Weekly Performance Digest

Overview

trader-memory-core

state/theses/th_*.yaml

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

--state-dir state/theses

--from-date

--to-date

--to-date

--output-dir reports/

Step 2: Read the report

reports/weekly_digest_<to-date>.json

reports/weekly_digest_<to-date>.md

Step 3 (optional): Feed downstream

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

)

{
  "schema_version": "1.0",
  "report_type": "weekly_performance_digest",
  "period": {"from": "2026-06-13", "to": "2026-06-20"},
  "generated_at": "2026-06-20T21:39:07Z",
  "summary": {
    "total_trades": 2, "winners": 1, "losers": 1, "breakeven": 0,
    "win_rate": 0.5, "expectancy": 25.0, "profit_factor": 2.0,
    "total_realized_pnl": 50.0, "total_realized_pnl_pct": 4.17
  },
  "metrics": {
    "avg_winner": 100.0, "avg_loser": -50.0,
    "largest_winner": 100.0, "largest_loser": -50.0,
    "avg_holding_days_winners": 9.0, "avg_holding_days_losers": 6.0,
    "r_multiple_avg": 0.25, "r_multiple_stdev": 1.06,
    "avg_mae_pct": -3.75, "avg_mfe_pct": 4.5
  },
  "pattern_analysis": {
    "by_source_skill": {"...": {"wins": 1, "losses": 0, "total": 1, "win_rate": 1.0}},
    "by_exit_reason": {}, "by_thesis_type": {}, "by_sector": {},
    "by_mechanism_tag": {}, "by_screening_grade": {}
  },
  "partial_trims": {"count": 0, "total_realized_pnl": 0.0, "trims": []},
  "lessons": {"top_wins": [], "top_losses": [], "process_improvements": []}
}

Markdown (

weekly_digest_<to-date>.md

)

# Weekly Performance Digest

## Executive Summary

## Performance Metrics

## Pattern Analysis

## Lessons Learned

### Top Winners

### Top Losers

### Process Improvements

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.