PyFolio - Portfolio Analytics

A comprehensive Python library for portfolio performance and risk analysis

English

PyFolio is a powerful Python library designed for comprehensive portfolio performance analysis and risk analytics. This enhanced fork includes additional features like a modern Flask web interface and extensive Chinese data support.

✨ Key Features

📊 Comprehensive Analytics: Returns, risk metrics, drawdowns, and performance attribution
📈 Rich Visualizations: Professional charts with matplotlib and seaborn
🌐 Web Interface: Modern Flask-based dashboard for interactive analysis
📝 Tear Sheets: Automated comprehensive reports
🔄 Multi-Format Support: Both English and Chinese data formats
⚡ High Performance: Optimized calculations using empyrical
🧪 Well Tested: Extensive test suite across multiple Python versions

🚀 Quick Start

Installation

Windows:

install_win.bat

Unix/Linux/macOS:

./install_unix.sh

Manual Installation:

pip install -e .

Basic Usage

import pandas as pd
import pyfolio as pf

# Load your returns data
returns = pd.Series([0.01, 0.02, -0.01, 0.03], 
                   index=pd.date_range('2023-01-01', periods=4))

# Create a comprehensive tear sheet
pf.create_returns_tear_sheet(returns)

# Or use the modern web interface
pf.create_full_tear_sheet_by_flask(returns, run_flask_app=True)

Web Dashboard Demo

# Run the Flask demo
python examples/demo_flask.py

This creates a professional financial dashboard with:

Key performance metrics cards
Interactive charts and visualizations
Collapsible detailed statistics sections
Responsive design optimized for financial data

📋 Available Tear Sheets

Function	Description
`create_returns_tear_sheet()`	Basic returns analysis
`create_full_tear_sheet()`	Comprehensive analysis with positions
`create_position_tear_sheet()`	Position-level analysis
`create_txn_tear_sheet()`	Transaction analysis
`create_round_trip_tear_sheet()`	Round trip trade analysis
`create_risk_tear_sheet()`	Risk factor analysis
`create_perf_attrib_tear_sheet()`	Performance attribution

🔧 Development

Testing

# Run all tests with parallel execution
pytest tests/ -n 4

# Run specific test file
pytest tests/test_timeseries.py

# Run with coverage
pytest tests/ --cov=pyfolio

Building

# Build package
python setup.py build

# Create distribution
python setup.py sdist bdist_wheel

📁 Project Structure

pyfolio/
├── pyfolio/           # Main package
│   ├── tears.py       # Main tear sheet interface
│   ├── timeseries.py  # Time series analysis
│   ├── plotting.py    # Visualization functions
│   ├── risk.py        # Risk metrics
│   ├── pos.py         # Position analysis
│   ├── txn.py         # Transaction analysis
│   └── flask_app.py   # Web interface
├── examples/          # Usage examples
├── tests/             # Test suite
└── templates/         # Flask templates

🌟 Core Metrics

PyFolio calculates a comprehensive set of performance and risk metrics:

Performance Metrics:

Total Returns, Annual Returns, Cumulative Returns
Sharpe Ratio, Sortino Ratio, Calmar Ratio
Alpha, Beta, Max Drawdown, Volatility

Risk Analytics:

Value at Risk (VaR), Conditional VaR
Rolling metrics and time-series analysis
Factor exposure and performance attribution

🔗 Dependencies

empyrical: Performance calculations (from GitHub)
pandas: Data manipulation and analysis
numpy: Numerical computing
scipy: Scientific computing
matplotlib: Plotting and visualization
flask: Web interface framework

📄 License

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

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

中文

PyFolio 是一个功能强大的 Python 投资组合分析库，专为全面的投资组合绩效分析和风险分析而设计。这个增强版本包含现代化的 Flask Web 界面和广泛的中文数据支持。

✨ 主要特性

📊 全面分析: 收益率、风险指标、回撤和绩效归因
📈 丰富可视化: 使用 matplotlib 和 seaborn 的专业图表
🌐 Web 界面: 基于 Flask 的现代化交互式分析仪表板
📝 分析报告: 自动化综合报告生成
🔄 多格式支持: 支持英文和中文数据格式
⚡ 高性能: 使用 empyrical 优化计算
🧪 完善测试: 跨多个 Python 版本的广泛测试套件

🚀 快速开始

安装

Windows:

install_win.bat

Unix/Linux/macOS:

./install_unix.sh

手动安装:

pip install -e .

基础用法

import pandas as pd
import pyfolio as pf

# 加载收益率数据
returns = pd.Series([0.01, 0.02, -0.01, 0.03], 
                   index=pd.date_range('2023-01-01', periods=4))

# 创建综合分析报告
pf.create_returns_tear_sheet(returns)

# 或使用现代化 Web 界面
pf.create_full_tear_sheet_by_flask(returns, run_flask_app=True)

Web 仪表板演示

# 运行 Flask 演示
python examples/demo_flask.py

这将创建一个专业的金融仪表板，包含：

关键绩效指标卡片
交互式图表和可视化
可折叠的详细统计部分
针对金融数据优化的响应式设计

📋 可用分析报告

函数	描述
`create_returns_tear_sheet()`	基础收益率分析
`create_full_tear_sheet()`	包含持仓的综合分析
`create_position_tear_sheet()`	持仓级别分析
`create_txn_tear_sheet()`	交易分析
`create_round_trip_tear_sheet()`	往返交易分析
`create_risk_tear_sheet()`	风险因子分析
`create_perf_attrib_tear_sheet()`	绩效归因分析

🔧 开发

测试

# 并行运行所有测试
pytest tests/ -n 4

# 运行特定测试文件
pytest tests/test_timeseries.py

# 运行覆盖率测试
pytest tests/ --cov=pyfolio

构建

# 构建包
python setup.py build

# 创建分发包
python setup.py sdist bdist_wheel

📁 项目结构

pyfolio/
├── pyfolio/           # 主包
│   ├── tears.py       # 主要分析报告接口
│   ├── timeseries.py  # 时间序列分析
│   ├── plotting.py    # 可视化函数
│   ├── risk.py        # 风险指标
│   ├── pos.py         # 持仓分析
│   ├── txn.py         # 交易分析
│   └── flask_app.py   # Web 界面
├── examples/          # 使用示例
├── tests/             # 测试套件
└── templates/         # Flask 模板

🌟 核心指标

PyFolio 计算全面的绩效和风险指标集：

绩效指标:

总收益率、年化收益率、累计收益率
夏普比率、索提诺比率、卡玛比率
Alpha、Beta、最大回撤、波动率

风险分析:

风险价值 (VaR)、条件风险价值
滚动指标和时间序列分析
因子暴露和绩效归因

🔗 依赖库

empyrical: 绩效计算 (来自 GitHub)
pandas: 数据处理和分析
numpy: 数值计算
scipy: 科学计算
matplotlib: 绘图和可视化
flask: Web 界面框架

📄 许可证

本项目采用 Apache 2.0 许可证 - 详情请参阅 LICENSE 文件。

🤝 贡献

欢迎贡献！请随时提交 Pull Request。

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