# signal-postmortem

Records trade signal outcomes, compares predicted direction against realized returns, and drafts weight fixes for the aggregator.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 3300 installs · ★ 3267
- Author: tradermonty
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/tradermonty/claude-trading-skills/tree/HEAD/skills/signal-postmortem/SKILL.md
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/skills/signal-postmortem

## What it does

The skill takes signal records in JSON, from `edge-signal-aggregator` or screeners, and checks the predicted direction against realized returns at 5 and 20 days. Every signal lands in one of four buckets: true positive, false positive, missed opportunity, regime mismatch. The rules live in `references/outcome-classification.md`.

Two scripts do the work. `postmortem_recorder.py` logs outcomes; `postmortem_analyzer.py` builds summaries by skill, ticker and month, and writes weight suggestions plus improvement backlog entries. Summary reports go to `reports/` as `postmortem_summary_YYYY-MM-DD.md`.

You need Python 3.9+ and `requests`. The FMP API key is optional — without it you record outcomes by hand with `--exit-price` and `--exit-date`. Weight adjustments only kick in at 20 signals or more, so a thin sample is a reason to hold off rather than trust the numbers.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add tradermonty/claude-trading-skills --skill signal-postmortem -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/signal-postmortem/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/signal-postmortem/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add tradermonty/claude-trading-skills --skill signal-postmortem -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/signal-postmortem/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add tradermonty/claude-trading-skills --skill signal-postmortem -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/signal-postmortem/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install tradermonty/claude-trading-skills signal-postmortem
```

2. **Manually: put the files in place**
- File: `.github/skills/signal-postmortem/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add tradermonty/claude-trading-skills --skill signal-postmortem -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/tradermonty/claude-trading-skills --path skills/signal-postmortem
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/signal-postmortem/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add tradermonty/claude-trading-skills --skill signal-postmortem -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/tradermonty/claude-trading-skills/tree/HEAD/skills/signal-postmortem/SKILL.md

## When to use it
- log a closed trade and classify its outcome
- spot systematic false positives from one skill
- build a monthly signal quality summary
- draft weight suggestions for edge-signal-aggregator
- separate a skill failure from a market regime shift

## Example prompts
- review signals in `state/signals/aggregated_signals_2026-03-10.json` at 5 and 20 days
- record outcome for signal `sig_aapl_20260310_abc`: exit 178.50 on 2026-03-15
- generate a postmortem summary grouped by skill and month

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 33cd0470bc3d28ff.

BEGIN THIRD-PARTY FILE 33cd0470bc3d28ff
````text
---
name: signal-postmortem
description: Record and analyze post-trade outcomes for signals generated by edge pipeline and other skills. Track false positives, missed opportunities, and regime mismatches. Feed results back to edge-signal-aggregator weights and skill improvement backlog.
---

# Signal Postmortem

## Overview

Signal Postmortem records and analyzes the outcomes of trading signals generated by the edge pipeline, screeners, and other skills. It compares predicted edge direction against 5-day and 20-day realized returns, categorizes outcomes (true positive, false positive, missed opportunity, regime mismatch), and generates feedback for edge-signal-aggregator weight adjustments and skill improvement backlog entries.

## When to Use

- After a trade has been closed and you want to record the outcome
- When reviewing a batch of signals that have reached their holding period (5 or 20 days)
- To identify systematic false positive patterns from specific skills
- To generate feedback for edge-signal-aggregator weight calibration
- When building a skill improvement backlog from decision quality metrics
- For periodic (weekly/monthly) signal quality audits

## Prerequisites

- Python 3.9+
- FMP API key (optional, for fetching realized returns if not provided manually)
- Standard library + `requests` for API calls
- Input: signal records in JSON format (from edge-signal-aggregator or screener outputs)

### API Key Setup (Optional)

If you want to automatically fetch price data for return calculations, set up the FMP API key:

```bash
export FMP_API_KEY=your_api_key_here
```

Alternatively, pass the key via command line with `--api-key YOUR_KEY`. Without an API key, you can still record outcomes manually by providing `--exit-price` and `--exit-date`.

## Workflow

### Step 1: Prepare Signal Records

Gather closed or matured signal records. Each record should include:
- `signal_id`: Unique identifier
- `ticker`: Stock symbol
- `signal_date`: Date signal was generated
- `predicted_direction`: LONG or SHORT
- `source_skill`: Which skill generated the signal
- `entry_price`: Price at signal generation (optional, for manual override)

```bash
# Example: List signals ready for postmortem (5+ days old)
python3 skills/signal-postmortem/scripts/postmortem_recorder.py \
  --list-ready \
  --signals-dir state/signals/ \
  --min-days 5
```

### Step 2: Record Outcomes

Run the postmortem recorder to fetch realized returns and classify outcomes.

```bash
python3 skills/signal-postmortem/scripts/postmortem_recorder.py \
  --signals-file state/signals/aggregated_signals_2026-03-10.json \
  --holding-periods 5,20 \
  --output-dir reports/
```
````
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