""" Backtest #17 — Liquidity Sweep Filter ====================================== Base: SMC-Only v4 (Backtest #1) Added: Liquidity Sweep as entry filter/enhancer RTM Theory: - BSL sweep (buyside liquidity taken) → smart money selling → confirms SELL - SSL sweep (sellside liquidity taken) → smart money buying → confirms BUY - Trade only when SMC signal aligns with recent sweep direction Liquidity Zone Detection: - Uses rolling std/mean (CV) to find equal-high / equal-low clusters - BSL: cluster of equal highs (stop losses of shorts) - SSL: cluster of equal lows (stop losses of longs) - Sweep: price pierces through level but closes back (rejection) Modes: A) FILTER: Only trade when matching sweep detected within lookback B) BOOST: Trade normally, but allow wider tolerance & lower CV when sweep matches Usage: python backtests/backtest_17_liquidity_sweep.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 from openpyxl import Workbook from openpyxl.styles import Font, Alignment, PatternFill, Border, Side from openpyxl.chart import LineChart, Reference from openpyxl.utils import get_column_letter 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" sweep_type: str = "" # "BSL", "SSL", or "" entry_source: str = "SMC" # "SMC" (normal) or "SWEEP+SMC" (sweep confirmed) @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 # Liquidity sweep stats sweep_confirmed_trades: int = 0 sweep_blocked_trades: int = 0 bsl_sweeps_detected: int = 0 ssl_sweeps_detected: int = 0 # ─── Liquidity Sweep Calculator ─────────────────────────────── def calculate_liquidity_zones_multi( df: pl.DataFrame, cv_thresholds: List[float] = [0.001, 0.002, 0.003], window_size: int = 20, ) -> pl.DataFrame: """ Calculate liquidity zones with multiple CV thresholds. Returns columns: - bsl_level, ssl_level: Using tightest threshold (0.001) - liquidity_sweep: "BSL" or "SSL" using tightest - liq_sweep_relaxed: "BSL" or "SSL" using most relaxed threshold - For each threshold: bsl_{t}, ssl_{t}, sweep_{t} """ # Rolling stats df = df.with_columns([ pl.col("high").rolling_std(window_size=window_size).alias("_high_std"), pl.col("low").rolling_std(window_size=window_size).alias("_low_std"), pl.col("high").rolling_mean(window_size=window_size).alias("_high_mean"), pl.col("low").rolling_mean(window_size=window_size).alias("_low_mean"), ]) sweep_cols = [] for cv_t in cv_thresholds: suffix = f"_{int(cv_t * 10000)}" # e.g., _10, _20, _30 # Detect clusters at this threshold df = df.with_columns([ pl.when( (pl.col("_high_std") / pl.col("_high_mean")) < cv_t ).then(pl.col("high")).otherwise(None).alias(f"bsl{suffix}"), pl.when( (pl.col("_low_std") / pl.col("_low_mean")) < cv_t ).then(pl.col("low")).otherwise(None).alias(f"ssl{suffix}"), ]) # Forward fill df = df.with_columns([ pl.col(f"bsl{suffix}").forward_fill().alias(f"_bsl_ff{suffix}"), pl.col(f"ssl{suffix}").forward_fill().alias(f"_ssl_ff{suffix}"), ]) # Detect sweeps df = df.with_columns([ pl.when( (pl.col("high") > pl.col(f"_bsl_ff{suffix}").shift(1)) & (pl.col("close") < pl.col(f"_bsl_ff{suffix}").shift(1)) ).then(pl.lit("BSL")) .when( (pl.col("low") < pl.col(f"_ssl_ff{suffix}").shift(1)) & (pl.col("close") > pl.col(f"_ssl_ff{suffix}").shift(1)) ).then(pl.lit("SSL")) .otherwise(None) .alias(f"sweep{suffix}"), ]) sweep_cols.append(f"sweep{suffix}") # Cleanup per-threshold temp cols df = df.drop([f"_bsl_ff{suffix}", f"_ssl_ff{suffix}"]) # Primary sweep = tightest threshold primary_suffix = f"_{int(cv_thresholds[0] * 10000)}" relaxed_suffix = f"_{int(cv_thresholds[-1] * 10000)}" df = df.with_columns([ pl.col(f"sweep{primary_suffix}").alias("liquidity_sweep"), pl.col(f"sweep{relaxed_suffix}").alias("liq_sweep_relaxed"), ]) # Cleanup