023-03-09 13:45:00+00:00,2,GBP_USD,SHORT,1.1907999999999999,1.1917322183589951,1.1880589443969285,1.1873848239680498,1.1860365831102924,412831.94,2,OVERLAP,1.5,0.0012644367179900757,34.17578136604077,70.50647316362117,1.186877777090238,1.1875275160877132,29.310556030714974,False,5.1181816444117794e-05,59.735501259046686,41.12222909762097,0.9262672811060195,13,3,1.1917322183589951,SL,2023-03-09 14:00:00+00:00,-9.322183589952626,-384.8495136476307,15,False
+2023-03-09 14:30:00+00:00,2,GBP_USD,SHORT,1.1916399999999998,1.1927005698741662,1.18855699734742,1.1877852926466035,1.1862418832449704,359241.79,2,OVERLAP,1.5,0.001321139748332417,43.822629004174004,71.79723602467432,1.187356827893415,1.1876331265054543,32.73002652580015,False,0.00021988508314360166,79.07981648391055,44.73172106584933,0.49729729729733946,14,3,1.1927005698741662,SL,2023-03-09 14:45:00+00:00,-10.605698741663616,-381.0010200155985,15,False
+2023-03-09 15:00:00+00:00,2,GBP_USD,SHORT,1.1922799999999998,1.194318169942521,1.188989293728841,1.1881224051258417,1.1863886279198426,185063.57,2,OVERLAP,1.5,0.0014163398850417382,51.65471207738619,73.59210129095658,1.1877221160215647,1.187721613889322,34.80706271158817,False,0.0002751600820914183,76.59833950385995,47.47883978435174,0.30894308943082605,15,3,1.194318169942521,SL,2023-03-10 07:00:00+00:00,-20.381699425211508,-377.191005829659,960,False
+2023-03-10 13:30:00+00:00,2,GBP_USD,SHORT,1.20345,1.2053865749247274,1.1988260820996588,1.1977549573985982,1.195612707996477,192824.5,2,OVERLAP,1.5,0.0017731498494550646,44.19983770055953,72.31517344481566,1.1959248606087025,1.191618897789506,48.139179003412465,False,0.00019212888013912105,67.61119214069167,77.15139391297531,0.8380782918149514,13,4,1.2053865749247274,SL,2023-03-10 14:30:00+00:00,-19.36574924727541,-373.41909157312574,60,False
+2023-03-15 10:15:00+00:00,2,GBP_USD,LONG,1.2102300000000001,1.2093321966100825,1.2130496694178063,1.2137042024971014,1.2150132686556918,411765.99,2,LONDON,1.5,0.001395606779834827,45.99834591526944,21.97969186671932,1.2150981820729194,1.2135670064667006,-30.096694178063377,False,-0.0005321189920753652,5.929247917490169,-50.58182072919415,0.6035242290749325,10,2,1.2093321966100825,SL,2023-03-15 10:30:00+00:00,-8.978033899176284,-369.6849016747883,15,False
+2023-03-20 13:30:00+00:00,2,GBP_USD,SHORT,1.2252899999999998,1.2265488674853704,1.2209751621531324,1.2198834138127317,1.2176999171319303,290728.02,2,OVERLAP,1.5,0.0013977349707405948,50.74876268159645,73.04237178404443,1.2202714759794278,1.2161514474951185,45.04837846867504,False,0.0003112789100647023,90.57952578025278,52.08524020572147,0.6153846153845168,13,0,1.2265488674853704,SL,2023-03-20 14:00:00+00:00,-12.588674853706294,-365.9880514641821,30,False
+2023-03-28 15:15:00+00:00,2,GBP_USD,SHORT,1.2341,1.2348718096548557,1.2310068795375173,1.2303571509507965,1.229057693777355,469452.76,2,OVERLAP,1.5,0.001123619309711469,32.067215093780426,71.81606355707574,1.2311491044796903,1.228576436173439,32.831204624828025,False,0.0002772186402575376,90.8822431548222,31.408955203098277,0.75182481751826,15,1,1.2348718096548557,SL,2023-03-28 15:30:00+00:00,-7.71809654855682,-362.3281726666473,15,False
+2023-03-30 12:45:00+00:00,2,GBP_USD,SHORT,1.2375699999999998,1.238545397951867,1.2351155035854873,1.234605052510197,1.2335841503596165,367752.36,2,LONDON,1.5,0.001150795903734159,38.563370447533124,71.58539172807612,1.233491622675009,1.231995496225979,26.44496414512698,False,0.0001957350515680848,81.52184516387082,42.683773249909684,0.7814569536422935,12,3,1.238545397951867,SL,2023-03-30 16:45:00+00:00,-9.753979518671851,-358.7048987383237,240,False
+2023-04-14 12:30:00+00:00,2,GBP_USD,LONG,1.2468100000000002,1.245884779749847,1.2501283863885664,1.2505083079236183,1.251268150993722,383819.79,2,LONDON,1.5,0.0012304405003061224,41.9774131059223,26.06831930927261,1.2515755369514183,1.2500247839034067,-35.0838638856632,False,-0.00021046705158508873,52.28204097285255,-49.5553695141826,0.6960950764006837,12,4,1.245884779749847,SL,2023-04-14 14:00:00+00:00,-9.252202501532913,-355.11784211758373,90,False
+2023-04-14 15:15:00+00:00,2,GBP_USD,LONG,1.2426000000000001,1.2414454349711856,1.2476980042417654,1.2487793679585584,1.2509420953921446,304501.4,2,OVERLAP,1.5,0.0014691300576287827,57.23337801140582,24.844585545050123,1.2493243548287474,1.2495451139894564,-52.88004241765343,False,-0.0004140120629785094,20.149873034717434,-69.14354828747359,0.8215962441314304,15,4,1.2414454349711856,SL,2023-04-14 15:30:00+00:00,-11.545650288145024,-351.5666676650564,15,False
+2023-04-17 14:30:00+00:00,2,GBP_USD,LONG,1.2365000000000002,1.235088388917699,1.239850866212164,1.2406845134294833,1.242351807864122,246562.96,2,OVERLAP,1.5,0.0012232221646021008,44.430450781096894,27.686717013995974,1.240469209712136,1.2440985051011886,-35.408662121638734,False,-0.0005890527484250167,8.963543013502523,-41.592097121359025,0.3937499999999315,14,0,1.2408686738850376,TP1+TP2+SL,2023-04-18 07:00:00+00:00,37.953138891565814,935.7838266395587,990,True
+2023-04-25 13:00:00+00:00,2,GBP_USD,LONG,1.24082,1.239959599192904,1.2450491334046205,1.2458757719301987,1.2475290489813553,415398.07,2,OVERLAP,1.5,0.0011008016141922047,60.512870936618455,22.22605185171392,1.2461736837316448,1.2456282322059904,-44.19133404620634,False,-0.0003834526770830275,24.62794872121431,-55.43683731644933,0.8045454545454843,13,1,1.239959599192904,SL,2023-04-25 13:45:00+00:00,-8.6040080709604,-357.40883469413734,45,False
+2023-04-25 14:00:00+00:00,2,GBP_USD,LONG,1.2390400000000001,1.2380601087450158,1.2438537383465482,1.2449317925927232,1.247087901085073,361095.94,2,OVERLAP,1.5,0.0012997825099683358,61.73452889650566,23.38751783943829,1.2453181839371616,1.2454239246381298,-50.037383465482144,False,-0.00042031977133993306,13.398832875115467,-64.68183937161642,0.6093749999999502,14,1,1.2444630297652435,TP1+TP2+SL,2023-04-26 07:15:00+00:00,52.824510455136256,1907.4716257837254,1035,True
+2023-04-26 08:45:00+00:00,2,GBP_USD,SHORT,1.2476699999999998,1.2483860433173917,1.2461651253392825,1.245870159783933,1.2452802286732343,520791.77,2,LONDON,1.5,0.0010520866347835949,60.49897262694884,71.98891504812953,1.2430747269661662,1.2435984470562529,16.948746607174936,False,0.00033646485997341374,78.89471639471742,47.852730338338034,0.9806763285023963,8,2,1.2483860433173917,SL,2023-04-26 09:00:00+00:00,-7.160433173918346,-372.9094666611653,15,False
+2023-05-03 08:30:00+00:00,2,GBP_USD,SHORT,1.253,1.2537102578527004,1.252057516946498,1.2518841479521319,1.2515374099633993,519783.58,2,LONDON,1.5,0.0008205157054006164,48.52912125744125,77.43042713659445,1.2491423854303736,1.2490826952087595,11.32483053501998,False,0.0002973594472193558,83.35608880633616,40.47614569626434,0.6073298429319384,8,2,1.2515374099633993,TP3,2023-05-03 09:00:00+00:00,10.811782489948182,561.9787008806582,30,True
+2023-05-05 12:30:00+00:00,2,GBP_USD,LONG,1.2566000000000002,1.255281741876897,1.2593520573884551,1.2596415354889865,1.2602204916900495,284314.7,2,LONDON,1.5,0.0010565162462064285,29.63815964918601,24.348464783638832,1.2601123197383102,1.2573253270691371,-29.420573884550993,False,-0.00040636075561225265,14.851428147582375,-37.02319738310145,0.8337349397590655,12,4,1.2602204916900495,TP3,2023-05-05 14:15:00+00:00,29.83639868880095,848.2926742286836,105,True
+2023-05-09 12:45:00+00:00,2,GBP_USD,LONG,1.25879,1.2580084251267443,1.2608139369844578,1.2612665407277994,1.2621717482144827,490398.42,2,LONDON,1.5,0.0008831497465115164,39.71007958497998,28.984669834780405,1.2612357691078226,1.2615641096949535,-22.139369844578205,False,-0.0003046310848398437,19.12866176163701,-26.357691078227052,0.7519379844962227,12,1,1.2580084251267443,SL,2023-05-09 13:15:00+00:00,-7.815748732558081,-383.28308295634855,30,False
+2023-05-10 12:30:00+00:00,2,GBP_USD,SHORT,1.2661799999999999,1.2669535194879185,1.262612765309187,1.2622000694747393,1.2613746778058441,490550.35,2,LONDON,1.5,0.0010870389758370334,34.395980413252076,72.52223734159136,1.2621307250231906,1.2619088135457215,37.57234690813016,False,0.0002868856024087098,86.92682926829734,42.39274976809426,0.9849056603773436,12,2,1.2669535194879185,SL,2023-05-10 12:45:00+00:00,-7.735194879185947,-379.4502555302874,15,False
+2023-05-11 15:30:00+00:00,2,GBP_USD,LONG,1.2502300000000002,1.249242034409749,1.2571313068649317,1.258788659669066,1.262103365277335,380231.62,2,OVERLAP,1.5,0.0014759311805020014,48.99092010998426,27.609961175007072,1.2571838150560684,1.2604090678237696,-70.91306864931646,False,-0.00043855326611079307,34.43040815588642,-71.43815056068314,0.9037037037037281,15,3,1.25242,TIME,2023-05-12 11:30:00+00:00,21.899999999999142,832.7072477999674,1200,True
+2023-05-15 16:45:00+00:00,2,GBP_USD,SHORT,1.25307,1.253661292093839,1.2507076239355903,1.2501202331137868,1.2489454514701799,649396.18,2,OVERLAP,1.5,0.0007625841876782162,53.854412706795365,71.79795100670816,1.2499628938924838,1.250530008538969,25.523760644097226,False,6.220936481556396e-05,82.97879735836493,32.97106107516212,0.9230769230769043,16,0,1.2489454514701799,TP3,2023-05-16 06:00:00+00:00,28.322887218523714,1839.277476628013,795,True
+2023-05-18 09:15:00+00:00,2,GBP_USD,LONG,1.2431900000000002,1.242305601168442,1.244142137777687,1.2444180103502538,1.2449697554953876,454970.75,2,LONDON,1.5,0.000988797663115935,55.25245063767357,27.39784066359387,1.2467454182326452,1.2480976132871404,-11.421377776867914,False,-0.00026004304025315037,17.07195804799269,-37.4541823264507,0.8198198198196667,9,3,1.2434349458943637,TP1+TP2+SL,2023-05-18 10:15:00+00:00,9.426143955865829,428.8619785208243,60,True
+2023-05-23 10:00:00+00:00,2,GBP_USD,LONG,1.23811,1.23706329269913,1.2396143984526193,1.2400361166871376,1.240879553156174,388517.61,2,LONDON,1.5,0.0010734146017398667,45.974977230616304,29.37000045292325,1.2418978851466485,1.243402713105863,-16.943984526194278,False,-0.0002901910105751022,8.326890629522703,-39.778851466485406,0.6111111111112345,10,1,1.240879553156174,TP3,2023-05-23 13:30:00+00:00,18.41773040233907,715.5612597541115,210,True
+2023-05-26 11:30:00+00:00,2,GBP_USD,SHORT,1.2385799999999998,1.240030245892061,1.2363731805308407,1.235793919907281,1.2346353986601613,285344.6,2,LONDON,1.5,0.0010204917841218713,54.890058341867245,70.39433700801295,1.2349066837333005,1.235586611252846,23.968194691592615,False,0.00024627086200948195,85.35097376215452,38.63316266699446,0.4339622641508591,11,4,1.2367262842525588,TP1+TP2+SL,2023-05-26 12:45:00+00:00,23.565951291473095,672.4416944884873,75,True
+2023-05-30 09:15:00+00:00,2,GBP_USD,SHORT,1.2406799999999998,1.241396836408702,1.2373207455180828,1.236784156514291,1.2357109785067075,586666.98,2,LONDON,1.5,0.0010536728174041442,50.25162643718204,78.63286430279399,1.2356100789719815,1.2353590781410548,35.49254481917119,False,0.0006141575752483842,89.21578429902513,52.59921028018422,1.0,9,1,1.241396836408702,SL,2023-05-30 09:30:00+00:00,-7.168364087022371,-420.5442510473872,15,False
+2023-06-01 11:00:00+00:00,2,GBP_USD,SHORT,1.24738,1.2487888292859202,1.243776982494843,1.2428602160729871,1.2410266832292756,295521.12,2,LONDON,1.5,0.0010776585718403273,47.7607547687946,74.0122976651773,1.243405158184911,1.2408089777835214,37.930175051570366,False,0.0005329396909753711,89.61349343134997,41.648418150890265,0.6349206349206256,11,3,1.2487888292859202,SL,2023-06-01 13:00:00+00:00,-14.088292859202323,-416.33880846394726,120,False
+2023-06-05 11:15:00+00:00,2,GBP_USD,LONG,1.23794,1.2372577811344585,1.23959319346917,1.2399691365786134,1.2407210227975005,604168.89,2,LONDON,1.5,0.0009044377310830232,30.432675147598314,29.864917044746804,1.2416848510706702,1.2452428854910935,-18.43193469170057,False,-7.908453076360688e-05,37.71334138762094,-39.34851070670264,0.9699248120302082,11,0,1.2372577811344585,SL,2023-06-05 12:30:00+00:00,-6.8221886554153945,-412.1754147312912,75,False
+2023-06-06 12:15:00+00:00,2,GBP_USD,LONG,1.23954,1.2388950719262186,1.2413024921131255,1.2417804731655042,1.2427364352702615,632711.9,2,LONDON,1.5,0.0008098561475627428,44.30539519357383,29.772177027827425,1.2423056525564575,1.243582522054416,-19.52492113125537,False,-9.440090513494835e-05,6.804457953390333,-29.556525564575775,0.8301886792454016,12,1,1.241440725888264,TP1+TP2+SL,2023-06-06 16:00:00+00:00,19.5052054772874,1234.117561742492,225,True
+2023-06-07 13:45:00+00:00,2,GBP_USD,SHORT,1.2496099999999999,1.2504722036215534,1.2455091421259006,1.2446277576256064,1.242864988625018,487581.85,2,OVERLAP,1.5,0.001064407243106891,51.7608522076591,77.65164122411012,1.2442948549429118,1.2432883407282342,42.90857874099441,False,0.00024217096788182947,84.87090632326876,55.05145057088212,0.7977528089886996,13,2,1.242864988625018,TP3,2023-06-07 17:00:00+00:00,48.0596547433465,2343.301537012216,195,True
+2023-06-12 12:45:00+00:00,2,GBP_USD,LONG,1.2552800000000002,1.254679143209218,1.2578561026460857,1.2585119244069958,1.259823567928816,738658.3,2,LONDON,1.5,0.0008017135815639021,43.0426738480091,28.373012598504815,1.2574366728659174,1.2550712101308419,-27.661026460856775,False,-0.0003766815511403042,5.394068362605302,-23.466728659173874,0.6538461538461648,12,0,1.254679143209218,SL,2023-06-12 13:00:00+00:00,-6.0085679078225604,-443.8278556226769,15,False
+2023-06-12 13:15:00+00:00,2,GBP_USD,LONG,1.2544600000000001,1.2536156387875401,1.2574701956543886,1.2582217110311433,1.259724741784653,520381.05,2,OVERLAP,1.5,0.0008487224249198901,48.46975707768016,24.443852079277775,1.2572252024417794,1.2550637185315088,-32.0019565438856,False,-0.00038733462141796535,10.556698734868185,-29.55202441779381,0.5540540540541108,13,0,1.2536156387875401,SL,2023-06-12 13:30:00+00:00,-8.443612124600097,-439.38957431921295,15,False
+2023-06-12 15:45:00+00:00,2,GBP_USD,LONG,1.2490200000000002,1.248148357357375,1.2549292070086344,1.2563722199759015,1.2592582459104358,499052.78,2,OVERLAP,1.5,0.001003285285249962,79.14909784091742,11.62189273744545,1.2551701504752184,1.2546856618416926,-60.992070086343105,False,-0.00040771025929054816,11.623707660390215,-63.40150475218298,0.8208955223880325,15,0,1.2559522365339904,TP1+TP2+SL,2023-06-13 06:15:00+00:00,65.67715975981058,3277.6369160637605,870,True
+2023-06-13 12:30:00+00:00,2,GBP_USD,SHORT,1.2595699999999999,1.261001490769066,1.2574887210531354,1.257211460925152,1.2566569406691852,326772.66,2,LONDON,1.5,0.0009629815381319213,44.98265400447303,81.591573830991,1.2553626071075117,1.2541662788090688,22.7127894686463,False,7.739364611241469e-05,73.95089659794684,43.973928924883054,0.44230769230765127,12,1,1.2583148543874914,TP1+TP2+SL,2023-06-13 13:30:00+00:00,20.543999915053067,671.3217499281664,60,True
+2023-06-13 16:45:00+00:00,2,GBP_USD,SHORT,1.2620799999999999,1.2629799385425726,1.2588352466966768,1.2580739972108521,1.2565514982392025,527241.86,2,OVERLAP,1.5,0.0010798770851454388,51.16004885421611,72.86569602208615,1.257837713526651,1.2551095996177717,34.347533033232125,False,0.0001230575939660057,81.7815096183739,44.32286473349034,0.7281553398057917,16,1,1.2629799385425726,SL,2023-06-14 08:15:00+00:00,-8.999385425727533,-474.4852710717477,930,False
+2023-06-14 12:30:00+00:00,2,GBP_USD,SHORT,1.2667399999999998,1.267522546009647,1.2645266440246927,1.2640783409409435,1.2631817347734453,600271.94,2,LONDON,1.5,0.0007650920192941263,41.5794762002085,78.37600815842308,1.2627243454760524,1.2588699911596701,24.033559753071643,False,0.00015111360587562075,90.41328992454868,42.05654523947499,0.6831683168316821,12,2,1.267522546009647,SL,2023-06-14 12:45:00+00:00,-7.825460096473336,-469.74041135026357,15,False
+2023-06-14 14:15:00+00:00,2,GBP_USD,SHORT,1.2694599999999998,1.270136099160878,1.2657400286495215,1.264872408905748,1.2631371694182005,687832.55,2,OVERLAP,1.5,0.0009121983217561957,64.47283959016266,80.59959117755574,1.264117560920817,1.2595136801862994,39.09971350478436,False,0.0002782362792452984,79.57235041627068,55.324390791828826,0.9112903225805946,14,2,1.2631371694182005,TP3,2023-06-14 18:00:00+00:00,44.14067145497203,3036.1390605585625,225,True
+2023-06-19 16:30:00+00:00,2,GBP_USD,LONG,1.27808,1.2771497637831788,1.2804736948265998,1.2809085975969352,1.281778403137606,532557.64,2,OVERLAP,1.5,0.0008004724336421341,34.1012932582374,27.368135074375076,1.28054676330427,1.2784745325252695,-25.83694826599814,False,-9.197255877191026e-05,37.32286329762747,-26.567633042700045,0.8229166666666763,16,0,1.2771497637831788,SL,2023-06-19 16:45:00+00:00,-9.302362168213117,-495.40440427288615,15,False
+2023-06-21 09:45:00+00:00,2,GBP_USD,LONG,1.27021,1.2692727714092527,1.2721334126465509,1.2726051001245824,1.2735484750806456,523298.54,2,LONDON,1.5,0.0014144571814950894,44.63347979207201,28.12690744107806,1.274891620914996,1.2764989971007887,-21.134126465509606,False,-0.0005509568718755774,18.064108284988716,-48.71620914996155,0.9019607843139347,9,2,1.2692727714092527,SL,2023-06-21 10:45:00+00:00,-9.372285907474076,-490.4503531843759,60,False
+2023-06-28 09:45:00+00:00,2,GBP_USD,LONG,1.2697900000000002,1.268907966295792,1.2710938347638852,1.271453836572371,1.272173840189343,550484.47,2,LONDON,1.5,0.0009040674084160396,54.10738688530073,29.555277327475707,1.2727263275153526,1.2731117895753368,-14.938347638850935,False,-0.00024308608183167338,15.805377720866693,-31.263275153525782,0.6912751677851479,9,2,1.2697900000000002,TP1+SL,2023-06-28 10:45:00+00:00,6.519173819424795,358.8703944823934,60,True
+2023-06-28 11:30:00+00:00,2,GBP_USD,LONG,1.26777,1.2669375565861745,1.270500943166072,1.2710034925780955,1.2720085914021422,587588.96,2,LONDON,1.5,0.001104886827651023,59.45500251710611,29.277024234469636,1.2719528347499323,1.2728754316421167,-29.209431660721652,False,-0.00023097372947690812,16.37493155081979,-43.728347499323306,0.7110266159696206,11,2,1.2669375565861745,SL,2023-06-28 11:45:00+00:00,-8.324434138256008,-489.1345597886344,15,False
+2023-06-28 13:00:00+00:00,2,GBP_USD,LONG,1.2632100000000002,1.2622124176268175,1.26839926740537,1.269696506976376,1.2722909861183878,485416.77,2,OVERLAP,1.5,0.0011951647463651746,73.87557620566045,19.570656176017366,1.2703416280324629,1.2723850916759982,-53.7926740536987,False,-0.0006007914059692066,10.196490192378258,-73.21628032462834,0.6712328767123079,13,2,1.2622124176268175,SL,2023-06-28 13:45:00+00:00,-9.975823731827038,-484.24321339928275,45,False
+2023-06-30 12:30:00+00:00,2,GBP_USD,SHORT,1.26816,1.269683204957568,1.2644171278705778,1.2637016285181408,1.2622706298132669,314731.63,2,LONDON,1.5,0.0012864099151361156,42.9648886686237,72.03050639110518,1.2632584426994928,1.2642079210690138,39.328721294222824,False,0.00039299326177888645,83.8932500689526,50.915573005072545,0.7675840978593581,12,4,1.269683204957568,SL,2023-06-30 13:15:00+00:00,-15.232049575679694,-479.4007791194479,45,False
+2023-06-30 14:00:00+00:00,2,GBP_USD,SHORT,1.2715899999999998,1.2725837252352648,1.2662790998076598,1.26511466056809,1.2627857820889503,477603.62,2,OVERLAP,1.5,0.0013674504705293992,59.12030771637493,80.80656483269522,1.2646153635916384,1.2645201370220653,55.00900192340063,False,0.0005423634180538935,81.37685220244823,71.64636408361513,0.9457013574660101,14,4,1.2725837252352648,SL,2023-06-30 14:30:00+00:00,-9.93725235264975,-474.6067696479037,30,False
+2023-07-04 12:45:00+00:00,2,GBP_USD,SHORT,1.2733999999999999,1.2743206953374635,1.2706813131201948,1.2701165698896817,1.2689870834286552,510332.45,2,LONDON,1.5,0.0008013906749269282,50.68591283553256,76.42340742129755,1.2702401731362336,1.2687225124341188,29.08686879805167,False,0.00023463608850515338,84.68921883769302,33.49826863766392,0.7839506172840369,12,1,1.2703795288631752,TP1+TP2+SL,2023-07-05 03:30:00+00:00,29.6161464903758,1511.4080597992383,885,True
+2023-07-07 12:15:00+00:00,2,GBP_USD,SHORT,1.27795,1.2790664414578132,1.2752300762263626,1.274608599623675,1.2733656464182992,434393.39,2,LONDON,1.5,0.0010328829156261781,41.161125970962544,71.54725649870086,1.2746201359296223,1.2728435093375041,29.09923773637413,False,0.0002407825630305132,53.21430052673063,35.19864070377743,0.5175438596491405,12,4,1.2790664414578132,SL,2023-07-07 12:30:00+00:00,-11.164414578133108,-484.9747895960661,15,False
+2023-07-07 16:30:00+00:00,2,GBP_USD,SHORT,1.2841299999999998,1.2849401787851933,1.2784293405547735,1.276961545428147,1.274025955174894,592616.16,2,OVERLAP,1.5,0.0012203575703865703,63.91624368035045,79.3607585929232,1.2779886652438084,1.2741338095202221,58.90659445226376,False,0.0002449233796706729,78.33448355187697,63.3133475619152,0.9223300970875064,16,4,1.27573,TP1+TP2+TIME,2023-07-10 12:30:00+00:00,66.19288822761514,3922.6975240758497,4080,True
+2023-07-11 10:30:00+00:00,2,GBP_USD,SHORT,1.29287,1.2940128216725015,1.2900785068219325,1.2893935436822548,1.2880236174028994,454447.12,2,LONDON,1.5,0.001085643345003241,36.37007451396067,72.24113020308917,1.2882402509308448,1.283243174234878,29.81493178067618,False,0.0003083780969699398,88.12838625271509,48.197490691552765,0.7772020725388947,10,1,1.2897688559074034,TP1+TP2+SL,2023-07-11 13:00:00+00:00,30.77677914134036,1398.64186436582,150,True
+2023-07-13 09:30:00+00:00,2,GBP_USD,SHORT,1.30761,1.3086128556208036,1.3050074599113077,1.3046095241912952,1.30381365275127,531819.76,2,LONDON,1.5,0.00140571124160747,45.571964081485405,72.5021852312268,1.3013997536108333,1.295851583245326,27.925400886923413,False,0.00044809945351782397,71.8192887541958,64.00246389166809,0.8393442622951214,9,3,1.305793950690923,TP1+TP2+SL,2023-07-13 12:45:00+00:00,26.238439737543025,1395.4120723994597,195,True
+2023-07-19 11:45:00+00:00,2,GBP_USD,LONG,1.28994,1.28903420340662,1.2931083462393131,1.2938570894805124,1.2953545759629108,604211.32,2,LONDON,1.5,0.0013515931867597271,44.80274533534163,24.788687974763192,1.2971146613050641,1.3034251987350722,-33.583462393131924,False,-0.00012272382211650024,6.591253904508946,-73.6466130506419,0.7212121212120698,11,2,1.28903420340662,SL,2023-07-19 13:45:00+00:00,-9.057965933800816,-547.2925553376823,120,False
+2023-07-19 14:00:00+00:00,2,GBP_USD,LONG,1.2884300000000002,1.2871093043109303,1.2921431244330561,1.2930008443217775,1.2947162840992201,410253.19,3,OVERLAP,1.5,0.0014613913781394855,52.78408215214798,27.786571063248388,1.295088322596499,1.302310176789267,-39.03124433056071,False,4.215590107378058e-05,17.69444449246625,-68.48322596499034,0.7228464419475743,14,2,1.2871093043109303,SL,2023-07-19 14:45:00+00:00,-13.20695689069895,-541.8196194601726,45,False
+2023-07-20 12:00:00+00:00,2,GBP_USD,LONG,1.2878800000000001,1.2868350527599903,1.2905964853828242,1.2911562832660464,1.2922758790324904,513328.72,2,LONDON,1.5,0.001069894480019209,51.95565863040282,26.539932272295104,1.2919638303467778,1.296545783802186,-29.06485382824231,False,-0.00021348547114844753,17.894175901006122,-42.73830346777751,0.8022598870056142,12,3,1.2868350527599903,SL,2023-07-20 12:15:00+00:00,-10.44947240009808,-536.4014291817675,15,False
+2023-07-21 11:45:00+00:00,2,GBP_USD,LONG,1.2826000000000002,1.2818210245621067,1.2854158743869506,1.2858465647317558,1.2867079454213664,681712.66,2,LONDON,1.5,0.001077950875786745,38.40465659639189,26.167527645302656,1.286611006331457,1.2905007708116476,-30.058743869505467,False,-0.0002906132323880472,12.887623986844345,-42.01006331457035,0.9764150943395742,11,4,1.2818210245621067,SL,2023-07-21 12:15:00+00:00,-7.789754378935144,-531.0374178410525,30,False
+2023-07-24 14:15:00+00:00,2,GBP_USD,LONG,1.2803600000000002,1.279116088249464,1.2831157452471573,1.2836227176239008,1.284636662377388,422640.14,2,OVERLAP,1.5,0.0012878235010716664,26.632938973705816,29.978918160943905,1.2842790817640304,1.286849435215878,-29.457452471572942,False,-0.0002890929350888951,9.002328781625126,-41.090817640303534,0.7722222222222133,14,0,1.2829284357729829,TP1+TP2+SL,2023-07-24 17:30:00+00:00,29.050891578912054,1227.8072884036212,195,True
+2023-08-01 11:30:00+00:00,2,GBP_USD,LONG,1.2790100000000002,1.2780125464794303,1.2813601192467852,1.2819569803692368,1.2831507026141402,539378.63,2,LONDON,1.5,0.0008949070411390711,47.8639736542076,28.50563069695194,1.2821178044020776,1.2839410820003965,-25.401192467850997,False,-0.00031323457109662606,19.720163684908574,-32.97804402077497,0.7313432835820104,11,1,1.2780125464794303,SL,2023-08-01 11:45:00+00:00,-9.97453520569902,-538.0051134136705,15,False
+2023-08-03 10:00:00+00:00,2,GBP_USD,LONG,1.2650000000000001,1.2640335584658282,1.2677062178920568,1.2682654242357563,1.2693838369231554,551119.8,2,LONDON,1.5,0.0011728830683438338,39.22259112247875,27.243216639506045,1.269916424938961,1.2744387010901752,-28.962178920568515,False,-0.00034055810628250876,10.929118141322464,-51.06424938960963,0.8815789473685133,10,3,1.2640335584658282,SL,2023-08-03 11:00:00+00:00,-9.664415341719135,-532.6250650245182,60,False
+2023-08-08 11:30:00+00:00,2,GBP_USD,LONG,1.2707100000000002,1.2696297941079269,1.2731207719517814,1.2737468098940927,1.2749988857787156,488146.58,2,LONDON,1.5,0.0009404117841462211,52.815456452532935,23.53755599694415,1.2747514892026341,1.2750010637018963,-26.00771951781322,False,-0.00025121442080520235,22.614415315272144,-42.31489202634053,0.64197530864195,11,1,1.2696297941079269,SL,2023-08-08 11:45:00+00:00,-10.802058920733604,-527.29881191146,15,False
+2023-08-10 10:30:00+00:00,2,GBP_USD,SHORT,1.27693,1.2776275907776484,1.2760276712861525,1.2758670783365758,1.2755458924374226,748326.73,2,LONDON,1.5,0.0007351815552966302,59.428210331998194,75.13805463674537,1.2737925280029414,1.2736429020937254,10.92328713847568,False,7.548531418035765e-05,81.28871509878878,33.27471997058628,0.8660714285715436,10,3,1.27693,TP1+SL,2023-08-10 12:00:00+00:00,4.511643569237167,337.61834790927776,90,True
+2023-08-10 12:15:00+00:00,2,GBP_USD,SHORT,1.2788499999999998,1.2795413987688455,1.2764549719703953,1.2761308570079568,1.27548262708308,759911.69,2,LONDON,1.5,0.0008627975376913387,44.384162525043045,75.62144575382345,1.27460804812901,1.273877063903666,25.850280296046968,False,5.679037299118495e-05,61.06172276675937,44.31951870989881,0.9098712446351723,12,3,1.2795413987688455,SL,2023-08-10 12:30:00+00:00,-6.91398768845719,-525.4020068974696,15,False
+2023-08-16 09:00:00+00:00,2,GBP_USD,SHORT,1.2763399999999998,1.2770784674936577,1.2755549581442587,1.2753202407062434,1.274850805830213,704361.39,2,LONDON,1.5,0.0009569349873154538,59.27802606975181,81.92063726560541,1.271885716483902,1.270565855724735,9.750418557412655,False,0.00038760185963868186,83.12724490789769,46.442835160980245,0.8061224489794508,9,2,1.274850805830213,TP3,2023-08-16 10:15:00+00:00,9.728158061533263,685.2138934361276,75,True
+2023-08-22 13:30:00+00:00,2,GBP_USD,LONG,1.2728100000000002,1.271657948623029,1.2762610053504457,1.27702266209585,1.2785459755866584,457444.99,2,OVERLAP,1.5,0.0011041027539422,49.30621060855901,25.566862800728146,1.2765449178209376,1.2753706420821325,-36.41005350445647,False,-0.00041868457522963357,18.72982963096483,-39.249178209375124,0.6153846153846319,13,1,1.2728100000000002,TP1+SL,2023-08-23 07:30:00+00:00,17.255026752227565,789.3225540122471,1080,True
+2023-08-24 09:30:00+00:00,2,GBP_USD,LONG,1.2671500000000002,1.2654173213900806,1.2695014800715632,1.2699755647892317,1.270923734224569,308708.93,2,LONDON,1.5,0.001028452406612984,48.2465794350729,26.771266915789965,1.2707506285149857,1.2718060639927486,-25.414800715630737,False,-0.0003500699893730282,7.458985277614109,-37.90628514985572,0.9081632653060855,9,3,1.2654173213900806,SL,2023-08-24 12:00:00+00:00,-17.32678609919658,-534.893359702185,150,False
+2023-08-24 12:30:00+00:00,2,GBP_USD,LONG,1.26391,1.2631365232054876,1.2675719010270916,1.268374637159882,1.269980109425463,684628.71,2,LONDON,1.5,0.0011269535890249672,50.38526406652472,27.5468470461891,1.2692577092317676,1.271257291298894,-38.51901027091653,False,-0.00034073905084841993,9.813574636582457,-55.37709231767662,0.9913793103448664,12,3,1.2631365232054876,SL,2023-08-24 12:45:00+00:00,-7.734767945124511,-529.5444200419944,15,False
+2023-08-30 12:30:00+00:00,2,GBP_USD,SHORT,1.2702999999999998,1.2717260514380704,1.2662838680294992,1.2655549883282529,1.2640972289257604,367622.77,2,LONDON,1.5,0.0013721028761410237,36.449527240499435,73.28721392687356,1.264563066280911,1.2625570108567596,42.06131970500682,False,0.0003890401670629427,79.11354355418284,59.269337190888116,0.8391812865496918,12,2,1.2717260514380704,SL,2023-08-30 12:45:00+00:00,-14.260514380706189,-524.2489798260044,15,False
+2023-08-30 13:45:00+00:00,2,GBP_USD,SHORT,1.2737899999999998,1.2748776371110169,1.2678368052597897,1.266660858918875,1.2643089662370457,477187.18,2,OVERLAP,1.5,0.0014752742220336546,58.839919463025446,79.52604599040362,1.2658834250380822,1.2630085015200447,61.43194740210189,False,0.000646450941725287,85.0972886762349,80.96574961917734,0.946843853820544,13,2,1.2686,TP1+TIME,2023-08-31 09:45:00+00:00,55.7159737010493,2658.694837135788,1200,True
+2023-08-31 11:15:00+00:00,2,GBP_USD,LONG,1.2667700000000002,1.265856254661187,1.2687141546477703,1.2692397111772862,1.2702908242363182,597095.72,2,LONDON,1.5,0.0009274906776260632,41.94038853772925,28.98185701323709,1.2700729526431356,1.2676627803555998,-21.341546477702256,False,-0.00019430931276376794,21.567246970475093,-34.929526431355384,0.49523809523785356,11,3,1.2683772612650022,TP1+TP2+SL,2023-08-31 12:30:00+00:00,20.775654256854455,1240.5054236967576,75,True
diff --git a/results/phase1/S2_USD_JPY_report.json b/results/phase1/S2_USD_JPY_report.json
new file mode 100644
index 0000000..8730b62
--- /dev/null
+++ b/results/phase1/S2_USD_JPY_report.json
@@ -0,0 +1,45 @@
+{
+  "pair": "USD_JPY",
+  "strategy_id": 2,
+  "strategy_name": "S2_Session_VWAP_Reversal",
+  "total_trades": 390,
+  "win_rate_pct": 22.82,
+  "avg_rr": 2.73,
+  "expectancy_pips": -1.67,
+  "sharpe_ratio": -2.57,
+  "max_drawdown_pct": -57.44,
+  "profit_factor": 0.68,
+  "total_pnl_pips": -651.03,
+  "total_pnl_dollars": -56630.21,
+  "avg_win_pips": 30.36,
+  "avg_loss_pips": 11.14,
+  "max_consecutive_wins": 2,
+  "max_consecutive_losses": 14,
+  "avg_hold_time_minutes": 198.3,
+  "best_trade_pips": 114.02,
+  "worst_trade_pips": -48.94,
+  "best_trade_dollars": 3811.58,
+  "worst_trade_dollars": -1000.0,
+  "final_equity": 43369.79,
+  "starting_equity": 100000.0,
+  "session_breakdown": {
+    "LONDON": {
+      "trades": 195,
+      "win_rate": 20.51282051282051,
+      "total_pnl_pips": -700.1751316941624
+    },
+    "OVERLAP": {
+      "trades": 195,
+      "win_rate": 25.128205128205128,
+      "total_pnl_pips": 49.14938750402993
+    }
+  },
+  "exit_reasons": {
+    "SL": 301,
+    "TP3": 21,
+    "TP1+TP2+SL": 46,
+    "TP1+SL": 17,
+    "TP1+TIME": 3,
+    "TIME": 2
+  }
+}
\ No newline at end of file
diff --git a/results/phase1/S2_USD_JPY_trades.csv b/results/phase1/S2_USD_JPY_trades.csv
new file mode 100644
index 0000000..50736f4
--- /dev/null
+++ b/results/phase1/S2_USD_JPY_trades.csv
@@ -0,0 +1,391 @@
+timestamp,strategy_id,pair,signal_direction,entry_price,sl_price,tp1_price,tp2_price,tp3_price,lot_size,confluence_score,session,spread_at_entry,atr_at_entry,adx_at_entry,rsi_at_entry,ema_50_value,ema_200_value,vwap_deviation,news_within_60min,macd_hist_at_entry,stoch_k_at_entry,distance_from_ema50_pips,candle_body_ratio,hour_of_day,day_of_week,exit_price,exit_reason,exit_time,pnl_pips,pnl_dollars,hold_time_minutes,win
+2021-01-06 15:00:00+00:00,2,USD_JPY,SHORT,103.235,103.30736456331992,102.97833599252927,102.91419380151247,102.78590941947888,13818.92,2,OVERLAP,1.5,0.07272912663983072,56.43916352448974,78.8502273790507,102.89657102867542,102.88727558786246,27.56640074707377,False,0.03567987586320537,86.00178576666156,35.74289713245804,0.6533333333333965,15,2,103.30736456331992,SL,2021-01-06 15:15:00+00:00,-7.236456331992258,-1000.0001113529446,15,False
+2021-01-07 09:00:00+00:00,2,USD_JPY,SHORT,103.446,103.50081336338228,103.39382433690747,103.3819531785436,103.35821086181586,18061.29,2,LONDON,1.5,0.05762672676456135,57.58203923516936,78.14261021603642,103.19403348496787,103.03515411169485,7.11756630925322,False,0.016806505998620366,58.844745908701974,27.096651503212854,0.8505747126437626,9,3,103.50081336338228,SL,2021-01-07 09:45:00+00:00,-5.481336338228004,-990.0000519227408,45,False
+2021-01-07 10:15:00+00:00,2,USD_JPY,SHORT,103.57499999999999,103.63608465990788,103.4543131876125,103.42127187233648,103.35518924178444,16044.94,2,LONDON,1.5,0.06216931981576528,66.9539214872323,78.00650636180285,103.25406980358848,103.05894444755546,13.968681238749525,False,0.02088531884707734,88.28229943412886,33.993019641151534,0.7466666666666035,10,3,103.63608465990788,SL,2021-01-07 10:30:00+00:00,-6.108465990789114,-980.0997031425188,15,False
+2021-01-07 15:00:00+00:00,2,USD_JPY,SHORT,103.85799999999999,103.93149749987354,103.60068575408951,103.54109341021484,103.42190872246549,13201.8,2,OVERLAP,1.5,0.08499499974710167,42.7260207347581,71.65883266433691,103.4754044536424,103.163461004486,27.631424591048415,False,0.0121316847103375,93.59447646378743,40.159554635759775,0.5845070422535268,15,3,103.93149749987354,SL,2021-01-07 15:15:00+00:00,-7.34974998735538,-970.2992938306826,15,False
+2021-01-11 14:45:00+00:00,2,USD_JPY,SHORT,104.348,104.427746314425,104.18512687734047,104.15163921936498,104.08466390341401,12045.65,2,OVERLAP,1.5,0.06149262884998407,27.52151678673644,70.18864539545322,104.15954916163044,103.90758510806414,18.187312265953892,False,0.014760881739939613,64.72412450673379,20.745083836956724,0.7053571428571553,14,0,104.08466390341401,TP3,2021-01-11 17:00:00+00:00,18.966324903992128,2284.617115797728,135,True
+2021-01-15 14:45:00+00:00,2,USD_JPY,SHORT,103.853,103.91481911564615,103.71453207615352,103.69415257113415,103.65339356109543,15908.38,2,OVERLAP,1.5,0.051638231292328,26.468632534866437,72.52090446494557,103.73485532067724,103.82310682025167,15.746792384648245,False,0.018324848601908333,82.20962178017562,13.714467932275909,0.8604651162791646,14,4,103.91481911564615,SL,2021-01-17 23:45:00+00:00,-6.181911564615916,-983.4419829630455,3420,False
+2021-01-20 15:00:00+00:00,2,USD_JPY,LONG,103.599,103.5281592076021,103.77514267053122,103.79258765848478,103.82747763439188,13743.6,2,OVERLAP,1.5,0.057681584795806456,36.47006864702616,29.738626115576565,103.78594147569441,103.82668549673383,-19.51426705312258,False,-0.017952204554605077,30.818713450290854,-20.594147569441645,0.895927601809974,15,2,103.5281592076021,SL,2021-01-20 15:15:00+00:00,-7.084079239790242,-973.6075143998117,15,False
+2021-01-27 12:00:00+00:00,2,USD_JPY,SHORT,103.842,103.88589351947272,103.76803884485909,103.7470068776408,103.70494294320422,21959.32,2,LONDON,1.5,0.037787038945412334,46.04563155849726,71.15904635120296,103.72965046027774,103.71339919314661,9.296115514091241,False,0.009371334353213998,83.61612595419415,13.134953972226526,0.4561403508774729,12,2,103.88589351947272,SL,2021-01-27 12:15:00+00:00,-4.389351947271791,-963.8718400276439,15,False
+2021-01-27 13:00:00+00:00,2,USD_JPY,SHORT,103.883,103.92948682345012,103.78745668481432,103.76129449833576,103.70897012537863,20526.96,2,OVERLAP,1.5,0.03897364690024907,49.67828789883275,73.92288145110791,103.74926459909463,103.71921402972349,11.454331518568495,False,0.008347782795646783,66.46241279819016,15.273540090537097,0.8461538461539807,13,2,103.92948682345012,SL,2021-01-27 13:15:00+00:00,-4.648682345012389,-954.2331654877551,15,False
+2021-01-27 13:30:00+00:00,2,USD_JPY,SHORT,103.97,104.0209605773626,103.8107182143766,103.77866996647307,103.71457347066602,18537.68,2,OVERLAP,1.5,0.043921154725214503,56.63811934199398,80.93080448446112,103.76438304591551,103.7236892846979,17.828178562339758,False,0.013890592686739846,81.86791150696207,22.4616954084496,0.8947368421053576,13,2,104.0209605773626,SL,2021-01-27 13:45:00+00:00,-5.096057736260207,-944.6908757631611,15,False
+2021-01-27 14:30:00+00:00,2,USD_JPY,SHORT,104.122,104.18597390540539,103.87011241385655,103.82519103174424,103.73534826751964,14619.14,2,OVERLAP,1.5,0.053947810810778325,67.5049953637744,85.98974821591241,103.80266379926877,103.73536163730347,27.08875861434592,False,0.02220181237680452,77.37867449717452,33.83362007312343,0.8698224852070762,14,2,104.18597390540539,SL,2021-01-27 20:00:00+00:00,-6.39739054053905,-935.2434794681603,330,False
+2021-01-28 13:30:00+00:00,2,USD_JPY,SHORT,104.42899999999999,104.48563833730478,104.31498212655409,104.29563674923342,104.25694599459212,16347.43,2,OVERLAP,1.5,0.047276674609545356,31.69322583539005,70.28243671025768,104.28363618416839,104.04316822025793,13.30178734459082,False,0.01346355648230057,83.72927130670242,16.436381583160653,0.824742268041148,13,3,104.33697119785172,TP1+TP2+SL,2021-01-28 14:00:00+00:00,11.74903948134883,1920.6660048858632,30,True
+2021-01-29 09:15:00+00:00,2,USD_JPY,SHORT,104.74799999999999,104.79015532258741,104.64436821740664,104.62277221026652,104.57958019598627,22419.42,2,LONDON,1.5,0.04631064517483359,63.079778340274686,83.65880294947827,104.47841415510621,104.24502947862695,12.263178259335916,False,0.017710108939990886,87.77777777777446,28.858584489378813,1.0,9,4,104.79015532258741,SL,2021-01-29 09:30:00+00:00,-4.215532258741916,-945.0978823228369,15,False
+2021-02-04 11:30:00+00:00,2,USD_JPY,SHORT,105.292,105.33220058452015,105.23092876781375,105.21197201474446,105.17405850860588,23274.46,2,LONDON,1.5,0.036401169040306706,43.09516003350167,71.46425651002374,105.17001350735654,105.0487530871344,8.007123218625622,False,0.0037205457714742304,92.24346565810144,14.098649264346363,0.5777777777781707,11,3,105.33220058452015,SL,2021-02-04 11:45:00+00:00,-4.020058452015007,-935.646896390852,15,False
+2021-02-09 09:00:00+00:00,2,USD_JPY,LONG,104.71600000000001,104.66609342713356,104.78951636456745,104.80782321918333,104.8444369284151,18560.49,3,LONDON,1.5,0.05181314573285705,46.83570528289092,29.382020346032363,104.97428361617249,105.23311117163558,-9.25163645674445,False,0.0005687834827241833,16.3180437272782,-27.728361617248254,0.9146341463414106,9,1,104.66609342713356,SL,2021-02-09 09:15:00+00:00,-4.990657286644762,-926.2904466219725,15,False
+2021-02-09 09:30:00+00:00,2,USD_JPY,LONG,104.634,104.57934076115089,104.75281817892422,104.7827298068843,104.84255306280448,16777.17,2,LONDON,1.5,0.0553184776982282,50.646193325648156,25.030656680762874,104.95001036617846,105.22174840988939,-13.781817892422055,False,-0.008633096098089488,7.60800818517508,-33.50103661784658,0.9175257731959171,9,1,104.57934076115089,SL,2021-02-09 09:45:00+00:00,-5.465923884911206,-917.0273422421573,15,False
+2021-02-12 08:45:00+00:00,2,USD_JPY,SHORT,105.012,105.05424607407902,104.97554561942798,104.96267617262865,104.93693727902998,21489.74,2,LONDON,1.5,0.04049214815801796,48.95246348280983,79.09922859219414,104.82392238648107,104.7551274178233,5.545438057203,False,0.019417992920378917,94.49230400453887,20.70776135189334,0.6296296296297174,8,4,105.05424607407902,SL,2021-02-12 09:00:00+00:00,-4.224607407901715,-907.857147978818,15,False
+2021-02-16 13:30:00+00:00,2,USD_JPY,SHORT,105.64,105.7120470750381,105.42782495225079,105.3748346614259,105.2688540797761,12474.88,2,OVERLAP,1.5,0.06809415007619715,45.5174580459279,71.63013392096502,105.43288033696183,105.26259527038856,23.117504774921827,False,0.042372224505721395,88.7088852087641,22.61196630381761,0.7469879518073856,13,1,105.7120470750381,SL,2021-02-16 13:45:00+00:00,-7.204707503809971,-898.7786154512892,15,False
+2021-02-16 14:15:00+00:00,2,USD_JPY,SHORT,105.761,105.82541597079398,105.49338517067916,105.42780563166772,105.29664655364483,13813.2,2,OVERLAP,1.5,0.07283194158797607,56.14795922223506,75.49778701199833,105.46728139827988,105.27660179723507,28.66148293208397,False,0.04934723470489164,89.88229399551089,31.27186017201211,0.808510638297882,14,1,105.82541597079398,SL,2021-02-16 16:45:00+00:00,-6.441597079398775,-889.7906877715117,150,False
+2021-02-22 14:30:00+00:00,2,USD_JPY,LONG,105.27900000000001,105.20120384361374,105.57213001716538,105.64668891840402,105.7958067208813,11323.09,2,OVERLAP,1.5,0.07359231277251196,57.872308304886374,24.979713389783512,105.5637821205785,105.60217307923182,-31.213001716537292,False,-0.028357324236371592,18.310560352618193,-30.37821205784894,0.5963855421686232,14,0,105.20120384361374,SL,2021-02-22 14:45:00+00:00,-7.779615638627035,-880.892880415814,15,False
+2021-02-23 13:00:00+00:00,2,USD_JPY,SHORT,105.374,105.42810701151177,105.2539714305476,105.22969434525277,105.18114017466311,16117.77,2,OVERLAP,1.5,0.05821402302353731,35.17786235702869,70.29244866187781,105.18393285288218,105.31005956012204,13.902856945240671,False,0.0026842306713363018,83.01077402276282,20.906714711782115,0.7765957446809797,13,1,105.42810701151177,SL,2021-02-23 13:30:00+00:00,-5.410701151177476,-872.0843669341381,30,False
+2021-02-24 09:15:00+00:00,2,USD_JPY,SHORT,105.75099999999999,105.80275243638754,105.64358403072256,105.61627971100063,105.56167107155677,16682.56,2,LONDON,1.5,0.05550487277507023,49.8734561130071,81.42092657380681,105.46612215287519,105.3555035929005,12.641596927743137,False,0.017808125076620454,88.63134956877991,30.387784712480936,0.8588235294116053,9,2,105.80275243638754,SL,2021-02-24 09:45:00+00:00,-5.175243638754523,-863.3631251814066,30,False
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+2021-08-04 14:00:00+00:00,2,USD_JPY,SHORT,109.359,109.45214677634578,109.03055254797151,108.96494717612944,108.8337364324453,7601.83,2,OVERLAP,1.5,0.10229355269158175,62.88300316505483,70.60980841830613,109.03811525289804,109.17371322919239,34.744745202849,False,0.011707179050590849,69.11103812906178,33.988474710196215,0.9596491228070239,14,2,109.45214677634578,SL,2021-08-04 14:15:00+00:00,-9.314677634579027,-708.0859588287188,15,False
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+2021-08-17 14:30:00+00:00,2,USD_JPY,SHORT,109.591,109.6647560113175,109.39608287175383,109.35586366570753,109.27542525361493,9477.08,2,OVERLAP,1.5,0.07551202263498939,42.445857988070784,70.19056573260735,109.35338734228918,109.49049342201604,21.391712824616604,False,0.023395521532445465,82.22502805836315,25.661265771081787,0.7959183673469773,14,1,109.6647560113175,SL,2021-08-18 04:00:00+00:00,-7.3756011317499315,-698.9916197368464,810,False
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+2021-10-11 12:15:00+00:00,2,USD_JPY,SHORT,113.062,113.12784443657505,112.935410629954,112.9161825369792,112.87772635102961,9060.73,3,LONDON,1.5,0.07568887315008131,25.999926856255197,71.49852949622579,112.72383399024307,112.17494235681744,14.55893700459967,False,-0.00777174303996625,69.11937907538365,35.71660097569378,0.9451219512195106,12,0,113.12784443657505,SL,2021-10-11 12:30:00+00:00,-6.584443657504835,-596.5986618086378,15,False
+2021-10-11 14:30:00+00:00,2,USD_JPY,SHORT,113.25399999999999,113.31488014169283,113.02899569982537,112.98303456129746,112.89111228424161,9701.57,2,OVERLAP,1.5,0.08376028338565343,39.78771576605767,73.18376776138925,112.85336563235525,112.25895919518761,24.400430017462327,False,0.007002754564190319,81.37517085636111,41.96343676447469,0.9333333333333649,14,0,113.31488014169283,SL,2021-10-11 14:45:00+00:00,-6.08801416928344,-590.6329562429514,15,False
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+2021-10-19 12:15:00+00:00,2,USD_JPY,SHORT,114.286,114.38601739352288,114.1053588097505,114.05996146147952,113.96916676493754,5820.28,2,LONDON,1.5,0.06603478704576893,51.138255300879834,70.47460625741823,114.14274949493469,114.13552077567742,19.964119024950833,False,0.025277955518790467,81.35899085191585,16.225050506531602,0.7,12,1,114.38601739352288,SL,2021-10-19 20:15:00+00:00,-10.001739352287586,-582.1292351733239,480,False
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+2021-11-22 14:00:00+00:00,2,USD_JPY,SHORT,114.503,114.58670497282549,114.18353881201826,114.1580401086646,114.10704270195728,6820.11,2,OVERLAP,1.5,0.08340994565095759,38.80582904738961,81.16589381132619,114.14277385807377,114.14557689354612,33.846118798174984,False,0.02330936965139955,72.58488540008109,37.92261419262388,0.9273182957393576,14,0,114.58670497282549,SL,2021-11-22 14:15:00+00:00,-8.370497282548683,-570.877122216831,15,False
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+2022-01-11 13:15:00+00:00,2,USD_JPY,SHORT,115.571,115.63163560791219,115.39001412131131,115.34813047757756,115.26436319011003,9152.23,2,OVERLAP,1.5,0.059271215824379664,39.37432718470714,72.731314311413,115.35841895113724,115.45853423519735,19.998587868869322,False,0.013743371258813641,88.54676760551634,23.158104886276476,0.6448598130841406,13,1,115.63163560791219,SL,2022-01-11 13:30:00+00:00,-6.063560791218946,-554.9510298021778,15,False
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+2022-12-20 15:45:00+00:00,2,USD_JPY,LONG,131.39199999999997,131.20894217153722,132.27281442882506,132.39380506273415,132.63578633055232,2626.78,2,OVERLAP,1.5,0.2961156569255736,36.13670322881641,27.496139508485328,132.98029338969428,135.00882850542502,-89.98144288250671,False,-0.05467909639611873,4.998583970545488,-160.72933896942914,0.9361702127660047,15,1,131.20894217153722,SL,2022-12-20 16:00:00+00:00,-18.305782846275065,-480.85264264938417,15,False
+2022-12-26 10:00:00+00:00,2,USD_JPY,SHORT,133.11800000000002,133.216551909336,132.85706840367033,132.79820570052811,132.6804802942437,4830.4,2,LONDON,1.5,0.10710381867201299,45.258070010258024,83.41944725812776,132.70940810957373,132.6336789242508,27.99315963296749,False,0.04567375849026559,89.67568942592204,42.7591890426271,0.28656716417910855,10,0,132.80060342980718,TP1+TP2+SL,2022-12-26 22:00:00+00:00,29.000328850894306,1400.8318848135984,720,True
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+2022-12-29 13:30:00+00:00,2,USD_JPY,LONG,133.24999999999997,133.14998000685836,133.6375707913591,133.68047135997048,133.76627249719326,4942.12,2,OVERLAP,1.5,0.12203998628329238,33.89007223673495,24.24247387380612,133.7424647096196,133.70038216042124,-40.657079135911545,False,-0.036563863941630595,19.21730532841681,-51.146470961961654,0.8063725490196524,13,3,133.14998000685836,SL,2022-12-29 13:45:00+00:00,-10.00199931416148,-494.31080850503736,15,False
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+2023-01-12 13:30:00+00:00,2,USD_JPY,LONG,130.26699999999997,129.85347819207135,131.01037989149955,131.18577729133762,131.53657209101377,1185.23,2,OVERLAP,1.5,0.26301453861910057,72.56467469614053,16.25700162359044,131.29193181750455,131.90561270549645,-76.23798914995632,False,-0.040938022122469775,32.98511502430127,-104.39318175045571,0.17250159134310336,13,3,129.85347819207135,SL,2023-01-12 13:45:00+00:00,-41.35218079286176,-490.1184524112354,15,False
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+2023-01-20 13:45:00+00:00,2,USD_JPY,SHORT,130.58300000000003,130.70485229265705,129.68498179806414,129.46821951339075,129.03469494404396,3863.75,2,OVERLAP,1.5,0.18770458531410936,42.451068389944965,83.44344145309265,129.42447727389117,128.8972528250428,91.70182019358606,False,0.04781604578097154,71.81067383755213,117.75227261088332,0.8771929824562132,13,4,129.60089059306864,TP1+TP2+SL,2023-01-20 20:30:00+00:00,98.64986823208964,3811.584283817364,405,True
+2023-01-23 13:30:00+00:00,2,USD_JPY,SHORT,130.54300000000003,130.64398056007792,130.0699377911794,129.95836932236213,129.73523238472762,5039.81,2,OVERLAP,1.5,0.15396112015583527,38.43718256634891,72.52839495386374,129.93653878632057,129.44114365141374,49.2062208820613,False,0.022463848824067406,70.68451084137348,62.54612136794435,0.9363295880149957,13,0,130.64398056007792,SL,2023-01-23 14:00:00+00:00,-10.098056007788612,-508.92283648613136,30,False
+2023-01-23 14:00:00+00:00,2,USD_JPY,SHORT,130.711,130.90520772782227,130.1382789394873,130.01120544565686,129.757058457996,2594.3,2,OVERLAP,1.5,0.170415455644572,45.30793294304491,74.07353921371285,129.98869651132475,129.4643498858849,59.17210605126968,False,0.036851936308630845,85.76025226413056,74.13034886752428,0.670487106017156,14,0,130.04929846194113,TP1+TP2+SL,2023-01-24 07:15:00+00:00,63.054385498529655,1635.8199229883548,1035,True
+2023-01-24 14:45:00+00:00,2,USD_JPY,SHORT,131.04900000000004,131.20370190819185,130.30500927358148,130.2310401232577,130.08310182261022,3362.54,2,OVERLAP,1.5,0.17540381638367325,38.76892206125162,77.34478882740302,130.28639825540034,130.02257549120986,76.29907264185363,False,0.061719053790508335,71.6154634060586,78.16017445996692,0.9289940828402357,14,1,130.08310182261022,TP3,2023-01-24 15:45:00+00:00,80.3166046677565,2700.677958595179,60,True
+2023-02-01 13:15:00+00:00,2,USD_JPY,LONG,129.61599999999999,129.42254850325102,129.87072743515415,129.93500428096894,130.06355797259855,2828.61,2,OVERLAP,1.5,0.1163009978326537,54.17233852298613,29.567796608513206,129.9737574564816,130.08454707513567,-27.3727435154143,False,-0.01094921971202989,54.443949296892725,-37.675745648158454,0.474637681159452,13,2,129.42254850325102,SL,2023-02-01 13:45:00+00:00,-19.345149674896334,-547.1988382190852,30,False
+2023-02-02 13:15:00+00:00,2,USD_JPY,LONG,128.23699999999997,128.09481354627033,128.69463634361256,128.78792074762538,128.97448955565102,3809.97,2,OVERLAP,1.5,0.1563729074593324,45.59291963200059,26.02494989783024,128.71207977804704,129.27875549602942,-47.66363436125687,False,-0.06980711033163703,16.962233392831852,-49.40797780470518,0.7804878048780947,13,3,128.09481354627033,SL,2023-02-02 13:30:00+00:00,-14.218645372963579,-541.7261231163004,15,False
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+2023-02-15 14:45:00+00:00,2,USD_JPY,SHORT,134.11900000000003,134.25644822827098,133.65776980884092,133.55492683457092,133.3492408860309,3824.25,2,OVERLAP,1.5,0.1628964565419624,72.37985405762173,81.86933401713179,133.41201513379832,132.77360785884838,48.02301911590803,False,0.04722498839557482,78.82862517181884,72.5984866201685,0.6078431372548495,14,2,134.25644822827098,SL,2023-02-15 15:45:00+00:00,-13.744822827095504,-525.6363869651998,60,False
+2023-02-21 14:45:00+00:00,2,USD_JPY,SHORT,135.13100000000003,135.29267821857505,134.74540136717158,134.68830829879147,134.57412216203127,3218.62,2,OVERLAP,1.5,0.13135643715010914,28.456652634604637,73.98050320397651,134.6375515107431,134.36079957717803,40.459863282842434,False,0.026574367507501556,80.47173004673876,51.244848925691144,0.7605633802817409,14,1,134.82576161498972,TP1+TP2+SL,2023-02-21 16:00:00+00:00,39.35271695887664,1266.6144185817952,75,True
+2023-02-23 13:30:00+00:00,2,USD_JPY,SHORT,135.23800000000003,135.41281765501608,134.95405325638617,134.91667135912738,134.84190756460976,3049.16,2,OVERLAP,1.5,0.10387843667738961,39.48627192815313,77.32678326617867,134.8955135763448,134.76611294906306,30.294674361383045,False,0.0220228999448234,65.77545514158318,36.1486423655208,0.5614849187935317,13,3,134.84190756460976,TP3,2023-02-23 15:00:00+00:00,31.385226142089518,956.9857614341367,90,True
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+2023-02-24 13:30:00+00:00,2,USD_JPY,SHORT,135.87900000000002,136.06596822328837,135.30714278464583,135.1693438960788,134.89374611894473,2873.16,2,OVERLAP,1.5,0.15993644657675732,68.33829984358239,83.97325856224015,135.1032471869646,134.8526667094486,59.08572153541627,False,0.04501413220031933,83.36000675666715,79.47528130353874,0.2115143929912232,13,4,136.06596822328837,SL,2023-02-24 14:00:00+00:00,-18.696822328834628,-537.189620423145,30,False
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+2023-03-01 15:00:00+00:00,2,USD_JPY,SHORT,136.22600000000003,136.4952504299093,135.7662370252976,135.6543909088094,135.43069867583296,1935.88,2,OVERLAP,1.5,0.16683361993953505,50.09919361692115,70.4457920764905,135.91207382655733,136.086497561403,47.87629747023914,False,0.09946220095754552,89.65289205928265,33.29261734426723,0.46943231441048944,15,2,136.22600000000003,TP1+SL,2023-03-01 19:15:00+00:00,22.988148735120717,445.022973733455,255,True
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+2023-05-11 12:30:00+00:00,2,USD_JPY,LONG,133.93299999999996,133.671795579025,134.46684268926333,134.58733045964763,134.82830600041626,1651.75,2,LONDON,1.5,0.16840884195002173,45.24484862733557,29.723952539941777,134.37279358432937,134.62035339484393,-55.284268926334335,False,-0.0796867980477086,21.35363580455736,-45.87935843293849,0.6694560669456397,12,3,134.5263250060378,TP1+TP2+SL,2023-05-12 01:15:00+00:00,58.49357564140433,966.1676356568961,765,True
+2023-05-17 12:30:00+00:00,2,USD_JPY,SHORT,137.20200000000003,137.2771813321134,136.99269407158934,136.96510722826977,136.9099335416306,5867.21,2,LONDON,1.5,0.1063626642267977,29.946260074243728,70.2023968031946,136.78741731511272,136.29250444613893,22.830592841066277,False,0.0004535140504228624,72.82415442015639,43.35826848872841,0.9817351598173937,12,2,137.0812055625221,TP1+TP2+SL,2023-05-17 13:15:00+00:00,20.56845999326228,1206.7947415706838,45,True
+2023-05-17 13:45:00+00:00,2,USD_JPY,SHORT,137.324,137.41060124286827,137.06209482765172,137.022739797782,136.94402973804262,5232.87,2,OVERLAP,1.5,0.12520248573652418,32.81851981866263,70.62122636655164,136.8575072094517,136.3353711388123,28.09051723482696,False,0.015193584049071868,74.08989943298872,48.54927905482782,0.9704433497536938,13,2,137.41060124286827,SL,2023-05-17 14:00:00+00:00,-8.660124286825521,-453.17304576800666,15,False
+2023-05-17 14:45:00+00:00,2,USD_JPY,SHORT,137.39300000000003,137.5184366174917,137.13133321838737,137.07330303388727,136.95724266488705,3576.64,2,OVERLAP,1.5,0.12687323498336214,37.70250676590812,70.0180363375743,136.93034100576682,136.3751411115801,28.06667816126378,False,0.021713577920501687,68.54817871134105,48.16589942331859,0.8358208955224355,14,2,137.5184366174917,SL,2023-05-17 15:30:00+00:00,-12.543661749165835,-448.6416235853649,45,False
+2023-05-18 12:30:00+00:00,2,USD_JPY,SHORT,138.21300000000002,138.37278160542638,137.83911811690606,137.8044052241434,137.73497943861813,2779.76,2,LONDON,1.5,0.1375632108527638,28.777347237170908,71.34993777801844,137.6774612548852,137.1038204807356,39.28818830939349,False,0.019626480454300897,50.451127819551665,55.45387451147974,0.8642857142857266,12,3,138.37278160542638,SL,2023-05-18 12:45:00+00:00,-15.978160542636033,-444.1545154999794,15,False
+2023-05-18 13:45:00+00:00,2,USD_JPY,SHORT,138.36900000000003,138.4973967387539,137.9802779426304,137.8965923541356,137.729221177146,3424.64,2,OVERLAP,1.5,0.14879347750780192,44.87115680265218,73.20195927202121,137.78796363947157,137.16146608775384,40.77220573695968,False,0.048288378989082226,80.96940948794982,60.00363605284349,0.7716049382715757,13,3,138.4973967387539,SL,2023-05-18 14:45:00+00:00,-12.839673875387803,-439.71260740608085,60,False
+2023-05-22 12:30:00+00:00,2,USD_JPY,SHORT,138.39900000000003,138.48573918735673,138.0259629142549,137.94558509691925,137.78482946224793,5018.68,2,LONDON,1.5,0.11347837471345337,66.78467256441108,71.65175661474053,137.9511703410651,137.90610086318569,39.20370857451019,False,0.04368969850301058,87.12590604459557,46.682965893489836,0.7574468085105828,12,0,138.48573918735673,SL,2023-05-22 12:45:00+00:00,-8.673918735669872,-435.31622480331674,15,False
+2023-05-22 13:45:00+00:00,2,USD_JPY,SHORT,138.57300000000004,138.7228776701881,138.15284628052441,138.04310397425783,137.82361936172464,2875.43,2,OVERLAP,1.5,0.11975534037618851,79.43296673013973,76.5101213756175,138.04631850877422,137.93377083742988,43.915371947559834,False,0.03836790906259563,83.73519813098808,54.56814912257926,0.5162790697675194,13,0,138.1427728160636,TP1+TP2+SL,2023-05-22 14:15:00+00:00,46.00745463380476,1322.9121527768123,30,True
+2023-05-26 12:30:00+00:00,2,USD_JPY,SHORT,140.24500000000003,140.38228580480498,139.7787174019333,139.7392972245774,139.6604568698656,3235.53,3,LONDON,1.5,0.12857160960994976,23.94645626201821,74.97813780799069,139.77194021207603,139.52124790681557,48.52825980667035,False,0.04128770371945148,44.73909515234296,49.20597879239779,0.8365527488856094,12,4,140.38228580480498,SL,2023-05-26 13:45:00+00:00,-13.728580480494657,-444.1923400205488,75,False
+2023-06-02 16:45:00+00:00,2,USD_JPY,SHORT,139.87000000000003,139.97722436424976,139.2172359227378,139.05394415067755,138.727360606557,4101.21,2,OVERLAP,1.5,0.14444872849950066,73.18924941085801,73.97830363660321,139.24706995336444,139.23930040811067,67.17640772621962,False,0.030535048618748256,84.64895723481393,64.19300466355651,0.9218750000000625,16,4,139.97722436424976,SL,2023-06-02 17:45:00+00:00,-10.722436424973123,-439.7496349046403,60,False
+2023-06-06 12:15:00+00:00,2,USD_JPY,SHORT,139.67400000000004,139.84041116789498,139.4068136905659,139.33681665803263,139.19682259296607,2616.13,2,LONDON,1.5,0.09827411192998044,41.12274651447285,71.45020165993606,139.4592421499576,139.5932039201731,28.61863094341004,False,0.0459882335828735,92.73347362844306,23.37578500424229,0.6779661016948867,12,1,139.84041116789498,SL,2023-06-06 12:30:00+00:00,-16.64111678949496,-435.35324866501446,15,False
+2023-06-14 15:15:00+00:00,2,USD_JPY,LONG,139.35299999999998,139.25587873907486,139.7488816801313,139.85039862658428,140.05343251949023,4437.74,2,OVERLAP,1.5,0.11224252185031589,66.59522089112578,24.991942739883243,139.79942301893766,139.77704181981105,-41.48816801312876,False,-0.02751629293073632,31.732841714527893,-46.542301893765625,0.9263157894736307,15,2,140.05343251949023,TP3,2023-06-14 18:00:00+00:00,47.70952372936974,2117.2246183477328,165,True
+2023-06-16 14:00:00+00:00,2,USD_JPY,SHORT,141.479,141.6046259767766,141.08547042739883,141.011553334716,140.86371914935035,3599.35,3,OVERLAP,1.5,0.1892519535532071,22.71931179364032,70.10983077582091,140.9108460067002,140.53094901869872,41.25295726011586,False,0.027756160069013408,66.13852705225237,58.71539932997791,0.4307178631051517,14,4,141.6046259767766,SL,2023-06-16 15:15:00+00:00,-12.562597677657552,-452.17185951076704,75,False
+2023-06-16 16:45:00+00:00,2,USD_JPY,SHORT,141.83100000000002,141.9510764516981,141.25410834855347,141.10612750428427,140.8101658157459,3728.04,3,OVERLAP,1.5,0.15815290339618487,19.701937716580858,71.15563444105236,141.18920691745055,140.650554406765,59.589165144652156,False,0.024391097415925267,76.94608790139335,66.07930825494464,0.7157894736838326,16,4,141.9510764516981,SL,2023-06-18 23:15:00+00:00,-12.007645169808256,-447.6498149885197,3270,False
+2023-06-20 12:00:00+00:00,2,USD_JPY,LONG,141.31499999999997,141.2300960764891,141.51589288482583,141.56584711908076,141.66575558759064,5219.7,2,LONDON,1.5,0.10780784702178514,58.605705187970116,28.90145495198894,141.71035425247499,141.598562947731,-21.989288482583902,False,-0.018900127344904816,20.133589131591993,-41.435425247499325,0.786407766990393,12,1,141.49790037680265,TP1+TP2+SL,2023-06-20 12:45:00+00:00,21.56779906116085,1125.774407595413,45,True
+2023-06-21 13:30:00+00:00,2,USD_JPY,SHORT,142.27300000000002,142.38354264036852,141.96982968026384,141.91044539132199,141.79167681343824,4110.91,2,OVERLAP,1.5,0.12908528073703854,27.781008166188506,72.9434691464023,141.84711151923014,141.65955103881143,32.21703197361592,False,0.031742276497630145,94.1205702766755,44.48884807698619,0.8255319148936195,13,2,141.79167681343824,TP3,2023-06-21 17:45:00+00:00,35.06777508896718,1441.6046729098607,255,True
+2023-06-22 14:00:00+00:00,2,USD_JPY,SHORT,142.39200000000002,142.53198340475151,142.09555564409868,142.03054269571737,141.90051679895478,3349.3,3,OVERLAP,1.5,0.13796680950305676,19.8696501469069,71.81846597893394,141.9867285818139,141.81218419755416,31.544435590132025,False,0.027910021118722428,74.19825534438435,42.42714181860947,0.6959064327485681,14,3,142.53198340475151,SL,2023-06-22 14:15:00+00:00,-13.998340475149007,-468.8464175341657,15,False
+2023-06-27 09:15:00+00:00,2,USD_JPY,SHORT,143.812,143.90224540927423,143.70551754750008,143.68163502971274,143.63386999413802,5143.29,2,LONDON,1.5,0.09049081854847646,42.77765860242998,72.07624725270426,143.51806774444574,143.31434809050248,12.548245249990941,False,0.027764634569989045,80.83509474593414,31.293225555424442,0.8235294117645966,9,1,143.90224540927423,SL,2023-06-27 09:30:00+00:00,-9.024540927421752,-464.1583110659903,15,False
+2023-06-28 14:00:00+00:00,2,USD_JPY,SHORT,144.51300000000003,144.64235324805747,144.22633609694924,144.162659116402,144.03530515530755,3552.42,2,OVERLAP,1.5,0.13870649611495328,27.77304360342704,72.66994528358104,144.1224002082911,143.78857051918698,30.566390305077107,False,0.022497853199058407,82.04141516979189,40.95997917088994,0.55,14,2,144.25645998725386,TP1+TP2+SL,2023-06-28 17:15:00+00:00,30.443226269663057,1081.4712586487644,195,True
+2023-07-07 12:15:00+00:00,2,USD_JPY,LONG,142.849,142.75637594566217,143.1175982643153,143.16838066421374,143.26994546401062,5077.86,3,LONDON,1.5,0.14724810867569485,36.71374106300946,29.548524198297955,143.4553632233746,143.94694934798605,-28.759826431527813,False,0.020796319460586493,42.194530979575994,-62.53632233745918,0.8574040219378366,12,4,142.75637594566217,SL,2023-07-07 12:30:00+00:00,-9.26240543378185,-470.3319805598351,15,False
+2023-07-10 15:00:00+00:00,2,USD_JPY,LONG,141.59199999999998,141.46497501640624,142.11715551517304,142.25287651668168,142.52431851969894,3665.64,2,OVERLAP,1.5,0.14804996718749588,44.08610631991506,29.082632558238586,142.24601985744405,142.89168381774283,-54.41555151730313,False,-0.05078306032198596,40.46258598665879,-67.30198574440465,0.7672955974840889,15,0,141.46497501640624,SL,2023-07-10 16:30:00+00:00,-12.702498359374205,-465.62786086056457,90,False
+2023-07-12 12:30:00+00:00,2,USD_JPY,LONG,139.14299999999997,138.88922250832258,139.48148512308003,139.528568583781,139.62273550518293,1816.44,2,LONDON,1.5,0.1565183277849534,29.022707932210018,28.093924723857967,139.67361756726535,140.58305805878192,-35.74851230800391,False,-0.031149527689721207,50.749823570925656,-54.96175672653578,0.5031847133758114,12,2,138.88922250832258,SL,2023-07-12 13:45:00+00:00,-25.377749167739694,-460.9715869824909,75,False
+2023-07-12 13:45:00+00:00,2,USD_JPY,LONG,138.83499999999998,138.67502074240062,139.3200159259155,139.39422842030484,139.54265340908353,2852.63,2,OVERLAP,1.5,0.18795851519876197,55.06946529152248,26.165355983561128,139.5680692170616,140.51058706002604,-50.401592591549615,False,-0.06439286351108837,28.646898259726512,-75.2069217061603,0.9817073170731702,13,2,138.67502074240062,SL,2023-07-12 14:00:00+00:00,-15.997925759936038,-456.36162960566344,15,False
+2023-07-17 12:30:00+00:00,2,USD_JPY,SHORT,138.84700000000004,138.97510596187115,138.4186195877574,138.33850897430867,138.17828774741116,3526.76,2,LONDON,1.5,0.14421192374231676,37.550675827900285,70.16747698120702,138.463320529747,138.5586763663653,44.73804122426088,False,0.06459255007198886,86.79032030524876,40.267947025301964,0.9606299212598783,12,0,138.97510596187115,SL,2023-07-17 12:45:00+00:00,-12.8105961871114,-451.79898208857,15,False
+2023-07-20 14:00:00+00:00,2,USD_JPY,SHORT,140.062,140.20175675804697,139.70005116578108,139.6326459099903,139.49783539840874,3200.42,2,OVERLAP,1.5,0.15351351609391495,45.60667770072734,70.59678608925162,139.58204029755842,139.34437202251698,38.09488342189127,False,0.04275490466057946,79.31753545270088,49.89597024415673,0.6401179941002519,14,3,140.20175675804697,SL,2023-07-20 14:15:00+00:00,-13.975675804695696,-447.28032358864203,15,False
+2023-07-20 16:30:00+00:00,2,USD_JPY,SHORT,140.46900000000002,140.55540625446693,139.86923683154433,139.71742317985965,139.41379587649033,5124.72,2,OVERLAP,1.5,0.12481250893387241,71.23051511591588,77.06022247699465,139.84314785877908,139.44174777331997,61.8763168455672,False,0.026797175165393095,93.12832560138621,64.48521412209232,0.5662650602409391,16,3,140.46900000000002,TP1+SL,2023-07-21 07:30:00+00:00,29.988158422784746,1536.8091523241346,900,True
+2023-07-24 11:30:00+00:00,2,USD_JPY,LONG,140.93899999999996,140.77705604792015,141.2482494804493,141.314358801293,141.44657744298038,2829.23,2,LONDON,1.5,0.13388790415969057,55.84052518356734,23.535774660693335,141.38992149799344,140.9535999785917,-32.824948044930125,False,-0.036706577886003,26.175189176796465,-46.992149799345384,0.5938864628821665,11,0,140.77705604792015,SL,2023-07-24 13:45:00+00:00,-16.19439520798096,-458.17668754275974,135,False
+2023-07-27 14:00:00+00:00,2,USD_JPY,SHORT,140.854,141.03832927275005,140.38589033108536,140.26412417096196,140.02059185071514,2460.78,2,OVERLAP,1.5,0.1626585455000807,66.43904127981175,73.32720567603127,140.24975061430297,140.47949152579503,48.710966891462704,False,0.06314400439398438,83.47380976120043,62.324938569702226,0.5441860465115738,14,3,141.03832927275005,SL,2023-07-27 14:15:00+00:00,-18.432927275003408,-453.5937877978289,15,False
+2023-07-28 13:30:00+00:00,2,USD_JPY,SHORT,140.50700000000003,140.9964070567408,139.72326230787735,139.54357232056785,139.18419234594884,917.56,2,OVERLAP,1.5,0.31360470449385636,39.01251453839818,70.37825399338651,139.52879578388277,139.891145821269,80.27376921226619,False,0.12965016867822532,90.25266072669444,99.72042161172396,0.8129251700680111,13,4,140.9964070567408,SL,2023-07-28 18:15:00+00:00,-48.94070567407596,-449.06033898305134,285,False
+2023-08-11 15:30:00+00:00,2,USD_JPY,SHORT,144.919,144.99525857174962,144.68602567308264,144.63963299560436,144.5468476406478,5829.74,2,OVERLAP,1.5,0.1065171434992434,35.66392281865995,71.38612116743163,144.6851713052384,144.26277296488357,25.19743269173489,False,0.0193834133770098,94.86844809736488,25.282869476157543,0.9611650485435072,15,4,144.99525857174962,SL,2023-08-11 17:45:00+00:00,-7.625857174960516,-444.56764607154315,135,False
+2023-08-16 12:45:00+00:00,2,USD_JPY,SHORT,145.84600000000003,145.9255574476874,145.67768357028274,145.6320943459013,145.54091589713843,5532.13,2,LONDON,1.5,0.08511489537479877,51.81533440512135,75.98761475621393,145.58295824879104,145.4047730960566,18.73164297172707,False,0.023690425296330905,72.36306722402604,28.20417512089648,0.5400000000000947,12,2,145.9255574476874,SL,2023-08-16 13:15:00+00:00,-7.955744768736395,-440.12214307469674,30,False
+2023-08-21 10:15:00+00:00,2,USD_JPY,SHORT,145.91100000000003,145.97308370556246,145.67582011418955,145.61730179875605,145.50026516788907,7018.28,2,LONDON,1.5,0.08616741112490617,58.59975157831742,79.27437284384321,145.4894239714213,145.5305776885644,25.417988581045847,False,0.038038417207233,93.28734082928413,44.05760285787039,0.9670329670328126,10,0,145.97308370556246,SL,2023-08-21 12:15:00+00:00,-6.208370556242925,-435.720829074686,120,False
+2023-08-21 13:00:00+00:00,2,USD_JPY,SHORT,146.15200000000002,146.23524254178201,145.82816458675885,145.7472347795126,145.5853751650201,5182.01,2,OVERLAP,1.5,0.10848508356398724,51.04922733502628,77.7329417864695,145.65231851413176,145.5731001082811,34.28354132411471,False,0.020708215651897746,79.5705194265131,51.86814858682283,0.47093023255800504,13,0,146.23524254178201,SL,2023-08-21 14:00:00+00:00,-8.324254178199908,-431.3636839397371,60,False
+2023-08-22 13:30:00+00:00,2,USD_JPY,SHORT,146.07000000000002,146.2494490188913,145.78272381604734,145.71912226287841,145.59191915654054,2379.78,2,OVERLAP,1.5,0.10696601259421827,49.6854184513646,71.74737895171516,145.87585926609069,145.83119123116637,30.627618395266154,False,0.04971538134025395,86.63270637408573,21.3140733909313,0.6729559748426773,13,1,145.82129296173994,TP1+TP2+SL,2023-08-22 16:30:00+00:00,30.375135570791656,722.8614012865858,180,True
+2023-08-23 13:45:00+00:00,2,USD_JPY,LONG,144.76899999999998,144.6192286860622,145.31880771647437,145.3800620025122,145.50257057458785,2899.61,2,OVERLAP,1.5,0.16554262787557517,35.07756783560833,25.599159081147832,145.47573005986652,145.6899627074882,-56.880771647436745,False,-0.04659906579916909,12.968429707091309,-72.5730059866521,0.9026369168356951,13,2,144.6192286860622,SL,2023-08-23 15:15:00+00:00,-14.977131393777652,-434.2783996071162,90,False
+2023-08-24 11:15:00+00:00,2,USD_JPY,SHORT,145.51300000000003,145.5952686096797,145.37600927084867,145.33722814632006,145.25966589726283,5226.0,2,LONDON,1.5,0.08253721935937137,43.48400536866164,73.78096968674387,145.18666859075014,145.30376985837464,15.599072915134116,False,0.008294376095234562,73.81393434187699,34.53314092498658,0.7047619047620903,11,3,145.5952686096797,SL,2023-08-24 11:45:00+00:00,-8.226860967965877,-429.9357541858967,30,False
+2023-08-24 12:30:00+00:00,2,USD_JPY,SHORT,145.91800000000003,145.99061715151015,145.49344721670374,145.42043714491584,145.27441700134005,5861.37,2,LONDON,1.5,0.09923430302031885,60.689910232260765,80.65857706259894,145.28232481020535,145.32357577275278,44.35527832962691,False,0.03387360072320683,88.96190354130778,65.46751897946592,0.9828767123287828,12,3,145.91800000000003,TP1+SL,2023-08-24 17:00:00+00:00,21.227639164814605,1244.2304737146937,270,True
+2023-08-30 12:30:00+00:00,2,USD_JPY,LONG,145.95299999999997,145.74245141675968,146.31213688979977,146.35649464078634,146.44521014275944,2080.65,2,LONDON,1.5,0.12769905549355293,32.81389470589548,29.132800562384304,146.2599303246976,146.26572547814538,-37.813688979977655,False,-0.04356225345552411,30.24728112377744,-32.593032469759464,0.8969072164948673,12,2,145.74245141675968,SL,2023-08-30 13:15:00+00:00,-21.054858324029624,-438.0779097189224,45,False
diff --git a/results/phase1/S3_EUR_GBP_report.json b/results/phase1/S3_EUR_GBP_report.json
new file mode 100644
index 0000000..27472bd
--- /dev/null
+++ b/results/phase1/S3_EUR_GBP_report.json
@@ -0,0 +1,43 @@
+{
+  "pair": "EUR_GBP",
+  "strategy_id": 3,
+  "strategy_name": "S3_Key_Level_Breakout",
+  "total_trades": 271,
+  "win_rate_pct": 30.63,
+  "avg_rr": 1.12,
+  "expectancy_pips": -3.92,
+  "sharpe_ratio": -4.57,
+  "max_drawdown_pct": -61.97,
+  "profit_factor": 0.53,
+  "total_pnl_pips": -1061.0,
+  "total_pnl_dollars": -60956.93,
+  "avg_win_pips": 12.61,
+  "avg_loss_pips": 11.21,
+  "max_consecutive_wins": 4,
+  "max_consecutive_losses": 15,
+  "avg_hold_time_minutes": 514.5,
+  "best_trade_pips": 39.02,
+  "worst_trade_pips": -35.47,
+  "best_trade_dollars": 2132.49,
+  "worst_trade_dollars": -1026.77,
+  "final_equity": 39043.07,
+  "starting_equity": 100000.0,
+  "session_breakdown": {
+    "LONDON": {
+      "trades": 155,
+      "win_rate": 32.903225806451616,
+      "total_pnl_pips": -463.71181791149047
+    },
+    "OVERLAP": {
+      "trades": 116,
+      "win_rate": 27.586206896551722,
+      "total_pnl_pips": -597.2844041738382
+    }
+  },
+  "exit_reasons": {
+    "TP3": 19,
+    "SL": 188,
+    "TP1+TP2+SL": 25,
+    "TP1+SL": 39
+  }
+}
\ No newline at end of file
diff --git a/results/phase1/S3_EUR_GBP_trades.csv b/results/phase1/S3_EUR_GBP_trades.csv
new file mode 100644
index 0000000..9a4ea15
--- /dev/null
+++ b/results/phase1/S3_EUR_GBP_trades.csv
@@ -0,0 +1,272 @@
