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XauBot/backtests/backtest_23_confidence_weight.py
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GifariKemal e8355b3f62 feat: add 5 dashboard features — dark mode, trade history, backtests, model insights, alerts 
- Dark mode: class-based theme toggle with localStorage persistence and flash prevention
- Trade History (/trades): paginated table, stats cards, equity curve chart with DB API endpoints
- Backtest Viewer (/backtests): log parser for 35 backtest results, sidebar + detail + comparison tabs
- Model Insights: dashboard card + dialog showing feature importance, regime distribution, training history
- Alert/Signal Log (/alerts): signal stats, filterable table with execution tracking
- API: 8 new endpoints with psycopg2 DB connection pool
- Dark mode sweep across books page, about dialog, and all dashboard components
- Architecture docs rewritten with Mermaid diagrams (23 docs)
- README and FEATURES.md rewritten bilingual (Indonesian + English)
- main_live.py: write model_metrics.json on startup and retrain

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-09 05:46:54 +07:00

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"""
Backtest #23 — Confidence Weight Rebalance
============================================
Base: SMC-Only v4 (Backtest #1)
Modification: Rebalance SMC confidence weights to prioritize FVG/OB over BOS
Current weights:
base: 0.40
structure_aligned: 0.15
bos_choch: 0.12
fvg: 0.08
ob: 0.10
trend_strength: 0.10
fresh_level: 0.05
Hypothesis: FVG and OB are more reliable reversal signals than BOS.
Rebalancing should improve lot sizing decisions and potentially filter quality.
Configs:
A: Boost FVG+OB (fvg=0.14, ob=0.14, bos=0.06)
B: FVG dominant (fvg=0.18, ob=0.10, bos=0.06)
C: OB dominant (ob=0.18, fvg=0.10, bos=0.06)
D: Require FVG|OB (entry filter: must have FVG or OB to enter)
E: High min conf (require confidence >= 0.55 to enter)
Usage:
python backtests/backtest_23_confidence_weight.py
"""
import polars as pl
import pandas as pd
import numpy as np
from datetime import datetime, timedelta, date
from typing import Dict, List, Tuple, Optional
from dataclasses import dataclass, field
from enum import Enum
import sys
import os
from zoneinfo import ZoneInfo
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from src.mt5_connector import MT5Connector
from src.smc_polars import SMCAnalyzer, SMCSignal
from src.feature_eng import FeatureEngineer
from src.regime_detector import MarketRegimeDetector, MarketRegime
from src.ml_model import TradingModel
from src.config import get_config
from src.dynamic_confidence import DynamicConfidenceManager, create_dynamic_confidence, MarketQuality
from loguru import logger
logger.remove()
logger.add(sys.stderr, level="WARNING")
WIB = ZoneInfo("Asia/Jakarta")
# ─── Enums & Dataclasses ──────────────────────────────────────
class TradeResult(Enum):
WIN = "WIN"
LOSS = "LOSS"
BREAKEVEN = "BREAKEVEN"
class ExitReason(Enum):
TAKE_PROFIT = "take_profit"
SMART_TP = "smart_tp"
PEAK_PROTECT = "peak_protect"
EARLY_EXIT = "early_exit"
EARLY_CUT = "early_cut"
MAX_LOSS = "max_loss"
STALL = "stall"
TREND_REVERSAL = "trend_reversal"
TIMEOUT = "timeout"
WEEKEND_CLOSE = "weekend_close"
TRAILING_SL = "trailing_sl"
BREAKEVEN_EXIT = "breakeven_exit"
DAILY_LIMIT = "daily_limit"
REGIME_DANGER = "regime_danger"
MARKET_SIGNAL = "market_signal"
class TradingMode(Enum):
NORMAL = "normal"
RECOVERY = "recovery"
PROTECTED = "protected"
STOPPED = "stopped"
@dataclass
class SimulatedTrade:
ticket: int
entry_time: datetime
exit_time: datetime
direction: str
entry_price: float
exit_price: float
stop_loss: float
take_profit: float
lot_size: float
profit_usd: float
profit_pips: float
result: TradeResult
exit_reason: ExitReason
smc_confidence: float
regime: str
session: str
signal_reason: str
has_bos: bool = False
has_choch: bool = False
has_fvg: bool = False
has_ob: bool = False
atr_at_entry: float = 0.0
rr_ratio: float = 0.0
trading_mode: str = "normal"
@dataclass
class BacktestStats:
total_trades: int = 0
wins: int = 0
losses: int = 0
total_profit: float = 0.0
total_loss: float = 0.0
max_drawdown: float = 0.0
max_drawdown_usd: float = 0.0
win_rate: float = 0.0
profit_factor: float = 0.0
avg_win: float = 0.0
avg_loss: float = 0.0
avg_trade: float = 0.0
expectancy: float = 0.0
sharpe_ratio: float = 0.0
trades: List[SimulatedTrade] = field(default_factory=list)
equity_curve: List[float] = field(default_factory=list)
avoided_signals: int = 0
daily_limit_stops: int = 0
recovery_mode_trades: int = 0
filtered_signals: int = 0
# ─── Confidence Weight Backtest ───────────────────────────────
class ConfidenceWeightBacktest:
"""SMC-Only + custom confidence weights / entry filters."""
