jquantstats 0.0.23

Analytics for quants

jQuantStats: Portfolio Analytics for Quants

📊 Overview

jQuantStats is a Python library for portfolio analytics that helps quants and portfolio managers understand their performance through in-depth analytics and risk metrics. It provides tools for calculating various performance metrics and visualizing portfolio performance using interactive Plotly charts.

The library is inspired by QuantStats, but focuses on providing a clean, modern API with enhanced visualization capabilities. Key improvements include:

• Support for both pandas and polars DataFrames
• Modern interactive visualizations using Plotly
• Comprehensive test coverage with pytest
• Clean, well-documented API
• Efficient data processing with polars

✨ Features

• Performance Metrics: Calculate key metrics like Sharpe ratio, Sortino ratio, drawdowns, volatility, and more
• Risk Analysis: Analyze risk through metrics like Value at Risk (VaR), Conditional VaR, and drawdown analysis
• Interactive Visualizations: Create interactive plots for portfolio performance, drawdowns, and return distributions
• Benchmark Comparison: Compare your portfolio performance against benchmarks
• Pandas & Polars Support: Work with either pandas or polars DataFrames as input

📦 Installation

pip install jquantstats

For development:

pip install jquantstats[dev]

🚀 Quick Start

>>> # Import jquantstats
>>> import polars as pl
>>> from jquantstats import build_data

>>> # Create sample returns data
>>> returns = pl.DataFrame({
...     "Date": ["2023-01-01", "2023-01-02", "2023-01-03"],
...     "Asset1": [0.01, -0.02, 0.03],
...     "Asset2": [0.02, 0.01, -0.01]
... }).with_columns(pl.col("Date").str.to_date())
>>> returns
shape: (3, 3)
┌────────────┬────────┬────────┐
│ Date       ┆ Asset1 ┆ Asset2 │
│ ---        ┆ ---    ┆ ---    │
│ date       ┆ f64    ┆ f64    │
╞════════════╪════════╪════════╡
│ 2023-01-01 ┆ 0.01   ┆ 0.02   │
│ 2023-01-02 ┆ -0.02  ┆ 0.01   │
│ 2023-01-03 ┆ 0.03   ┆ -0.01  │
└────────────┴────────┴────────┘

>>> # Basic usage
>>> data = build_data(returns=returns)
>>>
>>> # With benchmark and risk-free rate
>>> benchmark = pl.DataFrame({
...     "Date": ["2023-01-01", "2023-01-02", "2023-01-03"],
...     "Market": [0.005, -0.01, 0.02]
... }).with_columns(pl.col("Date").str.to_date())
>>> benchmark
shape: (3, 2)
┌────────────┬────────┐
│ Date       ┆ Market │
│ ---        ┆ ---    │
│ date       ┆ f64    │
╞════════════╪════════╡
│ 2023-01-01 ┆ 0.005  │
│ 2023-01-02 ┆ -0.01  │
│ 2023-01-03 ┆ 0.02   │
└────────────┴────────┘

>>> data = build_data(
...     returns=returns,
...     benchmark=benchmark,
...     rf=0.0002,  # risk-free rate (e.g., 0.02% per day)
... )

>>> # Calculate statistics
>>> sharpe = data.stats.sharpe()
>>> sharpe
{'Asset1': np.float64(4.909200099205072),
 'Asset2': np.float64(8.08795106197808),
 'Market': np.float64(6.113591415853696)}

>>> volatility = data.stats.volatility()
>>> volatility
{'Asset1': np.float64(0.4807979478602905),
 'Asset2': np.float64(0.2918332857414772),
 'Market': np.float64(0.286574597618142)}

>>> # Create visualizations
>>> fig = data.plots.plot_snapshot(title="Portfolio Performance")
>>> type(fig)
<class 'plotly.graph_objs._figure.Figure'>
>>> # End of example

📚 Documentation

For detailed documentation, visit jQuantStats Documentation.

🔧 Requirements

• Python 3.10+
• numpy
• polars
• pandas
• plotly
• scipy

👥 Contributing

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

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

⚖️ License

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