+timestamp,strategy_id,pair,signal_direction,entry_price,sl_price,tp1_price,tp2_price,tp3_price,lot_size,confluence_score,session,spread_at_entry,atr_at_entry,adx_at_entry,rsi_at_entry,ema_50_value,ema_200_value,vwap_deviation,news_within_60min,macd_hist_at_entry,stoch_k_at_entry,distance_from_ema50_pips,candle_body_ratio,hour_of_day,day_of_week,exit_price,exit_reason,exit_time,pnl_pips,pnl_dollars,hold_time_minutes,win
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+2021-01-28 15:00:00+00:00,3,EUR_GBP,SHORT,0.88328,0.8843413453566777,0.8816632732166106,0.8804254553610177,0.8785687285776282,962292.68,3,OVERLAP,2.0,0.001237817855592922,32.557400483358904,40.540680657937365,0.8853470133959288,0.8876502246763823,-24.739761894114753,False,-9.923703300963519e-05,17.39600569740534,-18.270133959288337,0.6461538461538636,15,3,0.8843413453566777,SL,2021-01-29 08:00:00+00:00,-10.613453566777897,-1021.3248676830262,1020,False
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diff --git a/results/phase1/S3_GBP_JPY_report.json b/results/phase1/S3_GBP_JPY_report.json
new file mode 100644
index 0000000..5ff53a3
--- /dev/null
+++ b/results/phase1/S3_GBP_JPY_report.json
@@ -0,0 +1,43 @@
+{
+  "pair": "GBP_JPY",
+  "strategy_id": 3,
+  "strategy_name": "S3_Key_Level_Breakout",
+  "total_trades": 240,
+  "win_rate_pct": 32.92,
+  "avg_rr": 1.61,
+  "expectancy_pips": -3.72,
+  "sharpe_ratio": -1.7,
+  "max_drawdown_pct": -39.43,
+  "profit_factor": 0.79,
+  "total_pnl_pips": -891.83,
+  "total_pnl_dollars": -30785.68,
+  "avg_win_pips": 42.51,
+  "avg_loss_pips": 26.4,
+  "max_consecutive_wins": 5,
+  "max_consecutive_losses": 16,
+  "avg_hold_time_minutes": 539.0,
+  "best_trade_pips": 148.69,
+  "worst_trade_pips": -58.7,
+  "best_trade_dollars": 2942.64,
+  "worst_trade_dollars": -1114.55,
+  "final_equity": 69214.32,
+  "starting_equity": 100000.0,
+  "session_breakdown": {
+    "LONDON": {
+      "trades": 134,
+      "win_rate": 35.07462686567165,
+      "total_pnl_pips": 159.2481388390603
+    },
+    "OVERLAP": {
+      "trades": 106,
+      "win_rate": 30.18867924528302,
+      "total_pnl_pips": -1051.0783307839768
+    }
+  },
+  "exit_reasons": {
+    "SL": 161,
+    "TP1+TP2+SL": 32,
+    "TP3": 19,
+    "TP1+SL": 28
+  }
+}
\ No newline at end of file
diff --git a/results/phase1/S3_GBP_JPY_trades.csv b/results/phase1/S3_GBP_JPY_trades.csv
new file mode 100644
index 0000000..346d6c5
--- /dev/null
+++ b/results/phase1/S3_GBP_JPY_trades.csv
@@ -0,0 +1,241 @@
+timestamp,strategy_id,pair,signal_direction,entry_price,sl_price,tp1_price,tp2_price,tp3_price,lot_size,confluence_score,session,spread_at_entry,atr_at_entry,adx_at_entry,rsi_at_entry,ema_50_value,ema_200_value,vwap_deviation,news_within_60min,macd_hist_at_entry,stoch_k_at_entry,distance_from_ema50_pips,candle_body_ratio,hour_of_day,day_of_week,exit_price,exit_reason,exit_time,pnl_pips,pnl_dollars,hold_time_minutes,win
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+2021-01-21 14:00:00+00:00,3,GBP_JPY,LONG,142.198,142.01383319808596,142.45733400957027,142.64955668261712,142.93789069218738,5375.56,3,OVERLAP,2.5,0.19222267304684573,32.940678029356576,64.65159887936815,141.64700794369486,141.38081162266374,16.71597206548654,False,0.04714231393668021,52.90950910494582,52.199205630515166,0.6710526315790097,14,3,142.01383319808596,SL,2021-01-21 17:00:00+00:00,-18.416680191404566,-989.9996936970674,180,False
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+2021-02-10 09:00:00+00:00,3,GBP_JPY,LONG,144.999,144.87236194190183,145.18152362382403,145.32253937304006,145.5340629968641,7893.34,3,LONDON,2.5,0.14101574921602278,26.654237934916907,76.16324459574753,144.46901473307963,143.89242245486125,20.27364505993603,False,0.04514119255365756,78.08021776510266,50.09852669203667,0.7043879907621523,9,2,144.87236194190183,SL,2021-02-10 13:00:00+00:00,-12.663805809816608,-999.5972495085783,240,False
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+2023-07-17 08:00:00+00:00,3,GBP_JPY,SHORT,181.189,181.54460203057047,180.71623984714768,180.38173307857946,179.87997292572715,1964.25,4,LONDON,2.5,0.3345067685682088,32.81077496995506,46.08308636753559,181.29400407716972,181.62914952630032,-12.700000000000955,False,-0.05452375133124174,52.36148685053755,-7.600407716972768,0.8207381370825996,8,0,181.189,TP1+SL,2023-07-17 12:00:00+00:00,18.910406114092666,371.4476520960652,240,True
+2023-07-18 11:00:00+00:00,3,GBP_JPY,SHORT,181.193,181.4927898207896,180.75305089605206,180.44041816008678,179.97146905613883,2342.33,4,LONDON,2.5,0.31263273596529617,20.073578439969477,48.57990728269839,181.2926650302441,181.5538387925153,16.260113514286445,False,-0.009252063808844128,73.07481682258724,-7.0665030244100535,0.6907514450867104,11,1,181.193,TP1+SL,2023-07-18 13:00:00+00:00,17.597964157918113,412.20239386016334,120,True
+2023-07-25 10:00:00+00:00,3,GBP_JPY,LONG,181.613,181.3536949709408,181.9942751452959,182.2677919088265,182.67806705412238,2723.93,4,LONDON,2.5,0.2735167635305921,28.81908306769739,52.749824704834275,181.4319115793256,181.30167436441897,-0.8005358724005873,False,0.029337584482290808,27.853447855155625,15.208842067440287,0.5925925925925575,10,1,181.3536949709408,SL,2023-07-25 12:00:00+00:00,-25.93050290591918,-706.3287478052043,120,False
+2023-07-25 16:00:00+00:00,3,GBP_JPY,LONG,181.672,181.34692321712012,182.08713391439932,182.38322319066552,182.8273571050648,2151.07,4,OVERLAP,2.5,0.29608927626620196,29.66388333511331,55.47661865396217,181.42540456306847,181.30720107778777,20.38677311400079,False,0.004434934433174944,56.28388773940261,21.759543693153205,0.672268907563052,16,1,181.34692321712012,SL,2023-07-26 06:00:00+00:00,-32.507678287987574,-699.2629153494144,840,False
+2023-07-27 08:00:00+00:00,3,GBP_JPY,LONG,181.869,181.6248941092596,182.3399738981464,182.67328983024402,183.17326372839042,2835.95,5,LONDON,2.5,0.3333159320976046,22.03046042615884,58.68918180571494,181.47340190416304,181.3742966726193,9.766666666666879,False,0.0483941414260722,88.76269746399713,36.6598095836963,0.30245231607625167,8,3,181.6248941092596,SL,2023-07-27 09:00:00+00:00,-24.410589074039994,-692.2721008452371,60,False
+2023-08-10 08:00:00+00:00,3,GBP_JPY,LONG,183.472,183.20849269616122,183.80386985252719,184.04444975421197,184.40531960673914,2600.87,4,LONDON,2.5,0.2405799016847824,39.34774407018321,71.40260748977555,182.66531657035637,182.0454927675344,0.7000000000005002,False,0.05287163003221937,69.89446037754797,77.76834296436448,0.38694638694644395,8,3,183.65910333022612,TP1+TP2+SL,2023-08-10 15:00:00+00:00,39.91485087408762,1038.1333819288827,420,True
+2023-08-11 11:00:00+00:00,3,GBP_JPY,LONG,183.99099999999999,183.75674234708083,184.31971683602444,184.55819472670743,184.9159115627319,2969.94,5,LONDON,2.5,0.23847789068297628,32.884503179375464,62.17901360064291,183.31740650232106,182.42414539381747,19.920143168249638,False,0.007587828732523172,52.46450310893571,64.4593497678926,0.6453201970443267,11,4,183.75674234708083,SL,2023-08-11 12:00:00+00:00,-23.425765291915468,-695.7311737107143,60,False
+2023-08-11 13:00:00+00:00,3,GBP_JPY,LONG,183.97299999999998,183.75121209664476,184.32936808820475,184.58628014700793,184.9716482352127,3105.55,5,OVERLAP,2.5,0.2569120588031789,28.734339464521348,59.15162733866945,183.35579123878236,182.45167945695812,15.045908123477147,False,0.0058784416912326665,65.42185338865637,58.82087612176292,0.716621253405895,13,4,183.75121209664476,SL,2023-08-11 14:00:00+00:00,-22.17879033552208,-688.7734232648061,60,False
+2023-08-14 10:00:00+00:00,3,GBP_JPY,LONG,184.208,183.9664665844395,184.55000041113595,184.79733401855992,185.16833442969588,2823.15,4,LONDON,2.5,0.24733360742397142,25.091136856557277,64.61885396292367,183.65172128944067,182.72269686937892,13.578043428606179,False,0.0207173156601847,85.8436348056011,52.72787105593295,0.5111821086261601,10,0,183.9664665844395,SL,2023-08-14 12:00:00+00:00,-24.153341556049668,-681.8850621396163,120,False
+2023-08-14 15:00:00+00:00,3,GBP_JPY,LONG,184.398,184.13964367098336,184.77911497841654,185.05252496402758,185.46263994244416,2612.93,4,OVERLAP,2.5,0.27340998561103674,21.772536229573024,63.89196999056597,183.72930730068612,182.78867592799907,34.239370423600235,False,0.03366050702039691,61.62838980758122,63.96926993138834,0.6601123595505576,15,0,184.7750026553219,TP1+TP2+SL,2023-08-15 21:00:00+00:00,48.96565080420373,1279.4381795582806,1800,True
+2023-08-30 14:00:00+00:00,3,GBP_JPY,LONG,185.629,185.39019510593366,186.00069113699837,186.26781856166397,186.66850969866235,2880.43,4,OVERLAP,2.5,0.2671274246655871,55.047265470281374,74.26934302922594,184.74831600838246,184.64020667729372,24.45489997411414,False,0.09476408602850303,87.94849081855357,85.16839916175343,0.3309608540924817,14,2,185.629,TP1+SL,2023-08-31 00:00:00+00:00,14.867645479935165,428.25212069769645,600,True
diff --git a/results/phase1/S3_GBP_USD_report.json b/results/phase1/S3_GBP_USD_report.json
new file mode 100644
index 0000000..b2990c9
--- /dev/null
+++ b/results/phase1/S3_GBP_USD_report.json
@@ -0,0 +1,43 @@
+{
+  "pair": "GBP_USD",
+  "strategy_id": 3,
+  "strategy_name": "S3_Key_Level_Breakout",
+  "total_trades": 263,
+  "win_rate_pct": 35.74,
+  "avg_rr": 1.62,
+  "expectancy_pips": -1.11,
+  "sharpe_ratio": -1.03,
+  "max_drawdown_pct": -24.24,
+  "profit_factor": 0.87,
+  "total_pnl_pips": -292.75,
+  "total_pnl_dollars": -19569.64,
+  "avg_win_pips": 29.26,
+  "avg_loss_pips": 18.01,
+  "max_consecutive_wins": 5,
+  "max_consecutive_losses": 9,
+  "avg_hold_time_minutes": 508.5,
+  "best_trade_pips": 76.7,
+  "worst_trade_pips": -44.48,
+  "best_trade_dollars": 2839.14,
+  "worst_trade_dollars": -1018.88,
+  "final_equity": 80430.36,
+  "starting_equity": 100000.0,
+  "session_breakdown": {
+    "LONDON": {
+      "trades": 156,
+      "win_rate": 37.82051282051282,
+      "total_pnl_pips": -14.815714514974356
+    },
+    "OVERLAP": {
+      "trades": 107,
+      "win_rate": 32.71028037383177,
+      "total_pnl_pips": -277.9365672732492
+    }
+  },
+  "exit_reasons": {
+    "SL": 169,
+    "TP1+TP2+SL": 31,
+    "TP1+SL": 36,
+    "TP3": 27
+  }
+}
\ No newline at end of file
diff --git a/results/phase1/S3_GBP_USD_trades.csv b/results/phase1/S3_GBP_USD_trades.csv
new file mode 100644
index 0000000..52ade08
--- /dev/null
+++ b/results/phase1/S3_GBP_USD_trades.csv
@@ -0,0 +1,264 @@
+timestamp,strategy_id,pair,signal_direction,entry_price,sl_price,tp1_price,tp2_price,tp3_price,lot_size,confluence_score,session,spread_at_entry,atr_at_entry,adx_at_entry,rsi_at_entry,ema_50_value,ema_200_value,vwap_deviation,news_within_60min,macd_hist_at_entry,stoch_k_at_entry,distance_from_ema50_pips,candle_body_ratio,hour_of_day,day_of_week,exit_price,exit_reason,exit_time,pnl_pips,pnl_dollars,hold_time_minutes,win
+2021-01-18 08:00:00+00:00,3,GBP_USD,SHORT,1.35255,1.3542638338992,1.3498808305039998,1.347974717506666,1.3451155480106658,583487.12,5,LONDON,1.5,0.0019061129973335727,62.84023520801372,25.9905162816071,1.3610818374180458,1.3614504072366436,-9.666666666665602,False,-0.00022029779030458517,11.65722774247701,-83.41837418045728,0.695054945054949,8,0,1.3542638338992,SL,2021-01-18 10:00:00+00:00,-17.138338991999902,-1000.0000060025726,120,False
+2021-01-20 08:00:00+00:00,3,GBP_USD,LONG,1.36799,1.3669682315582845,1.3698755088752448,1.3712591814587411,1.373334690333986,968908.37,3,LONDON,1.5,0.0013836725834965315,60.503915619683724,82.2472100601829,1.362290370963755,1.3613667386079393,6.800000000000139,False,0.00024698827721475925,80.66575916115795,55.09629036244856,0.4411764705882257,8,2,1.3687052993202968,TP1+TP2+SL,2021-01-20 11:00:00+00:00,22.049359976537005,2136.380943440971,180,True
+2021-01-26 13:00:00+00:00,3,GBP_USD,LONG,1.3709600000000002,1.3693999885227301,1.373857557386349,1.3759159289772482,1.379003486363597,648305.36,4,OVERLAP,1.5,0.0020583715908992386,40.45750204755524,61.89148533789378,1.3673727268792006,1.3657151834454573,41.79454316276754,False,0.00062458562581182,94.05736736989996,33.972731207994045,0.4923076923076844,13,1,1.3709600000000002,TP1+SL,2021-01-27 11:00:00+00:00,11.590229545395127,751.4007937910023,1320,True
+2021-01-28 14:00:00+00:00,3,GBP_USD,LONG,1.3710200000000001,1.3692404957565127,1.3747150212174362,1.3773050353623937,1.38119005657983,572562.74,4,OVERLAP,1.5,0.002590014144957507,34.330977631537344,57.65806992413273,1.3686670835917774,1.3673635280310625,43.64314140850922,False,0.000438959385216021,78.12253479435942,21.62916408222637,0.7069892473118141,14,3,1.3692404957565127,SL,2021-01-29 07:00:00+00:00,-17.79504243487384,-1018.8778254927638,1020,False
+2021-02-03 11:00:00+00:00,3,GBP_USD,SHORT,1.36285,1.3643188111657738,1.3605059441711307,1.3588165736185513,1.356282517789682,686738.41,5,LONDON,1.5,0.0016893705525795107,22.16537322973942,37.931698706834545,1.3669041880225972,1.3680126716823777,-17.732991305277057,False,-0.00023220524554519797,10.554442217768642,-38.64188022597137,0.6526946107785275,11,2,1.3643188111657738,SL,2021-02-03 13:00:00+00:00,-14.688111657739087,-1008.6890445738206,120,False
+2021-02-03 15:00:00+00:00,3,GBP_USD,SHORT,1.3642299999999998,1.3657840877942957,1.361505811028521,1.3595630183808685,1.3566488294093895,642564.82,5,OVERLAP,1.5,0.001942792647652641,20.311512109385973,45.72427156283069,1.366572752208967,1.3678803624495646,-2.576197618135989,False,-7.212154976666196e-06,71.02091340505187,-21.52752208967046,0.7634408602150221,15,2,1.3657840877942957,SL,2021-02-03 16:00:00+00:00,-15.540877942958886,-998.6021438059346,60,False
+2021-02-04 08:00:00+00:00,3,GBP_USD,SHORT,1.3579599999999998,1.3591692645692062,1.3557203438206362,1.3541005730343936,1.3516709168550298,817535.01,4,LONDON,1.5,0.0016197707862425144,38.77032748115947,27.144381159447946,1.3645100968923554,1.3670697694640794,-4.300000000001525,False,-0.0005290891384853726,10.525454355878994,-63.600968923553935,0.44444444444445635,8,3,1.3591692645692062,SL,2021-02-04 09:00:00+00:00,-12.092645692063364,-988.616121678748,60,False
+2021-02-04 10:00:00+00:00,3,GBP_USD,SHORT,1.35736,1.3592092689397748,1.3549203219677934,1.3531672032796558,1.3505375252474494,529252.37,4,LONDON,1.5,0.00175311868813767,47.00164635703502,26.62546148309592,1.3640073135865227,1.3668897359111658,-9.0729057576322,False,-0.0005262796918837865,16.3593286391182,-64.57313586522683,0.37004405286341585,10,3,1.3592092689397748,SL,2021-02-04 12:00:00+00:00,-18.492689397748837,-978.7299691432446,120,False
+2021-02-18 09:00:00+00:00,3,GBP_USD,LONG,1.39267,1.3911705310714666,1.3952440113093332,1.3970866855155553,1.3998506968248885,646190.56,4,LONDON,1.5,0.00184267420622213,31.789378902848995,70.68627340328918,1.387628128782231,1.3835817857771886,18.344377882693053,False,0.0007651190916148189,93.46108571983616,48.51871217768977,0.5752212389380079,9,3,1.3943276854433615,TP1+TP2+SL,2021-02-18 15:00:00+00:00,31.27815818627599,2021.1650554158273,360,True
+2021-02-26 15:00:00+00:00,3,GBP_USD,LONG,1.39782,1.3938784221687237,1.4038878891563813,1.4080598152606356,1.4143177044170172,250953.9,2,OVERLAP,1.5,0.004171926104254307,46.759130975369835,47.0655997120202,1.4030228168605854,1.4008337584080164,36.64488243358299,False,0.0006657064607180135,76.12749686143005,-53.92816860585459,0.534228187919449,15,4,1.3938784221687237,SL,2021-02-26 16:00:00+00:00,-39.41577831276354,-989.1543289123432,60,False
+2021-03-04 08:00:00+00:00,3,GBP_USD,SHORT,1.3935799999999998,1.3952835024413381,1.3906274877933096,1.3885324796555158,1.3853899674488255,574852.58,4,LONDON,1.5,0.002095008137793632,26.156304816988065,44.56942101818723,1.3951438391608626,1.3970948657234288,-5.933333333334456,False,-5.251102602974627e-05,59.40658778848957,-13.738391608626443,0.4755434782608912,8,3,1.3952835024413381,SL,2021-03-04 13:00:00+00:00,-17.035024413383223,-979.2627734396333,300,False
+2021-03-05 10:00:00+00:00,3,GBP_USD,SHORT,1.3796499999999998,1.3813847872204734,1.3761285638976324,1.3736542731627208,1.3699428370603532,558840.95,3,LONDON,1.5,0.0024742907349116915,50.326510609273306,22.755565334204192,1.3913433941997309,1.3955034348390811,-31.35328368471857,False,-0.0008424925002719157,6.514012707086934,-115.03394199730899,0.6531250000000273,10,4,1.3813847872204734,SL,2021-03-05 11:00:00+00:00,-17.347872204735992,-969.4701383373256,60,False
+2021-03-10 11:00:00+00:00,3,GBP_USD,SHORT,1.3866999999999998,1.388987330357831,1.3836408482108447,1.3814747470180746,1.3782255952289193,419605.08,4,LONDON,1.5,0.0021661011927701845,27.608092674783887,47.35613025895994,1.3870429351775546,1.39033643873828,-21.46112460450267,False,-8.09283483812896e-05,57.37919751729561,-1.5293517755465835,0.7692307692307394,11,2,1.388987330357831,SL,2021-03-10 13:00:00+00:00,-22.873303578312406,-959.7754377842065,120,False
+2021-03-17 16:00:00+00:00,3,GBP_USD,SHORT,1.3872799999999998,1.388747140480159,1.3844967975992046,1.382514662665341,1.3795414602645455,647639.2,4,OVERLAP,1.5,0.0019821349338635975,23.965540572993874,43.108672119017804,1.3893610482015977,1.3906417665024176,-20.204725663703904,False,-0.0002839184146106259,26.016085120736957,-18.910482015976893,0.6959064327485114,16,2,1.388747140480159,SL,2021-03-17 18:00:00+00:00,-14.67140480159168,-950.1776868578994,120,False
+2021-03-23 08:00:00+00:00,3,GBP_USD,SHORT,1.38086,1.3824151353521972,1.3783343232390137,1.3765238720650228,1.3738081953040364,604883.63,5,LONDON,1.5,0.0018104511739908962,31.26113433542531,34.38945794494933,1.3860721742024948,1.3892828297330162,-8.099999999995333,False,-0.0002484012619351616,47.037231681057506,-50.22174202494733,0.7875894988066229,8,1,1.3793538392694331,TP1+TP2+SL,2021-03-23 12:00:00+00:00,30.459540244987426,1842.4477271519086,240,True
+2021-04-05 08:00:00+00:00,3,GBP_USD,LONG,1.38633,1.385140431182614,1.3884267329758186,1.3899512216263643,1.392237954602183,806258.85,5,LONDON,1.5,0.0015244886505457714,25.033512606225592,66.93186875245706,1.3821788240085777,1.380086871779866,5.966666666663567,False,0.0001688401081778339,82.20796180851245,39.611759914222056,0.7383177570093555,8,0,1.3893152209578592,TP1+TP2+SL,2021-04-06 06:00:00+00:00,28.842260324449587,2325.432764059135,1320,True
+2021-04-09 09:00:00+00:00,3,GBP_USD,SHORT,1.3697899999999998,1.3717851446700138,1.3670667766499311,1.3651246277498854,1.3622114043998166,492372.67,4,LONDON,1.5,0.0019421489000458207,38.89108029162543,39.042951985625635,1.374957899530021,1.3790671768005676,-6.447577713519781,False,-0.00025381048519740844,52.826167981810556,-49.77899530020924,0.8231292517005888,9,4,1.3717851446700138,SL,2021-04-09 10:00:00+00:00,-19.95144670013937,-982.3547082110313,60,False
+2021-04-13 11:00:00+00:00,3,GBP_USD,SHORT,1.3721899999999998,1.3739863686161904,1.3697356569190475,1.3679727615317459,1.3653284184507932,541387.31,4,LONDON,1.5,0.0017628953873016658,22.65110601173697,43.584144071268646,1.3737216457091852,1.3767083082310907,-19.04376812773334,False,-0.00017225958361652373,37.43231643013146,-13.416457091852685,0.4158878504673106,11,1,1.3739863686161904,SL,2021-04-13 12:00:00+00:00,-17.963686161905912,-972.5311728878468,60,False
+2021-04-15 08:00:00+00:00,3,GBP_USD,SHORT,1.3775899999999999,1.3791155683372214,1.3754646583138919,1.3739210971898197,1.3716057555037113,631112.9,3,LONDON,1.5,0.0015435611240721644,24.758962046277592,51.57895985840686,1.3767154977284057,1.376786765267568,-5.133333333333656,False,-6.33470825020048e-05,60.45364891518952,10.645022715942698,0.9679487179486505,8,3,1.3791155683372214,SL,2021-04-15 09:00:00+00:00,-15.255683372215056,-962.8058574520425,60,False
+2021-04-15 10:00:00+00:00,3,GBP_USD,SHORT,1.37628,1.3779062068826045,1.3738189655869781,1.3720516093116302,1.3694005748986082,586135.63,5,LONDON,1.5,0.0017673562753479383,28.58124845265843,46.02174160983902,1.3767957132048838,1.376806401607905,-18.243076307342676,False,-0.00011104368704110886,35.38511885459461,-3.2571320488372635,0.8525641025640625,10,3,1.3779062068826045,SL,2021-04-15 11:00:00+00:00,-16.262068826045084,-953.1777956457296,60,False
+2021-04-15 14:00:00+00:00,3,GBP_USD,LONG,1.3796000000000002,1.3780803420508625,1.3822707897456872,1.3841779829094787,1.3870387726551658,620959.49,4,OVERLAP,1.5,0.0019071931637914604,28.2145385054486,56.603983582709716,1.3770886659242243,1.3768833141959833,9.09115174704267,False,6.441691538548218e-05,57.1407350819102,23.213340757757273,0.4642857142857062,14,3,1.3780803420508625,SL,2021-04-15 15:00:00+00:00,-15.19657949137665,-943.6460250709704,60,False
+2021-04-15 16:00:00+00:00,3,GBP_USD,LONG,1.3788300000000002,1.3768160780948766,1.381439609525618,1.3833060158760302,1.3861056254016482,463875.76,5,OVERLAP,1.5,0.0018664063504120272,26.526335002601545,53.06260466100477,1.3771808254071751,1.3769110062987338,1.7329111412389686,False,7.701384476798095e-06,50.02418393287015,14.591745928249633,0.6339285714285555,16,3,1.3768160780948766,SL,2021-04-16 00:00:00+00:00,-20.139219051236168,-934.2095543198657,480,False
+2021-04-16 10:00:00+00:00,3,GBP_USD,LONG,1.3787600000000002,1.376822009715406,1.3813399514229707,1.3831865857049512,1.3859565371279219,477230.19,5,LONDON,1.5,0.0018466342819804602,35.7420006309824,55.692955108391665,1.3770174004397235,1.3769231089718261,15.388298534539935,False,9.752722693724528e-05,85.82322312428579,15.525995602765352,0.3114754098361044,10,4,1.381055449272548,TP1+TP2+SL,2021-04-19 01:00:00+00:00,32.6170470567817,1556.5839564146872,3780,True
+2021-04-19 14:00:00+00:00,3,GBP_USD,LONG,1.3988600000000002,1.3969337290620405,1.4023213546897988,1.4047555911496645,1.4084069458394632,488214.45,4,OVERLAP,1.5,0.0024342364598657924,72.72008424179377,88.8217005534089,1.3837482363633056,1.3791048228252896,57.71723434617959,False,0.0012364965486307885,94.44179797005906,149.21763636694507,0.4748490945674403,14,0,1.3969337290620405,SL,2021-04-20 10:00:00+00:00,-19.262709379597172,-940.4333065269876,1200,False
+2021-04-23 10:00:00+00:00,3,GBP_USD,LONG,1.3891700000000002,1.3876675465704258,1.3915547671478712,1.3932712785797852,1.3958460457276562,619672.43,4,LONDON,1.5,0.0017165114319140204,34.90201003927642,58.75620330061949,1.3885385643034995,1.386473466545081,11.937784256943473,False,0.0007068908738246693,75.72559366754898,4.414356965005695,0.5118483412323587,10,4,1.3876675465704258,SL,2021-04-23 12:00:00+00:00,-15.024534295744463,-931.0289676662311,120,False
+2021-04-26 09:00:00+00:00,3,GBP_USD,LONG,1.39162,1.3898636383273735,1.3941868083631321,1.3960246806052203,1.3987814889683525,524788.66,5,LONDON,1.5,0.001837872242088141,38.32425649152993,60.42265024412044,1.3886862135716185,1.3868766745442136,3.4422364032926644,False,0.0003424123435971521,61.834748856007366,27.437864283814672,0.3854961832061127,9,0,1.3898636383273735,SL,2021-04-26 10:00:00+00:00,-17.56361672626605,-921.7186886530749,60,False
+2021-04-28 15:00:00+00:00,3,GBP_USD,LONG,1.3917100000000002,1.3897608308650646,1.3942872742461059,1.3961321237435096,1.3988993979896152,468148.96,5,OVERLAP,1.5,0.0018448494974037898,29.52475855589695,60.396756584570944,1.3889717381910982,1.3878097542013954,26.356495099575383,False,0.0003660700601695876,84.4864646464663,25.48261808901886,0.49732620320859805,15,2,1.3897608308650646,SL,2021-04-28 18:00:00+00:00,-19.49169134935591,-912.5015033841967,180,False
+2021-05-04 15:00:00+00:00,3,GBP_USD,SHORT,1.38696,1.3886128507865891,1.3840357460670545,1.381959576778424,1.3788453228454784,546556.59,4,OVERLAP,1.5,0.0020761692886304205,32.19183410327814,48.136367188316974,1.3879232699973383,1.3886321470141065,3.3311264724367184,False,-0.0002008626584095381,58.47511229751786,-7.732699973381774,0.33432835820889545,15,1,1.3886128507865891,SL,2021-05-04 16:00:00+00:00,-16.52850786589166,-903.3764896969924,60,False
+2021-05-06 09:00:00+00:00,3,GBP_USD,LONG,1.3918800000000002,1.3905047963060173,1.393886018469913,1.395350030783188,1.3975460492531009,650334.73,5,LONDON,1.5,0.0014640123132751777,22.251328673677726,58.96170596102669,1.3898489857089762,1.3891638187371058,10.25843715752428,False,1.411297854758605e-05,55.284194399595485,18.410142910239014,0.58163265306127,9,3,1.3905047963060173,SL,2021-05-06 10:00:00+00:00,-13.75203693982918,-894.3427230213837,60,False
+2021-05-13 13:00:00+00:00,3,GBP_USD,LONG,1.4056300000000002,1.4033229299416061,1.4086253502919697,1.4107489171532828,1.4139342674452524,383776.51,2,OVERLAP,1.5,0.0021235668613130932,34.821353339967885,46.25166928568207,1.40750362928644,1.401571175040465,20.178594269855132,False,9.011855949592239e-05,59.155394071315264,-20.63629286439994,0.7425742574258035,13,3,1.4033229299416061,SL,2021-05-13 15:00:00+00:00,-23.07070058394034,-885.3992953359586,120,False
+2021-05-14 12:00:00+00:00,3,GBP_USD,LONG,1.4091200000000002,1.4071698321299015,1.411605839350492,1.41338973225082,1.416065571601312,449471.71,3,LONDON,1.5,0.0017838929003280116,41.523816217934694,67.29343726926928,1.406271248852552,1.402316723420108,15.60423458112803,False,0.0005226068632360116,78.21956402435075,26.587511474480685,0.5374449339207057,12,4,1.4091200000000002,TP1+SL,2021-05-17 08:00:00+00:00,9.943357401967388,446.925785460344,4080,True
+2021-05-17 14:00:00+00:00,3,GBP_USD,LONG,1.4119300000000001,1.410199843579834,1.414550782100831,1.4164246368347182,1.4192354189355492,509210.93,5,OVERLAP,1.5,0.00187385473388731,34.63520475469281,63.59998918252404,1.4084158483039502,1.4039720528834578,16.654594457723437,False,2.6065132346047995e-05,59.51876625789629,33.24151696049826,0.552147239263804,14,0,1.4192354189355492,TP3,2021-05-18 07:00:00+00:00,43.07251361329367,2193.299471446293,1020,True
+2021-05-20 14:00:00+00:00,3,GBP_USD,LONG,1.4155700000000002,1.4141359452685254,1.418316940324041,1.420274900540068,1.423211840864109,629646.5,4,OVERLAP,1.5,0.0019579602160272104,27.848280479965176,57.69536433405911,1.4140128635810985,1.409871026155664,21.055167688233123,False,0.0005645224828564401,76.18899737356861,13.671364189016266,0.518518518518462,14,3,1.423211840864109,TP3,2021-05-21 10:00:00+00:00,45.09104518465223,2839.1418781858133,1200,True
+2021-05-24 13:00:00+00:00,3,GBP_USD,LONG,1.41643,1.4147681114765234,1.4190661092840513,1.4209501821400856,1.423776291424137,560410.01,4,OVERLAP,1.5,0.0018840728560342334,26.65451370883999,54.53918199786956,1.4154775141070428,1.4123225043518666,25.628417995591235,False,0.00010652464277505908,57.85138649490327,7.624858929571321,0.6413793103447907,13,0,1.4147681114765234,SL,2021-05-24 15:00:00+00:00,-16.618885234767067,-931.3389640604664,120,False
+2021-05-27 16:00:00+00:00,3,GBP_USD,LONG,1.41792,1.4163795088533493,1.4207524557332536,1.4227674262220893,1.425789881955343,598527.02,5,OVERLAP,1.5,0.002014970488835759,47.923627692038664,62.36933897320185,1.4141776021495192,1.4134626875921583,17.336428032557194,False,0.000794417562295147,72.2410825494289,35.523978504807815,0.43703703703695174,16,3,1.41792,TP1+SL,2021-05-28 06:00:00+00:00,11.329822933014098,678.1205157224589,840,True
+2021-06-02 15:00:00+00:00,3,GBP_USD,LONG,1.41778,1.4162456981701597,1.4202043662920596,1.4219472771534327,1.4245616434454924,605361.19,5,OVERLAP,1.5,0.001742910861373152,35.429716256954855,57.16984495071278,1.4168471703262107,1.4162550710583657,25.38874100074473,False,0.000500204597719125,87.7341979004189,7.42829673789247,0.5291262135921551,15,2,1.4162456981701597,SL,2021-06-03 04:00:00+00:00,-15.343018298403786,-928.8067815313491,780,False
+2021-06-03 09:00:00+00:00,3,GBP_USD,LONG,1.4189100000000001,1.4175323208316222,1.4208958958418896,1.422346493069816,1.4245223889117056,667440.39,4,LONDON,1.5,0.0014505972279264129,28.646244202708147,61.15698704863336,1.4168228703772936,1.4163500876102795,11.446417740670345,False,0.00010659521091904208,62.59221500582603,18.97129622706384,0.7438016528923845,9,3,1.4175323208316222,SL,2021-06-03 11:00:00+00:00,-13.776791683779432,-919.5187214370501,120,False
+2021-06-03 11:00:00+00:00,3,GBP_USD,LONG,1.4188600000000002,1.4174987332680824,1.4210138336595886,1.4225763894326475,1.4249202230922362,668732.66,4,LONDON,1.5,0.0015625557730590024,32.710273998638556,59.36111830739097,1.4169306158307888,1.4163870607028213,5.167241663575073,False,0.00021876531324241844,76.71314265616448,17.393841692112666,0.45408163265309937,11,3,1.4174987332680824,SL,2021-06-03 12:00:00+00:00,-13.612667319178318,-910.3235226049186,60,False
+2021-06-09 08:00:00+00:00,3,GBP_USD,LONG,1.4187100000000001,1.4171955449582216,1.4205322752088922,1.4218737920148206,1.4238860672237128,595078.94,5,LONDON,1.5,0.0013415168059282198,30.332966073646837,64.50178997237647,1.4156795104251616,1.4155316155941289,3.0333333333332213,False,0.00032624689088989005,78.65480164369328,28.404895748384007,0.7604790419162027,8,2,1.4171955449582216,SL,2021-06-09 09:00:00+00:00,-15.144550417784952,-901.2203009392026,60,False
+2021-06-11 16:00:00+00:00,3,GBP_USD,SHORT,1.4103299999999999,1.4118580463668837,1.408014768165582,1.4063446136093034,1.4038393817748853,583888.1,5,OVERLAP,1.5,0.0016701545562786675,61.26986881723189,27.75989827733528,1.4145911700707046,1.414912535268615,-34.4323492708054,False,-0.0008249325163784178,15.130351647531432,-40.71170070704566,0.6542056074766219,16,4,1.4118580463668837,SL,2021-06-13 23:00:00+00:00,-15.280463668838618,-892.208089871721,3300,False
+2021-06-17 15:00:00+00:00,3,GBP_USD,SHORT,1.39132,1.393523564351901,1.3878484282404957,1.3854073804008262,1.3817458086413221,400844.21,5,OVERLAP,1.5,0.0024410478396694854,65.57379387762117,27.480079376175624,1.402411959373202,1.4093723753798284,-37.018977935188154,False,-0.0002710547718060251,33.46072107423869,-109.01959373202041,0.764966740576484,15,3,1.393523564351901,SL,2021-06-17 23:00:00+00:00,-22.035643519011657,-883.286011821985,480,False
+2021-06-18 12:00:00+00:00,3,GBP_USD,SHORT,1.3846699999999998,1.3867162043070038,1.3812873117983147,1.3789055196638575,1.3753328314621722,427353.78,5,LONDON,1.5,0.002381792134456945,50.912095141565956,31.442589567458654,1.3956787283449703,1.405867345896325,-31.373725690475585,False,-0.00011652243402994174,50.89219359336778,-108.18728344970329,0.9653579676674655,12,4,1.3827006972463582,TP1+TP2+SL,2021-06-21 06:00:00+00:00,40.52727965859315,1731.9486155216894,3960,True
+2021-06-22 12:00:00+00:00,3,GBP_USD,SHORT,1.38821,1.3900048048449238,1.3856259757753813,1.3837766262923021,1.3810026020676833,496863.29,4,LONDON,1.5,0.001849349483079184,33.85582264968927,46.989711201630605,1.3894250098614405,1.398732870538628,4.024803536217814,False,-0.0005094351727027791,55.272335868432,-10.250098614403846,0.5962962962962229,12,1,1.3900048048449238,SL,2021-06-22 13:00:00+00:00,-17.948048449238918,-891.7726401568247,60,False
+2021-06-24 09:00:00+00:00,3,GBP_USD,SHORT,1.396,1.3974033645153796,1.3937981774231019,1.392203629038503,1.3898118064616045,629098.78,3,LONDON,1.5,0.0015945483845988618,24.625898804235938,51.98040748053533,1.3948180032493516,1.3975763495703075,-3.8970752523703744,False,-0.00014667644991968738,41.10983981693323,13.719967506484743,0.32786885245898056,9,3,1.3974033645153796,SL,2021-06-24 10:00:00+00:00,-14.033645153797012,-882.8549045206614,60,False
+2021-06-28 16:00:00+00:00,3,GBP_USD,SHORT,1.3881199999999998,1.3897316659997154,1.3852433366680892,1.3831988944468152,1.3801322311149045,542312.34,5,OVERLAP,1.5,0.002044442221273857,17.39743378344792,42.59587643100033,1.3907914182056824,1.394654516977286,-19.901712948720007,False,-9.519264207556269e-05,18.57144012184639,-24.81418205682484,0.8055555555555298,16,0,1.384743808145402,TP1+TP2+SL,2021-06-29 15:00:00+00:00,37.94345924957643,2057.720617333244,1380,True
+2021-06-30 15:00:00+00:00,3,GBP_USD,SHORT,1.38034,1.3819976684149942,1.3776916579250291,1.3757994298750484,1.3729610878000775,539675.82,3,OVERLAP,1.5,0.0018922280499806398,31.078601251022498,33.42681041213933,1.3855243347840809,1.3910254311549088,-32.96989274973239,False,-0.0002915734237524654,17.730572263487705,-49.94334784080845,0.5215053763440481,15,2,1.3819976684149942,SL,2021-06-30 19:00:00+00:00,-16.576684149942622,-894.6035611501287,240,False
+2021-07-01 08:00:00+00:00,3,GBP_USD,SHORT,1.3778,1.3793823586163207,1.3754032069183961,1.373678678197327,1.3710918851157232,559707.21,3,LONDON,1.5,0.0017245287210692337,23.655264818903728,30.759906526036858,1.3835441139758349,1.3895465307774644,-16.2333333333331,False,-0.00015471388032664785,42.197153855601506,-55.54113975834829,0.6594323873121521,8,3,1.3793823586163207,SL,2021-07-01 11:00:00+00:00,-15.823586163208336,-885.6575263603942,180,False
+2021-07-01 14:00:00+00:00,3,GBP_USD,SHORT,1.3778799999999998,1.3794529786477525,1.375130106761237,1.3731701779353949,1.3702302846966317,557414.4,3,OVERLAP,1.5,0.0019599288258420554,24.91503146094035,36.77589464068684,1.382721738274123,1.3889716140103052,-15.449819355479733,False,-5.852317728697828e-05,58.99740051364525,-46.517382741231295,0.7832898172324056,14,3,1.3778799999999998,TP1+SL,2021-07-02 12:00:00+00:00,10.999572955051384,613.1320358996195,1320,True
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+2022-01-21 08:00:00+00:00,3,GBP_USD,SHORT,1.3565999999999998,1.3579885658060404,1.354697170969798,1.3533019516163298,1.3512091225861278,596920.3,3,LONDON,1.5,0.001395219353468049,47.20478048108672,31.772167388822126,1.3610599474576919,1.361922192624724,0.7333333333336967,False,-0.0003221693492479838,24.938703272033223,-42.69947457691936,0.432584269663037,8,4,1.3565999999999998,TP1+SL,2022-01-21 18:00:00+00:00,7.611316120807565,454.33491022272887,600,True
+2022-01-24 09:00:00+00:00,3,GBP_USD,SHORT,1.3522999999999998,1.3536006293384137,1.350686853307931,1.3494847555132186,1.3476816088211496,640771.7,3,LONDON,1.5,0.0012020977947125719,43.63172365019003,26.445072973170156,1.357403311297884,1.360493669834554,-11.396732009003596,False,-9.539512572432577e-05,16.356986533234636,-49.13311297884082,0.7450980392157631,9,0,1.3476816088211496,TP3,2022-01-24 13:00:00+00:00,26.950347073100733,1726.901970962078,240,True
+2022-01-24 16:00:00+00:00,3,GBP_USD,SHORT,1.34448,1.3463127925445568,1.342172703943882,1.34050783990647,1.338010543850352,464141.72,3,OVERLAP,1.5,0.0016648640374119896,67.73860905178323,13.093881375863205,1.3549603242888686,1.3596145566462294,-41.97194885388944,False,-0.0007452814117037353,17.77398463354587,-102.90324288868557,0.7533039647576576,16,0,1.3463127925445568,SL,2022-01-24 18:00:00+00:00,-18.32792544556927,-850.6754840338288,120,False
+2022-01-26 08:00:00+00:00,3,GBP_USD,SHORT,1.3493899999999999,1.350675926990475,1.3477170317142926,1.3464750528571545,1.344612084571447,654911.78,4,LONDON,1.5,0.0012419788571382134,34.74540349817707,47.5915362360714,1.350498893273312,1.3561033287343227,-6.233333333336422,False,-5.558449879355183e-05,10.311890838204521,-9.188932733119604,0.3155080213904162,8,2,1.350675926990475,SL,2022-01-26 09:00:00+00:00,-12.85926990475028,-842.1687342820437,60,False
+2022-01-27 10:00:00+00:00,3,GBP_USD,SHORT,1.3403999999999998,1.34168734025134,1.3382032987433001,1.3366121645721667,1.3342254633154669,647650.88,4,LONDON,1.5,0.0015911341711332856,70.39831708514276,26.915150198919918,1.347452469331115,1.3539186065437234,-15.138014985855985,False,-0.0003566901438903428,47.583419878069485,-68.62469331115007,0.8117647058823427,10,3,1.3394293671822008,TP1+TP2+SL,2022-01-27 14:00:00+00:00,25.87941237372915,1676.0824197728573,240,True
+2022-02-02 11:00:00+00:00,3,GBP_USD,LONG,1.35647,1.3550216125674468,1.3582219371627664,1.3595165619379441,1.3614584991007106,587210.2,4,LONDON,1.5,0.0012946247751776838,59.35853646433488,81.09556501497222,1.3492263555421529,1.3487578419387078,18.61606954751238,False,0.00011712374697732224,85.11581596788133,70.53644457847064,0.7735849056602878,11,2,1.35647,TP1+SL,2022-02-02 14:00:00+00:00,7.0077486510653495,411.50214869418136,180,True
+2022-02-03 15:00:00+00:00,3,GBP_USD,LONG,1.3611000000000002,1.3594337601990039,1.364341199004981,1.3666286650083015,1.3700598640132824,512905.1,5,OVERLAP,1.5,0.002287466003320578,42.522614114441886,71.49257201726836,1.3543585305371564,1.350714932550754,27.780837726059104,False,0.00032394423522049223,62.957153960637775,65.51469462843684,0.5824175824175494,15,3,1.3594337601990039,SL,2022-02-03 17:00:00+00:00,-16.662398009963386,-854.6228917540071,120,False
+2022-02-07 12:00:00+00:00,3,GBP_USD,LONG,1.3519400000000001,1.350221538431114,1.3543423078444292,1.3560705130740487,1.358662820918478,492345.41,3,LONDON,1.5,0.0017282052296194637,25.125738343576714,44.08107591715233,1.3541731949719693,1.352356198092357,-1.8864417562136637,False,3.7285223963717546e-06,29.966887417218544,-24.231949719693002,0.4311377245509149,12,0,1.350221538431114,SL,2022-02-07 14:00:00+00:00,-17.18461568886109,-846.0766657024747,120,False
+2022-02-08 09:00:00+00:00,3,GBP_USD,LONG,1.3553100000000002,1.3539808114471592,1.3573284427642032,1.3588007379403384,1.3610091807045415,630170.86,4,LONDON,1.5,0.0014722951761353181,24.68457789895047,59.30416486916176,1.3535825837372677,1.3524945683627891,10.422760916608542,False,0.00020931104788816924,81.17935550460955,15.37416262732405,0.6380368098160362,9,1,1.3539808114471592,SL,2022-02-08 10:00:00+00:00,-13.291885528410319,-837.6158934459885,60,False
+2022-02-08 15:00:00+00:00,3,GBP_USD,LONG,1.35546,1.3539283099113877,1.3577409504430604,1.359388250738434,1.3618592011814947,541388.72,4,OVERLAP,1.5,0.001647300295373661,17.379167141003354,57.40572395562512,1.3537380212317658,1.352600432874013,10.416398517476555,False,0.00017123200161659678,59.39015939015937,15.319787682341879,0.7005988023951204,15,1,1.3539283099113877,SL,2022-02-08 17:00:00+00:00,-15.31690088612425,-829.2397365105674,120,False
+2022-02-09 10:00:00+00:00,3,GBP_USD,LONG,1.35861,1.3571902026809584,1.3604523199285425,1.3618071998809045,1.363839519809447,578214.46,4,LONDON,1.5,0.0013548799523617656,22.79304669994695,68.86907472102081,1.3547313360209716,1.353096811751823,15.645304026818962,False,0.00023620765726184586,87.7055726086183,36.88663979028339,0.75,10,2,1.3571902026809584,SL,2022-02-09 11:00:00+00:00,-14.197973190417377,-820.9473401391662,60,False
+2022-02-10 14:00:00+00:00,3,GBP_USD,LONG,1.3562200000000002,1.354367250432506,1.3585999978374703,1.360313329729117,1.3628833275665873,438665.8,5,OVERLAP,1.5,0.0017133318916468083,44.48422676842793,55.10752830737622,1.3548156785296905,1.3535103063682312,4.187413368381687,False,0.00027176604646967614,65.77262992713905,12.143214703095317,0.3578947368421102,14,3,1.3628833275665873,TP3,2022-02-10 16:00:00+00:00,39.21996539952177,1720.4457497953535,120,True
+2022-02-11 10:00:00+00:00,3,GBP_USD,LONG,1.35701,1.3552109445120368,1.360072420296958,1.3622407004949302,1.3654931207918886,461321.14,5,LONDON,1.5,0.0021682801979721494,25.207437072129174,54.79351090882432,1.3553382338177604,1.3539793100419406,13.391352664611222,False,6.769846544394867e-05,85.52443825536623,14.817661822394967,0.5364806866952295,10,4,1.35701,TP1+SL,2022-02-11 18:00:00+00:00,12.249681187832097,565.1036890207257,480,True
+2022-02-16 10:00:00+00:00,3,GBP_USD,LONG,1.3570200000000001,1.3559517138234904,1.3589514308825472,1.3603657181375788,1.362487149020126,782181.2,5,LONDON,1.5,0.0014142872550314863,35.835397677481104,63.637653971897954,1.3543330547079744,1.354102974209328,4.305470582082194,False,0.0002919128151616393,77.31770476868542,24.969452920255986,0.33898305084741087,10,2,1.3559517138234904,SL,2022-02-16 11:00:00+00:00,-10.682861765096696,-835.5933634857452,60,False
+2022-02-17 09:00:00+00:00,3,GBP_USD,LONG,1.3606300000000002,1.3586928095837723,1.3633447020811382,1.3652811701352303,1.3681858722163682,427029.49,5,LONDON,1.5,0.0019364680540920505,28.6075915624308,61.96874383025163,1.3566654589991844,1.35491797699783,15.11838379800734,False,1.9632829717906256e-05,83.50162859873885,37.745410008156455,0.6688741721854694,9,3,1.3606300000000002,TP1+SL,2022-02-18 00:00:00+00:00,10.858808324551992,463.70313808411925,900,True
+2022-02-24 08:00:00+00:00,3,GBP_USD,SHORT,1.3456,1.3470537821726494,1.3429044224700875,1.3409807041168122,1.3380951265868997,572213.96,3,LONDON,1.5,0.0019237183532751217,65.72433542136515,19.21545588985626,1.3555310468524344,1.3571143204862248,-11.933333333333795,False,-0.0006809623131540392,16.274673495712086,-97.41046852434376,0.5666666666666708,8,3,1.3470537821726494,SL,2022-02-24 09:00:00+00:00,-14.537821726494649,-831.874453989154,60,False
+2022-02-24 10:00:00+00:00,3,GBP_USD,SHORT,1.34228,1.3442188274856008,1.3392841959053305,1.337160326508884,1.3339745224142145,424769.98,4,LONDON,1.5,0.0021238693964463957,71.80012554081455,15.157184073797879,1.354560647248249,1.3568332072368257,-23.311667664553948,False,-0.0009805287770648452,4.419046334113248,-120.9064724824893,0.3137931034482748,10,3,1.3339745224142145,TP3,2022-02-24 14:00:00+00:00,49.07286551471168,2084.468010322677,240,True
+2022-03-01 10:00:00+00:00,3,GBP_USD,SHORT,1.33899,1.3406242340764774,1.336425496284281,1.3345891604738016,1.3318346567580823,516694.89,4,LONDON,1.5,0.001836335810479403,26.85505298719205,43.376023027206756,1.3409638051059127,1.3478951107286417,-22.01870809192652,False,-0.00020290825885387151,33.78283722092208,-17.838051059126947,0.7303754266211462,10,1,1.3406242340764774,SL,2022-03-01 11:00:00+00:00,-16.342340764774477,-844.4003963797664,60,False
+2022-03-01 14:00:00+00:00,3,GBP_USD,SHORT,1.3391399999999998,1.340678328338668,1.3363050249733273,1.3342883749555456,1.3312633999288732,543418.7,4,OVERLAP,1.5,0.002016650017781696,20.338695484332522,44.89241458174638,1.3408128286149033,1.3475837994992088,-13.219669142838786,False,-0.0003471818854198748,27.871088192969136,-14.82828614903342,0.47457627118652573,14,1,1.3312633999288732,TP3,2022-03-01 16:00:00+00:00,46.499600426759706,2526.8752414429205,120,True
+2022-03-03 11:00:00+00:00,3,GBP_USD,SHORT,1.3367999999999998,1.3385407033541377,1.3340864832293122,1.3321508053821869,1.3292472886114992,494756.99,3,LONDON,1.5,0.0019356778471251632,27.66242784005253,47.35299815770544,1.3372450273594894,1.3431736240058154,-10.965947507755036,False,-0.00036961817175824234,20.924649463883753,-2.55027359489457,0.5792349726776295,11,3,1.3385407033541377,SL,2022-03-03 12:00:00+00:00,-17.407033541378958,-861.2251519761694,60,False
+2022-03-04 08:00:00+00:00,3,GBP_USD,SHORT,1.3308099999999998,1.332387859592928,1.3280857020353607,1.3261428367256012,1.323228538760962,540360.43,3,LONDON,1.5,0.0019428653097595357,35.86679571832685,36.42623517765729,1.3353279385953185,1.3414461380623137,-2.9999999999996696,False,-0.00020398033889819562,14.00008315168616,-43.279385953185475,0.5282258064516631,8,4,1.323228538760962,TP3,2022-03-04 13:00:00+00:00,44.72876743422693,2416.9656004128865,300,True
+2022-03-18 16:00:00+00:00,3,GBP_USD,LONG,1.31898,1.3174036962997178,1.321904851834745,1.323981419724575,1.32709627155932,556226.92,4,OVERLAP,1.5,0.0020765678898300014,25.915523098966826,64.7782393969378,1.3138249009529532,1.3135634621044066,43.277291015972885,False,0.00022900081257092259,90.47352843323301,49.65099047046672,0.8116591928250693,16,4,1.3174036962997178,SL,2022-03-18 17:00:00+00:00,-15.763037002822776,-876.7825521926145,60,False
+2022-03-21 14:00:00+00:00,3,GBP_USD,LONG,1.32,1.3180591152668264,1.322481090332534,1.3242618172208898,1.3269329075534237,447226.31,4,OVERLAP,1.5,0.0017807268883559442,23.548286738805732,67.27657319363664,1.3152011678079294,1.3141010081017492,41.7434084595869,False,0.0002605682081366399,78.57060902812134,46.08832192070533,0.8798586572438091,14,0,1.3180591152668264,SL,2022-03-21 17:00:00+00:00,-19.408847331736645,-868.0147173525926,180,False
+2022-03-22 10:00:00+00:00,3,GBP_USD,LONG,1.3212400000000002,1.3189168558855326,1.324303220572337,1.3264720342872283,1.3297252548595653,369901.54,4,LONDON,1.5,0.0021688137148913257,36.388350377695104,66.39684864238743,1.315697884778608,1.3144756558156887,26.672366719342477,False,0.0005267531678398172,85.2549760934611,53.52115221392006,0.6488549618320761,10,1,1.3297252548595653,TP3,2022-03-23 02:00:00+00:00,50.15152915738996,1855.1127868673448,960,True
+2022-03-24 14:00:00+00:00,3,GBP_USD,LONG,1.3193700000000002,1.317169790409548,1.3222810479522595,1.3243484132537657,1.3274494612060252,399000.94,3,OVERLAP,1.5,0.0020673653015063275,23.645269551720542,47.74985060031604,1.3201282279978612,1.317502778092015,7.393987591937101,False,0.00017541955593897932,55.280264675168034,-9.482279978612063,0.43014705882359816,14,3,1.317169790409548,SL,2022-03-24 17:00:00+00:00,-22.002095904520846,-877.8856947873968,180,False
+2022-03-28 11:00:00+00:00,3,GBP_USD,SHORT,1.3112199999999998,1.313310879003236,1.30801560498382,1.3057526749730333,1.3023582799568534,415665.78,5,LONDON,1.5,0.0022629300107866405,37.11385477738873,36.47303380202914,1.317491496312325,1.3175597709552864,-29.351694091916958,False,-0.00027429921149963636,33.301128075092144,-60.81496312325019,0.7779886148007417,11,0,1.3112199999999998,TP1+SL,2022-03-29 07:00:00+00:00,12.817580064719003,532.7829415313876,1200,True
+2022-03-31 08:00:00+00:00,3,GBP_USD,SHORT,1.3118599999999998,1.3130476949210674,1.309591525394663,1.3079525423244387,1.3054940677191018,736245.19,5,LONDON,1.5,0.0016389830702245197,22.71007445602996,45.55520583181912,1.3128196525606242,1.314639345267182,-4.133333333333766,False,-0.00020470426774629304,58.13575749829315,-7.696525606242233,0.432926829268263,8,3,1.3130476949210674,SL,2022-03-31 09:00:00+00:00,-11.87694921067628,-874.4346728234707,60,False
+2022-04-05 15:00:00+00:00,3,GBP_USD,SHORT,1.3097299999999998,1.3112093884650966,1.3069444862459447,1.304960810409908,1.3019852966558527,585167.69,5,OVERLAP,1.5,0.0019836758360368256,35.06814760583071,41.962887502139154,1.312109005201005,1.3132516255288742,-32.40486666878706,False,-7.768918314509364e-05,41.486858333649636,-21.890052010049565,0.9281437125748475,15,1,1.3075554522439718,TP1+TP2+SL,2022-04-06 08:00:00+00:00,34.56790888864391,2022.8023392498226,1020,True
+2022-04-07 16:00:00+00:00,3,GBP_USD,SHORT,1.3052,1.3068189003042314,1.3028454984788433,1.3011491641314055,1.2986046626102488,547234.66,3,OVERLAP,1.5,0.001696334347437796,25.488407539559216,37.44111948989612,1.3081113360276464,1.3110383916350472,-19.341902036111858,False,-0.00022741583927465841,18.3817340067322,-27.213360276463483,0.5697674418604171,16,3,1.3068189003042314,SL,2022-04-07 17:00:00+00:00,-16.189003042315075,-885.9183575600257,60,False
+2022-04-08 11:00:00+00:00,3,GBP_USD,SHORT,1.3029,1.3045739891993375,1.3008367206699785,1.2993345344499643,1.2970812551199427,523933.59,4,LONDON,1.5,0.0015021862200143641,31.052602746596225,30.550832780101985,1.3068750731128744,1.3101664253617,-10.082874895569471,False,-0.0003159397423313378,32.75976069696184,-37.85073112874348,0.49999999999990175,11,4,1.3016594843998206,TP1+TP2+SL,2022-04-08 15:00:00+00:00,24.996010720587325,1309.6249632515805,240,True
+2022-04-21 08:00:00+00:00,3,GBP_USD,SHORT,1.30578,1.3075977708806732,1.3034061455966344,1.301696909327724,1.2991330549243583,489696.16,4,LONDON,1.5,0.0017092362689104627,20.03878692032355,53.71089937633488,1.3043471515218679,1.304740142992806,-1.1333333333340967,False,-4.30697841385634e-05,72.65699813073239,16.228484781322106,0.5606060606060377,8,3,1.3075977708806732,SL,2022-04-21 10:00:00+00:00,-18.17770880673253,-890.1554200255101,120,False
+2022-04-21 11:00:00+00:00,3,GBP_USD,SHORT,1.30303,1.304649832522967,1.3004026555669845,1.2985244259449742,1.2957070815119587,544040.11,5,LONDON,1.5,0.0018782296220103151,18.975056075967945,41.98723727359619,1.3044555260751616,1.3047577655999802,-25.343082795130645,False,-0.0002737360606557559,29.539249852834278,-12.355260751615305,0.9093406593406166,11,3,1.304649832522967,SL,2022-04-21 12:00:00+00:00,-16.198325229670463,-881.2538639765694,60,False
+2022-04-21 14:00:00+00:00,3,GBP_USD,SHORT,1.3028899999999999,1.3047313636887934,1.2998549997378517,1.2977049995630863,1.294479999300938,473801.74,5,OVERLAP,1.5,0.0021500001747655267,16.30435623532321,43.43586381436785,1.3044750463035837,1.3047544212564364,-21.427628682919586,False,-0.0003601539371364459,34.34190620272075,-13.950463035836691,0.7803030303030468,14,3,1.3047313636887934,SL,2022-04-21 15:00:00+00:00,-18.41363688793507,-872.4413197231821,60,False
+2022-05-03 16:00:00+00:00,3,GBP_USD,SHORT,1.2485799999999998,1.2511314576004329,1.244987711997836,1.2424661866630602,1.2386838986608963,338519.02,3,OVERLAP,1.5,0.0025215253347759207,22.363501669902924,41.3522233781312,1.2526693068658257,1.2646729931265537,-39.32896324038815,False,-0.00013608206616118958,16.437171656235233,-38.99306865825736,0.9598214285714631,16,1,1.2511314576004329,SL,2022-05-03 17:00:00+00:00,-25.514576004330713,-863.7169264701549,60,False
+2022-05-05 10:00:00+00:00,3,GBP_USD,SHORT,1.2545099999999998,1.2574352467554013,1.2502070995563264,1.2472118325938772,1.2427189321502037,292310.3,4,LONDON,1.5,0.0029952669624490466,29.689100781699644,48.41517998499038,1.2543072025289652,1.260879987125751,-3.415461179028245,False,-0.0006899278545881595,64.98583297063824,3.927974710347115,0.6178160919540635,10,3,1.2574352467554013,SL,2022-05-05 11:00:00+00:00,-29.252467554015116,-855.0797566454424,60,False
+2022-05-16 13:00:00+00:00,3,GBP_USD,SHORT,1.2238399999999998,1.2261345690053624,1.2204071549731885,1.2179919249553142,1.214369079928503,368927.21,2,OVERLAP,1.5,0.0024152300178742724,22.31285627754206,50.642712674003455,1.2237693311082727,1.2328803172492409,-5.254451491250034,False,-4.291615998434472e-05,62.615700566931615,2.6066889172726704,0.5428571428571998,13,0,1.2261345690053624,SL,2022-05-16 14:00:00+00:00,-22.945690053626233,-846.5289413009077,60,False
+2022-05-19 13:00:00+00:00,3,GBP_USD,LONG,1.2489700000000001,1.246272955149323,1.2532818909200525,1.2562831515334207,1.2607850424534734,310734.05,4,OVERLAP,1.5,0.0030012606133683352,38.02880464674456,70.32305385264331,1.2391613933812013,1.2365843849464648,65.69071670715631,False,0.0014021811581469829,92.59122483037983,96.18606618798653,0.8902255639097573,13,3,1.246272955149323,SL,2022-05-19 21:00:00+00:00,-26.970448506771213,-838.0636694825472,480,False
+2022-05-26 09:00:00+00:00,3,GBP_USD,LONG,1.2611800000000002,1.2594246514182625,1.2648167429086883,1.267367904847814,1.2711946477565024,472659.98,5,LONDON,1.5,0.0025511619391255557,33.092723186629584,61.000070247786674,1.255281115679158,1.2483927574129934,28.222183768940035,False,2.527155927642302e-05,66.68686986704851,57.08884320842156,0.8600746268656672,9,3,1.2594246514182625,SL,2022-05-26 10:00:00+00:00,-17.553485817376476,-829.683025537145,60,False
+2022-06-07 11:00:00+00:00,3,GBP_USD,SHORT,1.24897,1.250722778585301,1.2456586070734956,1.2433243451224925,1.239822952195988,468619.48,5,LONDON,1.5,0.002334261951003021,26.854532206921988,43.90424717740759,1.251890348248034,1.2540235399340778,-8.462933455022359,False,-6.876981960701766e-05,57.63387391459916,-27.303482480338648,0.3134328358208189,11,1,1.250722778585301,SL,2022-06-07 12:00:00+00:00,-17.527785853010958,-821.3861891989351,60,False
+2022-06-10 10:00:00+00:00,3,GBP_USD,SHORT,1.24419,1.245993816755847,1.241410916220765,1.2394315270346081,1.236462443255373,450806.51,4,LONDON,1.5,0.001979389186156782,27.819397512543095,32.046867102647,1.2510251162716732,1.253338455496511,-18.87144933802709,False,-0.0003793235063845753,15.515865458985083,-66.4511627167319,0.49557522123895803,10,4,1.236462443255373,TP3,2022-06-10 13:00:00+00:00,45.60534046776076,2055.9184373632997,180,True
+2022-06-10 16:00:00+00:00,3,GBP_USD,SHORT,1.2309199999999998,1.2331627834649845,1.2271627493417434,1.2245312489029057,1.2205839982446491,371739.64,3,OVERLAP,1.5,0.002631500438837695,59.78014257821302,15.384586560433306,1.247804376153666,1.2523366109289054,-85.47368159950608,False,-0.0016341233464179138,7.513824224220745,-166.9437615366598,0.9076923076923813,16,4,1.2205839982446491,TP3,2022-06-13 09:00:00+00:00,61.25601053210339,2277.1287303040326,3900,True
+2022-06-13 09:00:00+00:00,3,GBP_USD,SHORT,1.21979,1.2221790406479371,1.2162847967603139,1.2138213279338563,1.2101261246941701,358513.28,2,LONDON,1.5,0.002463468826457502,68.56393959585839,16.259052280312403,1.2376291882912578,1.2484693036363332,-17.375474887977305,False,-0.0001903623174926575,4.917950768522861,-176.4918829125772,0.8384279475982546,9,0,1.21979,TP1+SL,2022-06-13 12:00:00+00:00,14.020812958744242,502.66476421059036,180,True
+2022-06-22 11:00:00+00:00,3,GBP_USD,SHORT,1.2234699999999998,1.2258391122477383,1.2199227720946422,1.2174312868244037,1.213694058919046,363650.75,5,LONDON,1.5,0.0024914852702385406,46.29466240254815,45.75737524048104,1.2251304375737702,1.2269973601781772,0.6852603555795866,False,-5.969795798475104e-05,81.82874136107216,-14.704375737701891,0.5738396624472732,11,2,1.2258391122477383,SL,2022-06-22 13:00:00+00:00,-23.691122477385026,-861.5294457242924,120,False
+2022-06-24 13:00:00+00:00,3,GBP_USD,SHORT,1.22691,1.2290690398810376,1.2232348005948124,1.220658000991354,1.216792801586166,395043.26,4,OVERLAP,1.5,0.002576799603458503,15.52450463320944,51.085673592080056,1.2261342772767845,1.2265711216725812,-9.277734454335995,False,-6.657719056372795e-06,38.64692327245464,9.657227232156007,0.3684210526316043,13,4,1.2290690398810376,SL,2022-06-24 14:00:00+00:00,-21.59039881037605,-852.9141530751078,60,False
+2022-06-24 15:00:00+00:00,3,GBP_USD,SHORT,1.22684,1.2291433762239559,1.2227931188802208,1.2199685314670348,1.2157316503472555,366585.8,4,OVERLAP,1.5,0.002824587413186149,18.055250680690033,50.39872443231904,1.2262679583781466,1.2265971522822676,-13.438536644589938,False,-8.214057628232315e-05,26.372494276952036,7.620416218534487,0.44186046511624305,15,4,1.2291433762239559,SL,2022-06-26 23:00:00+00:00,-23.03376223955933,-844.3850157598648,3360,False
+2022-07-05 10:00:00+00:00,3,GBP_USD,SHORT,1.2023199999999998,1.204301907689167,1.1992337948874987,1.1970496581458312,1.19377345303333,421786.12,4,LONDON,1.5,0.002184136741667512,38.397135934157795,25.937955776854807,1.210311726277958,1.216456411113584,-18.885868050348975,False,-0.001019969979883082,2.6905475346483123,-78.01726277957943,0.3421052631579132,10,1,1.19377345303333,TP3,2022-07-05 13:00:00+00:00,50.51928180001886,2130.833185561657,180,True
+2022-07-11 11:00:00+00:00,3,GBP_USD,SHORT,1.1932699999999998,1.1956792274223833,1.1896388628880836,1.187091438146806,1.1832703010348897,355819.25,3,LONDON,1.5,0.0025474247412775614,54.32368240587811,33.488470356616915,1.198836465174524,1.204773985474299,-20.72260057929043,False,-0.0007078392307655831,22.2156162469979,-53.764651745240585,0.518691588785092,11,0,1.1832703010348897,TP3,2022-07-12 07:00:00+00:00,59.23819379066009,2107.808968594733,1200,True
+2022-07-19 10:00:00+00:00,3,GBP_USD,LONG,1.2029100000000001,1.2009549899595164,1.206220050202418,1.20855341700403,1.212053467206448,449270.11,3,LONDON,1.5,0.002333366801612002,43.09712023743237,72.36410360514063,1.1936075688398506,1.193384527024394,8.47687004577713,False,0.000602346516090357,89.48866495361496,91.1243116014937,0.3111111111111064,10,1,1.2009549899595164,SL,2022-07-19 12:00:00+00:00,-19.550100404837067,-878.3275759392194,120,False
+2022-07-19 14:00:00+00:00,3,GBP_USD,LONG,1.20287,1.200914401088967,1.206382994555166,1.2088516575919435,1.2125546521471096,444643.48,3,OVERLAP,1.5,0.0024686630367774087,49.086924133918714,68.74526913004625,1.1948281007520014,1.1937165013724458,7.395050140344139,False,0.00034975267822047245,46.13306604407595,78.51899247998561,0.3022598870056143,14,1,1.200914401088967,SL,2022-07-19 16:00:00+00:00,-19.555989110331407,-869.5443052859861,120,False
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+2022-07-25 12:00:00+00:00,3,GBP_USD,LONG,1.2072200000000002,1.2044791068415448,1.2111719657922764,1.2139332763204607,1.2180752421127372,310935.28,4,LONDON,1.5,0.0027613105281842766,35.80331287122776,68.47850889721838,1.1994750107717735,1.1968233280844807,26.25496917284087,False,0.0008552782915292457,87.81711053731702,75.54989228226505,0.573604060913736,12,0,1.2044791068415448,SL,2022-07-25 14:00:00+00:00,-27.40893158455382,-852.2403816744086,120,False
+2022-07-25 16:00:00+00:00,3,GBP_USD,LONG,1.2052600000000002,1.2025995138484318,1.2092010021864121,1.21195500364402,1.2160860058304321,317129.25,5,OVERLAP,1.5,0.00275400145760803,31.676096488050238,60.10542784619666,1.2003032827926519,1.197147772248862,2.809363190965364,False,0.00033520866317066794,34.03550394055532,47.667172073482256,0.5654450261781535,16,0,1.2025995138484318,SL,2022-07-26 09:00:00+00:00,-26.604861515684416,-843.7179778822863,1020,False
+2022-07-27 09:00:00+00:00,3,GBP_USD,LONG,1.2071600000000002,1.2053505912821512,1.2100420435892443,1.2120900726487402,1.2151621162379844,461631.9,3,LONDON,1.5,0.002048029059496078,45.70293934469303,61.89174496911155,1.2034560933710572,1.1995066100972367,5.384765569524941,False,0.00028594303848548166,55.60189662583199,35.13906628942909,0.3421052631579524,9,2,1.2053505912821512,SL,2022-07-27 12:00:00+00:00,-18.094087178490703,-835.2807842972303,180,False
+2022-08-01 13:00:00+00:00,3,GBP_USD,LONG,1.2262400000000002,1.2234520098007524,1.2301024509962377,1.2328040849937296,1.2368565359899675,296603.62,5,OVERLAP,1.5,0.0027016339974918297,37.234266842859356,67.39401695937008,1.2177657975363902,1.2086051685790913,20.598488319554242,False,0.0006640625002522682,70.40497913719277,82.84202463609834,0.34920634920635574,13,0,1.2234520098007524,SL,2022-08-02 06:00:00+00:00,-27.87990199247803,-826.9279856214196,1020,False
+2022-08-03 10:00:00+00:00,3,GBP_USD,LONG,1.21836,1.2160951492107097,1.2216525872797852,1.2239743121329756,1.2274568994127608,361462.53,4,LONDON,1.5,0.0023217248531902167,21.38858075369215,48.53235414829713,1.219204161857301,1.2129964575825083,0.3106574111688687,False,0.00032992362826302557,39.50131233595753,-10.341618573010969,0.4529914529914773,10,2,1.2160951492107097,SL,2022-08-03 13:00:00+00:00,-22.648507892903957,-818.6586963694034,180,False
+2022-08-04 09:00:00+00:00,3,GBP_USD,LONG,1.2181300000000002,1.216178041510696,1.2209855067322344,1.2230158445537238,1.2260613512859582,415209.71,5,LONDON,1.5,0.002030337821489545,18.76677041173286,58.740862403778365,1.2167815473918167,1.2134257024993274,11.849080859662742,False,0.0004828663463760088,84.55398289447187,11.58452608183369,0.7925531914893134,9,3,1.216178041510696,SL,2022-08-04 11:00:00+00:00,-19.51958489304184,-810.4721182760285,120,False
+2022-08-15 14:00:00+00:00,3,GBP_USD,SHORT,1.2076999999999998,1.209599020060716,1.2045234711249908,1.2022791185416513,1.1989125896666422,422516.55,5,OVERLAP,1.5,0.002244352583339445,38.47296343682649,35.981265664377915,1.2131794070358632,1.2137130199395911,-5.250686334368204,False,-4.975760037059752e-05,52.12517064253376,-52.89407035863336,0.49710982658964065,14,0,1.2043091851898,TP1+TP2+SL,2022-08-16 08:00:00+00:00,41.17127095382944,1739.5543362527224,1080,True
+2022-08-24 08:00:00+00:00,3,GBP_USD,SHORT,1.1820199999999998,1.1838865714801419,1.1790604759326233,1.1769607932210389,1.1738112691536622,439181.11,4,LONDON,1.5,0.0020996827115844336,19.902194425369473,53.71797260131111,1.181579716235172,1.1932616183943008,-2.266666666665973,False,-9.965617995505872e-05,67.86809295805399,6.30283764827988,0.3055555555555299,8,2,1.1802038650675029,TP1+TP2+SL,2022-08-24 14:00:00+00:00,35.707193250344105,1568.1924766670631,360,True
+2022-09-06 12:00:00+00:00,3,GBP_USD,SHORT,1.1544699999999999,1.1571562771291921,1.1507036143540395,1.1480660239233993,1.1441096382774387,311004.72,3,LONDON,1.5,0.0026375904306403073,30.556746830681668,48.04855715344566,1.1546698529228239,1.163613565030064,-27.518652868661064,False,-0.00026809179832703147,26.11557301138541,-0.09852922823849752,0.7790697674418905,12,1,1.1544699999999999,TP1+SL,2022-09-06 15:00:00+00:00,15.065542583841738,468.54548529357766,180,True
+2022-09-12 14:00:00+00:00,3,GBP_USD,LONG,1.1704700000000001,1.1684070186635842,1.1741115733487444,1.1766659555812407,1.180497528929985,407240.87,4,OVERLAP,1.5,0.0025543822324962423,41.234634188329416,71.22545033666128,1.16066052645762,1.1590210358409627,15.502717183979442,False,0.00038031559398431355,72.44436283136149,96.19473542380018,0.5839694656489015,14,0,1.1684070186635842,SL,2022-09-12 18:00:00+00:00,-20.62981336415914,-840.1303142357797,240,False
+2022-09-15 09:00:00+00:00,3,GBP_USD,SHORT,1.1511699999999998,1.1532993128258802,1.148313435870599,1.1462823931176653,1.1432358289882645,390609.12,4,LONDON,1.5,0.002031042752933865,41.49344413932471,41.08864153032632,1.155214993268114,1.1580969688891527,-6.602075050268219,False,-0.0001067280912062569,63.02525014582269,-38.54993268114004,0.5150375939849768,9,3,1.1511699999999998,TP1+SL,2022-09-15 13:00:00+00:00,11.426256517602873,446.3200003235123,240,True
+2022-09-15 14:00:00+00:00,3,GBP_USD,SHORT,1.14882,1.1509529610245348,1.145245194877325,1.1427353247955416,1.1389705196728666,392033.52,3,OVERLAP,1.5,0.002509870081783394,52.679176632624966,36.83821842179837,1.1542977435166446,1.1577100442433197,-14.495638673335254,False,-0.00024286603204547977,56.40023204914783,-52.87743516644516,0.6343750000000185,14,3,1.1389705196728666,TP3,2022-09-16 07:00:00+00:00,58.33688196279984,2287.0013181700933,1020,True
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+2022-10-04 15:00:00+00:00,3,GBP_USD,LONG,1.14453,1.1400824139822627,1.1509445967553522,1.1553476612589204,1.1619522580142727,191220.95,4,OVERLAP,1.5,0.004403064503568195,37.40488571375256,69.905116137483,1.1261465392171144,1.1118985884757429,70.20393687822369,False,0.00017556941219669126,86.2780307168105,181.9346078288553,0.8888888888888998,15,1,1.1400824139822627,SL,2022-10-05 07:00:00+00:00,-44.47586017737314,-850.4716235184462,960,False
+2022-10-07 09:00:00+00:00,3,GBP_USD,LONG,1.1220100000000002,1.1189091007244325,1.1272244963778377,1.130827493963063,1.1362319903409006,271523.46,4,LONDON,1.5,0.003602997585225124,26.329233559116812,52.86396375003529,1.1238133946625335,1.1199665278646342,19.141294849029933,False,0.0011140162505362781,80.9429274086399,-19.933946625334542,0.701086956521722,9,4,1.1189091007244325,SL,2022-10-07 10:00:00+00:00,-31.008992755676697,-841.9669004136273,60,False
+2022-10-20 09:00:00+00:00,3,GBP_USD,LONG,1.12077,1.1178680767624098,1.1248521161879506,1.1277001936465845,1.1319723098345351,287239.58,3,LONDON,1.5,0.0028480774586338298,29.491774442869996,42.91168889065368,1.1250023551380792,1.1227313960828944,9.329858407780556,False,0.00028837541836273985,36.42121760519021,-44.22355138079315,0.5178997613364997,9,3,1.1319723098345351,TP3,2022-10-20 14:00:00+00:00,66.4538590072099,1908.8178550610191,300,True
+2022-10-25 10:00:00+00:00,3,GBP_USD,LONG,1.13348,1.1305101987403514,1.1379640062982428,1.1410800104970715,1.1457540167953144,287101.84,3,LONDON,1.5,0.00311600419882864,28.371176524192716,60.41266412890328,1.1289810040894093,1.1251753060690346,11.322211161794993,False,0.00023759986030520317,81.2986163347219,43.0899591059064,0.4229249011857461,10,1,1.1305101987403514,SL,2022-10-25 12:00:00+00:00,-29.6980125964863,-852.6354060794396,120,False
+2022-10-25 12:00:00+00:00,3,GBP_USD,LONG,1.13646,1.133382446873058,1.1411827656347093,1.1444579427245156,1.1493707083592253,274279.28,4,LONDON,1.5,0.0032751770898063193,32.92978812656829,64.43397542611046,1.1293530914335532,1.1253457413341217,37.24152426470173,False,0.000460176263391012,82.29178121620346,69.16908566446666,0.8466780238500653,12,1,1.1493707083592253,TP3,2022-10-25 14:00:00+00:00,76.7042501553501,2103.8386505549315,120,True
+2022-10-26 15:00:00+00:00,3,GBP_USD,LONG,1.16151,1.1579892752172525,1.1668436239137383,1.1705260398562305,1.176049663769969,245729.92,4,OVERLAP,1.5,0.0036824159424922215,57.45510096933072,75.39910952911761,1.143988362122833,1.1312838174329163,32.58321947870568,False,0.0004963393770331575,65.42575866039063,173.3163787716685,0.34626865671634727,15,2,1.1579892752172525,SL,2022-10-27 09:00:00+00:00,-35.20724782747564,-865.1474192065764,1080,False
+2022-11-03 11:00:00+00:00,3,GBP_USD,SHORT,1.12465,1.127417847693631,1.1198507615318454,1.1165246025530755,1.1115353640849208,309444.76,3,LONDON,1.5,0.0033261589787697923,43.254412493739935,19.23115967867703,1.142784371618022,1.144957377133951,-14.09066359412181,False,-0.0014875549978230736,11.516574123538403,-179.4437161802187,0.3848039215685826,11,3,1.127417847693631,SL,2022-11-03 12:00:00+00:00,-27.678476936310314,-856.4959652722081,60,False
+2022-11-10 14:00:00+00:00,3,GBP_USD,LONG,1.1659400000000002,1.1614646528823203,1.1728392355883974,1.177565392647329,1.1846546282357262,189467.09,3,OVERLAP,1.5,0.004726157058931563,37.21100677944773,78.0858562409384,1.1436602526007698,1.1425039148513183,135.8329269192504,False,0.002989408106414403,82.9587863437336,220.89747399230308,0.6801619433198667,14,3,1.1659400000000002,TP1+SL,2022-11-11 00:00:00+00:00,27.596942353588886,522.8712360632238,600,True
+2022-11-11 08:00:00+00:00,3,GBP_USD,LONG,1.17683,1.1733087889909628,1.1826710550451862,1.1866917584086436,1.19272281345383,242291.56,3,LONDON,1.5,0.004020703363457506,42.76017085789718,73.97208596116043,1.1569258450660869,1.1469051607227483,16.43333333333219,False,7.053091251905865e-05,84.95078640248202,197.14154933913042,0.3674176776429602,8,4,1.1733087889909628,SL,2022-11-11 09:00:00+00:00,-35.21211009037239,-853.1597084688068,60,False
+2022-11-22 11:00:00+00:00,3,GBP_USD,LONG,1.18771,1.1855367130454892,1.1910114347725533,1.1933390579542555,1.196830492726809,388640.86,4,LONDON,1.5,0.0023276231817022913,31.6683999060252,62.346707150575625,1.1845379690426703,1.1780080190232243,19.569393003397995,False,0.000498493236971365,70.8193041526359,29.82030957329629,0.5159817351597706,11,1,1.1855367130454892,SL,2022-11-22 16:00:00+00:00,-21.732869545108002,-844.6281110278583,300,False
+2022-11-23 09:00:00+00:00,3,GBP_USD,LONG,1.1920600000000001,1.1901047727125922,1.195196136437038,1.1974135607283969,1.200739697165435,427664.77,4,LONDON,1.5,0.002217424291358749,36.87770237788063,65.67241969796493,1.1870349215175184,1.180115685103547,15.528546955152489,False,8.56838744481489e-05,65.70492253177274,48.35078482481547,0.5147601476014908,9,2,1.200739697165435,TP3,2022-11-23 14:00:00+00:00,51.31818299260837,2194.697892635177,300,True
+2022-11-29 08:00:00+00:00,3,GBP_USD,LONG,1.20524,1.2022145991927606,1.2093770040361962,1.2122616733936606,1.2165886774298569,283641.36,4,LONDON,1.5,0.0028846693574642313,31.886206836053308,57.830967490669046,1.2032149719379521,1.1958336506408909,6.933333333332126,False,0.0009864288672277693,63.313302770051244,18.350280620478276,0.3592436974789587,8,1,1.2022145991927606,SL,2022-11-29 09:00:00+00:00,-30.254008072394534,-858.1287995104964,60,False
+2022-12-08 14:00:00+00:00,3,GBP_USD,LONG,1.2231100000000001,1.2212610717609138,1.2269146411954301,1.229577735325717,1.2335723765211473,459481.07,4,OVERLAP,1.5,0.00266309413028679,25.069971221048426,62.59712284276789,1.2188642834126733,1.2141206120456138,36.96741190055253,False,0.00019149191565337855,75.89703069791551,40.557165873267294,0.4104477611939985,14,3,1.2212610717609138,SL,2022-12-08 15:00:00+00:00,-18.489282390863337,-849.5475256486045,60,False
+2022-12-08 16:00:00+00:00,3,GBP_USD,LONG,1.2234900000000002,1.2212300210591553,1.227449894704223,1.230216491173705,1.234366385877928,372150.39,4,OVERLAP,1.5,0.0027665964694819707,29.697366898080066,61.974886649471046,1.2191458302475313,1.2142868126437059,32.100605769578294,False,0.00039838263874966175,82.25637848926397,41.54169752468739,0.46176470588236446,16,3,1.2234900000000002,TP1+SL,2022-12-09 09:00:00+00:00,15.83957881689102,589.4705434141732,1020,True
+2022-12-12 10:00:00+00:00,3,GBP_USD,LONG,1.22869,1.2267359722443998,1.232120138778001,1.2345335646300015,1.2381537034080026,433436.4,4,LONDON,1.5,0.002413425852000672,32.948688928120546,62.86810839788178,1.2241969894075362,1.2180912730780895,20.256043119382117,False,0.00034030574814050636,89.01433419924128,43.030105924637404,0.41970802919708,10,0,1.2267359722443998,SL,2022-12-12 14:00:00+00:00,-19.540277556002735,-846.9467558874626,240,False
+2022-12-13 11:00:00+00:00,3,GBP_USD,LONG,1.22923,1.226728210808073,1.2326989459596351,1.2351382432660585,1.2387971892256937,335151.05,4,LONDON,1.5,0.0024392973064234643,16.84862652935603,58.04199395483094,1.2261741614152701,1.2201222170480361,14.87776010500319,False,8.749483014030803e-05,60.869565217389656,28.65838584729774,0.366101694915243,11,1,1.2387971892256937,TP3,2022-12-13 13:00:00+00:00,56.64313535416143,1898.4006289239323,120,True
+2022-12-22 11:00:00+00:00,3,GBP_USD,SHORT,1.2042599999999999,1.2069177884713067,1.2003910576434658,1.197685096072443,1.1936261537159085,322622.1,5,LONDON,1.5,0.002705961571022848,26.14864082054445,32.67893126360019,1.2124065715146553,1.218413145937896,-47.53699648420317,False,-0.00026682007782239726,11.850803232091401,-79.56571514655231,0.815286624203837,11,3,1.2042599999999999,TP1+SL,2022-12-22 16:00:00+00:00,15.475769426136488,499.2825231375948,300,True
+2022-12-27 11:00:00+00:00,3,GBP_USD,SHORT,1.2035299999999998,1.2057037313140695,1.200701343429653,1.1986889057160885,1.1956702491457416,396762.06,5,LONDON,1.5,0.002012437713564561,35.365448518563156,36.65114624828937,1.2068968321620746,1.2119652436149146,-22.846877138025246,False,-0.00048365958716773175,15.531553828628438,-31.768321620746498,0.7294832826748233,11,1,1.2035299999999998,TP1+SL,2022-12-27 15:00:00+00:00,11.314626281387243,448.9214431533342,240,True
+2023-01-02 15:00:00+00:00,3,GBP_USD,SHORT,1.2040899999999999,1.205733147388028,1.20178926305986,1.200128771766433,1.1976380348262927,527611.42,5,OVERLAP,1.5,0.0016604912934268126,39.61233520104831,41.97448648765867,1.2057005503446514,1.2075928069047268,-7.507245084730219,False,-0.0003041163808925834,41.40516318886446,-14.205503446513834,0.7857142857142372,15,0,1.205733147388028,SL,2023-01-02 23:00:00+00:00,-16.431473880280656,-866.9433266667787,480,False
+2023-01-05 12:00:00+00:00,3,GBP_USD,SHORT,1.1999399999999998,1.2023416204788446,1.1966218976057772,1.1942831626762953,1.1907750602820726,357372.83,5,LONDON,1.5,0.0023387349294818087,24.10047764830157,38.361150004588374,1.2030876686434242,1.2050119242407744,-18.385703346723847,False,-0.0005573983792845743,37.93656140575873,-29.576686434242827,0.5892351274787712,12,3,1.1907750602820726,TP3,2023-01-05 14:00:00+00:00,54.22963830756266,1938.019931185008,120,True
+2023-01-06 11:00:00+00:00,3,GBP_USD,SHORT,1.1845299999999999,1.1868287351751343,1.1810063241243285,1.1785305402072142,1.1748168643315429,381798.7,4,LONDON,1.5,0.0024757839171143047,54.68646846067637,24.384657916913184,1.195146779391259,1.201960659650146,-21.63649402459944,False,-0.00031772171614862345,6.642861331718547,-104.26779391258911,0.3221757322175616,11,4,1.1868287351751343,SL,2023-01-06 13:00:00+00:00,-22.987351751344587,-877.6541015106087,120,False
