A collection of systematic trading algorithms built using Python for quantitative research, strategy development, backtesting, and portfolio analysis.
This repository contains five algorithmic trading systems implementing different quantitative investment strategies.
The objective is to explore financial markets using data-driven techniques, statistical models, and systematic decision making.
| Strategy | Description |
|---|---|
| Mean Reversion | Trades assets expected to revert to historical averages |
| Momentum | Captures persistent price trends |
| Statistical Arbitrage | Identifies temporary pricing inefficiencies |
| Options Trading | Volatility-based derivatives strategies |
| Portfolio Rebalancing | Dynamic asset allocation and optimization |
- Python
- Pandas
- NumPy
- Matplotlib
- SciPy
- Scikit-Learn
- yFinance
- Alpha Vantage API
- Plotly
✔ Strategy Backtesting
✔ Performance Analytics
✔ Risk Metrics
✔ Sharpe Ratio
✔ Maximum Drawdown
✔ CAGR
✔ Portfolio Optimization
✔ Signal Generation
✔ Data Visualization
Quant-Trading-Bots
│
├── Mean_Reversion_bot
├── Momentum_Bot
├── Options_Risk_Surface_Bot
├── Reinforcement_Learning_Bot
├── Statistical_Arbitrage_Bot
├── README.md
├── LICENSE
└── .gitignore
- Live Trading
- Alpaca API
- Interactive Brokers API
- Binance API
- Reinforcement Learning
- Machine Learning Signal Generation
Yashraj Verma