df = df.drop(["_high_std", "_low_std", "_high_mean", "_low_mean"]) return df # ─── Liquidity Sweep Backtest ───────────────────────────────── class LiquiditySweepBacktest: """SMC-Only + Liquidity Sweep 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, 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, # Liquidity Sweep params sweep_lookback: int = 15, # How many bars back to check for sweep sweep_mode: str = "filter", # "filter" = block without sweep, "boost" = enhance use_relaxed_cv: bool = True, # Use relaxed CV (0.003) instead of tight (0.001) ): 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 # Sweep params self.sweep_lookback = sweep_lookback self.sweep_mode = sweep_mode self.use_relaxed_cv = use_relaxed_cv 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 — exit ML checks disabled") 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 = 2170000 # ── 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 _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) # ── Check for recent liquidity sweep ── def _check_recent_sweep( self, df: pl.DataFrame, current_idx: int, direction: str, ) -> Tuple[bool, str]: """ Check if there's a recent liquidity sweep that confirms the trade direction. BSL sweep → confirms SELL (buyside stops hunted → smart money selling) SSL sweep → confirms BUY (sellside stops hunted → smart money buying) Returns: (sweep_found, sweep_type) """ sweep_col = "liq_sweep_relaxed" if self.use_relaxed_cv else "liquidity_sweep" if sweep_col not in df.columns: return False, "" start_idx = max(0, current_idx - self.sweep_lookback) sweeps = df[sweep_col].to_list() for j in range(start_idx, current_idx): sweep_val = sweeps[j] if sweep_val is None: continue # BSL sweep → SELL confirmation if direction == "SELL" and sweep_val == "BSL": return True, "BSL" # SSL sweep → BUY confirmation if direction == "BUY" and sweep_val == "SSL": return True, "SSL" return False, "" def _hours_to_golden(self, dt: datetime) -> float: """Hours until golden time (19:00 WIB). Returns 0 if already in golden.""" 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) # ── Full exit simulation (all 3 systems — synced with #1) ── def _simulate_trade_exit( self, df: pl.DataFrame, entry_idx: int, direction: str, entry_price: float, take_profit: float, stop_loss: float, lot_size: float, daily_loss_so_far: float, feature_cols: list, max_bars: int = 100, ) -> Tuple[float, float, ExitReason, int, float]: """ Simulate trade exit with ALL 3 exit systems synced with main_live.py: A) SmartPositionManager (breakeven, trailing, peak protect, market signal) B) SmartRiskManager (smart TP, early cut, stall, daily limit, reversal) C) Time/Trend exit (4h/6h/8h timeout, ATR momentum) """ pip_value = 10 # XAUUSD: 1 pip = $10 per lot highs = df["high"].to_list() lows = df["low"].to_list() closes = df["close"].to_list() times = df["time"].to_list() # ATR at entry 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 # ── State tracking (simulating SmartRiskManager PositionGuard) ── profit_history = [] price_history = [] peak_profit = 0.0 stall_count = 0 reversal_warnings = 0 # SmartPositionManager state current_sl = stop_loss # broker SL (mutable via trailing) breakeven_moved = False # Target TP profit for probability estimation 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 # ML prediction cache (evaluate every 4 bars like live) 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] # Current P/L 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 # Track history profit_history.append(current_profit) price_history.append(close) if