def __init__(
self,
capital: float = 5000.0,
max_daily_loss_percent: float = 5.0,
max_loss_per_trade_percent: float = 1.0,
base_lot_size: float = 0.01,
max_lot_size: float = 0.02,
recovery_lot_size: float = 0.01,
trend_reversal_threshold: float = 0.75,
max_concurrent_positions: int = 2,
breakeven_pips: float = 30.0,
trail_start_pips: float = 50.0,
trail_step_pips: float = 30.0,
min_profit_to_protect: float = 5.0,
max_drawdown_from_peak: float = 50.0,
trade_cooldown_bars: int = 10,
trend_reversal_mult: float = 0.6,
# Confidence weight overrides
w_bos: float = 0.12,
w_fvg: float = 0.08,
w_ob: float = 0.10,
# Entry filter
require_fvg_or_ob: bool = False,
min_confidence: float = 0.0, # 0 = no min filter
):
self.capital = capital
self.max_daily_loss_usd = capital * (max_daily_loss_percent / 100)
self.max_loss_per_trade = capital * (max_loss_per_trade_percent / 100)
self.base_lot_size = base_lot_size
self.max_lot_size = max_lot_size
self.recovery_lot_size = recovery_lot_size
self.trend_reversal_threshold = trend_reversal_threshold
self.max_concurrent_positions = max_concurrent_positions
self.breakeven_pips = breakeven_pips
self.trail_start_pips = trail_start_pips
self.trail_step_pips = trail_step_pips
self.min_profit_to_protect = min_profit_to_protect
self.max_drawdown_from_peak = max_drawdown_from_peak
self.trade_cooldown_bars = trade_cooldown_bars
self.trend_reversal_mult = trend_reversal_mult
# Custom weights
self.w_bos = w_bos
self.w_fvg = w_fvg
self.w_ob = w_ob
self.require_fvg_or_ob = require_fvg_or_ob
self.min_confidence = min_confidence
config = get_config()
self.smc = SMCAnalyzer(
swing_length=config.smc.swing_length,
ob_lookback=config.smc.ob_lookback,
)
self.features = FeatureEngineer()
self.dynamic_confidence = create_dynamic_confidence()
self.ml_model = TradingModel(model_path="models/xgboost_model.pkl")
try:
self.ml_model.load()
print(" ML model loaded (for exit evaluation)")
except Exception:
print(" [WARN] ML model not loaded")
self.regime_detector = MarketRegimeDetector(model_path="models/hmm_regime.pkl")
try:
self.regime_detector.load()
except Exception:
print(" [WARN] HMM model not loaded")
self._ticket_counter = 2230000
def _recalculate_confidence(
self,
signal_type: str,
market_structure: int,
has_break: bool,
has_fvg: bool,
has_ob: bool,
df_slice: pl.DataFrame,
) -> float:
"""Recalculate confidence with custom weights."""
conf = 0.40 # base
# Structure alignment
structure_aligned = (
(signal_type == "BUY" and market_structure == 1) or
(signal_type == "SELL" and market_structure == -1)
)
if structure_aligned:
conf += 0.15
# BOS/CHoCH (custom weight)
if has_break:
conf += self.w_bos
# FVG (custom weight)
if has_fvg:
conf += self.w_fvg
# OB (custom weight)
if has_ob:
conf += self.w_ob
# Trend strength
if df_slice is not None and "bos" in df_slice.columns:
recent_bos = df_slice.tail(20)["bos"].to_list()
if signal_type == "BUY":
bos_count = sum(1 for b in recent_bos if b == 1)
else:
bos_count = sum(1 for b in recent_bos if b == -1)
if bos_count >= 2:
conf += 0.10
return min(conf, 0.85)
# ── Session filter (synced) ──
def _get_session_from_time(self, dt: datetime) -> Tuple[str, bool, float]:
if dt.tzinfo is None:
dt = dt.replace(tzinfo=ZoneInfo("UTC"))
wib_time = dt.astimezone(WIB)
hour = wib_time.hour
if 6 <= hour < 15:
return "Sydney-Tokyo", True, 0.5
elif 15 <= hour < 16:
return "Tokyo-London Overlap", True, 0.75
elif 16 <= hour < 19:
return "London Early", True, 0.8
elif 19 <= hour < 24:
return "London-NY Overlap (Golden)", True, 1.0
elif 0 <= hour < 4:
return "NY Session", True, 0.9
else:
return "Off Hours", False, 0.0
def _hours_to_golden(self, dt: datetime) -> float:
if dt.tzinfo is None:
dt = dt.replace(tzinfo=ZoneInfo("UTC"))
wib = dt.astimezone(WIB)
if 19 <= wib.hour < 24:
return 0
target = wib.replace(hour=19, minute=0, second=0, microsecond=0)
if wib.hour >= 19:
target += timedelta(days=1)
return max(0, (target - wib).total_seconds() / 3600)
def _is_near_weekend_close(self, dt: datetime) -> bool:
if dt.tzinfo is None:
dt = dt.replace(tzinfo=ZoneInfo("UTC"))
wib = dt.astimezone(WIB)
if wib.weekday() == 5 and wib.hour >= 4 and wib.minute >= 30:
return True
return False
# ── Lot sizing (synced) ──
def _calculate_lot_size(self, confidence, regime, trading_mode, session_mult):
