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PerformanceFreeCommunityv1.0.0

weekly-performance-digest

Use when the user wants a weekly performance summary from closed trades — win rate, expectancy, profit factor, R-multiple, MAE/MFE, and win/loss pattern breakdowns.

About this skill

Use when the user wants a weekly performance summary from closed trades — win rate, expectancy, profit factor, R-multiple, MAE/MFE, and win/loss pattern breakdowns.

SKILL.md

weekly-performance-digest.SKILL.md

Place it in your Syntic skills folder. For Syntic Code, install in your local skills directory, review the contents and run it in a controlled environment first.

SKILL.md
---
name: weekly-performance-digest
description: Use when the user wants a weekly performance summary from closed trades — win rate, expectancy, profit factor, R-multiple, MAE/MFE, and win/loss pattern breakdowns.
category: Performance
version: 1.0.0
tools: []
---

# Weekly Performance Digest

Aggregate the trades closed during a week into a single performance report — pure calculation, descriptive only, no trade recommendations.

## When to Use

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; or for a quick "what worked / what didn't" snapshot grounded in real closed trades.

Do not use this for a single-trade deep review (use `trade-performance-coach`), for signal-level true/false-positive classification (use `signal-postmortem`), or for buy/sell recommendations or position sizing — this skill is descriptive only.

## Inputs

Read CLOSED theses tracked by `trader-memory-core` for the requested date range (default: trailing 7 days). A trade counts in a week if its actual exit date falls in that range and its status is CLOSED.

## How It Works

**Win/loss.** A trade with positive realized P&L is a winner, negative is a loser, zero is breakeven; win rate = winners / total trades.

**R-multiple.** P&L divided by (entry price − stop-loss price) × shares, using the plan's recorded stop-loss.

**Double-counting safeguard.** A CLOSED thesis's cumulative realized P&L already includes all trims plus the final leg — headline metrics use that cumulative value over CLOSED theses only. Partial trims from theses that are still open (PARTIALLY_CLOSED) are reported separately as informational and are never added into the headline totals or win rate. A position trimmed in week 1 and 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.

## Workflow

Compute headline metrics: win rate, expectancy, profit factor, average R-multiple, and MAE/MFE. Break results down across multiple pattern dimensions: source skill, exit reason, thesis type, sector, mechanism tag, and screening grade. Surface the week's biggest winners, losers, and process-improvement lessons.

## Output

Return, in chat, a report with:

- **Summary** — total trades, winners, losers, breakeven, win rate, expectancy, profit factor, total realized P&L (dollar and percent).
- **Metrics** — average/largest winner and loser, average holding days for winners vs. losers, R-multiple average and standard deviation, average MAE% and MFE%.
- **Pattern analysis** — win/loss breakdown by source skill, exit reason, thesis type, sector, mechanism tag, and screening grade.
- **Partial trims** — count and total realized P&L from trims on still-open positions, kept separate from headline totals.
- **Lessons** — top wins, top losses, and process improvements.

An empty week still produces a valid report with zeroed metrics rather than an error.

## Key Principles

1. **Closed trades only for headline numbers** — cumulative outcome figures, 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; the user decides.

Install

Syntic App

  1. 1Create a dedicated folder for this skill in your local skills library.
  2. 2Place SKILL.md into that folder.
  3. 3Restart Syntic and invoke this skill on matching tasks.

Syntic Code (CLI)

  1. 1Save SKILL.md in your local Syntic Code skills directory.
  2. 2Keep related files in the same skill folder.
  3. 3Run in a safe environment and validate outputs.