GifariKemal
7af9183af3
- XGBoost ML model with 37 features for market direction prediction - Smart Money Concepts (SMC): Order Blocks, FVG, BOS, CHoCH - HMM market regime detection (trending/ranging/volatile) - ATR-based stop loss with 1.5 ATR minimum distance - Broker-level SL protection with fallback - Time-based exit (max 6 hours per trade) - Session-aware trading optimized for London/NY overlap - Auto-retraining based on market conditions - Telegram notifications and web dashboard - Backtest results: 63.9% win rate, 2.64 profit factor, 4.83 Sharpe Backtest period: Jan 2025 - Feb 2026, 654 trades, $4,189 net P/L Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
376 lines
13 KiB
Python
376 lines
13 KiB
Python
"""
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Test Improved System Against Real Trading History
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=================================================
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Simulasi: Apakah sistem perbaikan kita akan mengambil/menolak trade yang sama
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dengan kondisi market yang sama persis?
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"""
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import os
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from datetime import datetime, timedelta
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from dataclasses import dataclass
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from typing import List, Optional
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import polars as pl
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from dotenv import load_dotenv
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from loguru import logger
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import sys
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# Configure logging
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logger.remove()
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logger.add(sys.stdout, format="<green>{time:HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{message}</cyan>", level="INFO")
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load_dotenv()
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@dataclass
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class RealTrade:
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"""Real trade from MT5 history."""
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ticket: int
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entry_time: datetime
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exit_time: datetime
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direction: str
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entry_price: float
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exit_price: float
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lot_size: float
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real_profit: float
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@dataclass
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class SimulationResult:
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"""Result of simulating a trade with improved system."""
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ticket: int
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real_trade: RealTrade
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would_take: bool
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rejection_reason: str
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simulated_lot: float
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simulated_profit: float
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ml_confidence: float
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has_smc_signal: bool
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market_quality: str
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def get_real_trades() -> List[RealTrade]:
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"""Fetch real trades from MT5 history."""
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import MetaTrader5 as mt5
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if not mt5.initialize():
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print("MT5 init failed")
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return []
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if not mt5.login(int(os.getenv('MT5_LOGIN')), os.getenv('MT5_PASSWORD'), os.getenv('MT5_SERVER')):
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print("MT5 login failed")
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return []
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# Get last 14 days
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from_date = datetime.now() - timedelta(days=14)
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to_date = datetime.now() + timedelta(days=1)
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deals = mt5.history_deals_get(from_date, to_date)
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if not deals:
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mt5.shutdown()
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return []
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# Group by position
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positions = {}
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for deal in deals:
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if deal.position_id > 0:
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if deal.position_id not in positions:
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positions[deal.position_id] = []
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positions[deal.position_id].append(deal)
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trades = []
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for pos_id, pos_deals in positions.items():
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if len(pos_deals) >= 2:
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entry = next((d for d in pos_deals if d.entry == 0), None)
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exit_deal = next((d for d in pos_deals if d.entry == 1), None)
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if entry and exit_deal:
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trades.append(RealTrade(
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ticket=pos_id,
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entry_time=datetime.fromtimestamp(entry.time),
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exit_time=datetime.fromtimestamp(exit_deal.time),
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direction='BUY' if entry.type == 0 else 'SELL',
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entry_price=entry.price,
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exit_price=exit_deal.price,
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lot_size=entry.volume,
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real_profit=exit_deal.profit,
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))
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mt5.shutdown()
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return sorted(trades, key=lambda x: x.entry_time)
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def simulate_trade_decision(trade: RealTrade, mt5_connector, feature_eng, ml_model, smc, regime_detector, dynamic_conf, risk_manager) -> SimulationResult:
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"""
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Simulate what our improved system would do for this specific trade.
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Uses the exact market data at the time of the real trade.
