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signal-postmortem

Analyze post-trade outcomes and generate weight adjustment suggestions.

2|Updated Apr 6, 2026
Search Tags:#feedback loop#trading#signal analysis#post-trade analysis#weight adjustment
One-click install
npx skills add https://github.com/k1064190/stock-expectation --skill signal-postmortem-k1064190
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: signal-postmortem
Source: https://github.com/k1064190/stock-expectation/tree/main/.claude/skills/signal-postmortem
Command: npx skills add https://github.com/k1064190/stock-expectation --skill signal-postmortem-k1064190

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, pandas, datetime, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill helps traders and analysts record and analyze post-trade outcomes for signals generated by the edge pipeline and other skills. It tracks false positives, missed opportunities, and regime mismatches to inform weight adjustments and skill improvement backlogs.

Core Features & Use Cases

  • Record Outcomes: Record the outcome of trades after they are closed.
  • Classify Outcomes: Automatically classify outcomes into categories like TRUE_POSITIVE, FALSE_POSITIVE, MISSED_OPPORTUNITY, and REGIME_MISMATCH.
  • Generate Feedback: Generate weight adjustment suggestions for edge-signal-aggregator and skill improvement backlog entries.
  • Use Case: After a trade is closed, use this Skill to record the outcome and compare the predicted direction against the realized return to identify areas for improvement in your trading strategy.

Quick Start

Run the postmortem recorder to record the outcome of a trade and generate feedback.

Frequently Asked Questions about signal-postmortem

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze post-trade outcomes to improve my trading signals?▼

To analyze post-trade outcomes, you record closed trades and compare predicted direction against realized return to classify results into categories like TRUE_POSITIVE or FALSE_POSITIVE. This generates weight adjustment suggestions and skill improvement backlog entries.

What is post-trade signal analysis and when do I need it?▼

Post-trade signal analysis is the process of recording and classifying closed trade results to evaluate signal performance. You need it after trades close to identify false positives, missed opportunities, and regime mismatches in your trading strategy.

How do I classify false positives and missed opportunities from my trading history?▼

You classify false positives and missed opportunities by inputting closed trade data in JSON format from edge-signal-aggregator or screeners. The system automatically categorizes outcomes into TRUE_POSITIVE, FALSE_POSITIVE, MISSED_OPPORTUNITY, and REGIME_MISMATCH.

Do I need an FMP API key to analyze post-trade signal performance?▼

An FMP API key is optional for fetching price data during post-trade signal analysis. The core requirements are Python, input in JSON format from edge-signal-aggregator or screeners, and the requests, pandas, and datetime dependencies.

Can I use pandas and Python to generate weight adjustment suggestions for my edge pipeline?▼

Yes, you can use Python with pandas to generate weight adjustment suggestions for edge-signal-aggregator. The system processes JSON input from your edge pipeline and outputs summary statistics alongside backlog entries for skill improvement.

What's the best way to record trading outcomes for feedback loop optimization?▼

The best way to record trading outcomes for feedback loop optimization is running the postmortem recorder on closed trades. It compares predicted direction against realized return and outputs weight adjustment suggestions and summary statistics for strategy improvement.

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