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

Elias Wu — Sun, 22 Mar 2026 06:22:24 +0000

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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 trying to get into it), especially:

quant students

researchers

anyone working with cross-sectional factors

It’s not meant to be a full trading system — more like a fast “idea validation tool”.

The project is still early stage, but usable.
Would really appreciate feedback or ideas on what to improve.

Comparison

From my experience using other tools:

vs Alphalens:
Alphalens is great for IC, but stops there.
AlphaPurify extends that into full backtesting + attribution.

vs Backtrader:
Backtrader is flexible, but you need to build everything yourself.
AlphaPurify is more opinionated and factor-focused.

vs Qlib:
Qlib is powerful but heavy.
AlphaPurify is much lighter and easier to start with.

vs QuantStats / Pyfolio:
Those focus on performance analysis, not factor testing.

So the goal here isn’t to replace them — just to make the factor workflow faster and simpler.

GitHub

pip install alphapurify

If you’ve done factor research before —
I’m curious: what part of the workflow do you find the most annoying or repetitive?

That’s probably what I should optimize next.

DEV Community: Elias Wu