gg

- goals_form now uses avg of last 5 historical matches instead of current match goals
- squad_quality removes current match goals/assists, uses only pre-match known data
- adds temporal filtering via match_id -> mst_utc mapping

.gitea/workflows/deploy.yml

@@ -25,11 +25,11 @@ jobs:
             --network iddaai_iddaai-network \
             -p 127.0.0.1:1810:3005 \
             -e NODE_ENV=production \
-            -e DATABASE_URL='postgresql://iddaai_user:IddaA1_S4crET!@iddaai-postgres:5432/iddaai_db?schema=public' \
+            -e DATABASE_URL='${{ secrets.DATABASE_URL }}' \
-            -e REDIS_HOST='iddaai-redis' \
+            -e REDIS_HOST='${{ secrets.REDIS_HOST }}' \
-            -e REDIS_PORT='6379' \
+            -e REDIS_PORT='${{ secrets.REDIS_PORT }}' \
-            -e REDIS_PASSWORD='IddaA1_Redis_Pass!' \
+            -e REDIS_PASSWORD='${{ secrets.REDIS_PASSWORD }}' \
-            -e AI_ENGINE_URL='http://iddaai-ai-engine:8000' \
+            -e AI_ENGINE_URL='${{ secrets.AI_ENGINE_URL }}' \
-            -e JWT_SECRET='b7V8jM2wP1L5mQxs2RdfFkAsLpI2oG!w' \
+            -e JWT_SECRET='${{ secrets.JWT_SECRET }}' \
             -e JWT_ACCESS_EXPIRATION='1d' \
             iddaai-be:latest /bin/sh -c "npx prisma migrate deploy && node dist/src/main.js"

Dockerfile

@@ -47,7 +47,7 @@ COPY --from=builder /app/dist ./dist
 COPY --from=builder /app/src/i18n ./dist/i18n
 
 # Copy league filter config files (critical: without these, feeder stores ALL matches)
-COPY top_leagues.json basketball_top_leagues.json ./
+COPY qualified_leagues.json top_leagues.json basketball_top_leagues.json ./
 
 # Set environment
 ENV NODE_ENV=production

ai-engine/config/config_loader.py

@@ -1,17 +1,19 @@
 import os
+import json
 import yaml
 from typing import Dict, Any, Optional
 
+
 class EnsembleConfig:
     _instance: Optional['EnsembleConfig'] = None
     _config: Dict[str, Any] = {}
-    
+
     def __new__(cls):
         if cls._instance is None:
             cls._instance = super(EnsembleConfig, cls).__new__(cls)
             cls._instance._load_config()
         return cls._instance
-    
+
     def _load_config(self):
         """Load configuration from YAML file."""
         config_path = os.path.join(os.path.dirname(__file__), 'ensemble_config.yaml')
@@ -22,12 +24,12 @@ class EnsembleConfig:
         except Exception as e:
             print(f"❌ Failed to load ensemble config: {e}")
             self._config = {}
-    
+
     def get(self, key: str, default: Any = None) -> Any:
         """Get configuration value by key (supports dot notation for nested keys)."""
         keys = key.split('.')
         value = self._config
-        
+
         try:
             for k in keys:
                 value = value[k]
@@ -35,12 +37,79 @@ class EnsembleConfig:
         except (KeyError, TypeError):
             return default
 
+
 # Singleton accessor
 def get_config() -> EnsembleConfig:
     return EnsembleConfig()
 
+
+# ── Market Thresholds Loader ────────────────────────────────────────────
+
+_market_thresholds_cache: Optional[Dict[str, Any]] = None
+
+
+def load_market_thresholds() -> Dict[str, Any]:
+    """
+    Load market thresholds from JSON config file.
+    Returns the full config dict with 'markets' and 'defaults' keys.
+    Caches after first load for performance.
+    """
+    global _market_thresholds_cache
+    if _market_thresholds_cache is not None:
+        return _market_thresholds_cache
+
+    config_path = os.path.join(os.path.dirname(__file__), 'market_thresholds.json')
+    try:
+        with open(config_path, 'r', encoding='utf-8') as f:
+            data = json.load(f)
+        _market_thresholds_cache = data
+        print(f"✅ Market thresholds loaded: {len(data.get('markets', {}))} markets (v={data.get('_meta', {}).get('version', '?')})")
+        return data
+    except Exception as e:
+        print(f"❌ Failed to load market thresholds: {e} — using built-in defaults")
+        _market_thresholds_cache = {"markets": {}, "defaults": {
+            "calibration": 0.55,
+            "min_conf": 55.0,
+            "min_play_score": 68.0,
+            "min_edge": 0.02,
+            "odds_band_min_sample": 0.0,
+            "odds_band_min_edge": 0.0,
+        }}
+        return _market_thresholds_cache
+
+
+def build_threshold_dict(field: str) -> Dict[str, float]:
+    """
+    Build a flat {market: value} dict for a specific threshold field.
+
+    Usage:
+        calibration_map = build_threshold_dict("calibration")
+        # → {"MS": 0.62, "DC": 0.82, ...}
+    """
+    data = load_market_thresholds()
+    markets = data.get("markets", {})
+    result: Dict[str, float] = {}
+    for market, cfg in markets.items():
+        if field in cfg:
+            result[market] = float(cfg[field])
+    return result
+
+
+def get_threshold_default(field: str) -> float:
+    """Get the default fallback value for a threshold field."""
+    data = load_market_thresholds()
+    defaults = data.get("defaults", {})
+    return float(defaults.get(field, 0.0))
+
+
 if __name__ == "__main__":
     # Test
     cfg = get_config()
     print(f"Weights: {cfg.get('engine_weights')}")
     print(f"Team Weight: {cfg.get('engine_weights.team')}")
+    print()
+    print("--- Market Thresholds ---")
+    for field in ["calibration", "min_conf", "min_play_score", "min_edge"]:
+        d = build_threshold_dict(field)
+        print(f"{field}: {d}")
+    print(f"Default calibration: {get_threshold_default('calibration')}")

