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shashankvemuri/Finance
150+ quantitative finance Python programs to help you gather, manipulate, and analyze stock market data
A flat collection of 150+ standalone Python scripts covering technical indicators, screening, ML price prediction, and portfolio analysis. It's an educational reference, not a library — each script runs independently and prints or plots results. Good starting point if you're learning quant finance and want working examples to read and modify.
Breadth is genuine: indicator coverage goes well beyond the basics (Chaikin oscillator, dynamic momentum index, Gann lines, CPR) and the portfolio section includes pairs trading, Monte Carlo, and factor analysis. Scripts are self-contained, so you can drop one into a notebook without untangling a dependency graph. The screener section (Minervini criteria, IBD RS rating) shows practical use of the indicators rather than just plotting them. ML section covers ARIMA, LSTM, Prophet, and sklearn classifiers — a reasonable survey of approaches actually used in practice.
No abstraction whatsoever — 150 scripts means 150 copies of the same yfinance data-fetch boilerplate, and changing a ticker or date range requires editing each file individually. Data dependencies are fragile: several scripts rely on web scraping Finviz or TradingView, Twitter/X API (now expensive), and a bundled chromedriver binary that will quietly break on any modern Chrome. The ML scripts treat prediction accuracy as the goal with no walk-forward validation or out-of-sample testing, which will give anyone trying to use them for actual trading a false sense of confidence. Last meaningful commit activity has slowed significantly; the Twitter screener and Robinhood bot are almost certainly broken against current APIs.