Quantalytics is a fast, modern Python library for generating quantitative performance metrics, interactive charts, and publication-ready reports. It is designed for strategy researchers, portfolio managers, and data scientists who want an ergonomic toolchain without the overhead of large monolithic frameworks.

• Descriptive Stats – Grab skew, kurtosis, total return, and CAGR via the lightweight qa.stats helpers.
• Analytics Helpers – Access payoff ratio, profit ratio, Kelly, omega, tail, and other advanced risk/efficiency diagnostics through qa.analytics.
• Performance Metrics – Compute Sharpe, Sortino, Calmar, max drawdown, annualized returns/volatility, and more in a single call.
• Interactive Visuals – Build Plotly-based charts for cumulative returns, rolling volatility, and drawdown analysis with sensible defaults.
• Beautiful Reports – Produce responsive HTML tear sheets with configurable sections, ready to export to PDF.
• Composable API – Small, well-typed functions that play nicely with pandas Series/DataFrames.
• Production Ready Packaging – Standards-based pyproject.toml, semantic versioning, and optional CLI hooks for release automation.

pip install quantalytics

import pandas as pd
import quantalytics as qa

returns = pd.Series(
    [0.01, 0.02, -0.005, 0.015, -0.01, 0.03],
    index=pd.date_range("2024-01-01", periods=6, freq="B"),
)

summary = qa.metrics.performance_summary(returns)
print(summary.sharpe, summary.calmar)

fig = qa.charts.cumulative_returns_chart(returns)
fig.show()

Full tutorials and API references live on our Docusaurus site: https://pattertj.github.io/quantalytics/. Start with the introduction, then dive into the stats, metrics, charts, or reports guides as needed.

MIT License. See LICENSE.