# Signal Postmortem

Record and analyze post-trade outcomes for signals generated by edge pipeline and other...

- 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 signal-postmortem -g -a claude-code -y`
Codex: `npx skills add tradermonty/claude-trading-skills --skill signal-postmortem -g -a codex -y`
Cursor: `npx skills add tradermonty/claude-trading-skills --skill signal-postmortem -g -a cursor -y`
Gemini CLI: `npx skills add tradermonty/claude-trading-skills --skill signal-postmortem -g -a gemini-cli -y`

## 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.

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

# 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)

The rest: https://github.com/tradermonty/claude-trading-skills/blob/HEAD/skills/signal-postmortem/SKILL.md