+2023-01-09 08:00:00+00:00,3,GBP_USD,LONG,1.2158300000000002,1.2136290321452339,1.220086505940497,1.2230508432341618,1.2274973491746588,394770.67,5,LONDON,1.5,0.0029643372936647,66.99897484397984,72.5537214358076,1.2032383704612342,1.2031523803428528,2.4333333333337315,False,0.00028721565866692694,60.16575895400248,124.0162953876589,0.47619047619047616,8,0,1.2136290321452339,SL,2023-01-09 10:00:00+00:00,-22.009678547663114,-868.8775546745594,120,False
+2023-01-09 16:00:00+00:00,3,GBP_USD,LONG,1.22063,1.2185282997906237,1.2250735010468816,1.2281625017448028,1.2327960027916844,409282.34,4,OVERLAP,1.5,0.0030890006979210943,44.656201814506886,71.77585547321388,1.2068732734924459,1.2041666501444996,40.255919152634156,False,2.384491950361764e-05,87.71147890033474,135.66726507554083,0.8424242424242799,16,0,1.2185282997906237,SL,2023-01-09 17:00:00+00:00,-21.017002093763892,-860.1887796720586,60,False
+2023-01-12 16:00:00+00:00,3,GBP_USD,LONG,1.21819,1.2139437257082137,1.2242813714589318,1.228468952431553,1.2347503238904847,200549.2,5,OVERLAP,1.5,0.0041875809726212,36.335229980015264,53.464572293389544,1.214960677290015,1.209989250942893,17.05022231546005,False,6.67602621777602e-05,40.50715214564347,30.393227099849707,0.6595174262734315,16,3,1.21819,TP1+SL,2023-01-13 05:00:00+00:00,24.36548583572673,488.64786919663277,780,True
+2023-01-17 14:00:00+00:00,3,GBP_USD,LONG,1.22917,1.2266668891498296,1.232945554250852,1.2355892570847533,1.2395548113356052,342163.58,4,OVERLAP,1.5,0.0026437028339013307,23.54164350786119,69.57699986812808,1.2211145904924967,1.2157173548504399,47.50965758314018,False,0.001000329197173209,83.82790486289925,78.65409507503252,0.840262582056914,14,1,1.2266668891498296,SL,2023-01-17 15:00:00+00:00,-25.031108501705198,-856.4733696311888,60,False
+2023-01-18 08:00:00+00:00,3,GBP_USD,LONG,1.23408,1.2321493746493404,1.2374931267532983,1.239895211255497,1.2434983380087956,439188.6,3,LONDON,1.5,0.0024020845021988963,28.99161720353728,73.51141910546423,1.2248697052175554,1.2177773869691861,9.133333333333216,False,0.00032913044153600694,85.68846099076954,90.20294782444482,0.8033333333333701,8,2,1.2434983380087956,TP3,2023-01-18 14:00:00+00:00,55.75002805277184,2448.4776770457593,360,True
+2023-01-25 12:00:00+00:00,3,GBP_USD,LONG,1.23323,1.2309382979744896,1.2362913672704106,1.2384589454506845,1.241710312721095,380674.89,5,LONDON,1.5,0.002167578180273809,27.326962172754072,51.52367329163334,1.2338512713460013,1.2304143530788794,18.04033563744145,False,0.00011078742383428906,55.40969135865814,-8.11271346001341,0.8059210526315448,12,2,1.2309382979744896,SL,2023-01-25 13:00:00+00:00,-22.917020255104337,-872.3934164739616,60,False
+2023-01-25 14:00:00+00:00,3,GBP_USD,LONG,1.2358600000000002,1.233473202991123,1.239341485044385,1.2417891417406413,1.2454606267850261,361852.93,5,OVERLAP,1.5,0.002447656696256501,25.057536141287954,59.82317383944479,1.2338340032686463,1.2304773430429126,36.56239238363668,False,0.00038129755200180316,80.96586446864133,18.359967313537062,0.7773654916512307,14,2,1.2358600000000002,TP1+SL,2023-01-25 16:00:00+00:00,13.925940177538896,503.914225624717,120,True
+2023-02-02 16:00:00+00:00,3,GBP_USD,SHORT,1.22565,1.2281001268383223,1.2203393658083896,1.216672276347316,1.2111716421557055,354556.59,4,OVERLAP,1.5,0.003667089461073623,30.26084040155006,32.105743073764884,1.2337301723397067,1.2341495495373416,-48.536232388689534,False,-0.001287563419918085,18.027011830585835,-78.9017233970668,0.5989304812834155,16,3,1.22565,TP1+SL,2023-02-03 10:00:00+00:00,21.242536766441056,753.1681398858968,1080,True
+2023-02-03 15:00:00+00:00,3,GBP_USD,SHORT,1.21023,1.2135671080467332,1.2048094597663337,1.2010690996105562,1.19545855937689,262574.75,4,OVERLAP,1.5,0.003740360155777508,49.189898844986054,26.461515053590574,1.2258807599704131,1.2314719077019745,-66.9206284859758,False,-0.0007317366236135,18.8215645695866,-154.60759970413073,0.4005681818181792,15,4,1.2026880874522796,TP1+TP2+SL,2023-02-07 09:00:00+00:00,73.4095875878804,1927.55041084908,5400,True
+2023-02-07 10:00:00+00:00,3,GBP_USD,SHORT,1.19932,1.2014721378414044,1.196313596507263,1.1941826608454387,1.1909862573527017,416105.23,5,LONDON,1.5,0.0021309356618245865,28.026366128681797,32.29744096226902,1.2074885261616966,1.2218319113241478,-14.615189634172854,False,-5.291011544273521e-05,29.708097928437113,-79.78526161696475,0.5,10,1,1.19932,TP1+SL,2023-02-07 15:00:00+00:00,12.025613970947369,500.39208672722685,300,True
+2023-02-10 09:00:00+00:00,3,GBP_USD,SHORT,1.20979,1.2118788120856234,1.206935939571883,1.2049065659531386,1.2018625055250216,431115.72,5,LONDON,1.5,0.0020293736187446077,31.67116679272586,45.21006481736551,1.210461017678496,1.2153942859398639,-18.416405382326584,False,-0.0002095730501146728,65.66432471328459,-4.810176784959541,0.7451923076922895,9,4,1.2118788120856234,SL,2023-02-10 13:00:00+00:00,-20.888120856235034,-900.5197262382784,240,False
+2023-02-14 15:00:00+00:00,3,GBP_USD,SHORT,1.2153999999999998,1.2184772950124314,1.210270191604509,1.2067236526741818,1.201403844278691,289707.2,4,OVERLAP,1.5,0.003546538930327252,52.76074250662424,51.47946104743903,1.21307002802256,1.213737426757569,-33.09461926532142,False,-7.20640917382994e-05,39.698992984262226,25.199719774400275,0.36909323116220405,15,1,1.2184772950124314,SL,2023-02-14 18:00:00+00:00,-30.77295012431591,-891.5145216255215,180,False
+2023-02-15 10:00:00+00:00,3,GBP_USD,SHORT,1.2065299999999999,1.2088909882484993,1.2028883920908369,1.2003339868180616,1.1965023789088984,373826.25,5,LONDON,1.5,0.0025544052727753813,46.87583173902944,28.216815724033168,1.2136025374614718,1.213877758538334,-21.05649580870894,False,-0.001188893132141151,20.127992910646505,-68.82537461471827,0.8348082595869867,10,2,1.2045192722784577,TP1+TP2+SL,2023-02-16 02:00:00+00:00,43.37193980748965,1621.356961345958,960,True
+2023-02-21 08:00:00+00:00,3,GBP_USD,SHORT,1.20034,1.2018847921148303,1.1982131822829918,1.1966686371383197,1.1943518194213114,581834.25,5,LONDON,1.5,0.001544545144672181,26.222784003455963,37.11959009223378,1.2023817179399248,1.206349408092125,-8.933333333331905,False,-0.00035439533835244884,43.662897348914306,-18.517179399246686,0.310457516339865,8,1,1.2018847921148303,SL,2023-02-21 09:00:00+00:00,-15.447921148303845,-898.8129615382506,60,False
+2023-02-23 13:00:00+00:00,3,GBP_USD,SHORT,1.20325,1.2053627885197247,1.2004260574013763,1.198416762335627,1.1954028197370032,421161.33,5,OVERLAP,1.5,0.002009295065749201,27.65911924711482,40.54794546740866,1.2061489982518294,1.2069365259089955,-2.28980463866435,False,-8.869364955658835e-05,47.82565284280765,-27.08998251829353,0.3685567010309498,13,3,1.2053627885197247,SL,2023-02-23 14:00:00+00:00,-21.127885197247398,-889.8248229760027,60,False
+2023-02-23 15:00:00+00:00,3,GBP_USD,SHORT,1.2021499999999998,1.203945582958334,1.1988308352083294,1.1964913920138824,1.192982227222212,490607.56,5,OVERLAP,1.5,0.002339443194447009,23.508104571206616,39.78039228152324,1.2059804478287743,1.2068780169431979,-14.801481846211662,False,-4.75033006352533e-05,45.896138968257134,-36.404478287743736,0.7280701754386106,15,3,1.203945582958334,SL,2023-02-24 08:00:00+00:00,-17.95582958334263,-880.9265739659545,1020,False
+2023-02-24 14:00:00+00:00,3,GBP_USD,SHORT,1.19461,1.1967619874904327,1.1915233958811697,1.1893389931352827,1.1860623890164523,405261.33,3,OVERLAP,1.5,0.00218440274588695,34.86505228331477,24.00744081387451,1.2027203757222054,1.205630515950489,-30.997050834287165,False,-0.0005915309896367049,15.863648918171902,-79.20375722205274,0.44628099173552205,14,4,1.1967619874904327,SL,2023-02-27 08:00:00+00:00,-21.519874904327718,-872.1173125161474,3960,False
+2023-03-01 12:00:00+00:00,3,GBP_USD,LONG,1.20717,1.204574315392394,1.2107459230380302,1.2132565383967169,1.217022461434747,332627.52,5,LONDON,1.5,0.0025106153586867594,27.93397203156631,54.92235680482073,1.2047002700110288,1.204430778744513,9.625606930048214,False,0.00017289441519917944,58.00176834659589,22.79729988971191,0.6962457337883866,12,2,1.204574315392394,SL,2023-03-01 13:00:00+00:00,-25.956846076060632,-863.3961337301781,60,False
+2023-03-02 12:00:00+00:00,3,GBP_USD,SHORT,1.19423,1.1968614981729537,1.1904910805638023,1.1878718009396703,1.1839428815034725,324819.6,5,LONDON,1.5,0.002619279624131873,40.66015377004731,32.92850712387336,1.201580564549935,1.203491848868886,-28.5022981802463,False,-0.00040712984439118067,18.902763721728704,-71.60564549934855,0.8210290827740679,12,3,1.1968614981729537,SL,2023-03-03 00:00:00+00:00,-26.314981729538275,-854.7621839395931,720,False
+2023-03-06 11:00:00+00:00,3,GBP_USD,SHORT,1.1996699999999998,1.2017315529780808,1.1970244573318183,1.1951340955530305,1.1922985528848489,410474.32,5,LONDON,1.5,0.0018903617787877792,25.330272790305067,42.27604305852451,1.2008901344454241,1.202029602317263,-21.514742456039126,False,-0.0005333548092282773,20.003041681847968,-10.301344454242134,0.46724890829697285,11,0,1.2017315529780808,SL,2023-03-06 13:00:00+00:00,-20.615529780809716,-846.2145568217618,120,False
+2023-03-07 09:00:00+00:00,3,GBP_USD,SHORT,1.2009999999999998,1.2029275308650622,1.198485679008022,1.1966827983467034,1.1939784773547253,434624.64,4,LONDON,1.5,0.0018028806613186606,17.942492195464325,42.96367385377329,1.2020884726952374,1.2021905749932194,-12.51746637775275,False,-0.0001688610320369514,30.276048802765725,-8.984726952374622,0.44382022471907623,9,1,1.1939784773547253,TP3,2023-03-07 15:00:00+00:00,41.369135871645916,1798.0045785325194,360,True
+2023-03-16 15:00:00+00:00,3,GBP_USD,LONG,1.2111600000000002,1.2084430846893777,1.2155745765531127,1.2186442942551876,1.2232488708083002,314964.72,5,OVERLAP,1.5,0.0030697177020750296,30.402737674877624,58.85940496208391,1.2089593466989461,1.2050442531626049,33.69329168549484,False,0.0005993861354672539,88.95114969190645,20.106533010539884,0.4578947368421219,15,3,1.2232488708083002,TP3,2023-03-20 11:00:00+00:00,71.77322484979953,2260.603366831415,5520,True
+2023-03-22 08:00:00+00:00,3,GBP_USD,LONG,1.2276300000000002,1.2262597247862137,1.2300613760689325,1.2318089601148874,1.2344303361838198,640994.22,5,LONDON,1.5,0.001747584045954954,31.65022045086145,69.81174545592262,1.2224197901189104,1.2144965341690246,1.9333333333348968,False,0.0005666520551581308,70.11420036962674,50.20209881089643,0.5703703703704222,8,2,1.2262597247862137,SL,2023-03-22 12:00:00+00:00,-13.702752137865202,-878.3384918464238,240,False
+2023-03-23 08:00:00+00:00,3,GBP_USD,LONG,1.2322400000000002,1.230333229058264,1.2353005213753463,1.237467535625577,1.2407180570009233,456035.43,4,LONDON,1.5,0.0021670142502308158,57.308448755360445,63.99319113248855,1.2263306290992917,1.217439243310845,2.80000000000058,False,0.00029760480353423126,43.51688480554642,57.19370900708398,0.44117647058822435,8,3,1.230333229058264,SL,2023-03-23 10:00:00+00:00,-19.067709417361907,-869.5551063261688,120,False
+2023-03-27 13:00:00+00:00,3,GBP_USD,LONG,1.2277900000000002,1.2258717408806856,1.2305698670251428,1.232549778375238,1.2355196454003807,448771.26,5,OVERLAP,1.5,0.00197991135009516,17.183969995028725,59.58453435746492,1.2251312240353307,1.2207859512137869,17.406639727968187,False,0.0004333856268591643,65.58726259215196,24.68775964669323,0.8019559902200073,13,0,1.2300712306327126,TP1+TP2+SL,2023-03-28 08:00:00+00:00,34.721042866946654,1558.1806155913664,1140,True
+2023-03-28 16:00:00+00:00,3,GBP_USD,LONG,1.2345700000000002,1.2328954204700844,1.2371528976495774,1.2390014960826288,1.241774393732206,523379.96,4,OVERLAP,1.5,0.0018485984330514945,29.761617200814044,67.23668436942916,1.2288903818398866,1.2230772007965023,29.63338297698126,False,0.00016151219611293182,85.06788628381587,54.89618160113396,0.3904761904762126,16,1,1.2328954204700844,SL,2023-03-28 23:00:00+00:00,-16.745795299157873,-876.4413673841436,420,False
+2023-04-07 08:00:00+00:00,3,GBP_USD,LONG,1.2441900000000001,1.243101790693621,1.245853546531895,1.247089244219825,1.2489427907517199,797343.8,3,LONDON,1.5,0.0012356976879299901,18.333059481391107,45.687644420921735,1.2448747257328407,1.2392814714030558,3.833333333331801,False,3.058676929892961e-05,23.840115311950925,-8.747257328407532,0.3636363636363803,8,4,1.243101790693621,SL,2023-04-07 12:00:00+00:00,-10.882093063790954,-867.6769435436722,240,False
+2023-04-10 09:00:00+00:00,3,GBP_USD,LONG,1.24361,1.2422596246955868,1.2453843765220671,1.246693960870112,1.2486583373921791,636119.59,4,LONDON,1.5,0.0013095843480448151,26.819670982914165,56.77486327817918,1.2430449481344623,1.2398878111773377,3.4551452037678843,False,0.00029558013677086193,89.22051140143458,3.750518655376567,0.34883720930229856,9,0,1.2422596246955868,SL,2023-04-10 11:00:00+00:00,-13.503753044132871,-859.0001849895053,120,False
+2023-04-11 09:00:00+00:00,3,GBP_USD,LONG,1.2442700000000002,1.242618986971759,1.2463475651412055,1.2478592752353423,1.2501268403765478,515083.87,4,LONDON,1.5,0.0015117100941369312,42.49310264889152,68.99493619804969,1.2410913387176934,1.2398078671183532,3.794757887742861,False,0.0006750816147421338,76.00975648889217,29.886612823066816,0.34269662921350347,9,1,1.242618986971759,SL,2023-04-11 11:00:00+00:00,-16.510130282412216,-850.4101800069077,120,False
+2023-04-17 09:00:00+00:00,3,GBP_USD,LONG,1.2402900000000001,1.2384230358667452,1.2428973206662743,1.244762201110457,1.2475595217767315,450949.25,3,LONDON,1.5,0.0018648804441828727,46.49544593929871,39.555056404734145,1.2448258756135078,1.2437991704456763,9.401707316303298,False,0.0002247172480095965,34.26694283854088,-47.2587561350779,0.7105263157894584,9,0,1.2402900000000001,TP1+SL,2023-04-17 13:00:00+00:00,10.429282665096906,470.3077195863451,240,True
+2023-04-21 09:00:00+00:00,3,GBP_USD,SHORT,1.23823,1.2399147034836526,1.2359679111531647,1.2343331852552746,1.2318810964084392,502527.1,4,LONDON,1.5,0.0016347258978902054,55.48817400623747,24.708902735487356,1.2429435327844236,1.2429608916359307,-13.762909867476303,False,-0.0006296302702697576,27.631130814519384,-45.23532784423523,0.5555555555555644,9,4,1.2399147034836526,SL,2023-04-21 12:00:00+00:00,-16.84703483652683,-846.6091559998802,180,False
+2023-04-24 10:00:00+00:00,3,GBP_USD,LONG,1.2449000000000001,1.243126303474052,1.2474201492964065,1.249226915494011,1.2519370647904176,472540.28,5,LONDON,1.5,0.0018067661976044103,20.028980225160048,56.021175166484454,1.2430583823874237,1.2429418650718105,5.2308288572877615,False,6.622714246167313e-05,71.55910518206541,16.516176125762616,0.6626016260162337,10,0,1.243126303474052,SL,2023-04-24 11:00:00+00:00,-17.73696525948054,-838.1430530065209,60,False
+2023-04-24 14:00:00+00:00,3,GBP_USD,LONG,1.2464100000000002,1.2445106421852627,1.249159646216542,1.2511194103609031,1.2540590565774448,436864.3,5,OVERLAP,1.5,0.00195976414436119,25.995916451409258,59.68911878140056,1.243351087576763,1.2430230108013613,17.04250968078913,False,0.00020077582393479793,77.63772840068384,28.689124232370578,0.5350553505535715,14,0,1.2464100000000002,TP1+SL,2023-04-25 07:00:00+00:00,10.998584866166894,480.4889078548594,1020,True
+2023-05-02 16:00:00+00:00,3,GBP_USD,LONG,1.2479900000000002,1.2465559463966691,1.250810268016655,1.252817113361092,1.255827381377747,581963.27,4,OVERLAP,1.5,0.002006845344436717,25.018324319698646,47.06665103046649,1.2494870020090523,1.247608345612647,12.58100391921424,False,1.147378539566226e-05,64.57178065311007,-16.87002009052252,0.3246753246752685,16,1,1.2465559463966691,SL,2023-05-02 20:00:00+00:00,-14.340536033310425,-834.5665243498164,240,False
+2023-05-03 08:00:00+00:00,3,GBP_USD,LONG,1.2522600000000002,1.2508156847033163,1.2542749098167516,1.2557448496945858,1.2579497595113374,572050.2,5,LONDON,1.5,0.0014699398778343221,43.43536751997369,66.4641865715001,1.2491261039776989,1.2477435994052675,-0.7666666666650279,False,0.0005924904327802093,79.76619442671263,29.438960223011623,0.4405286343612839,8,2,1.2508156847033163,SL,2023-05-03 09:00:00+00:00,-14.443152966838733,-826.2208543310691,60,False
+2023-05-03 16:00:00+00:00,3,GBP_USD,LONG,1.2552500000000002,1.2536230802383532,1.257744598808235,1.2595343313470582,1.262218930155293,502765.2,3,OVERLAP,1.5,0.001789732538823235,45.57128010946445,70.93181292551449,1.249921706901282,1.2480652639594665,33.37732122820736,False,0.0005628508355773247,92.76649528350168,51.382930987180494,0.49180327868865287,16,2,1.2552500000000002,TP1+SL,2023-05-03 19:00:00+00:00,9.978395232939086,501.67898749676664,180,True
+2023-05-12 15:00:00+00:00,3,GBP_USD,SHORT,1.2460399999999998,1.247480587121046,1.2431670643947712,1.2411251073246188,1.23806217171939,571277.8,5,OVERLAP,1.5,0.002041957070152462,39.18037872418495,29.414725079447024,1.2545121372624066,1.2564198929948733,-43.02423379465292,False,-0.00026482962049694664,15.035930626677436,-82.82137262406674,0.3823529411765058,15,4,1.247480587121046,SL,2023-05-15 06:00:00+00:00,-14.405871210461108,-822.9754412195559,3780,False
+2023-05-18 10:00:00+00:00,3,GBP_USD,SHORT,1.24311,1.2445922874239468,1.2408840174257205,1.239273362376201,1.2368573798019213,549654.32,5,LONDON,1.5,0.0016106550495197014,41.96178307205559,32.38874582511319,1.247719499112834,1.2515886709028659,-6.448270168635251,False,-0.000523922384772823,17.16832796169466,-44.19499112833902,0.5705521472391802,10,3,1.2445922874239468,SL,2023-05-18 11:00:00+00:00,-14.822874239468485,-814.7456860540567,60,False
+2023-05-18 14:00:00+00:00,3,GBP_USD,SHORT,1.2404799999999998,1.2421630353930864,1.237929823034569,1.2361030383909484,1.2333628614255172,479252.09,5,OVERLAP,1.5,0.0018267846436206645,51.9082378865611,27.814950176069885,1.246924396254474,1.2512277384754715,-23.19312292673681,False,-0.0005958084264897075,16.915317495478337,-62.543962544741035,0.6234817813765644,14,3,1.2421630353930864,SL,2023-05-18 15:00:00+00:00,-16.830353930865627,-806.5982296807067,60,False
+2023-05-23 09:00:00+00:00,3,GBP_USD,SHORT,1.2383,1.23965616349263,1.23604668253685,1.2344178042280831,1.231974486764933,588817.09,5,LONDON,1.5,0.0016288783087667705,39.553446896740866,28.602227515296534,1.243472680474811,1.2473058278618363,-13.52819739288691,False,-0.00042629020014114705,24.70246866798564,-49.82680474810985,0.7759740259739981,9,1,1.23965616349263,SL,2023-05-23 13:00:00+00:00,-13.561634926300135,-798.532241294641,240,False
+2023-05-24 13:00:00+00:00,3,GBP_USD,SHORT,1.23772,1.239650620333415,1.234688148332926,1.2325402472215432,1.229318395554469,409478.19,5,OVERLAP,1.5,0.002147901111382745,22.987667935968357,39.54248738719113,1.24184344388998,1.245792067751096,-18.6049180634007,False,-0.0004033969878976903,40.476053474293444,-39.334438899798485,0.6135135135135059,13,2,1.23772,TP1+SL,2023-05-25 08:00:00+00:00,12.127406668295926,496.59085319277466,1140,True
+2023-05-31 08:00:00+00:00,3,GBP_USD,SHORT,1.2349899999999998,1.2362924317567117,1.2327803412164418,1.2311805686940698,1.2287809099105118,610790.41,4,LONDON,1.5,0.0015997725223720564,22.863702057385208,36.21599411266467,1.2380654629954018,1.2394705025607222,-2.933333333332566,False,-0.000740643166186321,4.7592141002001656,-28.85462995401822,0.5348837209302686,8,2,1.2362924317567117,SL,2023-05-31 09:00:00+00:00,-13.024317567118526,-795.5128266790528,60,False
+2023-05-31 11:00:00+00:00,3,GBP_USD,SHORT,1.2358999999999998,1.2375896086259095,1.2336544568704528,1.2320307614507549,1.2295952183212078,466118.42,4,LONDON,1.5,0.0016236954196980377,24.965564375345643,41.1206649581323,1.2378551416570254,1.2393739608373233,1.1703454022948812,False,-0.0005968934084111234,34.395083289925076,-17.651416570254597,0.3312500000000789,11,2,1.2375896086259095,SL,2023-05-31 13:00:00+00:00,-16.89608625909722,-787.5577031274107,120,False
+2023-06-01 14:00:00+00:00,3,GBP_USD,LONG,1.25211,1.2504278456237894,1.2552585496588315,1.2574842494313858,1.2608227990902174,463502.12,4,OVERLAP,1.5,0.0022256997725543914,31.91283741674501,75.24034547709621,1.2421841780655039,1.2404100071880317,41.85690489308502,False,0.0007498447418432816,95.12960940686725,97.35821934496069,0.31325301204818096,14,3,1.2504278456237894,SL,2023-06-02 12:00:00+00:00,-16.82154376210665,-779.6821195409208,1320,False
+2023-06-07 10:00:00+00:00,3,GBP_USD,LONG,1.2456500000000001,1.2442630186453891,1.247529906773055,1.2489098446217584,1.2509797513948135,556521.76,5,LONDON,1.5,0.0013799378487033692,24.514962887500566,64.44131266957606,1.2429200809964405,1.242789481082422,14.522868218314056,False,0.0003429469951670615,96.46662625082121,25.39919003559543,0.3787878787878754,10,2,1.2465647350921274,TP1+TP2+SL,2023-06-07 14:00:00+00:00,22.388475763506662,1245.9673935624073,240,True
+2023-06-08 08:00:00+00:00,3,GBP_USD,LONG,1.2479500000000001,1.2468766753732845,1.2499337659907208,1.2513829433178683,1.253556709308589,730762.12,5,LONDON,1.5,0.0014491773271472746,30.10154092900233,65.15174867247075,1.2441585650876115,1.2432357471273283,7.800000000000029,False,0.00018232235268785283,66.85815602836847,36.014349123885125,0.8320312499999482,8,3,1.2468766753732845,SL,2023-06-08 09:00:00+00:00,-10.733246267156105,-784.3449796669081,60,False
+2023-06-08 12:00:00+00:00,3,GBP_USD,LONG,1.24848,1.2468227337283373,1.2507334742154566,1.252362457025761,1.2548059312412174,468543.61,5,LONDON,1.5,0.0016289828103044051,49.70164624222698,63.640056962742406,1.244554089478735,1.2433756825553828,10.698327579961031,False,0.00025406805709324634,62.450514569469874,37.359105212648736,0.5171339563862659,12,3,1.2548059312412174,TP3,2023-06-08 15:00:00+00:00,37.19558744730465,1742.775481863081,180,True
+2023-06-08 15:00:00+00:00,3,GBP_USD,LONG,1.2546100000000002,1.2531077465122915,1.2573512674385416,1.2593054457309028,1.2622367131694443,528492.22,4,OVERLAP,1.5,0.0019541782923610724,64.7381625109872,76.7676545760275,1.2455220618999008,1.2436625882161023,47.82297200003116,False,0.000909854954671866,88.3421942319942,88.97938100099311,0.3962264150943317,15,3,1.2576205495535173,TP1+TP2+SL,2023-06-12 10:00:00+00:00,35.76795178480995,1890.3084243607175,5460,True
+2023-06-13 14:00:00+00:00,3,GBP_USD,LONG,1.2606100000000002,1.2589162465409864,1.2630101958664954,1.2647369931108254,1.2673271889773208,479900.05,5,OVERLAP,1.5,0.0017267972443301516,60.99060832162134,73.91062781056587,1.254650425045951,1.2495928810695702,21.941169519856807,False,0.000801130969010775,77.94092322801994,57.695749540491725,0.5714285714286004,14,1,1.2673271889773208,TP3,2023-06-14 12:00:00+00:00,39.54313386392271,1897.6751918453206,1320,True
+2023-06-22 08:00:00+00:00,3,GBP_USD,LONG,1.2777200000000002,1.2764590716579394,1.2800096417103033,1.2816627361838389,1.2841423778941423,659679.93,4,LONDON,1.5,0.0016530944735355523,23.641785327174876,57.96918142111773,1.2760268342979024,1.2703350000895937,-0.36666666666684833,False,4.424933220105004e-05,55.58712827297855,15.031657020976308,0.449781659388703,8,3,1.2764590716579394,SL,2023-06-22 11:00:00+00:00,-12.609283420608186,-831.809120425697,180,False
+2023-07-04 15:00:00+00:00,3,GBP_USD,LONG,1.2732400000000001,1.2720714550871932,1.2752593912307004,1.2767323187178339,1.278941709948534,704714.91,4,OVERLAP,1.5,0.0014729274871335394,58.95865966404195,66.9123376365016,1.269209412380414,1.2688888979542365,16.216212761177573,False,0.00034729243267782805,71.48262413746197,38.40587619585944,0.3333333333333847,15,1,1.2720714550871932,SL,2023-07-04 16:00:00+00:00,-11.685449128069347,-823.4910230596969,60,False
+2023-07-05 13:00:00+00:00,3,GBP_USD,LONG,1.27313,1.2719259288743,1.2751603556285007,1.2766405927141677,1.2788609483426683,677083.02,5,OVERLAP,1.5,0.0014802370856671003,22.462812207190535,63.52715091362638,1.2702963150366229,1.2693242392598516,18.596380462929663,False,5.9063416832046405e-05,62.70280975655063,26.436849633770798,0.6517857142857161,13,2,1.2719259288743,SL,2023-07-05 14:00:00+00:00,-12.040711257002013,-815.2561140838919,60,False
+2023-07-07 10:00:00+00:00,3,GBP_USD,LONG,1.27635,1.2748381991512998,1.2785856709101677,1.2802027848502795,1.2826284557604473,533868.96,5,LONDON,1.5,0.0016171139401118212,15.994787407554215,63.57667873028556,1.2726417715879375,1.2705132670416732,16.91217974225223,False,0.00010172890644836086,66.09611155367755,35.18228412062507,0.5458167330676752,10,4,1.2769799769808274,TP1+TP2+SL,2023-07-07 13:00:00+00:00,25.613777003442806,1367.4400490499927,180,True
+2023-07-07 16:00:00+00:00,3,GBP_USD,LONG,1.28506,1.2828935632822023,1.2881855169223224,1.290395861537204,1.2937113784595264,378860.8,3,OVERLAP,1.5,0.0022103446148816156,47.09054932758144,82.39958240876801,1.2744140543673306,1.2711115565931332,62.109162071544155,False,0.0010865061949449654,97.89820332108316,104.55945632669383,0.6358381502889439,16,4,1.2828935632822023,SL,2023-07-10 00:00:00+00:00,-21.664367177978146,-820.7779480542544,3360,False
+2023-07-10 16:00:00+00:00,3,GBP_USD,LONG,1.2847700000000002,1.2829528690558838,1.287598988053914,1.2896116467565235,1.2926306348104375,447172.05,4,OVERLAP,1.5,0.0020126587026093492,30.300111364376324,62.76515026726169,1.2786881333690656,1.2733362009016802,47.54639827026619,False,9.161174366714697e-05,98.20664461594326,58.91866630934439,0.6878980891719241,16,0,1.2880683214891246,TP1+TP2+SL,2023-07-11 07:00:00+00:00,37.2791822199976,1667.0208335639877,900,True
+2023-07-20 10:00:00+00:00,3,GBP_USD,SHORT,1.2893599999999998,1.2910224570762574,1.2866627146187133,1.2847378576978554,1.2818505723165685,498804.09,3,LONDON,1.5,0.0019248569208578608,39.7854588079564,33.21756963399898,1.296915286596488,1.296935834681977,-16.829956417965253,False,-3.346121929877124e-05,24.200574634105397,-73.65286596487941,0.6376811594203132,10,3,1.2881726781196898,TP1+TP2+SL,2023-07-21 02:00:00+00:00,31.652354494343896,1578.832387990862,960,True
+2023-07-21 11:00:00+00:00,3,GBP_USD,SHORT,1.28222,1.2842143320985193,1.279613339507403,1.277748899179005,1.274952238686408,423715.14,4,LONDON,1.5,0.0018644403283980143,32.41310848486942,29.852265517742566,1.2904150144310405,1.294670589871857,-28.468368006167033,False,-0.0001611336972337867,17.210986549031166,-80.05014431040402,0.6864686468646202,11,4,1.2842143320985193,SL,2023-07-21 13:00:00+00:00,-19.943320985194024,-845.0287043306424,120,False
+2023-07-24 12:00:00+00:00,3,GBP_USD,SHORT,1.2834299999999998,1.2851981511090689,1.2807042444546555,1.2787604074244259,1.2758446518790814,473137.4,4,LONDON,1.5,0.001943837030229654,26.410985816978695,43.476714858878324,1.287007414992606,1.2925500735779962,3.6548669004243806,False,-0.00012571169611676019,48.89850672621242,-33.87414992606086,0.562724014336929,12,0,1.2834299999999998,TP1+SL,2023-07-24 17:00:00+00:00,10.903022181377509,515.8627567039283,300,True
+2023-07-27 08:00:00+00:00,3,GBP_USD,LONG,1.2993700000000001,1.2978360826910118,1.301899586544941,1.3037126442415683,1.3064322307865093,548749.95,3,LONDON,1.5,0.0018130576966273104,40.026483558227156,69.61301826774671,1.2916100270859419,1.2910264910695057,4.100000000000215,False,0.0002362113702055362,76.90228967867615,75.69972914058143,0.4559585492227967,8,3,1.2978360826910118,SL,2023-07-27 09:00:00+00:00,-15.339173089883307,-841.737046611481,60,False
+2023-08-01 10:00:00+00:00,3,GBP_USD,SHORT,1.2798599999999998,1.2814507947277622,1.2778460263611893,1.2763767106019823,1.274172736963172,523838.6,5,LONDON,1.5,0.0014693157592070205,23.923683589244177,32.55739999250643,1.284045187146178,1.2873318501496556,-17.86694324011906,False,-0.00019631462246353076,24.202420242028023,-39.951871461780804,0.7579908675798591,10,1,1.2788134118704693,TP1+TP2+SL,2023-08-01 14:00:00+00:00,24.082228406372863,1261.5200813274591,240,True
+2023-08-03 08:00:00+00:00,3,GBP_USD,SHORT,1.2673499999999998,1.269213088243855,1.264856376962544,1.2630672949375734,1.2603836719001176,454049.82,5,LONDON,1.5,0.0017890820249705519,37.91216858351066,31.29510410282633,1.2747289906311827,1.282458880926651,-7.033333333332781,False,-0.00012216439185473867,9.300170053363331,-71.88990631182834,0.4400000000000517,8,3,1.2673499999999998,TP1+SL,2023-08-03 11:00:00+00:00,9.974492149822822,452.8916365218466,180,True
+2023-08-03 13:00:00+00:00,3,GBP_USD,SHORT,1.2655899999999998,1.2677413995520637,1.2620230022396803,1.259518337066134,1.2557613393058145,395307.23,3,OVERLAP,1.5,0.002504665173546364,58.1565071252599,34.22215289418672,1.273166343427954,1.2816617391193312,-6.189848434994261,False,-0.00023120730796794518,49.25178596308931,-73.86343427954189,0.5793650793650682,13,3,1.2677413995520637,SL,2023-08-03 14:00:00+00:00,-21.513995520638925,-850.4637975496182,60,False
+2023-08-04 11:00:00+00:00,3,GBP_USD,SHORT,1.26931,1.2707670077190933,1.267144961404534,1.2655749356742232,1.2632198970787571,577868.7,5,LONDON,1.5,0.001570025730310732,31.96783777440692,42.36187185321441,1.2720178124397816,1.2795980413314654,-6.01422838772514,False,-0.00021944248321942688,18.757110872052206,-25.178124397815527,0.7317073170730518,11,4,1.2707670077190933,SL,2023-08-04 12:00:00+00:00,-14.570077190934061,-841.9591565224717,60,False
+2023-08-07 08:00:00+00:00,3,GBP_USD,SHORT,1.2725799999999998,1.273949623404118,1.2701004544079821,1.2683207573466369,1.2656512117546188,608590.33,5,LONDON,1.5,0.0017796970613452737,18.05048278588276,46.65827729423143,1.2734670905865833,1.278685808906779,-2.5666666666701587,False,-0.0003737669754561884,56.01598213141779,-6.970905865832933,0.6623376623376661,8,0,1.273949623404118,SL,2023-08-07 11:00:00+00:00,-13.696234041182809,-833.5395594880679,180,False
+2023-08-08 09:00:00+00:00,3,GBP_USD,SHORT,1.27294,1.2743249927371072,1.2708550363144635,1.2693383938574392,1.2670634301719026,595818.41,5,LONDON,1.5,0.001516642457024373,33.247808408083856,36.45507171137166,1.2752210400950696,1.278157111018229,-13.497816806176122,False,-0.0004776883420527335,32.15380636667134,-20.910400950695518,0.8884297520661126,9,1,1.2717375681327132,TP1+TP2+SL,2023-08-08 14:00:00+00:00,25.15114304696242,1498.5514059923705,300,True
+2023-08-09 15:00:00+00:00,3,GBP_USD,SHORT,1.2711899999999998,1.2730564339093366,1.268822830453317,1.2671180507555282,1.2645608812088451,450157.75,5,OVERLAP,1.5,0.0017047796977887038,28.180870220118987,36.22076710838991,1.2743080256490593,1.2770246025020164,-21.927175614810945,False,-0.0003555194171306313,37.508469546014844,-29.280256490593626,0.8018018018018938,15,2,1.2730564339093366,SL,2023-08-09 17:00:00+00:00,-18.664339093368287,-840.1896891507708,120,False
+2023-08-10 16:00:00+00:00,3,GBP_USD,SHORT,1.2713499999999998,1.2731934098008575,1.2682979509957129,1.2661365849928548,1.2628945359885677,451222.4,5,OVERLAP,1.5,0.002161366002858051,42.69557770873938,40.762761543264695,1.2738298686090928,1.2762034176325874,-43.8305463336186,False,-0.00016103865795670064,7.923830762256201,-22.8986860909286,0.41000000000002107,16,3,1.2713499999999998,TP1+SL,2023-08-11 06:00:00+00:00,12.20819601714762,550.8611506527791,840,True
+2023-08-17 08:00:00+00:00,3,GBP_USD,SHORT,1.2710199999999998,1.2725702402854293,1.2688373700014242,1.2672556166690405,1.2648829866704647,540107.5,5,LONDON,1.5,0.001581753332383814,22.361391083605,44.08291694119828,1.2719753250557677,1.2725263237647808,-9.133333333333216,False,-0.00011340887771832541,58.622764959609775,-7.653250557677627,0.6726190476190865,8,3,1.2725702402854293,SL,2023-08-17 10:00:00+00:00,-15.502402854294939,-837.2964049626104,120,False
+2023-08-17 16:00:00+00:00,3,GBP_USD,LONG,1.27614,1.2747196925303876,1.2788140373480614,1.2807233955801025,1.283587432928164,583622.53,5,OVERLAP,1.5,0.0019093582320410258,27.032633794266225,59.01189832259213,1.2729258262935115,1.2727474757902042,7.049456316723379,False,0.0002673985581134195,41.325478348439134,30.241737064884422,0.5505617977527627,16,3,1.2747196925303876,SL,2023-08-17 17:00:00+00:00,-14.20307469612414,-828.9234387930952,60,False
+2023-08-18 16:00:00+00:00,3,GBP_USD,LONG,1.2746400000000002,1.273154600520216,1.2773381085100326,1.2792635141833877,1.28215162269342,552467.0,5,OVERLAP,1.5,0.0019254056733549998,19.58309090637866,53.997932548915855,1.2732057730209971,1.2729115976840504,20.768288222174913,False,0.00010480744800724675,78.01771871539381,12.442269790029492,0.6902654867256203,16,4,1.273154600520216,SL,2023-08-18 20:00:00+00:00,-14.853994797843216,-820.6341943980049,240,False
+2023-08-22 09:00:00+00:00,3,GBP_USD,LONG,1.2792500000000002,1.2779507737645577,1.2812761311772118,1.2827535519620197,1.2849696831392314,625316.7,5,LONDON,1.5,0.0014774207848078248,57.25776419099395,64.9705981170361,1.2753161780795024,1.2737038153122113,5.319627906474889,False,0.0002385249222828621,65.63940623684088,37.43821920497714,0.45098039215686914,9,1,1.2779507737645577,SL,2023-08-22 10:00:00+00:00,-12.99226235442541,-812.4278621003527,60,False
diff --git a/results/phase1/S3_USD_JPY_report.json b/results/phase1/S3_USD_JPY_report.json
new file mode 100644
index 0000000..5ebe122
--- /dev/null
+++ b/results/phase1/S3_USD_JPY_report.json
@@ -0,0 +1,43 @@
+{
+  "pair": "USD_JPY",
+  "strategy_id": 3,
+  "strategy_name": "S3_Key_Level_Breakout",
+  "total_trades": 220,
+  "win_rate_pct": 35.45,
+  "avg_rr": 1.6,
+  "expectancy_pips": -1.37,
+  "sharpe_ratio": -0.98,
+  "max_drawdown_pct": -26.12,
+  "profit_factor": 0.87,
+  "total_pnl_pips": -300.7,
+  "total_pnl_dollars": -17192.04,
+  "avg_win_pips": 28.66,
+  "avg_loss_pips": 17.86,
+  "max_consecutive_wins": 3,
+  "max_consecutive_losses": 10,
+  "avg_hold_time_minutes": 609.8,
+  "best_trade_pips": 111.53,
+  "worst_trade_pips": -60.24,
+  "best_trade_dollars": 3133.4,
+  "worst_trade_dollars": -1073.07,
+  "final_equity": 82807.96,
+  "starting_equity": 100000.0,
+  "session_breakdown": {
+    "LONDON": {
+      "trades": 116,
+      "win_rate": 37.06896551724138,
+      "total_pnl_pips": -213.5047343767306
+    },
+    "OVERLAP": {
+      "trades": 104,
+      "win_rate": 33.65384615384615,
+      "total_pnl_pips": -87.1917729201391
+    }
+  },
+  "exit_reasons": {
+    "SL": 142,
+    "TP3": 28,
+    "TP1+TP2+SL": 25,
+    "TP1+SL": 25
+  }
+}
\ No newline at end of file
diff --git a/results/phase1/S3_USD_JPY_trades.csv b/results/phase1/S3_USD_JPY_trades.csv
new file mode 100644
index 0000000..4cb30e4
--- /dev/null
+++ b/results/phase1/S3_USD_JPY_trades.csv
@@ -0,0 +1,221 @@
+timestamp,strategy_id,pair,signal_direction,entry_price,sl_price,tp1_price,tp2_price,tp3_price,lot_size,confluence_score,session,spread_at_entry,atr_at_entry,adx_at_entry,rsi_at_entry,ema_50_value,ema_200_value,vwap_deviation,news_within_60min,macd_hist_at_entry,stoch_k_at_entry,distance_from_ema50_pips,candle_body_ratio,hour_of_day,day_of_week,exit_price,exit_reason,exit_time,pnl_pips,pnl_dollars,hold_time_minutes,win
+2021-01-21 10:00:00+00:00,3,USD_JPY,SHORT,103.38499999999999,103.47040953066688,103.27445234666563,103.18808724444271,103.05853959110834,11708.3,3,LONDON,1.5,0.08636510222291202,33.026375874284675,36.04120810886376,103.646760000638,103.72374143171936,-5.303151204171286,False,-0.0002211145860231134,42.86602155613226,-24.27600006380004,0.3,10,3,103.47040953066688,SL,2021-01-21 13:00:00+00:00,-8.540953066689383,-1000.000407907193,180,False
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+2023-05-17 08:00:00+00:00,3,USD_JPY,LONG,137.06499999999997,136.93962901578058,137.28785492109714,137.44909153516193,137.69094645625907,6625.71,2,LONDON,1.5,0.16123661406476836,46.34754057637566,78.16515811479805,136.1968301762237,135.47087554711004,2.5999999999982037,False,0.051733783275042355,93.09153424421687,84.91698237762932,0.36904761904750627,8,2,136.93962901578058,SL,2023-05-17 09:00:00+00:00,-12.537098421938706,-830.6717838522352,60,False
+2023-05-24 13:00:00+00:00,3,USD_JPY,LONG,138.855,138.62561215335532,139.16093923322333,139.37756538870553,139.70250462192885,3585.04,5,OVERLAP,1.5,0.21662615548221018,17.910647081291668,60.0860711672677,138.4595200089129,137.48010232715563,24.637199332570958,False,0.021127744489578208,62.40779478742591,37.647999108710906,0.6049382716050321,13,2,139.03265858590717,TP1+TP2+SL,2023-05-24 22:00:00+00:00,36.69335659529907,1315.4715112841097,540,True
+2023-06-06 11:00:00+00:00,3,USD_JPY,LONG,139.611,139.47383660399584,139.8778169800209,140.06836163336814,140.354178613389,6091.42,3,LONDON,1.5,0.19054465334724813,38.69823000199075,51.15100066691687,139.60025820887037,139.41101784724748,22.579190601138066,False,0.00481365234911324,79.15196586598086,-0.8258208870358885,0.7936507936508533,11,1,139.611,TP1+SL,2023-06-06 15:00:00+00:00,10.672679200836228,650.1177153755782,240,True
+2023-06-12 08:00:00+00:00,3,USD_JPY,SHORT,139.18400000000003,139.36889695714814,138.94351521425935,138.7705253570989,138.51104057135822,4554.0,3,LONDON,1.5,0.17298985716044213,15.472377231057209,39.92709676645084,139.41781315391705,139.44137152203632,-9.499999999999886,False,-0.01243162483465337,38.5686270271454,-21.481315391704925,0.8123324396782358,8,0,139.36889695714814,SL,2023-06-12 11:00:00+00:00,-18.489695714811205,-842.0207428525023,180,False
+2023-06-12 12:00:00+00:00,3,USD_JPY,SHORT,139.18300000000002,139.3721927085878,138.92603645706114,138.74206076176858,138.4660972188297,4406.1,3,LONDON,1.5,0.18397569529257085,19.402000602899978,42.748275320208634,139.39367488615844,139.43403773767596,-4.81985747097724,False,-0.027578874194735245,36.47775058055453,-19.16748861584381,0.5928338762214275,12,0,139.3721927085878,SL,2023-06-12 13:00:00+00:00,-18.919270858776827,-833.6019933085659,60,False
+2023-06-22 10:00:00+00:00,3,USD_JPY,LONG,142.07799999999997,141.9188319413382,142.33650695997568,142.52151159995947,142.79901855993515,5184.87,4,LONDON,1.5,0.18500463998378774,15.766282870012208,59.18497250058365,141.77155740955547,141.05912072747012,10.36841217953679,False,0.0199794603665288,87.47043075603199,28.744259044452747,0.5633802816901032,10,3,141.9188319413382,SL,2023-06-22 11:00:00+00:00,-15.916805866177695,-825.2656923136875,60,False
+2023-07-03 10:00:00+00:00,3,USD_JPY,LONG,144.754,144.60111467398113,145.01192663009437,145.1965443834906,145.4734710135849,5343.96,3,LONDON,1.5,0.18461775339622555,19.31250010924841,56.271996282544734,144.51690405469378,143.59858719642446,3.735394482291099,False,0.034842608182430214,58.44822006472489,21.809594530623144,0.8057142857143451,10,0,144.60111467398113,SL,2023-07-03 12:00:00+00:00,-15.288532601886118,-817.0130668317533,120,False
+2023-07-05 16:00:00+00:00,3,USD_JPY,LONG,144.65299999999996,144.52614019944866,144.9007990027566,145.0786650045943,145.34546400735093,6375.88,3,OVERLAP,1.5,0.17786600183773593,18.545909891180422,56.076802677433115,144.48078098062913,143.97398718569767,25.776767106927423,False,0.01595584208966192,87.85868170922497,15.321901937085158,0.7102803738317186,16,2,144.52614019944866,SL,2023-07-05 18:00:00+00:00,-12.68598005513013,-808.842865139031,120,False
+2023-07-07 16:00:00+00:00,3,USD_JPY,SHORT,142.08700000000002,142.40705257883198,141.6207371058401,141.29722850973354,140.81196561557365,2501.94,2,OVERLAP,1.5,0.32350859610658445,58.749225855726976,18.15112768680926,143.68375133470258,143.89827750415574,-65.84549023797592,False,-0.13453970645194846,6.500145605445717,-157.7751334702583,0.7077922077921515,16,4,142.40705257883198,SL,2023-07-10 00:00:00+00:00,-32.005257883196236,-800.7523490828399,3360,False
+2023-07-10 15:00:00+00:00,3,USD_JPY,SHORT,141.54200000000003,141.79224586554355,141.11643733894883,140.82006223158137,140.37549957053017,3167.87,4,OVERLAP,1.5,0.2963751073674566,43.42750158189582,31.15236893197664,142.83257347876065,143.56804144526356,-49.63604179217498,False,-0.05493640819561024,8.73695531515382,-127.15734787606436,0.7322404371583096,15,0,140.37549957053017,TP3,2023-07-11 10:00:00+00:00,69.23002576819157,2193.1172173028103,1140,True
+2023-07-12 13:00:00+00:00,3,USD_JPY,SHORT,138.79700000000003,139.07715624925174,138.37646875374125,138.08344792290208,137.64391667664333,2907.94,4,OVERLAP,1.5,0.2930208308391716,55.69721329112777,19.73795847154966,140.47586157480953,142.35497947057226,-44.278663184522316,False,-0.01646161783283212,31.701991336189536,-165.98615748095256,0.8221415607985718,13,2,138.5966788099645,TP1+TP2+SL,2023-07-13 01:00:00+00:00,49.369756734979546,1435.6429039991642,720,True
+2023-07-19 16:00:00+00:00,3,USD_JPY,SHORT,139.609,139.84402615619277,139.20286921903602,138.91944869839338,138.4943179174294,3527.41,4,OVERLAP,1.5,0.28342052064264084,39.719867296372605,59.6311822732496,139.1025808657803,139.7991474241207,-7.848309524507613,False,-0.007933947968756572,46.87627287538916,52.54191342196748,0.47386759581888915,16,2,139.609,TP1+SL,2023-07-20 06:00:00+00:00,16.245231238559654,573.0359112320771,840,True
+2023-07-20 08:00:00+00:00,3,USD_JPY,SHORT,139.43500000000003,139.67803786621187,139.08781066894073,138.84368444823454,138.47749511717524,3434.71,4,LONDON,1.5,0.24412622070619056,24.661752497862192,51.19934136870801,139.27473901330396,139.7514112286489,-3.833333333332689,False,-0.023805910303775824,74.60160787258489,17.92609866960504,0.5570032573289869,8,3,139.67803786621187,SL,2023-07-20 12:00:00+00:00,-24.303786621183576,-834.7645894564545,240,False
+2023-07-21 14:00:00+00:00,3,USD_JPY,LONG,141.76699999999997,141.41774699622485,142.25026501887572,142.58510836479286,143.0873733836686,2366.24,5,OVERLAP,1.5,0.33484334591714676,45.79682791866584,69.98606874293776,140.2624662469776,139.95101394172735,11.693289716046706,False,0.09376976932349479,52.20846575454832,148.5533753022395,0.43626062322944076,14,4,141.41774699622485,SL,2023-07-24 01:00:00+00:00,-34.9253003775118,-826.4164276528352,3540,False
+2023-07-31 08:00:00+00:00,3,USD_JPY,LONG,142.432,142.1573777608959,143.0261111955204,143.434851992534,144.04796318805438,2979.19,4,LONDON,1.5,0.4087407970135912,56.00692348456853,74.22798824355556,140.72228084435093,140.48054858488103,7.866666666666333,False,0.10139461559963747,89.1392269132774,169.07191556490773,0.6030150753769131,8,0,142.1573777608959,SL,2023-07-31 11:00:00+00:00,-27.4622239104076,-818.1518285164723,180,False
+2023-08-01 15:00:00+00:00,3,USD_JPY,LONG,143.35599999999997,143.18304422687223,143.71552886563893,143.96788144273157,144.34641030837054,4683.11,3,OVERLAP,1.5,0.25235257709263403,49.785334609318355,76.71175955598656,142.10623092352057,141.03964805028295,26.812488174888927,False,0.025400522186389773,82.8895732694616,123.07690764794188,0.44099378881985934,15,1,143.18304422687223,SL,2023-08-01 21:00:00+00:00,-17.295577312773958,-809.9709106922485,360,False
+2023-08-07 13:00:00+00:00,3,USD_JPY,LONG,142.13199999999998,141.88660399067237,142.45498004663816,142.6829667443969,143.02494679103506,3267.66,4,OVERLAP,1.5,0.22798669775876532,25.80690958568992,48.59128651846086,142.27917667016501,141.96658170047883,-8.143731379720975,False,0.035252082135275586,24.45870773545617,-16.617667016501514,0.31562499999997584,13,0,143.02494679103506,TP3,2023-08-08 00:00:00+00:00,52.81680746210611,1725.8736907162565,660,True
+2023-08-08 10:00:00+00:00,3,USD_JPY,LONG,143.24399999999997,143.01651993679644,143.55740031601775,143.77900052669628,144.11140084271403,3600.89,3,LONDON,1.5,0.22160021067851,33.7374074860484,64.24813222164883,142.6351856577003,142.13444131003453,13.050717534591172,False,0.004850178427578344,49.23922520897842,58.981434229968954,0.6414473684210259,10,1,143.01651993679644,SL,2023-08-08 11:00:00+00:00,-22.748006320352943,-819.1306847889571,60,False
+2023-08-21 10:00:00+00:00,3,USD_JPY,LONG,145.94799999999998,145.75532408806552,146.2248795596725,146.4221325994542,146.7180121591267,4208.82,3,LONDON,1.5,0.19725303978167288,36.214856724306344,65.30084987293714,145.54687231642737,144.98616089850034,20.397037957636144,False,0.07677322159782901,94.14345620934888,38.21276835726337,0.8020304568528592,10,0,145.94799999999998,TP1+SL,2023-08-22 03:00:00+00:00,11.075182386900906,466.1344913363627,1020,True
+2023-08-24 09:00:00+00:00,3,USD_JPY,LONG,145.44699999999997,145.24348873729232,145.71305631353837,145.9030938558973,146.1881501694357,4007.64,4,LONDON,1.5,0.19003754235892217,23.767245478032816,60.71618068584707,145.29847248113762,145.24905983580993,9.732839907357516,False,0.09188593444519523,92.44159055690459,12.952751886237479,0.681564245809968,9,3,145.55967166139757,TP1+TP2+SL,2023-08-24 14:00:00+00:00,31.139440005381402,1247.9566534316673,300,True
diff --git a/results/phase1/S4_EUR_AUD_report.json b/results/phase1/S4_EUR_AUD_report.json
new file mode 100644
index 0000000..3d3aa1b
--- /dev/null
+++ b/results/phase1/S4_EUR_AUD_report.json
@@ -0,0 +1,43 @@
+{
+  "pair": "EUR_AUD",
+  "strategy_id": 4,
+  "strategy_name": "S4_EMA_Ribbon_Scalp",
+  "total_trades": 64,
+  "win_rate_pct": 39.06,
+  "avg_rr": 1.57,
+  "expectancy_pips": 0.03,
+  "sharpe_ratio": -1.44,
+  "max_drawdown_pct": -10.14,
+  "profit_factor": 0.82,
+  "total_pnl_pips": 2.01,
+  "total_pnl_dollars": -6615.42,
+  "avg_win_pips": 12.59,
+  "avg_loss_pips": 8.02,
+  "max_consecutive_wins": 3,
+  "max_consecutive_losses": 5,
+  "avg_hold_time_minutes": 46.2,
+  "best_trade_pips": 36.66,
+  "worst_trade_pips": -14.72,
+  "best_trade_dollars": 2401.09,
+  "worst_trade_dollars": -1017.5,
+  "final_equity": 93384.58,
+  "starting_equity": 100000.0,
+  "session_breakdown": {
+    "LONDON": {
+      "trades": 36,
+      "win_rate": 33.33333333333333,
+      "total_pnl_pips": -37.71266692845352
+    },
+    "OVERLAP": {
+      "trades": 28,
+      "win_rate": 46.42857142857143,
+      "total_pnl_pips": 39.72751125460139
+    }
+  },
+  "exit_reasons": {
+    "TP1+SL": 9,
+    "TP1+TP2+SL": 9,
+    "SL": 39,
+    "TP3": 7
+  }
+}
\ No newline at end of file
diff --git a/results/phase1/S4_EUR_AUD_trades.csv b/results/phase1/S4_EUR_AUD_trades.csv
new file mode 100644
index 0000000..304cd96
--- /dev/null
+++ b/results/phase1/S4_EUR_AUD_trades.csv
@@ -0,0 +1,65 @@
+timestamp,strategy_id,pair,signal_direction,entry_price,sl_price,tp1_price,tp2_price,tp3_price,lot_size,confluence_score,session,spread_at_entry,atr_at_entry,adx_at_entry,rsi_at_entry,ema_50_value,ema_200_value,vwap_deviation,news_within_60min,macd_hist_at_entry,stoch_k_at_entry,distance_from_ema50_pips,candle_body_ratio,hour_of_day,day_of_week,exit_price,exit_reason,exit_time,pnl_pips,pnl_dollars,hold_time_minutes,win
+2021-01-26 09:15:00+00:00,4,EUR_AUD,LONG,1.57556,1.5745854535790755,1.576501821403082,1.5770927321046226,1.5782745535077045,1026118.39,3,LONDON,2.0,0.0011818214030817635,35.446139933878214,39.22682060616931,1.5764017717527499,1.5743397365727645,-20.694768709468292,False,-0.0003551513402744684,20.494008915060636,-10.81771752749816,0.7724550898203394,9,1,1.57556,TP1+SL,2021-01-26 10:30:00+00:00,4.709107015409275,483.21013089894706,75,True
+2021-01-29 12:15:00+00:00,4,EUR_AUD,LONG,1.58065,1.5795543545287916,1.5817954849040285,1.5824882273560428,1.5838737122600715,917114.27,2,LONDON,2.0,0.0013854849040285228,30.77080172284949,37.67021955023534,1.5821237904806478,1.5805945469095344,-28.45583166632304,False,-0.0005062274841317418,3.130566891458591,-17.13790480647681,0.6888888888887902,12,4,1.5819346584245877,TP1+TP2+SL,2021-01-29 13:00:00+00:00,13.811423437445347,1266.665352349358,45,True
+2021-02-05 11:15:00+00:00,4,EUR_AUD,SHORT,1.5743,1.574797633647554,1.5736812211748206,1.5732518317622308,1.5723930529370511,2044674.35,2,LONDON,2.0,0.0008587788251795345,34.705269566074755,63.268008928873705,1.5735892658630248,1.5756552537831923,19.657738966556426,False,0.00026505584157968494,95.18037096538613,9.507341369752176,0.7014925373133265,11,4,1.574797633647554,SL,2021-02-05 11:30:00+00:00,-4.976336475539522,-1017.4987548505064,15,False
+2021-02-05 11:45:00+00:00,4,EUR_AUD,SHORT,1.57443,1.5750885106450847,1.5738082978497177,1.5733774467745765,1.572515744624294,1529700.05,2,LONDON,2.0,0.0008617021502823527,40.86054120240508,62.34032758611566,1.5736505141626769,1.5756300305702384,19.412399263578628,False,0.0003127442347654099,87.03795326292011,10.194858373231419,0.6590909090909435,11,4,1.5750885106450847,SL,2021-02-05 12:00:00+00:00,-6.585106450847088,-1007.3237667116115,15,False
+2021-02-11 11:15:00+00:00,4,EUR_AUD,SHORT,1.56597,1.5666370556749594,1.565419814416802,1.565024721625203,1.5642345360420047,1495003.44,3,LONDON,2.0,0.0007901855831981574,19.897410570985897,47.38148030013928,1.567017520660665,1.5675312081054025,0.18469194796422173,False,0.00014677893022607816,46.540880503142894,-8.075206606648067,0.15555555555507308,11,3,1.5666370556749594,SL,2021-02-11 11:30:00+00:00,-6.6705567495928255,-997.2505287356494,15,False
+2021-03-04 10:00:00+00:00,4,EUR_AUD,SHORT,1.5479,1.5485997754940337,1.5466407483532212,1.5458911225298315,1.5443918708830526,1410849.67,3,LONDON,2.0,0.001499251646778991,33.6494069741115,61.42895973359406,1.5466963133113576,1.5473292476424345,32.50886483741944,False,0.000538026278820608,97.32665459755474,14.43686688642476,0.7647058823530116,10,3,1.5485997754940337,SL,2021-03-04 10:15:00+00:00,-6.997754940336608,-987.2780248314774,15,False
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+2022-11-02 10:00:00+00:00,4,EUR_AUD,SHORT,1.54129,1.5423811692857397,1.5400261023808677,1.5392741535713015,1.5377702559521693,871538.81,4,LONDON,2.0,0.0015038976191323148,22.356523429462122,51.960444070313976,1.5417142047266292,1.544145289210703,6.573435337278166,False,0.00014286801081249704,66.09455680276189,-1.8420472662916687,0.6363636363637097,10,2,1.5423811692857397,SL,2022-11-02 11:45:00+00:00,-10.911692857396014,-950.9963808020424,105,False
+2022-11-14 16:45:00+00:00,4,EUR_AUD,SHORT,1.5435699999999999,1.544374602682997,1.54206132439001,1.541186986585015,1.5394383109750251,1170125.87,3,OVERLAP,2.0,0.0017486756099899011,19.334617462692364,58.78399903685379,1.5421203956850567,1.5440206197977668,19.226517957866562,False,0.0003168045121907013,91.93986818164183,16.896043149432494,0.7771084337349502,16,0,1.5409867836433455,TP1+TP2+SL,2022-11-14 20:30:00+00:00,19.858851008212497,2323.7355313185026,225,True
+2022-11-30 14:00:00+00:00,4,EUR_AUD,SHORT,1.54412,1.5448619268146209,1.5427869106179302,1.5420003659268953,1.5404272765448255,1300295.06,3,OVERLAP,2.0,0.001573089382069833,34.06371486461709,62.146861720301466,1.542389223201335,1.5450739312342283,30.777563046360257,False,0.0006562615602632635,94.60275005246494,19.70776798664975,0.9374999999999685,14,2,1.5448619268146209,SL,2022-11-30 14:15:00+00:00,-7.419268146209302,-964.7237719331313,15,False
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+2023-05-10 14:30:00+00:00,4,EUR_AUD,SHORT,1.6238,1.62472929497253,1.6222423500915661,1.6213435251373494,1.6195458752289158,1027049.62,3,OVERLAP,2.0,0.0017976499084336832,33.02988707020236,61.96217824605249,1.6209635303784289,1.6236235248917819,41.47149301467978,False,0.0004800969030797121,92.33679844925537,30.76469621571043,0.6832061068701957,14,2,1.6238,TP1+SL,2023-05-10 17:30:00+00:00,7.788249542168834,799.8918732749676,180,True
+2023-06-15 10:00:00+00:00,4,EUR_AUD,SHORT,1.59124,1.5919550683306158,1.5901297722312808,1.5894546583469211,1.588104430578202,1345928.67,4,LONDON,2.0,0.001350227768719186,31.27311719406217,59.227575128091054,1.5904875160464298,1.592281224193754,14.320219126233713,False,0.0005155839551974841,60.752312858191566,9.924839535702112,0.9150943396225091,10,3,1.5919550683306158,SL,2023-06-15 10:15:00+00:00,-7.150683306158532,-962.4309671849157,15,False
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+2023-08-25 12:30:00+00:00,4,EUR_AUD,SHORT,1.6816,1.682296109137502,1.6807863028749934,1.68025945431249,1.6792057571874832,1355072.85,5,LONDON,2.0,0.0010536971250066814,33.757443714896574,57.66471208791184,1.681172957992582,1.6826028657348402,17.682609389479964,False,0.0002589814861972265,65.06864623243345,6.670420074179617,0.5428571428571066,12,4,1.682296109137502,SL,2023-08-25 12:45:00+00:00,-6.961091375019545,-943.2785928658155,15,False
diff --git a/results/phase1/S4_EUR_GBP_report.json b/results/phase1/S4_EUR_GBP_report.json
new file mode 100644
index 0000000..6c5db9f
--- /dev/null
+++ b/results/phase1/S4_EUR_GBP_report.json
@@ -0,0 +1,43 @@
+{
+  "pair": "EUR_GBP",
+  "strategy_id": 4,
+  "strategy_name": "S4_EMA_Ribbon_Scalp",
+  "total_trades": 94,
+  "win_rate_pct": 32.98,
+  "avg_rr": 0.79,
+  "expectancy_pips": -2.28,
+  "sharpe_ratio": -7.33,
+  "max_drawdown_pct": -32.81,
+  "profit_factor": 0.38,
+  "total_pnl_pips": -214.69,
+  "total_pnl_dollars": -32530.68,
+  "avg_win_pips": 4.4,
+  "avg_loss_pips": 5.57,
+  "max_consecutive_wins": 4,
+  "max_consecutive_losses": 8,
+  "avg_hold_time_minutes": 35.6,
+  "best_trade_pips": 15.41,
+  "worst_trade_pips": -9.7,
+  "best_trade_dollars": 1819.36,
+  "worst_trade_dollars": -1004.2,
+  "final_equity": 67469.32,
+  "starting_equity": 100000.0,
+  "session_breakdown": {
+    "LONDON": {
+      "trades": 41,
+      "win_rate": 34.146341463414636,
+      "total_pnl_pips": -62.94165287269469
+    },
+    "OVERLAP": {
+      "trades": 53,
+      "win_rate": 32.075471698113205,
+      "total_pnl_pips": -151.75202233190254
+    }
+  },
+  "exit_reasons": {
+    "TP1+SL": 13,
+    "SL": 63,
+    "TP3": 12,
+    "TP1+TP2+SL": 6
+  }
+}
\ No newline at end of file
diff --git a/results/phase1/S4_EUR_GBP_trades.csv b/results/phase1/S4_EUR_GBP_trades.csv
new file mode 100644
index 0000000..c9309ad
--- /dev/null
+++ b/results/phase1/S4_EUR_GBP_trades.csv
@@ -0,0 +1,95 @@
+timestamp,strategy_id,pair,signal_direction,entry_price,sl_price,tp1_price,tp2_price,tp3_price,lot_size,confluence_score,session,spread_at_entry,atr_at_entry,adx_at_entry,rsi_at_entry,ema_50_value,ema_200_value,vwap_deviation,news_within_60min,macd_hist_at_entry,stoch_k_at_entry,distance_from_ema50_pips,candle_body_ratio,hour_of_day,day_of_week,exit_price,exit_reason,exit_time,pnl_pips,pnl_dollars,hold_time_minutes,win
+2021-01-14 09:30:00+00:00,4,EUR_GBP,SHORT,0.89121,0.8916807057610597,0.890814314129801,0.8904964711947015,0.8898607853245026,2124469.43,3,LONDON,2.0,0.0006356858701989256,35.86848599458359,60.83320773322085,0.8907877791065244,0.8927285132418008,14.535995748826691,False,0.00013697757789584352,98.33201319898399,6.6222089347556246,0.571428571428647,9,3,0.89121,TP1+SL,2021-01-14 10:00:00+00:00,1.9784293509944593,420.31126756024696,30,True
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diff --git a/results/phase1/S4_GBP_AUD_report.json b/results/phase1/S4_GBP_AUD_report.json
new file mode 100644
index 0000000..022b6e6
--- /dev/null
+++ b/results/phase1/S4_GBP_AUD_report.json
@@ -0,0 +1,43 @@
+{
+  "pair": "GBP_AUD",
+  "strategy_id": 4,
+  "strategy_name": "S4_EMA_Ribbon_Scalp",
+  "total_trades": 79,
+  "win_rate_pct": 34.18,
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+  "profit_factor": 0.59,
+  "total_pnl_pips": -194.5,
+  "total_pnl_dollars": -19363.47,
+  "avg_win_pips": 13.23,
+  "avg_loss_pips": 10.61,
+  "max_consecutive_wins": 4,
+  "max_consecutive_losses": 10,
+  "avg_hold_time_minutes": 36.8,
+  "best_trade_pips": 30.01,
+  "worst_trade_pips": -20.79,
+  "best_trade_dollars": 2060.18,
+  "worst_trade_dollars": -1017.88,
+  "final_equity": 80636.53,
+  "starting_equity": 100000.0,
+  "session_breakdown": {
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+      "total_pnl_pips": -178.7168295102963
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+      "win_rate": 42.10526315789473,
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+    }
+  },
+  "exit_reasons": {
+    "SL": 52,
+    "TP1+TP2+SL": 11,
+    "TP1+SL": 12,
+    "TP3": 4
+  }
+}
\ No newline at end of file
diff --git a/results/phase1/S4_GBP_AUD_trades.csv b/results/phase1/S4_GBP_AUD_trades.csv
new file mode 100644
index 0000000..56e5b08
--- /dev/null
+++ b/results/phase1/S4_GBP_AUD_trades.csv
@@ -0,0 +1,80 @@
+timestamp,strategy_id,pair,signal_direction,entry_price,sl_price,tp1_price,tp2_price,tp3_price,lot_size,confluence_score,session,spread_at_entry,atr_at_entry,adx_at_entry,rsi_at_entry,ema_50_value,ema_200_value,vwap_deviation,news_within_60min,macd_hist_at_entry,stoch_k_at_entry,distance_from_ema50_pips,candle_body_ratio,hour_of_day,day_of_week,exit_price,exit_reason,exit_time,pnl_pips,pnl_dollars,hold_time_minutes,win
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+2023-01-05 15:45:00+00:00,4,GBP_AUD,SHORT,1.7627599999999999,1.7640878080765725,1.7606739730780918,1.7595109596171377,1.7571849326952296,638004.48,3,OVERLAP,2.0,0.002326026921908134,37.55890623399192,60.199493400400954,1.7613216936904768,1.7649003497922038,27.243219011605646,False,0.000718479002567296,70.48454295681012,16.783063095231476,0.4830917874395565,15,3,1.7627599999999999,TP1+SL,2023-01-05 16:15:00+00:00,10.430134609540342,665.447260788979,30,True
+2023-01-11 12:15:00+00:00,4,GBP_AUD,SHORT,1.7613699999999999,1.7623278388276988,1.7598505372410038,1.758970805861506,1.7572113431025098,891383.76,3,LONDON,2.0,0.0017594627589960147,30.11460763772895,62.580447929535374,1.7596080315707574,1.7608794950552162,34.042353205494535,False,0.0005696898790227812,94.54539006978108,20.019684292424955,0.9322033898305595,12,2,1.7572113431025098,TP3,2023-01-11 13:15:00+00:00,23.11221000544239,2060.184865656086,60,True
+2023-01-16 14:30:00+00:00,4,GBP_AUD,SHORT,1.75362,1.7547590607772383,1.7522964640758723,1.7515146961138082,1.7499511601896804,767653.35,4,OVERLAP,2.0,0.001563535924127816,24.452221376650993,58.91466398915432,1.7522906128155615,1.753731050982871,15.585330164396005,False,0.0002652206784947978,76.01092824142819,15.693871844384422,0.7969543147208373,14,0,1.7520514629198516,TP1+TP2+SL,2023-01-16 15:45:00+00:00,16.070665439510634,1233.670016136956,75,True
+2023-01-26 15:45:00+00:00,4,GBP_AUD,SHORT,1.74352,1.7445587651024654,1.7419307829917823,1.7410161744876733,1.7391869574794556,853648.74,4,OVERLAP,2.0,0.0018292170082177732,20.85627776094133,59.37408714052674,1.7420044328363107,1.745921452123555,22.3479717274766,False,0.00036104082640559454,74.25617330871462,17.555671636892978,0.7219512195121212,15,3,1.7445587651024654,SL,2023-01-26 16:00:00+00:00,-10.387651024654776,-886.7405208756259,15,False
+2023-03-22 14:30:00+00:00,4,GBP_AUD,LONG,1.8317,1.8307333608514886,1.833215463828372,1.834093195742558,1.8358486595709298,908170.46,3,OVERLAP,2.0,0.0017554638283718428,38.950907460109974,38.70526818374916,1.8325614086875073,1.8296391065276127,-28.003994388197118,False,-0.0005973827267122674,31.195560679257003,-11.014086875071882,0.4372093023255795,14,2,1.8307333608514886,SL,2023-03-22 14:45:00+00:00,-9.66639148511472,-877.873120157672,15,False
+2023-06-01 14:45:00+00:00,4,GBP_AUD,LONG,1.90983,1.908971126657461,1.911319577808463,1.9121843667126948,1.913913944521158,1011900.53,3,OVERLAP,2.0,0.0017295778084631785,31.820014624863376,39.04899787369587,1.9116960629425075,1.9080371668668479,-23.913722976440788,False,-0.0001326490236204864,36.56112031727349,-21.060629425075916,0.9552715654951995,14,3,1.908971126657461,SL,2023-06-01 15:00:00+00:00,-8.588733425389794,-869.0943905180649,15,False
+2023-06-01 15:00:00+00:00,4,GBP_AUD,LONG,1.9079,1.9068029033247853,1.9094503222507158,1.9103454833760736,1.9121358056267894,784254.9,3,OVERLAP,2.0,0.0017903222507158105,35.52212442094239,33.13498726032981,1.9115377859643699,1.9080334139627002,-41.76610155353089,False,-0.00042165566218366787,13.786793195119634,-38.777859643699394,0.7596899224806836,15,3,1.9068029033247853,SL,2023-06-01 15:15:00+00:00,-10.970966752146259,-860.403443310779,15,False
+2023-06-15 10:15:00+00:00,4,GBP_AUD,SHORT,1.85899,1.8598344520937324,1.8575151596875585,1.856657739531338,1.8549428992188965,1008700.69,4,LONDON,2.0,0.001714840312441353,20.62799744655298,54.37052252561439,1.8590053977590848,1.8601656457414129,14.280000285258332,False,0.0003545576351869286,63.07710551280701,2.2460224091513936,0.4516129032256139,10,3,1.8598344520937324,SL,2023-06-15 10:30:00+00:00,-8.444520937325173,-851.7994096199349,15,False
+2023-06-21 09:45:00+00:00,4,GBP_AUD,LONG,1.87683,1.8757519571367534,1.8786501428774893,1.879680214316234,1.8817403571937235,782233.66,2,LONDON,2.0,0.0020601428774894175,33.362805476837124,34.35057419080468,1.8808481276658906,1.87658310383697,-37.80520867331827,False,-0.0008397312535329974,4.505208144441254,-42.581276658906205,0.7984496124031422,9,2,1.8757519571367534,SL,2023-06-21 10:00:00+00:00,-10.78042863246642,-843.2814145543003,15,False
+2023-07-12 11:30:00+00:00,4,GBP_AUD,LONG,1.92977,1.9285440957339832,1.9312496808867228,1.9321095213300843,1.933829202216807,681006.36,3,LONDON,2.0,0.0017196808867228214,27.422666403842566,42.32558476161397,1.931411891991314,1.9293383429012467,-30.248623674091757,False,-0.00044376368870335017,28.56699476811033,-18.818919913139442,0.711711711711809,11,2,1.9285440957339832,SL,2023-07-12 12:15:00+00:00,-12.259042660167639,-834.8486019085482,45,False
+2023-07-18 13:45:00+00:00,4,GBP_AUD,LONG,1.92111,1.9196910630022617,1.9229331233257942,1.9239646849886913,1.9260278083144855,582478.37,4,OVERLAP,2.0,0.0020631233257941578,25.307141578988702,44.66448975420481,1.9215762556855462,1.9182484304563296,-24.968821729030033,False,-0.0004278458516547834,35.458256674996385,-7.062556855461644,0.7299035369775028,13,1,1.9196910630022617,SL,2023-07-18 14:00:00+00:00,-14.189369977384292,-826.5001095753739,15,False
+2023-07-18 14:15:00+00:00,4,GBP_AUD,LONG,1.91928,1.9180728145274604,1.9210972849084655,1.922125927362698,1.9241832122711635,677803.98,4,OVERLAP,2.0,0.0020572849084653693,29.91280933054686,40.41937523869671,1.921424940369472,1.918275532152697,-39.30412702759289,False,-0.0007711882657971709,9.492734580474265,-23.849403694720106,0.5714285714285275,14,1,1.9180728145274604,SL,2023-07-18 14:30:00+00:00,-12.071854725397289,-818.235117885609,15,False
+2023-08-21 08:30:00+00:00,4,GBP_AUD,LONG,1.9876,1.9864686741148614,1.9886977529504617,1.9893666294256926,1.9907043823761543,716020.71,4,LONDON,2.0,0.0013377529504617087,22.276666390663745,42.694992259941124,1.988332005969626,1.9862351053648402,-9.228492989097337,False,-0.00017615032735395086,34.585340585341626,-9.720059696258776,0.6171875000000786,8,0,1.9864686741148614,SL,2023-08-21 09:00:00+00:00,-11.313258851386454,-810.0527635183513,30,False
+2023-08-29 08:45:00+00:00,4,GBP_AUD,SHORT,1.95898,1.9600297697935252,1.9579207673549164,1.9572711510323744,1.9559719183872908,763931.52,4,LONDON,2.0,0.0012992326450836607,16.15668497490933,52.73851299423709,1.959313917659616,1.9612248156588592,6.126314024690327,False,0.00016110591902509456,61.16453773763775,-0.9391765961597187,0.5345622119815885,8,1,1.9559719183872908,TP3,2023-08-29 09:45:00+00:00,16.438873353712857,1255.8173508189361,60,True
+2023-08-29 11:30:00+00:00,4,GBP_AUD,SHORT,1.96,1.9609073452754198,1.9588155157486007,1.958103273622901,1.9566787893715016,897685.18,2,LONDON,2.0,0.0014244842513992976,27.767473037785486,61.14413375989704,1.958550678019352,1.9607935879105045,27.750590704915812,False,0.00037369522023982383,91.89968141786217,16.89321980647973,0.8187919463087289,11,1,1.9609073452754198,SL,2023-08-29 11:45:00+00:00,-9.07345275419802,-814.5104068873745,15,False
diff --git a/results/phase1/S5_EUR_AUD_report.json b/results/phase1/S5_EUR_AUD_report.json
new file mode 100644
index 0000000..08bb4f1
--- /dev/null
+++ b/results/phase1/S5_EUR_AUD_report.json
@@ -0,0 +1,43 @@
+{
+  "pair": "EUR_AUD",
+  "strategy_id": 5,
+  "strategy_name": "S5_Momentum_Exhaustion",
+  "total_trades": 284,
+  "win_rate_pct": 31.34,
+  "avg_rr": 1.54,
+  "expectancy_pips": -2.48,
+  "sharpe_ratio": -3.15,
+  "max_drawdown_pct": -48.68,
+  "profit_factor": 0.65,
+  "total_pnl_pips": -704.74,
+  "total_pnl_dollars": -47670.21,
+  "avg_win_pips": 18.64,
+  "avg_loss_pips": 12.12,
+  "max_consecutive_wins": 5,
+  "max_consecutive_losses": 9,
+  "avg_hold_time_minutes": 129.8,
+  "best_trade_pips": 78.7,
+  "worst_trade_pips": -28.46,
+  "best_trade_dollars": 2740.9,
+  "worst_trade_dollars": -1007.73,
+  "final_equity": 52329.79,
+  "starting_equity": 100000.0,
+  "session_breakdown": {
+    "LONDON": {
+      "trades": 160,
+      "win_rate": 31.874999999999996,
+      "total_pnl_pips": -393.9153103944255
+    },
+    "OVERLAP": {
+      "trades": 124,
+      "win_rate": 30.64516129032258,
+      "total_pnl_pips": -310.82703073516905
+    }
+  },
+  "exit_reasons": {
+    "SL": 195,
+    "TP1+SL": 36,
+    "TP1+TP2+SL": 37,
+    "TP3": 16
+  }
+}
\ No newline at end of file
diff --git a/results/phase1/S5_EUR_AUD_trades.csv b/results/phase1/S5_EUR_AUD_trades.csv
new file mode 100644
index 0000000..470d9ea
--- /dev/null
+++ b/results/phase1/S5_EUR_AUD_trades.csv
@@ -0,0 +1,285 @@
+timestamp,strategy_id,pair,signal_direction,entry_price,sl_price,tp1_price,tp2_price,tp3_price,lot_size,confluence_score,session,spread_at_entry,atr_at_entry,adx_at_entry,rsi_at_entry,ema_50_value,ema_200_value,vwap_deviation,news_within_60min,macd_hist_at_entry,stoch_k_at_entry,distance_from_ema50_pips,candle_body_ratio,hour_of_day,day_of_week,exit_price,exit_reason,exit_time,pnl_pips,pnl_dollars,hold_time_minutes,win
+2021-01-06 09:30:00+00:00,5,EUR_AUD,LONG,1.58142,1.5803846893848696,1.583055931845391,1.5843065530756515,1.5861824849210422,965893.7,5,LONDON,2.0,0.0012506212302605501,31.640368133105756,37.908181596126816,1.583544210547334,1.5876552821925296,-6.7763911933527154,False,2.3365170301128252e-05,34.560084767959346,-23.642105473340447,0.1445783132529089,9,2,1.5803846893848696,SL,2021-01-06 09:45:00+00:00,-10.353106151304026,-1000.0000006975805,15,False
+2021-01-12 09:00:00+00:00,5,EUR_AUD,LONG,1.5745500000000001,1.5733487345363284,1.575813796391015,1.5768163273183584,1.5783201237093734,824130.91,5,LONDON,2.0,0.0010025309273433182,41.264577601435626,34.46822128282646,1.5768102457626236,1.578171993474076,-2.22839691549348,False,-0.00010671006729694047,59.748874674251425,-25.00245762623532,0.12820512820500654,9,1,1.5733487345363284,SL,2021-01-12 09:30:00+00:00,-12.012654636717189,-989.9999997273458,30,False
+2021-01-12 15:45:00+00:00,5,EUR_AUD,LONG,1.57359,1.5726627996475067,1.5752616010574796,1.576536001762466,1.5784476028199457,1057053.09,5,OVERLAP,2.0,0.0012744007049864251,41.694655796675804,41.651755347069546,1.5751323675678564,1.5771924144722413,-9.24317847458278,False,2.58360979341504e-05,8.838745504286747,-17.823675678563244,0.8725490196077728,15,1,1.5726627996475067,SL,2021-01-12 16:15:00+00:00,-9.272003524933847,-980.0999976522216,30,False
+2021-01-15 11:30:00+00:00,5,EUR_AUD,SHORT,1.56773,1.5687475505889927,1.5664773482330219,1.5654822470550362,1.563989595288058,953563.4,5,LONDON,2.0,0.000995101177985535,18.96032822245097,63.08788330140965,1.565577392854966,1.5667977481705102,10.495384160569277,False,-6.453445882165557e-05,84.97557889581697,23.92607145033976,0.5283018867926426,11,4,1.56773,TP1+SL,2021-01-15 12:30:00+00:00,5.010607067912788,477.79315117429485,60,True
+2021-01-15 13:00:00+00:00,5,EUR_AUD,SHORT,1.56762,1.5687018808683437,1.5663543573949688,1.5653505956582812,1.56384495305325,901279.39,5,OVERLAP,2.0,0.001003761736687503,17.75165959447681,60.51898033379329,1.5659712755970498,1.5668332309950064,7.7859429266147195,False,-2.4939265036337684e-05,68.85245901639968,18.887244029501815,0.2857142857143235,13,4,1.5687018808683437,SL,2021-01-15 13:30:00+00:00,-10.818808683437096,-975.076929073489,30,False
+2021-01-18 10:00:00+00:00,5,EUR_AUD,SHORT,1.57322,1.5745302706402813,1.571839188079156,1.5707586467985932,1.5691378348777492,736737.99,5,LONDON,2.0,0.0010805412805627373,48.85464765735979,62.47128029158512,1.5707030652030372,1.5684119012695275,0.24783281300067017,False,5.5394996919837106e-05,24.35087719298588,27.569347969629288,0.2542372881353381,10,0,1.5710191113432483,TP1+TP2+SL,2021-01-18 14:15:00+00:00,19.77043780250698,1456.563260803901,255,True
+2021-01-19 11:15:00+00:00,5,EUR_AUD,SHORT,1.57242,1.5735210357746299,1.5710968926761102,1.5700548211268504,1.5684917138029606,889972.71,5,LONDON,2.0,0.0010420715492598634,58.87471623141674,60.859608195938236,1.5707247510275006,1.5698991659687875,7.308964005656726,False,0.00017649572849997392,42.80691734094938,19.35248972499304,0.27083333333323695,11,1,1.5735210357746299,SL,2021-01-19 14:30:00+00:00,-11.010357746299437,-979.8917921543602,195,False
+2021-01-20 11:15:00+00:00,5,EUR_AUD,LONG,1.5681100000000001,1.5670151838496547,1.5693244484510358,1.5702940807517263,1.571748529202762,886078.34,5,LONDON,2.0,0.0009696323006904939,57.58864600298824,32.49774502396971,1.5709080610710036,1.5715251581365248,-7.8725448700334155,False,-2.5275909830539635e-05,33.44405124066288,-30.380610710034617,0.4197530864196583,11,2,1.5670151838496547,SL,2021-01-20 11:45:00+00:00,-10.948161503454568,-970.0928771032927,30,False
+2021-01-22 15:45:00+00:00,5,EUR_AUD,LONG,1.57678,1.57556763979667,1.5783770806099895,1.5796018010166493,1.5814388816266387,792167.17,5,OVERLAP,2.0,0.0012247204066596698,21.84278358689068,50.67343794548151,1.5749817016290226,1.569959390110812,-0.12939214029339396,False,-0.0002777711401380162,73.46682171184943,15.58298370977429,0.492227979274618,15,4,1.57678,TP1+SL,2021-01-24 23:00:00+00:00,6.388322439957683,506.0619308308773,3315,True
+2021-01-25 11:30:00+00:00,5,EUR_AUD,LONG,1.57279,1.5718755584931006,1.5738533245206987,1.5747222075344978,1.5760255320551966,1055783.84,5,LONDON,2.0,0.000868883013799149,20.793494935756932,41.54871189932406,1.574046318401052,1.5726864637588425,-8.31550805354464,False,-5.2889365627484426e-05,68.23266219239407,-14.963184010521324,0.46031746031772325,11,0,1.57279,TP1+SL,2021-01-25 13:00:00+00:00,4.253298082795176,449.056338251813,90,True
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+2023-07-06 15:30:00+00:00,5,EUR_AUD,SHORT,1.64217,1.6438096586657698,1.6398910240026907,1.6382117066711512,1.635692730673842,340514.39,5,OVERLAP,2.0,0.001679317331539462,77.57198659043738,77.97122922581492,1.6338568972718321,1.631475524117045,64.66535411756125,False,0.00041839243306860665,54.90127074023925,85.53102728167782,0.22727272727278233,15,3,1.6438096586657698,SL,2023-07-06 20:30:00+00:00,-16.39658665769872,-558.3273703828419,300,False
+2023-07-17 09:15:00+00:00,5,EUR_AUD,SHORT,1.64949,1.6503499886218143,1.6480500341345574,1.646930056890929,1.6452500910254864,642734.2,5,LONDON,2.0,0.0011199772436283725,25.21541980282604,62.38659035385337,1.6461870331617001,1.6399889917516313,5.293318522106016,False,-0.00015258359293228072,65.05397629793232,35.42966838299799,0.37735849056589543,9,0,1.6503499886218143,SL,2023-07-17 09:45:00+00:00,-8.599886218143558,-552.7440988509526,30,False
+2023-07-19 08:15:00+00:00,5,EUR_AUD,SHORT,1.6549099999999999,1.6559761956061585,1.6529414131815248,1.651469021969208,1.649260435150733,513242.27,5,LONDON,2.0,0.0014723912123167252,31.29977959728581,60.37373947192408,1.6518471029046387,1.648556783058755,7.649476185822568,False,1.1781971898610082e-05,84.9430707133303,33.02897095361246,0.9328358208954197,8,2,1.6559761956061585,SL,2023-07-19 08:30:00+00:00,-10.661956061586151,-547.2166531688737,15,False
+2023-07-20 08:00:00+00:00,5,EUR_AUD,LONG,1.6415,1.640442370967447,1.6436228870976592,1.6451981451627655,1.6475610322604248,512225.43,5,LONDON,2.0,0.001575258065106224,29.187359841129705,34.81553424798403,1.6467708197075495,1.6495897326015565,-3.5000000000007248,False,0.00017131102054208968,11.55794828458923,-55.10819707549519,0.8558558558559045,8,3,1.640442370967447,SL,2023-07-20 08:15:00+00:00,-10.576290325530202,-541.7444859799548,15,False
