Signal Postmortem

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:

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export FMP_API_KEY=your_api_key_here

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)

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# Example: List signals ready for postmortem (5+ days old)
python3 skills/signal-postmortem/scripts/postmortem_recorder.py \
  --list-ready \
  --signals-dir state/signals/ \
  --min-days 5

Step 2: Record Outcomes

Run the postmortem recorder to fetch realized returns and classify outcomes.

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python3 skills/signal-postmortem/scripts/postmortem_recorder.py \
  --signals-file state/signals/aggregated_signals_2026-03-10.json \
  --holding-periods 5,20 \
  --output-dir reports/

For manual outcome recording (when price data is already available):

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python3 skills/signal-postmortem/scripts/postmortem_recorder.py \
  --signal-id sig_aapl_20260310_abc \
  --exit-price 178.50 \
  --exit-date 2026-03-15 \
  --outcome-notes "Closed at target, +3.2% in 5 days" \
  --output-dir reports/

Step 3: Classify Outcomes

The recorder automatically classifies each signal into one of four categories:

Classification rules are documented in references/outcome-classification.md.

Step 4: Generate Feedback Files

Generate feedback for downstream consumers:

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# Generate weight adjustment suggestions for edge-signal-aggregator
python3 skills/signal-postmortem/scripts/postmortem_analyzer.py \
  --postmortems-dir reports/postmortems/ \
  --generate-weight-feedback \
  --output-dir reports/

# Generate skill improvement backlog entries
python3 skills/signal-postmortem/scripts/postmortem_analyzer.py \
  --postmortems-dir reports/postmortems/ \
  --generate-improvement-backlog \
  --output-dir reports/

Step 5: Review Summary Statistics

Generate aggregate statistics by skill, by ticker, and by time period:

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python3 skills/signal-postmortem/scripts/postmortem_analyzer.py \
  --postmortems-dir reports/postmortems/ \
  --summary \
  --group-by skill,month \
  --output-dir reports/

Output Format

Postmortem Record (JSON)

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{
  "schema_version": "1.0",
  "postmortem_id": "pm_sig_aapl_20260310_abc",
  "signal_id": "sig_aapl_20260310_abc",
  "ticker": "AAPL",
  "signal_date": "2026-03-10",
  "source_skill": "edge-signal-aggregator",
  "predicted_direction": "LONG",
  "entry_price": 172.50,
  "realized_returns": {
    "5d": 0.032,
    "20d": 0.058
  },
  "exit_price": 178.50,
  "exit_date": "2026-03-15",
  "holding_days": 5,
  "outcome_category": "TRUE_POSITIVE",
  "regime_at_signal": "RISK_ON",
  "regime_at_exit": "RISK_ON",
  "outcome_notes": "Clean breakout, held through minor pullback",
  "recorded_at": "2026-03-17T10:30:00Z"
}

Weight Feedback (JSON)

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{
  "schema_version": "1.0",
  "generated_at": "2026-03-17T10:35:00Z",
  "analysis_period": {
    "from": "2026-02-01",
    "to": "2026-03-15"
  },
  "skill_adjustments": [
    {
      "skill": "vcp-screener",
      "current_weight": 1.0,
      "suggested_weight": 0.85,
      "reason": "15% false positive rate in RISK_OFF regime",
      "sample_size": 42
    }
  ],
  "confidence": "MEDIUM",
  "min_sample_threshold": 20
}

Skill Improvement Backlog Entry (YAML)

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- skill: vcp-screener
  issue_type: false_positive_cluster
  severity: medium
  evidence:
    false_positive_rate: 0.15
    sample_size: 42
    regime_correlation: RISK_OFF
  suggested_action: "Add regime filter or reduce signal confidence in RISK_OFF"
  generated_by: signal-postmortem
  generated_at: "2026-03-17T10:35:00Z"

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