""" Backtest #29 — Confluence Scoring ================================== Base: #28B (Smart BE 0.5x ATR) — 741 trades, 79.8% WR, $2,464, Sharpe 3.23 Idea: Require minimum number of SMC confirmations (BOS, CHoCH, FVG, OB) before entering. Currently any single SMC signal triggers entry. By requiring more confirmations, we filter weak signals and keep only high-quality setups. Configs: A: Min 2 SMC elements (any 2 of BOS/CHoCH/FVG/OB) B: Min confidence >= 0.55 (threshold filter) C: Min confidence >= 0.60 D: A+B combined (2 elements + conf >= 0.55) E: Min 3 SMC elements (BOS/CHoCH + FVG or OB) Usage: python backtests/backtest_29_confluence_scoring.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" smc_element_count: int = 0 # NEW: count of SMC elements @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 session_blocked: int = 0 confluence_filtered: int = 0 # NEW # ─── Confluence Scoring Backtest ───────────────────────────── class ConfluenceScoringBacktest: """#28B base + confluence scoring entry filter.""" 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, 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, # #24B base skip_tokyo_london: bool = True, early_cut_momentum: float = -50.0, early_cut_loss_pct: float = 30.0, be_mult: float = 2.0, trail_start_mult: float = 4.0, trail_step_mult: float = 3.0, # #28B: Smart breakeven be_profit_lock_atr_mult: float = 0.5, # ═══ #29 CONFLUENCE SCORING PARAMS ═══ min_smc_elements: int = 0, # Minimum number of SMC elements (BOS, CHoCH, FVG, OB) min_confidence: float = 0.0, # Minimum confidence threshold ): 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.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 self.skip_tokyo_london = skip_tokyo_london self.early_cut_momentum = early_cut_momentum self.early_cut_loss_pct = early_cut_loss_pct self.be_mult = be_mult self.trail_start_mult = trail_start_mult self.trail_step_mult = trail_step_mult self.be_profit_lock_atr_mult = be_profit_lock_atr_mult # #29 params self.min_smc_elements = min_smc_elements 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 = 2290000 def _get_session_from_time(self, dt): 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: if self.skip_tokyo_london: return "Tokyo-London Overlap", False, 0.0 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): 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): if dt.tzinfo is None: dt = dt.replace(tzinfo=ZoneInfo("UTC")) wib = dt.astimezone(WIB) return wib.weekday() == 5 and wib.hour >= 4 and wib.minute >= 30 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) 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, ): 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] adaptive_breakeven_pips = atr * self.be_mult adaptive_trail_start_pips = atr * self.trail_start_mult adaptive_trail_step_pips = atr * self.trail_step_mult reversal_momentum_threshold = atr * self.trend_reversal_mult min_loss_for_reversal_exit = atr * 0.8 # #28B: Smart breakeven profit lock if self.be_profit_lock_atr_mult > 0: be_lock_distance = atr * self.be_profit_lock_atr_mult else: be_lock_distance = 2.0 profit_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 = current_pips else: current_pips = (entry_price - close) / 0.1 pip_profit_from_entry = current_pips current_profit = current_pips * pip_value * lot_size profit_history.append(current_profit) 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.0 TP hit 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 # A.0b Trailing SL hit 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 >= adaptive_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 >= adaptive_trail_start_pips else ExitReason.BREAKEVEN_EXIT return pips * pip_value * lot_size, pips, reason, i, current_sl # A.1 Breakeven (#28B: Smart — lock profit at entry + ATR*0.5) if pip_profit_from_entry >= adaptive_breakeven_pips and not breakeven_moved: if direction == "BUY": current_sl = entry_price + be_lock_distance else: current_sl = entry_price - be_lock_distance breakeven_moved = True # A.2 Trailing SL if pip_profit_from_entry >= adaptive_trail_start_pips: trail_distance = adaptive_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 # A.3 Peak protect 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 # A.4 Market analysis if bars_since_entry % 5 == 0 and bars_since_entry >= 5 and i >= 20: ma_fast = np.mean(closes[i-4:i+1]) ma_slow = np.mean(closes[i-19:i+1]) trend = "BULLISH" if ma_fast > ma_slow * 1.001 else ("BEARISH" if ma_fast < ma_slow * 0.999 else "NEUTRAL") 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") or (direction == "SELL" and cached_ml_signal == "BUY"): should_exit = True; urgency += 2 if rsi_val: if (rsi_val > 75 and direction == "BUY") or (rsi_val < 25 and direction == "SELL"): should_exit = True; urgency += 2 if (direction == "BUY" and trend == "BEARISH" and mom_dir == "BEARISH") or \ (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 # A.5 Weekend close if self._is_near_weekend_close(current_time): if current_profit > 0 or current_profit > -10: return current_profit, current_pips, ExitReason.WEEKEND_CLOSE, i, close # B.1 Smart TP 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 # B.2 Smart Early Exit 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 # B.3 Early cut if current_profit < 0: loss_percent_of_max = abs(current_profit) / self.max_loss_per_trade * 100 if momentum < self.early_cut_momentum and loss_percent_of_max >= self.early_cut_loss_pct: return current_profit, current_pips, ExitReason.EARLY_CUT, i, close # B.4 Trend Reversal is_ml_reversal = False if (direction == "BUY" and cached_ml_signal == "SELL" and cached_ml_confidence >= self.trend_reversal_threshold) or \ (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 # B.5 Max loss 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 # B.6 Stall 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 # B.7 Daily loss limit 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 if bars_since_entry >= 16 and current_profit < 5 and not profit_growing: if current_profit >= 0 or current_profit > -15: return current_profit, current_pips, ExitReason.TIMEOUT, i, close if bars_since_entry >= 24 and (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 # C.2 ATR trend reversal 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) or \ (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] pips = ((final_price - entry_price) if direction == "BUY" else (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" #29 Min SMC elements: {self.min_smc_elements}, Min confidence: {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: if session_name == "Tokyo-London Overlap": stats.session_blocked += 1 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 ELEMENT 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 # ═══ #29: COUNT SMC ELEMENTS ═══ smc_element_count = sum([has_bos, has_choch, has_fvg, has_ob]) # ═══ #29: CONFLUENCE FILTER ═══ if self.min_smc_elements > 0 and smc_element_count < self.min_smc_elements: stats.confluence_filtered += 1 continue 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 confidence = smc_signal.confidence 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 = (smc_signal.confidence + ml_confidence) / 2 if regime == "high_volatility": confidence *= 0.9 # ═══ #29: CONFIDENCE FILTER ═══ if self.min_confidence > 0 and confidence < self.min_confidence: stats.confluence_filtered += 1 continue 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, smc_element_count=smc_element_count, ) 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 — #29 Confluence Scoring") print("Base: #28B (Smart BE 0.5x ATR) | Modified: Confluence entry filters") 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") # ═══ ANALYZE ELEMENT DISTRIBUTION BEFORE BACKTEST ═══ print("\n SMC Element Distribution (pre-analysis)...") if "bos" in df.columns: bos_count = df.filter(pl.col("bos") != 0).height choch_count = df.filter(pl.col("choch") != 0).height if "choch" in df.columns else 0 fvg_bull = df.filter(pl.col("is_fvg_bull") == True).height if "is_fvg_bull" in df.columns else 0 fvg_bear = df.filter(pl.col("is_fvg_bear") == True).height if "is_fvg_bear" in df.columns else 0 ob_count = df.filter(pl.col("ob") != 0).height if "ob" in df.columns else 0 print(f" BOS: {bos_count} bars | CHoCH: {choch_count} bars | FVG: {fvg_bull + fvg_bear} bars | OB: {ob_count} bars") baseline_28b_pnl = 2463.80 # ═══ CONFIGS ═══ configs = [ # (name, min_smc_elements, min_confidence) ("A: Min 2 SMC elements", 2, 0.0), # Require 2 of BOS/CHoCH/FVG/OB ("B: Min conf >= 0.55", 0, 0.55), # Confidence threshold ("C: Min conf >= 0.60", 0, 0.60), # Higher confidence ("D: 2 elem + conf>=0.55", 2, 0.55), # Combined ("E: Min 3 SMC elements", 3, 0.0), # Strict: 3 out of 4 elements ] all_results = [] for cfg_name, min_elem, min_conf in