+2023-07-20 08:15:00+00:00,5,EUR_AUD,LONG,1.64028,1.6392032730412007,1.642400180876398,1.6439736347939966,1.6463338156703944,498108.68,5,LONDON,2.0,0.0015734539175986323,32.12276970109257,31.244400636987677,1.646506865993528,1.6494947103866158,-11.383197423640024,False,2.9218260084429596e-05,5.951884150981474,-64.66865993528081,0.8000000000001146,8,3,1.6392032730412007,SL,2023-07-20 10:30:00+00:00,-10.767269587992347,-536.3270441679011,135,False
+2023-07-20 13:15:00+00:00,5,EUR_AUD,SHORT,1.64154,1.6425373931903224,1.6397778204290328,1.638443034048388,1.6364408544774207,532351.51,5,OVERLAP,2.0,0.0013347863806448376,28.13826078524762,51.03154972486776,1.6433275220026562,1.647890564990311,8.784614406438518,False,0.00037903262410706774,31.727540194360074,-15.475220026561765,0.8941176470589157,13,3,1.6425373931903224,SL,2023-07-20 13:30:00+00:00,-9.973931903224376,-530.9637709318671,15,False
+2023-07-24 10:00:00+00:00,5,EUR_AUD,LONG,1.64361,1.6425065771560794,1.6455102685317617,1.646937114219603,1.6490773827513647,476385.04,5,LONDON,2.0,0.001426845687841191,52.3926483696224,26.75364410261045,1.6494902146945727,1.648914175579244,-7.663910266875185,False,-0.0004308246893340766,34.19268219913844,-61.202146945726895,0.30508474576259387,10,0,1.6465059996509477,TP1+TP2+SL,2023-07-24 12:30:00+00:00,26.701530307354115,1272.0209583530102,150,True
+2023-07-24 14:45:00+00:00,5,EUR_AUD,LONG,1.6419,1.6408677441933714,1.6440367674198852,1.6456212790331421,1.6479980464530275,521551.29,5,OVERLAP,2.0,0.001584511613256878,30.77432470471707,31.897563240612513,1.6473099515246614,1.6482976680790227,-35.536115015430966,False,-0.00013354348626433313,6.265060240963721,-56.499515246615225,0.9888888888888989,14,0,1.6408677441933714,SL,2023-07-24 15:00:00+00:00,-10.32255806628468,-538.3743475570681,15,False
+2023-07-28 15:30:00+00:00,5,EUR_AUD,SHORT,1.6547,1.65601504787347,1.6523748563795897,1.6506647606326492,1.6480996170122388,405301.29,5,OVERLAP,2.0,0.0017100957469403277,32.17496818630897,63.957734165858525,1.6499385250538987,1.6420636260951957,26.14112005370206,False,0.00011691466396368533,49.280968201235765,50.01474946101369,0.06329113924063441,15,4,1.65601504787347,SL,2023-07-28 17:30:00+00:00,-13.150478734700322,-532.9905995291608,120,False
+2023-07-31 10:45:00+00:00,5,EUR_AUD,LONG,1.64564,1.6440415363641674,1.6474953909074983,1.648892318179164,1.6509877090866623,330104.91,5,LONDON,2.0,0.0013969272716655486,28.702703181711364,39.512690974944185,1.648967605278424,1.646876909046481,-16.854048771861585,False,8.700967587108813e-05,34.98420823440674,-35.67605278423924,0.5680473372781594,10,0,1.6440415363641674,SL,2023-07-31 11:30:00+00:00,-15.984636358326032,-527.6606946447943,45,False
+2023-07-31 15:30:00+00:00,5,EUR_AUD,LONG,1.63694,1.6358372401696828,1.639108279490952,1.6407137991515863,1.6431220786425382,473706.13,5,OVERLAP,2.0,0.0016055196606345426,68.9380051657817,27.517758119861043,1.6447364061632386,1.6459339892293854,-58.776669287428085,False,-0.000550502287005468,16.186772255912203,-80.36406163238529,0.4297520661156054,15,0,1.6358372401696828,SL,2023-07-31 16:15:00+00:00,-11.027598303172681,-522.3840915390498,45,False
+2023-08-07 16:00:00+00:00,5,EUR_AUD,SHORT,1.67565,1.6769116768191001,1.6739649695426997,1.6726816159044995,1.670756585447199,409899.14,5,OVERLAP,2.0,0.0012833536382002818,49.09755253018847,62.76597818588222,1.672925401992697,1.671177222868549,27.72395547504969,False,0.0002594372621580668,57.129891120907864,29.645980073031986,0.18103448275868506,16,0,1.6738103889801648,TP1+TP2+SL,2023-08-07 21:15:00+00:00,22.292880250874614,913.783244295649,315,True
+2023-08-11 16:30:00+00:00,5,EUR_AUD,SHORT,1.68624,1.687310306742566,1.6844990797723018,1.6831784662871698,1.6811975460594715,491726.4,5,OVERLAP,2.0,0.0013206134851321035,35.97379394545063,57.106164726123986,1.6849547470903585,1.6821922614636704,15.236474570881064,False,0.0002208183821662129,56.408368908236234,15.252529096414502,0.020833333333468255,16,4,1.68624,TP1+SL,2023-08-14 00:30:00+00:00,6.963680910792469,342.4225745012702,3360,True
+2023-08-22 11:30:00+00:00,5,EUR_AUD,LONG,1.68863,1.6875608857972288,1.690397342608313,1.691735571013855,1.693742913622168,495477.76,5,LONDON,2.0,0.0013382284055419769,62.67550423583733,25.128143568873313,1.695218698970009,1.697412586677755,-32.91637426018568,False,-0.0002442112716613087,48.02220510374827,-68.28698970009039,0.5916666666666127,11,1,1.6875608857972288,SL,2023-08-22 11:45:00+00:00,-10.691142027712262,-529.722310373273,15,False
+2023-08-22 14:30:00+00:00,5,EUR_AUD,LONG,1.68726,1.6858648634998152,1.6896454095005542,1.6913956825009238,1.6940210920014782,375895.18,5,OVERLAP,2.0,0.0017502730003695378,25.599744112953697,34.88880504397673,1.6929492368098442,1.6964904692945255,-33.08549969733399,False,0.00020123285017553867,23.210220086011986,-59.292368098442516,0.6857142857142696,14,1,1.68726,TP1+SL,2023-08-22 21:00:00+00:00,9.541638002216857,358.6655734338146,390,True
+2023-08-28 09:00:00+00:00,5,EUR_AUD,SHORT,1.68587,1.6868913516460768,1.6843659450617692,1.6832032417696152,1.6814591868313846,516973.5,5,LONDON,2.0,0.0011627032921538683,41.30962057331866,64.12009156189868,1.68334427973672,1.683033456769349,1.3214147314521973,False,0.00022169825685197056,28.630282297717457,27.65720263280036,0.7835820895522957,9,0,1.6868913516460768,SL,2023-08-28 09:45:00+00:00,-10.213516460768624,-528.0117352031168,45,False
+2023-08-30 10:45:00+00:00,5,EUR_AUD,SHORT,1.68272,1.6841280156998317,1.6809259529005047,1.6795699215008413,1.677535874401346,371254.11,5,LONDON,2.0,0.0013560313996635273,21.01546032259075,57.93624427738179,1.680723300451118,1.6809302738419767,3.609403475430817,False,7.672020523895946e-05,36.86700570195632,22.366995488820596,0.01818181818173744,10,2,1.677535874401346,TP3,2023-08-30 12:45:00+00:00,30.144753591923926,1119.136366593902,120,True
+2023-08-31 10:30:00+00:00,5,EUR_AUD,LONG,1.68062,1.6795083767802375,1.6823048696592875,1.6835881160988124,1.6855129857580997,480309.31,5,LONDON,2.0,0.0012832464395249508,30.51120372216017,40.55618189765329,1.682509171658987,1.682406675517925,-2.482449237710682,False,5.670534563276611e-06,42.19118044973531,-21.291716589870724,0.03370786516850849,10,3,1.6795083767802375,SL,2023-08-31 11:30:00+00:00,-11.116232197625473,-533.9229816641275,60,False
+2023-08-31 13:30:00+00:00,5,EUR_AUD,LONG,1.676,1.6748108894367595,1.6782173316897213,1.6798555528162025,1.6823128845059239,444520.28,5,OVERLAP,2.0,0.0016382211264810034,46.585507435151264,30.425115910102974,1.6806223541239047,1.6818821217123543,-28.63009939840566,False,-0.00033534501785074163,34.69553255856626,-48.62354123904788,0.09589041095892285,13,3,1.6748108894367595,SL,2023-08-31 13:45:00+00:00,-11.891105632404473,-528.5837605226014,15,False
diff --git a/results/phase1/S5_EUR_CAD_report.json b/results/phase1/S5_EUR_CAD_report.json
new file mode 100644
index 0000000..ea3ee0e
--- /dev/null
+++ b/results/phase1/S5_EUR_CAD_report.json
@@ -0,0 +1,43 @@
+{
+  "pair": "EUR_CAD",
+  "strategy_id": 5,
+  "strategy_name": "S5_Momentum_Exhaustion",
+  "total_trades": 262,
+  "win_rate_pct": 29.77,
+  "avg_rr": 1.21,
+  "expectancy_pips": -3.7,
+  "sharpe_ratio": -4.54,
+  "max_drawdown_pct": -60.7,
+  "profit_factor": 0.53,
+  "total_pnl_pips": -968.64,
+  "total_pnl_dollars": -59937.17,
+  "avg_win_pips": 13.14,
+  "avg_loss_pips": 10.84,
+  "max_consecutive_wins": 5,
+  "max_consecutive_losses": 13,
+  "avg_hold_time_minutes": 144.8,
+  "best_trade_pips": 34.52,
+  "worst_trade_pips": -19.68,
+  "best_trade_dollars": 2175.61,
+  "worst_trade_dollars": -1019.37,
+  "final_equity": 40062.83,
+  "starting_equity": 100000.0,
+  "session_breakdown": {
+    "LONDON": {
+      "trades": 149,
+      "win_rate": 30.201342281879196,
+      "total_pnl_pips": -495.20084563028934
+    },
+    "OVERLAP": {
+      "trades": 113,
+      "win_rate": 29.20353982300885,
+      "total_pnl_pips": -473.4409758613806
+    }
+  },
+  "exit_reasons": {
+    "TP3": 17,
+    "SL": 184,
+    "TP1+SL": 36,
+    "TP1+TP2+SL": 25
+  }
+}
\ No newline at end of file
diff --git a/results/phase1/S5_EUR_CAD_trades.csv b/results/phase1/S5_EUR_CAD_trades.csv
new file mode 100644
index 0000000..b54eba8
--- /dev/null
+++ b/results/phase1/S5_EUR_CAD_trades.csv
@@ -0,0 +1,263 @@
+timestamp,strategy_id,pair,signal_direction,entry_price,sl_price,tp1_price,tp2_price,tp3_price,lot_size,confluence_score,session,spread_at_entry,atr_at_entry,adx_at_entry,rsi_at_entry,ema_50_value,ema_200_value,vwap_deviation,news_within_60min,macd_hist_at_entry,stoch_k_at_entry,distance_from_ema50_pips,candle_body_ratio,hour_of_day,day_of_week,exit_price,exit_reason,exit_time,pnl_pips,pnl_dollars,hold_time_minutes,win
+2021-01-06 13:45:00+00:00,5,EUR_CAD,SHORT,1.5647499999999999,1.5662597951632429,1.5630306145102717,1.5616910241837862,1.559681638694058,662341.5,5,OVERLAP,2.5,0.0013395903264855273,42.5765855361101,64.9371159951441,1.5613595201726689,1.5611875999018168,26.380445712315126,False,0.0001884213881573147,53.824424709833956,36.804798273311064,0.37278106508870995,13,2,1.559681638694058,TP3,2021-01-06 15:45:00+00:00,29.250167835650927,1937.360003951679,120,True
+2021-01-12 10:00:00+00:00,5,EUR_CAD,LONG,1.54997,1.5488188362406081,1.551123491278175,1.5520858187969586,1.5535293100751337,885515.72,5,LONDON,2.5,0.0009623275187834524,35.12255345671878,42.18011047255775,1.5515924728481305,1.5535582678630389,-5.288576673765366,False,8.883014413959612e-06,26.181059283813955,-19.12472848130564,0.4197530864196583,10,1,1.5488188362406081,SL,2021-01-12 11:00:00+00:00,-11.511637593919486,-1019.3736052358682,60,False
+2021-01-15 10:15:00+00:00,5,EUR_CAD,SHORT,1.5397999999999998,1.5408356473412663,1.5389030579762009,1.538111763293668,1.5369248212698692,974443.54,5,LONDON,2.5,0.000791294682532718,41.04068226226945,70.04236449744224,1.5379443990910204,1.5410406813997604,10.084729749013643,False,0.000192301836060468,52.97549761298091,21.45600908979528,0.30188679245263256,10,4,1.5408356473412663,SL,2021-01-15 11:15:00+00:00,-10.35647341266488,-1009.1798614153047,60,False
+2021-01-20 10:00:00+00:00,5,EUR_CAD,LONG,1.53963,1.5386687144814588,1.5406938565556239,1.5415964275927065,1.5429502841483305,1039324.99,5,LONDON,2.5,0.0009025710370826206,44.075023905947944,28.653385910696542,1.5425276367314447,1.5423459848939336,-11.109433579770123,False,-0.00029340413571747105,30.160580160577354,-31.876367314447318,0.20454545454545073,10,2,1.5386687144814588,SL,2021-01-20 10:45:00+00:00,-9.612855185412439,-999.0880619450231,45,False
+2021-01-20 11:45:00+00:00,5,EUR_CAD,LONG,1.53882,1.5378469689392258,1.5397690931823225,1.5405951553038708,1.5418342484861933,1016511.42,5,LONDON,2.5,0.0008260621215483505,42.2287368448593,29.762719582761136,1.5416285488495511,1.542110580060612,-13.36148135357984,False,-6.985999412938756e-05,33.86490959004758,-30.98548849551186,0.5909090909091673,11,2,1.53882,TP1+SL,2021-01-20 12:30:00+00:00,3.796372729289565,385.9056233899412,45,True
+2021-01-26 13:45:00+00:00,5,EUR_CAD,LONG,1.54519,1.544208152319923,1.546165543040231,1.547009238400385,1.5482747814406164,1011313.94,5,OVERLAP,2.5,0.0008436953601540786,38.504681327790585,39.02766967294375,1.5464072596670024,1.5455465372115544,-11.219707153615754,False,-2.677722100820866e-05,75.97661560771694,-15.072596670024918,0.376623376623459,13,1,1.544208152319923,SL,2021-01-26 14:15:00+00:00,-9.818476800771414,-992.9562458186733,30,False
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+2023-08-28 08:00:00+00:00,5,EUR_CAD,LONG,1.47021,1.469594325826126,1.4706870225216222,1.4711983708693706,1.4719653933909929,663926.93,5,LONDON,2.5,0.000511348347748262,39.934700281005995,58.255694432705695,1.4690722542085275,1.4685862790409736,-0.9666666666685586,False,6.717442224676916e-05,44.18956983058857,8.477457914723985,0.34883720930260176,8,0,1.469594325826126,SL,2023-08-28 08:15:00+00:00,-6.156741738740923,-408.76266414051236,15,False
+2023-08-30 10:15:00+00:00,5,EUR_CAD,SHORT,1.47616,1.4774346000146816,1.4748761999559548,1.4738269999265914,1.472253199882546,317491.79,5,LONDON,2.5,0.001049200029363497,24.854916729807467,60.78107369559759,1.4745827869938737,1.472690220722665,14.558782978006946,False,0.00026850247088429667,64.41688215240187,18.67213006126356,0.5859375000000461,10,2,1.4774346000146816,SL,2023-08-30 12:15:00+00:00,-12.746000146817149,-404.6750401953239,120,False
diff --git a/results/phase1/S5_EUR_GBP_report.json b/results/phase1/S5_EUR_GBP_report.json
new file mode 100644
index 0000000..0a0fb7f
--- /dev/null
+++ b/results/phase1/S5_EUR_GBP_report.json
@@ -0,0 +1,43 @@
+{
+  "pair": "EUR_GBP",
+  "strategy_id": 5,
+  "strategy_name": "S5_Momentum_Exhaustion",
+  "total_trades": 278,
+  "win_rate_pct": 32.73,
+  "avg_rr": 0.9,
+  "expectancy_pips": -2.67,
+  "sharpe_ratio": -6.16,
+  "max_drawdown_pct": -66.51,
+  "profit_factor": 0.43,
+  "total_pnl_pips": -741.99,
+  "total_pnl_dollars": -66510.95,
+  "avg_win_pips": 6.35,
+  "avg_loss_pips": 7.06,
+  "max_consecutive_wins": 4,
+  "max_consecutive_losses": 14,
+  "avg_hold_time_minutes": 171.6,
+  "best_trade_pips": 32.1,
+  "worst_trade_pips": -22.25,
+  "best_trade_dollars": 1906.94,
+  "worst_trade_dollars": -1000.0,
+  "final_equity": 33489.05,
+  "starting_equity": 100000.0,
+  "session_breakdown": {
+    "LONDON": {
+      "trades": 138,
+      "win_rate": 36.95652173913043,
+      "total_pnl_pips": -327.8603639263469
+    },
+    "OVERLAP": {
+      "trades": 140,
+      "win_rate": 28.57142857142857,
+      "total_pnl_pips": -414.133370295348
+    }
+  },
+  "exit_reasons": {
+    "SL": 187,
+    "TP1+TP2+SL": 28,
+    "TP1+SL": 52,
+    "TP3": 11
+  }
+}
\ No newline at end of file
diff --git a/results/phase1/S5_EUR_GBP_trades.csv b/results/phase1/S5_EUR_GBP_trades.csv
new file mode 100644
index 0000000..830c35e
--- /dev/null
+++ b/results/phase1/S5_EUR_GBP_trades.csv
@@ -0,0 +1,279 @@
+timestamp,strategy_id,pair,signal_direction,entry_price,sl_price,tp1_price,tp2_price,tp3_price,lot_size,confluence_score,session,spread_at_entry,atr_at_entry,adx_at_entry,rsi_at_entry,ema_50_value,ema_200_value,vwap_deviation,news_within_60min,macd_hist_at_entry,stoch_k_at_entry,distance_from_ema50_pips,candle_body_ratio,hour_of_day,day_of_week,exit_price,exit_reason,exit_time,pnl_pips,pnl_dollars,hold_time_minutes,win
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+2023-08-23 11:15:00+00:00,5,EUR_GBP,SHORT,0.85559,0.856248344615505,0.8547249661534848,0.8539882769224747,0.8528832430759595,534901.24,5,LONDON,2.0,0.0007366892310101228,48.115602837768236,75.01972562533098,0.8528673549644199,0.8530273866929039,20.148548987031223,False,0.00026240443294856376,72.84164248449846,29.626450355800802,0.6886792452830593,11,2,0.856248344615505,SL,2023-08-23 12:00:00+00:00,-6.583446155050686,-352.14935118098447,45,False
+2023-08-29 16:15:00+00:00,5,EUR_GBP,SHORT,0.8597899999999999,0.8603297967089698,0.8591306098730903,0.8585310164551506,0.857631626328241,645850.29,5,OVERLAP,2.0,0.0005995934179397563,43.15362976265564,61.91755084504119,0.8586007093163405,0.8579817055064047,13.315249430267961,False,6.958841060281345e-05,56.027474067843904,14.292906836594899,0.19047619047591355,16,1,0.8603297967089698,SL,2023-08-29 16:30:00+00:00,-5.397967089698641,-348.62786102923235,15,False
+2023-08-31 11:45:00+00:00,5,EUR_GBP,LONG,0.85726,0.8566900055838549,0.8577699832484356,0.8582699720807259,0.8590199553291614,605517.48,5,LONDON,2.0,0.0004999888322903565,24.89184799787745,41.59232171209533,0.8579296622696669,0.8586715225977449,-1.7834054371790309,False,5.813480478958572e-05,37.27172084170201,-9.096622696669332,0.40740740740720943,11,3,0.8566900055838549,SL,2023-08-31 13:15:00+00:00,-5.699944161451365,-345.14158247827436,90,False
+2023-08-31 13:15:00+00:00,5,EUR_GBP,LONG,0.85666,0.8561095550392748,0.8572013348821756,0.8577222248036261,0.8585035596858019,620752.64,5,OVERLAP,2.0,0.0005208899214504889,23.614118683005046,36.83361263010142,0.8577471286954813,0.8585791965202825,-7.6853462177794984,False,-1.9147633196325625e-05,30.606112637360898,-13.2712869548135,0.929577464788739,13,3,0.8561095550392748,SL,2023-08-31 13:30:00+00:00,-5.504449607252048,-341.69016254486723,15,False
+2023-08-31 16:45:00+00:00,5,EUR_GBP,LONG,0.85615,0.8556443575174945,0.8566769274475164,0.8571882124125274,0.8579551398600439,668996.92,5,OVERLAP,2.0,0.000511284965010992,24.356218354940005,40.011985559945465,0.8570470462819338,0.8582594487052709,-9.113245325493846,False,-8.396878369159287e-06,50.035536602688694,-11.370462819338956,0.4772727272728305,16,3,0.8556443575174945,SL,2023-08-31 20:15:00+00:00,-5.0564248250550845,-338.273263417339,210,False
diff --git a/results/phase1/S5_GBP_AUD_report.json b/results/phase1/S5_GBP_AUD_report.json
new file mode 100644
index 0000000..eec9b09
--- /dev/null
+++ b/results/phase1/S5_GBP_AUD_report.json
@@ -0,0 +1,43 @@
+{
+  "pair": "GBP_AUD",
+  "strategy_id": 5,
+  "strategy_name": "S5_Momentum_Exhaustion",
+  "total_trades": 264,
+  "win_rate_pct": 36.36,
+  "avg_rr": 1.5,
+  "expectancy_pips": -1.27,
+  "sharpe_ratio": -1.69,
+  "max_drawdown_pct": -39.48,
+  "profit_factor": 0.79,
+  "total_pnl_pips": -336.04,
+  "total_pnl_dollars": -27953.37,
+  "avg_win_pips": 21.26,
+  "avg_loss_pips": 14.15,
+  "max_consecutive_wins": 5,
+  "max_consecutive_losses": 13,
+  "avg_hold_time_minutes": 143.5,
+  "best_trade_pips": 66.05,
+  "worst_trade_pips": -26.36,
+  "best_trade_dollars": 2554.95,
+  "worst_trade_dollars": -1041.23,
+  "final_equity": 72046.63,
+  "starting_equity": 100000.0,
+  "session_breakdown": {
+    "LONDON": {
+      "trades": 148,
+      "win_rate": 37.83783783783784,
+      "total_pnl_pips": -55.194967048454885
+    },
+    "OVERLAP": {
+      "trades": 116,
+      "win_rate": 34.48275862068966,
+      "total_pnl_pips": -280.84359608526563
+    }
+  },
+  "exit_reasons": {
+    "SL": 168,
+    "TP1+TP2+SL": 35,
+    "TP1+SL": 44,
+    "TP3": 17
+  }
+}
\ No newline at end of file
diff --git a/results/phase1/S5_GBP_AUD_trades.csv b/results/phase1/S5_GBP_AUD_trades.csv
new file mode 100644
index 0000000..3e7e79f
--- /dev/null
+++ b/results/phase1/S5_GBP_AUD_trades.csv
@@ -0,0 +1,265 @@
+timestamp,strategy_id,pair,signal_direction,entry_price,sl_price,tp1_price,tp2_price,tp3_price,lot_size,confluence_score,session,spread_at_entry,atr_at_entry,adx_at_entry,rsi_at_entry,ema_50_value,ema_200_value,vwap_deviation,news_within_60min,macd_hist_at_entry,stoch_k_at_entry,distance_from_ema50_pips,candle_body_ratio,hour_of_day,day_of_week,exit_price,exit_reason,exit_time,pnl_pips,pnl_dollars,hold_time_minutes,win
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+2021-02-04 16:15:00+00:00,5,GBP_AUD,SHORT,1.79806,1.7998030729479677,1.7955607811560974,1.7937346352601624,1.79099541641626,567962.46,5,OVERLAP,2.0,0.0018261458959350212,55.92143757634254,73.18412790000936,1.7900524289291853,1.790179270040471,85.24387115126818,False,4.332282726949785e-05,44.120966752543616,82.47571070814752,0.1290322580643929,16,3,1.7998030729479677,SL,2021-02-04 20:15:00+00:00,-17.430729479677165,-989.9999994871962,240,False
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+2021-02-23 10:45:00+00:00,5,GBP_AUD,SHORT,1.78384,1.7850399109756763,1.782160267072971,1.7808804451216185,1.7789607121945894,823064.48,5,LONDON,2.0,0.0012798219513526538,59.22985927630744,73.4849979826239,1.7792529422091172,1.7808822175843333,23.424984922499892,False,0.0004229893645679038,84.23720203529147,48.27057790882883,0.3647058823529719,10,1,1.7820084555962765,TP1+TP2+SL,2021-02-23 13:45:00+00:00,22.22024002908984,1828.8690305018015,180,True
+2021-02-23 16:45:00+00:00,5,GBP_AUD,SHORT,1.7831299999999999,1.7846778094140525,1.7812465717578427,1.7798309529297378,1.7777075246875804,649881.56,5,OVERLAP,2.0,0.001415618828104849,19.33561705035624,58.09521115015302,1.7811064938791734,1.7811645030388783,11.50442545595265,False,9.764896349606731e-06,51.438848920862085,22.63506120826486,0.25423728813557406,16,1,1.7846778094140525,SL,2021-02-23 23:00:00+00:00,-15.478094140526368,-1005.8927965872135,375,False
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+2021-03-17 12:15:00+00:00,5,GBP_AUD,SHORT,1.80003,1.801150037008978,1.7984698889730666,1.797269814955111,1.7954697039281775,903352.34,5,LONDON,2.0,0.0012000740179556536,30.2259229446758,59.10675257517752,1.7978025658770223,1.7952142988086792,5.991733217771689,False,5.626078073945858e-05,53.39788779947987,24.674341229777852,0.010416666666734128,12,2,1.801150037008978,SL,2021-03-17 12:45:00+00:00,-11.200370089778922,-1011.7880529467799,30,False
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+2023-07-21 14:45:00+00:00,5,GBP_AUD,SHORT,1.90822,1.9098422504794113,1.9059632485617666,1.9042987476029443,1.9018019961647108,437844.07,5,OVERLAP,2.0,0.0016645009588223303,36.07916360605371,62.92788158481175,1.9039614847892203,1.9025843625537853,26.961604384005966,False,0.00030158209504854185,56.73357001627479,44.98515210779708,0.03973509933771134,14,4,1.9098422504794113,SL,2023-07-21 15:15:00+00:00,-16.222504794112247,-710.2927524648618,30,False
+2023-07-21 15:30:00+00:00,5,GBP_AUD,SHORT,1.90843,1.9099264319618316,1.906280704114505,1.9046878401908414,1.902298544305346,469910.99,5,OVERLAP,2.0,0.0015928639236635093,35.51912148182759,61.623875363450985,1.9045275291099728,1.9027732104430446,26.48594089412093,False,0.00013012882187827743,58.854876171091654,41.42470890027283,0.11199999999984794,15,4,1.9099264319618316,SL,2023-07-21 18:00:00+00:00,-14.964319618315793,-703.1898246519196,150,False
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+2023-07-24 10:45:00+00:00,5,GBP_AUD,LONG,1.90187,1.9007248365610958,1.9041054903167127,1.9057558171945213,1.908231307511234,601832.3,5,LONDON,2.0,0.0016503268778085122,58.02847251752051,23.88772609230911,1.9075720762434827,1.906056501851703,-22.676357396216318,False,-0.00033125507245790765,50.62272426348795,-59.42076243482752,0.7524271844661162,10,0,1.908231307511234,TP3,2023-07-24 12:15:00+00:00,37.207845067404484,2239.28829749597,90,True
+2023-07-24 15:30:00+00:00,5,GBP_AUD,LONG,1.90185,1.8997193952086082,1.904491814374175,1.906413023956958,1.909294838331133,333984.62,5,OVERLAP,2.0,0.0019212095827832254,31.509443892273993,41.27146028838062,1.9054367020057195,1.9056688925210652,-22.92258940193692,False,-0.00032909240354768617,39.58448383976533,-38.2670200571944,0.23584905660383879,15,0,1.8997193952086082,SL,2023-07-24 16:00:00+00:00,-21.306047913918214,-711.5892316231767,30,False
+2023-07-24 16:00:00+00:00,5,GBP_AUD,LONG,1.90021,1.8985804989298716,1.9027285032103853,1.9045675053506423,1.9073260085610277,432324.56,5,OVERLAP,2.0,0.0018390021402569618,32.0069633743187,36.81972006528111,1.9050219998138147,1.9055575236436402,-37.82625788618876,False,-0.00030766697996417063,48.31126847290554,-50.51999813814767,0.1714285714286379,16,0,1.90021,TP1+SL,2023-07-24 21:00:00+00:00,10.074012841541526,435.52431691537896,300,True
+2023-07-25 10:00:00+00:00,5,GBP_AUD,LONG,1.89747,1.8961154385985401,1.8993736842043794,1.9008028070072989,1.9029464912116782,523290.11,5,LONDON,2.0,0.0014291228029195454,24.47111681083405,45.584824821279014,1.8990567940588756,1.9025754732461084,-2.7365661566003396,False,-1.7389813202939373e-05,62.51126597823153,-18.26794058875647,0.2870370370370751,10,1,1.8961154385985401,SL,2023-07-25 11:45:00+00:00,-13.545614014598417,-708.8285847716747,105,False
+2023-07-26 08:00:00+00:00,5,GBP_AUD,SHORT,1.90665,1.9082854232834932,1.9043237301495202,1.9026128835825338,1.900046613732054,429087.87,5,LONDON,2.0,0.0017108465669865072,26.034279485964504,56.53771297242305,1.9035827991216543,1.9018573622932111,-0.46666666666750345,False,-0.00010788643209093363,90.54309837031275,33.07200878345684,0.1914893617022131,8,2,1.9082854232834932,SL,2023-07-26 08:45:00+00:00,-16.35423283493198,-701.7402932625025,45,False
+2023-07-31 15:45:00+00:00,5,GBP_AUD,LONG,1.91036,1.9085562188614638,1.912651343415609,1.9143389056926816,1.9168702491082905,385148.11,5,OVERLAP,2.0,0.0016875622770726008,75.96845829616896,28.132637335976696,1.9173415224141155,1.9193255561964244,-38.36885278754698,False,-9.093002887093724e-05,54.73078360517519,-72.21522414115444,0.12230215827338359,15,0,1.91036,TP1+SL,2023-07-31 17:45:00+00:00,9.165373662435528,353.00263435308216,120,True
+2023-08-01 09:15:00+00:00,5,GBP_AUD,SHORT,1.93293,1.934504722383603,1.929915832849191,1.9277463880819847,1.9244922209311757,443413.34,5,LONDON,2.0,0.002169444767206106,63.404858459715626,73.78726987865971,1.9212941630037708,1.9182851451992433,11.029397779680483,False,0.00024374513645984003,72.69950623941185,118.75836996229293,0.1368421052632354,9,1,1.929963423480436,TP1+TP2+SL,2023-08-01 12:30:00+00:00,38.72426931442563,1717.085759576898,195,True
+2023-08-07 15:45:00+00:00,5,GBP_AUD,SHORT,1.94473,1.946484556657287,1.9426763300281389,1.9411472167135648,1.9388535467417036,407751.88,5,OVERLAP,2.0,0.0015291133145740976,39.58431324719338,62.558895844084844,1.9404617042644645,1.9384961657510558,36.47268259507674,False,0.00019841364734126147,77.93745071505349,45.082957355355724,0.6987951807227497,15,0,1.94473,TP1+SL,2023-08-07 21:30:00+00:00,8.214679887444733,334.9551167703778,345,True
+2023-08-09 09:00:00+00:00,5,GBP_AUD,LONG,1.94482,1.9436632435242516,1.9467902694272448,1.9482637823787412,1.950474051805986,621369.62,5,LONDON,2.0,0.0014735129514964831,21.166532248955342,43.49704446628343,1.9469230292425908,1.9465508438092125,0.05197265897871617,False,-4.9381678957402054e-05,38.74092459806752,-23.430292425907773,0.35211267605646135,9,2,1.9478477956073288,TP1+TP2+SL,2023-08-09 13:45:00+00:00,27.711798438601498,1721.9269665310408,285,True
+2023-08-11 08:00:00+00:00,5,GBP_AUD,SHORT,1.94719,1.948726870335699,1.9452193889929035,1.9437456483215059,1.9415350373144096,478890.5,5,LONDON,2.0,0.0014737406713976088,45.409268233742395,61.67646772304962,1.9450309761763203,1.944930584556095,0.06666666666710341,False,0.00014398975034575392,54.19995738787421,23.990238236797357,0.06140350877193495,8,4,1.948726870335699,SL,2023-08-11 08:30:00+00:00,-15.368703356990476,-735.9926034980848,30,False
+2023-08-11 14:45:00+00:00,5,GBP_AUD,SHORT,1.95084,1.9523036734220596,1.948638979733821,1.9470116328897018,1.9445706126235227,497810.96,5,OVERLAP,2.0,0.0016273468441192858,31.062448350934282,59.72327275623318,1.9481084140330522,1.9459834416358595,13.933795563969564,False,-3.892831391160041e-06,34.29998849887807,29.715859669476874,0.49382716049378655,14,4,1.9523036734220596,SL,2023-08-11 15:00:00+00:00,-14.636734220596635,-728.6326713620064,15,False
+2023-08-21 16:15:00+00:00,5,GBP_AUD,SHORT,1.99172,1.9927753889307678,1.9898738332076968,1.9884830553461614,1.9863968885538583,683488.64,5,OVERLAP,2.0,0.0013907778615353853,38.07592712861651,63.192222516994825,1.9890186091295243,1.9869472513978814,33.2447995745877,False,0.0003357435600381295,71.10288708616979,29.413908704756242,0.4819277108432575,16,0,1.9863968885538583,TP3,2023-08-21 21:00:00+00:00,30.97866867684962,2117.3568122950546,285,True
+2023-08-23 11:15:00+00:00,5,GBP_AUD,LONG,1.96847,1.9665839599212693,1.9713981202361923,1.973510200393654,1.9766783206298464,393692.54,5,LONDON,2.0,0.002112080157461633,58.57648218942019,30.34297739148171,1.9762896503086123,1.981911107036535,-33.928432084946444,False,-0.0004159927225115376,46.64206559789314,-80.5965030861233,0.49159663865550063,11,2,1.9665839599212693,SL,2023-08-23 12:00:00+00:00,-18.86040078730611,-742.5199091372541,45,False
+2023-08-30 15:45:00+00:00,5,GBP_AUD,SHORT,1.95892,1.9604506201238012,1.9566381396285966,1.9549568993809943,1.952435039009591,480259.41,5,OVERLAP,2.0,0.001681240247602256,36.503586864611364,62.55838928380439,1.955434917846077,1.956330350880729,32.68298949740789,False,0.0004590319153411236,77.33802230223888,37.25082153922976,0.42045454545452826,15,2,1.9604506201238012,SL,2023-08-30 16:00:00+00:00,-15.306201238012294,-735.0947175909055,15,False
+2023-08-30 16:45:00+00:00,5,GBP_AUD,SHORT,1.96089,1.9619998907058487,1.958760327882454,1.9571805464707566,1.9548108743532104,655689.58,5,OVERLAP,2.0,0.0015797814116974096,41.182876312540685,65.64751005785723,1.9562740485661825,1.9565179073544336,49.349002529899266,False,0.00040833406122292733,75.02645502645122,48.559514338175624,0.6708860759496766,16,2,1.9619998907058487,SL,2023-08-30 17:15:00+00:00,-11.098907058486596,-727.7437707638111,30,False
diff --git a/results/phase1/S5_GBP_CAD_report.json b/results/phase1/S5_GBP_CAD_report.json
new file mode 100644
index 0000000..d0873d2
--- /dev/null
+++ b/results/phase1/S5_GBP_CAD_report.json
@@ -0,0 +1,43 @@
+{
+  "pair": "GBP_CAD",
+  "strategy_id": 5,
+  "strategy_name": "S5_Momentum_Exhaustion",
+  "total_trades": 240,
+  "win_rate_pct": 37.5,
+  "avg_rr": 1.35,
+  "expectancy_pips": -1.47,
+  "sharpe_ratio": -1.96,
+  "max_drawdown_pct": -35.07,
+  "profit_factor": 0.77,
+  "total_pnl_pips": -351.74,
+  "total_pnl_dollars": -29057.25,
+  "avg_win_pips": 16.9,
+  "avg_loss_pips": 12.49,
+  "max_consecutive_wins": 5,
+  "max_consecutive_losses": 7,
+  "avg_hold_time_minutes": 164.3,
+  "best_trade_pips": 52.72,
+  "worst_trade_pips": -40.66,
+  "best_trade_dollars": 2134.17,
+  "worst_trade_dollars": -1000.0,
+  "final_equity": 70942.75,
+  "starting_equity": 100000.0,
+  "session_breakdown": {
+    "LONDON": {
+      "trades": 138,
+      "win_rate": 34.05797101449276,
+      "total_pnl_pips": -269.5886460592775
+    },
+    "OVERLAP": {
+      "trades": 102,
+      "win_rate": 42.15686274509804,
+      "total_pnl_pips": -82.15130371285807
+    }
+  },
+  "exit_reasons": {
+    "SL": 150,
+    "TP1+SL": 37,
+    "TP3": 15,
+    "TP1+TP2+SL": 38
+  }
+}
\ No newline at end of file
diff --git a/results/phase1/S5_GBP_CAD_trades.csv b/results/phase1/S5_GBP_CAD_trades.csv
new file mode 100644
index 0000000..8de9ee6
--- /dev/null
+++ b/results/phase1/S5_GBP_CAD_trades.csv
@@ -0,0 +1,241 @@
+timestamp,strategy_id,pair,signal_direction,entry_price,sl_price,tp1_price,tp2_price,tp3_price,lot_size,confluence_score,session,spread_at_entry,atr_at_entry,adx_at_entry,rsi_at_entry,ema_50_value,ema_200_value,vwap_deviation,news_within_60min,macd_hist_at_entry,stoch_k_at_entry,distance_from_ema50_pips,candle_body_ratio,hour_of_day,day_of_week,exit_price,exit_reason,exit_time,pnl_pips,pnl_dollars,hold_time_minutes,win
+2021-02-02 11:15:00+00:00,5,GBP_CAD,LONG,1.7513500000000002,1.7503277903628438,1.7526866289114686,1.7537710481857811,1.7553976770972497,978272.91,5,LONDON,2.5,0.0010844192743124175,60.07923076908048,32.227445730884426,1.7537876917416417,1.754544648907083,-8.450994361814956,False,-6.035633904094692e-05,64.11365387779385,-27.27691741641669,0.1578947368419515,11,1,1.7503277903628438,SL,2021-02-02 13:15:00+00:00,-10.222096371563794,-999.9999963710154,120,False
+2021-02-04 14:30:00+00:00,5,GBP_CAD,SHORT,1.7528199999999998,1.754143896116605,1.750308311650185,1.748440519416975,1.74563883106716,747792.81,5,OVERLAP,2.5,0.0018677922332099698,63.45784722686425,75.65294834543747,1.7444188058401342,1.7455968344827628,89.88705247335415,False,0.0008151296538982471,52.734798801718,86.91194159865745,0.45528455284554975,14,3,1.754143896116605,SL,2021-02-04 16:00:00+00:00,-13.238961166051055,-989.9999971842196,90,False
+2021-02-04 16:00:00+00:00,5,GBP_CAD,SHORT,1.7537099999999999,1.755090125496537,1.7513896235103887,1.7496493725173146,1.7470389960277033,710152.81,5,OVERLAP,2.5,0.0017402509930741971,46.52082886546415,73.89660117511697,1.7463739345734643,1.7460612437816967,80.25598956802992,False,2.694287666410401e-05,79.20809083564363,76.26065426535655,0.4437086092715183,16,3,1.7537099999999999,TP1+SL,2021-02-05 00:00:00+00:00,9.281505958444567,659.1287537421155,480,True
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diff --git a/results/phase1/phase1_summary.json b/results/phase1/phase1_summary.json
new file mode 100644
index 0000000..9c0ad35
--- /dev/null
+++ b/results/phase1/phase1_summary.json
@@ -0,0 +1,872 @@
+{
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+        "trades": 113,
+        "win_rate": 29.20353982300885,
+        "total_pnl_pips": -473.4409758613806
+      }
+    },
+    "exit_reasons": {
+      "TP3": 17,
+      "SL": 184,
+      "TP1+SL": 36,
+      "TP1+TP2+SL": 25
+    }
+  }
+}
\ No newline at end of file
diff --git a/sql/alter_fx_candles_v2.sql b/sql/alter_fx_candles_v2.sql
new file mode 100644
index 0000000..f9e779d
--- /dev/null
+++ b/sql/alter_fx_candles_v2.sql
@@ -0,0 +1,22 @@
+-- Schema migration: add columns for 5-strategy indicator set
+-- Run against Supabase SQL editor or via psql
+
+ALTER TABLE fx_candles ADD COLUMN IF NOT EXISTS sma_50 DOUBLE PRECISION;
+ALTER TABLE fx_candles ADD COLUMN IF NOT EXISTS sma_100 DOUBLE PRECISION;
+ALTER TABLE fx_candles ADD COLUMN IF NOT EXISTS sma_200 DOUBLE PRECISION;
+ALTER TABLE fx_candles ADD COLUMN IF NOT EXISTS ema_50 DOUBLE PRECISION;
+ALTER TABLE fx_candles ADD COLUMN IF NOT EXISTS ema_100 DOUBLE PRECISION;
+ALTER TABLE fx_candles ADD COLUMN IF NOT EXISTS ema_200 DOUBLE PRECISION;
+ALTER TABLE fx_candles ADD COLUMN IF NOT EXISTS macd DOUBLE PRECISION;
+ALTER TABLE fx_candles ADD COLUMN IF NOT EXISTS macd_signal DOUBLE PRECISION;
+ALTER TABLE fx_candles ADD COLUMN IF NOT EXISTS macd_hist DOUBLE PRECISION;
+ALTER TABLE fx_candles ADD COLUMN IF NOT EXISTS adx_14 DOUBLE PRECISION;
+ALTER TABLE fx_candles ADD COLUMN IF NOT EXISTS stoch_k DOUBLE PRECISION;
+ALTER TABLE fx_candles ADD COLUMN IF NOT EXISTS stoch_d DOUBLE PRECISION;
+ALTER TABLE fx_candles ADD COLUMN IF NOT EXISTS session_vwap DOUBLE PRECISION;
+ALTER TABLE fx_candles ADD COLUMN IF NOT EXISTS vwap_upper_1_5 DOUBLE PRECISION;
+ALTER TABLE fx_candles ADD COLUMN IF NOT EXISTS vwap_upper_2 DOUBLE PRECISION;
+ALTER TABLE fx_candles ADD COLUMN IF NOT EXISTS vwap_lower_1_5 DOUBLE PRECISION;
+ALTER TABLE fx_candles ADD COLUMN IF NOT EXISTS vwap_lower_2 DOUBLE PRECISION;
+ALTER TABLE fx_candles ADD COLUMN IF NOT EXISTS vwap_upper_2_5 DOUBLE PRECISION;
+ALTER TABLE fx_candles ADD COLUMN IF NOT EXISTS vwap_lower_2_5 DOUBLE PRECISION;
diff --git a/src/backtest_phase1.py b/src/backtest_phase1.py
new file mode 100644
index 0000000..cadf31a
--- /dev/null
+++ b/src/backtest_phase1.py
@@ -0,0 +1,207 @@
+"""
+Phase 1 Backtest Runner.
+
+Runs all 5 strategies across their assigned pairs using TRAINING data only.
+Generates rich trade logs and performance reports.
+Saves results to results/phase1/.
+"""
+import os
+import sys
+import json
+import time
+
+import pandas as pd
+import numpy as np
+
+# Add project root to path
+sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
+
+from src.indicators.technical import compute_all_indicators
+from src.backtester.engine import Backtester
+from src.strategies_pkg import (
+    STRATEGIES, STRATEGY_PAIRS, STRATEGY_TIMEFRAMES,
+    S1_MA_Breakout, S2_VWAP_Reversal, S3_KeyLevel_Breakout,
+    S4_EMA_Ribbon, S5_Momentum_Exhaustion,
+)
+
+PROCESSED_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "data", "processed")
+RESULTS_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "results", "phase1")
+
+
+def load_data(pair: str, timeframe: str) -> pd.DataFrame:
+    """Load training data and compute indicators."""
+    filepath = os.path.join(PROCESSED_DIR, f"{pair}_{timeframe}.csv")
+    if not os.path.exists(filepath):
+        print(f"  WARNING: {filepath} not found")
+        return None
+
+    df = pd.read_csv(filepath, index_col=0, parse_dates=True)
+    df.index.name = "timestamp"
+
+    # Compute all indicators
+    df = compute_all_indicators(df)
+
+    return df
+
+
+def run_strategy_backtest(strategy_id: int, pair: str):
+    """Run a single strategy on a single pair."""
+    strat_cls = STRATEGIES[strategy_id]
+    tf_config = STRATEGY_TIMEFRAMES[strategy_id]
+    primary_tf = tf_config["primary"]
+    filter_tf = tf_config["filter"]
+
+    print(f"  Loading {pair} {primary_tf}...", end=" ")
+    data = load_data(pair, primary_tf)
+    if data is None:
+        return None
+    print(f"{len(data)} bars")
+
+    htf_data = None
+    if filter_tf:
+        print(f"  Loading {pair} {filter_tf} (HTF filter)...", end=" ")
+        htf_data = load_data(pair, filter_tf)
+        if htf_data is not None:
+            print(f"{len(htf_data)} bars")
+        else:
+            print("not available")
+
+    # Instantiate strategy
+    strategy = strat_cls()
+
+    # Run backtest
+    bt = Backtester(
+        data=data,
+        strategy=strategy,
+        pair=pair,
+        starting_equity=100_000.0,
+        htf_data=htf_data,
+    )
+
+    print(f"  Running backtest...", end=" ")
+    start_time = time.time()
+    report = bt.run()
+    elapsed = time.time() - start_time
+    print(f"done in {elapsed:.1f}s")
+
+    # Get trade log
+    trade_log = bt.get_trade_log_df()
+
+    return report, trade_log
+
+
+def save_results(strategy_id: int, strategy_name: str, pair: str,
+                 report: dict, trade_log: pd.DataFrame):
+    """Save results to results/phase1/."""
+    os.makedirs(RESULTS_DIR, exist_ok=True)
+
+    prefix = f"S{strategy_id}_{pair}"
+
+    # Save report JSON
+    report_path = os.path.join(RESULTS_DIR, f"{prefix}_report.json")
+    with open(report_path, "w") as f:
+        json.dump(report, f, indent=2, default=str)
+
+    # Save trade log CSV
+    if len(trade_log) > 0:
+        log_path = os.path.join(RESULTS_DIR, f"{prefix}_trades.csv")
+        trade_log.to_csv(log_path, index=False)
+
+    return report_path
+
+
+def print_report_summary(report: dict):
+    """Print a concise report summary."""
+    total = report.get("total_trades", 0)
+    if total == 0:
+        print(f"    No trades generated")
+        return
+
+    print(f"    Trades: {total}")
+    print(f"    Win Rate: {report.get('win_rate_pct', 0):.1f}%")
+    print(f"    Avg RR: {report.get('avg_rr', 0):.2f}")
+    print(f"    Expectancy: {report.get('expectancy_pips', 0):.2f} pips")
+    print(f"    Sharpe: {report.get('sharpe_ratio', 0):.2f}")
+    print(f"    Max DD: {report.get('max_drawdown_pct', 0):.2f}%")
+    print(f"    Profit Factor: {report.get('profit_factor', 0):.2f}")
+    print(f"    Total PnL: {report.get('total_pnl_pips', 0):.1f} pips / ${report.get('total_pnl_dollars', 0):.2f}")
+    print(f"    Final Equity: ${report.get('final_equity', 0):,.2f}")
+
+    exits = report.get("exit_reasons", {})
+    if exits:
+        print(f"    Exits: {exits}")
+
+
+def main():
+    # Allow filtering strategies via command line: python backtest_phase1.py 2 3 4 5
+    selected = None
+    if len(sys.argv) > 1:
+        selected = [int(x) for x in sys.argv[1:]]
+
+    print("=" * 70)
+    print("PHASE 1 BACKTEST RUNNER")
+    print("Using TRAINING data only (Jan 2021 - Aug 2023)")
+    print("=" * 70)
+
+    all_results = {}
+
+    for strategy_id in sorted(STRATEGIES.keys()):
+        if selected and strategy_id not in selected:
+            continue
+        strat_cls = STRATEGIES[strategy_id]
+        strat_name = strat_cls().name
+        pairs = STRATEGY_PAIRS[strategy_id]
+        tf = STRATEGY_TIMEFRAMES[strategy_id]
+
+        print(f"\n{'='*70}")
+        print(f"Strategy {strategy_id}: {strat_name}")
+        print(f"Pairs: {', '.join(pairs)}")
+        print(f"Primary TF: {tf['primary']}, Filter TF: {tf['filter'] or 'None'}")
+        print(f"{'='*70}")
+
+        strategy_trades_total = 0
+
+        for pair in pairs:
+            print(f"\n  --- {pair} ---")
+            result = run_strategy_backtest(strategy_id, pair)
+            if result is None:
+                print(f"    SKIPPED (data not available)")
+                continue
+
+            report, trade_log = result
+            save_results(strategy_id, strat_name, pair, report, trade_log)
+            print_report_summary(report)
+
+            total_trades = report.get("total_trades", 0)
+            strategy_trades_total += total_trades
+
+            key = f"S{strategy_id}_{pair}"
+            all_results[key] = report
+
+        print(f"\n  >>> S{strategy_id} TOTAL TRADES: {strategy_trades_total}")
+        if strategy_trades_total < 200:
+            print(f"  >>> WARNING: Below 200 trade minimum!")
+
+    # Final summary
+    print("\n" + "=" * 70)
+    print("PHASE 1 SUMMARY")
+    print("=" * 70)
+    print(f"\n{'Strategy':<35} {'Trades':>7} {'WinRate':>8} {'Sharpe':>8} {'PnL Pips':>10}")
+    print("-" * 70)
+
+    for key in sorted(all_results.keys()):
+        r = all_results[key]
+        print(f"{key:<35} {r.get('total_trades', 0):>7} "
+              f"{r.get('win_rate_pct', 0):>7.1f}% "
+              f"{r.get('sharpe_ratio', 0):>8.2f} "
+              f"{r.get('total_pnl_pips', 0):>10.1f}")
+
+    # Save master summary
+    summary_path = os.path.join(RESULTS_DIR, "phase1_summary.json")
+    with open(summary_path, "w") as f:
+        json.dump(all_results, f, indent=2, default=str)
+    print(f"\nMaster summary saved to: {summary_path}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/backtest_strategies.py b/src/backtest_strategies.py
new file mode 100644
index 0000000..5e5acce
--- /dev/null
+++ b/src/backtest_strategies.py
@@ -0,0 +1,670 @@
+# src/backtest_strategies.py
+"""
+Multi-strategy backtester for 5 trading strategies.
+
+Extends the existing backtester with:
+- 3 take-profit levels with custom partial close percentages
+- Trailing stop for TP3 runner (trail by Nx ATR)
+- Time-based exits (max candles before forced close)
+- Confidence-based position sizing (1%/1.5%/2% risk)
+- News event filter (reuse is_near_event from backtester)
+
+Global risk rules:
+- 5% daily drawdown halt
+- 1.5:1 minimum RR check before entry
+- No entries within 30 min of major news
+
+Usage:
+    python src/backtest_strategies.py
+"""
+
+import os
+import json
+import math
+from pathlib import Path
+
+import pandas as pd
+import numpy as np
+from supabase import create_client
+
+from config_loader import load_config, get_project_root
+from backtester import (
+    fetch_candles_from_supabase,
+    fetch_calendar_for_backtest,
+    is_near_event,
+    _prepare_calendar_index,
+    compute_metrics,
+    compute_monthly_pnl,
+)
+from strategies import STRATEGY_REGISTRY
+
+
+# ---------------------------------------------------------------------------
+# Confidence-based position sizing
+# ---------------------------------------------------------------------------
+
+def _get_risk_pct(confidence):
+    """Map confidence score to risk percentage of equity."""
+    if confidence >= 4:
+        return 0.02   # 2% risk for high confidence
+    elif confidence >= 2:
+        return 0.015  # 1.5% risk for medium
+    else:
+        return 0.01   # 1% risk for low
+
+
+# ---------------------------------------------------------------------------
+# Extended backtest engine with 3 TPs + trailing stop + time exit
+# ---------------------------------------------------------------------------
+
+def run_backtest_3tp(df, strategy_cfg, tp_splits=None, trail_atr_mult=0.0,
+                     max_bars=0, calendar_df=None, event_buffer_minutes=30):
+    """
+    Backtest engine with 3 take-profit levels, trailing stop, and time exit.
+
+    Parameters
+    ----------
+    df : DataFrame with signal, sl_price, tp1_price, tp2_price, tp3_price,
+         confidence columns.
+    strategy_cfg : dict with max_drawdown_pct, starting_equity.
+    tp_splits : list of 3 floats, e.g. [0.40, 0.40, 0.20] for TP1/TP2/TP3.
+    trail_atr_mult : float, ATR multiplier for trailing stop on TP3 runner.
+                     0.0 = no trailing stop.
+    max_bars : int, max bars to hold before forced close. 0 = no limit.
+    calendar_df : DataFrame of economic events for news filter.
+    event_buffer_minutes : int, minutes buffer around news events.
+
+    Returns dict with equity_curve, trades, metrics.
+    """
+    if tp_splits is None:
+        tp_splits = [0.40, 0.40, 0.20]
+
+    max_dd_pct = strategy_cfg.get("max_drawdown_pct", 0.05)
+    starting_equity = strategy_cfg.get("starting_equity", 100_000.0)
+    min_rr = strategy_cfg.get("min_reward_risk", 1.5)
+
+    equity = starting_equity
+    peak_equity = equity
+    daily_start_equity = equity
+    stopped = False
+
+    # Position state
+    in_position = False
+    direction = 0
+    entry_price = 0.0
+    sl_price = 0.0
+    tp1_price = 0.0
+    tp2_price = 0.0
+    tp3_price = 0.0
+    position_size = 0.0
+    tp1_closed = False
+    tp2_closed = False
+    bars_in_trade = 0
+    trail_active = False
+    trailing_sl = 0.0
+
+    # Calendar filter
+    cal_indexed = _prepare_calendar_index(calendar_df)
+    blocked_by_calendar = 0
+
+    equity_curve = []
+    trades = []
+
+    times = df.index.tolist()
+    signals = df["signal"].values
+    closes = df["close"].values
+    highs = df["high"].values
+    lows = df["low"].values
+    sl_col = df["sl_price"].values
+    tp1_col = df["tp1_price"].values
+    tp2_col = df["tp2_price"].values
+    tp3_col = df["tp3_price"].values if "tp3_price" in df.columns else np.full(len(df), np.nan)
+    conf_col = df["confidence"].values if "confidence" in df.columns else np.ones(len(df), dtype=int)
+    atr_col = df["atr_14"].values if "atr_14" in df.columns else np.full(len(df), np.nan)
+
+    current_date = None
+
+    for i in range(len(df)):
+        bar_time = times[i]
+        bar_high = highs[i]
+        bar_low = lows[i]
+        bar_close = closes[i]
+
+        # Daily drawdown reset
+        bar_date = pd.Timestamp(bar_time).date()
+        if bar_date != current_date:
+            current_date = bar_date
+            daily_start_equity = equity
+            if stopped:
+                stopped = False  # reset daily halt
+
+        if stopped:
+            equity_curve.append(equity)
+            continue
+
+        # --- Exit logic for active position ---
+        if in_position:
+            bars_in_trade += 1
+            remaining_splits = []
+            if not tp1_closed:
+                remaining_splits = tp_splits  # all 3 portions
+            elif not tp2_closed:
+                remaining_splits = [0, tp_splits[1], tp_splits[2]]
+            else:
+                remaining_splits = [0, 0, tp_splits[2]]
+
+            remaining_pct = sum(remaining_splits)
+            remaining_size = position_size * remaining_pct
+
+            # Update trailing stop if active
+            if trail_active and trail_atr_mult > 0 and not np.isnan(atr_col[i]):
+                if direction == 1:
+                    new_trail = bar_high - trail_atr_mult * atr_col[i]
+                    trailing_sl = max(trailing_sl, new_trail)
+                elif direction == -1:
+                    new_trail = bar_low + trail_atr_mult * atr_col[i]
+                    trailing_sl = min(trailing_sl, new_trail)
+
+            # Effective SL (use trailing if active and better)
+            effective_sl = sl_price
+            if trail_active:
+                if direction == 1:
+                    effective_sl = max(sl_price, trailing_sl)
+                else:
+                    effective_sl = min(sl_price, trailing_sl)
+
+            # Time-based forced exit
+            if max_bars > 0 and bars_in_trade >= max_bars:
+                pnl = remaining_size * (bar_close - entry_price) / entry_price * direction
+                equity += pnl
+                trades.append({
+                    "time": bar_time, "side": "TIME_EXIT",
+                    "price": bar_close, "position_size": 0.0,
+                    "equity": round(equity, 2), "drawdown": 0.0,
+                    "pnl": round(pnl, 2),
+                })
+                in_position = False
+                tp1_closed = False
+                tp2_closed = False
+                trail_active = False
+
+            elif direction == 1:  # LONG
+                # SL check
+                if bar_low <= effective_sl:
+                    pnl = remaining_size * (effective_sl - entry_price) / entry_price
+                    equity += pnl
+                    trades.append({
+                        "time": bar_time,
+                        "side": "TRAIL_SL_LONG" if trail_active and effective_sl == trailing_sl else "SL_EXIT_LONG",
+                        "price": effective_sl, "position_size": 0.0,
+                        "equity": round(equity, 2), "drawdown": 0.0,
+                        "pnl": round(pnl, 2),
+                    })
+                    in_position = False
+                    tp1_closed = False
+                    tp2_closed = False
+                    trail_active = False
+
+                # TP1
+                elif not tp1_closed and tp_splits[0] > 0 and bar_high >= tp1_price:
+                    close_size = position_size * tp_splits[0]
+                    pnl = close_size * (tp1_price - entry_price) / entry_price
+                    equity += pnl
+                    trades.append({
+                        "time": bar_time, "side": "TP1_LONG",
+                        "price": tp1_price,
+                        "position_size": round(position_size * (tp_splits[1] + tp_splits[2]), 2),
+                        "equity": round(equity, 2), "drawdown": 0.0,
+                        "pnl": round(pnl, 2),
+                    })
+                    tp1_closed = True
+                    sl_price = entry_price  # move to breakeven
+
+                    # Check TP2 same bar
+                    if tp_splits[1] > 0 and bar_high >= tp2_price:
+                        close_size2 = position_size * tp_splits[1]
+                        pnl2 = close_size2 * (tp2_price - entry_price) / entry_price
+                        equity += pnl2
+                        trades.append({
+                            "time": bar_time, "side": "TP2_LONG",
+                            "price": tp2_price,
+                            "position_size": round(position_size * tp_splits[2], 2),
+                            "equity": round(equity, 2), "drawdown": 0.0,
+                            "pnl": round(pnl2, 2),
+                        })
+                        tp2_closed = True
+                        # Activate trailing stop for runner
+                        if tp_splits[2] > 0 and trail_atr_mult > 0:
+                            trail_active = True
+                            trailing_sl = tp2_price - trail_atr_mult * atr_col[i] if not np.isnan(atr_col[i]) else entry_price
+
+                        # Check TP3 same bar
+                        if tp_splits[2] > 0 and not np.isnan(tp3_price) and bar_high >= tp3_price:
+                            close_size3 = position_size * tp_splits[2]
+                            pnl3 = close_size3 * (tp3_price - entry_price) / entry_price
+                            equity += pnl3
+                            trades.append({
+                                "time": bar_time, "side": "TP3_LONG",
+                                "price": tp3_price, "position_size": 0.0,
+                                "equity": round(equity, 2), "drawdown": 0.0,
+                                "pnl": round(pnl3, 2),
+                            })
+                            in_position = False
+                            tp1_closed = False
+                            tp2_closed = False
+                            trail_active = False
+
+                # TP2 (after TP1)
+                elif tp1_closed and not tp2_closed and tp_splits[1] > 0 and bar_high >= tp2_price:
+                    close_size = position_size * tp_splits[1]
+                    pnl = close_size * (tp2_price - entry_price) / entry_price
+                    equity += pnl
+                    trades.append({
+                        "time": bar_time, "side": "TP2_LONG",
+                        "price": tp2_price,
+                        "position_size": round(position_size * tp_splits[2], 2),
+                        "equity": round(equity, 2), "drawdown": 0.0,
+                        "pnl": round(pnl, 2),
+                    })
+                    tp2_closed = True
+                    if tp_splits[2] > 0 and trail_atr_mult > 0:
+                        trail_active = True
+                        trailing_sl = tp2_price - trail_atr_mult * atr_col[i] if not np.isnan(atr_col[i]) else entry_price
+
+                    if tp_splits[2] <= 0:
+                        in_position = False
+                        tp1_closed = False
+                        tp2_closed = False
+                        trail_active = False
+                    elif not np.isnan(tp3_price) and bar_high >= tp3_price:
+                        close_size3 = position_size * tp_splits[2]
+                        pnl3 = close_size3 * (tp3_price - entry_price) / entry_price
+                        equity += pnl3
+                        trades.append({
+                            "time": bar_time, "side": "TP3_LONG",
+                            "price": tp3_price, "position_size": 0.0,
+                            "equity": round(equity, 2), "drawdown": 0.0,
+                            "pnl": round(pnl3, 2),
+                        })
+                        in_position = False
+                        tp1_closed = False
+                        tp2_closed = False
+                        trail_active = False
+
+                # TP3 (after TP1+TP2)
+                elif tp1_closed and tp2_closed and tp_splits[2] > 0:
+                    if not np.isnan(tp3_price) and bar_high >= tp3_price:
+                        close_size = position_size * tp_splits[2]
+                        pnl = close_size * (tp3_price - entry_price) / entry_price
+                        equity += pnl
+                        trades.append({
+                            "time": bar_time, "side": "TP3_LONG",
+                            "price": tp3_price, "position_size": 0.0,
+                            "equity": round(equity, 2), "drawdown": 0.0,
+                            "pnl": round(pnl, 2),
+                        })
+                        in_position = False
+                        tp1_closed = False
+                        tp2_closed = False
+                        trail_active = False
+
+            elif direction == -1:  # SHORT
+                if bar_high >= effective_sl:
+                    pnl = remaining_size * (entry_price - effective_sl) / entry_price
+                    equity += pnl
+                    trades.append({
+                        "time": bar_time,
+                        "side": "TRAIL_SL_SHORT" if trail_active and effective_sl == trailing_sl else "SL_EXIT_SHORT",
+                        "price": effective_sl, "position_size": 0.0,
+                        "equity": round(equity, 2), "drawdown": 0.0,
+                        "pnl": round(pnl, 2),
+                    })
+                    in_position = False
+                    tp1_closed = False
+                    tp2_closed = False
+                    trail_active = False
+
+                elif not tp1_closed and tp_splits[0] > 0 and bar_low <= tp1_price:
+                    close_size = position_size * tp_splits[0]
+                    pnl = close_size * (entry_price - tp1_price) / entry_price
+                    equity += pnl
+                    trades.append({
+                        "time": bar_time, "side": "TP1_SHORT",
+                        "price": tp1_price,
+                        "position_size": round(position_size * (tp_splits[1] + tp_splits[2]), 2),
+                        "equity": round(equity, 2), "drawdown": 0.0,
+                        "pnl": round(pnl, 2),
+                    })
+                    tp1_closed = True
+                    sl_price = entry_price
+
+                    if tp_splits[1] > 0 and bar_low <= tp2_price:
+                        close_size2 = position_size * tp_splits[1]
+                        pnl2 = close_size2 * (entry_price - tp2_price) / entry_price
+                        equity += pnl2
+                        trades.append({
+                            "time": bar_time, "side": "TP2_SHORT",
+                            "price": tp2_price,
+                            "position_size": round(position_size * tp_splits[2], 2),
+                            "equity": round(equity, 2), "drawdown": 0.0,
+                            "pnl": round(pnl2, 2),
+                        })
+                        tp2_closed = True
+                        if tp_splits[2] > 0 and trail_atr_mult > 0:
+                            trail_active = True
+                            trailing_sl = tp2_price + trail_atr_mult * atr_col[i] if not np.isnan(atr_col[i]) else entry_price
+
+                        if tp_splits[2] > 0 and not np.isnan(tp3_price) and bar_low <= tp3_price:
+                            close_size3 = position_size * tp_splits[2]
+                            pnl3 = close_size3 * (entry_price - tp3_price) / entry_price
+                            equity += pnl3
+                            trades.append({
+                                "time": bar_time, "side": "TP3_SHORT",
+                                "price": tp3_price, "position_size": 0.0,
+                                "equity": round(equity, 2), "drawdown": 0.0,
+                                "pnl": round(pnl3, 2),
+                            })
+                            in_position = False
+                            tp1_closed = False
+                            tp2_closed = False
+                            trail_active = False
+
+                elif tp1_closed and not tp2_closed and tp_splits[1] > 0 and bar_low <= tp2_price:
+                    close_size = position_size * tp_splits[1]
+                    pnl = close_size * (entry_price - tp2_price) / entry_price
+                    equity += pnl
+                    trades.append({
+                        "time": bar_time, "side": "TP2_SHORT",
+                        "price": tp2_price,
+                        "position_size": round(position_size * tp_splits[2], 2),
+                        "equity": round(equity, 2), "drawdown": 0.0,
+                        "pnl": round(pnl, 2),
+                    })
+                    tp2_closed = True
+                    if tp_splits[2] > 0 and trail_atr_mult > 0:
+                        trail_active = True
+                        trailing_sl = tp2_price + trail_atr_mult * atr_col[i] if not np.isnan(atr_col[i]) else entry_price
+
+                    if tp_splits[2] <= 0:
+                        in_position = False
+                        tp1_closed = False
+                        tp2_closed = False
+                        trail_active = False
+                    elif not np.isnan(tp3_price) and bar_low <= tp3_price:
+                        close_size3 = position_size * tp_splits[2]
+                        pnl3 = close_size3 * (entry_price - tp3_price) / entry_price
+                        equity += pnl3
+                        trades.append({
+                            "time": bar_time, "side": "TP3_SHORT",
+                            "price": tp3_price, "position_size": 0.0,
+                            "equity": round(equity, 2), "drawdown": 0.0,
+                            "pnl": round(pnl3, 2),
+                        })
+                        in_position = False
+                        tp1_closed = False
+                        tp2_closed = False
+                        trail_active = False
+
+                elif tp1_closed and tp2_closed and tp_splits[2] > 0:
+                    if not np.isnan(tp3_price) and bar_low <= tp3_price:
+                        close_size = position_size * tp_splits[2]
+                        pnl = close_size * (entry_price - tp3_price) / entry_price
+                        equity += pnl
+                        trades.append({
+                            "time": bar_time, "side": "TP3_SHORT",
+                            "price": tp3_price, "position_size": 0.0,
+                            "equity": round(equity, 2), "drawdown": 0.0,
+                            "pnl": round(pnl, 2),
+                        })
+                        in_position = False
+                        tp1_closed = False
+                        tp2_closed = False
+                        trail_active = False
+
+        # --- New entry (only when flat) ---
+        if not in_position and not stopped:
+            sig = signals[i]
+            if sig in (1, -1) and not np.isnan(sl_col[i]):
+                # News filter
+                if is_near_event(bar_time, cal_indexed, event_buffer_minutes):
+                    blocked_by_calendar += 1
+                    equity_curve.append(equity)
+                    continue
+
+                # Minimum RR check
+                risk = abs(bar_close - sl_col[i])
+                reward = abs(tp1_col[i] - bar_close) if not np.isnan(tp1_col[i]) else 0
+                if risk > 0 and reward / risk < min_rr:
+                    equity_curve.append(equity)
+                    continue
+
+                # Confidence-based sizing
+                risk_pct = _get_risk_pct(int(conf_col[i]))
+                direction = sig
+                entry_price = bar_close
+                sl_price = sl_col[i]
+                tp1_price = tp1_col[i]
+                tp2_price = tp2_col[i]
+                tp3_price = tp3_col[i] if not np.isnan(tp3_col[i]) else tp2_col[i]
+                position_size = equity * risk_pct
+                tp1_closed = False
+                tp2_closed = False
+                trail_active = False
+                trailing_sl = 0.0
+                bars_in_trade = 0
+                in_position = True
+
+                side_label = "BUY" if sig == 1 else "SELL_SHORT"
+                trades.append({
+                    "time": bar_time, "side": side_label,
+                    "price": bar_close,
+                    "position_size": round(position_size, 2),
+                    "equity": round(equity, 2), "drawdown": 0.0,
+                    "pnl": 0.0,
+                })
+
+        # Update peak and drawdown
+        if equity > peak_equity:
+            peak_equity = equity
+
+        # 5% daily drawdown halt
+        daily_dd = (daily_start_equity - equity) / daily_start_equity if daily_start_equity > 0 else 0
+        if daily_dd >= max_dd_pct:
+            if in_position:
+                remaining_pct = sum(tp_splits) if not tp1_closed else (
+                    tp_splits[1] + tp_splits[2] if not tp2_closed else tp_splits[2]
+                )
+                remaining_size = position_size * remaining_pct
+                pnl = remaining_size * (bar_close - entry_price) / entry_price * direction
+                equity += pnl
+                trades.append({
+                    "time": bar_time, "side": "DD_EXIT",
+                    "price": bar_close, "position_size": 0.0,
+                    "equity": round(equity, 2),
+                    "drawdown": round(daily_dd, 6),
+                    "pnl": round(pnl, 2),
+                })
+                in_position = False
+                tp1_closed = False
+                tp2_closed = False
+                trail_active = False
+            stopped = True
+            print(f"  Daily drawdown {max_dd_pct:.1%} breached at {bar_time}. Halting for day.")
+
+        equity_curve.append(equity)
+
+    equity_series = pd.Series(equity_curve, index=df.index, name="equity")
+    metrics = compute_metrics(equity_series, trades, starting_equity)
+
+    if blocked_by_calendar > 0:
+        print(f"  Calendar filter blocked {blocked_by_calendar} entries.")
+        metrics["blocked_by_calendar"] = blocked_by_calendar
+
+    return {
+        "equity_curve": equity_series,
+        "trades": trades,
+        "metrics": metrics,
+    }
+
+
+# ---------------------------------------------------------------------------
+# Save results (extended with strategy_name)
+# ---------------------------------------------------------------------------
+
+def save_strategy_results(strategy_key, strategy_name, instrument, granularity,
+                          results, metrics):
+    """Save backtest results with strategy name in the JSON summary."""
+    root = get_project_root()
+    logs_dir = root / "logs"
+    logs_dir.mkdir(exist_ok=True)
+
+    trades = results["trades"]
+    equity_curve = results["equity_curve"]
+
+    # Trade log CSV
+    if trades:
+        trade_df = pd.DataFrame(trades)
+        trade_df["instrument"] = instrument
+        trade_df["granularity"] = granularity
+        trade_df["strategy"] = strategy_name
+        base_cols = ["time", "strategy", "instrument", "granularity", "side", "price",
+                     "position_size", "equity", "drawdown", "pnl"]
+        cols = [c for c in base_cols if c in trade_df.columns]
+        trade_df = trade_df[cols]
+        csv_path = logs_dir / f"backtest_trades_{strategy_key}_{instrument}_{granularity}.csv"
+        trade_df.to_csv(csv_path, index=False)
+        print(f"  Trade log: {csv_path}")
+
+    # Monthly P&L
+    starting_equity = metrics.get("starting_equity", 100_000.0)
+    monthly = compute_monthly_pnl(equity_curve, starting_equity)
+
+    # JSON summary
+    summary = {
+        "strategy_name": strategy_name,
+        "strategy_key": strategy_key,
+        "instrument": instrument,
+        "granularity": granularity,
+        "run_time": pd.Timestamp.now(tz="UTC").isoformat(),
+        "data_range": {
+            "start": str(equity_curve.index[0]) if len(equity_curve) > 0 else "",
+            "end": str(equity_curve.index[-1]) if len(equity_curve) > 0 else "",
+            "bars": len(equity_curve),
+        },
+        "metrics": metrics,
+        "monthly_pnl": monthly,
+    }
+    json_path = logs_dir / f"backtest_summary_{strategy_key}_{strategy_name}_{instrument}_{granularity}.json"
+    with open(json_path, "w") as f:
+        json.dump(summary, f, indent=2, default=str)
+    print(f"  Summary: {json_path}")
+
+    # Console summary
+    print(f"\n  --- {strategy_name} | {instrument} / {granularity} ---")
+    print(f"  Return: {metrics['total_return_pct']:.4f}% | DD: {metrics['max_drawdown_pct']:.4f}%")
+    print(f"  Trades: {metrics['num_trades']} ({metrics['round_trips']} RTs) | Win: {metrics['win_rate_pct']:.1f}%")
+    print(f"  Sharpe: {metrics['sharpe_ratio']:.4f} | Final: ${metrics['final_equity']:,.2f}")
+    print()
+
+
+# ---------------------------------------------------------------------------
+# Main
+# ---------------------------------------------------------------------------
+
+def main():
+    cfg = load_config()
+
+    supabase_url = os.getenv("SUPABASE_URL")
+    supabase_key = os.getenv("SUPABASE_KEY")
+    if not supabase_url or not supabase_key:
+        raise SystemExit("Missing SUPABASE_URL or SUPABASE_KEY in config/.env")
+    sb = create_client(supabase_url, supabase_key)
+    table = cfg.get("supabase", {}).get("table", "fx_candles")
+
+    cal_cfg = cfg.get("economic_calendar", {})
+    cal_enabled = cal_cfg.get("enabled", False)
+    event_buffer = cal_cfg.get("event_buffer_minutes", 30)
+
+    strategy_cfg = {
+        "max_drawdown_pct": 0.05,
+        "starting_equity": 100_000.0,
+        "min_reward_risk": 1.5,
+    }
+
+    print("=" * 70)
+    print("Multi-Strategy Backtester")
+    print(f"Strategies: {list(STRATEGY_REGISTRY.keys())}")
+    print("=" * 70)
+
+    for skey, sinfo in STRATEGY_REGISTRY.items():
+        strategy_name = sinfo["name"]
+        signal_func = sinfo["func"]
+        pairs = sinfo["pairs"]
+        entry_tf = sinfo["entry_tf"]
+        filter_tf = sinfo.get("filter_tf")
+        tp_splits = sinfo.get("tp_splits", [0.40, 0.40, 0.20])
+        trail_atr = sinfo.get("trail_atr_mult", 0.0)
+        max_bars = sinfo.get("max_bars", 0)
+
+        print(f"\n{'='*70}")
+        print(f"Strategy: {strategy_name}")
+        print(f"Pairs: {pairs} | Entry: {entry_tf} | Filter: {filter_tf}")
+        print(f"TP splits: {tp_splits} | Trail ATR: {trail_atr} | Max bars: {max_bars}")
+        print(f"{'='*70}")
+
+        for instrument in pairs:
+            print(f"\n--- {instrument} ---")
+
+            # Load entry timeframe data
+            df_entry = fetch_candles_from_supabase(instrument, entry_tf, sb, table)
+            if df_entry.empty:
+                print(f"  No {entry_tf} data for {instrument}. Skipping.")
+                continue
+
+            # Load filter timeframe data if needed
+            df_filter = None
+            if filter_tf:
+                df_filter = fetch_candles_from_supabase(instrument, filter_tf, sb, table)
+                if df_filter.empty:
+                    print(f"  No {filter_tf} data for {instrument}. Proceeding without filter.")
+                    df_filter = None
+
+            # Load calendar
+            calendar_df = None
+            if cal_enabled:
+                calendar_df = fetch_calendar_for_backtest(instrument, sb, cfg)
+
+            # Generate signals
+            print(f"  Generating {strategy_name} signals...")
+            df_signals = signal_func(df_entry, df_filter=df_filter)
+
+            signal_count = (df_signals["signal"] != 0).sum()
+            print(f"  Signals generated: {signal_count} on {len(df_signals)} bars")
+
+            if signal_count == 0:
+                print(f"  No signals. Skipping backtest.")
+                continue
+
+            # Run backtest
+            print(f"  Running backtest...")
+            results = run_backtest_3tp(
+                df_signals, strategy_cfg,
+                tp_splits=tp_splits,
+                trail_atr_mult=trail_atr,
+                max_bars=max_bars,
+                calendar_df=calendar_df,
+                event_buffer_minutes=event_buffer,
+            )
+
+            save_strategy_results(
+                skey, strategy_name, instrument, entry_tf,
+                results, results["metrics"],
+            )
+
+    print("\n" + "=" * 70)
+    print("All strategy backtests complete.")
+    print("=" * 70)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/backtester.py b/src/backtester.py
index 144f3ba..241a1da 100644
--- a/src/backtester.py
+++ b/src/backtester.py
@@ -102,7 +102,7 @@ def fetch_candles_from_supabase(instrument, granularity, supabase_client, table)
     """
     print(f"Fetching {instrument} / {granularity} from Supabase...")
 
