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pyfolio – Performance and Risk Analysis for Zipline

pyfolio - Performance and Risk Analysis for Quantopian/Zipline

pyfolio – Performance and Risk Analysis for Zipline

Pyfolio is a Python library for performance and risk analysis of financial portfolios.

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Pyfolio is a Python library for portfolio and risk analysis. It allows users to analyze and visualize their trading strategies in a simple and powerful way. Pyfolio provides a set of tools to help users assess their strategies, including performance metrics, risk metrics, and plotting tools. Performance metrics measure a strategy’s return, risk-adjusted return, and drawdowns. Risk metrics measure a strategy’s volatility, maximum drawdown, and VaR. Plotting tools allow users to visualize their strategy’s returns, cumulative returns, rolling returns, and drawdowns. Pyfolio also provides an interactive dashboard that allows users to compare their strategy with a benchmark.

Pyfolio is designed to be used with the Python programming language, making it easy for users to integrate their trading strategies into their own code. Pyfolio also supports integration with popular data sources such as Google Finance and Quandl. Pyfolio is open source and free to use, making it an ideal choice for users who want to analyze and visualize their trading strategies.

Pyfolio is a powerful tool for portfolio and risk analysis. It provides a set of tools to help users assess their strategies, including performance and risk metrics, and plotting tools. Pyfolio is designed to be used with the Python programming language and supports integration with popular data sources. It is open source and free to use, making it an ideal choice for users who want to analyze and visualize their trading strategies.

Overall, Pyfolio is an easy to use library that provides users with a set of tools to help them analyze and visualize their trading strategies. It is open source and free to use, making it an ideal choice for users looking for a powerful portfolio and risk analysis tool.

Check out the full post at github.io.