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Empyrical: Common financial risk and performance metrics in Python

Empyrical: Common financial risk and performance metrics in Python

Empyrical: Common financial risk and performance metrics in Python

This article explains the performance metrics used to evaluate investment strategies, such as Sharpe Ratio, Sortino Ratio, and Omega Ratio.

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Quantopian is a platform that provides a suite of tools for quantitative finance. The suite, called empyrical, is designed to help people analyze and understand their portfolio performance. It includes a number of features, such as performance metrics, risk analysis, and portfolio optimization. The suite is built on top of the open source Python library, Pandas. It is designed to be simple to use and understand, and is compatible with most major Python libraries.

Empyrical provides a range of performance metrics. These include returns, volatility, Sharpe ratio, Alpha, Beta, and more. It also provides risk analysis, such as maximum drawdown and Value-at-Risk. Additionally, it offers portfolio optimization capabilities, such as portfolio diversification and risk-adjusted returns.

Quantopian has designed empyrical to be easy to use. It is designed to be compatible with most major Python libraries, and it has a user-friendly interface. Additionally, it has detailed documentation and tutorials to help users get started.

Overall, Quantopian’s empyrical suite is designed to provide users with the tools they need to analyze and understand their portfolio performance. It provides performance metrics, risk analysis, and portfolio optimization capabilities, and is simple to use and understand.

Check out the full post at github.io.