Weekly Performance Digest

Generate a weekly performance summary from closed trader-memory-core theses — win rate, expectancy, profit factor, R-multiple, MAE/MFE, and win/loss pattern analysis by source skill, exit reason, thesis type, sector, and mechanism. No API required; pure local calculation.

No API

Download Skill Package (.skill) View Source on GitHub

Table of Contents

1. Overview

Weekly Performance Digest aggregates the trades you closed during a week into a single performance report. It reads CLOSED theses tracked by trader-memory-core (state/theses/th_*.yaml), computes headline metrics (win rate, expectancy, profit factor, R-multiple, MAE/MFE), breaks results down across several pattern dimensions (source skill, exit reason, thesis type, sector, mechanism tag, screening grade), and surfaces the week’s biggest winners, losers, and lessons. Output is a JSON record plus a human-readable Markdown report. Pure calculation — no API key required.

2. When to Use

  • At the end of a trading week to review aggregate realized performance
  • To measure win rate and expectancy across all closed positions
  • To see which source skills, exit reasons, sectors, or mechanisms drove wins vs losses
  • To feed a month-end review (combine four weekly digests) or a postmortem
  • For a quick “what worked / what didn’t” snapshot grounded in real closed trades

3. Prerequisites

  • Python 3.9+ with PyYAML (already a repo dependency)
  • A trader-memory-core state directory of thesis YAML files (state/theses/)
  • No API key required

4. Quick Start 

python3 skills/weekly-performance-digest/scripts/generate_weekly_digest.py \
  --state-dir state/theses \
  --from-date 2026-06-13 --to-date 2026-06-20 \
  --output-dir reports/ -v

5. Workflow

Step 1: Run the digest for a week 

python3 skills/weekly-performance-digest/scripts/generate_weekly_digest.py \
  --state-dir state/theses \
  --from-date 2026-06-13 --to-date 2026-06-20 \
  --output-dir reports/ -v

Defaults: --state-dir state/theses, --from-date = 7 days before --to-date, --to-date = today, --output-dir reports/. With no date flags it digests the trailing 7 days.

Step 2: Read the report

The run writes reports/weekly_digest_<to-date>.json and reports/weekly_digest_<to-date>.md. Review the Markdown for the executive summary, metrics table, pattern breakdowns, and top winners/losers; consume the JSON downstream.

Step 3 (optional): Feed downstream

Combine several weekly JSON digests for a monthly review, or pass the JSON to a postmortem/coach step. The skill is descriptive — act on its findings via your normal review process.

6. Resources

References:

  • skills/weekly-performance-digest/references/weekly-digest-metrics.md

Scripts:

  • skills/weekly-performance-digest/scripts/generate_weekly_digest.py