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>

src/strategies_pkg/s1_ma_breakout.py

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+"""
+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)