Calculating Daily Strategy Returns from Cumulative Backtest Performance

Summary

This short forum post asks how to export a backtest’s daily returns to a CSV file and where to place a suggested calculation in the BigQuant workflow. The proposed approach reads a performance DataFrame, adds one to the cumulative algorithm return, computes the percentage change between observations, and fills the first missing value with zero. This converts a cumulative return series into period-by-period returns, which can then be included in an export.

The post does not provide a complete CSV-writing example or answer the question about where the calculation belongs. It also gives no guidance on the performance object’s sampling frequency, column availability, or handling of missing dates. Those details matter: the computed values are daily only if the underlying observations are daily, and the first value is set to zero by convention rather than derived from a prior observation. Treat this as a partial implementation hint, not a full export procedure.

Key ideas

• Daily returns can be derived from a cumulative return series by taking percentage changes in one plus cumulative return.
• The suggested calculation reads backtest performance data and adds a new return column.
• The first missing change is replaced with zero, a convention that should be understood when interpreting the series.
• The post does not explain where to run the calculation or how to write the result to CSV.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.