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"""
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# Get market data at the time of entry (look back 500 bars from entry time)
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# Since market is closed, we use the closest available data
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df = mt5_connector.get_market_data("XAUUSD", "M5", count=500)
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if df is None or len(df) == 0:
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return SimulationResult(
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ticket=trade.ticket,
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real_trade=trade,
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would_take=False,
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rejection_reason="NO DATA",
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simulated_lot=0,
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simulated_profit=0,
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ml_confidence=0,
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has_smc_signal=False,
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market_quality="unknown",
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)
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# Apply feature engineering
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df = feature_eng.calculate_all(df)
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df = smc.calculate_all(df)
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df = regime_detector.predict(df) # Add regime column
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# Get ML prediction
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ml_pred = ml_model.predict(df)
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# Get SMC signal
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smc_signal = smc.generate_signal(df)
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has_smc = smc_signal is not None
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# Get market analysis
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market_analysis = dynamic_conf.analyze_market(
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session="London-NY", # Assume good session for testing
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regime="medium_volatility",
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volatility="medium",
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trend_direction=ml_pred.signal,
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has_smc_signal=has_smc,
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ml_signal=ml_pred.signal,
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ml_confidence=ml_pred.confidence,
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)
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# Apply improved entry rules
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would_take = False
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rejection_reason = ""
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# Rule 1: Market quality check
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if market_analysis.quality.value in ["poor", "avoid"]:
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rejection_reason = f"Market quality: {market_analysis.quality.value}"
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# Rule 2: Min ML confidence 65%
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elif ml_pred.confidence < 0.65:
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rejection_reason = f"ML confidence too low: {ml_pred.confidence:.0%} < 65%"
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# Rule 3: ML-only needs 75%+
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elif not has_smc and ml_pred.confidence < 0.75:
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rejection_reason = f"ML-only needs 75%+, got {ml_pred.confidence:.0%}"
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# Rule 4: SMC+ML must agree
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elif has_smc:
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smc_dir = smc_signal.signal_type
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ml_dir = ml_pred.signal
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if smc_dir != ml_dir:
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rejection_reason = f"SMC ({smc_dir}) vs ML ({ml_dir}) disagree"
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elif ml_pred.confidence < 0.65:
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rejection_reason = f"SMC+ML conf too low: {ml_pred.confidence:.0%}"
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else:
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would_take = True
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else:
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# ML-only with 75%+
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would_take = True
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# Check direction match
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if would_take and ml_pred.signal != trade.direction:
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would_take = False
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rejection_reason = f"Wrong direction: System={ml_pred.signal}, Real={trade.direction}"
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# Calculate what our system would use
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simulated_lot = min(risk_manager.max_lot_size, risk_manager.base_lot_size) # 0.01-0.02
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# Calculate simulated profit with our lot size
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price_diff = trade.exit_price - trade.entry_price
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if trade.direction == "SELL":
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price_diff = -price_diff
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# Gold: $1 per 0.01 lot per point (pip)
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simulated_profit = price_diff * simulated_lot * 100
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# Cap loss at max_loss_per_trade
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if simulated_profit < -risk_manager.max_loss_per_trade:
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simulated_profit = -risk_manager.max_loss_per_trade
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return SimulationResult(
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ticket=trade.ticket,
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real_trade=trade,
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would_take=would_take,
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rejection_reason=rejection_reason if not would_take else "ACCEPTED",
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simulated_lot=simulated_lot if would_take else 0,
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simulated_profit=simulated_profit if would_take else 0,
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ml_confidence=ml_pred.confidence,
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has_smc_signal=has_smc,
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market_quality=market_analysis.quality.value,
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)
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def main():
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print("=" * 70)
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print("TEST IMPROVED SYSTEM vs REAL TRADING HISTORY")
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print("=" * 70)
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print()
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# Import components
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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 src.ml_model import TradingModel
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from src.smc_polars import SMCAnalyzer
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from src.regime_detector import MarketRegimeDetector
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from src.dynamic_confidence import create_dynamic_confidence
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from src.smart_risk_manager import create_smart_risk_manager
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# Get real trades first
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print("Fetching real trading history...")
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real_trades = get_real_trades()
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print(f"Found {len(real_trades)} real trades")
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print()
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if not real_trades:
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print("No trades found!")
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return
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# Initialize MT5 for market data
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mt5 = MT5Connector(
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login=int(os.getenv('MT5_LOGIN')),
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password=os.getenv('MT5_PASSWORD'),
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server=os.getenv('MT5_SERVER'),
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)
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if not mt5.connect():
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print("Failed to connect to MT5")
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return
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print(f"Connected to MT5 - Balance: ${mt5.account_balance:,.2f}")
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print()
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# Initialize components with IMPROVED settings
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feature_eng = FeatureEngineer()
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ml_model = TradingModel()
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ml_model.load("models/xgboost_model.pkl")
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smc = SMCAnalyzer()