current_profit > peak_profit: peak_profit = current_profit bars_since_entry = i - entry_idx # ── ML prediction (every 4 bars, synced with live) ── 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 calculation (synced with PositionGuard.calculate_momentum) ── 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 checks (every bar) # ════════════════════════════════════════════════ # A.0 TP hit by price action (high/low) if direction == "BUY" and high >= take_profit: pips = (take_profit - entry_price) / 0.1 profit = pips * pip_value * lot_size return profit, pips, ExitReason.TAKE_PROFIT, i, take_profit elif direction == "SELL" and low <= take_profit: pips = (entry_price - take_profit) / 0.1 profit = pips * pip_value * lot_size return profit, pips, ExitReason.TAKE_PROFIT, i, take_profit # A.0b Trailing SL hit check if breakeven_moved and current_sl > 0: if direction == "BUY" and low <= current_sl: pips = (current_sl - entry_price) / 0.1 profit = pips * pip_value * lot_size reason = ExitReason.TRAILING_SL if pip_profit_from_entry >= self.trail_start_pips else ExitReason.BREAKEVEN_EXIT return profit, pips, reason, i, current_sl elif direction == "SELL" and high >= current_sl: pips = (entry_price - current_sl) / 0.1 profit = pips * pip_value * lot_size reason = ExitReason.TRAILING_SL if pip_profit_from_entry >= self.trail_start_pips else ExitReason.BREAKEVEN_EXIT return profit, pips, reason, i, current_sl # A.1 Breakeven move (after 30 pips / $3 profit) if pip_profit_from_entry >= self.breakeven_pips and not breakeven_moved: if direction == "BUY": current_sl = entry_price + 2 # 2 points buffer else: current_sl = entry_price - 2 breakeven_moved = True # A.2 Trailing SL (after 50 pips / $5 profit) 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 # A.3 Peak profit drawdown protection (50% drawdown from peak for $5+ profit) 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: trend + momentum + RSI (synced with position_manager) if bars_since_entry % 5 == 0 and bars_since_entry >= 5: # Trend analysis (5-bar vs 20-bar MA) 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 momentum 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 check 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 # Strong ML opposite signal 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 # RSI extremes 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 # Trend + momentum reversal 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 # Close on strong opposite signal with profit (synced) if should_exit and current_profit > self.min_profit_to_protect / 2: return current_profit, current_pips, ExitReason.MARKET_SIGNAL, i, close # High urgency with any profit if urgency >= 7 and current_profit > 0: return current_profit, current_pips, ExitReason.MARKET_SIGNAL, i, close # A.5 Weekend close check 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 checks # ════════════════════════════════════════════════ # B.1 Smart TP ($15+ with momentum analysis — synced evaluate_position CHECK 1) if current_profit >= 15: # Hard TP at $40 if current_profit >= 40: return current_profit, current_pips, ExitReason.SMART_TP, i, close # Momentum-based TP: profit $25+ but momentum dropping if current_profit >= 25 and momentum < -30: return current_profit, current_pips, ExitReason.SMART_TP, i, close # Peak protection: profit turun ke 60% dari peak if peak_profit > 30 and current_profit < peak_profit * 0.6: return current_profit, current_pips, ExitReason.PEAK_PROTECT, i, close # Low TP probability: profit $20+ tapi kemungkinan TP rendah if current_profit >= 20: # Simplified TP probability (synced with