skyboi94/quant-trading-projects

Quantitative Trading Projects is a collection of 12 self-contained Python notebooks that each teach a different concept in algorithmic trading and quantitative finance, built around Indian stock market data like the Nifty 50 index and NSE listed stocks. Every project has its own notebook plus a README explaining the idea, the method, and the results, so each one can be studied on its own. The topics span a wide range of the field: building a reusable backtesting framework with a mean reversion strategy, an event driven trading engine using Bollinger Bands, a simulated RSI trading bot connected to a real broker API, next day price direction prediction with logistic regression, ARIMA based time series forecasting, Black-Scholes options pricing, the option Greeks that measure price sensitivity, statistical arbitrage through cointegrated pairs trading, volatility trading with straddles, VWAP based order execution used by institutional traders, portfolio optimization for the best risk adjusted return, and a simplified simulation of market making. Getting started means cloning the repository, installing the Python dependencies listed in a requirements file, and opening the notebooks in Jupyter. The projects rely on common data science and finance libraries including NumPy, pandas, SciPy, statsmodels, and scikit-learn, plus Yahoo Finance for live market data and the Kite Connect broker API for the live trading example. The README notes that market data is pulled live, so results will differ from the numbers shown in each notebook, and that the broker API project uses placeholder credentials rather than real ones. It states plainly that every project is for educational purposes only, is not financial advice, and that past backtested performance does not guarantee future results.