Quantitative Trading Projects
July 8, 2026 · View on GitHub
A collection of 12 hands-on quantitative finance and algorithmic trading projects in Python, built around Indian markets (Nifty 50, NSE stocks). Each project is a self-contained Jupyter notebook with its own README explaining the concept, methodology, and results.
Projects
| # | Project | Topic | Key skills |
|---|---|---|---|
| 01 | Back-Testing Framework | Reusable signal backtester + z-score mean reversion | Sharpe, drawdown, look-ahead bias |
| 02 | Bollinger Band Mean Reversion | Event-driven engine with plug-in entry/exit rules | Trade logs, profit factor, fees |
| 03 | Live Trading API | RSI bot on Zerodha Kite Connect (simulated) | Broker APIs, RSI, order management |
| 04 | ML Stock Prediction | Next-day Nifty direction via logistic regression | Feature engineering, classification metrics |
| 05 | Time Series Forecasting | ARIMA forecast of Nifty closes | ARIMA, MAE, holdout evaluation |
| 06 | Black-Scholes Pricing | European call pricing + mispricing detection | Risk-neutral pricing |
| 07 | Options Greeks | Delta/Gamma/Theta/Vega/Rho sensitivity plots | Greeks, moneyness, time decay |
| 08 | Statistical Arbitrage | Cointegrated pairs trading on Nifty stocks | Engle-Granger, hedge ratios, z-scores |
| 09 | Volatility Trading | Long straddle backtest split by vol regime | Implied vs realized vol, straddles |
| 10 | VWAP Execution | Institutional order slicing vs VWAP/TWAP | Volume profiles, slippage (bps) |
| 11 | Portfolio Optimization | Max-Sharpe weights vs equal-weighted | Mean-variance optimization, SLSQP |
| 12 | Market-Making Simulation | Quote bid/ask, manage inventory, track P&L | Microstructure, inventory risk |
Setup
git clone https://github.com/<your-username>/quant-trading-projects.git
cd quant-trading-projects
pip install -r requirements.txt
jupyter notebook
Tech stack
Python · NumPy · pandas · SciPy · statsmodels · scikit-learn · matplotlib · yfinance · Kite Connect
Notes
- Market data is fetched live from Yahoo Finance, so exact numbers will vary from the sample outputs shown in each README.
- Project 03 uses broker API credentials. Placeholders are committed; never commit real API keys — see that project's README.
Disclaimer
All projects are for educational purposes only and do not constitute financial or investment advice. Backtested results do not guarantee future performance.