gg

2026-04-21 16:53:56 +03:00
parent 1346924387
commit 2ccd6831eb
26 changed files with 430403 additions and 3 deletions
@@ -0,0 +1,94 @@
+from __future__ import annotations
+
+import json
+import sys
+from pathlib import Path
+
+import psycopg2
+from psycopg2.extras import RealDictCursor
+
+
+AI_ENGINE_DIR = Path(__file__).resolve().parents[1]
+if str(AI_ENGINE_DIR) not in sys.path:
+    sys.path.insert(0, str(AI_ENGINE_DIR))
+
+from services.single_match_orchestrator import SingleMatchOrchestrator
+
+
+def _resolve_dsn() -> str:
+    env_path = AI_ENGINE_DIR / ".env"
+    if env_path.exists():
+        for line in env_path.read_text(encoding="utf-8").splitlines():
+            if line.startswith("DATABASE_URL="):
+                return line.split("=", 1)[1].strip().split("?schema=")[0]
+    raise SystemExit("DATABASE_URL not found in ai-engine/.env")
+
+
+def _fetch_matches(dsn: str, limit: int = 60) -> list[str]:
+    query = """
+        SELECT m.id
+        FROM matches m
+        WHERE m.status = 'FT'
+          AND m.sport = 'football'
+          AND m.score_home IS NOT NULL
+          AND m.score_away IS NOT NULL
+        ORDER BY m.mst_utc DESC
+        LIMIT %s
+    """
+    with psycopg2.connect(dsn) as conn:
+        with conn.cursor(cursor_factory=RealDictCursor) as cur:
+            cur.execute(query, (limit,))
+            return [str(row["id"]) for row in cur.fetchall()]
+
+
+def _score_prediction(package: dict) -> dict[str, float]:
+    rows = package.get("bet_summary", []) or []
+    playable = [row for row in rows if row.get("playable")]
+    return {
+        "playable_count": float(len(playable)),
+        "avg_edge": round(
+            sum(float(row.get("ev_edge", 0.0)) for row in playable) / len(playable),
+            4,
+        )
+        if playable
+        else 0.0,
+        "avg_confidence": round(
+            sum(float(row.get("calibrated_confidence", 0.0)) for row in playable)
+            / len(playable),
+            2,
+        )
+        if playable
+        else 0.0,
+    }
+
+
+def main() -> None:
+    dsn = _resolve_dsn()
+    match_ids = _fetch_matches(dsn)
+    orchestrator = SingleMatchOrchestrator()
+
+    results: list[dict[str, object]] = []
+    for match_id in match_ids:
+        orchestrator.engine_mode = "v25"
+        v25 = orchestrator.analyze_match(match_id)
+        orchestrator.engine_mode = "v26"
+        v26 = orchestrator.analyze_match(match_id)
+        if not v25 or not v26:
+            continue
+        results.append(
+            {
+                "match_id": match_id,
+                "v25": _score_prediction(v25),
+                "v26": _score_prediction(v26),
+                "v25_main": (v25.get("main_pick") or {}).get("pick"),
+                "v26_main": (v26.get("main_pick") or {}).get("pick"),
+            }
+        )
+
+    out_path = AI_ENGINE_DIR / "reports" / "backtest_v26_shadow.json"
+    out_path.write_text(json.dumps(results, indent=2), encoding="utf-8")
+    print(f"[OK] Shadow backtest summary written to {out_path}")
+
+
+if __name__ == "__main__":
+    main()