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>
104 lines
3.2 KiB
Markdown
104 lines
3.2 KiB
Markdown
# Smart Automatic Trading BOT + AI
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An intelligent automated trading system for XAUUSD (Gold) using Machine Learning and Smart Money Concepts (SMC).
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## Features
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- **ML-Powered Predictions**: XGBoost model with 37 features for market direction prediction
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- **Smart Money Concepts (SMC)**: Order Blocks, Fair Value Gaps, Break of Structure, Change of Character
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- **HMM Regime Detection**: Hidden Markov Model for market regime classification
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- **Dynamic Risk Management**: ATR-based stop loss, position sizing, and smart exits
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- **Session-Aware Trading**: Optimized for different market sessions (Sydney, London, NY)
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- **Auto-Retraining**: Models automatically retrain based on market conditions
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- **Telegram Notifications**: Real-time trade alerts and market updates
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- **Web Dashboard**: Real-time monitoring interface
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## Performance (Backtest Jan 2025 - Feb 2026)
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| Metric | Value |
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|--------|-------|
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| Total Trades | 654 |
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| Win Rate | 63.9% |
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| Net P/L | $4,189.52 |
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| Profit Factor | 2.64 |
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| Max Drawdown | 2.2% |
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| Sharpe Ratio | 4.83 |
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## Architecture
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```
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├── main_live.py # Main trading orchestrator
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├── src/
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│ ├── ml_model.py # XGBoost ML model
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│ ├── smc_polars.py # Smart Money Concepts analyzer
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│ ├── regime_detector.py # HMM market regime detection
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│ ├── smart_risk_manager.py # Risk management system
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│ ├── feature_eng.py # Feature engineering
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│ ├── mt5_connector.py # MetaTrader 5 connection
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│ ├── session_filter.py # Trading session management
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│ └── ...
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├── backtests/
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│ ├── backtest_live_sync.py # Main backtest (synced with live)
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│ └── archive/ # Historical backtest scripts
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├── models/ # Trained ML models (.pkl)
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├── data/ # Market data and trade logs
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├── docs/ # Documentation
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└── web-dashboard/ # Next.js monitoring dashboard
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```
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## Risk Management
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- **ATR-Based Stop Loss**: Minimum 1.5 ATR distance
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- **Broker-Level Protection**: Emergency SL at broker level
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- **Time-Based Exit**: Max 6 hours per trade
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- **Daily Loss Limit**: 5% of capital
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- **Position Limit**: Max 2 concurrent positions
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## Installation
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1. Clone the repository
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2. Install dependencies:
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```bash
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pip install -r requirements.txt
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```
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3. Copy `.env.example` to `.env` and configure:
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- MT5 credentials
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- Telegram bot token
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- Database connection
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4. Train models:
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```bash
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python train_models.py
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```
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5. Run the bot:
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```bash
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python main_live.py
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```
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## Configuration
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Key settings in `.env`:
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- `MT5_LOGIN`, `MT5_PASSWORD`, `MT5_SERVER` - MetaTrader 5 credentials
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- `TELEGRAM_BOT_TOKEN`, `TELEGRAM_CHAT_ID` - Telegram notifications
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- `CAPITAL` - Trading capital amount
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- `SYMBOL` - Trading symbol (default: XAUUSD)
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## Backtest
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Run backtest with threshold tuning:
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```bash
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python backtests/backtest_live_sync.py --tune
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```
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Run backtest with specific threshold:
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```bash
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python backtests/backtest_live_sync.py --threshold 0.50 --save
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```
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## Disclaimer
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This software is for educational purposes only. Trading involves substantial risk of loss. Past performance is not indicative of future results. Use at your own risk.
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## License
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MIT License
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