I built a small Python library for factor research — mainly because I got tired of stitching everything together myself.

Showcase Every time I wanted to test a factor idea, the workflow was always the same: clean the factor neutralize / standardize run IC build long-short portfolios analyze exposures And I kept rewriting the same pipeline over and over again. So I built AlphaPurify — a lightweight library that tries to handle the whole factor research loop in one place. What My Project Does AlphaPurify is a Python library for factor construction, preprocessing, backtesting, and return attribution. The idea is pretty simple: give it a DataFrame with time, asset, price, and factor — and it handles the rest. It currently supports: Factor preprocessing (winsorization, standardization, neutralization, etc.) IC / Rank IC analysis Quantile-based long / short / long-short backtests Factor return attribution (multi-factor exposures) Interactive reports (via Plotly) It’s fully vectorized + multiprocessing, so it runs pretty fast even on large datasets. Target Audience People who already do factor research (or are