ai-engine/config/market_thresholds.json

@@ -0,0 +1,115 @@
+{
+  "_meta": {
+    "version": "v34",
+    "description": "Market-specific thresholds for the betting engine pipeline — V34 odds-aware gate fix",
+    "rule": "max_reachable (100 × calibration) MUST be > min_conf + 8",
+    "updated_at": "2026-05-10",
+    "changelog": "V34: Reduced min_edge to realistic levels for odds-aware V25 model. Model output ≈ market-implied, so large EV edges are mathematically impossible."
+  },
+  "markets": {
+    "MS": {
+      "calibration": 0.62,
+      "min_conf": 20.0,
+      "min_play_score": 28.0,
+      "min_edge": 0.005,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.005
+    },
+    "DC": {
+      "calibration": 0.82,
+      "min_conf": 40.0,
+      "min_play_score": 50.0,
+      "min_edge": 0.003,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.005
+    },
+    "OU15": {
+      "calibration": 0.84,
+      "min_conf": 45.0,
+      "min_play_score": 50.0,
+      "min_edge": 0.003,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.005
+    },
+    "OU25": {
+      "calibration": 0.68,
+      "min_conf": 30.0,
+      "min_play_score": 40.0,
+      "min_edge": 0.005,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.005
+    },
+    "OU35": {
+      "calibration": 0.60,
+      "min_conf": 20.0,
+      "min_play_score": 30.0,
+      "min_edge": 0.008,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.008
+    },
+    "BTTS": {
+      "calibration": 0.65,
+      "min_conf": 30.0,
+      "min_play_score": 40.0,
+      "min_edge": 0.005,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.005
+    },
+    "HT": {
+      "calibration": 0.58,
+      "min_conf": 20.0,
+      "min_play_score": 28.0,
+      "min_edge": 0.01,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.008
+    },
+    "HT_OU05": {
+      "calibration": 0.68,
+      "min_conf": 35.0,
+      "min_play_score": 42.0,
+      "min_edge": 0.005,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.005
+    },
+    "HT_OU15": {
+      "calibration": 0.60,
+      "min_conf": 25.0,
+      "min_play_score": 32.0,
+      "min_edge": 0.008,
+      "odds_band_min_sample": 8.0,
+      "odds_band_min_edge": 0.008
+    },
+    "OE": {
+      "calibration": 0.62,
+      "min_conf": 35.0,
+      "min_play_score": 32.0,
+      "min_edge": 0.005
+    },
+    "CARDS": {
+      "calibration": 0.58,
+      "min_conf": 30.0,
+      "min_play_score": 35.0,
+      "min_edge": 0.008
+    },
+    "HCAP": {
+      "calibration": 0.56,
+      "min_conf": 25.0,
+      "min_play_score": 30.0,
+      "min_edge": 0.015
+    },
+    "HTFT": {
+      "calibration": 0.45,
+      "min_conf": 10.0,
+      "min_play_score": 18.0,
+      "min_edge": 0.02
+    }
+  },
+  "defaults": {
+    "calibration": 0.55,
+    "min_conf": 55.0,
+    "min_play_score": 60.0,
+    "min_edge": 0.008,
+    "odds_band_min_sample": 0.0,
+    "odds_band_min_edge": 0.0
+  }
+}

ai-engine/core/engines/player_predictor.py

@@ -18,15 +18,20 @@ from features.sidelined_analyzer import get_sidelined_analyzer
 
