Author: XiaoYinXu

AlphaLens Modify is a modified version of the original AlphaLens library with updated dependencies and compatibility fixes for modern Python environments.

This project includes comprehensive factor analysis capabilities. Here are some example results:

本项目包含全面的因子分析功能。以下是一些示例结果：

The Information Coefficient measures the predictive power of alpha factors, showing the correlation between factor values and future returns.

Cumulative returns demonstrate the long-term performance of factor-based investment strategies over time.

Group analysis shows how different factor quantiles perform, helping identify the most effective investment segments.

Turnover analysis evaluates the stability and trading frequency of factor-based strategies.

import alphalens_modify as al
import pandas as pd

# Load your factor data and pricing data
factor_data = pd.read_csv('factor_data.csv')
price_data = pd.read_csv('price_data.csv')
factor_data['date'] = pd.to_datetime(factor_data['date'])
price_data['date'] = pd.to_datetime(price_data['date'])
factor_data = factor_data.set_index(['date', 'instrument'])
price_data = price_data.pivot(values='close', index='date', columns='instrument')

# Get clean factor and forward returns
factor_returns = al.utils.get_clean_factor_and_forward_returns(
    factor_data, 
    price_data, 
    periods=[1, 5, 10]
)

# Create comprehensive analysis
al.tears.create_summary_tear_sheet(factor_returns)
al.tears.create_returns_tear_sheet(factor_returns)
al.tears.create_information_tear_sheet(factor_returns)

pip install alphalens-modify

git clone https://github.com/GenjiYin/alphalens-modify.git
cd alphalens-modify
pip install -e .

• Python >= 3.12
• pandas >= 2.2.3
• numpy >= 1.26.4
• scipy >= 1.14.1
• statsmodels >= 0.14.5
• matplotlib >= 3.9.2
• seaborn >= 0.13.2
• IPython

• Factor Performance Analysis: Analyze the predictive power of alpha factors
• Information Coefficient: Calculate and visualize IC metrics
• Quantile Analysis: Performance analysis by factor quantiles
• Group Analysis: Sector-based and custom group analysis
• Turnover Analysis: Evaluate factor stability and trading frequency
• Event Studies: Analyze factor performance around specific events

The library provides a complete factor analysis workflow:

1. Data Preparation: Clean and prepare factor and price data
2. Performance Metrics: Calculate IC, returns, turnover metrics
3. Visualization: Generate comprehensive charts and plots
4. Reporting: Create detailed tear sheets for analysis

See the included Jupyter notebook market_cap_factor_analys.ipynb for a complete example using market capitalization factor analysis.

Note: To run the market_cap_factor_analys.ipynb notebook, you need to download the test_data folder from the GitHub repository:

1. Go to the GitHub repository
2. Navigate to the test_data folder
3. Download all the files in the test_data folder
4. Place them in a test_data directory in your local project root

The notebook requires these data files to demonstrate the factor analysis functionality.

If you encounter any issues or have questions, please open an issue on GitHub.

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

XiaoYinXu - 965418170@qq.com

This project is based on the original AlphaLens library by Quantopian. Thanks to the original contributors for their work on the factor analysis framework.