ranaroussi/quantstats
Quantstats
QuantStats is an open-source Python library that calculates risk and return metrics from a portfolio return series and generates comprehensive HTML tear sheets. It computes dozens of financial statistics—including Sharpe ratio, drawdown, and volatility—in a single pass over the input data, using vectorized pandas operations for efficiency.
The library distinguishes itself by combining portfolio performance analysis with Monte Carlo simulation, which models thousands of random return paths to estimate the probability of reaching financial targets or hitting loss thresholds. It produces self-contained HTML reports that assemble metric tables and Matplotlib-generated charts—such as drawdown plots, rolling statistics, monthly heatmaps, and return distributions—into a styled document suitable for sharing or offline review.
Beyond its core reporting and simulation capabilities, QuantStats supports multi-format report generation and provides a rolling window statistics engine for time-varying metrics like rolling Sharpe ratio. The library accepts portfolio returns as a pandas Series or DataFrame, making it straightforward to integrate into existing Python-based analysis workflows.
Features
- Portfolio Performance Metrics - Analyzes investment portfolio returns with risk metrics, drawdown analysis, and performance statistics in a single workflow.
- HTML Analysis Reports - Assembles metrics and plots into self-contained HTML reports for sharing or offline review.
- Monte Carlo Simulators - Simulates thousands of random return sequences using Monte Carlo methods to estimate portfolio outcome probabilities.
- Asset Price Path Simulators - Implements Geometric Brownian Motion to simulate thousands of asset price paths for portfolio analysis.
- Risk Simulations - Simulates thousands of return paths to estimate probabilities of reaching financial targets or hitting loss thresholds.
- Portfolio Return Path Simulators - Simulates thousands of random return paths to estimate portfolio outcome probabilities.
- Financial Performance Metrics - Computes Sharpe ratio, drawdown, volatility, and other quantitative statistics from time-series return data.
- Performance Charting - Creates visualizations of portfolio behavior such as drawdowns, rolling statistics, and return distributions.
- Financial Performance Reports - Generates multi-page reports in basic, full, or HTML formats for portfolio analysis.
- Rolling Statistical Aggregators - Computes time-varying metrics like rolling Sharpe ratio and volatility by applying windowed functions over return series.
- Portfolio Performance Plotters - Generates plots for returns, drawdowns, rolling statistics, and monthly heatmaps.
- Statistical Distribution Visualizers - Plots return distributions, monthly heatmaps, rolling statistics, and drawdown charts to understand portfolio behavior.
- Financial Analytics - Portfolio management and performance analytics.
- Financial Analytics Tools - Portfolio analytics and performance reporting for quants.
- Trading and Backtesting - Portfolio analytics for quantitative traders.
Star history
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
