- Overview
- Features
- Technology Stack
- Project Structure
- Installation & Setup
- Configuration
- Usage
- Performance
- Visualization
- Example Output
- Extensibility
- Contributing
- License
- Author
The AI Trading Bot is a state-of-the-art, institution-grade algorithmic trading system built using cutting-edge machine learning techniques and advanced quantitative finance methodologies. This comprehensive solution is designed for robust, adaptive, and highly customizable trading across asset classes.
- Multi-dimensional regime detection: Volatility, trend, momentum, and market stress regimes for optimized market entry/exit.
- Advanced feature engineering: Technical indicators, market microstructure signals, cross-asset correlations, and time-based cyclical features.
- Machine Learning pipeline: Ensembled classifiers (Random Forest, ExtraTrees, XGBoost) with calibrated probability outputs.
- Dynamic thresholding: Regime-based and adaptive threshold optimizers for dynamic entry criteria.
- Risk management: Kelly criterion-based position sizing, adaptive stop-loss/take-profit, trailing stops, and sophisticated drawdown control.
- Comprehensive backtesting: Realistic trading simulation with transaction cost models (brokerage, taxes, market impact).
- Extensive performance metrics: CAGR, Sharpe, Sortino, Omega ratios, drawdowns, and detailed trade statistics.
- Interactive visualizations: Plotly dashboards for performance, risk, trade analysis, and heatmaps.
- Modular and configurable: Customize via config files for risk parameters and model settings.
- Python 3.11
- NumPy, Pandas, Numba (performance, data manipulation)
- Scikit-learn, XGBoost, LightGBM (machine learning)
- yFinance (financial data)
- Plotly (interactive visualizations)
AI-Trading-Bot/
├── Trading_Bot
├── reports/
│ └── [visual analysis reports]
├── .github/
│ └── workflows/
| └──python-ci.yml
├── requirements.txt
├── readme.md
└── license
- Python 3.11 or higher
pip install -r requirements.txtAdjust trading parameters, risk tolerance, target volatility, etc. in the config file.
Default:
risk_tolerance: 0.02
target_volatility: 0.15
max_drawdown: 0.12
trading_horizon: 'daily'
position_sizing: 'kelly'
asset: 'RELIANCE'
years: 5- Configure parameters in
config/TradingConfig.yaml. - Run the analysis:
python main.py- Output reports and interactive dashboards are saved with versioned timestamps under
/reportsalong with full logs.
Default: Runs analysis on RELIANCE stock over 5 years, generating detailed performance reports and dashboards.
- Demonstrated predictive signal quality with AUC > 0.55
- Controlled drawdown levels below 12%
- Configurable trading horizons and position sizing
======================
AI TRADING BOT ANALYSIS - RELIANCE
==========================================================================================
SIGNAL QUALITY ASSESSMENT:
Model AUC: 0.559
Model Accuracy: 0.546
SIGNAL STRENGTH: STRONG - Good predictive power
STRATEGY PERFORMANCE:
Total Return: 7.66%
Sharpe Ratio: -2.069
Win Rate: 71.0%
Number of Trades: 53
Max Drawdown: -0.57%
OVERALL ASSESSMENT:
PROMISING: Good signals but execution needs work
- Easily extend feature engineering by adding new indicators in
FeatureBuilder. - Customize ML models or add new algorithms in
models/. - Plug in alternative data sources or live data feeds.
- Expand backtesting for other asset classes.
Contributions, bug reports, and feature requests are welcome!
- Fork the repository.
- Create your feature branch (
git checkout -b feature/fooBar). - Commit your changes (
git commit -am 'Add some fooBar'). - Push to the branch (
git push origin feature/fooBar). - Open a Pull Request.
This project is licensed under the MIT License. See the LICENSE file for details.
Developed by Aaditya V — Aspiring Quantitative Engineer and AI Enthusiast
For queries or collaboration opportunities, please reach out via GitHub or email: [email protected]