research-run

To run Python backtests reproducibly, you implement research code on a dedicated Git branch, tag blocks with # EXPERIMENT, and validate outputs using pytest to generate structured execution reports.

The best way to track experiment changes is enforcing a structured Git workflow with explicit # EXPERIMENT tags, ensuring all backtest modifications are traceable and auditable for collaboration.

You use pytest to validate event study backtest outputs by writing test cases that verify your Python research code execution, ensuring the documented results match the expected experiment design.

Yes, you need Git to manage Python research experiments because this workflow requires a branch-based process to isolate backtest code, enforce experiment tagging, and document changes for reproducibility.

Yes, you can apply this experiment workflow to event studies across various topics, as it guides Python-based research implementation, backtesting, and result validation independent of the specific subject matter.