if trading_mode == TradingMode.STOPPED:
return 0
lot = self.base_lot_size
if trading_mode in (TradingMode.RECOVERY, TradingMode.PROTECTED):
lot = self.recovery_lot_size
else:
if confidence >= 0.65:
lot = self.max_lot_size
elif confidence >= 0.55:
lot = self.base_lot_size
else:
lot = self.recovery_lot_size
if regime.lower() in ["high_volatility", "crisis"]:
lot = self.recovery_lot_size
lot = max(0.01, lot * session_mult)
return round(lot, 2)
# ── Full exit simulation (synced with baseline #1) ──
def _simulate_trade_exit(
self, df, entry_idx, direction, entry_price, take_profit, stop_loss,
lot_size, daily_loss_so_far, feature_cols, max_bars=100,
) -> Tuple[float, float, ExitReason, int, float]:
pip_value = 10
highs = df["high"].to_list()
lows = df["low"].to_list()
closes = df["close"].to_list()
times = df["time"].to_list()
atr = 12.0
if "atr" in df.columns:
atr_list = df["atr"].to_list()
if entry_idx < len(atr_list) and atr_list[entry_idx] is not None:
atr = atr_list[entry_idx]
reversal_momentum_threshold = atr * self.trend_reversal_mult
min_loss_for_reversal_exit = atr * 0.8
profit_history = []
price_history = []
peak_profit = 0.0
stall_count = 0
reversal_warnings = 0
current_sl = stop_loss
breakeven_moved = False
if direction == "BUY":
target_tp_profit = (take_profit - entry_price) / 0.1 * pip_value * lot_size
else:
target_tp_profit = (entry_price - take_profit) / 0.1 * pip_value * lot_size
cached_ml_signal = ""
cached_ml_confidence = 0.5
for i in range(entry_idx + 1, min(entry_idx + max_bars, len(df))):
high = highs[i]
low = lows[i]
close = closes[i]
current_time = times[i]
if direction == "BUY":
current_pips = (close - entry_price) / 0.1
pip_profit_from_entry = (close - entry_price) / 0.1
else:
current_pips = (entry_price - close) / 0.1
pip_profit_from_entry = (entry_price - close) / 0.1
current_profit = current_pips * pip_value * lot_size
profit_history.append(current_profit)
price_history.append(close)
if current_profit > peak_profit:
peak_profit = current_profit
bars_since_entry = i - entry_idx
if bars_since_entry % 4 == 0 and self.ml_model.fitted:
try:
df_slice = df.head(i + 1)
ml_pred = self.ml_model.predict(df_slice, feature_cols)
cached_ml_signal = ml_pred.signal
cached_ml_confidence = ml_pred.confidence
except Exception:
pass
momentum = 0.0
if len(profit_history) >= 3:
recent = profit_history[-5:] if len(profit_history) >= 5 else profit_history
profit_change = recent[-1] - recent[0]
momentum = max(-100, min(100, (profit_change / 10) * 50))
profit_growing = momentum > 0
# A) SmartPositionManager
if direction == "BUY" and high >= take_profit:
pips = (take_profit - entry_price) / 0.1
return pips * pip_value * lot_size, pips, ExitReason.TAKE_PROFIT, i, take_profit
elif direction == "SELL" and low <= take_profit:
pips = (entry_price - take_profit) / 0.1
return pips * pip_value * lot_size, pips, ExitReason.TAKE_PROFIT, i, take_profit
if breakeven_moved and current_sl > 0:
if direction == "BUY" and low <= current_sl:
pips = (current_sl - entry_price) / 0.1
reason = ExitReason.TRAILING_SL if pip_profit_from_entry >= self.trail_start_pips else ExitReason.BREAKEVEN_EXIT
return pips * pip_value * lot_size, pips, reason, i, current_sl
elif direction == "SELL" and high >= current_sl:
pips = (entry_price - current_sl) / 0.1
reason = ExitReason.TRAILING_SL if pip_profit_from_entry >= self.trail_start_pips else ExitReason.BREAKEVEN_EXIT
return pips * pip_value * lot_size, pips, reason, i, current_sl
if pip_profit_from_entry >= self.breakeven_pips and not breakeven_moved:
if direction == "BUY":
current_sl = entry_price + 2
else:
current_sl = entry_price - 2
breakeven_moved = True
if pip_profit_from_entry >= self.trail_start_pips:
trail_distance = self.trail_step_pips * 0.1
if direction == "BUY":
new_trail_sl = close - trail_distance
if new_trail_sl > current_sl:
current_sl = new_trail_sl
else:
new_trail_sl = close + trail_distance
if current_sl == 0 or new_trail_sl < current_sl:
current_sl = new_trail_sl
if peak_profit > self.min_profit_to_protect:
drawdown_pct = ((peak_profit - current_profit) / peak_profit) * 100 if peak_profit > 0 else 0
if drawdown_pct > self.max_drawdown_from_peak:
return current_profit, current_pips, ExitReason.PEAK_PROTECT, i, close
if bars_since_entry % 5 == 0 and bars_since_entry >= 5:
if i >= 20:
ma_fast = np.mean(closes[i-4:i+1])
ma_slow = np.mean(closes[i-19:i+1])
trend = "NEUTRAL"
if ma_fast > ma_slow * 1.001:
trend = "BULLISH"
elif ma_fast < ma_slow * 0.999:
trend = "BEARISH"
roc = (closes[i] / closes[max(0,i-4)] - 1) * 100
mom_dir = "BULLISH" if roc > 0.3 else ("BEARISH" if roc < -0.3 else "NEUTRAL")
rsi_val = None
if "rsi" in df.columns:
rsi_list = df["rsi"].to_list()
if i < len(rsi_list):
rsi_val = rsi_list[i]
urgency = 0
should_exit = False
if cached_ml_confidence > 0.75:
if direction == "BUY" and cached_ml_signal == "SELL":
should_exit = True
urgency += 2
elif direction == "SELL" and cached_ml_signal == "BUY":
should_exit = True
urgency += 2
if rsi_val:
if rsi_val > 75 and direction == "BUY":
should_exit = True
urgency += 2
elif rsi_val < 25 and direction == "SELL":
should_exit = True
urgency += 2
if direction == "BUY" and trend == "BEARISH" and mom_dir == "BEARISH":
should_exit = True
urgency += 3
elif direction == "SELL" and trend == "BULLISH" and mom_dir == "BULLISH":
should_exit = True
urgency += 3
if should_exit and current_profit > self.min_profit_to_protect / 2:
return current_profit, current_pips, ExitReason.MARKET_SIGNAL, i, close
if urgency >= 7 and current_profit > 0:
return current_profit, current_pips, ExitReason.MARKET_SIGNAL, i, close
if self._is_near_weekend_close(current_time):
if current_profit > 0:
return current_profit, current_pips, ExitReason.WEEKEND_CLOSE, i, close
elif current_profit > -10:
return current_profit, current_pips, ExitReason.WEEKEND_CLOSE, i, close
# B) SmartRiskManager
if current_profit >= 15:
if current_profit >= 40:
return current_profit, current_pips, ExitReason.SMART_TP, i, close
if current_profit >= 25 and momentum < -30:
return current_profit, current_pips, ExitReason.SMART_TP, i, close
if peak_profit > 30 and current_profit < peak_profit * 0.6:
return current_profit, current_pips, ExitReason.PEAK_PROTECT, i, close
if current_profit >= 20:
progress = (current_profit / target_tp_profit) * 100 if target_tp_profit > 0 else 0
progress_score = min(40, max(0, progress * 0.4))
momentum_score = ((momentum + 100) / 200) * 30
time_penalty = min(10, bars_since_entry / 4 * 2)
tp_probability = progress_score + momentum_score + 10 - time_penalty
if tp_probability < 25:
return current_profit, current_pips, ExitReason.SMART_TP, i, close
if 5 <= current_profit < 15:
if momentum < -50 and cached_ml_confidence >= 0.65:
is_reversal = (
(direction == "BUY" and cached_ml_signal == "SELL") or
(direction == "SELL" and cached_ml_signal == "BUY")
)
if is_reversal:
return current_profit, current_pips, ExitReason.EARLY_EXIT, i, close
if current_profit < 0:
loss_percent_of_max = abs(current_profit) / self.max_loss_per_trade * 100
if momentum < -30 and loss_percent_of_max >= 30:
return current_profit, current_pips, ExitReason.EARLY_CUT, i, close
is_ml_reversal = False
if direction == "BUY" and cached_ml_signal == "SELL" and cached_ml_confidence >= self.trend_reversal_threshold:
is_ml_reversal = True
reversal_warnings += 1
elif direction == "SELL" and cached_ml_signal == "BUY" and cached_ml_confidence >= self.trend_reversal_threshold:
is_ml_reversal = True
reversal_warnings += 1
loss_moderate = abs(current_profit) > (self.max_loss_per_trade * 0.4)
if is_ml_reversal and current_profit < -8 and loss_moderate:
return current_profit, current_pips, ExitReason.TREND_REVERSAL, i, close
if reversal_warnings >= 3 and current_profit < -10:
return current_profit, current_pips, ExitReason.TREND_REVERSAL, i, close
if current_profit <= -(self.max_loss_per_trade * 0.50):
htg = self._hours_to_golden(current_time)
if htg <= 1 and htg > 0 and momentum > -40:
pass
else:
return current_profit, current_pips, ExitReason.MAX_LOSS, i, close
if len(profit_history) >= 10:
recent_range = max(profit_history[-10:]) - min(profit_history[-10:])
if recent_range < 3 and current_profit < -15:
stall_count += 1
if stall_count >= 5:
return current_profit, current_pips, ExitReason.STALL, i, close
potential_daily_loss = daily_loss_so_far + abs(min(0, current_profit))
if potential_daily_loss >= self.max_daily_loss_usd:
return current_profit, current_pips, ExitReason.DAILY_LIMIT, i, close
# C) Time-based exit
if bars_since_entry >= 16:
if current_profit < 5 and not profit_growing:
if current_profit >= 0:
return current_profit, current_pips, ExitReason.TIMEOUT, i, close
elif current_profit > -15:
return current_profit, current_pips, ExitReason.TIMEOUT, i, close
if bars_since_entry >= 24:
if current_profit < 10 or not profit_growing:
return current_profit, current_pips, ExitReason.TIMEOUT, i, close
if bars_since_entry >= 32:
return current_profit, current_pips, ExitReason.TIMEOUT, i, close
if bars_since_entry > 10:
recent_closes = closes[i-5:i+1]
mom = recent_closes[-1] - recent_closes[0]
if direction == "BUY" and mom < -reversal_momentum_threshold:
if current_profit < -min_loss_for_reversal_exit:
return current_profit, current_pips, ExitReason.TREND_REVERSAL, i, close
elif direction == "SELL" and mom > reversal_momentum_threshold:
if current_profit < -min_loss_for_reversal_exit:
return current_profit, current_pips, ExitReason.TREND_REVERSAL, i, close
final_idx = min(entry_idx + max_bars - 1, len(df) - 1)
final_price = closes[final_idx]
if direction == "BUY":
pips = (final_price - entry_price) / 0.1
else:
pips = (entry_price - final_price) / 0.1
return pips * pip_value * lot_size, pips, ExitReason.TIMEOUT, final_idx, final_price
# ── Main run ──
def run(self, df, start_date=None, end_date=None, initial_capital=5000.0):
stats = BacktestStats()
capital = initial_capital
peak_capital = initial_capital
stats.equity_curve.append(capital)
daily_loss = 0.0
daily_profit = 0.0
daily_trades = 0
consecutive_losses = 0
trading_mode = TradingMode.NORMAL
current_date = None
feature_cols = []
if self.ml_model.fitted and self.ml_model.feature_names:
feature_cols = [f for f in self.ml_model.feature_names if f in df.columns]
times = df["time"].to_list()
start_idx = next((i for i, t in enumerate(times) if t >= start_date), 100) if start_date else 100
end_idx = next((i for i, t in enumerate(times) if t > end_date), len(df) - 100) if end_date else len(df) - 100
last_trade_idx = -self.trade_cooldown_bars * 2
print(f" Weights: bos={self.w_bos}, fvg={self.w_fvg}, ob={self.w_ob}")
print(f" Require FVG|OB: {self.require_fvg_or_ob}, Min conf: {self.min_confidence}")
print(f" Date range: {times[start_idx]} to {times[end_idx - 1]}")
print(f" Total bars: {end_idx - start_idx}")
for i in range(start_idx, end_idx):
if i - last_trade_idx < self.trade_cooldown_bars:
continue
current_time = times[i]
trade_date = current_time.date() if hasattr(current_time, 'date') else current_time
if current_date is None or trade_date != current_date:
daily_loss = 0.0
daily_profit = 0.0
daily_trades = 0
current_date = trade_date
if consecutive_losses < 2:
trading_mode = TradingMode.NORMAL
if trading_mode == TradingMode.STOPPED:
continue
session_name, can_trade, lot_mult = self._get_session_from_time(current_time)
if not can_trade:
continue
if hasattr(current_time, 'weekday') and current_time.weekday() >= 5:
continue
df_slice = df.head(i + 1)
regime = "normal"
try:
if self.regime_detector.fitted:
regime_state = self.regime_detector.get_current_state(df_slice)
if regime_state:
regime = regime_state.regime.value
if regime_state.regime == MarketRegime.CRISIS:
continue
if regime_state.recommendation == "SLEEP":
continue
except Exception:
pass
try:
ml_signal = ""
ml_confidence = 0.5
if self.ml_model.fitted and feature_cols:
ml_pred = self.ml_model.predict(df_slice, feature_cols)
ml_signal = ml_pred.signal
ml_confidence = ml_pred.confidence
market_analysis = self.dynamic_confidence.analyze_market(
session=session_name, regime=regime, volatility="medium",
trend_direction=regime, has_smc_signal=True,
ml_signal=ml_signal, ml_confidence=ml_confidence,
)
if market_analysis.quality == MarketQuality.AVOID:
stats.avoided_signals += 1
continue
except Exception:
pass
try:
smc_signal = self.smc.generate_signal(df_slice)
except Exception:
continue
if smc_signal is None:
continue
# SMC component detection
recent_df = df_slice.tail(10)
recent_bos = recent_df["bos"].to_list() if "bos" in df_slice.columns else []
recent_choch = recent_df["choch"].to_list() if "choch" in df_slice.columns else []
recent_fvg_bull = recent_df["is_fvg_bull"].to_list() if "is_fvg_bull" in df_slice.columns else []