-    # Supabase JS-style pagination: fetch in pages of 1000
+    # Supabase pagination: fetch in pages of 1000
     all_rows = []
     page_size = 1000
     offset = 0
@@ -113,14 +113,14 @@ def fetch_candles_from_supabase(instrument, granularity, supabase_client, table)
             .eq("instrument", instrument)
             .eq("granularity", granularity)
             .order("time", desc=False)
-            .range(offset, offset + page_size - 1)
+            .range(offset, offset + page_size)
             .execute()
         )
         rows = resp.data or []
         all_rows.extend(rows)
         if len(rows) < page_size:
             break
-        offset += page_size
+        offset += len(rows)
 
     if not all_rows:
         print(f"  No data found for {instrument} / {granularity}.")
diff --git a/src/backtester/__init__.py b/src/backtester/__init__.py
new file mode 100644
index 0000000..73bc4c7
--- /dev/null
+++ b/src/backtester/__init__.py
@@ -0,0 +1 @@
+from .engine import Backtester
diff --git a/src/backtester/engine.py b/src/backtester/engine.py
new file mode 100644
index 0000000..1ca4ea6
--- /dev/null
+++ b/src/backtester/engine.py
@@ -0,0 +1,658 @@
+"""
+Event-driven backtesting engine for fx-quant Phase 1.
+
+Processes one candle at a time. No lookahead bias.
+Supports:
+  - Spread and slippage modeling
+  - 3-level take-profit with partial closes
+  - Trailing stop on runner position
+  - Time-based exits
+  - Rich trade logging (20+ features per signal)
+  - News filter (30 min buffer)
+  - Confidence-based position sizing (1-2% risk)
+"""
+import numpy as np
+import pandas as pd
+from dataclasses import dataclass, field
+from typing import Optional
+
+
+# ---------------------------------------------------------------------------
+# Spread Configuration (in pips)
+# ---------------------------------------------------------------------------
+SPREAD_PIPS = {
+    "EUR_USD": 1.5, "GBP_USD": 1.5,
+    "GBP_AUD": 2.0, "EUR_AUD": 2.0,
+    "EUR_GBP": 2.0, "GBP_JPY": 2.5,
+    "USD_JPY": 1.5, "GBP_CAD": 2.5,
+    "EUR_CAD": 2.5, "EUR_NZD": 2.5,
+}
+
+# Pip value per pair
+PIP_SIZE = {
+    "EUR_USD": 0.0001, "GBP_USD": 0.0001, "EUR_AUD": 0.0001,
+    "GBP_AUD": 0.0001, "EUR_GBP": 0.0001, "GBP_CAD": 0.0001,
+    "EUR_CAD": 0.0001, "EUR_NZD": 0.0001,
+    "USD_JPY": 0.01, "GBP_JPY": 0.01,
+}
+
+# Major news events (simplified: first Friday of month = NFP, plus key dates)
+# In production, use a calendar API. For backtesting 2021-2024, we hardcode
+# a pattern: block trading around the first Friday of each month (NFP) and
+# known high-impact recurring events.
+MAJOR_NEWS_DAY_OF_WEEK = 4  # Friday
+MAJOR_NEWS_WEEK = 1  # First full week of month
+
+
+@dataclass
+class Position:
+    """Represents an open position."""
+    entry_time: pd.Timestamp
+    direction: str  # 'LONG' or 'SHORT'
+    entry_price: float
+    sl_price: float
+    tp1_price: float
+    tp2_price: float
+    tp3_price: float
+    initial_size: float
+    current_size: float
+    tp_splits: tuple  # e.g. (0.40, 0.40, 0.20)
+    trail_atr_mult: float
+    max_bars: int
+    bars_held: int = 0
+    tp1_hit: bool = False
+    tp2_hit: bool = False
+    tp3_hit: bool = False
+    trailing_sl: Optional[float] = None
+    strategy_id: int = 0
+    confluence_score: int = 0
+    signal_features: dict = field(default_factory=dict)
+    realized_pnl: float = 0.0  # Tracks PnL from partial closes
+
+
+@dataclass
+class TradeRecord:
+    """Rich trade log entry for Phase 3 feature engineering."""
+    timestamp: pd.Timestamp = None
+    strategy_id: int = 0
+    pair: str = ""
+    signal_direction: str = ""
+    entry_price: float = 0.0
+    sl_price: float = 0.0
+    tp1_price: float = 0.0
+    tp2_price: float = 0.0
+    tp3_price: float = 0.0
+    lot_size: float = 0.0
+    confluence_score: int = 0
+    session: str = ""
+    spread_at_entry: float = 0.0
+    atr_at_entry: float = 0.0
+    adx_at_entry: float = 0.0
+    rsi_at_entry: float = 0.0
+    ema_50_value: float = 0.0
+    ema_200_value: float = 0.0
+    vwap_deviation: float = 0.0
+    news_within_60min: bool = False
+    macd_hist_at_entry: float = 0.0
+    stoch_k_at_entry: float = 0.0
+    distance_from_ema50_pips: float = 0.0
+    candle_body_ratio: float = 0.0
+    hour_of_day: int = 0
+    day_of_week: int = 0
+    exit_price: float = 0.0
+    exit_reason: str = ""
+    exit_time: pd.Timestamp = None
+    pnl_pips: float = 0.0
+    pnl_dollars: float = 0.0
+    hold_time_minutes: int = 0
+    win: bool = False
+
+
+class Backtester:
+    """Event-driven backtesting engine."""
+
+    def __init__(self, data: pd.DataFrame, strategy, pair: str,
+                 starting_equity: float = 100_000.0,
+                 htf_data: pd.DataFrame = None):
+        """
+        Args:
+            data: Primary timeframe OHLCV with indicators pre-computed.
+            strategy: Strategy object with check_signal(hist_data, current, htf_row) method.
+            pair: Currency pair string e.g. 'EUR_USD'.
+            starting_equity: Starting account equity.
+            htf_data: Higher timeframe data with indicators (for multi-TF strategies).
+        """
+        self.data = data
+        self.strategy = strategy
+        self.pair = pair
+        self.equity = starting_equity
+        self.starting_equity = starting_equity
+        self.peak_equity = starting_equity
+        self.htf_data = htf_data
+
+        self.pip = PIP_SIZE.get(pair, 0.0001)
+        self.spread = SPREAD_PIPS.get(pair, 2.0) * self.pip
+
+        self.open_positions: list[Position] = []
+        self.closed_trades: list[TradeRecord] = []
+        self.equity_curve = []
+        self.daily_pnl = {}
+
+    def _get_session(self, hour: int) -> str:
+        if 0 <= hour < 8:
+            return "ASIAN"
+        elif 8 <= hour < 13:
+            return "LONDON"
+        elif 13 <= hour < 17:
+            return "OVERLAP"
+        else:
+            return "NY"
+
+    def _get_slippage(self, hour: int) -> float:
+        """Slippage in price units. High-volume sessions get less slippage."""
+        if 8 <= hour < 17:  # London + NY overlap
+            return 0.4 * self.pip
+        else:
+            return 1.0 * self.pip
+
+    def _is_near_news(self, timestamp: pd.Timestamp) -> bool:
+        """Simple news filter: first Friday of each month (NFP proxy) +/- 30 min."""
+        # Check if current day is first Friday of month
+        if timestamp.weekday() != 4:  # Not Friday
+            return False
+        if timestamp.day > 7:  # Not first week
+            return False
+        # NFP typically at 13:30 UTC
+        if 13 <= timestamp.hour <= 14:
+            return True
+        return False
+
+    def _calculate_position_size(self, sl_distance: float,
+                                 confluence_score: int) -> float:
+        """Fixed 1% risk position sizing."""
+        if sl_distance <= 0:
+            return 0.0
+
+        risk_pct = 0.01  # Flat 1% risk for consistency
+        risk_amount = self.equity * risk_pct
+        # Position size = risk_amount / SL distance in price
+        position_size = risk_amount / sl_distance
+        return round(position_size, 2)
+
+    def _get_htf_row(self, timestamp: pd.Timestamp) -> Optional[pd.Series]:
+        """Get the most recent FULLY CLOSED higher-timeframe candle."""
+        if self.htf_data is None:
+            return None
+        # Only use HTF candles that closed BEFORE current timestamp
+        valid = self.htf_data[self.htf_data.index < timestamp]
+        if len(valid) == 0:
+            return None
+        return valid.iloc[-1]
+
+    def _apply_spread_to_entry(self, price: float, direction: str) -> float:
+        """Apply spread to entry price."""
+        if direction == "LONG":
+            return price + self.spread  # Buy at ask
+        else:
+            return price - self.spread  # Sell at bid (lower)
+
+    def _apply_slippage_to_entry(self, price: float, direction: str,
+                                 hour: int) -> float:
+        """Apply slippage to entry price."""
+        slip = self._get_slippage(hour)
+        if direction == "LONG":
+            return price + slip  # Slippage works against us
+        else:
+            return price - slip
+
+    def _check_daily_drawdown(self, timestamp: pd.Timestamp) -> bool:
+        """Check if 5% daily drawdown has been breached."""
+        date_key = timestamp.date()
+        if date_key not in self.daily_pnl:
+            self.daily_pnl[date_key] = 0.0
+        return self.daily_pnl[date_key] <= -0.05 * self.starting_equity
+
+    def _update_positions(self, candle: pd.Series, i: int):
+        """Check SL/TP/trailing/time exits for all open positions."""
+        to_close = []
+
+        for pos in self.open_positions:
+            pos.bars_held += 1
+            high = candle["high"]
+            low = candle["low"]
+            close_price = candle["close"]
+            current_atr = candle.get("atr_14", 0)
+
+            # Determine effective SL
+            effective_sl = pos.trailing_sl if pos.trailing_sl is not None else pos.sl_price
+
+            if pos.direction == "LONG":
+                # Check SL
+                if low <= effective_sl:
+                    self._close_position(pos, effective_sl, "SL", candle)
+                    to_close.append(pos)
+                    continue
+
+                # Check TP1
+                if not pos.tp1_hit and high >= pos.tp1_price:
+                    close_size = pos.initial_size * pos.tp_splits[0]
+                    self._partial_close(pos, pos.tp1_price, close_size, "TP1", candle)
+                    pos.tp1_hit = True
+                    # Move SL to breakeven after TP1
+                    pos.trailing_sl = pos.entry_price
+
+                # Check TP2
+                if not pos.tp2_hit and pos.tp1_hit and high >= pos.tp2_price:
+                    close_size = pos.initial_size * pos.tp_splits[1]
+                    self._partial_close(pos, pos.tp2_price, close_size, "TP2", candle)
+                    pos.tp2_hit = True
+
+                # Check TP3
+                if not pos.tp3_hit and pos.tp2_hit and high >= pos.tp3_price:
+                    self._close_position(pos, pos.tp3_price, "TP3", candle)
+                    to_close.append(pos)
+                    continue
+
+                # Trailing stop for runner (after TP2)
+                if pos.tp2_hit and current_atr > 0:
+                    new_trail = high - pos.trail_atr_mult * current_atr
+                    if pos.trailing_sl is None or new_trail > pos.trailing_sl:
+                        pos.trailing_sl = new_trail
+
+            else:  # SHORT
+                # Check SL
+                if high >= effective_sl:
+                    self._close_position(pos, effective_sl, "SL", candle)
+                    to_close.append(pos)
+                    continue
+
+                # Check TP1
+                if not pos.tp1_hit and low <= pos.tp1_price:
+                    close_size = pos.initial_size * pos.tp_splits[0]
+                    self._partial_close(pos, pos.tp1_price, close_size, "TP1", candle)
+                    pos.tp1_hit = True
+                    pos.trailing_sl = pos.entry_price
+
+                # Check TP2
+                if not pos.tp2_hit and pos.tp1_hit and low <= pos.tp2_price:
+                    close_size = pos.initial_size * pos.tp_splits[1]
+                    self._partial_close(pos, pos.tp2_price, close_size, "TP2", candle)
+                    pos.tp2_hit = True
+
+                # Check TP3
+                if not pos.tp3_hit and pos.tp2_hit and low <= pos.tp3_price:
+                    self._close_position(pos, pos.tp3_price, "TP3", candle)
+                    to_close.append(pos)
+                    continue
+
+                # Trailing stop for runner
+                if pos.tp2_hit and current_atr > 0:
+                    new_trail = low + pos.trail_atr_mult * current_atr
+                    if pos.trailing_sl is None or new_trail < pos.trailing_sl:
+                        pos.trailing_sl = new_trail
+
+            # Time-based exit
+            if pos.max_bars > 0 and pos.bars_held >= pos.max_bars:
+                self._close_position(pos, close_price, "TIME", candle)
+                to_close.append(pos)
+                continue
+
+        for pos in to_close:
+            if pos in self.open_positions:
+                self.open_positions.remove(pos)
+
+    def _partial_close(self, pos: Position, exit_price: float,
+                       close_size: float, reason: str, candle: pd.Series):
+        """Close a partial portion of a position."""
+        if pos.direction == "LONG":
+            pnl_per_unit = exit_price - pos.entry_price
+        else:
+            pnl_per_unit = pos.entry_price - exit_price
+
+        pnl = pnl_per_unit * close_size
+        self.equity += pnl
+        pos.current_size -= close_size
+        pos.realized_pnl += pnl
+
+        date_key = candle.name.date() if hasattr(candle.name, 'date') else None
+        if date_key:
+            self.daily_pnl[date_key] = self.daily_pnl.get(date_key, 0.0) + pnl
+
+        if self.equity > self.peak_equity:
+            self.peak_equity = self.equity
+
+    def _close_position(self, pos: Position, exit_price: float,
+                        reason: str, candle: pd.Series):
+        """Fully close remaining position and log the trade."""
+        remaining = pos.current_size
+        if remaining <= 0:
+            remaining = 0.01  # avoid zero
+
+        if pos.direction == "LONG":
+            pnl_per_unit = exit_price - pos.entry_price
+        else:
+            pnl_per_unit = pos.entry_price - exit_price
+
+        final_pnl = pnl_per_unit * remaining
+        self.equity += final_pnl
+
+        # Total PnL = partial closes + final close
+        total_pnl = pos.realized_pnl + final_pnl
+        total_pnl_pips = total_pnl / (pos.initial_size * self.pip) if pos.initial_size > 0 else 0
+
+        date_key = candle.name.date() if hasattr(candle.name, 'date') else None
+        if date_key:
+            self.daily_pnl[date_key] = self.daily_pnl.get(date_key, 0.0) + final_pnl
+
+        if self.equity > self.peak_equity:
+            self.peak_equity = self.equity
+
+        exit_time = candle.name
+        hold_minutes = 0
+        if hasattr(exit_time, 'timestamp') and hasattr(pos.entry_time, 'timestamp'):
+            hold_minutes = int((exit_time - pos.entry_time).total_seconds() / 60)
+
+        # Build detailed exit reason (e.g. "TP1+TP2+SL" instead of just "SL")
+        exit_detail = reason
+        if reason != "TP3":
+            parts = []
+            if pos.tp1_hit:
+                parts.append("TP1")
+            if pos.tp2_hit:
+                parts.append("TP2")
+            parts.append(reason)
+            exit_detail = "+".join(parts)
+
+        features = pos.signal_features
+        record = TradeRecord(
+            timestamp=pos.entry_time,
+            strategy_id=pos.strategy_id,
+            pair=self.pair,
+            signal_direction=pos.direction,
+            entry_price=pos.entry_price,
+            sl_price=pos.sl_price,
+            tp1_price=pos.tp1_price,
+            tp2_price=pos.tp2_price,
+            tp3_price=pos.tp3_price,
+            lot_size=pos.initial_size,
+            confluence_score=pos.confluence_score,
+            session=features.get("session", ""),
+            spread_at_entry=features.get("spread_at_entry", 0),
+            atr_at_entry=features.get("atr_at_entry", 0),
+            adx_at_entry=features.get("adx_at_entry", 0),
+            rsi_at_entry=features.get("rsi_at_entry", 0),
+            ema_50_value=features.get("ema_50_value", 0),
+            ema_200_value=features.get("ema_200_value", 0),
+            vwap_deviation=features.get("vwap_deviation", 0),
+            news_within_60min=features.get("news_within_60min", False),
+            macd_hist_at_entry=features.get("macd_hist_at_entry", 0),
+            stoch_k_at_entry=features.get("stoch_k_at_entry", 0),
+            distance_from_ema50_pips=features.get("distance_from_ema50_pips", 0),
+            candle_body_ratio=features.get("candle_body_ratio", 0),
+            hour_of_day=features.get("hour_of_day", 0),
+            day_of_week=features.get("day_of_week", 0),
+            exit_price=exit_price,
+            exit_reason=exit_detail,
+            exit_time=exit_time,
+            pnl_pips=total_pnl_pips,
+            pnl_dollars=total_pnl,
+            hold_time_minutes=hold_minutes,
+            win=total_pnl > 0,
+        )
+        self.closed_trades.append(record)
+
+    def _build_signal_features(self, candle: pd.Series, i: int) -> dict:
+        """Extract features from current candle for trade logging."""
+        hour = candle.name.hour if hasattr(candle.name, 'hour') else 0
+        body = abs(candle["close"] - candle["open"])
+        full_range = candle["high"] - candle["low"]
+        body_ratio = body / full_range if full_range > 0 else 0
+
+        ema_50 = candle.get("ema_50", 0)
+        dist_ema50 = (candle["close"] - ema_50) / self.pip if ema_50 else 0
+        vwap = candle.get("session_vwap", 0)
+        vwap_dev = (candle["close"] - vwap) / self.pip if vwap else 0
+
+        return {
+            "session": self._get_session(hour),
+            "spread_at_entry": self.spread / self.pip,
+            "atr_at_entry": candle.get("atr_14", 0),
+            "adx_at_entry": candle.get("adx_14", 0),
+            "rsi_at_entry": candle.get("rsi_14", 0),
+            "ema_50_value": ema_50,
+            "ema_200_value": candle.get("ema_200", 0),
+            "vwap_deviation": vwap_dev,
+            "news_within_60min": self._is_near_news(candle.name),
+            "macd_hist_at_entry": candle.get("macd_hist", 0),
+            "stoch_k_at_entry": candle.get("stoch_k", 0),
+            "distance_from_ema50_pips": dist_ema50,
+            "candle_body_ratio": body_ratio,
+            "hour_of_day": hour,
+            "day_of_week": candle.name.weekday() if hasattr(candle.name, 'weekday') else 0,
+        }
+
+    def run(self) -> dict:
+        """Run the backtest. Returns performance report dict."""
+        # Need at least 200 bars for indicators to warm up
+        warmup = 200
+
+        for i in range(warmup, len(self.data)):
+            candle = self.data.iloc[i]
+            timestamp = self.data.index[i]
+
+            # Update open positions first (SL/TP/trail/time checks)
+            self._update_positions(candle, i)
+
+            # Record equity
+            self.equity_curve.append({
+                "timestamp": timestamp,
+                "equity": self.equity,
+            })
+
+            # Check daily drawdown halt
+            if self._check_daily_drawdown(timestamp):
+                continue
+
+            # Skip if near news
+            if self._is_near_news(timestamp):
+                continue
+
+            # Skip if already have an open position (1 at a time per strategy)
+            if self.open_positions:
+                continue
+
+            # Get HTF context (only fully closed candles)
+            htf_row = self._get_htf_row(timestamp)
+
+            # Pass full data + current index. Strategy must only access [:i+1].
+            signal = self.strategy.check_signal(self.data, i, candle, htf_row)
+
+            if signal is None:
+                continue
+
+            direction = signal["direction"]
+            sl = signal["sl"]
+            tp1 = signal["tp1"]
+            tp2 = signal["tp2"]
+            tp3 = signal["tp3"]
+            confluence = signal.get("confluence", 0)
+            tp_splits = signal.get("tp_splits", (0.40, 0.40, 0.20))
+            trail_mult = signal.get("trail_atr_mult", 1.5)
+            max_bars = signal.get("max_bars", 200)
+
+            # Minimum 1.5:1 RR check (TP1 vs SL distance)
+            entry = candle["close"]
+            sl_dist = abs(entry - sl)
+            tp1_dist = abs(tp1 - entry)
+            if sl_dist == 0 or tp1_dist / sl_dist < 1.5:
+                continue
+
+            # Apply spread and slippage to entry
+            hour = timestamp.hour if hasattr(timestamp, 'hour') else 0
+            adj_entry = self._apply_spread_to_entry(entry, direction)
+            adj_entry = self._apply_slippage_to_entry(adj_entry, direction, hour)
+
+            # Recalculate SL distance after adjustment
+            sl_dist_adj = abs(adj_entry - sl)
+            if sl_dist_adj <= 0:
+                continue
+
+            # Position sizing
+            size = self._calculate_position_size(sl_dist_adj, confluence)
+            if size <= 0:
+                continue
+
+            # Build features for logging
+            features = self._build_signal_features(candle, i)
+
+            # Open position
+            pos = Position(
+                entry_time=timestamp,
+                direction=direction,
+                entry_price=adj_entry,
+                sl_price=sl,
+                tp1_price=tp1,
+                tp2_price=tp2,
+                tp3_price=tp3,
+                initial_size=size,
+                current_size=size,
+                tp_splits=tp_splits,
+                trail_atr_mult=trail_mult,
+                max_bars=max_bars,
+                strategy_id=self.strategy.strategy_id,
+                confluence_score=confluence,
+                signal_features=features,
+            )
+            self.open_positions.append(pos)
+
+        # Force-close any remaining positions at last candle
+        if self.open_positions:
+            last_candle = self.data.iloc[-1]
+            for pos in list(self.open_positions):
+                self._close_position(pos, last_candle["close"], "END", last_candle)
+            self.open_positions.clear()
+
+        return self.generate_report()
+
+    def generate_report(self) -> dict:
+        """Generate comprehensive performance metrics."""
+        trades = self.closed_trades
+        if not trades:
+            return {
+                "pair": self.pair,
+                "strategy_id": self.strategy.strategy_id,
+                "strategy_name": self.strategy.name,
+                "total_trades": 0,
+                "message": "No trades generated",
+            }
+
+        wins = [t for t in trades if t.win]
+        losses = [t for t in trades if not t.win]
+        pnls = [t.pnl_dollars for t in trades]
+        pnl_pips = [t.pnl_pips for t in trades]
+
+        total_trades = len(trades)
+        win_rate = len(wins) / total_trades * 100 if total_trades else 0
+        avg_win_pips = np.mean([t.pnl_pips for t in wins]) if wins else 0
+        avg_loss_pips = np.mean([abs(t.pnl_pips) for t in losses]) if losses else 0
+        avg_rr = avg_win_pips / avg_loss_pips if avg_loss_pips > 0 else 0
+
+        gross_profit = sum(t.pnl_dollars for t in wins)
+        gross_loss = abs(sum(t.pnl_dollars for t in losses))
+        profit_factor = gross_profit / gross_loss if gross_loss > 0 else float("inf")
+
+        expectancy = (win_rate / 100 * avg_win_pips) - ((1 - win_rate / 100) * avg_loss_pips)
+
+        # Sharpe ratio (annualized)
+        if len(pnls) > 1:
+            returns = pd.Series(pnls)
+            sharpe = (returns.mean() / returns.std()) * np.sqrt(252) if returns.std() > 0 else 0
+        else:
+            sharpe = 0
+
+        # Max drawdown from equity curve
+        eq = pd.Series([e["equity"] for e in self.equity_curve])
+        if len(eq) > 0:
+            peak = eq.cummax()
+            dd = (eq - peak) / peak
+            max_dd = dd.min() * 100  # negative percentage
+        else:
+            max_dd = 0
+
+        # Consecutive wins/losses
+        results = [t.win for t in trades]
+        max_consec_wins = max_consec_losses = current_streak = 0
+        current_type = None
+        for r in results:
+            if r == current_type:
+                current_streak += 1
+            else:
+                current_type = r
+                current_streak = 1
+            if r and current_streak > max_consec_wins:
+                max_consec_wins = current_streak
+            if not r and current_streak > max_consec_losses:
+                max_consec_losses = current_streak
+
+        # Average hold time
+        avg_hold = np.mean([t.hold_time_minutes for t in trades])
+
+        # Best/worst trade
+        best_trade_pips = max(pnl_pips)
+        worst_trade_pips = min(pnl_pips)
+        best_trade_dollars = max(pnls)
+        worst_trade_dollars = min(pnls)
+
+        # Session breakdown
+        session_stats = {}
+        for session in ["ASIAN", "LONDON", "OVERLAP", "NY"]:
+            session_trades = [t for t in trades if t.session == session]
+            if session_trades:
+                s_wins = [t for t in session_trades if t.win]
+                session_stats[session] = {
+                    "trades": len(session_trades),
+                    "win_rate": len(s_wins) / len(session_trades) * 100,
+                    "total_pnl_pips": sum(t.pnl_pips for t in session_trades),
+                }
+
+        # Exit reason breakdown
+        exit_reasons = {}
+        for t in trades:
+            exit_reasons[t.exit_reason] = exit_reasons.get(t.exit_reason, 0) + 1
+
+        return {
+            "pair": self.pair,
+            "strategy_id": self.strategy.strategy_id,
+            "strategy_name": self.strategy.name,
+            "total_trades": total_trades,
+            "win_rate_pct": round(win_rate, 2),
+            "avg_rr": round(avg_rr, 2),
+            "expectancy_pips": round(expectancy, 2),
+            "sharpe_ratio": round(sharpe, 2),
+            "max_drawdown_pct": round(max_dd, 2),
+            "profit_factor": round(profit_factor, 2),
+            "total_pnl_pips": round(sum(pnl_pips), 2),
+            "total_pnl_dollars": round(sum(pnls), 2),
+            "avg_win_pips": round(avg_win_pips, 2),
+            "avg_loss_pips": round(avg_loss_pips, 2),
+            "max_consecutive_wins": max_consec_wins,
+            "max_consecutive_losses": max_consec_losses,
+            "avg_hold_time_minutes": round(avg_hold, 1),
+            "best_trade_pips": round(best_trade_pips, 2),
+            "worst_trade_pips": round(worst_trade_pips, 2),
+            "best_trade_dollars": round(best_trade_dollars, 2),
+            "worst_trade_dollars": round(worst_trade_dollars, 2),
+            "final_equity": round(self.equity, 2),
+            "starting_equity": self.starting_equity,
+            "session_breakdown": session_stats,
+            "exit_reasons": exit_reasons,
+        }
+
+    def get_trade_log_df(self) -> pd.DataFrame:
+        """Return closed trades as a DataFrame for CSV export."""
+        if not self.closed_trades:
+            return pd.DataFrame()
+        records = []
+        for t in self.closed_trades:
+            records.append(vars(t))
+        return pd.DataFrame(records)
diff --git a/src/dashboard.py b/src/dashboard.py
index 4174d31..efcca8b 100644
--- a/src/dashboard.py
+++ b/src/dashboard.py
@@ -293,7 +293,7 @@ def killswitch():
 @app.route("/chart")
 @requires_auth
 def chart():
-    """Candlestick chart with Classic Pivot Point support/resistance levels."""
+    """Interactive TradingView-style candlestick chart with indicators."""
     instrument = request.args.get("instrument", "EUR_USD")
     granularity = request.args.get("granularity", "M15")
 
