Agent skill · data analytics · tradermonty
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.
Why this skill is useful
Provides specific calculations and metrics for performance analysis that the AI wouldn't reliably generate on its own.
What it needs
Requires python, pyyaml installed locally. About 3k tokens when loaded. Last updated 2026-08-06. 2,581 stars on the source repository.
What this skill does
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 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/weeklydigest<to-date>.json and reports/weeklydigest<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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills tradermonty/weekly-performance-digest