recent_fvg_bear = recent_df["is_fvg_bear"].to_list() if "is_fvg_bear" in df_slice.columns else []
recent_obs = recent_df["ob"].to_list() if "ob" in df_slice.columns else []
has_bos = 1 in recent_bos or -1 in recent_bos
has_choch = 1 in recent_choch or -1 in recent_choch
has_fvg = any(recent_fvg_bull) or any(recent_fvg_bear)
has_ob = 1 in recent_obs or -1 in recent_obs
# ═══ #23: ENTRY FILTER — Require FVG or OB ═══
if self.require_fvg_or_ob and not has_fvg and not has_ob:
stats.filtered_signals += 1
continue
# Market structure for confidence calc
market_structure = 0
if "bos" in df_slice.columns:
recent_bos_vals = df_slice.tail(20)["bos"].to_list()
bull_bos = sum(1 for b in recent_bos_vals if b == 1)
bear_bos = sum(1 for b in recent_bos_vals if b == -1)
if bull_bos > bear_bos:
market_structure = 1
elif bear_bos > bull_bos:
market_structure = -1
has_break = has_bos or has_choch
# ═══ #23: CUSTOM CONFIDENCE CALCULATION ═══
confidence = self._recalculate_confidence(
signal_type=smc_signal.signal_type,
market_structure=market_structure,
has_break=has_break,
has_fvg=has_fvg,
has_ob=has_ob,
df_slice=df_slice,
)
# ═══ #23: MIN CONFIDENCE FILTER ═══
if self.min_confidence > 0 and confidence < self.min_confidence:
stats.filtered_signals += 1
continue
# ML agreement boost (synced)
ml_agrees = (
(smc_signal.signal_type == "BUY" and ml_signal == "BUY") or
(smc_signal.signal_type == "SELL" and ml_signal == "SELL")
)
if ml_agrees:
confidence = (confidence + ml_confidence) / 2
if regime == "high_volatility":
confidence *= 0.9
atr_at_entry = 12.0
if "atr" in df_slice.columns:
atr_val = df_slice.tail(1)["atr"].item()
if atr_val is not None and atr_val > 0:
atr_at_entry = atr_val
lot_size = self._calculate_lot_size(confidence, regime, trading_mode, lot_mult)
if lot_size <= 0:
continue
if trading_mode == TradingMode.RECOVERY:
stats.recovery_mode_trades += 1
entry_price = smc_signal.entry_price
take_profit_price = smc_signal.take_profit
stop_loss_price = smc_signal.stop_loss
risk = abs(entry_price - stop_loss_price)
rr = abs(take_profit_price - entry_price) / risk if risk > 0 else 0
profit, pips, exit_reason, exit_idx, exit_price = self._simulate_trade_exit(
df=df, entry_idx=i, direction=smc_signal.signal_type,
entry_price=entry_price, take_profit=take_profit_price,
stop_loss=stop_loss_price, lot_size=lot_size,
daily_loss_so_far=daily_loss, feature_cols=feature_cols,
)
self._ticket_counter += 1
result = TradeResult.WIN if profit > 0 else (TradeResult.LOSS if profit < 0 else TradeResult.BREAKEVEN)
trade = SimulatedTrade(
ticket=self._ticket_counter,
entry_time=current_time,
exit_time=times[exit_idx] if exit_idx < len(times) else times[-1],
direction=smc_signal.signal_type,
entry_price=entry_price, exit_price=exit_price,
stop_loss=stop_loss_price, take_profit=take_profit_price,
lot_size=lot_size, profit_usd=profit, profit_pips=pips,
result=result, exit_reason=exit_reason,
smc_confidence=confidence, regime=regime,
session=session_name, signal_reason=smc_signal.reason,
has_bos=has_bos, has_choch=has_choch,
has_fvg=has_fvg, has_ob=has_ob,
atr_at_entry=atr_at_entry, rr_ratio=rr,
trading_mode=trading_mode.value,
)
stats.trades.append(trade)
stats.total_trades += 1
daily_trades += 1
capital += profit
if profit > 0:
stats.wins += 1
stats.total_profit += profit
daily_profit += profit
consecutive_losses = 0
if trading_mode == TradingMode.RECOVERY:
trading_mode = TradingMode.NORMAL
else:
stats.losses += 1
stats.total_loss += abs(profit)
daily_loss += abs(profit)
consecutive_losses += 1
if daily_loss >= self.max_daily_loss_usd:
trading_mode = TradingMode.STOPPED
stats.daily_limit_stops += 1
elif consecutive_losses >= 3 or daily_loss >= self.max_daily_loss_usd * 0.6:
trading_mode = TradingMode.PROTECTED
elif consecutive_losses >= 2:
trading_mode = TradingMode.RECOVERY
if capital > peak_capital:
peak_capital = capital
drawdown_pct = (peak_capital - capital) / peak_capital * 100
drawdown_usd = peak_capital - capital
if drawdown_pct > stats.max_drawdown:
stats.max_drawdown = drawdown_pct
stats.max_drawdown_usd = drawdown_usd
stats.equity_curve.append(capital)
last_trade_idx = exit_idx
if stats.total_trades % 100 == 0:
print(f" {stats.total_trades} trades processed...")
