""" Backtest #20 — Early Cut Tuning ================================ Base: SMC-Only v4 (Backtest #1) Modified: Tune early_cut exit sensitivity Current baseline early cut logic (B.3): if current_profit < 0: loss_percent_of_max = abs(current_profit) / max_loss_per_trade * 100 if momentum < -30 and loss_percent_of_max >= 30: exit EARLY_CUT From corrected #19B results: early_cut accounts for 12.4% of all exits Many early_cut trades might have recovered if given more time Configurations: A: momentum < -40 (more patient on momentum, keep loss at 30%) B: momentum < -50 (very patient on momentum) C: momentum < -30, loss >= 40% (allow bigger losses before cutting) D: momentum < -30, loss >= 50% (allow even bigger losses) E: momentum < -40, loss >= 40% (combined patience) F: Disable early cut entirely Usage: python backtests/backtest_20_early_cut_tune.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" @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 # ─── Early Cut Tune Backtest ───────────────────────────────── class EarlyCutTuneBacktest: """SMC-Only + Tunable early cut parameters.""" 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, # Early cut tuning params early_cut_momentum: float = -30.0, # momentum threshold (default: -30) early_cut_loss_pct: float = 30.0, # loss % of max threshold (default: 30%) early_cut_enabled: bool = True, # can disable entirely ): 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 # Early cut params self.early_cut_momentum = early_cut_momentum self.early_cut_loss_pct = early_cut_loss_pct self.early_cut_enabled = early_cut_enabled 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 = 2200000 # ── 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: """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) 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 #1, early cut tunable) ── 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 — identical to baseline #1 EXCEPT: B.3 Early cut uses self.early_cut_momentum and self.early_cut_loss_pct """ 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 checks # ════════════════════════════════════════════════ # A.0 TP hit 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 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 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 # A.2 Trailing SL 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 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: 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 # A.5 Weekend 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 checks # ════════════════════════════════════════════════ # 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 — TUNABLE THRESHOLDS ║ # ╚══════════════════════════════════════════════╝ if self.early_cut_enabled and 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: 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 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 detection 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 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 # 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: 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 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 (synced with #1) ── 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 ec_info = f"mom<{self.early_cut_momentum}, loss>={self.early_cut_loss_pct}%" if not self.early_cut_enabled: ec_info = "DISABLED" print(f" Early cut: {ec_info}") 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 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 = 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 = 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 — #20 Early Cut Tuning") print("Base: SMC-Only v4 | Modified: Early cut sensitivity") 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 ═══ configs = [ { "name": "A: mom<-40", "early_cut_momentum": -40.0, "early_cut_loss_pct": 30.0, "early_cut_enabled": True, }, { "name": "B: