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>
41 lines
718 B
Plaintext
41 lines
718 B
Plaintext
# Smart Automatic Trading BOT + AI
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# Requirements for Hybrid AI Forex Trading System (XAUUSD)
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# Python 3.11+ required
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# Core Data Engine (Rust-based, NOT Pandas)
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polars>=1.37.0
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pyarrow>=15.0.0
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# Broker Connection
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MetaTrader5>=5.0.5572
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# Machine Learning
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xgboost>=2.1.0
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scikit-learn>=1.4.0
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hmmlearn>=0.3.3
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joblib>=1.4.0
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# Asynchronous Processing
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asyncio-throttle>=1.0.2
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# Logging and Monitoring
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loguru>=0.7.2
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# Environment Variables
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python-dotenv>=1.0.1
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# Numerical Computing (for numpy-based SMC algorithms)
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numpy>=1.26.0
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# HTTP Client (for Telegram)
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aiohttp>=3.9.0
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# PostgreSQL Database
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psycopg2-binary>=2.9.9
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# Optional: For backtesting
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# vectorbt>=0.26.2
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# Optional: Connection pooling
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# asyncpg>=0.29.0
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