Python Tools for Strategy Performance Metrics and Reporting
Summary
The document asks how to calculate measures such as maximum drawdown, Sharpe ratio, and Treynor measure from a strategy’s returns. Replies point to several Python tools: Backtrader for a broader backtesting environment with analyzers, empyrical for risk and performance calculations, pyfolio for portfolio performance visualization, and Quantiacs for statistics based on an equity curve.
The recommendations distinguish metric calculation from the surrounding workflow: a library may provide formulas, while a backtesting framework can also support simulation, optimization, brokerage integration, and plots. The evidence is limited to user recommendations and personal experience; there is no benchmark comparing metric definitions, accuracy, maintenance, or suitability for a returns-only input. Before adopting a tool, researchers should check whether its assumptions and return-frequency conventions match their own calculations. The document is a starting point for tool selection rather than a technical guide to performance measurement.
Key ideas
- Empyrical is suggested for calculating common risk and performance measures from return data.
- Pyfolio is mentioned for visualizing portfolio performance through time.
- Backtrader offers analyzers within a larger backtesting framework.
- Quantiacs is described as producing statistics from an equity curve.
- The recommendations are anecdotal and do not compare calculation conventions or metric definitions.
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Full text
# Is there a python library to generate performance metrics from returns of the strategy? # Is there a python library to generate performance metrics from returns of the strategy? I am backtesting a strategy and have data generated from the returns of the strategy. Now I need performance metrics like maximum drawdown, Sharpe ratio, Treynor measure etc., I am writing functions individually. I am looking for a library which can generate these metrics taking the returns as input. ## Answer by Jaspal Singh Rathour (score 1, accepted) https://quant.stackexchange.com/a/41613 Try backtrader at at Backtrader.com. It is a python based open source backtester with great documentation. You can implement analysers as part of your back test and get various performance metrics https://www.backtrader.com/docu/analyzers/analyzers.html?highlight=performance[Analysers][1]. I have used both Quantiacs and Backtrader and found that Quantiacs was limited to functionality required to enter their quant competition, however Backtrader is a full backtesting solution, including optimisisation, broker integration, useful graph output, multiprocessor support and framework that allows great flexibility in making your ideas come to life. One huge difference is the community support - just take a look for yourself. I posted a question on quantiacs about six months ago and I am still waiting for a response. On backtrader I often post in the evening and have a response by the morning. Good luck with whatever you choose. ## Answer by ernestoeperez88 (score 3) https://quant.stackexchange.com/a/41359 Check out empyrical. This library provides methods for calculating several risk and performance metrics. pyfolio is also a great tool for visualizing your portfolio's performance over time. ## Answer by user35489 (score 1) https://quant.stackexchange.com/a/41361 Have you looked at Quantiacs? Their Python toolkit generates a plethora of statistics based on the equity curve. You may want to take a look: www.quantiacs.com/For-Quants/GetStarted.aspx.
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