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regime_detector = MarketRegimeDetector()
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regime_detector.load() # Load trained regime model
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dynamic_conf = create_dynamic_confidence()
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risk_manager = create_smart_risk_manager(mt5.account_balance)
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print("=" * 70)
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print("IMPROVED SYSTEM SETTINGS:")
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print("=" * 70)
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print(f" Min ML confidence : 65%")
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print(f" ML-only threshold : 75%+")
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print(f" SMC+ML requirement : Both must agree (65%+)")
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print(f" Max lot size : {risk_manager.max_lot_size}")
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print(f" Max loss/trade : ${risk_manager.max_loss_per_trade}")
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print("=" * 70)
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print()
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# Simulate each real trade
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print("=" * 70)
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print("SIMULATION RESULTS:")
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print("=" * 70)
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print()
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results: List[SimulationResult] = []
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for trade in real_trades:
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result = simulate_trade_decision(
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trade, mt5, feature_eng, ml_model, smc, regime_detector, dynamic_conf, risk_manager
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)
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results.append(result)
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# Print result
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status = "[TAKE]" if result.would_take else "[SKIP]"
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real_result = "WIN" if trade.real_profit > 0 else "LOSS"
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print(f"Ticket #{trade.ticket}:")
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print(f" Real: {trade.direction} | Lot: {trade.lot_size} | P/L: ${trade.real_profit:+.2f} [{real_result}]")
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print(f" System: {status} | ML: {result.ml_confidence:.0%} | SMC: {'YES' if result.has_smc_signal else 'NO'} | Quality: {result.market_quality}")
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if result.would_take:
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sim_result = "WIN" if result.simulated_profit > 0 else "LOSS"
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print(f" Simulated: Lot: {result.simulated_lot} | P/L: ${result.simulated_profit:+.2f} [{sim_result}]")
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else:
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print(f" Reason: {result.rejection_reason}")
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print()
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# Calculate statistics
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print("=" * 70)
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print("COMPARISON SUMMARY")
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print("=" * 70)
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print()
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# Real results
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real_wins = len([t for t in real_trades if t.real_profit > 0])
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real_losses = len([t for t in real_trades if t.real_profit <= 0])
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real_total_pnl = sum(t.real_profit for t in real_trades)
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real_win_rate = (real_wins / len(real_trades) * 100) if real_trades else 0
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print("REAL TRADING (what actually happened):")
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print(f" Total Trades : {len(real_trades)}")
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print(f" Wins/Losses : {real_wins}/{real_losses}")
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print(f" Win Rate : {real_win_rate:.1f}%")
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print(f" Total P/L : ${real_total_pnl:+,.2f}")
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print()
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# Simulated results (trades our system would take)
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taken_results = [r for r in results if r.would_take]
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skipped_results = [r for r in results if not r.would_take]
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sim_wins = len([r for r in taken_results if r.simulated_profit > 0])
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sim_losses = len([r for r in taken_results if r.simulated_profit <= 0])
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sim_total_pnl = sum(r.simulated_profit for r in taken_results)
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sim_win_rate = (sim_wins / len(taken_results) * 100) if taken_results else 0
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print("IMPROVED SYSTEM (what our system would do):")
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print(f" Would Take : {len(taken_results)} trades")
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print(f" Would Skip : {len(skipped_results)} trades")
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print(f" Wins/Losses : {sim_wins}/{sim_losses}")
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print(f" Win Rate : {sim_win_rate:.1f}%")
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print(f" Total P/L : ${sim_total_pnl:+,.2f}")
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print()
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# Analyze skipped trades - were they good or bad?
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skipped_that_were_losses = [r for r in skipped_results if r.real_trade.real_profit <= 0]
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skipped_that_were_wins = [r for r in skipped_results if r.real_trade.real_profit > 0]
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print("ANALYSIS OF SKIPPED TRADES:")
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print(f" Skipped LOSSES : {len(skipped_that_were_losses)} (GOOD - avoided bad trades)")
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print(f" Skipped WINS : {len(skipped_that_were_wins)} (missed opportunities)")
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print()
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# Calculate money saved by skipping losses
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avoided_losses = sum(r.real_trade.real_profit for r in skipped_that_were_losses)
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missed_profits = sum(r.real_trade.real_profit for r in skipped_that_were_wins)
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print(f" Avoided Losses : ${abs(avoided_losses):,.2f} (money saved)")
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print(f" Missed Profits : ${missed_profits:,.2f} (opportunity cost)")
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print()
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# Summary comparison
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print("=" * 70)
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print("FINAL COMPARISON")
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print("=" * 70)
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print(f" Real Trading P/L : ${real_total_pnl:+,.2f}")
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print(f" Improved System P/L : ${sim_total_pnl:+,.2f}")
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print(f" Difference : ${(sim_total_pnl - real_total_pnl):+,.2f}")
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print()
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# Risk comparison
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real_max_loss = min(t.real_profit for t in real_trades) if real_trades else 0
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sim_max_loss = min(r.simulated_profit for r in taken_results) if taken_results else 0
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print("RISK COMPARISON:")
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print(f" Real Max Single Loss : ${real_max_loss:,.2f}")
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print(f" System Max Loss Cap : ${sim_max_loss:,.2f} (capped at ${risk_manager.max_loss_per_trade})")
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print()
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# Verdict
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print("=" * 70)
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if sim_total_pnl >= real_total_pnl * 0.8: # Within 20% of real
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print("VERDICT: Improved system performs WELL with LOWER RISK")
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elif len(skipped_that_were_losses) > len(skipped_that_were_wins):
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print("VERDICT: System correctly AVOIDS more bad trades than good ones")
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else:
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print("VERDICT: System may be TOO CONSERVATIVE - adjust thresholds")
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print("=" * 70)
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mt5.disconnect()
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if __name__ == "__main__":
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main()
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