AlphaPurify Python library for financial data aggregation, factor construction, IC testing, factor return attribution, full-pipeline backtesting, and large-scale experimentation to help quants rapidly validate ideas.
alphapurify.FactorAnalyzer— for IC testing and quantile portfolio analysis to evaluate factor predictive ability.alphapurify.AlphaPurifier— for factor preprocessing, including 40+ Winsorization, Neutralization, and Standardization methods.alphapurify.Database— for reading, writing, and aggregating financial and factor datasets.alphapurify.Exposures— for factor correlation analysis and factor-based return attribution.
Compared with traditional factor research tools, You merely just need a Dataframe.
• Optimized for single-machine research
Many independent researchers work on a single laptop where memory overflow and slow computation are common issues.
AlphaPurify is designed with optimized caching, vectorized computation, and multiprocessing wherever possible.
For example, a 15-year daily dataset of the CSI 300 universe can complete full factor evaluation — including long-only, long-short, short portfolios and IC analysis — in around 30 seconds on a typical laptop.
• Adaptive to arbitrary bar frequency
AlphaPurify works with any bar frequency (daily, hourly, minute-level, etc.).
Return aggregation automatically adapts to the data frequency, while allowing users to explicitly specify the horizon if needed.
The framework is carefully designed to strictly prevent look-ahead bias.
• Professional factor preprocessing toolkit
AlphaPurify provides 40+ built-in preprocessing methods for factor research, including common operations such as:
- winsorization
- neutralization
- standardization
This allows researchers to rapidly experiment with different factor cleaning pipelines.
• Lightweight high-performance data backend
AlphaPurify integrates a fast Parquet + DuckDB data layer for factor storage and aggregation.
This avoids the need for configuring complex database systems while still providing high-performance querying and fast factor construction workflows.
Users can easily install AlphaPurify by pip according to the following command.
pip install alphapurifyNote: 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.
| datetime | symbol | close | volume | factor | 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 |
from alphapurify import AlphaPurifier, FactorAnalyzer, Pure_Exposures
# 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 = Pure_Exposures(
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()
More detailed documentation and examples will be released soon.
Elias Wu