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name: settle description: Compare predictions against settled results, update the learning feedback loop, and report ROI/P&L. Use when the user wants to settle bets, grade a run, see how picks performed, or refresh calibration after results come in.

Settle & Grade

Evaluate predictions against actual results and refresh the calibration loop.

Steps

1. Settle the latest run (or a specific saved run):

   python tippmix.py compare
   python tippmix.py compare --run data/predictions/20260609_074456.json
   

2. This writes data/comparisons/compare_<timestamp>.json, appends data/archive/comparisons.jsonl, and updates data/lessons.json (market_adjustments, missed_predictions, notes).
3. Read the comparison output and report:
   • Per-item results (🟢 NYERT / 🔴 VESZÍTETT / 🟡 FOLYAMATBAN).
   • Summary: hit rate, ROI, and HUF balance.
   • Which markets moved their calibration offset and why (from lessons.json notes).
4. Combo legs settle individually — a pushed/void leg pays the winning legs, it does not zero the combo. Verify this held if a combo looks mis-graded.

Notes

• Markets with < 5 settled predictions keep adjustment = 0 (not enough signal).
• Adjustment formula: clamp(round((actual_win_rate - expected_win_rate) * 50), -20, 20).
• If many picks are still 🟡 FOLYAMATBAN, results haven't landed yet (tennis/esports settlement can lag) — re-run later rather than forcing conclusions.
• For a full historical view across all runs, use /backtest.