Add backtest pipeline, betting_brain filters, score coherence + social v3
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>
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@@ -39,6 +39,31 @@ class BettingBrain:
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SNIPER_BLOCKED_MARKETS = {"HT", "HTFT", "OE", "CARDS", "HT_OU05", "HT_OU15"}
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# Markets that lose money under every filter combination per the
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# diagnostic backtest (1000 matches). Until calibration is rebuilt for
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# these specifically, force NO_BET. Re-evaluate after each backtest run.
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MUTED_MARKETS = {"BTTS"}
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# Per-market optimal filter envelopes derived from the diagnostic
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# backtest grid search (reports/filter_optimization_patch.json). Any
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# pick falling OUTSIDE this envelope is vetoed. Tightens the playable
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# band to the ROI-positive zone identified empirically.
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#
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# Each entry: {min_conf, min_edge, max_edge, min_odds, max_odds,
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# min_reliability, require_v27_agree}
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MARKET_OPTIMAL_FILTERS = {
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"MS": {
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"min_edge": -0.05, "max_edge": 0.15,
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"min_odds": 1.20, "max_odds": 10.0,
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"min_reliability": 0.0, "require_v27_agree": True,
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},
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"OU25": {
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"min_edge": -1.0, "max_edge": 0.15,
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"min_odds": 1.80, "max_odds": 10.0,
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"min_reliability": 0.0, "require_v27_agree": False,
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},
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}
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MARKET_PRIORS = {
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"DC": 4.0,
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"OU15": 3.0,
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@@ -86,6 +111,36 @@ class BettingBrain:
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watchlist.sort(key=self._candidate_sort_key, reverse=True)
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no_value.sort(key=self._candidate_sort_key, reverse=True)
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# ── SCORE COHERENCE FILTER ──────────────────────────────────────
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# If the model also produced a score prediction (e.g. 1-0), pick
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# main_pick from the subset of candidates that would WIN at that
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# score. Stops the system from recommending OU25 Üst while also
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# predicting 1-0 (only 1 goal). Falls back to original list if no
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# coherent candidate exists.
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coherent_set = self._score_consistent_markets(guarded)
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coherent_flag = False
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if coherent_set:
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def is_coherent(row: Dict[str, Any]) -> bool:
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m = str(row.get("market") or "")
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p = str(row.get("pick") or "")
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return (m, p) in coherent_set
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approved_coh = [r for r in approved if is_coherent(r)]
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watchlist_coh = [r for r in watchlist if is_coherent(r)]
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if approved_coh:
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approved = approved_coh
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coherent_flag = True
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elif watchlist_coh:
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# No coherent BET candidates — at least promote a coherent
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# watch over an incoherent BET.
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watchlist = watchlist_coh + [r for r in watchlist if not is_coherent(r)]
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coherent_flag = True
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# Tag every row so the UI/diagnostics can see what happened
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for row in judged_rows.values():
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row.setdefault("betting_brain", {})
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row["betting_brain"]["score_coherent"] = is_coherent(row)
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original_main = guarded.get("main_pick") or {}
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main_pick = None
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decision = "NO_BET"
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@@ -142,10 +197,11 @@ class BettingBrain:
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rejected = [d for d in decisions if d.get("action") == "REJECT"]
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guarded["betting_brain"] = {
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"version": "judge-v1",
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"version": "judge-v2-score-coherent",
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"decision": decision,
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"reason": decision_reason,
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"main_pick_key": main_key or None,
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"score_coherent_filter_applied": coherent_flag,
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"approved_count": len(approved),
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"watchlist_count": len(watchlist),
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"rejected_count": len(rejected),
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@@ -243,6 +299,44 @@ class BettingBrain:
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if play_score < 50.0 and not is_value_sniper:
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vetoes.append("play_score_too_low")
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# ── HARD EV-EDGE VETO ───────────────────────────────────────────
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# Diagnostic backtest (1000 maç, 524 settled bet) gösterdi ki
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# ev_edge < 0 olan bahisler %76 of all picks ve ROI yaklaşık -%16.
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# ev_edge < 0 = "model market'in altında olasılık veriyor" = vig'i
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# yiyemeyeceğimiz negative-EV bahis. Hard veto: oynama.
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# Sniper override hâlâ geçer (yüksek convicted alternatif pick'ler).
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if ev_edge < 0.0 and not is_value_sniper:
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vetoes.append("negative_ev_edge")
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issues.append(f"ev_edge={ev_edge:.3f}_below_zero")
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# Trap edge: bizim diagnostic backtest'te ev_edge >= 0.20 olan tüm
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# bahisler kaybediyordu (n=10, hepsi -%25+ ROI). Model market'i bu
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# kadar yanlış buluyorsa muhtemelen modelin kendisinin yanlış olduğu
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# bir senaryo (eksik info, tuhaf maç, vs.) — oynama.
