Individual skill

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.

skills/signal-postmortem/SKILL.md

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Repository stars (not skill ratings): 3,768
Repository last updated: Nov 10, 2026
License: MIT

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SKILL.mdcore · 6,756 bytes
SKILL.mdcore · 6,756 bytes
SKILL.mdcore · 6,756 bytes
SKILL.mdcore · 6,756 bytes
SKILL.mdcore · 6,756 bytes
SKILL.mdcore · 6,756 bytes
SKILL.mdcore · 6,756 bytes
SKILL.mdcore · 6,756 bytes
references/feedback-integration.mddoc · 5,261 bytes
references/outcome-classification.mddoc · 5,118 bytes
requirements.txtdoc · 128 bytes
scripts/postmortem_analyzer.pyruns · 19,249 bytes
scripts/postmortem_recorder.pyruns · 14,898 bytes
scripts/tests/conftest.pyruns · 230 bytes
scripts/tests/test_cli_workflow.pyruns · 12,150 bytes
scripts/tests/test_postmortem_analyzer.pyruns · 11,351 bytes
scripts/tests/test_postmortem_recorder.pyruns · 7,654 bytes

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# 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