PositionGuard.get_tp_probability) 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) # 2 points per hour 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 ($5-15 profit + reversal, synced CHECK 2) 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: loss significant + momentum negative (synced CHECK 3) 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 # B.4 Trend Reversal: ML 75%+ opposite (synced CHECK 4) 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 # B.5 Max loss per trade — 50% of max (synced CHECK 5) if current_profit <= -(self.max_loss_per_trade * 0.50): # Last chance hold if golden time very close (synced) htg = self._hours_to_golden(current_time) if htg <= 1 and htg > 0 and momentum > -40: pass # Hold — last chance for recovery else: return current_profit, current_pips, ExitReason.MAX_LOSS, i, close # B.6 Stall detection (synced CHECK 5b) 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 (synced CHECK 6) 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 (synced CHECK 8) # ════════════════════════════════════════════════ # Check ML agreement for timeout decision ml_agrees = ( (direction == "BUY" and cached_ml_signal == "BUY") or (direction == "SELL" and cached_ml_signal == "SELL") ) # 4+ hours: exit if stuck (synced) 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 # 6+ hours: exit unless significantly profitable AND growing (synced) if bars_since_entry >= 24: if current_profit < 10 or not profit_growing: return current_profit, current_pips, ExitReason.TIMEOUT, i, close # 8+ hours: hard max (synced) if bars_since_entry >= 32: return current_profit, current_pips, ExitReason.TIMEOUT, i, close # C.2 ATR trend reversal (synced with original backtest) 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 # End of data — close at last price 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 profit = pips * pip_value * lot_size return profit, pips, ExitReason.TIMEOUT, final_idx, final_price # ── Main run ── def run( self, df: pl.DataFrame, start_date: Optional[datetime] = None, end_date: Optional[datetime] = None, initial_capital: float = 5000.0, ) -> BacktestStats: 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] # Pre-extract sweep data for fast lookup sweep_col = "liq_sweep_relaxed" if self.use_relaxed_cv else "liquidity_sweep" has_sweep_data = sweep_col in df.columns if has_sweep_data: sweep_list = df[sweep_col].to_list() else: sweep_list = [None] * len(df) 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 # Count total sweeps in range for stats for i in range(start_idx, end_idx): sv = sweep_list[i] if sv == "BSL": stats.bsl_sweeps_detected += 1 elif sv == "SSL": stats.ssl_sweeps_detected += 1 mode_label = "FILTER" if self.sweep_mode == "filter" else "BOOST" cv_label = "relaxed (CV<0.003)" if self.use_relaxed_cv else "tight (CV<0.001)" print(f"\n Running SMC + Liquidity Sweep ({mode_label}) backtest...") print(f" Sweep detection: {cv_label}") print(f" Sweep lookback: {self.sweep_lookback} bars") print(f" BSL sweeps in range: {stats.bsl_sweeps_detected}") print(f" SSL sweeps in range: {stats.ssl_sweeps_detected}") 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] # Daily reset 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 check 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 # Dynamic confidence AVOID filter 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 # SMC signal try: smc_signal = self.smc.generate_signal(df_slice) except Exception: continue if smc_signal is None: continue # ═══ LIQUIDITY SWEEP CHECK ═══ sweep_found = False sweep_type = "" # Check for recent sweep within lookback window check_start = max(0, i - self.sweep_lookback) for j in range(check_start, i): sv = sweep_list[j] if sv is None: continue # BSL sweep → SELL confirmation if smc_signal.signal_type == "SELL" and sv == "BSL": sweep_found = True sweep_type = "BSL" break # SSL sweep → BUY confirmation if smc_signal.signal_type == "BUY" and sv == "SSL": sweep_found = True sweep_type = "SSL" break # Apply sweep mode if self.sweep_mode == "filter" and not sweep_found: stats.sweep_blocked_trades += 1 continue if sweep_found: stats.sweep_confirmed_trades += 1 # SMC details 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 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 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 # Lot size 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 # Execute trade 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, sweep_type=sweep_type, entry_source="SWEEP+SMC" if sweep_found else "SMC", ) stats.trades.append(trade) # Update state 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...") # Final statistics 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 # ─── XLSX Report ─────────────────────────────────────────────── def generate_xlsx_report(stats: BacktestStats, filepath: str, start_date, end_date, sweep_mode, use_relaxed, lookback): wb = Workbook() header_font = Font(name="Calibri", bold=True, size=12, color="FFFFFF") header_fill = PatternFill(start_color="1F4E79", end_color="1F4E79", fill_type="solid") subheader_font = Font(name="Calibri", bold=True, size=10) subheader_fill = PatternFill(start_color="D6E4F0", end_color="D6E4F0", fill_type="solid") win_fill = PatternFill(start_color="C6EFCE", end_color="C6EFCE", fill_type="solid") loss_fill = PatternFill(start_color="FFC7CE", end_color="FFC7CE", fill_type="solid") border = Border(left=Side(style="thin"), right=Side(style="thin"), top=Side(style="thin"), bottom=Side(style="thin")) net_pnl = stats.total_profit - stats.total_loss ws = wb.active ws.title = "Summary" ws.sheet_properties.tabColor = "1F4E79" ws.merge_cells("A1:F1") ws["A1"] = "XAUBot AI — #17 Liquidity Sweep Backtest" ws["A1"].font = Font(name="Calibri", bold=True, size=16, color="1F4E79") ws["A2"] = f"Period: {start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')}" ws["A3"] = f"Mode: {sweep_mode.upper()} | CV: {'relaxed' if use_relaxed else 'tight'} | Lookback: {lookback}" summary_data = [ ("Performance Metrics", "", True), ("Total Trades", stats.total_trades, False), ("Wins", stats.wins, False), ("Losses", stats.losses, False), ("Win Rate", f"{stats.win_rate:.1f}%", False), ("Avoided (AVOID)", stats.avoided_signals, False), ("", "", False), ("Profit - Loss", "", True), ("Total Profit", f"${stats.total_profit:,.2f}", False), ("Total Loss", f"${stats.total_loss:,.2f}", False), ("Net PnL", f"${net_pnl:,.2f}", False), ("Profit Factor", f"{stats.profit_factor:.2f}", False), ("", "", False), ("Risk Metrics", "", True), ("Max Drawdown", f"{stats.max_drawdown:.1f}%", False), ("Max Drawdown ($)", f"${stats.max_drawdown_usd:,.2f}", False), ("Avg Win", f"${stats.avg_win:,.2f}", False), ("Avg Loss", f"${stats.avg_loss:,.2f}", False), ("Expectancy", f"${stats.expectancy:,.2f}", False), ("Sharpe Ratio", f"{stats.sharpe_ratio:.2f}", False), ("", "", False), ("Sweep Stats", "", True), ("BSL Sweeps Detected", stats.bsl_sweeps_detected, False), ("SSL Sweeps Detected", stats.ssl_sweeps_detected, False), ("Sweep-Confirmed Trades", stats.sweep_confirmed_trades, False), ("Sweep-Blocked Trades", stats.sweep_blocked_trades, False), ] row = 5 for label, value, is_header in summary_data: ws.cell(row=row, column=1, value=label) ws.cell(row=row, column=2, value=value) if