if stats.total_trades > 0:
stats.win_rate = stats.wins / stats.total_trades * 100
stats.avg_win = stats.total_profit / stats.wins if stats.wins > 0 else 0
stats.avg_loss = stats.total_loss / stats.losses if stats.losses > 0 else 0
stats.avg_trade = (stats.total_profit - stats.total_loss) / stats.total_trades
stats.profit_factor = stats.total_profit / stats.total_loss if stats.total_loss > 0 else float("inf")
win_prob = stats.wins / stats.total_trades
loss_prob = stats.losses / stats.total_trades
stats.expectancy = (win_prob * stats.avg_win) - (loss_prob * stats.avg_loss)
returns = [t.profit_usd for t in stats.trades]
if len(returns) > 1:
avg_return = np.mean(returns)
std_return = np.std(returns)
stats.sharpe_ratio = (avg_return / std_return) * np.sqrt(252) if std_return > 0 else 0
return stats
# ─── Main ──────────────────────────────────────────────────────
def main():
print("=" * 70)
print("XAUBOT AI — #23 Confidence Weight Rebalance")
print("Base: SMC-Only v4 | Modified: Prioritize FVG/OB over BOS")
print("=" * 70)
config = get_config()
mt5 = MT5Connector(
login=config.mt5_login, password=config.mt5_password,
server=config.mt5_server, path=config.mt5_path,
)
mt5.connect()
print(f"\nConnected to MT5")
print("Fetching XAUUSD M15 historical data...")
df = mt5.get_market_data(symbol="XAUUSD", timeframe="M15", count=50000)
if len(df) == 0:
print("ERROR: No data")
mt5.disconnect()
return
print(f" Received {len(df)} bars")
times = df["time"].to_list()
print(f" Data range: {times[0]} to {times[-1]}")
end_date = datetime.now()
start_date = datetime(2025, 8, 1)
data_start = times[0]
if hasattr(data_start, 'replace') and data_start.tzinfo:
start_date = start_date.replace(tzinfo=data_start.tzinfo)
end_date = end_date.replace(tzinfo=data_start.tzinfo)
if data_start > start_date:
start_date = data_start + timedelta(days=5)
print(f"\n Backtest period: {start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')}")
print("\nCalculating indicators...")
