feat: Smart AI Trading Bot for XAUUSD with ML and SMC
- 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>
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GifariKemal
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"""
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Simulation Test - Test the improved trading system without real trades.
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Uses real market data but only simulates decisions.
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"""
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import asyncio
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import sys
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from datetime import datetime, timedelta
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from loguru import logger
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from dotenv import load_dotenv
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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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async def run_simulation():
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"""Run simulation test with improved settings."""
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print("=" * 60)
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print("SIMULATION TEST - IMPROVED TRADING SYSTEM")
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print("=" * 60)
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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, SMCSignal
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from src.regime_detector import MarketRegimeDetector
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from src.session_filter import SessionFilter
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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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# Initialize
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import os
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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")
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print(f"Balance: ${mt5.account_balance:,.2f}")
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print(f"Equity: ${mt5.account_equity:,.2f}")
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print()
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# Components
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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 = MarketRegimeDetector()
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regime.load()
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session_filter = SessionFilter()
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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("=" * 60)
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print("IMPROVED SETTINGS:")
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print("=" * 60)
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print(f" ML-only threshold: 85%+ required")
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print(f" SMC+ML: Both MUST agree")
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print(f" Market quality: Skip POOR and AVOID")
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print(f" Min ML confidence: 70%")
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print(f" Trade cooldown: 5 minutes")
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print(f" Max lot: 0.02")
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print(f" Max loss/trade: $30")
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print(f" Max daily loss: 2%")
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print("=" * 60)
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print()
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# Fetch data
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symbol = "XAUUSD"
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df = mt5.get_market_data(symbol, "M5", count=500)
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if df is None or len(df) == 0:
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print("Failed to fetch data (market might be closed)")
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print("Using last available data...")
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df = mt5.get_market_data(symbol, "M5", count=500)
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if df is None or len(df) == 0:
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print("Still no data - market is closed")
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mt5.disconnect()
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return
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print(f"Fetched {len(df)} bars of {symbol} M5 data")
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print(f"Latest price: ${df['close'][-1]:,.2f}")
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print()
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# Feature engineering
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df = feature_eng.calculate_all(df)
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# Add SMC features (required by ML model)
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df = smc.calculate_all(df)
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# Regime detection
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df = regime.predict(df) # Adds regime columns to df
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regime_state = regime.get_current_state(df) # Get regime state object
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print(f"Current Regime: {regime_state.regime.value if regime_state else 'N/A'}")
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print(f"Recommendation: {regime_state.recommendation if regime_state else 'N/A'}")
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print()
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# Session check
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can_trade, reason, _ = session_filter.can_trade()
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session_info = session_filter.get_status_report()
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print(f"Session: {session_info.get('current_session', 'Unknown')}")
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print(f"Can Trade: {can_trade} - {reason}")
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print()
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# ML Prediction
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feature_cols = [c for c in df.columns if c in ml_model.feature_names]
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ml_pred = ml_model.predict(df, feature_cols)
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print(f"ML Prediction: {ml_pred.signal} ({ml_pred.confidence:.0%})")
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print()
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# SMC Signal
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smc_signal = smc.generate_signal(df)
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if smc_signal:
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print(f"SMC Signal: {smc_signal.signal_type} ({smc_signal.confidence:.0%})")
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print(f" Entry: {smc_signal.entry_price:.2f}")
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print(f" SL: {smc_signal.stop_loss:.2f}")
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print(f" TP: {smc_signal.take_profit:.2f}")
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else:
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print("SMC Signal: NONE")
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print()
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# Dynamic Confidence Analysis
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market_analysis = dynamic_conf.analyze_market(
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session=session_info.get('current_session', 'Unknown'),
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regime=regime_state.regime.value,
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volatility=session_info.get('volatility', 'medium'),
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trend_direction=regime_state.regime.value,
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has_smc_signal=(smc_signal is not None),
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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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print("=" * 60)
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print("MARKET ANALYSIS:")
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print("=" * 60)
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print(f" Quality: {market_analysis.quality.value.upper()}")
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print(f" Score: {market_analysis.score}")
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print(f" Threshold: {market_analysis.confidence_threshold:.0%}")
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print()
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for reason in market_analysis.reasons:
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print(f" {reason}")
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print()
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# Entry Decision
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print("=" * 60)
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print("ENTRY DECISION (SIMULATION):")
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print("=" * 60)
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# Check conditions
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should_trade = False
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trade_reason = ""
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# 1. Market quality check
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if market_analysis.quality.value in ["poor", "avoid"]:
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trade_reason = f"SKIP: Market quality {market_analysis.quality.value}"
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# 2. ML confidence check
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elif ml_pred.confidence < 0.70:
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trade_reason = f"SKIP: ML confidence {ml_pred.confidence:.0%} < 70%"
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# 3. ML-only (no SMC)
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elif smc_signal is None:
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if ml_pred.confidence >= 0.85:
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should_trade = True
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trade_reason = f"TRADE (ML-ONLY): {ml_pred.signal} at {ml_pred.confidence:.0%}"
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else:
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trade_reason = f"SKIP: ML-only needs 85%+, got {ml_pred.confidence:.0%}"
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# 4. SMC + ML combination
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else:
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ml_agrees = (
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(smc_signal.signal_type == "BUY" and ml_pred.signal == "BUY") or
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(smc_signal.signal_type == "SELL" and ml_pred.signal == "SELL")
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)
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if ml_agrees:
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should_trade = True
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trade_reason = f"TRADE (SMC+ML): {smc_signal.signal_type} - Both agree!"
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else:
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trade_reason = f"SKIP: SMC={smc_signal.signal_type} vs ML={ml_pred.signal} - Disagree"
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print(f" {trade_reason}")
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print()
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if should_trade:
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# Calculate lot size
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lot = risk_manager.calculate_lot_size(
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entry_price=df['close'][-1],
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confidence=ml_pred.confidence,
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regime=regime_state.regime.value,
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)
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print(f" Simulated Trade:")
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print(f" Direction: {ml_pred.signal}")
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print(f" Lot Size: {lot}")
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print(f" Entry: ${df['close'][-1]:,.2f}")
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else:
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print(f" No trade - waiting for better conditions")
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print()
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print("=" * 60)
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print("SIMULATION COMPLETE")
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print("=" * 60)
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mt5.disconnect()
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if __name__ == "__main__":
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asyncio.run(run_simulation())
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