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if ev_edge >= 0.20 and not is_value_sniper:
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vetoes.append("ev_edge_too_high_trap")
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issues.append(f"ev_edge={ev_edge:.3f}_trap_range")
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# ── MUTED MARKETS (grid search showed no profitable config) ──
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if market in self.MUTED_MARKETS and not is_value_sniper:
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vetoes.append("market_muted_by_backtest")
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issues.append(f"market_{market}_muted")
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# ── PER-MARKET OPTIMAL ENVELOPE (from grid search) ──
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envelope = self.MARKET_OPTIMAL_FILTERS.get(market)
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if envelope and not is_value_sniper:
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if ev_edge < envelope["min_edge"]:
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vetoes.append("outside_envelope_edge_low")
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if ev_edge > envelope["max_edge"]:
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vetoes.append("outside_envelope_edge_high")
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if odds and odds < envelope["min_odds"]:
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vetoes.append("outside_envelope_odds_low")
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if odds and odds > envelope["max_odds"]:
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vetoes.append("outside_envelope_odds_high")
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if odds_rel < envelope["min_reliability"]:
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vetoes.append("outside_envelope_reliability_low")
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if envelope["require_v27_agree"] and consensus != "AGREE":
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vetoes.append("outside_envelope_v27_must_agree")
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if divergence is not None:
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if divergence >= self.HARD_DIVERGENCE and not is_value_sniper:
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score -= 42.0
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@@ -635,6 +729,112 @@ class BettingBrain:
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return self._safe_float(ou25.get(key)) if key else None
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return None
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def _score_consistent_markets(self, package: Dict[str, Any]) -> Optional[set]:
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"""Build the set of (market, pick) tuples that WOULD WIN if the
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model's own score prediction came true. We use this as a coherence
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gate: if the model is confident about a 1-0 outcome but also wants
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to play OU25 Üst, those two beliefs contradict each other — and the
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score prediction is the more informative one because it aggregates
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all market signals into a single most-likely scenario.
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Returns None if the score prediction is missing or malformed; in
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that case we skip the coherence check.
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"""
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score_pred = package.get("score_prediction") or {}
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ft_raw = str(score_pred.get("ft") or score_pred.get("full_time") or "").strip()
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ht_raw = str(score_pred.get("ht") or score_pred.get("half_time") or "").strip()
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def parse(s: str) -> Optional[Tuple[int, int]]:
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for sep in ("-", ":", "–"):
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if sep in s:
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parts = s.split(sep, 1)
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try:
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return int(parts[0].strip()), int(parts[1].strip())
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except (ValueError, IndexError):
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return None
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return None
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ft = parse(ft_raw)
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if ft is None:
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return None
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ht = parse(ht_raw)
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fh, fa = ft
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total = fh + fa
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consistent: set = set()
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# MS / 1X2 — single outcome
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if fh > fa:
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consistent.add(("MS", "1"))
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consistent.add(("ML", "1"))
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elif fh < fa:
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consistent.add(("MS", "2"))
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consistent.add(("ML", "2"))
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else:
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consistent.add(("MS", "X"))
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consistent.add(("ML", "X"))
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# DC — two of three legs win at any score
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if fh >= fa:
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consistent.add(("DC", "1X"))
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if fh <= fa:
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consistent.add(("DC", "X2"))
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if fh != fa:
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consistent.add(("DC", "12"))
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# Over/Under main lines
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for line, market in ((0.5, "OU05"), (1.5, "OU15"),
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(2.5, "OU25"), (3.5, "OU35"), (4.5, "OU45")):
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if total > line:
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for p in ("Üst", "Ust", "Over", "OVER"):
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consistent.add((market, p))
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elif total < line:
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for p in ("Alt", "Under", "UNDER"):
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consistent.add((market, p))
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# total == line → push, neither side wins → don't add
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# BTTS — both teams score
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if fh > 0 and fa > 0:
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for p in ("Var", "KG Var", "Yes", "YES"):
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consistent.add(("BTTS", p))
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else:
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for p in ("Yok", "KG Yok", "No", "NO"):
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consistent.add(("BTTS", p))
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# OE — total goals odd/even
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if total % 2 == 1:
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for p in ("Tek", "Odd", "ODD"):
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consistent.add(("OE", p))
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else:
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for p in ("Çift", "Cift", "Even", "EVEN"):
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consistent.add(("OE", p))
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# HT-only markets (need HT score)
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if ht is not None:
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hh, ha = ht
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ht_total = hh + ha
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if hh > ha:
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consistent.add(("HT", "1"))
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elif hh < ha:
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consistent.add(("HT", "2"))
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else:
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consistent.add(("HT", "X"))
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for line, market in ((0.5, "HT_OU05"), (1.5, "HT_OU15"), (2.5, "HT_OU25")):
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if ht_total > line:
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for p in ("Üst", "Ust", "Over"):
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consistent.add((market, p))
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elif ht_total < line:
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for p in ("Alt", "Under"):
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consistent.add((market, p))
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# HTFT — single combo
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ht_o = "1" if hh > ha else "2" if hh < ha else "X"
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ft_o = "1" if fh > fa else "2" if fh < fa else "X"
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consistent.add(("HTFT", f"{ht_o}/{ft_o}"))
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consistent.add(("HTFT", f"{ht_o}{ft_o}"))
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return consistent
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def _triple_value(self, package: Dict[str, Any], key: Optional[str]) -> Optional[Dict[str, Any]]:
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if not key:
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return None
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