is_header: ws.cell(row=row, column=1).font = subheader_font ws.cell(row=row, column=1).fill = subheader_fill ws.cell(row=row, column=2).fill = subheader_fill if label == "Net PnL": ws.cell(row=row, column=2).font = Font(bold=True, color="006100" if net_pnl > 0 else "9C0006") row += 1 ws.column_dimensions["A"].width = 24 ws.column_dimensions["B"].width = 18 # Exit Reason Breakdown exit_counts = {} for t in stats.trades: reason = t.exit_reason.value exit_counts[reason] = exit_counts.get(reason, 0) + 1 ws.cell(row=5, column=4, value="Exit Reasons") ws.cell(row=5, column=4).font = subheader_font ws.cell(row=5, column=4).fill = subheader_fill ws.cell(row=5, column=5).fill = subheader_fill ws.cell(row=5, column=6).fill = subheader_fill row = 6 for reason, count in sorted(exit_counts.items(), key=lambda x: -x[1]): pct = count / stats.total_trades * 100 if stats.total_trades > 0 else 0 ws.cell(row=row, column=4, value=reason) ws.cell(row=row, column=5, value=count) ws.cell(row=row, column=6, value=f"{pct:.1f}%") row += 1 # Sweep-confirmed vs normal performance row += 1 ws.cell(row=row, column=4, value="Sweep Analysis") ws.cell(row=row, column=4).font = subheader_font ws.cell(row=row, column=4).fill = subheader_fill for c in range(5, 8): ws.cell(row=row, column=c).fill = subheader_fill row += 1 for lbl, col in [("Source", 4), ("Trades", 5), ("WR", 6), ("Net PnL", 7)]: ws.cell(row=row, column=col, value=lbl).font = Font(bold=True) row += 1 for source in ["SWEEP+SMC", "SMC"]: st = [t for t in stats.trades if t.entry_source == source] sw = sum(1 for t in st if t.result == TradeResult.WIN) sp = sum(t.profit_usd for t in st) swr = sw / len(st) * 100 if st else 0 ws.cell(row=row, column=4, value=source) ws.cell(row=row, column=5, value=len(st)) ws.cell(row=row, column=6, value=f"{swr:.1f}%") ws.cell(row=row, column=7, value=f"${sp:,.2f}") row += 1 # Trade Log sheet ws2 = wb.create_sheet("Trade Log") headers = [ "Ticket", "Entry Time", "Exit Time", "Dir", "Entry", "Exit", "SL", "TP", "Lot", "Profit ($)", "Pips", "Result", "Exit Reason", "Conf", "Regime", "Session", "Signal", "Sweep", "Source", "BOS", "CHoCH", "FVG", "OB", ] for col, h in enumerate(headers, 1): cell = ws2.cell(row=1, column=col, value=h) cell.font = header_font cell.fill = header_fill for ri, t in enumerate(stats.trades, 2): vals = [ t.ticket, t.entry_time.strftime("%Y-%m-%d %H:%M"), t.exit_time.strftime("%Y-%m-%d %H:%M"), t.direction, t.entry_price, t.exit_price, t.stop_loss, t.take_profit, t.lot_size, round(t.profit_usd, 2), round(t.profit_pips, 1), t.result.value, t.exit_reason.value, round(t.smc_confidence, 2), t.regime, t.session, t.signal_reason, t.sweep_type, t.entry_source, "Y" if t.has_bos else "", "Y" if t.has_choch else "", "Y" if t.has_fvg else "", "Y" if t.has_ob else "", ] for ci, v in enumerate(vals, 1): cell = ws2.cell(row=ri, column=ci, value=v) cell.border = border if ci == 10 and isinstance(v, (int, float)): cell.fill = win_fill if v > 0 else (loss_fill if v < 0 else PatternFill()) for col in range(1, len(headers) + 1): ws2.column_dimensions[get_column_letter(col)].width = max(11, len(headers[col - 1]) + 3) # Equity Curve sheet ws3 = wb.create_sheet("Equity Curve") for c, h in enumerate(["Trade #", "Equity", "Drawdown ($)"], 1): ws3.cell(row=1, column=c, value=h).font = header_font ws3.cell(row=1, column=c).fill = header_fill peak = stats.equity_curve[0] if stats.equity_curve else 5000 for idx, eq in enumerate(stats.equity_curve): if eq > peak: peak = eq ws3.cell(row=idx + 2, column=1, value=idx) ws3.cell(row=idx + 2, column=2, value=round(eq, 2)) ws3.cell(row=idx + 2, column=3, value=round(peak - eq, 2)) if len(stats.equity_curve) > 1: chart = LineChart() chart.title = "Equity Curve" chart.style = 10 chart.y_axis.title = "Equity ($)" chart.width = 30 chart.height = 15 data = Reference(ws3, min_col=2, min_row=1, max_row=len(stats.equity_curve) + 1) chart.add_data(data, titles_from_data=True) chart.series[0].graphicalProperties.line.width = 20000 ws3.add_chart(chart, "E2") wb.save(filepath) print(f"\n Report saved: {filepath}") def generate_log(stats: BacktestStats, filepath: str, start_date, end_date, sweep_mode, use_relaxed, lookback): net_pnl = stats.total_profit - stats.total_loss lines = [] lines.append("=" * 80) lines.append("XAUBOT AI — #17 Liquidity Sweep Backtest") lines.append(f"Mode: {sweep_mode.upper()} | CV: {'relaxed (0.003)' if use_relaxed else 'tight (0.001)'} | Lookback: {lookback}") lines.append("=" * 80) lines.append(f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") lines.append(f"Period: {start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')}") lines.append("") lines.append("--- SWEEP STATS ---") lines.append(f" BSL Sweeps Detected: {stats.bsl_sweeps_detected}") lines.append(f" SSL Sweeps Detected: {stats.ssl_sweeps_detected}") lines.append(f" Sweep-Confirmed Trades: {stats.sweep_confirmed_trades}") lines.append(f" Sweep-Blocked Trades: {stats.sweep_blocked_trades}") lines.append("") lines.append("--- PERFORMANCE ---") lines.append(f" Total Trades: {stats.total_trades}") lines.append(f" Wins: {stats.wins}") lines.append(f" Losses: {stats.losses}") lines.append(f" Win Rate: {stats.win_rate:.1f}%") lines.append(f" Net PnL: ${net_pnl:,.2f}") lines.append(f" Profit Factor: {stats.profit_factor:.2f}") lines.append(f" Max Drawdown: {stats.max_drawdown:.1f}% (${stats.max_drawdown_usd:,.2f})") lines.append(f" Avg Win: ${stats.avg_win:,.2f}") lines.append(f" Avg Loss: ${stats.avg_loss:,.2f}") lines.append(f" Expectancy: ${stats.expectancy:,.2f}") lines.append(f" Sharpe Ratio: {stats.sharpe_ratio:.2f}") lines.append("") # Sweep-confirmed vs normal breakdown lines.append("--- ENTRY SOURCE BREAKDOWN ---") for source in ["SWEEP+SMC", "SMC"]: st = [t for t in stats.trades if t.entry_source == source] sw = sum(1 for t in st if t.result == TradeResult.WIN) sp = sum(t.profit_usd for t in st) swr = sw / len(st) * 100 if st else 0 lines.append(f" {source:12s}: {len(st):3d} trades, {swr:5.1f}% WR, ${sp:>8,.2f}") lines.append("") lines.append("--- EXIT REASONS ---") exit_counts = {} for t in 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 / stats.total_trades * 100 if stats.total_trades > 0 else 0 lines.append(f" {reason:20s}: {count:4d} ({pct:5.1f}%)") lines.append("") lines.append("--- DIRECTION ---") for d in ["BUY", "SELL"]: dt = [t for t in 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 lines.append(f" {d}: {len(dt)} trades, {dwr:.1f}% WR, ${dp:,.2f}") lines.append("") lines.append("--- TRADE LOG ---") lines.append(f"{'#':>4} {'Entry Time':>16} {'Dir':>4} {'Entry':>10} {'Exit':>10} {'P/L($)':>8} {'Result':>6} {'Exit Reason':>18} {'Sweep':>5} {'Source':>10}") lines.append("-" * 120) for idx, t in enumerate(stats.trades, 1): lines.append( f"{idx:4d} {t.entry_time.strftime('%Y-%m-%d %H:%M'):>16} {t.direction:>4} " f"{t.entry_price:>10.2f} {t.exit_price:>10.2f} {t.profit_usd:>8.2f} " f"{t.result.value:>6} {t.exit_reason.value:>18} {t.sweep_type:>5} {t.entry_source:>10}" ) lines.append("\n" + "=" * 80) with open(filepath, "w", encoding="utf-8") as f: f.write("\n".join(lines)) print(f" Log saved: {filepath}") # ─── Main ────────────────────────────────────────────────────── def main(): BASELINE_NET = 1449.86 # Backtest #1 baseline print("=" * 