Evaluating Trading Backtests with Pyfolio Return and Risk Reports
Summary
This article shows how to turn an FMZ backtest capital curve into a more detailed performance report with Pyfolio. The workflow exports the backtest’s profit and loss series as CSV, optionally obtains daily market data for a benchmark, converts the data into the format expected by the library, and generates a tear sheet. The author describes measures in the report, including annualized and cumulative returns, volatility, Sharpe, drawdown, Omega, Sortino, value at risk, tail ratio, and stability. A benchmark can support Alpha and Beta analysis; a designated live start date can also separate simulated or backtested returns from later results.
The article argues that comparing results before and after the live start date may help reveal possible overfitting when performance differs substantially. It provides an implementation walkthrough and sample output, but does not establish that any particular strategy is robust. Annualized returns are theoretical rather than realized yearly returns, and the metrics require careful interpretation. Benchmark choice and the quality of input return data also shape the assessment.
Key ideas
- Pyfolio can produce a multi-metric tear sheet from a strategy return series.
- A benchmark series enables additional relative performance measures such as Alpha and Beta.
- A live start date can separate backtest returns from live returns for comparison.
- Large differences between simulated and live periods may indicate possible overfitting.
- Risk and return measures summarize different properties and do not by themselves prove robustness.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.