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.
npx skills add BaggaT236/AI-Trading-Skills --skill weekly-performance-digest --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Weekly Performance Digest ## 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. ## 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 ## When Not to Use - For a single-trade deep review — use `trade-performance-coach` - For signal-level true/false-positive classification — use `signal-postmortem` - For buy/sell recomme
- Overview
- When to Use
- When Not to Use
- Prerequisites
- Workflow
- Step 1: Run the digest for a week
- Step 2: Read the report
- Step 3 (optional): Feed downstream
- How It Works
- Output Format
- JSON (weeklydigest<to-date>.json)
- Markdown (weeklydigest<to-date>.md)
- Resources
- Key Principles
python3 skills/weekly-performance-digest/scripts/generate_weekly_digest.py \
What does the weekly-performance-digest skill do?
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.
How do I install it?
Run `npx skills add BaggaT236/AI-Trading-Skills --skill weekly-performance-digest --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BaggaT236/AI-Trading-Skills, a repository with 118 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.