214 lines
8.3 KiB
Python
214 lines
8.3 KiB
Python
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"""
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Quick runner for v0.6.0 FIXED backtest
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======================================
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Usage:
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python backtests/v0.6.0_fixed/run_backtest.py --days 90
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python backtests/v0.6.0_fixed/run_backtest.py --days 30 --save
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"""
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import sys
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import os
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from dotenv import load_dotenv
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# Add parent to path
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
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# Load .env file
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load_dotenv()
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from datetime import datetime, timedelta
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from loguru import logger
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from src.mt5_connector import MT5Connector
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from src.feature_eng import FeatureEngineer
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from backtest_v0_6_0_fixed import BacktestFixed
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import argparse
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def main():
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parser = argparse.ArgumentParser(description="Run XAUBot AI v0.6.0 FIXED Backtest")
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parser.add_argument("--days", type=int, default=90, help="Days to backtest (default: 90)")
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parser.add_argument("--save", action="store_true", help="Save results to CSV")
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args = parser.parse_args()
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logger.info("=" * 80)
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logger.info("XAUBOT AI v0.6.0 FIXED - BACKTEST RUNNER")
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logger.info("=" * 80)
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logger.info("")
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logger.info("PROFESSOR'S FIXES APPLIED:")
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logger.info(" [FIX 1] Fuzzy Thresholds: 70-90% tiered (was fixed 90%)")
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logger.info(" [FIX 2] Trajectory Calibration: regime penalty + uncertainty")
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logger.info(" [FIX 3] Session Filter: Sydney/Tokyo DISABLED (00:00-10:00)")
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logger.info(" [FIX 4] Unicode Fix: ASCII only (no emojis)")
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logger.info(" [FIX 5] Max Loss: $25/trade (was $50)")
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logger.info("")
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logger.info(f"Backtest Period: {args.days} days")
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logger.info("=" * 80)
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logger.info("")
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# Load MT5 credentials from environment
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mt5_login = int(os.getenv("MT5_LOGIN", "0"))
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mt5_password = os.getenv("MT5_PASSWORD", "")
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mt5_server = os.getenv("MT5_SERVER", "")
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mt5_path = os.getenv("MT5_PATH", "")
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if mt5_login == 0 or not mt5_password or not mt5_server:
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logger.error("MT5 credentials not found in .env file")
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logger.error("Please set: MT5_LOGIN, MT5_PASSWORD, MT5_SERVER")
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return
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# Load data
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logger.info("Step 1/4: Connecting to MT5...")
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connector = MT5Connector(
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login=mt5_login,
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password=mt5_password,
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server=mt5_server,
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path=mt5_path if mt5_path else None
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)
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if not connector.connect():
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logger.error("Failed to connect to MT5")
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return
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end_date = datetime.now()
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start_date = end_date - timedelta(days=args.days)
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# Calculate bars needed: 90 days × 24 hours × 4 (M15) = ~8640 bars
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bars_needed = args.days * 24 * 4
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logger.info(f"Step 2/4: Loading XAUUSD M15 data (last {args.days} days, ~{bars_needed} bars)...")
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df = connector.get_market_data("XAUUSD", "M15", count=bars_needed)
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if df is None or len(df) == 0:
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logger.error("Failed to load data")
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connector.disconnect()
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return
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logger.info(f" Loaded {len(df)} bars")
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logger.info(f" Date range: {df['time'].min()} to {df['time'].max()}")
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# Add features
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logger.info("Step 3/4: Engineering features...")
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features = FeatureEngineer()
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df = features.calculate_all(df)
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# Add missing SMC and regime features with defaults (for TESTING MODE)
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import polars as pl
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missing_features = ['swing_high', 'swing_low', 'fvg_signal', 'ob', 'bos', 'choch', 'market_structure']
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for feat in missing_features:
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if feat not in df.columns:
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df = df.with_columns([pl.lit(0).alias(feat)])
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# Add regime if missing (will be filled by regime detector later)
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if 'regime' not in df.columns:
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df = df.with_columns([pl.lit(0).alias("regime")]) # 0=ranging (numeric)
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else:
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# Encode regime strings to numbers if exists
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regime_map = {"ranging": 0, "trending": 1, "volatile": 2}
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df = df.with_columns([
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pl.col("regime").map_dict(regime_map, default=0).alias("regime")
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])
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logger.info(f" Added {len(df.columns)} features (including {len(missing_features)} SMC placeholders)")
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# Run backtest
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logger.info("Step 4/4: Running backtest with FIXED logic...")