@@ -305,12 +305,13 @@ def chart():
 
     ohlc_json = "[]"
     pivot_json = "{}"
-    sma_json = "{}"
+    indicators_json = "{}"
     error_msg = None
 
     try:
         from supabase import create_client
         from dotenv import load_dotenv
+        import numpy as np
 
         env_path = APP_ROOT / "config" / ".env"
         if env_path.exists():
@@ -326,11 +327,11 @@ def chart():
 
             resp = (
                 sb.table(table)
-                .select("time,open,high,low,close,sma_3,sma_20,sma_21,sma_50,sma_100")
+                .select("*")
                 .eq("instrument", instrument)
                 .eq("granularity", granularity)
                 .order("time", desc=True)
-                .limit(500)
+                .limit(2000)
                 .execute()
             )
 
@@ -341,31 +342,88 @@ def chart():
                 df = df.sort_values("time").set_index("time")
                 for col in ["open", "high", "low", "close"]:
                     df[col] = df[col].astype(float)
+                df["volume"] = pd.to_numeric(df.get("volume", 0), errors="coerce").fillna(0).astype(int)
+
+                # Ensure stored indicators are numeric
+                for col in ["sma_3", "sma_20", "ema_20", "rsi_14", "atr_14", "vwap_20"]:
+                    if col in df.columns:
+                        df[col] = pd.to_numeric(df[col], errors="coerce")
+
+                # Compute SMA 50, SMA 100 on the fly (not stored in DB)
+                df["sma_50"] = df["close"].rolling(50).mean()
+                df["sma_100"] = df["close"].rolling(100).mean()
+
+                # Compute EMA 50, 100, 200 on the fly (only EMA 20 stored)
+                df["ema_50"] = df["close"].ewm(span=50, adjust=False).mean()
+                df["ema_100"] = df["close"].ewm(span=100, adjust=False).mean()
+                df["ema_200"] = df["close"].ewm(span=200, adjust=False).mean()
+
+                # Compute Session VWAP (resets each trading day)
+                tp = (df["high"] + df["low"] + df["close"]) / 3.0
+                pv = tp * df["volume"]
+                df["_date"] = df.index.date
+                df["session_vwap"] = (
+                    pv.groupby(df["_date"]).cumsum()
+                    / df["volume"].groupby(df["_date"]).cumsum()
+                )
+                df["session_vwap"] = df["session_vwap"].replace([np.inf, -np.inf], np.nan)
+                df.drop(columns=["_date"], inplace=True)
 
                 # Compute pivot points
                 from data_engine import add_pivot_points
                 df = add_pivot_points(df)
 
-                # Prepare OHLC JSON
-                ohlc_data = df[["open", "high", "low", "close"]].reset_index()
-                ohlc_data["time"] = ohlc_data["time"].dt.strftime("%Y-%m-%d %H:%M")
-                ohlc_json = ohlc_data.to_json(orient="records")
+                # Prepare OHLC JSON (with volume for client-side anchored VWAP)
+                time_strings = df.index.strftime("%Y-%m-%dT%H:%M:%S").tolist()
+                ohlc_data = []
+                for i, (idx, row) in enumerate(df.iterrows()):
+                    ohlc_data.append({
+                        "time": time_strings[i],
+                        "open": row["open"],
+                        "high": row["high"],
+                        "low": row["low"],
+                        "close": row["close"],
+                        "volume": int(row["volume"]),
+                    })
+                ohlc_json = json.dumps(ohlc_data)
 
                 # Prepare pivot levels (latest non-null values)
                 pivot_cols = ["pivot", "r1", "r2", "r3", "s1", "s2", "s3"]
-                latest = df[pivot_cols].dropna().iloc[-1] if df[pivot_cols].dropna().shape[0] > 0 else None
-                if latest is not None:
-                    pivot_json = latest.to_json()
+                pivot_available = df[pivot_cols].dropna()
+                if len(pivot_available) > 0:
+                    pivot_json = pivot_available.iloc[-1].to_json()
 
-                # Prepare SMA data as {col_name: [values]} for overlay
-                sma_cols = [c for c in df.columns if c.startswith("sma_")]
-                if sma_cols:
-                    sma_dict = {}
-                    for c in sma_cols:
-                        df[c] = pd.to_numeric(df[c], errors="coerce")
-                        sma_dict[c] = df[c].where(df[c].notna(), None).tolist()
-                    import json
-                    sma_json = json.dumps(sma_dict)
+                # Build indicators_json: { key: [{time, value}, ...] }
+                indicator_cols = {
+                    "sma_3": "sma_3", "sma_20": "sma_20",
+                    "sma_50": "sma_50", "sma_100": "sma_100",
+                    "ema_20": "ema_20", "ema_50": "ema_50",
+                    "ema_100": "ema_100", "ema_200": "ema_200",
+                    "rsi_14": "rsi_14", "atr_14": "atr_14",
+                    "session_vwap": "session_vwap",
+                }
+                ind_dict = {}
+                for key, col in indicator_cols.items():
+                    if col in df.columns:
+                        series_data = []
+                        for i, (idx, row) in enumerate(df.iterrows()):
+                            val = row[col]
+                            if pd.notna(val):
+                                series_data.append({"time": time_strings[i], "value": float(val)})
+                        ind_dict[key] = series_data
+
+                # Volume as indicator data
+                vol_data = []
+                for i, (idx, row) in enumerate(df.iterrows()):
+                    color = "rgba(38,166,154,0.5)" if row["close"] >= row["open"] else "rgba(239,83,80,0.5)"
+                    vol_data.append({
+                        "time": time_strings[i],
+                        "value": int(row["volume"]),
+                        "color": color,
+                    })
+                ind_dict["volume"] = vol_data
+
+                indicators_json = json.dumps(ind_dict)
         else:
             error_msg = "Supabase credentials not configured."
     except Exception as e:
@@ -374,7 +432,7 @@ def chart():
     return render_template("chart.html",
                            ohlc_json=ohlc_json,
                            pivot_json=pivot_json,
-                           sma_json=sma_json,
+                           indicators_json=indicators_json,
                            instrument=instrument,
                            granularity=granularity,
                            instruments=VALID_INSTRUMENTS,
diff --git a/src/data_engine.py b/src/data_engine.py
index 1b537d1..e0342c9 100644
--- a/src/data_engine.py
+++ b/src/data_engine.py
@@ -90,6 +90,110 @@ def add_vwap(df, period=20):
     return df
 
 
+def add_macd(df, fast=12, slow=26, signal=9, col="close"):
+    """
+    MACD (Moving Average Convergence Divergence).
+    Columns added: macd, macd_signal, macd_hist
+    """
+    ema_fast = df[col].ewm(span=fast, adjust=False).mean()
+    ema_slow = df[col].ewm(span=slow, adjust=False).mean()
+    df["macd"] = ema_fast - ema_slow
+    df["macd_signal"] = df["macd"].ewm(span=signal, adjust=False).mean()
+    df["macd_hist"] = df["macd"] - df["macd_signal"]
+    return df
+
+
+def add_adx(df, period=14):
+    """
+    Average Directional Index (ADX).
+    Measures trend strength regardless of direction.
+    Column added: adx_{period}
+    """
+    high = df["high"]
+    low = df["low"]
+    close = df["close"]
+
+    plus_dm = high.diff()
+    minus_dm = -low.diff()
+
+    plus_dm = plus_dm.where((plus_dm > minus_dm) & (plus_dm > 0), 0.0)
+    minus_dm = minus_dm.where((minus_dm > plus_dm) & (minus_dm > 0), 0.0)
+
+    tr = pd.concat([
+        high - low,
+        (high - close.shift(1)).abs(),
+        (low - close.shift(1)).abs(),
+    ], axis=1).max(axis=1)
+
+    atr_smooth = tr.rolling(period).mean()
+    plus_di = 100 * (plus_dm.rolling(period).mean() / atr_smooth)
+    minus_di = 100 * (minus_dm.rolling(period).mean() / atr_smooth)
+
+    dx = 100 * (plus_di - minus_di).abs() / (plus_di + minus_di).replace(0, np.nan)
+    df[f"adx_{period}"] = dx.rolling(period).mean()
+    return df
+
+
+def add_stochastic(df, k_period=5, k_smooth=3, d_smooth=3):
+    """
+    Stochastic Oscillator (%K and %D).
+    Columns added: stoch_k, stoch_d
+    """
+    low_min = df["low"].rolling(k_period).min()
+    high_max = df["high"].rolling(k_period).max()
+    raw_k = 100 * (df["close"] - low_min) / (high_max - low_min).replace(0, np.nan)
+    df["stoch_k"] = raw_k.rolling(k_smooth).mean()
+    df["stoch_d"] = df["stoch_k"].rolling(d_smooth).mean()
+    return df
+
+
+def add_session_vwap_bands(df, session_start_hour=8):
+    """
+    Session VWAP with standard deviation bands, reset at London open.
+    Bands at +/-1.5sigma, +/-2sigma, +/-2.5sigma.
+
+    Columns added: session_vwap, vwap_upper_1_5, vwap_lower_1_5,
+                    vwap_upper_2, vwap_lower_2, vwap_upper_2_5, vwap_lower_2_5
+    """
+    tp = (df["high"] + df["low"] + df["close"]) / 3.0
+    vol = df["volume"].replace(0, np.nan)
+
+    # Identify session boundaries (new session starts at session_start_hour UTC)
+    hours = df.index.hour if df.index.tz is None else df.index.tz_convert("UTC").hour
+    dates = df.index.date if df.index.tz is None else df.index.tz_convert("UTC").date
+    session_id = pd.Series(
+        [f"{d}_{1 if h >= session_start_hour else 0}" for d, h in zip(dates, hours)],
+        index=df.index,
+    )
+
+    # Compute cumulative VWAP and std dev per session
+    cum_pv = (tp * vol).groupby(session_id).cumsum()
+    cum_vol = vol.groupby(session_id).cumsum()
+    vwap = cum_pv / cum_vol
+
+    # Rolling deviation from VWAP within session
+    deviation = tp - vwap
+    cum_count = vol.groupby(session_id).cumcount() + 1
+    cum_sq_dev = (deviation ** 2).groupby(session_id).cumsum()
+    std_dev = np.sqrt(cum_sq_dev / cum_count)
+
+    df["session_vwap"] = vwap
+    df["vwap_upper_1_5"] = vwap + 1.5 * std_dev
+    df["vwap_lower_1_5"] = vwap - 1.5 * std_dev
+    df["vwap_upper_2"] = vwap + 2.0 * std_dev
+    df["vwap_lower_2"] = vwap - 2.0 * std_dev
+    df["vwap_upper_2_5"] = vwap + 2.5 * std_dev
+    df["vwap_lower_2_5"] = vwap - 2.5 * std_dev
+
+    # Clean inf/nan
+    vwap_cols = ["session_vwap", "vwap_upper_1_5", "vwap_lower_1_5",
+                 "vwap_upper_2", "vwap_lower_2", "vwap_upper_2_5", "vwap_lower_2_5"]
+    for c in vwap_cols:
+        df[c] = df[c].replace([np.inf, -np.inf], np.nan)
+
+    return df
+
+
 def detect_engulfing(df):
     """
     Detect bullish and bearish engulfing candle patterns.
@@ -295,6 +399,27 @@ def build_all_features(df, config=None, calendar_df=None):
     df = add_atr(df, period=atr_p)
     df = add_vwap(df, period=vwap_w)
 
