fahricansecer
988ee2f50d
betting_brain.py: - HARD_MIN_SAMPLES=50 floor for calibrator bypass - ev_edge < 0 + >= 0.20 hard vetoes - BTTS muted (grid search found no profitable config) - Per-market optimal envelopes (MS, OU25) - Score coherence filter: main_pick must agree with score prediction - HTFT reversal cross-check for MS picks feature_builder.py / data_loader.py: - Real home/away_position from data (was hardcoded 10) - Cup detection wired into UpsetEngine - _estimate_league_position with 300-day season filter New scripts: - diagnostic_backtest.py: per-bet diagnostic backtest with loss patterns - optimize_filters.py: grid search per-market optimal thresholds - analyze_backtest_csv.py: root-cause hypothesis testing on CSV - compare_backtests.py: side-by-side validation with verdict - test_score_coherence.py: smoke test for coherence filter (20/20 pass) Reports: - diagnostic_backtest_20260525_024437 (50-match smoke) - diagnostic_backtest_20260525_035649 (1000-match in-sample) - filter_optimization_patch.json (grid search winners per market) Social poster v3: - satori + resvg HTML/CSS rendering pipeline - Twemoji football/basketball + flag SVGs - caption SEO: 12 curated hashtags per post - image SEO: descriptive filenames + .json metadata sidecar - /health, /preview-png, /run-now endpoints Docs: - mds/SESSION_HANDOFF.md: full session state for cross-machine continuity - mds/SOCIAL_POSTER_SETUP.md: API keys + test commands Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
55 lines
2.0 KiB
Python
55 lines
2.0 KiB
Python
"""Smoke test for the score-coherence filter using the LAFC vs Sounders
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1-0 scenario from production. Verifies that markets that contradict the
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predicted score are correctly excluded from the coherent set, and that
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the markets the model got right are all included.
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"""
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import os, sys
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from services.betting_brain import BettingBrain
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brain = BettingBrain()
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pkg = {
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"score_prediction": {"ft": "1-0", "ht": "0-0"},
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}
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coh = brain._score_consistent_markets(pkg)
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print(f"Predicted: 1-0 (HT 0-0)")
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print(f"Coherent set size: {len(coh)}")
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print()
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# Each pick the system actually offered for the LAFC match, with whether
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# it was the *actual* winning pick.
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test_picks = [
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("MS", "1", True, "correct"),
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("MS", "2", False, "wrong"),
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("MS", "X", False, "wrong"),
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("DC", "1X", True, "correct"),
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("DC", "12", True, "correct"),
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("DC", "X2", False, "wrong"),
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("OU25", "Üst", False, "WRONG — system featured this"),
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("OU25", "Alt", True, "correct"),
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("OU35", "Alt", True, "correct"),
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("OU35", "Üst", False, "wrong"),
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("BTTS", "Var", False, "wrong"),
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("BTTS", "Yok", True, "correct"),
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("HT", "X", True, "correct"),
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("HT", "1", False, "wrong"),
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("HTFT", "X/1", True, "correct"),
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("HTFT", "1/1", False, "wrong (HT was 0-0)"),
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("HT_OU05", "Üst", False, "wrong"),
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("HT_OU05", "Alt", True, "correct"),
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("OE", "Çift", False, "wrong (1 is odd)"),
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("OE", "Tek", True, "correct"),
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]
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print(f"{'market':<10}{'pick':<10}{'real-win?':<12}{'in-coherent?':<14}{'match?'}")
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print("-" * 60)
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ok = 0
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for market, pick, would_win, note in test_picks:
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in_coh = (market, pick) in coh
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match = "✓" if in_coh == would_win else "✗ MISMATCH"
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if in_coh == would_win: ok += 1
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print(f"{market:<10}{pick:<10}{str(would_win):<12}{str(in_coh):<14}{match} {note}")
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print()
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print(f"Result: {ok}/{len(test_picks)} picks correctly classified")
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