70) print("XAUBOT AI — #17 Liquidity Sweep Filter") print("Base: SMC-Only v4 | Added: Liquidity Sweep entry filter") 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 received") 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) # Calculate liquidity zones with multiple CV thresholds print(" Calculating liquidity zones (multi-CV)...") df = calculate_liquidity_zones_multi( df, cv_thresholds=[0.001, 0.002, 0.003], window_size=20, ) # Count sweeps at each threshold for diagnostics for cv_t in [0.001, 0.002, 0.003]: suffix = f"_{int(cv_t * 10000)}" col = f"sweep{suffix}" if col in df.columns: bsl_count = (df[col] == "BSL").sum() ssl_count = (df[col] == "SSL").sum() print(f" CV={cv_t}: BSL={bsl_count}, SSL={ssl_count} sweeps") 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") # ═══ Run both modes: FILTER with relaxed CV, then BOOST ═══ results = {} for mode, use_relaxed, lookback in [ ("filter", True, 15), # Relaxed CV + filter mode ("filter", True, 30), # Wider lookback ("filter", False, 15), # Tight CV + filter mode ]: label = f"{mode}_{'relaxed' if use_relaxed else 'tight'}_lb{lookback}" print(f"\n{'='*60}") print(f" Config: {label}") bt = LiquiditySweepBacktest( capital=5000.0, max_daily_loss_percent=5.0, max_loss_per_trade_percent=1.0, base_lot_size=0.01, max_lot_size=0.02, recovery_lot_size=0.01, breakeven_pips=30.0, trail_start_pips=50.0, trail_step_pips=30.0, min_profit_to_protect=5.0, max_drawdown_from_peak=50.0, trade_cooldown_bars=10, trend_reversal_mult=0.6, sweep_lookback=lookback, sweep_mode=mode, use_relaxed_cv=use_relaxed, ) 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 results[label] = (stats, net_pnl, mode, use_relaxed, lookback) print(f"\n [{label}] 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" Sweep confirmed: {stats.sweep_confirmed_trades} | Blocked: {stats.sweep_blocked_trades}") print(f" vs BASELINE: ${net_pnl - BASELINE_NET:+,.2f}") # ═══ Print comparison table ═══ print("\n" + "=" * 70) print("#17 LIQUIDITY SWEEP — ALL CONFIGURATIONS") print("=" * 70) print(f"\n {'Config':<35} {'Trades':>6} {'WR':>6} {'Net PnL':>10} {'DD':>6} {'Sharpe':>7} {'PF':>5} {'vs Base':>10}") print(" " + "-" * 95) print(f" {'BASELINE (#1 SMC-Only)':<35} {'686':>6} {'72.2%':>6} {'$1,449.86':>10} {'5.4%':>6} {'1.98':>7} {'1.52':>5} {'—':>10}") best_label = None best_pnl = -float("inf") for label, (stats, net_pnl, mode, use_relaxed, lookback) in results.items(): diff = net_pnl - BASELINE_NET print(f" {label:<35} {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} ${diff:>+9,.2f}") if net_pnl > best_pnl: best_pnl = net_pnl best_label = label # ═══ Save best config ═══ if best_label and best_label in results: best_stats, best_net, best_mode, best_relaxed, best_lb = results[best_label] print(f"\n Best config: {best_label}") # Direction breakdown 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 reports timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") output_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "17_liquidity_sweep_results") os.makedirs(output_dir, exist_ok=True) log_path = os.path.join(output_dir, f"liq_sweep_{timestamp}.log") xlsx_path = os.path.join(output_dir, f"liq_sweep_{timestamp}.xlsx") generate_log(best_stats, log_path, start_date, end_date, best_mode, best_relaxed, best_lb) generate_xlsx_report(best_stats, xlsx_path, start_date, end_date, best_mode, best_relaxed, best_lb) print("\n" + "=" * 70) print(f"Output: {output_dir}") print(f" Log: {os.path.basename(log_path)}") print(f" Report: {os.path.basename(xlsx_path)}") print("=" * 70) mt5.disconnect() print("Backtest complete!") if __name__ == "__main__": main()