weekly-performance-digest

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

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

Closed trades only for headline numbers — cumulative outcome.*, keyed on exit date.
No double-counting — partial trims are informational and excluded from totals.
Pattern attribution — every win/loss is attributed across multiple dimensions.
Descriptive, not prescriptive — the digest reports; you decide.