configs: print(f"\n{'=' * 60}") print(f" Config: {cfg_name}") bt = ConfluenceScoringBacktest( min_smc_elements=min_elem, 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_28b_pnl buy_trades = [t for t in stats.trades if t.direction == "BUY"] sell_trades = [t for t in stats.trades if t.direction == "SELL"] buy_wins = sum(1 for t in buy_trades if t.result == TradeResult.WIN) sell_wins = sum(1 for t in sell_trades if t.result == TradeResult.WIN) buy_wr = buy_wins / len(buy_trades) * 100 if buy_trades else 0 sell_wr = sell_wins / len(sell_trades) * 100 if sell_trades else 0 buy_pnl = sum(t.profit_usd for t in buy_trades) sell_pnl = sum(t.profit_usd for t in sell_trades) # Element distribution for trades taken elem_dist = {} for t in stats.trades: c = t.smc_element_count elem_dist[c] = elem_dist.get(c, 0) + 1 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" Confluence filtered: {stats.confluence_filtered}") print(f" BUY: {len(buy_trades)}, {buy_wr:.1f}% WR, ${buy_pnl:,.2f}") print(f" SELL: {len(sell_trades)}, {sell_wr:.1f}% WR, ${sell_pnl:,.2f}") print(f" Element dist: {dict(sorted(elem_dist.items()))}") print(f" vs #28B: ${diff:+,.2f}") all_results.append((cfg_name, stats, net_pnl, diff, stats.confluence_filtered, elem_dist)) # ═══ FINAL SUMMARY ═══ print(f"\n{'=' * 70}") print("#29 CONFLUENCE SCORING — ALL CONFIGURATIONS") print("=" * 70) print(f"\n {'Config':<25} {'Trades':>6} {'WR':>6} {'Net PnL':>10} {'DD':>6} {'Sharpe':>7} {'PF':>5} {'Filt':>5} {'vs #28B':>10}") print(f" {'-' * 90}") print(f" {'#24B (base) ':<25} {'739':>6} {'80.4%':>6} {'$2,235':>10} {'3.4%':>6} {'2.87':>7} {'1.77':>5} {'—':>5} {'—':>10}") print(f" {'#28B (smart BE)':<25} {'741':>6} {'79.8%':>6} {'$2,464':>10} {'3.5%':>6} {'3.23':>7} {'1.83':>5} {'—':>5} {'—':>10}") for cfg_name, stats, net_pnl, diff, filt, elem_dist in all_results: print(f" {cfg_name:<25} {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} {filt:>5} ${diff:>+9,.2f}") best_pnl = -999999 best_name = "" best_stats = None for entry in all_results: if entry[2] > best_pnl: best_pnl = entry[2] best_name = entry[0] best_stats = entry[1] print(f"\n Best config: {best_name}") # Element analysis for best config print(f"\n Element Analysis (best config):") for elem_count in sorted(set(t.smc_element_count for t in best_stats.trades)): elem_trades = [t for t in best_stats.trades if t.smc_element_count == elem_count] elem_wins = sum(1 for t in elem_trades if t.result == TradeResult.WIN) elem_wr = elem_wins / len(elem_trades) * 100 if elem_trades else 0 elem_pnl = sum(t.profit_usd for t in elem_trades) print(f" {elem_count} elements: {len(elem_trades)} trades, {elem_wr:.1f}% WR, ${elem_pnl:,.2f}") # Direction analysis print(f"\n Direction (best config):") buy_trades = [t for t in best_stats.trades if t.direction == "BUY"] sell_trades = [t for t in best_stats.trades if t.direction == "SELL"] buy_wins = sum(1 for t in buy_trades if t.result == TradeResult.WIN) sell_wins = sum(1 for t in sell_trades if t.result == TradeResult.WIN) buy_wr = buy_wins / len(buy_trades) * 100 if buy_trades else 0 sell_wr = sell_wins / len(sell_trades) * 100 if sell_trades else 0 buy_pnl = sum(t.profit_usd for t in buy_trades) sell_pnl = sum(t.profit_usd for t in sell_trades) print(f" BUY: {len(buy_trades)} trades, {buy_wr:.1f}% WR, ${buy_pnl:,.2f}") print(f" SELL: {len(sell_trades)} trades, {sell_wr:.1f}% WR, ${sell_pnl:,.2f}") # Exit reasons print(f"\n Exit Reasons (best config):") 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__)), "29_confluence_scoring_results") os.makedirs(output_dir, exist_ok=True) log_path = os.path.join(output_dir, f"confluence_{timestamp}.log") with open(log_path, "w") as f: f.write(f"#29 Confluence Scoring Results\n") f.write(f"Generated: {datetime.now()}\n") f.write(f"Base: #28B (741 trades, 79.8% WR, $2,464)\n\n") for cfg_name, stats, net_pnl, diff, filt, elem_dist in all_results: f.write(f" {cfg_name}: {stats.total_trades} trades, {stats.win_rate:.1f}% WR, " f"${net_pnl:,.2f}, DD: {stats.max_drawdown:.1f}%, " f"Sharpe: {stats.sharpe_ratio:.2f}, PF: {stats.profit_factor:.2f}, " f"Filtered: {filt}, Elem dist: {dict(sorted(elem_dist.items()))}, " f"vs #28B: ${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"confluence_{timestamp}.xlsx") gen_xlsx(best_stats, xlsx_path, start_date, end_date) print(f"\n Report saved: {xlsx_path}") 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()