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.

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: 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) Step 2: Record Outcomes Run the postmortem recorder to fetch realized returns and classify outcomes. For manual outcome recording (when price data is already available): Step 3: Classify Outcomes The recorder automatically classifies each signal into one of four categories: Category Definition TRUE POSITIVE Predicted direction matched realized return sign FALSE POSITIVE Predicted direction opposite to realized return MISSED OPPORTUNITY Signal not taken but would have been profitable REGIME MISMATCH Signal failed due to market regime change Classification rules are documented in references/outcome classification.md . Step 4: Generate Feedback Files Generate feedback for downstream consumers: Step 5: Review Summary Statistics Generate aggregate statistics by skill, by ticker, and by time period: Output Format Postmortem Record (JSON) Weight Feedback (JSON) Skill Improvement Backlog Entry (YAML) Summary Report (Markdown) Reports are saved to reports/ with filenames postmortem summary YYYY MM DD.md . Resources scripts/postmortem recorder.py Records individual signal outcomes scripts/postmortem analyzer.py Generates feedback and summary statistics references/outcome classification.md Classification rules and edge cases references/feedback integration.md How to integrate feedback with downstream skills Key Principles 1. Honest Attribution Every outcome is attributed to its source skill for accountability 2. Regime Awareness Regime context is recorded to distinguish skill failure from market regime shifts 3. Minimum Sample Size Weight adjustments require 20+ signals for statistical validity 4. Feedback Loop Closure Results flow back to improve both signal aggregation and skill quality

signal-postmortem