signal-postmortem
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.
npx skills add BaggaT236/AI-Trading-Skills --skill signal-postmortem --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 d
- Overview
- When to Use
- Prerequisites
- API Key Setup (Optional)
- Workflow
- Step 1: Prepare Signal Records
- Step 2: Record Outcomes
- Step 3: Classify Outcomes
- Step 4: Generate Feedback Files
- Step 5: Review Summary Statistics
- Output Format
- Postmortem Record (JSON)
- Weight Feedback (JSON)
- Skill Improvement Backlog Entry (YAML)
export FMP_API_KEY=your_api_key_here python3 skills/signal-postmortem/scripts/postmortem_recorder.py \ Generate weight adjustment suggestions for edge-signal-aggregator python3 skills/signal-postmortem/scripts/postmortem_analyzer.py \ Generate skill improvement backlog entries
What does the signal-postmortem skill do?
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.
How do I install it?
Run `npx skills add BaggaT236/AI-Trading-Skills --skill signal-postmortem --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BaggaT236/AI-Trading-Skills, a repository with 118 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.