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logger.info("")
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bt = BacktestFixed(
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ml_threshold=0.30, # TESTING: Relaxed for more signals
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signal_confirmation=1, # TESTING: Accept signal immediately
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max_loss_per_trade=25.0, # FIX 5
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trade_cooldown_bars=5, # TESTING: Reduced cooldown
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)
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stats = bt.run(df)
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# Print detailed results
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print("\n" + "=" * 80)
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print("BACKTEST RESULTS - XAUBot AI v0.6.0 FIXED")
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print("=" * 80)
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print(f"\nPERFORMANCE METRICS:")
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print(f" Total Trades: {stats.total_trades}")
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print(f" Wins: {stats.wins}")
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print(f" Losses: {stats.losses}")
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print(f" Win Rate: {stats.win_rate:.1f}%")
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print(f"\nPROFIT ANALYSIS:")
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print(f" Avg Win: ${stats.avg_win:.2f}")
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print(f" Avg Loss: ${stats.avg_loss:.2f}")
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print(f" Win/Loss Ratio: 1:{stats.avg_loss/stats.avg_win:.2f}" if stats.avg_win > 0 else " Win/Loss Ratio: N/A")
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print(f" Micro Profits (<$1): {stats.micro_profits}/{stats.wins} ({stats.micro_profit_pct:.0f}%)")
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print(f"\nRISK METRICS:")
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print(f" Sharpe Ratio: {stats.sharpe_ratio:.2f}")
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print(f" Profit Factor: {stats.profit_factor:.2f}")
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print(f" Expectancy: ${stats.expectancy:.2f}/trade")
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print(f" Max Drawdown: {stats.max_drawdown:.1f}% (${stats.max_drawdown_usd:.2f})")
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print(f"\nNET RESULTS:")
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net_profit = stats.total_profit - stats.total_loss
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print(f" Total Profit: ${stats.total_profit:.2f}")
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print(f" Total Loss: -${stats.total_loss:.2f}")
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print(f" Net P/L: ${net_profit:.2f}")
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# Target comparison
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print(f"\n" + "-" * 80)
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print("PROFESSOR'S TARGET COMPARISON:")
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print("-" * 80)
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print(f"{'Metric':<25} | {'Target':>12} | {'Actual':>12} | {'Status':>10}")
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print("-" * 80)
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targets = [
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("Avg Win", "$8-12", f"${stats.avg_win:.2f}", stats.avg_win >= 8),
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("RR Ratio", "1.5:1 or better", f"1:{stats.avg_loss/stats.avg_win:.2f}" if stats.avg_win > 0 else "N/A",
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(stats.avg_loss/stats.avg_win <= 1.5) if stats.avg_win > 0 else False),
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("Micro Profits", "<20%", f"{stats.micro_profit_pct:.0f}%", stats.micro_profit_pct < 20),
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("Win Rate", "62-65%", f"{stats.win_rate:.1f}%", 62 <= stats.win_rate <= 67),
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("Sharpe Ratio", "1.5+", f"{stats.sharpe_ratio:.2f}", stats.sharpe_ratio >= 1.5),
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]
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for name, target, actual, met in targets:
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status = "PASS" if met else "FAIL"
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status_symbol = "[OK]" if met else "[X]"
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print(f"{name:<25} | {target:>12} | {actual:>12} | {status_symbol:>10}")
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print("=" * 80)
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# Exit reason breakdown
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print(f"\nEXIT REASON BREAKDOWN:")
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exit_reasons = {}
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for trade in stats.trades:
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reason = trade.exit_reason.value
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if reason not in exit_reasons:
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exit_reasons[reason] = []
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exit_reasons[reason].append(trade.profit_usd)
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for reason, profits in sorted(exit_reasons.items(), key=lambda x: len(x[1]), reverse=True):
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count = len(profits)
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avg_profit = sum(profits) / count
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print(f" {reason:<20}: {count:>3} trades (avg ${avg_profit:>6.2f})")
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# Save if requested
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if args.save:
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import csv
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output_file = f"backtests/v0.6.0_fixed/results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv"
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os.makedirs(os.path.dirname(output_file), exist_ok=True)
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with open(output_file, 'w', newline='') as f:
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writer = csv.writer(f)
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writer.writerow([
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'Ticket', 'Entry Time', 'Exit Time', 'Direction', 'Entry Price', 'Exit Price',
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'Profit USD', 'Profit Pips', 'Result', 'Exit Reason', 'Fuzzy Conf',
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'Trajectory Pred', 'Peak Profit', 'Regime', 'Session'
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])
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for t in stats.trades:
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writer.writerow([
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t.ticket, t.entry_time, t.exit_time, t.direction, t.entry_price, t.exit_price,
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f"{t.profit_usd:.2f}", f"{t.profit_pips:.1f}", t.result.value, t.exit_reason.value,
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f"{t.fuzzy_confidence:.3f}", f"{t.trajectory_predicted:.2f}", f"{t.peak_profit:.2f}",
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t.regime, t.session
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])
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logger.info(f"\nResults saved to: {output_file}")
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connector.disconnect()
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logger.info("\nBacktest completed!")
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if __name__ == "__main__":
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main()
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