Quantitative Fund Management Toolkit — A modular and extensible toolkit for building, testing, and analyzing quantitative investment strategies.
Designed for personal fund managers, family portfolios, or quants who want full control over data, strategy, and reporting.
- Data Ingestion: Import data from APIs, CSV, or live feeds
- Backtesting Engine: Test your strategies across historical data
- Portfolio Construction: Build portfolios with rules like equal-weight, risk parity, or optimization
- Execution Simulator: Simulate realistic trades with slippage, fees, and rebalancing
- Factor Analysis (Momentum, Value, Quality, etc.)
- Alpha & Beta calculations
- Risk Metrics (Sharpe Ratio, Volatility, Max Drawdown)
- Portfolio Optimization (Mean-Variance, Black-Litterman)
- Strategy Builder (scripted or GUI)
- Signal Generator (rule-based or ML-assisted)
- Rebalancing Scheduler
- Scenario Testing (e.g., interest rate spike, recession)
- Performance Dashboard
- Drawdown and Volatility Charts
- Benchmark Comparisons
- Export to PDF / Excel / JSON
- Role-based Access Control (Admin, Viewer, Analyst)
- Encrypted storage (configurable)
- Audit Logs for transparency
- Machine Learning module for signal prediction
- Sentiment Analysis (news, social media)
- API Integrations for live trading platforms
- Python (Pandas, NumPy, Backtrader, Scikit-learn)
- Streamlit / Dash (for UI)
- MongoDB / SQLite (for storage)
- Plotly / Matplotlib (for visualization)
- Manage personal/family portfolios with quant models
- Backtest and validate alpha strategies
- Build your own robo-advisor
- Learn and experiment with quantitative investing
git clone https://github.com/triphopMahithi/QFundToolkit.git
cd QFundToolkit
pip install -r requirements.txt
python app.py # or streamlit run dashboard.py