alphalens-modify 0.1.0
A modified version of alphalens with updated dependencies and fixes
License: Apache-2.0
Requires Python: >=3.12
Keywords: finance, quantitative, factor, analysis, investment

# AlphaLens Modify

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  <a href="README_CN.md">🇨🇳 中文</a> | 🇺🇸 English
</div>

**Author: XiaoYinXu**

AlphaLens Modify is a modified version of the original AlphaLens library with updated dependencies and compatibility fixes for modern Python environments.

## 📊 **Analysis Results Showcase**

*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：*

### 🔍 **Information Coefficient (IC) Analysis**
![IC Value Distribution](figures/IC_value_distribution.png)

The Information Coefficient measures the predictive power of alpha factors, showing the correlation between factor values and future returns.

### 📈 **Cumulative Return Analysis**
![Cumulative Returns](figures/cumlative_return.png)

Cumulative returns demonstrate the long-term performance of factor-based investment strategies over time.

### 📊 **Group Mean Return Analysis**
![Group Mean Returns](figures/group_mean_return.png)

Group analysis shows how different factor quantiles perform, helping identify the most effective investment segments.

### 🔄 **Factor Turnover Analysis**
![Turnover Analysis](figures/turnover.png)

Turnover analysis evaluates the stability and trading frequency of factor-based strategies.

## 🚀 **Quick Start**

```python
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')

# 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)
```

## 📦 **Installation**

### From PyPI
```bash
pip install alphalens-modify
```

### From Source
```bash
git clone https://github.com/GenjiYin/alphalens-modify.git
cd alphalens-modify
pip install -e .
```

## 📋 **Requirements**

- Python >= 3.12
- pandas >= 1.0.0
- numpy >= 1.16.0
- empyrical >= 0.5.0
- scipy >= 1.0.0
- statsmodels >= 0.9.0
- matplotlib >= 3.0.0
- seaborn >= 0.9.0
- IPython >= 7.0.0

## 🎯 **Key Features**

- **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

## 📊 **Complete Analysis Pipeline**

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

## 🔧 **Example Usage**

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](https://github.com/GenjiYin/alphalens-modify)
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.

## 🤝 **Support**

If you encounter any issues or have questions, please open an issue on [GitHub](https://github.com/GenjiYin/alphalens-modify/issues).

## 📄 **License**

This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.

## 👤 **Author**

**XiaoYinXu** - 965418170@qq.com

## 🙏 **Acknowledgments**

This project is based on the original AlphaLens library by Quantopian. Thanks to the original contributors for their work on the factor analysis framework.