mom<-50", "early_cut_momentum": -50.0, "early_cut_loss_pct": 30.0, "early_cut_enabled": True, }, { "name": "C: loss>=40%", "early_cut_momentum": -30.0, "early_cut_loss_pct": 40.0, "early_cut_enabled": True, }, { "name": "D: loss>=50%", "early_cut_momentum": -30.0, "early_cut_loss_pct": 50.0, "early_cut_enabled": True, }, { "name": "E: mom<-40+loss>=40%", "early_cut_momentum": -40.0, "early_cut_loss_pct": 40.0, "early_cut_enabled": True, }, { "name": "F: disabled", "early_cut_momentum": -30.0, "early_cut_loss_pct": 30.0, "early_cut_enabled": False, }, ] baseline_pnl = 1449.86 baseline_stoch_pnl = 1320.41 all_results = [] for cfg in configs: print(f"\n{'=' * 60}") print(f" Config: {cfg['name']}") bt = EarlyCutTuneBacktest( early_cut_momentum=cfg["early_cut_momentum"], early_cut_loss_pct=cfg["early_cut_loss_pct"], early_cut_enabled=cfg["early_cut_enabled"], ) 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 # Count early_cut exits ec_count = sum(1 for t in stats.trades if t.exit_reason == ExitReason.EARLY_CUT) ec_pct = ec_count / stats.total_trades * 100 if stats.total_trades > 0 else 0 # Early cut P/L breakdown ec_trades = [t for t in stats.trades if t.exit_reason == ExitReason.EARLY_CUT] ec_total_loss = sum(t.profit_usd for t in ec_trades) 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" Early cuts: {ec_count} ({ec_pct:.1f}%) | EC total P/L: ${ec_total_loss:,.2f}") print(f" vs BASELINE: ${net_pnl - baseline_pnl:+,.2f}") all_results.append({ "name": cfg["name"], "stats": stats, "net_pnl": net_pnl, "ec_count": ec_count, "ec_pct": ec_pct, "ec_pnl": ec_total_loss, }) # ═══ Summary ═══ print(f"\n{'=' * 70}") print("#20 EARLY CUT TUNING — ALL CONFIGURATIONS") print("=" * 70) print(f"\n {'Config':<25} {'Trades':>6} {'WR':>6} {'Net PnL':>10} {'DD':>6} {'Sharpe':>7} {'PF':>5} {'EC#':>5} {'EC%':>6} {'EC P/L':>10} {'vs Base':>10}") print(f" {'-' * 110}") print(f" {'BASELINE (#1)':<25} {'686':>6} {'72.2%':>6} {'$1,449.86':>10} {'5.4%':>6} {'1.98':>7} {'1.52':>5} {'—':>5} {'—':>6} {'—':>10} {'—':>10}") print(f" {'#8 Stoch+Sell':<25} {'416':>6} {'76.7%':>6} {'$1,320.41':>10} {'2.8%':>6} {'3.17':>7} {'1.76':>5} {'—':>5} {'—':>6} {'—':>10} {'—':>10}") best_result = None best_pnl = -999999 for r in all_results: s = r["stats"] diff = r["net_pnl"] - baseline_pnl print(f" {r['name']:<25} {s.total_trades:>6} {s.win_rate:>5.1f}% ${r['net_pnl']:>9,.2f} {s.max_drawdown:>5.1f}% {s.sharpe_ratio:>7.2f} {s.profit_factor:>5.2f} {r['ec_count']:>5} {r['ec_pct']:>5.1f}% ${r['ec_pnl']:>9,.2f} ${diff:>+9,.2f}") if r["net_pnl"] > best_pnl: best_pnl = r["net_pnl"] best_result = r print(f"\n Best config: {best_result['name']}") # Direction breakdown for best best_stats = best_result["stats"] 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 for best 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__)), "20_early_cut_results") os.makedirs(output_dir, exist_ok=True) log_path = os.path.join(output_dir, f"early_cut_{timestamp}.log") with open(log_path, "w") as f: f.write(f"#20 Early Cut Tuning Results\n") f.write(f"Generated: {datetime.now()}\n\n") for r in all_results: s = r["stats"] f.write(f"Config: {r['name']}\n") f.write(f" Trades: {s.total_trades}, WR: {s.win_rate:.1f}%, Net: ${r['net_pnl']:,.2f}\n") f.write(f" DD: {s.max_drawdown:.1f}%, Sharpe: {s.sharpe_ratio:.2f}, PF: {s.profit_factor:.2f}\n") f.write(f" Early cuts: {r['ec_count']} ({r['ec_pct']:.1f}%), EC P/L: ${r['ec_pnl']:,.2f}\n\n") print(f" Log saved: {log_path}") # XLSX for best config try: from backtests.backtest_01_smc_only import generate_xlsx_report as gen_xlsx xlsx_path = os.path.join(output_dir, f"early_cut_{timestamp}.xlsx") gen_xlsx(best_stats, xlsx_path, start_date, end_date) except Exception as e: print(f" [WARN] XLSX generation skipped: {e}") mt5.disconnect() print(f"\n{'=' * 70}") print(f"Output: {output_dir}") print(f" Log: early_cut_{timestamp}.log") print("=" * 70) print("Backtest complete!") if __name__ == "__main__": main()