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 recommendations or position sizing, this skill is descriptive only

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

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