Signal Postmortem

This is your feedback loop for trading signals. It takes signals from your edge pipeline or screeners, waits for the holding period (5 or 20 days), then records whether they actually worked. It classifies outcomes as true positives, false positives, missed opportunities, or regime mismatches, then generates weight adjustments for your signal aggregator and flags systematic problems for your skill improvement backlog. The regime awareness is smart: it distinguishes between a screener that's broken versus one that just doesn't work in RISK_OFF conditions. Requires 20+ signals before suggesting weight changes, which keeps you from overreacting to noise. If you're running multiple signal sources and want to know which ones actually make money, this closes that loop.

skills.sh installs: 3.5K
GitHub stars: 3.7K
last updated: Aug 21, 2026

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Install

$ npx -y skills add tradermonty/claude-trading-skills --skill signal-postmortem --agent claude-code

Installs into .claude/skills of the current project.

Files

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)