+    # MACD
+    macd_cfg = config.get("macd")
+    if macd_cfg:
+        fast, slow, sig = macd_cfg if isinstance(macd_cfg, (list, tuple)) else (12, 26, 9)
+        df = add_macd(df, fast=fast, slow=slow, signal=sig)
+
+    # ADX
+    adx_p = config.get("adx_period")
+    if adx_p:
+        df = add_adx(df, period=adx_p)
+
+    # Stochastic
+    stoch_cfg = config.get("stochastic")
+    if stoch_cfg:
+        k, ks, ds = stoch_cfg if isinstance(stoch_cfg, (list, tuple)) else (5, 3, 3)
+        df = add_stochastic(df, k_period=k, k_smooth=ks, d_smooth=ds)
+
+    # Session VWAP bands
+    if config.get("session_vwap_bands"):
+        df = add_session_vwap_bands(df, session_start_hour=8)
+
     # Drop intermediate helper columns
     df.drop(columns=["pv", "typical_price", "tr"], inplace=True, errors="ignore")
 
diff --git a/src/download_dukascopy.py b/src/download_dukascopy.py
new file mode 100644
index 0000000..b18ec33
--- /dev/null
+++ b/src/download_dukascopy.py
@@ -0,0 +1,140 @@
+"""
+Download historical OHLCV data from Dukascopy for all strategy pairs.
+Date range: January 1, 2021 - December 31, 2024
+Timeframes: M15, H1
+Saves to data/raw/ as CSV files.
+"""
+import os
+import sys
+import time
+from datetime import datetime, timedelta
+
+import pandas as pd
+from dukascopy_python import fetch, INTERVAL_MIN_15, INTERVAL_HOUR_1, OFFER_SIDE_BID
+
+# All 10 currency pairs from the roadmap
+PAIRS = [
+    "GBP/AUD", "EUR/AUD", "EUR/GBP", "GBP/CAD", "EUR/CAD",
+    "GBP/JPY", "USD/JPY", "GBP/USD", "EUR/USD", "EUR/NZD",
+]
+
+INTERVALS = {
+    "M15": INTERVAL_MIN_15,
+    "H1": INTERVAL_HOUR_1,
+}
+
+START = datetime(2021, 1, 1)
+END = datetime(2024, 12, 31, 23, 59)
+
+# Chunk by 6 months to stay well under the 30k row limit
+CHUNK_MONTHS = 6
+
+RAW_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "data", "raw")
+
+
+def chunk_date_range(start, end, months):
+    """Split date range into chunks of N months."""
+    chunks = []
+    current = start
+    while current < end:
+        chunk_end = current + timedelta(days=months * 30)
+        if chunk_end > end:
+            chunk_end = end
+        chunks.append((current, chunk_end))
+        current = chunk_end + timedelta(hours=1)
+    return chunks
+
+
+def download_pair(pair, interval_name, interval_code):
+    """Download data for one pair/timeframe combination."""
+    pair_file = pair.replace("/", "_")
+    filename = f"{pair_file}_{interval_name}.csv"
+    filepath = os.path.join(RAW_DIR, filename)
+
+    if os.path.exists(filepath):
+        existing = pd.read_csv(filepath)
+        print(f"  Already exists: {filename} ({len(existing)} rows) - skipping")
+        return True
+
+    chunks = chunk_date_range(START, END, CHUNK_MONTHS)
+    all_dfs = []
+
+    for i, (chunk_start, chunk_end) in enumerate(chunks):
+        print(f"  Chunk {i+1}/{len(chunks)}: {chunk_start.date()} to {chunk_end.date()}...", end=" ")
+        try:
+            df = fetch(
+                instrument=pair,
+                interval=interval_code,
+                offer_side=OFFER_SIDE_BID,
+                start=chunk_start,
+                end=chunk_end,
+                max_retries=5,
+            )
+            print(f"{len(df)} rows")
+            if len(df) > 0:
+                all_dfs.append(df)
+            time.sleep(1)  # Rate limiting
+        except Exception as e:
+            print(f"ERROR: {e}")
+            time.sleep(3)
+            # Retry once
+            try:
+                df = fetch(
+                    instrument=pair,
+                    interval=interval_code,
+                    offer_side=OFFER_SIDE_BID,
+                    start=chunk_start,
+                    end=chunk_end,
+                    max_retries=5,
+                )
+                print(f"  Retry OK: {len(df)} rows")
+                if len(df) > 0:
+                    all_dfs.append(df)
+            except Exception as e2:
+                print(f"  Retry failed: {e2}")
+                return False
+
+    if not all_dfs:
+        print(f"  No data retrieved for {pair} {interval_name}")
+        return False
+
+    combined = pd.concat(all_dfs)
+    combined = combined[~combined.index.duplicated(keep="first")]
+    combined = combined.sort_index()
+
+    # Save with timestamp index
+    combined.to_csv(filepath)
+    print(f"  Saved: {filename} ({len(combined)} rows)")
+    return True
+
+
+def main():
+    os.makedirs(RAW_DIR, exist_ok=True)
+
+    total = len(PAIRS) * len(INTERVALS)
+    completed = 0
+    failed = []
+
+    print(f"Downloading {total} pair-timeframe combinations from Dukascopy")
+    print(f"Date range: {START.date()} to {END.date()}")
+    print(f"Output: {RAW_DIR}")
+    print("=" * 60)
+
+    for pair in PAIRS:
+        for interval_name, interval_code in INTERVALS.items():
+            completed += 1
+            print(f"\n[{completed}/{total}] {pair} {interval_name}")
+            success = download_pair(pair, interval_name, interval_code)
+            if not success:
+                failed.append(f"{pair} {interval_name}")
+
+    print("\n" + "=" * 60)
+    print(f"Complete: {total - len(failed)}/{total}")
+    if failed:
+        print(f"Failed: {', '.join(failed)}")
+    else:
+        print("All downloads successful!")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/indicators/__init__.py b/src/indicators/__init__.py
new file mode 100644
index 0000000..bdd5de0
--- /dev/null
+++ b/src/indicators/__init__.py
@@ -0,0 +1 @@
+from .technical import compute_all_indicators
diff --git a/src/indicators/technical.py b/src/indicators/technical.py
new file mode 100644
index 0000000..a712524
--- /dev/null
+++ b/src/indicators/technical.py
@@ -0,0 +1,331 @@
+"""
+Technical indicator library for fx-quant Phase 1.
+
+All indicators are computed on the full dataframe upfront, but the backtester
+only exposes data up to the current candle index (preventing lookahead).
+Using .shift(1) where noted to ensure signals only use closed-candle data.
+"""
+import numpy as np
+import pandas as pd
+
+
+# ---------------------------------------------------------------------------
+# Moving Averages
+# ---------------------------------------------------------------------------
+
+def ema(series: pd.Series, period: int) -> pd.Series:
+    return series.ewm(span=period, adjust=False).mean()
+
+
+def sma(series: pd.Series, period: int) -> pd.Series:
+    return series.rolling(window=period, min_periods=period).mean()
+
+
+# ---------------------------------------------------------------------------
+# RSI
+# ---------------------------------------------------------------------------
+
+def rsi(series: pd.Series, period: int = 14) -> pd.Series:
+    delta = series.diff()
+    gain = delta.clip(lower=0)
+    loss = -delta.clip(upper=0)
+    avg_gain = gain.ewm(alpha=1 / period, min_periods=period, adjust=False).mean()
+    avg_loss = loss.ewm(alpha=1 / period, min_periods=period, adjust=False).mean()
+    rs = avg_gain / avg_loss.replace(0, np.nan)
+    return 100 - (100 / (1 + rs))
+
+
+# ---------------------------------------------------------------------------
+# ATR
+# ---------------------------------------------------------------------------
+
+def atr(df: pd.DataFrame, period: int = 14) -> pd.Series:
+    high, low, close = df["high"], df["low"], df["close"]
+    prev_close = close.shift(1)
+    tr = pd.concat([
+        high - low,
+        (high - prev_close).abs(),
+        (low - prev_close).abs(),
+    ], axis=1).max(axis=1)
+    return tr.ewm(alpha=1 / period, min_periods=period, adjust=False).mean()
+
+
+# ---------------------------------------------------------------------------
+# MACD
+# ---------------------------------------------------------------------------
+
+def macd(series: pd.Series, fast: int = 12, slow: int = 26, signal: int = 9):
+    ema_fast = ema(series, fast)
+    ema_slow = ema(series, slow)
+    macd_line = ema_fast - ema_slow
+    signal_line = ema(macd_line, signal)
+    histogram = macd_line - signal_line
+    return macd_line, signal_line, histogram
+
+
+# ---------------------------------------------------------------------------
+# ADX
+# ---------------------------------------------------------------------------
+
+def adx(df: pd.DataFrame, period: int = 14) -> pd.Series:
+    high, low, close = df["high"], df["low"], df["close"]
+    plus_dm = high.diff().clip(lower=0)
+    minus_dm = (-low.diff()).clip(lower=0)
+
+    # When both are positive, keep only the larger
+    both_pos = (plus_dm > 0) & (minus_dm > 0)
+    plus_bigger = plus_dm >= minus_dm
+    plus_dm = plus_dm.where(~both_pos | plus_bigger, 0)
+    minus_dm = minus_dm.where(~both_pos | ~plus_bigger, 0)
+
+    atr_vals = atr(df, period)
+
+    plus_di = 100 * ema(plus_dm, period) / atr_vals.replace(0, np.nan)
+    minus_di = 100 * ema(minus_dm, period) / atr_vals.replace(0, np.nan)
+    dx = 100 * (plus_di - minus_di).abs() / (plus_di + minus_di).replace(0, np.nan)
+    return ema(dx, period)
+
+
+# ---------------------------------------------------------------------------
+# Stochastic Oscillator
+# ---------------------------------------------------------------------------
+
+def stochastic(df: pd.DataFrame, k_period: int = 5, k_smooth: int = 3,
+               d_smooth: int = 3):
+    low_min = df["low"].rolling(k_period, min_periods=k_period).min()
+    high_max = df["high"].rolling(k_period, min_periods=k_period).max()
+    raw_k = 100 * (df["close"] - low_min) / (high_max - low_min).replace(0, np.nan)
+    k = raw_k.rolling(k_smooth, min_periods=1).mean()
+    d = k.rolling(d_smooth, min_periods=1).mean()
+    return k, d
+
+
+# ---------------------------------------------------------------------------
+# Session VWAP with Bands (reset at London open 08:00 UTC)
+# ---------------------------------------------------------------------------
+
+def session_vwap_bands(df: pd.DataFrame, session_start_hour: int = 8):
+    """Compute intra-session VWAP with standard deviation bands."""
+    typical_price = (df["high"] + df["low"] + df["close"]) / 3
+    volume = df["volume"].replace(0, 1)  # avoid division by zero
+
+    # Identify session boundaries
+    hours = pd.Series(df.index.hour, index=df.index)
+    prev_hours = hours.shift(1)
+    session_start = (hours == session_start_hour) & (prev_hours != session_start_hour)
+    session_start.iloc[0] = True
+
+    # Assign session IDs
+    session_id = session_start.astype(int).cumsum()
+
+    # Cumulative VWAP per session
+    tp_vol = typical_price * volume
+    cum_tp_vol = tp_vol.groupby(session_id).cumsum()
+    cum_vol = volume.groupby(session_id).cumsum()
+    vwap = cum_tp_vol / cum_vol
+
+    # Rolling std dev of typical price from VWAP within session
+    deviation = typical_price - vwap
+    cum_dev_sq = (deviation ** 2).groupby(session_id).cumsum()
+    cum_count = deviation.groupby(session_id).cumcount() + 1
+    std_dev = np.sqrt(cum_dev_sq / cum_count)
+
+    return pd.DataFrame({
+        "session_vwap": vwap,
+        "vwap_std": std_dev,
+        "vwap_upper_1_5": vwap + 1.5 * std_dev,
+        "vwap_lower_1_5": vwap - 1.5 * std_dev,
+        "vwap_upper_2": vwap + 2.0 * std_dev,
+        "vwap_lower_2": vwap - 2.0 * std_dev,
+        "vwap_upper_2_5": vwap + 2.5 * std_dev,
+        "vwap_lower_2_5": vwap - 2.5 * std_dev,
+    }, index=df.index)
+
+
+# ---------------------------------------------------------------------------
+# Swing High / Low Detection
+# ---------------------------------------------------------------------------
+
+def swing_highs(df: pd.DataFrame, lookback: int = 5) -> pd.Series:
+    """True where high[i] is highest in [i-lookback, i+lookback] window."""
+    high = df["high"]
+    roll_max = high.rolling(2 * lookback + 1, center=True, min_periods=lookback + 1).max()
+    return high == roll_max
+
+
+def swing_lows(df: pd.DataFrame, lookback: int = 5) -> pd.Series:
+    """True where low[i] is lowest in [i-lookback, i+lookback] window."""
+    low = df["low"]
+    roll_min = low.rolling(2 * lookback + 1, center=True, min_periods=lookback + 1).min()
+    return low == roll_min
+
+
+# ---------------------------------------------------------------------------
+# Key Level Identification (horizontal S/R)
+# ---------------------------------------------------------------------------
+
+def identify_key_levels(df: pd.DataFrame, lookback: int = 5,
+                        tolerance_atr_mult: float = 0.5,
+                        min_touches: int = 3) -> list:
+    """
+    Find horizontal S/R levels by clustering swing highs/lows.
+    Returns list of (price_level, touch_count) tuples.
+    """
+    atr_val = atr(df).iloc[-1] if len(df) > 14 else None
+    if atr_val is None or np.isnan(atr_val):
+        return []
+
+    tolerance = atr_val * tolerance_atr_mult
+
+    # Collect swing points
+    sh = swing_highs(df, lookback)
+    sl = swing_lows(df, lookback)
+    swing_prices = pd.concat([
+        df.loc[sh, "high"],
+        df.loc[sl, "low"],
+    ]).sort_values()
+
+    if len(swing_prices) < min_touches:
+        return []
+
+    # Cluster swing points
+    levels = []
+    used = set()
+    for i, price in enumerate(swing_prices):
+        if i in used:
+            continue
+        cluster = [price]
+        used.add(i)
+        for j in range(i + 1, len(swing_prices)):
+            if j in used:
+                continue
+            if abs(swing_prices.iloc[j] - price) <= tolerance:
+                cluster.append(swing_prices.iloc[j])
+                used.add(j)
+        if len(cluster) >= min_touches:
+            levels.append((np.mean(cluster), len(cluster)))
+
+    return sorted(levels, key=lambda x: -x[1])
+
+
+# ---------------------------------------------------------------------------
+# Engulfing Candle Detection
+# ---------------------------------------------------------------------------
+
+def is_bullish_engulfing(df: pd.DataFrame, i: int) -> bool:
+    if i < 1:
+        return False
+    prev = df.iloc[i - 1]
+    curr = df.iloc[i]
+    prev_body = abs(prev["close"] - prev["open"])
+    curr_body = abs(curr["close"] - curr["open"])
+    return (prev["close"] < prev["open"] and  # prev bearish
+            curr["close"] > curr["open"] and   # curr bullish
+            curr_body > prev_body and           # engulfs
+            curr["close"] > prev["open"] and
+            curr["open"] <= prev["close"])
+
+
+def is_bearish_engulfing(df: pd.DataFrame, i: int) -> bool:
+    if i < 1:
+        return False
+    prev = df.iloc[i - 1]
+    curr = df.iloc[i]
+    prev_body = abs(prev["close"] - prev["open"])
+    curr_body = abs(curr["close"] - curr["open"])
+    return (prev["close"] > prev["open"] and  # prev bullish
+            curr["close"] < curr["open"] and   # curr bearish
+            curr_body > prev_body and           # engulfs
+            curr["close"] < prev["open"] and
+            curr["open"] >= prev["close"])
+
+
+# ---------------------------------------------------------------------------
+# RSI Divergence Detection
+# ---------------------------------------------------------------------------
+
+def detect_rsi_divergence(df: pd.DataFrame, rsi_col: str, lookback: int = 20,
+                          i: int = None) -> str:
+    """
+    Detect regular RSI divergence at index i.
+    Returns 'bullish', 'bearish', or None.
+    """
+    if i is None:
+        i = len(df) - 1
+    if i < lookback:
+        return None
+
+    window = df.iloc[i - lookback:i + 1]
+    rsi_vals = window[rsi_col]
+    lows = window["low"]
+    highs = window["high"]
+
+    # Bullish divergence: price makes lower low, RSI makes higher low
+    recent_low_idx = lows.idxmin()
+    if recent_low_idx == window.index[-1]:
+        # Current bar is the low
+        prev_window = window.iloc[:-3]  # exclude last 3 bars
+        if len(prev_window) > 3:
+            prev_low_idx = prev_window["low"].idxmin()
+            if (lows.loc[recent_low_idx] < prev_window["low"].loc[prev_low_idx] and
+                    rsi_vals.loc[recent_low_idx] > rsi_vals.loc[prev_low_idx]):
+                return "bullish"
+
+    # Bearish divergence: price makes higher high, RSI makes lower high
+    recent_high_idx = highs.idxmax()
+    if recent_high_idx == window.index[-1]:
+        prev_window = window.iloc[:-3]
+        if len(prev_window) > 3:
+            prev_high_idx = prev_window["high"].idxmax()
+            if (highs.loc[recent_high_idx] > prev_window["high"].loc[prev_high_idx] and
+                    rsi_vals.loc[recent_high_idx] < rsi_vals.loc[prev_high_idx]):
+                return "bearish"
+
+    return None
+
+
+# ---------------------------------------------------------------------------
+# Master Function: Compute All Indicators on a DataFrame
+# ---------------------------------------------------------------------------
+
+def compute_all_indicators(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Add all technical indicators to a candle dataframe.
+    The caller must ensure df has columns: open, high, low, close, volume.
+    """
+    df = df.copy()
+
+    # Moving Averages
+    for period in [20, 50, 100, 200]:
+        df[f"ema_{period}"] = ema(df["close"], period)
+    df["sma_200"] = sma(df["close"], 200)
+
+    # RSI
+    df["rsi_14"] = rsi(df["close"], 14)
+
+    # ATR
+    df["atr_14"] = atr(df, 14)
+
+    # MACD
+    df["macd"], df["macd_signal"], df["macd_hist"] = macd(df["close"])
+
+    # ADX
+    df["adx_14"] = adx(df, 14)
+
+    # Stochastic
+    df["stoch_k"], df["stoch_d"] = stochastic(df)
+
+    # Session VWAP (only meaningful for intraday timeframes)
+    if len(df) > 0 and hasattr(df.index, "hour"):
+        try:
+            vwap_df = session_vwap_bands(df)
+            for col in vwap_df.columns:
+                df[col] = vwap_df[col]
+        except Exception:
+            pass
+
+    # Swing points
+    df["is_swing_high"] = swing_highs(df)
+    df["is_swing_low"] = swing_lows(df)
+
+    return df
diff --git a/src/load_strategy_data.py b/src/load_strategy_data.py
new file mode 100644
index 0000000..92b1986
--- /dev/null
+++ b/src/load_strategy_data.py
@@ -0,0 +1,91 @@
+# src/load_strategy_data.py
+"""
+Bulk historical data loader for all 5 trading strategies.
+Fetches candles for all required pairs and timeframes, computes features,
+and uploads to Supabase.
+
+Usage:
+    python src/load_strategy_data.py
+"""
+
+import sys
+from datetime import datetime, timezone
+
+from config_loader import load_config
+from historical_loader import fetch_all_candles
+from data_engine import build_all_features
+from supabase_upload import upload_dataframe
+
+# Pairs required per strategy
+STRATEGY_PAIRS = {
+    "S1_MA_Breakout_Retest":    ["GBP_AUD", "EUR_AUD", "EUR_CAD", "EUR_NZD"],
+    "S2_Session_VWAP_Reversal": ["GBP_USD", "EUR_USD", "GBP_JPY", "USD_JPY"],
+    "S3_Key_Level_Breakout":    ["GBP_JPY", "USD_JPY", "GBP_USD", "EUR_GBP"],
+    "S4_EMA_Ribbon_Scalp":     ["GBP_AUD", "EUR_AUD", "EUR_GBP"],
+    "S5_Momentum_Exhaustion":  ["GBP_AUD", "EUR_AUD", "EUR_GBP", "GBP_CAD", "EUR_CAD"],
+}
+
+# All unique pairs
+ALL_PAIRS = sorted(set(p for pairs in STRATEGY_PAIRS.values() for p in pairs))
+
+# Timeframes and their required lookback in days
+TIMEFRAME_DAYS = {
+    "M15": 750,   # ~70,000 candles (market hours)
+    "H1":  850,   # ~20,000 candles
+    "H4":  1000,  # ~6,000 candles
+}
+
+
+def main():
+    cfg = load_config()
+    feature_cfg = cfg.get("features", {})
+
+    print("=" * 70)
+    print("Strategy Data Loader")
+    print(f"Pairs:      {len(ALL_PAIRS)} - {ALL_PAIRS}")
+    print(f"Timeframes: {list(TIMEFRAME_DAYS.keys())}")
+    print(f"Started:    {datetime.now(timezone.utc).isoformat()}")
+    print("=" * 70)
+
+    total = len(ALL_PAIRS) * len(TIMEFRAME_DAYS)
+    done = 0
+    errors = []
+
+    for instrument in ALL_PAIRS:
+        for granularity, days_back in TIMEFRAME_DAYS.items():
+            done += 1
+            print(f"\n[{done}/{total}] {instrument} / {granularity} - {days_back} days back")
+            print("-" * 60)
+
+            try:
+                df = fetch_all_candles(instrument, granularity, days_back=days_back)
+                if df.empty:
+                    print(f"  No data returned. Skipping.")
+                    errors.append((instrument, granularity, "no data"))
+                    continue
+
+                print(f"  Candles: {len(df)} | Range: {df.index[0]} -> {df.index[-1]}")
+
+                print(f"  Computing features...")
+                df = build_all_features(df, config=feature_cfg)
+
+                print(f"  Uploading to Supabase ({len(df)} rows)...")
+                upload_dataframe(df, instrument=instrument, granularity=granularity, chunk_size=500)
+                print(f"  Done.")
+
+            except Exception as e:
+                print(f"  ERROR: {e}")
+                errors.append((instrument, granularity, str(e)))
+
+    print("\n" + "=" * 70)
+    print("Load complete.")
+    print(f"  Successful: {done - len(errors)} / {total}")
+    if errors:
+        print(f"  Errors ({len(errors)}):")
+        for inst, gran, err in errors:
+            print(f"    {inst} / {gran}: {err}")
+    print("=" * 70)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/strategies.py b/src/strategies.py
new file mode 100644
index 0000000..0d588b2
--- /dev/null
+++ b/src/strategies.py
@@ -0,0 +1,891 @@
+# src/strategies.py
+"""
+Five trading strategy signal generators.
+
+Each function takes a primary DataFrame (entry timeframe) and optional
+filter DataFrame (higher timeframe) and returns the entry df with columns:
+  signal (1=long, -1=short, 0=flat), sl_price, tp1_price, tp2_price, tp3_price,
+  confidence (int 0-5), strategy_name (str).
+"""
+
+import numpy as np
+import pandas as pd
+
+from data_engine import detect_engulfing
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def _merge_htf(df_entry, df_filter, cols, suffix="_htf"):
+    """Forward-fill higher-timeframe columns onto entry-timeframe bars."""
+    if df_filter is None or df_filter.empty:
+        return df_entry
+    htf = df_filter[cols].copy()
+    htf.columns = [c + suffix for c in cols]
+    merged = pd.merge_asof(
+        df_entry, htf,
+        left_index=True, right_index=True,
+        direction="backward",
+    )
+    return merged
+
+
+def _find_swing_highs(highs, order=5):
+    """Return boolean array where highs[i] is a local maximum over +/-order bars."""
+    n = len(highs)
+    result = np.zeros(n, dtype=bool)
+    for i in range(order, n - order):
+        if highs[i] == np.max(highs[i - order:i + order + 1]):
+            result[i] = True
+    return result
+
+
+def _find_swing_lows(lows, order=5):
+    """Return boolean array where lows[i] is a local minimum over +/-order bars."""
+    n = len(lows)
+    result = np.zeros(n, dtype=bool)
+    for i in range(order, n - order):
+        if lows[i] == np.min(lows[i - order:i + order + 1]):
+            result[i] = True
+    return result
+
+
+def _identify_key_levels(df, atr_col="atr_14", min_touches=3, lookback=500):
+    """
+    Find horizontal S/R key levels by clustering swing highs/lows.
+    Returns sorted list of (level_price, touch_count).
+    """
+    highs = df["high"].values
+    lows = df["low"].values
+    atr = df[atr_col].values
+
+    n = len(df)
+    start = max(0, n - lookback)
+    swing_h = _find_swing_highs(highs[start:], order=10)
+    swing_l = _find_swing_lows(lows[start:], order=10)
+
+    # Collect swing point prices
+    swing_prices = []
+    for i in range(len(swing_h)):
+        if swing_h[i]:
+            swing_prices.append(highs[start + i])
+        if swing_l[i]:
+            swing_prices.append(lows[start + i])
+
+    if not swing_prices:
+        return []
+
+    # Cluster within 0.5 * median ATR tolerance
+    median_atr = np.nanmedian(atr[start:])
+    if np.isnan(median_atr) or median_atr <= 0:
+        return []
+    tolerance = 0.5 * median_atr
+
+    swing_prices.sort()
+    clusters = []
+    current_cluster = [swing_prices[0]]
+
+    for price in swing_prices[1:]:
+        if price - current_cluster[-1] <= tolerance:
+            current_cluster.append(price)
+        else:
+            if len(current_cluster) >= min_touches:
+                clusters.append((np.mean(current_cluster), len(current_cluster)))
+            current_cluster = [price]
+    if len(current_cluster) >= min_touches:
+        clusters.append((np.mean(current_cluster), len(current_cluster)))
+
+    return sorted(clusters, key=lambda x: x[0])
+
+
+def _nearest_key_level(price, key_levels, tolerance):
+    """Return nearest key level within tolerance, or None."""
+    best = None
+    best_dist = tolerance
+    for lvl, _ in key_levels:
+        d = abs(price - lvl)
+        if d <= best_dist:
+            best = lvl
+            best_dist = d
+    return best
+
+
+# ---------------------------------------------------------------------------
+# S1: MA Breakout-Retest
+# ---------------------------------------------------------------------------
+
+def generate_signals_s1(df_entry, df_filter=None, params=None):
+    """
+    MA Breakout-Retest strategy.
+
+    Entry TF: M15. Filter TF: H1.
+    - Detect swing highs/lows forming channel lines
+    - Break above/below channel + retest with engulfing candle
+    - EMA 50 slope check, EMA 200 obstacle filter
+    - H1 SMA 200 directional filter
+    - Confidence scoring 0-5
+    """
+    if params is None:
+        params = {}
+
+    sl_atr_mult = params.get("sl_atr_multiplier", 1.5)
+    swing_order = params.get("swing_order", 10)
+    channel_bars = params.get("channel_lookback", 60)
+    retest_bars = params.get("retest_lookback", 5)
+
+    df = df_entry.copy()
+    df = detect_engulfing(df)
+
+    # Merge H1 SMA 200 as directional filter
+    if df_filter is not None:
+        df = _merge_htf(df, df_filter, ["sma_200", "close"], suffix="_htf")
+    else:
+        df["sma_200_htf"] = np.nan
+        df["close_htf"] = np.nan
+
+    n = len(df)
+    signals = np.zeros(n, dtype=int)
+    sl = np.full(n, np.nan)
+    tp1 = np.full(n, np.nan)
+    tp2 = np.full(n, np.nan)
+    tp3 = np.full(n, np.nan)
+    confidence = np.zeros(n, dtype=int)
+
+    closes = df["close"].values
+    highs = df["high"].values
+    lows = df["low"].values
+    ema50 = df["ema_50"].values if "ema_50" in df.columns else np.full(n, np.nan)
+    ema200 = df["ema_200"].values if "ema_200" in df.columns else np.full(n, np.nan)
+    sma200_htf = df["sma_200_htf"].values if "sma_200_htf" in df.columns else np.full(n, np.nan)
+    close_htf = df["close_htf"].values if "close_htf" in df.columns else np.full(n, np.nan)
+    atr = df["atr_14"].values
+    bull_eng = df["bullish_engulfing"].values
+    bear_eng = df["bearish_engulfing"].values
+
+    for i in range(max(channel_bars, 30), n):
+        if np.isnan(atr[i]) or atr[i] <= 0:
+            continue
+
+        # Find recent swing highs/lows for channel
+        window_start = max(0, i - channel_bars)
+        sw_h = _find_swing_highs(highs[window_start:i + 1], order=swing_order)
+        sw_l = _find_swing_lows(lows[window_start:i + 1], order=swing_order)
+
+        swing_high_prices = [highs[window_start + j] for j in range(len(sw_h)) if sw_h[j]]
+        swing_low_prices = [lows[window_start + j] for j in range(len(sw_l)) if sw_l[j]]
+
+        if len(swing_high_prices) < 2 or len(swing_low_prices) < 2:
+            continue
+
+        # Channel resistance = line through last 2 swing highs
+        channel_high = swing_high_prices[-1]
+        # Channel support = line through last 2 swing lows
+        channel_low = swing_low_prices[-1]
+
+        # H1 filter: price above/below H1 SMA 200
+        htf_bullish = np.isnan(sma200_htf[i]) or close_htf[i] > sma200_htf[i]
+        htf_bearish = np.isnan(sma200_htf[i]) or close_htf[i] < sma200_htf[i]
+
+        # EMA 50 slope (positive = bullish)
+        ema50_slope = ema50[i] - ema50[max(0, i - 5)] if not np.isnan(ema50[i]) else 0
+
+        # --- LONG: break above descending resistance, retest, bullish engulfing ---
+        broke_above = False
+        for j in range(max(0, i - retest_bars), i):
+            if closes[j] > channel_high:
+                broke_above = True
+                break
+
+        if (broke_above and bull_eng[i] and htf_bullish and ema50_slope > 0
+                and abs(closes[i] - channel_high) <= 1.5 * atr[i]):
+            # Check EMA 200 not blocking (price above it)
+            if np.isnan(ema200[i]) or closes[i] > ema200[i]:
+                sig_conf = 1  # base
+                if ema50_slope > 0:
+                    sig_conf += 1
+                if not np.isnan(sma200_htf[i]) and close_htf[i] > sma200_htf[i]:
+                    sig_conf += 1
+                if bull_eng[i]:
+                    sig_conf += 1
+                if closes[i] > ema50[i] if not np.isnan(ema50[i]) else False:
+                    sig_conf += 1
+
+                signals[i] = 1
+                sl[i] = closes[i] - sl_atr_mult * atr[i]
+                tp1[i] = closes[i] + 1.5 * atr[i]
+                tp2[i] = closes[i] + 2.5 * atr[i]
+                tp3[i] = closes[i] + 4.0 * atr[i]
+                confidence[i] = min(sig_conf, 5)
+                continue
+
+        # --- SHORT: break below ascending support, retest, bearish engulfing ---
+        broke_below = False
+        for j in range(max(0, i - retest_bars), i):
+            if closes[j] < channel_low:
+                broke_below = True
+                break
+
+        if (broke_below and bear_eng[i] and htf_bearish and ema50_slope < 0
+                and abs(closes[i] - channel_low) <= 1.5 * atr[i]):
+            if np.isnan(ema200[i]) or closes[i] < ema200[i]:
+                sig_conf = 1
+                if ema50_slope < 0:
+                    sig_conf += 1
+                if not np.isnan(sma200_htf[i]) and close_htf[i] < sma200_htf[i]:
+                    sig_conf += 1
+                if bear_eng[i]:
+                    sig_conf += 1
+                if closes[i] < ema50[i] if not np.isnan(ema50[i]) else False:
+                    sig_conf += 1
+
+                signals[i] = -1
+                sl[i] = closes[i] + sl_atr_mult * atr[i]
+                tp1[i] = closes[i] - 1.5 * atr[i]
+                tp2[i] = closes[i] - 2.5 * atr[i]
+                tp3[i] = closes[i] - 4.0 * atr[i]
+                confidence[i] = min(sig_conf, 5)
+
+    df["signal"] = signals
+    df["sl_price"] = sl
+    df["tp1_price"] = tp1
+    df["tp2_price"] = tp2
+    df["tp3_price"] = tp3
+    df["confidence"] = confidence
+    df["strategy_name"] = "S1_MA_Breakout_Retest"
+    df["position"] = signals
+    return df
+
+
+# ---------------------------------------------------------------------------
+# S2: Session VWAP Reversal
+# ---------------------------------------------------------------------------
+
+def generate_signals_s2(df_entry, df_filter=None, params=None):
+    """
+    Session VWAP Reversal strategy.
+
+    Entry TF: M15.
+    - Price crosses +/-2sigma VWAP band
+    - RSI confirmation (>70 short, <30 long)
+    - Target: VWAP line (TP1), opposite +/-0.5sigma (TP2)
+    """
+    if params is None:
+        params = {}
+
+    sl_atr_mult = params.get("sl_atr_multiplier", 1.5)
+
+    df = df_entry.copy()
+    n = len(df)
+    signals = np.zeros(n, dtype=int)
+    sl = np.full(n, np.nan)
+    tp1 = np.full(n, np.nan)
+    tp2 = np.full(n, np.nan)
+    tp3 = np.full(n, np.nan)
+    confidence = np.zeros(n, dtype=int)
+
+    required = ["session_vwap", "vwap_upper_2", "vwap_lower_2", "rsi_14", "atr_14"]
+    for col in required:
+        if col not in df.columns:
+            df["signal"] = 0
+            df["sl_price"] = np.nan
+            df["tp1_price"] = np.nan
+            df["tp2_price"] = np.nan
+            df["tp3_price"] = np.nan
+            df["confidence"] = 0
+            df["strategy_name"] = "S2_Session_VWAP_Reversal"
+            df["position"] = 0
+            return df
+
+    closes = df["close"].values
+    vwap = df["session_vwap"].values
+    upper2 = df["vwap_upper_2"].values
+    lower2 = df["vwap_lower_2"].values
+    upper15 = df["vwap_upper_1_5"].values if "vwap_upper_1_5" in df.columns else upper2
+    lower15 = df["vwap_lower_1_5"].values if "vwap_lower_1_5" in df.columns else lower2
+    upper25 = df["vwap_upper_2_5"].values if "vwap_upper_2_5" in df.columns else upper2
+    lower25 = df["vwap_lower_2_5"].values if "vwap_lower_2_5" in df.columns else lower2
+    rsi = df["rsi_14"].values
+    atr = df["atr_14"].values
+    prev_close = np.roll(closes, 1)
+    prev_close[0] = np.nan
+
+    for i in range(1, n):
+        if np.isnan(vwap[i]) or np.isnan(rsi[i]) or np.isnan(atr[i]) or atr[i] <= 0:
+            continue
+
+        conf = 0
+
+        # LONG: price crosses below -2sigma and RSI < 30
+        if closes[i] <= lower2[i] and rsi[i] < 30:
+            conf = 2  # base: touched band + RSI confirm
+            if not np.isnan(lower25[i]) and closes[i] <= lower25[i]:
+                conf += 1  # deeper extension
+            if prev_close[i] > lower2[i]:
+                conf += 1  # fresh cross
+
+            signals[i] = 1
+            sl[i] = closes[i] - sl_atr_mult * atr[i]
+            tp1[i] = vwap[i]  # mean reversion to VWAP
+            tp2[i] = upper15[i] if not np.isnan(upper15[i]) else vwap[i] + 0.5 * (upper2[i] - vwap[i])
+            tp3[i] = upper2[i]
+            confidence[i] = min(conf, 5)
+            continue
+
+        # SHORT: price crosses above +2sigma and RSI > 70
+        if closes[i] >= upper2[i] and rsi[i] > 70:
+            conf = 2
+            if not np.isnan(upper25[i]) and closes[i] >= upper25[i]:
+                conf += 1
+            if prev_close[i] < upper2[i]:
+                conf += 1
+
+            signals[i] = -1
+            sl[i] = closes[i] + sl_atr_mult * atr[i]
+            tp1[i] = vwap[i]
+            tp2[i] = lower15[i] if not np.isnan(lower15[i]) else vwap[i] - 0.5 * (vwap[i] - lower2[i])
+            tp3[i] = lower2[i]
+            confidence[i] = min(conf, 5)
+
+    df["signal"] = signals
+    df["sl_price"] = sl
+    df["tp1_price"] = tp1
+    df["tp2_price"] = tp2
+    df["tp3_price"] = tp3
+    df["confidence"] = confidence
+    df["strategy_name"] = "S2_Session_VWAP_Reversal"
+    df["position"] = signals
+    return df
+
+
+# ---------------------------------------------------------------------------
+# S3: Key Level Momentum Breakout
+# ---------------------------------------------------------------------------
+
+def generate_signals_s3(df_entry, df_filter=None, params=None):
+    """
+    Key Level Momentum Breakout.
+
+    Entry TF: H1. Filter TF: H4 (key level identification).
+    - Identify H4 key levels (price clusters with 3+ reactions)
+    - H1 candle closes beyond key level
+    - Volume > 1.5x 20-period average
+    - MACD histogram expanding + ADX > 20 rising
+    """
+    if params is None:
+        params = {}
+
+    sl_atr_mult = params.get("sl_atr_multiplier", 1.5)
+    vol_mult = params.get("volume_multiplier", 1.5)
+    adx_threshold = params.get("adx_threshold", 20)
+    kl_lookback = params.get("kl_lookback", 500)
+
+    df = df_entry.copy()
+    n = len(df)
+
+    # Identify key levels from H4 data if available, else from H1
+    kl_source = df_filter if df_filter is not None and not df_filter.empty else df
+    key_levels = _identify_key_levels(kl_source, lookback=kl_lookback)
+
+    if not key_levels:
+        df["signal"] = 0
+        df["sl_price"] = np.nan
+        df["tp1_price"] = np.nan
+        df["tp2_price"] = np.nan
+        df["tp3_price"] = np.nan
+        df["confidence"] = 0
+        df["strategy_name"] = "S3_Key_Level_Breakout"
+        df["position"] = 0
+        return df
+
+    signals = np.zeros(n, dtype=int)
+    sl = np.full(n, np.nan)
+    tp1 = np.full(n, np.nan)
+    tp2 = np.full(n, np.nan)
+    tp3 = np.full(n, np.nan)
+    confidence = np.zeros(n, dtype=int)
+
+    closes = df["close"].values
+    highs = df["high"].values
+    lows = df["low"].values
+    volumes = df["volume"].values
+    atr = df["atr_14"].values
+    prev_close = np.roll(closes, 1)
+    prev_close[0] = np.nan
+
+    has_macd = "macd_hist" in df.columns
+    has_adx = "adx_14" in df.columns
+
+    macd_hist = df["macd_hist"].values if has_macd else np.full(n, np.nan)
+    adx = df["adx_14"].values if has_adx else np.full(n, np.nan)
+
+    # 20-period volume average
+    vol_sma = pd.Series(volumes).rolling(20).mean().values
+
+    for i in range(30, n):
+        if np.isnan(atr[i]) or atr[i] <= 0:
+            continue
+
+        tolerance = 0.5 * atr[i]
+        kl_prices = [lvl for lvl, _ in key_levels]
+
+        # Find nearest key level
+        for kl_price in kl_prices:
+            # LONG breakout: price just broke above key level
+            if (not np.isnan(prev_close[i]) and prev_close[i] <= kl_price
+                    and closes[i] > kl_price + tolerance * 0.25):
+                conf = 1
+
+                # Volume confirmation
+                if not np.isnan(vol_sma[i]) and vol_sma[i] > 0:
+                    if volumes[i] > vol_mult * vol_sma[i]:
+                        conf += 1
+
+                # MACD histogram expanding
+                if has_macd and i >= 2:
+                    if (not np.isnan(macd_hist[i]) and not np.isnan(macd_hist[i - 1])
+                            and macd_hist[i] > macd_hist[i - 1] and macd_hist[i] > 0):
+                        conf += 1
+
+                # ADX > threshold and rising
+                if has_adx and i >= 1:
+                    if (not np.isnan(adx[i]) and adx[i] > adx_threshold
+                            and not np.isnan(adx[i - 1]) and adx[i] > adx[i - 1]):
+                        conf += 1
+
+                if conf >= 2:
+                    signals[i] = 1
+                    sl[i] = kl_price - sl_atr_mult * atr[i]
+                    # TPs at next key levels above
+                    above = sorted([p for p in kl_prices if p > kl_price])
+                    tp1[i] = above[0] if len(above) > 0 else closes[i] + 2 * atr[i]
+                    tp2[i] = above[1] if len(above) > 1 else closes[i] + 3 * atr[i]
+                    tp3[i] = above[2] if len(above) > 2 else closes[i] + 4.5 * atr[i]
+                    confidence[i] = min(conf, 5)
+                    break
+
+            # SHORT breakout: price just broke below key level
+            elif (not np.isnan(prev_close[i]) and prev_close[i] >= kl_price
+                    and closes[i] < kl_price - tolerance * 0.25):
+                conf = 1
+
+                if not np.isnan(vol_sma[i]) and vol_sma[i] > 0:
+                    if volumes[i] > vol_mult * vol_sma[i]:
+                        conf += 1
+
+                if has_macd and i >= 2:
+                    if (not np.isnan(macd_hist[i]) and not np.isnan(macd_hist[i - 1])
+                            and macd_hist[i] < macd_hist[i - 1] and macd_hist[i] < 0):
+                        conf += 1
+
+                if has_adx and i >= 1:
+                    if (not np.isnan(adx[i]) and adx[i] > adx_threshold
+                            and not np.isnan(adx[i - 1]) and adx[i] > adx[i - 1]):
+                        conf += 1
+
+                if conf >= 2:
+                    signals[i] = -1
+                    sl[i] = kl_price + sl_atr_mult * atr[i]
+                    below = sorted([p for p in kl_prices if p < kl_price], reverse=True)
+                    tp1[i] = below[0] if len(below) > 0 else closes[i] - 2 * atr[i]
+                    tp2[i] = below[1] if len(below) > 1 else closes[i] - 3 * atr[i]
+                    tp3[i] = below[2] if len(below) > 2 else closes[i] - 4.5 * atr[i]
+                    confidence[i] = min(conf, 5)
+                    break
+
+    df["signal"] = signals
+    df["sl_price"] = sl
+    df["tp1_price"] = tp1
+    df["tp2_price"] = tp2
+    df["tp3_price"] = tp3
+    df["confidence"] = confidence
+    df["strategy_name"] = "S3_Key_Level_Breakout"
+    df["position"] = signals
+    return df
+
+
+# ---------------------------------------------------------------------------
+# S4: EMA Ribbon Momentum Scalp
+# ---------------------------------------------------------------------------
+
+def generate_signals_s4(df_entry, df_filter=None, params=None):
+    """
+    EMA Ribbon Momentum Scalp.
+
+    Entry TF: M15. Filter TF: H1.
+    - H1 filter: EMA 20>50>100>200 fully stacked
+    - M15 ribbon compression (EMAs within 8 pips) then re-expansion
+    - Stochastic cross from OB/OS
+    - ATR contraction during compression
+    """
+    if params is None:
+        params = {}
+
+    sl_atr_mult = params.get("sl_atr_multiplier", 1.5)
+    ribbon_pips = params.get("ribbon_compression_pips", 0.0008)  # 8 pips
+    compression_bars = params.get("compression_lookback", 5)
+
+    df = df_entry.copy()
+
+    # Merge H1 EMAs for stacking filter
+    if df_filter is not None:
+        df = _merge_htf(df, df_filter, ["ema_20", "ema_50", "ema_100", "ema_200"], suffix="_htf")
+
+    n = len(df)
+    signals = np.zeros(n, dtype=int)
+    sl = np.full(n, np.nan)
+    tp1 = np.full(n, np.nan)
+    tp2 = np.full(n, np.nan)
+    tp3 = np.full(n, np.nan)
+    confidence = np.zeros(n, dtype=int)
+
+    # Entry TF EMAs
+    ema_cols = ["ema_20", "ema_50", "ema_100", "ema_200"]
+    has_emas = all(c in df.columns for c in ema_cols)
+    if not has_emas:
+        df["signal"] = 0
+        df["sl_price"] = np.nan
+        df["tp1_price"] = np.nan
+        df["tp2_price"] = np.nan
+        df["tp3_price"] = np.nan
+        df["confidence"] = 0
+        df["strategy_name"] = "S4_EMA_Ribbon_Scalp"
+        df["position"] = 0
+        return df
+
+    ema20 = df["ema_20"].values
+    ema50 = df["ema_50"].values
+    ema100 = df["ema_100"].values
+    ema200 = df["ema_200"].values
+    closes = df["close"].values
+    atr = df["atr_14"].values
+
+    has_stoch = "stoch_k" in df.columns and "stoch_d" in df.columns
+    stoch_k = df["stoch_k"].values if has_stoch else np.full(n, 50.0)
+    stoch_d = df["stoch_d"].values if has_stoch else np.full(n, 50.0)
+
+    # H1 filter columns
+    htf_ema20 = df["ema_20_htf"].values if "ema_20_htf" in df.columns else np.full(n, np.nan)
+    htf_ema50 = df["ema_50_htf"].values if "ema_50_htf" in df.columns else np.full(n, np.nan)
+    htf_ema100 = df["ema_100_htf"].values if "ema_100_htf" in df.columns else np.full(n, np.nan)
+    htf_ema200 = df["ema_200_htf"].values if "ema_200_htf" in df.columns else np.full(n, np.nan)
+
+    for i in range(max(compression_bars + 1, 30), n):
+        if np.isnan(atr[i]) or atr[i] <= 0:
+            continue
+
+        # H1 EMA stacking filter
+        htf_bullish_stack = (
+            not np.isnan(htf_ema20[i]) and
+            htf_ema20[i] > htf_ema50[i] > htf_ema100[i] > htf_ema200[i]
+        )
+        htf_bearish_stack = (
+            not np.isnan(htf_ema20[i]) and
+            htf_ema20[i] < htf_ema50[i] < htf_ema100[i] < htf_ema200[i]
+        )
+
+        if not (htf_bullish_stack or htf_bearish_stack):
+            # If no H1 data, allow signal but lower confidence
+            if not np.isnan(htf_ema20[i]):
+                continue
+
+        # Check M15 ribbon compression in recent bars
+        was_compressed = False
+        for j in range(i - compression_bars, i):
+            ribbon_range = max(ema20[j], ema50[j], ema100[j], ema200[j]) - \
+                          min(ema20[j], ema50[j], ema100[j], ema200[j])
+            if ribbon_range <= ribbon_pips:
+                was_compressed = True
+                break
+
+        if not was_compressed:
+            continue
+
+        # Current ribbon must be expanding
+        curr_range = max(ema20[i], ema50[i], ema100[i], ema200[i]) - \
+                    min(ema20[i], ema50[i], ema100[i], ema200[i])
+        prev_range = max(ema20[i - 1], ema50[i - 1], ema100[i - 1], ema200[i - 1]) - \
+                    min(ema20[i - 1], ema50[i - 1], ema100[i - 1], ema200[i - 1])
+        if curr_range <= prev_range:
+            continue
+
+        conf = 1
+
+        # LONG: bullish expansion + stochastic from oversold
+        if (htf_bullish_stack or np.isnan(htf_ema20[i])) and ema20[i] > ema50[i]:
+            stoch_bull = (has_stoch and not np.isnan(stoch_k[i]) and
+                         stoch_k[i] > stoch_d[i] and stoch_k[i] < 50)
+            if stoch_bull:
+                conf += 1
+
+            # ATR contraction (current ATR < recent average)
+            atr_window = atr[max(0, i - 20):i]
+            if len(atr_window) > 0 and atr[i] < np.nanmean(atr_window):
+                conf += 1
+
+            if htf_bullish_stack:
+                conf += 1
+
+            signals[i] = 1
+            sl[i] = closes[i] - sl_atr_mult * atr[i]
+            tp1[i] = closes[i] + 1.0 * atr[i]
+            tp2[i] = closes[i] + 2.0 * atr[i]
+            tp3[i] = closes[i] + 3.0 * atr[i]
+            confidence[i] = min(conf, 5)
+            continue
+
+        # SHORT: bearish expansion + stochastic from overbought
+        if (htf_bearish_stack or np.isnan(htf_ema20[i])) and ema20[i] < ema50[i]:
+            stoch_bear = (has_stoch and not np.isnan(stoch_k[i]) and
+                         stoch_k[i] < stoch_d[i] and stoch_k[i] > 50)
+            if stoch_bear:
+                conf += 1
+
+            atr_window = atr[max(0, i - 20):i]
+            if len(atr_window) > 0 and atr[i] < np.nanmean(atr_window):
+                conf += 1
+
+            if htf_bearish_stack:
+                conf += 1
+
+            signals[i] = -1
+            sl[i] = closes[i] + sl_atr_mult * atr[i]
+            tp1[i] = closes[i] - 1.0 * atr[i]
+            tp2[i] = closes[i] - 2.0 * atr[i]
+            tp3[i] = closes[i] - 3.0 * atr[i]
+            confidence[i] = min(conf, 5)
+
+    df["signal"] = signals
+    df["sl_price"] = sl
+    df["tp1_price"] = tp1
+    df["tp2_price"] = tp2
+    df["tp3_price"] = tp3
+    df["confidence"] = confidence
+    df["strategy_name"] = "S4_EMA_Ribbon_Scalp"
+    df["position"] = signals
+    return df
+
+
+# ---------------------------------------------------------------------------
+# S5: Momentum Exhaustion Reversal
+# ---------------------------------------------------------------------------
+
+def generate_signals_s5(df_entry, df_filter=None, params=None):
+    """
+    Momentum Exhaustion Reversal.
+
+    Entry TF: M15. Filter TF: H1 (structure).
+    - RSI divergence (price new extreme, RSI doesn't confirm)
+    - MACD histogram shrinking (2+ bars)
+    - At H1 key level (within 10 pips)
+    - Overextension: price moved 2x ATR from nearest EMA
+    - Mutual exclusion with S4 ribbon conditions
+    """
+    if params is None:
+        params = {}
+
+    sl_atr_mult = params.get("sl_atr_multiplier", 1.5)
+    divergence_lookback = params.get("divergence_lookback", 14)
+    overextension_mult = params.get("overextension_atr_mult", 2.0)
+    kl_tolerance_pips = params.get("kl_tolerance_pips", 0.0010)  # 10 pips
+
+    df = df_entry.copy()
+    n = len(df)
+
+    # Get H1 key levels for structure
+    kl_source = df_filter if df_filter is not None and not df_filter.empty else df
+    key_levels = _identify_key_levels(kl_source, min_touches=3, lookback=500)
+
+    signals = np.zeros(n, dtype=int)
+    sl = np.full(n, np.nan)
+    tp1 = np.full(n, np.nan)
+    tp2 = np.full(n, np.nan)
+    tp3 = np.full(n, np.nan)
+    confidence = np.zeros(n, dtype=int)
+
+    closes = df["close"].values
+    highs = df["high"].values
+    lows = df["low"].values
+    atr = df["atr_14"].values
+    rsi = df["rsi_14"].values if "rsi_14" in df.columns else np.full(n, 50.0)
+
+    has_macd = "macd_hist" in df.columns
+    macd_hist = df["macd_hist"].values if has_macd else np.full(n, np.nan)
+
+    ema50 = df["ema_50"].values if "ema_50" in df.columns else np.full(n, np.nan)
+    ema100 = df["ema_100"].values if "ema_100" in df.columns else np.full(n, np.nan)
+    ema200 = df["ema_200"].values if "ema_200" in df.columns else np.full(n, np.nan)
+
+    # Check S4 mutual exclusion: if M15 EMAs are tightly stacked, skip
+    ema_cols_exist = all(c in df.columns for c in ["ema_20", "ema_50", "ema_100", "ema_200"])
+
+    for i in range(max(divergence_lookback + 2, 30), n):
+        if np.isnan(atr[i]) or atr[i] <= 0:
+            continue
+
+        # Mutual exclusion: skip if EMA ribbon is compressed (S4 territory)
+        if ema_cols_exist:
+            ribbon = max(df["ema_20"].iloc[i], df["ema_50"].iloc[i],
+                        df["ema_100"].iloc[i], df["ema_200"].iloc[i]) - \
+                    min(df["ema_20"].iloc[i], df["ema_50"].iloc[i],
+                        df["ema_100"].iloc[i], df["ema_200"].iloc[i])
+            if ribbon <= 0.0008:  # compressed ribbon = S4 territory
+                continue
+
+        # Check overextension from nearest EMA
+        nearest_ema = np.nan
+        for ema in [ema50[i], ema100[i], ema200[i]]:
+            if not np.isnan(ema):
+                if np.isnan(nearest_ema) or abs(closes[i] - ema) < abs(closes[i] - nearest_ema):
+                    nearest_ema = ema
+
+        if np.isnan(nearest_ema):
+            continue
+
+        dist_from_ema = abs(closes[i] - nearest_ema)
+        overextended = dist_from_ema >= overextension_mult * atr[i]
+
+        if not overextended:
+            continue
+
+        # Check at key level
+        at_kl = False
+        if key_levels:
+            kl = _nearest_key_level(closes[i], key_levels, kl_tolerance_pips)
+            at_kl = kl is not None
+
+        # RSI divergence detection
+        lb_start = max(0, i - divergence_lookback)
+
+        # Bearish divergence: price makes new high but RSI doesn't
+        price_new_high = highs[i] >= np.max(highs[lb_start:i])
+        rsi_lower_high = rsi[i] < np.max(rsi[lb_start:i]) if not np.isnan(rsi[i]) else False
+        bearish_div = price_new_high and rsi_lower_high
+
+        # Bullish divergence: price makes new low but RSI doesn't
+        price_new_low = lows[i] <= np.min(lows[lb_start:i])
+        rsi_higher_low = rsi[i] > np.min(rsi[lb_start:i]) if not np.isnan(rsi[i]) else False
+        bullish_div = price_new_low and rsi_higher_low
+
+        # MACD histogram shrinking (2+ bars)
+        macd_shrinking_bull = False
+        macd_shrinking_bear = False
+        if has_macd and i >= 2:
+            if (not np.isnan(macd_hist[i]) and not np.isnan(macd_hist[i - 1])
+                    and not np.isnan(macd_hist[i - 2])):
+                # Bearish: positive histogram shrinking
+                if macd_hist[i - 2] > macd_hist[i - 1] > macd_hist[i] > 0:
+                    macd_shrinking_bear = True
+                # Bullish: negative histogram shrinking (getting less negative)
+                if macd_hist[i - 2] < macd_hist[i - 1] < macd_hist[i] < 0:
+                    macd_shrinking_bull = True
+
+        # LONG reversal: price overextended below EMA, bullish divergence
+        if closes[i] < nearest_ema and bullish_div:
+            conf = 1
+            if macd_shrinking_bull:
+                conf += 1
+            if at_kl:
+                conf += 1
+            if rsi[i] < 30:
+                conf += 1
+            if overextension_mult >= 2.5:
+                conf += 1
+
+            if conf >= 2:
+                signals[i] = 1
+                sl[i] = closes[i] - sl_atr_mult * atr[i]
+                tp1[i] = nearest_ema
+                tp2[i] = closes[i] + 2.0 * atr[i]
+                tp3[i] = closes[i] + 3.5 * atr[i]
+                confidence[i] = min(conf, 5)
+                continue
+
+        # SHORT reversal: price overextended above EMA, bearish divergence
+        if closes[i] > nearest_ema and bearish_div:
+            conf = 1
+            if macd_shrinking_bear:
+                conf += 1
+            if at_kl:
+                conf += 1
+            if rsi[i] > 70:
+                conf += 1
+            if overextension_mult >= 2.5:
+                conf += 1
+
+            if conf >= 2:
+                signals[i] = -1
+                sl[i] = closes[i] + sl_atr_mult * atr[i]
+                tp1[i] = nearest_ema
+                tp2[i] = closes[i] - 2.0 * atr[i]
+                tp3[i] = closes[i] - 3.5 * atr[i]
+                confidence[i] = min(conf, 5)
+
+    df["signal"] = signals
+    df["sl_price"] = sl
+    df["tp1_price"] = tp1
+    df["tp2_price"] = tp2
+    df["tp3_price"] = tp3
+    df["confidence"] = confidence
+    df["strategy_name"] = "S5_Momentum_Exhaustion"
+    df["position"] = signals
+    return df
+
+
+# ---------------------------------------------------------------------------
+# Strategy registry
+# ---------------------------------------------------------------------------
+
+STRATEGY_REGISTRY = {
+    "S1": {
+        "name": "S1_MA_Breakout_Retest",
+        "func": generate_signals_s1,
+        "pairs": ["GBP_AUD", "EUR_AUD", "EUR_CAD", "EUR_NZD"],
+        "entry_tf": "M15",
+        "filter_tf": "H1",
+        "tp_splits": [0.40, 0.40, 0.20],  # TP1: 40%, TP2: 40%, TP3: 20% runner
+        "trail_atr_mult": 1.0,
+        "max_bars": 96,  # 24 hours on M15
+    },
+    "S2": {
+        "name": "S2_Session_VWAP_Reversal",
+        "func": generate_signals_s2,
+        "pairs": ["GBP_USD", "EUR_USD", "GBP_JPY", "USD_JPY"],
+        "entry_tf": "M15",
+        "filter_tf": None,
+        "tp_splits": [0.50, 0.35, 0.15],
+        "trail_atr_mult": 0.75,
+        "max_bars": 48,  # 12 hours on M15
+    },
+    "S3": {
+        "name": "S3_Key_Level_Breakout",
+        "func": generate_signals_s3,
+        "pairs": ["GBP_JPY", "USD_JPY", "GBP_USD", "EUR_GBP"],
+        "entry_tf": "H1",
+        "filter_tf": "H4",
+        "tp_splits": [0.70, 0.30, 0.0],  # No TP3 runner
+        "trail_atr_mult": 0.0,
+        "max_bars": 48,  # 48 hours on H1
+    },
+    "S4": {
+        "name": "S4_EMA_Ribbon_Scalp",
+        "func": generate_signals_s4,
+        "pairs": ["GBP_AUD", "EUR_AUD", "EUR_GBP"],
+        "entry_tf": "M15",
+        "filter_tf": "H1",
+        "tp_splits": [0.50, 0.35, 0.15],
+        "trail_atr_mult": 0.5,
+        "max_bars": 32,  # 8 hours on M15
+    },
+    "S5": {
+        "name": "S5_Momentum_Exhaustion",
+        "func": generate_signals_s5,
+        "pairs": ["GBP_AUD", "EUR_AUD", "EUR_GBP", "GBP_CAD", "EUR_CAD"],
+        "entry_tf": "M15",
+        "filter_tf": "H1",
+        "tp_splits": [0.40, 0.40, 0.20],
+        "trail_atr_mult": 1.0,
+        "max_bars": 64,  # 16 hours on M15
+    },
+}
diff --git a/src/strategies_pkg/__init__.py b/src/strategies_pkg/__init__.py
new file mode 100644
index 0000000..b5a5c3b
--- /dev/null
+++ b/src/strategies_pkg/__init__.py
@@ -0,0 +1,31 @@
+from .s1_ma_breakout import S1_MA_Breakout
+from .s2_vwap_reversal import S2_VWAP_Reversal
+from .s3_key_level_breakout import S3_KeyLevel_Breakout
+from .s4_ema_ribbon import S4_EMA_Ribbon
+from .s5_momentum_exhaustion import S5_Momentum_Exhaustion
+
+STRATEGIES = {
+    1: S1_MA_Breakout,
+    2: S2_VWAP_Reversal,
+    3: S3_KeyLevel_Breakout,
+    4: S4_EMA_Ribbon,
+    5: S5_Momentum_Exhaustion,
+}
+
+# Which pairs each strategy trades
+STRATEGY_PAIRS = {
+    1: ["GBP_AUD", "EUR_AUD", "EUR_CAD", "EUR_NZD"],
+    2: ["GBP_USD", "EUR_USD", "GBP_JPY", "USD_JPY"],
+    3: ["GBP_JPY", "USD_JPY", "GBP_USD", "EUR_GBP"],
+    4: ["GBP_AUD", "EUR_AUD", "EUR_GBP"],
+    5: ["GBP_AUD", "EUR_AUD", "EUR_GBP", "GBP_CAD", "EUR_CAD"],
+}
+
+# Primary and filter timeframes
+STRATEGY_TIMEFRAMES = {
+    1: {"primary": "M15", "filter": "H1"},
+    2: {"primary": "M15", "filter": None},
+    3: {"primary": "H1", "filter": None},  # Uses internal key level detection
+    4: {"primary": "M15", "filter": "H1"},
+    5: {"primary": "M15", "filter": "H1"},
+}
diff --git a/src/strategies_pkg/base.py b/src/strategies_pkg/base.py
new file mode 100644
index 0000000..df65684
--- /dev/null
+++ b/src/strategies_pkg/base.py
@@ -0,0 +1,27 @@
+"""Base strategy interface."""
+from typing import Optional
+import pandas as pd
+
+
+class BaseStrategy:
+    strategy_id: int = 0
+    name: str = "BaseStrategy"
+
+    def check_signal(self, data: pd.DataFrame, idx: int,
+                     current: pd.Series,
+                     htf_row: Optional[pd.Series] = None) -> Optional[dict]:
+        """
+        Check for a trade signal at the current candle.
+
+        Args:
+            data: Full precomputed dataframe (must only access [:idx+1]).
+            idx: Current bar index into data.
+            current: The current candle (data.iloc[idx]).
+            htf_row: Most recent fully-closed higher timeframe candle (or None).
+
+        Returns:
+            dict with keys: direction, sl, tp1, tp2, tp3, confluence,
+                            tp_splits, trail_atr_mult, max_bars
+            or None if no signal.
+        """
+        raise NotImplementedError
diff --git a/src/strategies_pkg/s1_ma_breakout.py b/src/strategies_pkg/s1_ma_breakout.py
new file mode 100644
index 0000000..50be6f4
--- /dev/null
+++ b/src/strategies_pkg/s1_ma_breakout.py
@@ -0,0 +1,177 @@
+"""
+Strategy 1: MA Breakout-Retest (Option 3 - no trendlines).
+
+Uses MA structure + key level breaks + candle confirmation.
+Entry TF: M15, Filter TF: H1 (200 SMA directional filter).
+
+Entry conditions (LONG):
+  - EMA 50 > EMA 100 > EMA 200 (trend alignment)
+  - Price pulls back to EMA 50 zone (within 1.0x ATR)
+  - Bullish confirmation candle (close > open, close > prev close, body > 30% range)
+  - H1 close > H1 200 SMA (HTF filter)
+  - Session filter: London/NY hours only (08:00-17:00 UTC)
+  - Confluence >= 2
+
+Boosters (confluence 0-5):
+  - Volume above 20-period average
+  - RSI between 40-60 (not overextended)
+  - MACD histogram positive and rising
+  - ADX > 20 (trending)
+  - Price above session VWAP
+"""
+from typing import Optional
+import numpy as np
+import pandas as pd
+from .base import BaseStrategy
+
+
+class S1_MA_Breakout(BaseStrategy):
+    strategy_id = 1
+    name = "S1_MA_Breakout_Retest"
+
+    def check_signal(self, data: pd.DataFrame, idx: int,
+                     current: pd.Series,
+                     htf_row: Optional[pd.Series] = None) -> Optional[dict]:
+        if idx < 50:
+            return None
+
+        # Session filter: only London + NY (08:00-17:00 UTC)
+        hour = current.name.hour if hasattr(current.name, 'hour') else 0
+        if hour < 8 or hour >= 17:
+            return None
+
+        atr_val = current.get("atr_14", 0)
+        if atr_val <= 0 or np.isnan(atr_val):
+            return None
+
+        ema_50 = current.get("ema_50", np.nan)
+        ema_100 = current.get("ema_100", np.nan)
+        ema_200 = current.get("ema_200", np.nan)
+        if any(np.isnan(v) for v in [ema_50, ema_100, ema_200]):
+            return None
+
+        close = current["close"]
+        open_p = current["open"]
+        prev = data.iloc[idx - 1]
+        prev_close = prev["close"]
+
+        # Candle body filter: body must be > 30% of range (no dojis)
+        body = abs(close - open_p)
+        full_range = current["high"] - current["low"]
+        if full_range <= 0 or body / full_range < 0.3:
+            return None
+
+        # LONG setup
+        if ema_50 > ema_100 > ema_200:
+            # HTF filter
+            if htf_row is not None:
+                htf_sma200 = htf_row.get("sma_200", np.nan)
+                if not np.isnan(htf_sma200) and htf_row.get("close", 0) <= htf_sma200:
+                    return None
+
+            # Pullback to EMA 50 zone (within 1.0x ATR - tightened from 1.5x)
+            dist_to_ema50 = close - ema_50
+            if dist_to_ema50 < 0 or dist_to_ema50 > 1.0 * atr_val:
+                return None
+
+            # Bullish confirmation candle
+            if not (close > open_p and close > prev_close):
+                return None
+
+            # Not too far from EMAs (avoid chasing)
+            if close - ema_200 > 5 * atr_val:
+                return None
+
+            confluence = self._calc_confluence(data, idx, current, "LONG")
+
+            # Require minimum confluence of 2
+            if confluence < 2:
+                return None
+
+            sl = current["low"] - 0.5 * atr_val
+            tp1 = close + 1.5 * atr_val
+            tp2 = close + 2.5 * atr_val
+            tp3 = close + 4.0 * atr_val
+
+            return {
+                "direction": "LONG",
+                "sl": sl, "tp1": tp1, "tp2": tp2, "tp3": tp3,
+                "confluence": confluence,
+                "tp_splits": (0.50, 0.30, 0.20),
+                "trail_atr_mult": 1.5,
+                "max_bars": 200,
+            }
+
+        # SHORT setup
+        if ema_50 < ema_100 < ema_200:
+            if htf_row is not None:
+                htf_sma200 = htf_row.get("sma_200", np.nan)
+                if not np.isnan(htf_sma200) and htf_row.get("close", 0) >= htf_sma200:
+                    return None
+
+            dist_to_ema50 = ema_50 - close
+            if dist_to_ema50 < 0 or dist_to_ema50 > 1.0 * atr_val:
+                return None
+
+            if not (close < open_p and close < prev_close):
+                return None
+
+            if ema_200 - close > 5 * atr_val:
+                return None
+
+            confluence = self._calc_confluence(data, idx, current, "SHORT")
+
+            if confluence < 2:
+                return None
+
+            sl = current["high"] + 0.5 * atr_val
+            tp1 = close - 1.5 * atr_val
+            tp2 = close - 2.5 * atr_val
+            tp3 = close - 4.0 * atr_val
+
+            return {
+                "direction": "SHORT",
+                "sl": sl, "tp1": tp1, "tp2": tp2, "tp3": tp3,
+                "confluence": confluence,
+                "tp_splits": (0.50, 0.30, 0.20),
+                "trail_atr_mult": 1.5,
+                "max_bars": 200,
+            }
+
+        return None
+
+    def _calc_confluence(self, data, idx, current, direction):
+        confluence = 0
+
+        # Volume above average
+        if "volume" in current.index:
+            vol_avg = data["volume"].iloc[max(0, idx - 20):idx].mean()
+            if vol_avg > 0 and current["volume"] > vol_avg:
+                confluence += 1
+
+        # RSI between 40-60
+        rsi = current.get("rsi_14", 50)
+        if 40 <= rsi <= 60:
+            confluence += 1
+
+        # MACD histogram confirmation
+        macd_h = current.get("macd_hist", 0)
+        prev_macd_h = data.iloc[idx - 1].get("macd_hist", 0)
+        if direction == "LONG" and macd_h > 0 and macd_h > prev_macd_h:
+            confluence += 1
+        elif direction == "SHORT" and macd_h < 0 and macd_h < prev_macd_h:
+            confluence += 1
+
+        # ADX > 20
+        if current.get("adx_14", 0) > 20:
+            confluence += 1
+
+        # VWAP alignment
+        vwap = current.get("session_vwap", 0)
+        if vwap:
+            if direction == "LONG" and current["close"] > vwap:
+                confluence += 1
+            elif direction == "SHORT" and current["close"] < vwap:
+                confluence += 1
+
+        return min(confluence, 5)
diff --git a/src/strategies_pkg/s2_vwap_reversal.py b/src/strategies_pkg/s2_vwap_reversal.py
new file mode 100644
index 0000000..11ff658
--- /dev/null
+++ b/src/strategies_pkg/s2_vwap_reversal.py
@@ -0,0 +1,140 @@
+"""
+Strategy 2: Session VWAP Reversal.
+
+Entry TF: M15, No HTF filter needed.
+
+Entry conditions (LONG):
+  - Price crosses below VWAP -2 sigma band
+  - RSI < 30 (oversold confirmation)
+  - Session filter: London/NY hours only (08:00-17:00 UTC)
+  - Minimum band width: vwap_std > 0.3 * ATR
+
+Entry conditions (SHORT):
+  - Price crosses above VWAP +2 sigma band
+  - RSI > 70 (overbought confirmation)
+
+TP1: Return to VWAP, TP2: Opposite 0.5 sigma band
+SL: Beyond session high/low OR 1x ATR (whichever tighter)
+"""
+from typing import Optional
+import numpy as np
+import pandas as pd
+from .base import BaseStrategy
+
+
+class S2_VWAP_Reversal(BaseStrategy):
+    strategy_id = 2
+    name = "S2_Session_VWAP_Reversal"
+
+    def check_signal(self, data: pd.DataFrame, idx: int,
+                     current: pd.Series,
+                     htf_row: Optional[pd.Series] = None) -> Optional[dict]:
+        if idx < 30:
+            return None
+
+        # Session filter: only London + NY (08:00-17:00 UTC)
+        hour = current.name.hour if hasattr(current.name, 'hour') else 0
+        if hour < 8 or hour >= 17:
+            return None
+
+        vwap = current.get("session_vwap", None)
+        upper_2 = current.get("vwap_upper_2", None)
+        lower_2 = current.get("vwap_lower_2", None)
+
+        if vwap is None or upper_2 is None or lower_2 is None:
+            return None
+        if any(np.isnan(v) for v in [vwap, upper_2, lower_2]):
+            return None
+
+        # Skip if bands are too tight (early in session, no deviation yet)
+        vwap_std = current.get("vwap_std", 0)
+        if vwap_std is None or np.isnan(vwap_std) or vwap_std <= 0:
+            return None
+
+        atr_val = current.get("atr_14", 0)
+        if atr_val <= 0 or np.isnan(atr_val):
+            return None
+
+        # Minimum band width: std must be meaningful relative to ATR
+        if vwap_std < 0.3 * atr_val:
+            return None
+
+        rsi = current.get("rsi_14", 50)
+        if np.isnan(rsi):
+            return None
+
+        close = current["close"]
+        prev = data.iloc[idx - 1]
+
+        # Session high/low for SL
+        start = max(0, idx - 80)
+        session_high = data["high"].iloc[start:idx + 1].max()
+        session_low = data["low"].iloc[start:idx + 1].min()
+
+        # LONG: price below -2 sigma + RSI < 30 + price just crossed below
+        if close < lower_2 and rsi < 30:
+            prev_lower_2 = prev.get("vwap_lower_2", None)
+            if prev_lower_2 is not None and not np.isnan(prev_lower_2):
+                if prev["close"] >= prev_lower_2:
+                    return self._build_long(close, vwap, atr_val, vwap_std,
+                                            session_low, current)
+
+        # SHORT: price above +2 sigma + RSI > 70 + price just crossed above
+        if close > upper_2 and rsi > 70:
+            prev_upper_2 = prev.get("vwap_upper_2", None)
+            if prev_upper_2 is not None and not np.isnan(prev_upper_2):
+                if prev["close"] <= prev_upper_2:
+                    return self._build_short(close, vwap, atr_val, vwap_std,
+                                             session_high, current)
+
+        return None
+
+    def _build_long(self, close, vwap, atr_val, vwap_std, session_low, current):
+        sl_session = session_low - 0.5 * atr_val
+        sl_atr = close - 1.5 * atr_val  # Wider SL for mean reversion
+        sl = max(sl_session, sl_atr)
+
+        tp1 = vwap
+        tp2 = vwap + 0.5 * vwap_std
+        tp3 = vwap + 1.5 * vwap_std
+
+        confluence = 2
+        if current.get("adx_14", 30) < 25:
+            confluence += 1
+        macd_h = current.get("macd_hist", 0)
+        if macd_h > 0:
+            confluence += 1
+
+        return {
+            "direction": "LONG",
+            "sl": sl, "tp1": tp1, "tp2": tp2, "tp3": tp3,
+            "confluence": confluence,
+            "tp_splits": (0.50, 0.35, 0.15),
+            "trail_atr_mult": 1.0,
+            "max_bars": 80,
+        }
+
+    def _build_short(self, close, vwap, atr_val, vwap_std, session_high, current):
+        sl_session = session_high + 0.5 * atr_val
+        sl_atr = close + 1.5 * atr_val  # Wider SL for mean reversion
+        sl = min(sl_session, sl_atr)
+
+        tp1 = vwap
+        tp2 = vwap - 0.5 * vwap_std
+        tp3 = vwap - 1.5 * vwap_std
+
+        confluence = 2
+        if current.get("adx_14", 30) < 25:
+            confluence += 1
+        macd_h = current.get("macd_hist", 0)
+        if macd_h < 0:
+            confluence += 1
+
+        return {
+            "direction": "SHORT",
+            "sl": sl, "tp1": tp1, "tp2": tp2, "tp3": tp3,
+            "confluence": confluence,
+            "tp_splits": (0.50, 0.35, 0.15),
+            "trail_atr_mult": 1.0,
+            "max_bars": 80,
+        }
diff --git a/src/strategies_pkg/s3_key_level_breakout.py b/src/strategies_pkg/s3_key_level_breakout.py
new file mode 100644
index 0000000..b9f7c0c
--- /dev/null
+++ b/src/strategies_pkg/s3_key_level_breakout.py
@@ -0,0 +1,156 @@
+"""
+Strategy 3: Key Level Momentum Breakout.
+
+Entry TF: H1. Key levels identified from swing point clusters.
+
+Entry conditions (LONG):
+  - H1 candle closes above a key level (horizontal S/R with 2+ touches)
+  - MACD histogram same sign as direction
+  - ADX > 15
+  - Candle body > 30% of range (conviction candle)
+
+SL: Back inside key level + 1x ATR buffer
+TP1: 1.5x ATR, TP2: 2.5x ATR, TP3: 4x ATR
+"""
+from typing import Optional
+import numpy as np
+import pandas as pd
+from .base import BaseStrategy
+from ..indicators.technical import identify_key_levels
+
+
+class S3_KeyLevel_Breakout(BaseStrategy):
+    strategy_id = 3
+    name = "S3_Key_Level_Breakout"
+
+    def __init__(self):
+        self._cached_levels = None
+        self._cache_idx = -1
+
+    def check_signal(self, data: pd.DataFrame, idx: int,
+                     current: pd.Series,
+                     htf_row: Optional[pd.Series] = None) -> Optional[dict]:
+        if idx < 100:
+            return None
+
+        # Session filter: London/NY only (08:00-17:00 UTC)
+        hour = current.name.hour if hasattr(current.name, 'hour') else 0
+        if hour < 8 or hour >= 17:
+            return None
+
+        atr_val = current.get("atr_14", 0)
+        if atr_val <= 0 or np.isnan(atr_val):
+            return None
+
+        # Recalculate key levels every 20 bars using larger lookback
+        if self._cached_levels is None or idx - self._cache_idx >= 20:
+            start = max(0, idx - 1000)
+            window = data.iloc[start:idx]  # exclude current bar
+            self._cached_levels = identify_key_levels(
+                window, lookback=5, tolerance_atr_mult=0.75, min_touches=2
+            )
+            self._cache_idx = idx
+
+        if not self._cached_levels:
+            return None
+
+        close = current["close"]
+        prev_close = data.iloc[idx - 1]["close"]
+
+        # Candle body filter
+        body = abs(close - current["open"])
+        full_range = current["high"] - current["low"]
+        if full_range <= 0 or body / full_range < 0.3:
+            return None
+
+        # MACD
+        macd_h = current.get("macd_hist", 0)
+
+        # ADX
+        adx_val = current.get("adx_14", 0)
+        if adx_val < 15:
+            return None
+
+        for level_price, touch_count in self._cached_levels:
+            tolerance = 0.3 * atr_val
+
+            # LONG breakout: close above level, prev close was at or below
+            if close > level_price + tolerance and prev_close <= level_price + tolerance:
+                if macd_h <= 0:
+                    continue
+
+                # EMA alignment: 50 > 200 for LONG
+                ema_50 = current.get("ema_50", 0)
+                ema_200 = current.get("ema_200", 0)
+                if ema_50 and ema_200 and ema_50 <= ema_200:
+                    continue
+
+                confluence = self._calc_confluence(current, data, idx, "LONG", touch_count)
+
+                sl = level_price - 0.3 * atr_val  # Tight SL just inside key level
+                tp1 = close + 1.5 * atr_val
+                tp2 = close + 2.5 * atr_val
+                tp3 = close + 4.0 * atr_val
+
+                return {
+                    "direction": "LONG",
+                    "sl": sl, "tp1": tp1, "tp2": tp2, "tp3": tp3,
+                    "confluence": confluence,
+                    "tp_splits": (0.40, 0.40, 0.20),
+                    "trail_atr_mult": 2.0,
+                    "max_bars": 150,
+                }
+
+            # SHORT breakout: close below level, prev close was at or above
+            if close < level_price - tolerance and prev_close >= level_price - tolerance:
+                if macd_h >= 0:
+                    continue
+
+                # EMA alignment: 50 < 200 for SHORT
+                ema_50 = current.get("ema_50", 0)
+                ema_200 = current.get("ema_200", 0)
+                if ema_50 and ema_200 and ema_50 >= ema_200:
+                    continue
+
+                confluence = self._calc_confluence(current, data, idx, "SHORT", touch_count)
+
+                sl = level_price + 0.3 * atr_val  # Tight SL just inside key level
+                tp1 = close - 1.5 * atr_val
+                tp2 = close - 2.5 * atr_val
+                tp3 = close - 4.0 * atr_val
+
+                return {
+                    "direction": "SHORT",
+                    "sl": sl, "tp1": tp1, "tp2": tp2, "tp3": tp3,
+                    "confluence": confluence,
+                    "tp_splits": (0.40, 0.40, 0.20),
+                    "trail_atr_mult": 2.0,
+                    "max_bars": 150,
+                }
+
+        return None
+
+    def _calc_confluence(self, current, data, idx, direction, touch_count):
+        confluence = 1  # breakout confirmed
+
+        # More touches = stronger level
+        if touch_count >= 3:
+            confluence += 1
+        if touch_count >= 5:
+            confluence += 1
+
+        rsi = current.get("rsi_14", 50)
+        if direction == "LONG" and 50 < rsi < 75:
+            confluence += 1
+        elif direction == "SHORT" and 25 < rsi < 50:
+            confluence += 1
+
+        ema_50 = current.get("ema_50", 0)
+        ema_200 = current.get("ema_200", 0)
+        if ema_50 and ema_200:
+            if direction == "LONG" and ema_50 > ema_200:
+                confluence += 1
+            elif direction == "SHORT" and ema_50 < ema_200:
+                confluence += 1
+
+        return min(confluence, 5)
diff --git a/src/strategies_pkg/s4_ema_ribbon.py b/src/strategies_pkg/s4_ema_ribbon.py
new file mode 100644
index 0000000..78ce304
--- /dev/null
+++ b/src/strategies_pkg/s4_ema_ribbon.py
@@ -0,0 +1,173 @@
+"""
+Strategy 4: EMA Ribbon Momentum Scalp.
+
+Entry TF: M15, Filter TF: H1.
+
+H1 Filter: EMA 20 > 50 > 100 > 200 (LONG) or reversed (SHORT)
+
+M15 Entry (LONG):
+  - EMA ribbon compressed (EMAs within 1.0x ATR)
+  - Ribbon re-expanding (current width > prev width)
+  - Stochastic turning from oversold
+  - Session filter: London/NY (08:00-17:00 UTC)
+
+SL: Below compression low - 0.5x ATR
+TP1: 1x ATR, TP2: 1.5x ATR, TP3: 2.5x ATR
+"""
+from typing import Optional
+import numpy as np
+import pandas as pd
+from .base import BaseStrategy
+
+
+class S4_EMA_Ribbon(BaseStrategy):
+    strategy_id = 4
+    name = "S4_EMA_Ribbon_Scalp"
+
+    def check_signal(self, data: pd.DataFrame, idx: int,
+                     current: pd.Series,
+                     htf_row: Optional[pd.Series] = None) -> Optional[dict]:
+        if idx < 50:
+            return None
+
+        # Session filter
+        hour = current.name.hour if hasattr(current.name, 'hour') else 0
+        if hour < 8 or hour >= 17:
+            return None
+
+        atr_val = current.get("atr_14", 0)
+        if atr_val <= 0 or np.isnan(atr_val):
+            return None
+
+        if htf_row is None:
+            return None
+
+        # H1 EMA stack check
+        htf_ema20 = htf_row.get("ema_20", np.nan)
+        htf_ema50 = htf_row.get("ema_50", np.nan)
+        htf_ema100 = htf_row.get("ema_100", np.nan)
+        htf_ema200 = htf_row.get("ema_200", np.nan)
+        if any(np.isnan(v) for v in [htf_ema20, htf_ema50, htf_ema100, htf_ema200]):
+            return None
+
+        long_stack = htf_ema20 > htf_ema50 > htf_ema100 > htf_ema200
+        short_stack = htf_ema20 < htf_ema50 < htf_ema100 < htf_ema200
+
+        if not long_stack and not short_stack:
+            return None
+
+        # M15 ribbon EMAs
+        ema_20 = current.get("ema_20", np.nan)
+        ema_50 = current.get("ema_50", np.nan)
+        ema_100 = current.get("ema_100", np.nan)
+        if any(np.isnan(v) for v in [ema_20, ema_50, ema_100]):
+            return None
+
+        ribbon_width = max(ema_20, ema_50, ema_100) - min(ema_20, ema_50, ema_100)
+        compression_threshold = 1.0 * atr_val  # relaxed from 0.5x
+
+        # Check for recent compression (look back 5-20 bars)
+        was_compressed = False
+        compression_low = current["low"]
+        compression_high = current["high"]
+        min_compression_width = float('inf')
+
+        for j in range(max(0, idx - 20), idx):
+            bar = data.iloc[j]
+            e20 = bar.get("ema_20", np.nan)
+            e50 = bar.get("ema_50", np.nan)
+            e100 = bar.get("ema_100", np.nan)
+            if any(np.isnan(v) for v in [e20, e50, e100]):
+                continue
+            w = max(e20, e50, e100) - min(e20, e50, e100)
+            if w <= compression_threshold:
+                was_compressed = True
+                min_compression_width = min(min_compression_width, w)
+                compression_low = min(compression_low, bar["low"])
+                compression_high = max(compression_high, bar["high"])
+
+        if not was_compressed:
+            return None
+
+        # Current bar: ribbon must be expanding (wider than min compression)
+        if ribbon_width <= min_compression_width * 1.2:
+            return None
+
+        # Stochastic
+        stoch_k = current.get("stoch_k", 50)
+        stoch_d = current.get("stoch_d", 50)
+
+        if long_stack:
+            # Stochastic turning up or in bullish zone
+            if not (stoch_k > stoch_d or stoch_k < 50):
+                return None
+            if not (ema_20 >= ema_50):  # Ribbon expanding upward
+                return None
+
+            confluence = self._calc_confluence(data, idx, current, atr_val, "LONG")
+
+            # Use tighter SL: max of (compression_low, close - 0.8*ATR)
+            sl_compression = compression_low - 0.3 * atr_val
+            sl_atr = current["close"] - 0.8 * atr_val
+            sl = max(sl_compression, sl_atr)
+            tp1 = current["close"] + 1.0 * atr_val
+            tp2 = current["close"] + 1.5 * atr_val
+            tp3 = current["close"] + 2.5 * atr_val
+
+            return {
+                "direction": "LONG",
+                "sl": sl, "tp1": tp1, "tp2": tp2, "tp3": tp3,
+                "confluence": confluence,
+                "tp_splits": (0.50, 0.30, 0.20),
+                "trail_atr_mult": 1.0,
+                "max_bars": 60,
+            }
+
+        elif short_stack:
+            if not (stoch_k < stoch_d or stoch_k > 50):
+                return None
+            if not (ema_20 <= ema_50):
+                return None
+
+            confluence = self._calc_confluence(data, idx, current, atr_val, "SHORT")
+
+            sl_compression = compression_high + 0.3 * atr_val
+            sl_atr = current["close"] + 0.8 * atr_val
+            sl = min(sl_compression, sl_atr)
+            tp1 = current["close"] - 1.0 * atr_val
+            tp2 = current["close"] - 1.5 * atr_val
+            tp3 = current["close"] - 2.5 * atr_val
+
+            return {
+                "direction": "SHORT",
+                "sl": sl, "tp1": tp1, "tp2": tp2, "tp3": tp3,
+                "confluence": confluence,
+                "tp_splits": (0.50, 0.30, 0.20),
+                "trail_atr_mult": 1.0,
+                "max_bars": 60,
+            }
+
+        return None
+
+    def _calc_confluence(self, data, idx, current, atr_val, direction):
+        confluence = 2  # HTF alignment + compression/expansion
+
+        # ATR contracting
+        atr_avg = data["atr_14"].iloc[max(0, idx - 50):idx].mean()
+        if atr_val < atr_avg:
+            confluence += 1
+
+        # Volume rising
+        if "volume" in current.index:
+            vol_avg = data["volume"].iloc[max(0, idx - 20):idx].mean()
+            if vol_avg > 0 and current["volume"] > vol_avg:
+                confluence += 1
+
+        # RSI not extreme
+        rsi = current.get("rsi_14", 50)
+        if direction == "LONG" and 40 < rsi < 65:
+            confluence += 1
+        elif direction == "SHORT" and 35 < rsi < 60:
+            confluence += 1
+
+        return min(confluence, 5)
diff --git a/src/strategies_pkg/s5_momentum_exhaustion.py b/src/strategies_pkg/s5_momentum_exhaustion.py
new file mode 100644
index 0000000..c4740a8
--- /dev/null
+++ b/src/strategies_pkg/s5_momentum_exhaustion.py
@@ -0,0 +1,193 @@
+"""
+Strategy 5: Momentum Exhaustion Reversal.
+
+Entry TF: M15, Filter TF: H1 (structure/key levels).
+
+Mandatory conditions (all 4 required):
+  1. RSI divergence (price new extreme, RSI doesn't confirm)
+  2. MACD histogram shrinking (2+ consecutive smaller bars)
+  3. Near key level (within 0.75x ATR)
+  4. Price overextended: moved 1.0x+ ATR from nearest EMA
+  - Session filter: London/NY (08:00-17:00 UTC)
+
+Mutual exclusion: Cannot fire if S4 ribbon is compressed.
+"""
+from typing import Optional
+import numpy as np
+import pandas as pd
+from .base import BaseStrategy
+from ..indicators.technical import identify_key_levels
+
+
+class S5_Momentum_Exhaustion(BaseStrategy):
+    strategy_id = 5
+    name = "S5_Momentum_Exhaustion"
+
+    def __init__(self):
+        self._cached_levels = None
+        self._cache_idx = -1
+
+    def check_signal(self, data: pd.DataFrame, idx: int,
+                     current: pd.Series,
+                     htf_row: Optional[pd.Series] = None) -> Optional[dict]:
+        if idx < 50:
+            return None
+
+        # Session filter
+        hour = current.name.hour if hasattr(current.name, 'hour') else 0
+        if hour < 8 or hour >= 17:
+            return None
+
+        atr_val = current.get("atr_14", 0)
+        if atr_val <= 0 or np.isnan(atr_val):
+            return None
+
+        # Condition 1: RSI divergence
+        divergence = self._detect_divergence(data, idx)
+        if divergence is None:
+            return None
+
+        # Condition 2: MACD histogram shrinking
+        if not self._macd_shrinking(data, idx):
+            return None
+
+        # Condition 3: Near key level
+        if not self._near_key_level(data, idx, current, atr_val):
+            return None
+
+        # Condition 4: Overextension from EMA
+        if not self._overextended(current, atr_val):
+            return None
+
+        # Mutual exclusion with S4 ribbon
+        if self._s4_active(current, atr_val):
+            return None
+
+        confluence = 4  # All mandatory met
+
+        # Booster: declining volume (required for entry - reduces false signals)
+        if not self._volume_declining(data, idx):
+            return None
+        confluence = 5
+
+        close = current["close"]
+        if divergence == "bullish":
+            sl = current["low"] - 0.5 * atr_val  # Tight SL for reversal
+            tp1 = close + 1.5 * atr_val
+            tp2 = close + 2.5 * atr_val
+            tp3 = close + 4.0 * atr_val
+            direction = "LONG"
+        else:
+            sl = current["high"] + 0.5 * atr_val  # Tight SL for reversal
+            tp1 = close - 1.5 * atr_val
+            tp2 = close - 2.5 * atr_val
+            tp3 = close - 4.0 * atr_val
+            direction = "SHORT"
+
+        return {
+            "direction": direction,
+            "sl": sl, "tp1": tp1, "tp2": tp2, "tp3": tp3,
+            "confluence": confluence,
+            "tp_splits": (0.40, 0.40, 0.20),
+            "trail_atr_mult": 1.5,
+            "max_bars": 120,
+        }
+
+    def _detect_divergence(self, data, idx):
+        """Detect RSI divergence over lookback window."""
+        lookback = 20
+        if idx < lookback + 3:
+            return None
+
+        rsi_col = "rsi_14"
+        if rsi_col not in data.columns:
+            return None
+
+        window = data.iloc[idx - lookback:idx + 1]
+        rsi_vals = window[rsi_col]
+        lows = window["low"]
+        highs = window["high"]
+
+        # Bullish: current low is near the lowest in window, but RSI is higher
+        current_low = data.iloc[idx]["low"]
+        min_low_idx = lows.iloc[:-3].idxmin()  # exclude last 3 bars
+        if min_low_idx is not None:
+            prev_low = lows.loc[min_low_idx]
+            if current_low <= prev_low * 1.001:  # current near or below prev low
+                if rsi_vals.iloc[-1] > rsi_vals.loc[min_low_idx]:
+                    return "bullish"
+
+        # Bearish: current high is near highest, but RSI is lower
+        current_high = data.iloc[idx]["high"]
+        max_high_idx = highs.iloc[:-3].idxmax()
+        if max_high_idx is not None:
+            prev_high = highs.loc[max_high_idx]
+            if current_high >= prev_high * 0.999:
+                if rsi_vals.iloc[-1] < rsi_vals.loc[max_high_idx]:
+                    return "bearish"
+
+        return None
+
+    def _macd_shrinking(self, data, idx):
+        if idx < 3:
+            return False
+        h0 = abs(data.iloc[idx].get("macd_hist", 0))
+        h1 = abs(data.iloc[idx - 1].get("macd_hist", 0))
+        h2 = abs(data.iloc[idx - 2].get("macd_hist", 0))
+        return h0 < h1 and h1 < h2  # 2 consecutive shrinks
+
+    def _near_key_level(self, data, idx, current, atr_val):
+        # Recache every 50 bars
+        if self._cached_levels is None or idx - self._cache_idx >= 50:
+            start = max(0, idx - 500)
+            window = data.iloc[start:idx]
+            self._cached_levels = identify_key_levels(
+                window, lookback=5, tolerance_atr_mult=0.75, min_touches=2
+            )
+            self._cache_idx = idx
+
+        if not self._cached_levels:
+            return False
+
+        close = current["close"]
+        tolerance = 0.75 * atr_val  # relaxed from 0.5x
+
+        for level_price, _ in self._cached_levels:
+            if abs(close - level_price) <= tolerance:
+                return True
+        return False
+
+    def _overextended(self, current, atr_val):
+        close = current["close"]
+        ema_50 = current.get("ema_50", np.nan)
+        ema_100 = current.get("ema_100", np.nan)
+
+        distances = []
+        if not np.isnan(ema_50):
+            distances.append(abs(close - ema_50))
+        if not np.isnan(ema_100):
+            distances.append(abs(close - ema_100))
+
+        if not distances:
+            return False
+
+        # Relaxed from 1.5x to 1.0x ATR
+        return min(distances) >= 1.0 * atr_val
+
+    def _s4_active(self, current, atr_val):
+        ema_20 = current.get("ema_20", np.nan)
+        ema_50 = current.get("ema_50", np.nan)
+        ema_100 = current.get("ema_100", np.nan)
+        if any(np.isnan(v) for v in [ema_20, ema_50, ema_100]):
+            return False
+        ribbon_width = max(ema_20, ema_50, ema_100) - min(ema_20, ema_50, ema_100)
+        return ribbon_width <= 1.0 * atr_val  # match S4's relaxed threshold
+
+    def _volume_declining(self, data, idx):
+        if idx < 5 or "volume" not in data.columns:
+            return False
+        vols = data["volume"].iloc[idx - 4:idx + 1].values
+        if np.any(np.isnan(vols)):
+            return False
+        diffs = np.diff(vols)
+        return np.sum(diffs < 0) >= 3
diff --git a/src/validate_and_split_data.py b/src/validate_and_split_data.py
new file mode 100644
index 0000000..550a0b9
--- /dev/null
+++ b/src/validate_and_split_data.py
@@ -0,0 +1,214 @@
+"""
+Data Validation and Train/Test Split Script.
+
+Validates raw Dukascopy data quality, then splits:
+  - Training set (70%): Jan 2021 - Aug 2023 -> data/processed/
+  - Test set (30%): Sep 2023 - Dec 2024 -> data/test_set/
+
+Per the roadmap: test set must NOT be accessed during Phases 1-3.
+"""
+import os
+import sys
+import glob
+
+import pandas as pd
+import numpy as np
+
+RAW_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "data", "raw")
+PROCESSED_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "data", "processed")
+TEST_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "data", "test_set")
+
+# Split date: training ends Aug 31 2023, test starts Sep 1 2023
+SPLIT_DATE = "2023-09-01"
+
+# Expected timeframe intervals
+EXPECTED_INTERVALS = {
+    "M15": pd.Timedelta(minutes=15),
+    "H1": pd.Timedelta(hours=1),
+}
+
+# Pip values for spread sanity checks (approximate)
+PIP_VALUES = {
+    "EUR_USD": 0.0001, "GBP_USD": 0.0001, "EUR_AUD": 0.0001,
+    "GBP_AUD": 0.0001, "EUR_GBP": 0.0001, "GBP_CAD": 0.0001,
+    "EUR_CAD": 0.0001, "EUR_NZD": 0.0001,
+    "USD_JPY": 0.01, "GBP_JPY": 0.01,
+}
+
+
+def load_raw_csv(filepath):
+    """Load a raw Dukascopy CSV file."""
+    df = pd.read_csv(filepath, index_col=0, parse_dates=True)
+    df.index.name = "timestamp"
+    return df
+
+
+def validate_data(df, pair, timeframe):
+    """Run validation checks on a dataframe. Returns list of issues."""
+    issues = []
+    tf_delta = EXPECTED_INTERVALS[timeframe]
+    pip = PIP_VALUES.get(pair, 0.0001)
+
+    # 1. Check for missing candles (gaps in timestamp sequence)
+    diffs = df.index.to_series().diff().dropna()
+    # Filter to only market hours gaps (ignore weekends)
+    # A "gap" is any diff > 2x expected interval (allows for some market closures)
+    large_gaps = diffs[diffs > tf_delta * 4]
+    # Exclude weekend gaps (Fri->Sun typically)
+    weekday_gaps = []
+    for ts, gap in large_gaps.items():
+        # Skip if gap starts on Friday and ends on Sunday/Monday (weekend)
+        prev_ts = ts - gap
+        if prev_ts.weekday() == 4 and ts.weekday() in [0, 6]:  # Fri->Sun or Fri->Mon
+            continue
+        # Skip Christmas/New Year period
+        if prev_ts.month == 12 and prev_ts.day >= 24:
+            continue
+        if ts.month == 1 and ts.day <= 3:
+            continue
+        weekday_gaps.append((ts, gap))
+
+    if len(weekday_gaps) > 20:
+        issues.append(f"WARNING: {len(weekday_gaps)} non-weekend gaps > 4x interval")
+    elif len(weekday_gaps) > 0:
+        issues.append(f"INFO: {len(weekday_gaps)} non-weekend gaps (likely holidays)")
+
+    # 2. Check for zero-volume candles
+    if "volume" in df.columns:
+        zero_vol = (df["volume"] == 0).sum()
+        if zero_vol > 0:
+            issues.append(f"WARNING: {zero_vol} zero-volume candles ({zero_vol/len(df)*100:.2f}%)")
+
+    # 3. Check for extreme outliers (candles with >10x average range)
+    candle_range = df["high"] - df["low"]
+    avg_range = candle_range.mean()
+    extreme = (candle_range > avg_range * 10).sum()
+    if extreme > 0:
+        issues.append(f"WARNING: {extreme} candles with range >10x average")
+
+    # 4. Check for duplicate timestamps
+    dupes = df.index.duplicated().sum()
+    if dupes > 0:
+        issues.append(f"ERROR: {dupes} duplicate timestamps")
+
+    # 5. Check date range coverage
+    start = df.index.min()
+    end = df.index.max()
+    issues.append(f"INFO: Date range {start.date()} to {end.date()}")
+
+    # 6. Session coverage check
+    hours = df.index.hour
+    asian_count = ((hours >= 0) & (hours < 8)).sum()
+    london_count = ((hours >= 8) & (hours < 16)).sum()
+    ny_count = ((hours >= 13) & (hours < 22)).sum()
+    if asian_count == 0:
+        issues.append("ERROR: No Asian session data")
+    if london_count == 0:
+        issues.append("ERROR: No London session data")
+    if ny_count == 0:
+        issues.append("ERROR: No NY session data")
+
+    # 7. Basic OHLC sanity
+    bad_ohlc = ((df["high"] < df["low"]) | (df["high"] < df["open"]) |
+                (df["high"] < df["close"]) | (df["low"] > df["open"]) |
+                (df["low"] > df["close"])).sum()
+    if bad_ohlc > 0:
+        issues.append(f"ERROR: {bad_ohlc} candles with invalid OHLC relationships")
+
+    return issues
+
+
+def split_and_save(df, pair, timeframe):
+    """Split data into train/test and save to respective directories."""
+    split_ts = pd.Timestamp(SPLIT_DATE, tz=df.index.tz) if df.index.tz else pd.Timestamp(SPLIT_DATE)
+
+    train = df[df.index < split_ts]
+    test = df[df.index >= split_ts]
+
+    filename = f"{pair}_{timeframe}.csv"
+
+    train_path = os.path.join(PROCESSED_DIR, filename)
+    test_path = os.path.join(TEST_DIR, filename)
+
+    train.to_csv(train_path)
+    test.to_csv(test_path)
+
+    return len(train), len(test)
+
+
+def main():
+    os.makedirs(PROCESSED_DIR, exist_ok=True)
+
+    csv_files = sorted(glob.glob(os.path.join(RAW_DIR, "*.csv")))
+    if not csv_files:
+        print(f"No CSV files found in {RAW_DIR}")
+        sys.exit(1)
+
+    print(f"Found {len(csv_files)} files in {RAW_DIR}")
+    print(f"Split date: {SPLIT_DATE}")
+    print(f"Training -> {PROCESSED_DIR}")
+    print(f"Test -> {TEST_DIR}")
+    print("=" * 70)
+
+    summary = []
+    all_ok = True
+
+    for filepath in csv_files:
+        basename = os.path.basename(filepath).replace(".csv", "")
+        parts = basename.rsplit("_", 1)
+        pair = parts[0]
+        timeframe = parts[1]
+
+        print(f"\n--- {pair} {timeframe} ---")
+
+        df = load_raw_csv(filepath)
+        print(f"  Raw rows: {len(df)}")
+
+        # Remove duplicates
+        before = len(df)
+        df = df[~df.index.duplicated(keep="first")]
+        df = df.sort_index()
+        if len(df) < before:
+            print(f"  Removed {before - len(df)} duplicate rows")
+
+        # Validate
+        issues = validate_data(df, pair, timeframe)
+        for issue in issues:
+            level = issue.split(":")[0]
+            print(f"  {issue}")
+            if level == "ERROR":
+                all_ok = False
+
+        # Split and save
+        train_count, test_count = split_and_save(df, pair, timeframe)
+        print(f"  Train: {train_count} rows | Test: {test_count} rows | Split: {train_count/(train_count+test_count)*100:.1f}%/{test_count/(train_count+test_count)*100:.1f}%")
+
+        summary.append({
+            "pair": pair,
+            "timeframe": timeframe,
+            "raw_rows": len(df),
+            "train_rows": train_count,
+            "test_rows": test_count,
+            "issues": len([i for i in issues if i.startswith("ERROR") or i.startswith("WARNING")]),
+        })
+
+    # Summary table
+    print("\n" + "=" * 70)
+    print(f"{'Pair':<12} {'TF':<5} {'Raw':>8} {'Train':>8} {'Test':>8} {'Issues':>7}")
+    print("-" * 55)
+    for s in summary:
+        print(f"{s['pair']:<12} {s['timeframe']:<5} {s['raw_rows']:>8} {s['train_rows']:>8} {s['test_rows']:>8} {s['issues']:>7}")
+
+    total_train = sum(s["train_rows"] for s in summary)
+    total_test = sum(s["test_rows"] for s in summary)
+    print("-" * 55)
+    print(f"{'TOTAL':<18} {sum(s['raw_rows'] for s in summary):>8} {total_train:>8} {total_test:>8}")
+
+    if all_ok:
+        print("\nAll validation checks passed. Data is ready for Phase 1.")
+    else:
+        print("\nSome validation checks failed. Review errors above.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/templates/backtest.html b/templates/backtest.html
index d9c72b9..def7c61 100644
--- a/templates/backtest.html
+++ b/templates/backtest.html
@@ -11,7 +11,7 @@
 {% for s in summaries %}
 <div class="card mb-4">
   <div class="card-header">
-    <strong>{{ s.instrument }} / {{ s.granularity }}</strong>
+    <strong>{% if s.strategy_name %}{{ s.strategy_name }} &mdash; {% endif %}{{ s.instrument }} / {{ s.granularity }}</strong>
     <span class="text-muted float-end">Run: {{ s.run_time[:19] }} | {{ s.data_range.bars | default(0) }} bars ({{ s.data_range.start[:10] }} to {{ s.data_range.end[:10] }})</span>
   </div>
   <div class="card-body">
diff --git a/templates/chart.html b/templates/chart.html
index 7a73622..4f113fa 100644
--- a/templates/chart.html
+++ b/templates/chart.html
@@ -2,131 +2,613 @@
 {% block title %}Chart - fx-quant{% endblock %}
 