features = FeatureEngineer()
smc = SMCAnalyzer(swing_length=config.smc.swing_length, ob_lookback=config.smc.ob_lookback)
df = features.calculate_all(df, include_ml_features=True)
df = smc.calculate_all(df)
regime_detector = MarketRegimeDetector(model_path="models/hmm_regime.pkl")
try:
regime_detector.load()
df = regime_detector.predict(df)
print(" HMM regime loaded")
except Exception:
print(" [WARN] HMM not available")
print(" Indicators calculated")
# ═══ CONFIGURATIONS ═══
baseline_pnl = 1449.86
configs = [
# name, w_bos, w_fvg, w_ob, require_fvg_ob, min_conf
("A: boost_fvg_ob", 0.06, 0.14, 0.14, False, 0.0),
("B: fvg_dominant", 0.06, 0.18, 0.10, False, 0.0),
("C: ob_dominant", 0.06, 0.10, 0.18, False, 0.0),
("D: require_fvg|ob", 0.12, 0.08, 0.10, True, 0.0),
("E: min_conf_0.55", 0.12, 0.08, 0.10, False, 0.55),
]
all_results = []
for cfg_name, w_bos, w_fvg, w_ob, req_fvg_ob, min_conf in configs:
print(f"\n{'=' * 60}")
print(f" Config: {cfg_name}")
bt = ConfidenceWeightBacktest(
w_bos=w_bos,
w_fvg=w_fvg,
w_ob=w_ob,
require_fvg_or_ob=req_fvg_ob,
min_confidence=min_conf,
)
stats = bt.run(df=df, start_date=start_date, end_date=end_date, initial_capital=5000.0)
net_pnl = stats.total_profit - stats.total_loss
diff = net_pnl - baseline_pnl
print(f"\n [{cfg_name}] Results:")
print(f" Trades: {stats.total_trades} | WR: {stats.win_rate:.1f}%")
print(f" Net PnL: ${net_pnl:,.2f} | PF: {stats.profit_factor:.2f}")
print(f" Max DD: {stats.max_drawdown:.1f}% | Sharpe: {stats.sharpe_ratio:.2f}")
print(f" Filtered: {stats.filtered_signals}")
print(f" vs BASELINE: ${diff:+,.2f}")
all_results.append((cfg_name, stats, net_pnl, diff))
# ═══ SUMMARY ═══
print(f"\n{'=' * 70}")
print("#23 CONFIDENCE WEIGHT — ALL CONFIGURATIONS")
print("=" * 70)
print(f"\n {'Config':<22} {'Trades':>6} {'WR':>6} {'Net PnL':>10} {'DD':>6} {'Sharpe':>7} {'PF':>5} {'Filt':>5} {'vs Base':>10}")
print(f" {'-' * 85}")
print(f" {'BASELINE (#1)':<22} {'686':>6} {'72.2%':>6} {'$1,449.86':>10} {'5.4%':>6} {'1.98':>7} {'1.52':>5} {'—':>5} {'—':>10}")
print(f" {'#8 Stoch+Sell':<22} {'416':>6} {'76.7%':>6} {'$1,320.41':>10} {'2.8%':>6} {'3.17':>7} {'1.76':>5} {'—':>5} {'—':>10}")
best_pnl = -999999
best_name = ""
best_stats = None
for cfg_name, stats, net_pnl, diff in all_results:
print(f" {cfg_name:<22} {stats.total_trades:>6} {stats.win_rate:>5.1f}% ${net_pnl:>9,.2f} {stats.max_drawdown:>5.1f}% {stats.sharpe_ratio:>7.2f} {stats.profit_factor:>5.2f} {stats.filtered_signals:>5} ${diff:>+9,.2f}")
if net_pnl > best_pnl:
best_pnl = net_pnl
best_name = cfg_name
best_stats = stats
print(f"\n Best config: {best_name}")
# Direction
print(f"\n Direction:")
for d in ["BUY", "SELL"]:
dt = [t for t in best_stats.trades if t.direction == d]
dw = sum(1 for t in dt if t.result == TradeResult.WIN)
dp = sum(t.profit_usd for t in dt)
dwr = dw / len(dt) * 100 if dt else 0
print(f" {d}: {len(dt)} trades, {dwr:.1f}% WR, ${dp:,.2f}")
# Exit reasons
print(f"\n Exit Reasons:")
exit_counts = {}
for t in best_stats.trades:
r = t.exit_reason.value
exit_counts[r] = exit_counts.get(r, 0) + 1
for reason, count in sorted(exit_counts.items(), key=lambda x: -x[1]):
pct = count / best_stats.total_trades * 100 if best_stats.total_trades > 0 else 0
print(f" {reason:20s}: {count} ({pct:.1f}%)")
# Save
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
output_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "23_confidence_weight_results")
os.makedirs(output_dir, exist_ok=True)
log_path = os.path.join(output_dir, f"conf_weight_{timestamp}.log")
with open(log_path, "w") as f:
f.write(f"#23 Confidence Weight Rebalance Results\n")
f.write(f"Generated: {datetime.now()}\n\n")
for cfg_name, stats, net_pnl, diff in all_results:
f.write(f" {cfg_name}: {stats.total_trades} trades, {stats.win_rate:.1f}% WR, ${net_pnl:,.2f}, filtered: {stats.filtered_signals}, vs base: ${diff:+,.2f}\n")
f.write(f"\nBest: {best_name}\n")
print(f" Log saved: {log_path}")
try:
from backtests.backtest_01_smc_only import generate_xlsx_report as gen_xlsx
xlsx_path = os.path.join(output_dir, f"conf_weight_{timestamp}.xlsx")
gen_xlsx(best_stats, xlsx_path, start_date, end_date)
except Exception as e:
print(f" [WARN] XLSX: {e}")
mt5.disconnect()
print(f"\n{'=' * 70}")
print(f"Output: {output_dir}")
print(f" Log: {os.path.basename(log_path)}")
print("=" * 70)
print("Backtest complete!")
if __name__ == "__main__":
main()
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