signal-postmortem

To analyze trading signal outcomes, you record post-trade results and categorize them as true positives, false positives, missed opportunities, or regime mismatches. This generates feedback for weight adjustment and strategy refinement based on historical performance.

Yes, you need an FMP API key for post-trade return calculations. The Skill uses the Python requests library to call the FMP API, requiring this key to fetch the necessary market data for accurate signal outcome analysis.

Outcome classification categorizes trading signals by recording exit prices and dates, then sorting results into true positives, false positives, missed opportunities, or regime mismatches to generate targeted feedback for skill improvement.

Yes, you can adjust edge-signal-aggregator weights. By analyzing historical outcomes and classifying results, the Skill generates feedback that directly informs and modifies the weight adjustments within your trading strategy.

The best way to record a completed trade outcome is to provide the signal ID, exit price, and exit date. This records the transaction details for the analysis pipeline to categorize the result and generate strategy feedback.