 {% block content %}
-<div class="row mb-3">
-  <div class="col">
-    <h4>Price Chart with Pivot Points</h4>
-  </div>
-  <div class="col-auto">
-    <form method="get" action="/chart" class="d-flex gap-2">
-      <select name="instrument" class="form-select form-select-sm" onchange="this.form.submit()">
+<style>
+  /* Dark chart container - scoped to chart page only */
+  .chart-wrapper {
+    background: #131722;
+    border-radius: 8px;
+    padding: 0;
+    overflow: hidden;
+  }
+  .chart-toolbar {
+    background: #1e222d;
+    padding: 8px 12px;
+    display: flex;
+    align-items: center;
+    gap: 10px;
+    flex-wrap: wrap;
+    border-bottom: 1px solid #2a2e39;
+  }
+  .chart-toolbar label {
+    color: #787b86;
+    font-size: 0.8rem;
+    margin: 0;
+  }
+  .chart-toolbar select {
+    background: #2a2e39;
+    color: #d1d4dc;
+    border: 1px solid #363a45;
+    border-radius: 4px;
+    padding: 4px 8px;
+    font-size: 0.82rem;
+    outline: none;
+  }
+  .chart-toolbar select:focus {
+    border-color: #2962ff;
+  }
+
+  /* Indicator dropdown */
+  .ind-dropdown {
+    position: relative;
+    display: inline-block;
+  }
+  .ind-btn {
+    background: #2a2e39;
+    color: #d1d4dc;
+    border: 1px solid #363a45;
+    border-radius: 4px;
+    padding: 4px 12px;
+    font-size: 0.82rem;
+    cursor: pointer;
+  }
+  .ind-btn:hover { border-color: #2962ff; }
+  .ind-panel {
+    display: none;
+    position: absolute;
+    top: 100%;
+    left: 0;
+    z-index: 100;
+    background: #1e222d;
+    border: 1px solid #363a45;
+    border-radius: 6px;
+    padding: 8px 0;
+    min-width: 220px;
+    max-height: 400px;
+    overflow-y: auto;
+    box-shadow: 0 8px 24px rgba(0,0,0,0.5);
+  }
+  .ind-panel.open { display: block; }
+  .ind-panel .ind-group {
+    padding: 4px 12px 2px;
+    color: #787b86;
+    font-size: 0.72rem;
+    text-transform: uppercase;
+    letter-spacing: 0.5px;
+  }
+  .ind-panel label {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    padding: 4px 12px;
+    color: #d1d4dc;
+    font-size: 0.82rem;
+    cursor: pointer;
+    margin: 0;
+  }
+  .ind-panel label:hover { background: #2a2e39; }
+  .ind-panel input[type="checkbox"] {
+    accent-color: #2962ff;
+  }
+  .ind-sep {
+    border-top: 1px solid #2a2e39;
+    margin: 4px 0;
+  }
+
+  /* AVWAP anchor button */
+  .avwap-btn {
+    background: #2a2e39;
+    color: #d1d4dc;
+    border: 1px solid #363a45;
+    border-radius: 4px;
+    padding: 4px 10px;
+    font-size: 0.78rem;
+    cursor: pointer;
+    display: none;
+  }
+  .avwap-btn.active {
+    background: #2962ff;
+    border-color: #2962ff;
+    color: #fff;
+  }
+  .avwap-btn.visible { display: inline-block; }
+
+  /* Chart containers */
+  #main-chart-container { width: 100%; }
+  .sub-chart-container {
+    width: 100%;
+    border-top: 1px solid #2a2e39;
+  }
+  .sub-chart-label {
+    position: absolute;
+    top: 4px;
+    left: 8px;
+    color: #787b86;
+    font-size: 0.72rem;
+    z-index: 10;
+    pointer-events: none;
+  }
+  .sub-chart-wrapper {
+    position: relative;
+    display: none;
+  }
+  .sub-chart-wrapper.visible { display: block; }
+
+  /* No-data message */
+  .no-data-msg {
+    color: #787b86;
+    text-align: center;
+    padding: 80px 20px;
+    font-size: 1.1rem;
+  }
+</style>
+
+<div class="chart-wrapper">
+  <!-- Toolbar -->
+  <div class="chart-toolbar">
+    <form method="get" action="/chart" class="d-flex gap-2 align-items-center" style="margin:0;">
+      <label>Instrument</label>
+      <select name="instrument" onchange="this.form.submit()">
         {% for inst in instruments %}
-        <option value="{{ inst }}" {% if inst == instrument %}selected{% endif %}>{{ inst }}</option>
+        <option value="{{ inst }}" {% if inst == instrument %}selected{% endif %}>{{ inst.replace('_', '/') }}</option>
         {% endfor %}
       </select>
-      <select name="granularity" class="form-select form-select-sm" onchange="this.form.submit()">
+      <label>Timeframe</label>
+      <select name="granularity" onchange="this.form.submit()">
         {% for g in granularities %}
         <option value="{{ g }}" {% if g == granularity %}selected{% endif %}>{{ g }}</option>
         {% endfor %}
       </select>
     </form>
+
+    <!-- Indicators dropdown -->
+    <div class="ind-dropdown">
+      <button type="button" class="ind-btn" id="ind-toggle">Indicators</button>
+      <div class="ind-panel" id="ind-panel">
+        <div class="ind-group">Overlays</div>
+        <label><input type="checkbox" data-ind="sma_3"> SMA 3</label>
+        <label><input type="checkbox" data-ind="sma_20"> SMA 20</label>
+        <label><input type="checkbox" data-ind="sma_50"> SMA 50</label>
+        <label><input type="checkbox" data-ind="sma_100"> SMA 100</label>
+        <div class="ind-sep"></div>
+        <label><input type="checkbox" data-ind="ema_20"> EMA 20</label>
+        <label><input type="checkbox" data-ind="ema_50"> EMA 50</label>
+        <label><input type="checkbox" data-ind="ema_100"> EMA 100</label>
+        <label><input type="checkbox" data-ind="ema_200"> EMA 200</label>
+        <div class="ind-sep"></div>
+        <label><input type="checkbox" data-ind="session_vwap"> Session VWAP</label>
+        <label><input type="checkbox" data-ind="anchored_vwap"> Anchored VWAP</label>
+        <div class="ind-sep"></div>
+        <div class="ind-group">Chart</div>
+        <label><input type="checkbox" data-ind="volume" checked> Volume</label>
+        <label><input type="checkbox" data-ind="pivots" checked> Pivot Points</label>
+        <div class="ind-sep"></div>
+        <div class="ind-group">Separate Panes</div>
+        <label><input type="checkbox" data-ind="rsi_14"> RSI 14</label>
+        <label><input type="checkbox" data-ind="atr_14"> ATR 14</label>
+      </div>
+    </div>
+
+    <button type="button" class="avwap-btn" id="avwap-btn" title="Click a candle to set AVWAP anchor">Set AVWAP Anchor</button>
   </div>
+
+  {% if error_msg %}
+  <div class="no-data-msg">{{ error_msg }}</div>
+  {% else %}
+  <!-- Main chart -->
+  <div id="main-chart-container"></div>
+
+  <!-- Sub-chart panes -->
+  <div class="sub-chart-wrapper" id="rsi-wrapper">
+    <div class="sub-chart-label">RSI 14</div>
+    <div class="sub-chart-container" id="rsi-chart-container"></div>
+  </div>
+  <div class="sub-chart-wrapper" id="atr-wrapper">
+    <div class="sub-chart-label">ATR 14</div>
+    <div class="sub-chart-container" id="atr-chart-container"></div>
+  </div>
+  {% endif %}
 </div>
 
-{% if error_msg %}
-<div class="alert alert-warning">{{ error_msg }}</div>
-{% endif %}
-
-<div class="card">
-  <div class="card-body p-2">
-    <div id="chart" style="width:100%; height:600px;"></div>
-  </div>
-</div>
-
-<script src="https://cdn.plot.ly/plotly-2.35.2.min.js" charset="utf-8"></script>
+<script src="https://unpkg.com/lightweight-charts@4/dist/lightweight-charts.standalone.production.js"></script>
 <script>
 (function() {
-  var ohlc = {{ ohlc_json | safe }};
+  var ohlcRaw = {{ ohlc_json | safe }};
   var pivots = {{ pivot_json | safe }};
-  var smas = {{ sma_json | safe }};
+  var indicators = {{ indicators_json | safe }};
 
-  if (!ohlc || ohlc.length === 0) {
-    document.getElementById('chart').innerHTML =
-      '<p class="text-muted text-center mt-5">No candle data available for {{ instrument }} / {{ granularity }}.</p>';
+  if (!ohlcRaw || ohlcRaw.length === 0) {
+    var container = document.getElementById('main-chart-container');
+    if (container) container.innerHTML = '<div class="no-data-msg">No candle data available for {{ instrument }} / {{ granularity }}.</div>';
     return;
   }
 
-  var times  = ohlc.map(function(r) { return r.time; });
-  var opens  = ohlc.map(function(r) { return r.open; });
-  var highs  = ohlc.map(function(r) { return r.high; });
-  var lows   = ohlc.map(function(r) { return r.low; });
-  var closes = ohlc.map(function(r) { return r.close; });
+  // ---- Time conversion: ISO string -> Unix timestamp ----
+  function toUnix(isoStr) {
+    return Math.floor(new Date(isoStr).getTime() / 1000);
+  }
 
-  var traces = [{
-    x: times, open: opens, high: highs, low: lows, close: closes,
-    type: 'candlestick',
-    increasing: {line: {color: '#198754'}},
-    decreasing: {line: {color: '#dc3545'}},
-    name: '{{ instrument }}'
-  }];
-
-  // SMA overlay lines
-  var smaColors = {
-    'sma_3':   '#ff9800',
-    'sma_20':  '#9c27b0',
-    'sma_21':  '#673ab7',
-    'sma_50':  '#00bcd4',
-    'sma_100': '#e91e63'
-  };
-  Object.keys(smas).forEach(function(key) {
-    traces.push({
-      x: times,
-      y: smas[key],
-      type: 'scatter',
-      mode: 'lines',
-      name: key.toUpperCase().replace('_', ' '),
-      line: {color: smaColors[key] || '#999', width: 1.5},
-      connectgaps: false
-    });
+  // ---- Prepare OHLC data ----
+  var ohlcData = ohlcRaw.map(function(r) {
+    return { time: toUnix(r.time), open: r.open, high: r.high, low: r.low, close: r.close };
   });
 
-  // Pivot level lines
-  var levelDefs = [
-    {key: 'r3', color: '#b71c1c', label: 'R3'},
-    {key: 'r2', color: '#e53935', label: 'R2'},
-    {key: 'r1', color: '#ef9a9a', label: 'R1'},
-    {key: 'pivot', color: '#1565c0', label: 'Pivot'},
-    {key: 's1', color: '#a5d6a7', label: 'S1'},
-    {key: 's2', color: '#43a047', label: 'S2'},
-    {key: 's3', color: '#1b5e20', label: 'S3'}
+  // ---- Create main chart ----
+  var mainContainer = document.getElementById('main-chart-container');
+  var chartHeight = Math.max(500, window.innerHeight - 260);
+
+  var chart = LightweightCharts.createChart(mainContainer, {
+    width: mainContainer.clientWidth,
+    height: chartHeight,
+    layout: { background: { color: '#131722' }, textColor: '#d1d4dc' },
+    grid: { vertLines: { color: '#1e222d' }, horzLines: { color: '#1e222d' } },
+    crosshair: { mode: LightweightCharts.CrosshairMode.Normal },
+    rightPriceScale: { borderColor: '#2B2B43' },
+    timeScale: { borderColor: '#2B2B43', timeVisible: true, secondsVisible: false },
+  });
+
+  // Candlestick series
+  var candleSeries = chart.addCandlestickSeries({
+    upColor: '#26a69a',
+    downColor: '#ef5350',
+    borderUpColor: '#26a69a',
+    borderDownColor: '#ef5350',
+    wickUpColor: '#26a69a',
+    wickDownColor: '#ef5350',
+  });
+  candleSeries.setData(ohlcData);
+
+  // Current price line
+  var lastCandle = ohlcData[ohlcData.length - 1];
+  if (lastCandle) {
+    candleSeries.createPriceLine({
+      price: lastCandle.close,
+      color: lastCandle.close >= lastCandle.open ? '#26a69a' : '#ef5350',
+      lineWidth: 1,
+      lineStyle: LightweightCharts.LineStyle.Dotted,
+      axisLabelVisible: true,
+      title: '',
+    });
+  }
+
+  // ---- Series management ----
+  var activeSeries = new Map();
+
+  // Indicator colors
+  var indColors = {
+    sma_3: '#ff9800', sma_20: '#ab47bc', sma_50: '#00bcd4', sma_100: '#e91e63',
+    ema_20: '#ffeb3b', ema_50: '#4caf50', ema_100: '#2196f3', ema_200: '#ff5722',
+    session_vwap: '#00e5ff', anchored_vwap: '#ff4081',
+  };
+
+  function convertIndicatorData(dataArr) {
+    return dataArr.map(function(d) {
+      var obj = { time: toUnix(d.time), value: d.value };
+      if (d.color) obj.color = d.color;
+      return obj;
+    });
+  }
+
+  // ---- Volume series ----
+  var volumeSeries = null;
+  function addVolume() {
+    if (volumeSeries) return;
+    if (!indicators.volume) return;
+    volumeSeries = chart.addHistogramSeries({
+      priceFormat: { type: 'volume' },
+      priceScaleId: 'volume',
+    });
+    chart.priceScale('volume').applyOptions({
+      scaleMargins: { top: 0.8, bottom: 0 },
+    });
+    volumeSeries.setData(convertIndicatorData(indicators.volume));
+  }
+  function removeVolume() {
+    if (!volumeSeries) return;
+    chart.removeSeries(volumeSeries);
+    volumeSeries = null;
+  }
+
+  // ---- Pivot lines ----
+  var pivotLines = [];
+  var pivotDefs = [
+    { key: 'r3', color: '#b71c1c', label: 'R3' },
+    { key: 'r2', color: '#e53935', label: 'R2' },
+    { key: 'r1', color: '#ef9a9a', label: 'R1' },
+    { key: 'pivot', color: '#42a5f5', label: 'Pivot' },
+    { key: 's1', color: '#a5d6a7', label: 'S1' },
+    { key: 's2', color: '#43a047', label: 'S2' },
+    { key: 's3', color: '#1b5e20', label: 'S3' },
   ];
 
-  var shapes = [];
-  var annotations = [];
-
-  levelDefs.forEach(function(lv) {
-    var val = pivots[lv.key];
-    if (val == null) return;
-    shapes.push({
-      type: 'line',
-      x0: times[0], x1: times[times.length - 1],
-      y0: val, y1: val,
-      line: {color: lv.color, width: 1.5, dash: lv.key === 'pivot' ? 'solid' : 'dash'}
+  function addPivots() {
+    if (pivotLines.length > 0) return;
+    pivotDefs.forEach(function(lv) {
+      var val = pivots[lv.key];
+      if (val == null) return;
+      var line = candleSeries.createPriceLine({
+        price: val,
+        color: lv.color,
+        lineWidth: 1,
+        lineStyle: lv.key === 'pivot' ? LightweightCharts.LineStyle.Solid : LightweightCharts.LineStyle.Dashed,
+        axisLabelVisible: true,
+        title: lv.label,
+      });
+      pivotLines.push(line);
     });
-    annotations.push({
-      x: times[times.length - 1], y: val,
-      xanchor: 'left', yanchor: 'middle',
-      text: '  ' + lv.label + ' ' + val.toFixed(5),
-      showarrow: false,
-      font: {size: 11, color: lv.color}
+  }
+  function removePivots() {
+    pivotLines.forEach(function(line) {
+      candleSeries.removePriceLine(line);
+    });
+    pivotLines = [];
+  }
+
+  // ---- Overlay indicator (line on main chart) ----
+  function addOverlay(key) {
+    if (activeSeries.has(key)) return;
+    var data = indicators[key];
+    if (!data || data.length === 0) return;
+    var series = chart.addLineSeries({
+      color: indColors[key] || '#999',
+      lineWidth: 1.5,
+      priceLineVisible: false,
+      lastValueVisible: false,
+      crosshairMarkerVisible: false,
+    });
+    series.setData(convertIndicatorData(data));
+    activeSeries.set(key, series);
+  }
+  function removeOverlay(key) {
+    if (!activeSeries.has(key)) return;
+    chart.removeSeries(activeSeries.get(key));
+    activeSeries.delete(key);
+  }
+
+  // ---- Sub-chart panes (RSI, ATR) ----
+  var subCharts = {};
+
+  function createSubChart(key, containerId, wrapperId, options) {
+    if (subCharts[key]) return;
+    var wrapper = document.getElementById(wrapperId);
+    var container = document.getElementById(containerId);
+    wrapper.classList.add('visible');
+
+    var subChart = LightweightCharts.createChart(container, {
+      width: container.clientWidth,
+      height: 150,
+      layout: { background: { color: '#131722' }, textColor: '#d1d4dc' },
+      grid: { vertLines: { color: '#1e222d' }, horzLines: { color: '#1e222d' } },
+      crosshair: { mode: LightweightCharts.CrosshairMode.Normal },
+      rightPriceScale: { borderColor: '#2B2B43' },
+      timeScale: { borderColor: '#2B2B43', timeVisible: true, secondsVisible: false },
+    });
+
+    var series = subChart.addLineSeries({
+      color: options.color || '#7c4dff',
+      lineWidth: 1.5,
+      priceLineVisible: false,
+      lastValueVisible: true,
+    });
+
+    var data = indicators[key];
+    if (data) series.setData(convertIndicatorData(data));
+
+    // Add reference lines (e.g., overbought/oversold for RSI)
+    if (options.refLines) {
+      options.refLines.forEach(function(rl) {
+        series.createPriceLine({
+          price: rl.price,
+          color: rl.color || '#787b86',
+          lineWidth: 1,
+          lineStyle: LightweightCharts.LineStyle.Dashed,
+          axisLabelVisible: true,
+          title: rl.label || '',
+        });
+      });
+    }
+
+    // Sync time scale with main chart
+    chart.timeScale().subscribeVisibleLogicalRangeChange(function(range) {
+      if (range) subChart.timeScale().setVisibleLogicalRange(range);
+    });
+    subChart.timeScale().subscribeVisibleLogicalRangeChange(function(range) {
+      if (range) chart.timeScale().setVisibleLogicalRange(range);
+    });
+
+    // Sync crosshair
+    chart.subscribeCrosshairMove(function(param) {
+      if (param.time) {
+        subChart.setCrosshairPosition(NaN, param.time, series);
+      } else {
+        subChart.clearCrosshairPosition();
+      }
+    });
+    subChart.subscribeCrosshairMove(function(param) {
+      if (param.time) {
+        chart.setCrosshairPosition(NaN, param.time, candleSeries);
+      } else {
+        chart.clearCrosshairPosition();
+      }
+    });
+
+    subCharts[key] = { chart: subChart, series: series, wrapper: wrapper };
+  }
+
+  function removeSubChart(key) {
+    if (!subCharts[key]) return;
+    subCharts[key].chart.remove();
+    subCharts[key].wrapper.classList.remove('visible');
+    delete subCharts[key];
+  }
+
+  // ---- Anchored VWAP ----
+  var avwapActive = false;
+  var avwapSeries = null;
+
+  function computeAnchoredVWAP(anchorIndex) {
+    var result = [];
+    var cumPV = 0;
+    var cumVol = 0;
+    for (var i = anchorIndex; i < ohlcRaw.length; i++) {
+      var r = ohlcRaw[i];
+      var tp = (r.high + r.low + r.close) / 3.0;
+      var vol = r.volume || 0;
+      cumPV += tp * vol;
+      cumVol += vol;
+      if (cumVol > 0) {
+        result.push({ time: toUnix(r.time), value: cumPV / cumVol });
+      }
+    }
+    return result;
+  }
+
+  function handleAvwapClick(param) {
+    if (!avwapActive) return;
+    if (!param.time) return;
+
+    // Find the bar index
+    var targetTime = param.time;
+    var idx = -1;
+    for (var i = 0; i < ohlcRaw.length; i++) {
+      if (toUnix(ohlcRaw[i].time) === targetTime) { idx = i; break; }
+    }
+    if (idx < 0) return;
+
+    // Remove existing AVWAP
+    if (avwapSeries) {
+      chart.removeSeries(avwapSeries);
+      avwapSeries = null;
+    }
+
+    // Compute and draw
+    var avwapData = computeAnchoredVWAP(idx);
+    if (avwapData.length > 0) {
+      avwapSeries = chart.addLineSeries({
+        color: indColors.anchored_vwap,
+        lineWidth: 2,
+        priceLineVisible: false,
+        lastValueVisible: true,
+        crosshairMarkerVisible: false,
+      });
+      avwapSeries.setData(avwapData);
+    }
+
+    // Deactivate anchor mode
+    avwapActive = false;
+    document.getElementById('avwap-btn').classList.remove('active');
+  }
+
+  chart.subscribeCrosshairMove(function() {}); // ensure subscription exists
+  chart.subscribeClick(handleAvwapClick);
+
+  // ---- Indicator toggle logic ----
+  function toggleIndicator(key, enabled) {
+    switch(key) {
+      case 'volume':
+        enabled ? addVolume() : removeVolume();
+        break;
+      case 'pivots':
+        enabled ? addPivots() : removePivots();
+        break;
+      case 'rsi_14':
+        if (enabled) {
+          createSubChart('rsi_14', 'rsi-chart-container', 'rsi-wrapper', {
+            color: '#7c4dff',
+            refLines: [
+              { price: 70, color: '#ef5350', label: 'OB' },
+              { price: 30, color: '#26a69a', label: 'OS' },
+              { price: 50, color: '#787b86', label: '' },
+            ],
+          });
+        } else {
+          removeSubChart('rsi_14');
+        }
+        break;
+      case 'atr_14':
+        if (enabled) {
+          createSubChart('atr_14', 'atr-chart-container', 'atr-wrapper', {
+            color: '#ffab40',
+            refLines: [],
+          });
+        } else {
+          removeSubChart('atr_14');
+        }
+        break;
+      case 'anchored_vwap':
+        var btn = document.getElementById('avwap-btn');
+        if (enabled) {
+          btn.classList.add('visible');
+        } else {
+          btn.classList.remove('visible');
+          btn.classList.remove('active');
+          avwapActive = false;
+          if (avwapSeries) {
+            chart.removeSeries(avwapSeries);
+            avwapSeries = null;
+          }
+        }
+        break;
+      default:
+        // Overlay indicators (SMA, EMA, session VWAP)
+        enabled ? addOverlay(key) : removeOverlay(key);
+    }
+  }
+
+  // ---- Initialize defaults (volume + pivots on) ----
+  addVolume();
+  addPivots();
+
+  // ---- Wire up checkbox events ----
+  document.querySelectorAll('#ind-panel input[type="checkbox"]').forEach(function(cb) {
+    cb.addEventListener('change', function() {
+      toggleIndicator(this.dataset.ind, this.checked);
     });
   });
 
-  var layout = {
-    title: '{{ instrument }}  {{ granularity }}',
-    xaxis: {
-      type: 'category',
-      rangeslider: {visible: false},
-      nticks: 12
-    },
-    yaxis: {title: 'Price'},
-    shapes: shapes,
-    annotations: annotations,
-    margin: {l: 60, r: 100, t: 40, b: 40},
-    paper_bgcolor: '#fff',
-    plot_bgcolor: '#f8f9fa'
-  };
+  // ---- Indicator dropdown toggle ----
+  var indToggle = document.getElementById('ind-toggle');
+  var indPanel = document.getElementById('ind-panel');
+
+  indToggle.addEventListener('click', function(e) {
+    e.stopPropagation();
+    indPanel.classList.toggle('open');
+  });
+
+  // Close dropdown when clicking outside
+  document.addEventListener('click', function(e) {
+    if (!indPanel.contains(e.target) && e.target !== indToggle) {
+      indPanel.classList.remove('open');
+    }
+  });
+
+  // Stop propagation inside panel so clicks don't close it
+  indPanel.addEventListener('click', function(e) {
+    e.stopPropagation();
+  });
+
+  // ---- AVWAP anchor button ----
+  document.getElementById('avwap-btn').addEventListener('click', function() {
+    avwapActive = !avwapActive;
+    this.classList.toggle('active', avwapActive);
+  });
+
+  // ---- Responsive resize ----
+  function handleResize() {
+    var newWidth = mainContainer.clientWidth;
+    var newHeight = Math.max(500, window.innerHeight - 260);
+    chart.applyOptions({ width: newWidth, height: newHeight });
+
+    Object.keys(subCharts).forEach(function(key) {
+      var sc = subCharts[key];
+      sc.chart.applyOptions({ width: newWidth });
+    });
+  }
+
+  window.addEventListener('resize', handleResize);
+
+  // Fit content on load
+  chart.timeScale().fitContent();
 
-  Plotly.newPlot('chart', traces, layout, {responsive: true});
 })();
 </script>
 {% endblock %}