# AlphaPurify
**Repository Path**: cangmj/AlphaPurify
## Basic Information
- **Project Name**: AlphaPurify
- **Description**: mirror: https://github.com/eliasswu/AlphaPurify
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 1
- **Forks**: 1
- **Created**: 2026-05-11
- **Last Updated**: 2026-05-26
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
English README | 简体中文 README | 繁体中文 README
# AlphaPurify: Factor research for quants
**AlphaPurify** Python library for factor construction, preprocessing, backtesting, and factor return attributions to help quants rapidly validate ideas.
---
## 4 Main Modules:
1.**`alphapurify.FactorAnalyzer`** — for IC/ Rank IC testing and Long/ Short/ Long-Short quantile backtests.
2.**`alphapurify.AlphaPurifier`** — for factor preprocessing, including 40+ Winsorization, Neutralization, and Standardization methods (e.g., ridge regression, lasso regression, PCA decomposition, etc.).
3.**`alphapurify.Database`** — for financial data aggregation, factor construction, and factor storage.
4.**`alphapurify.Exposures`** — for factor correlation analysis and factor-based return attribution.
---
## Pipeline Overview
---
### Full Documents & Examples: **[English Docs](./examples)**
---
## Key Features:
### - Extremely Fast — Processes 4 Millions+ rows (15 years CSI 300) including long/short, long-short, IC backtests and creates 4 interactive reports in under 25 seconds (on a standard i7 CPU).
### - Stable at Scale — Reliably handles tens of millions of rows (minute-level data) with memory-optimized design to prevent overflow.
### - 40+ Preprocessing Methods — Built-in professional factor cleaning tools supporting workflows from ultra high-frequency to low-frequency data.
### - Flexible Horizons — Supports unlimited rebalance periods and IC lookback windows simultaneously for rich multi-dimensional factor analysis.
---
## AlphaPurify vs Other Quant Libraries
| Feature / Library | AlphaPurify | Qlib | Backtrader | Alphalens | QuantStats | Pyfolio |
|:------------------|:------------|:--------|:------------|:------------|:-------------|:-------------|
| Computation Speed | 🚀 Very Fast (Rust vectorized + multiprocessing) | ❌ Slow (heavy infrastructure) | ⚠️ Medium | ✅ Fast | no backtest | no backtest |
| Factor Preprocessing (40+) | ✅ Built-in | ⚠️ Limited | ❌ No | ❌ No | ❌ No | ❌ No |
| IC Analysis | ✅ Native | ✅ Yes | ❌ No | ✅Yes | ❌ No | ❌ No |
| Long / Short / Long-Short Rebalancing Quantile Backtest | ✅ Native | ✅ Yes | ⚠️ Indirect | ❌ No | ❌ No | ❌ No |
| Factor Return Attribution | ✅ Native | ⚠️ Indirect | ❌ No | ❌ No | ❌ No | ❌ No |
| Multi-Frequency Support | ✅ Any (microsecond → yearly) | ⚠️ Limited | ⚠️ Mostly daily | ⚠️ Mostly daily | ⚠️ Limited | ⚠️ Limited |
| Setup Complexity | 🟢 Low | 🔴 High | 🟡 Medium | 🟢 Low | 🟢 Low | 🟢 Low |
| Data Backend Support | ✅ Parquet + DuckDB | ⚠️ Custom infra | ❌ None | ❌ None | ❌ None | ❌ None |
While ``AlphaPurify`` may look similar to ``Alphalens``, it goes far beyond IC analysis and simple graphs.
It supports long, short, and long-short rebalancing backtests, factor cleaning, atributions and delivers a new generation of interactive visualizations by Plotly.
``AlphaPurify`` is different from libraries like ``QuantStats`` and ``Pyfolio``, which primarily focus on analyzing return curves and portfolio performance, not backtests. Compared to tools like ``Qlib`` and ``Backtrader``, ``AlphaPurify`` directly provides a lightweight, fast factor-driven rebalancing backtesting framework — eliminating the need for users to build custom pipelines or infrastructure in these libraries.
In short, ``AlphaPurify`` provide quants with a whole factor testing pipeline and beautiful interactive reports to rapidly validate ideas.
---
## Quick Start
### 1.Install with pip
Users can easily install ``AlphaPurify`` by pip according to the following command.
```bash
pip install alphapurify
```
**Note**: pip will install the latest stable ``AlphaPurify``. However, the main branch of AlphaPurify is in active development. If you want to test the latest scripts or functions in the main branch. Please install ``AlphaPurify`` with clone.
---
### 2.Load your DataFrame
| datetime | symbol | close | volume | alpha_003 | momentum_12_1 | vol_60 | beta_252 |
|:-------------------|:------|------:|------:|------:|--------------:|------:|--------:|
| 2024-01-01 09:30 | AAPL | 189.9 | 120034 | 0.42 | 0.15 | 0.21 | 1.08 |
| 2024-01-01 09:31 | AAPL | 190.0 | 98321 | 0.38 | 0.16 | 0.22 | 1.07 |
| 2024-01-01 09:32 | AAPL | 190.4 | 101245 | 0.41 | 0.17 | 0.23 | 1.06 |
| 2024-01-01 09:30 | MSFT | 378.5 | 84211 | -0.15 | -0.05 | 0.18 | 0.95 |
| 2024-01-01 09:31 | MSFT | 378.9 | 90122 | -0.12 | -0.04 | 0.19 | 0.96 |
| 2024-01-01 09:32 | MSFT | 379.1 | 95433 | -0.08 | -0.03 | 0.20 | 0.97 |
**P.S. Your DataFrame must include a time column, an asset identifier column, a price column, and your factor column to ensure proper usage.**
---
### 3.Creating backtesting reports
```bash
from alphapurify import AlphaPurifier, FactorAnalyzer
# preprocess
df = (
AlphaPurifier(df, factor_col="alpha_003")
.winsorize(method="mad")
.standardize(method="zscore")
.to_result()
)
#backtest
FA = FactorAnalyzer(base_df=df,
trade_date_col='datetime',
symbol_col='symbol',
price_col='close',
factor_name='alpha_003')
FA.run()
FA.create_long_return_sheet()
FA.create_long_short_return_sheet()
FA.create_short_return_sheet()
FA.create_single_fac_ic_sheet()
#contributions of other factors
Ex = PureExposures(
base_df=df,
trade_date_col='datetime',
symbol_col='symbol',
price_col='close',
factor_name='alpha_003',
exposure_cols=['momentum_12_1', 'vol_60', 'beta_252'],
)
Ex.run()
Ex.plot_pure_exposures()
Ex.plot_pure_returns()
Ex.plot_pure_exposures_and_returns()
Ex.plot_correlations()
```
---
## Examples of Backtesting Reports
### Portfolio for long positions only:
### Return attributions of other factors:
---
### If you like AlphaPurify, please star & fork this project to support the development!
---
**Elias Wu**