alphapurify 0.1.1

High-performance quantitative factor analysis and purification toolkit

AlphaPurify

AlphaPurify is a high-performance quantitative factor analysis and purification toolkit designed for institutional-grade research workflows.

It provides a fully modular, vectorized, and multiprocessing-enabled framework for factor cleaning, evaluation, exposure decomposition, and portfolio attribution — built on a modern Polars-based architecture for large-scale cross-sectional datasets.

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🚀 Key Features

• ⚡ High Performance

  • Nearly fully vectorized architecture powered by Polars
  • Optimized for large-scale cross-sectional panel data
  • Memory-efficient structural safeguards

• 🧩 Fully Modular Design

  • Each module can be used independently
  • Seamlessly integrated into custom research pipelines
  • Minimal coupling between components

• 📊 Comprehensive Factor Research Engine

  • Cross-sectional IC analysis
  • Horizon autocorrelation
  • Quantile portfolio backtesting
  • Turnover measurement
  • Industry-level attribution
  • Long–short, long-only, and short-only evaluation

• 🧪 Advanced Factor Cleaning Toolkit

  • 40+ preprocessing techniques
  • Robust winsorization
  • Regression-based neutralization
  • Polynomial & robust regression options
  • Advanced standardization methods

• 📈 Exposure & Return Attribution

  • Systematic exposure decomposition
  • Residual alpha estimation
  • Cumulative attribution curves
  • Interactive Plotly visualizations

• 🕒 Frequency-Agnostic

  • Supports intraday, daily, weekly, and high-frequency datasets
  • No structural modifications required

• 🛡 Look-Ahead Bias Protection

  • Forward return construction safeguards
  • Rebalancing alignment protection
  • Parameter-level anti-leakage controls

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📦 Installation

pip install alphapurify

## 📊 Example Workflow

from alphapurify import AlphaPurifier, FactorAnalyzer

# Load your DataFrame
df = ...

# Clean factor
cleaned = (
    AlphaPurifier(df, factor_col="alpha")
    .winsorize(method="mad")
    .neutralize(neutralizer_cols=["size", "industry"])
    .standardize(method="zscore")
    .to_result()
)