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.

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/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. How It Works Trade selection. A trade counts in a week if its exit.actual date falls in [from date, to date] and status == CLOSED . Win/loss. outcome.pnl dollars 0 is a winner, < 0 a loser, == 0 breakeven; win rate = winners / total trades . R multiple. pnl dollars / ((entry.actual price − exit.stop loss) × position.shares) . (Stop loss is read from exit.stop loss , per the real thesis schema.) Double counting safeguard. A CLOSED thesis's outcome.pnl dollars is the cumulative realized P&L across all trims plus the final leg. Headline metrics use that cumulative value over CLOSED theses only. The separate partial trims block scans status history[] of PARTIALLY CLOSED theses only (still open) and is reported for information — it is never added into the headline totals/win rate. A position trimmed in week 1 then closed in week 2 therefore shows as a partial trim in week 1 and inside week 2's CLOSED headline; that is intended, not a duplicate. Output Format JSON ( weekly digest <to date .json ) Markdown ( weekly digest <to date .md ) Sections: Weekly Performance Digest , Executive Summary , Performance Metrics , Pattern Analysis , Lessons Learned ( Top Winners / Top Losers / Process Improvements ). An empty week still produces a valid report with zeroed metrics (exit code 0). Resources scripts/generate weekly digest.py — digest generator (JSON + Markdown) references/weekly digest metrics.md — metric formulas and interpretation Key Principles 1. Closed trades only for headline numbers — cumulative outcome. , keyed on exit date. 2. No double counting — partial trims are informational and excluded from totals. 3. Pattern attribution — every win/loss is attributed across multiple dimensions. 4. Descriptive, not prescriptive — the digest reports; you decide.

weekly-performance-digest