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ranaroussi avatar

ranaroussi/quantstats

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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.

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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.
6,717 stars·1,113 forks·Python·apache-2.0·84 views

Star history

Star history chart for ranaroussi/quantstatsStar history chart for ranaroussi/quantstats

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does ranaroussi/quantstats do?

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.

What are the main features of ranaroussi/quantstats?

The main features of ranaroussi/quantstats are: Portfolio Performance Metrics, HTML Analysis Reports, Monte Carlo Simulators, Asset Price Path Simulators, Risk Simulations, Portfolio Return Path Simulators, Financial Performance Metrics, Performance Charting.

Which projects share features with ranaroussi/quantstats?

Projects with overlapping indexed features include: quantopian/pyfolio — Portfolio and risk analytics in Python. quantopian/alphalens — Alphalens is a quantitative alpha factor analysis library designed to measure the predictive power of financial… matplotlib/mplfinance — mplfinance is a financial time-series plotter and market data visualization framework built on Matplotlib. It is… jerbouma/fundamentalanalysis — FundamentalAnalysis is a comprehensive financial analysis library, quantitative finance framework, and macroeconomic… polakowo/vectorbt — VectorBT is a vectorized trading strategy backtesting framework that simulates thousands of strategy configurations in… llmquant/quant-wiki — quant-wiki is a comprehensive knowledge base and structured reference for quantitative finance, financial engineering,…

Projects sharing features with Quantstats

These projects share indexed features with Quantstats. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • quantopian/pyfolioquantopian avatar

    quantopian/pyfolio

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    Portfolio and risk analytics in Python

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  • quantopian/alphalensquantopian avatar

    quantopian/alphalens

    4,143View on GitHub↗

    Alphalens is a quantitative alpha factor analysis library designed to measure the predictive power of financial factors. It serves as a computational toolset for processing financial time series and calculating performance metrics to evaluate quantitative trading hypotheses. The library distinguishes itself through the use of quantile-based data binning to analyze return distributions across different factor strength levels. It aligns historical alpha signals with forward-looking price changes to isolate predictive effects and transforms these metrics into heatmaps and time-series charts for

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  • matplotlib/mplfinancematplotlib avatar

    matplotlib/mplfinance

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    mplfinance is a financial time-series plotter and market data visualization framework built on Matplotlib. It is designed to render market data frames into specialized charts, including candlesticks, OHLC bars, Renko bricks, and point-and-figure columns. The library distinguishes itself through a dedicated market data framework that manages trading calendars and non-trading periods, ensuring accurate temporal spacing by collapsing gaps during holidays. It also provides a system for technical analysis charting, enabling the overlay of moving averages, volume bars, and other technical indicator

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  • jerbouma/fundamentalanalysisJerBouma avatar

    JerBouma/FundamentalAnalysis

    4,974View on GitHub↗

    FundamentalAnalysis is a comprehensive financial analysis library, quantitative finance framework, and macroeconomic data integrator. It provides tools for computing financial ratios, executing corporate health metrics, and pricing derivatives and bonds using mathematical models. The project integrates diverse data streams, including global economic indicators, real-time market quotes, and standardized corporate financial statements. It features a technical analysis engine for generating momentum and volatility indicators, as well as a portfolio performance analyzer for tracking risk-adjusted

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