Backtest Performance APIs for Parameter Search and Reinforcement Learning

Summary

The document proposes adding a simple API to retrieve selected strategy performance metrics over a specified date range. Examples include the Sharpe ratio, annualized return, benchmark return, and alpha. The request responds to a gap between an existing visual backtest interface and programmatic access to performance summaries.

The proposed interface would make it easier to automate parameter searches, since candidate strategies could be compared directly using chosen metrics. It also suggests that programmatic metrics could support more complex deep-learning or reinforcement-learning workflows, framing reinforcement learning as repeated local optimization of a strategy objective such as Sharpe ratio. This is a feature proposal rather than a description of an implemented API or a demonstrated research result. It does not specify metric definitions, data formats, treatment of costs, or safeguards against overfitting when optimizing across many trials; those details would matter for reliable use.

Key ideas

• The proposal seeks programmatic access to backtest performance metrics over chosen date ranges.
• Suggested metrics include risk-adjusted return, annualized return, benchmark return, and alpha.
• An API could streamline automated parameter searches by making results easier to compare.
• The author connects metric access to potential deep-learning and reinforcement-learning workflows.
• The document is a request and does not establish that the API exists or validate its research benefits.

Tags

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