weekly-performance-digest

To generate a weekly trading performance summary, run the Python script with your state directory and date range. It analyzes closed theses to compute win rate, expectancy, profit factor, R-multiple, and MAE/MFE metrics. You need Python 3.9+ with PyYAML installed.

A weekly trading performance digest computes win rate, expectancy, profit factor, R-multiple, and MAE/MFE. It also performs win/loss pattern analysis and breaks down performance data by source skill, exit reason, thesis type, sector, and mechanism tag.

Yes, you need Python 3.9 or higher with the PyYAML dependency installed to analyze trading thesis performance. The script also requires a 'trader-memory-core' state directory containing your closed trading theses data to function correctly.

You can analyze trading performance by exit reason and sector by running the weekly digest script. It automatically breaks down performance metrics across various dimensions including source skill, exit reason, thesis type, sector, and mechanism tag.

The best way to review closed trading theses for weekly performance analysis is using an automated digest script. It processes your state directory data to output comprehensive performance metrics and multi-dimensional breakdowns, helping you identify areas of improvement.