# Weekly Performance Digest

Generate a weekly performance summary from closed trader-memory-core theses, win rate...

- Repository: https://github.com/tradermonty/claude-trading-skills (3536 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add tradermonty/claude-trading-skills --skill weekly-performance-digest -g -a claude-code -y`
Codex: `npx skills add tradermonty/claude-trading-skills --skill weekly-performance-digest -g -a codex -y`
Cursor: `npx skills add tradermonty/claude-trading-skills --skill weekly-performance-digest -g -a cursor -y`
Gemini CLI: `npx skills add tradermonty/claude-trading-skills --skill weekly-performance-digest -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

```bash
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 rest: https://github.com/tradermonty/claude-trading-skills/blob/HEAD/skills/weekly-performance-digest/SKILL.md