 @dataclass
 class PlayerPrediction:
-    """Player engine prediction output."""
+    """Player engine prediction output.
-    home_squad_quality: float = 50.0  # 0-100
+
-    away_squad_quality: float = 50.0
+    IMPORTANT: squad_quality uses the SAME composite formula as
-    squad_diff: float = 0.0  # -100 to +100
+    extract_training_data.py so that inference values match the
+    distribution the model was trained on (~3-36 range).
+    """
+    home_squad_quality: float = 12.0   # training-scale composite (~3-36)
+    away_squad_quality: float = 12.0
+    squad_diff: float = 0.0            # home - away (training scale)
     home_key_players: int = 0
     away_key_players: int = 0
-    home_missing_impact: float = 0.0  # 0-1, how much weaker due to missing players
+    home_missing_impact: float = 0.0   # 0-1, how much weaker due to missing players
     away_missing_impact: float = 0.0
-    home_goals_form: int = 0  # Goals in last 5 matches
+    home_goals_form: int = 0           # Goals in last 5 matches
     away_goals_form: int = 0
     lineup_available: bool = False
     confidence: float = 0.0
@@ -100,10 +105,12 @@ class PlayerPredictorEngine:
                 "home_goals_last_5": home_analysis.total_goals_last_5,
                 "home_assists_last_5": home_analysis.total_assists_last_5,
                 "home_key_players": home_analysis.key_players_count,
+                "home_forwards": home_analysis.forward_count or 2,
                 "away_starting_11": away_analysis.starting_count or 11,
                 "away_goals_last_5": away_analysis.total_goals_last_5,
                 "away_assists_last_5": away_analysis.total_assists_last_5,
                 "away_key_players": away_analysis.key_players_count,
+                "away_forwards": away_analysis.forward_count or 2,
             }
         elif match_id:
             # Try to get from database
@@ -131,13 +138,31 @@ class PlayerPredictorEngine:
         away_goals = features.get("away_goals_last_5", 0)
         home_key = features.get("home_key_players", 0)
         away_key = features.get("away_key_players", 0)
+        home_assists = features.get("home_assists_last_5", 0)
+        away_assists = features.get("away_assists_last_5", 0)
+        home_starting = features.get("home_starting_11", 11)
+        away_starting = features.get("away_starting_11", 11)
+        home_fwd = features.get("home_forwards", 2)
+        away_fwd = features.get("away_forwards", 2)
         
-        # Calculate squad quality (0-100)
+        # Calculate squad quality — MUST match extract_training_data.py formula
-        # Based on: goals scored, key players, assists
+        # Formula: starting_count * 0.3 + goals * 2.0 + assists * 1.0
-        home_quality = min(100, 50 + (home_goals * 3) + (home_key * 5) + 
+        #          + key_players * 3.0 + fwd_count * 1.5
-                          features.get("home_assists_last_5", 0) * 2)
+        # Typical range: ~3 – 36 (model trained on this distribution)
-        away_quality = min(100, 50 + (away_goals * 3) + (away_key * 5) + 
+        home_quality = (
-                          features.get("away_assists_last_5", 0) * 2)
+            home_starting * 0.3 +
+            home_goals * 2.0 +
+            home_assists * 1.0 +
+            home_key * 3.0 +
+            home_fwd * 1.5
+        )
+        away_quality = (
+            away_starting * 0.3 +
+            away_goals * 2.0 +
+            away_assists * 1.0 +
+            away_key * 3.0 +
+            away_fwd * 1.5
+        )
         
         # Squad difference
         squad_diff = home_quality - away_quality
@@ -186,8 +211,10 @@ class PlayerPredictorEngine:
         Calculate 1X2 probability modifiers based on squad analysis.
         
         Returns modifiers to apply to base probabilities.
+        squad_diff is in training scale (~-33 to +33), normalize to -1..+1.
         """
-        diff = prediction.squad_diff / 100  # -1 to +1
+        diff = prediction.squad_diff / 33.0  # training-scale normalisation
+        diff = max(-1.0, min(1.0, diff))      # clamp
         
         return {
             "home_modifier": 1.0 + (diff * 0.3),  # Up to +/-30%

ai-engine/models/v25_ensemble.py

@@ -141,8 +141,10 @@ class V25Predictor:
     Each market (MS, OU25, BTTS) has its own trained models.
     """
     
-    # Feature columns (82 features, NO target leakage)
+    # Feature columns — loaded dynamically from feature_cols.json to stay
-    FEATURE_COLS = [
+    # in sync with the trained models.  The hardcoded list below is only a
+    # fallback in case the JSON file is missing.
+    _FALLBACK_FEATURE_COLS = [
         # ELO Features (8)
         'home_overall_elo', 'away_overall_elo', 'elo_diff',
         'home_home_elo', 'away_away_elo',
@@ -178,6 +180,17 @@ class V25Predictor:
         'odds_ht_ou15_o', 'odds_ht_ou15_u',
         'odds_btts_y', 'odds_btts_n',
         
+        # Odds Presence Flags (20)
+        'odds_ms_h_present', 'odds_ms_d_present', 'odds_ms_a_present',
+        'odds_ht_ms_h_present', 'odds_ht_ms_d_present', 'odds_ht_ms_a_present',
+        'odds_ou05_o_present', 'odds_ou05_u_present',
+        'odds_ou15_o_present', 'odds_ou15_u_present',
+        'odds_ou25_o_present', 'odds_ou25_u_present',
+        'odds_ou35_o_present', 'odds_ou35_u_present',
+        'odds_ht_ou05_o_present', 'odds_ht_ou05_u_present',
+        'odds_ht_ou15_o_present', 'odds_ht_ou15_u_present',
+        'odds_btts_y_present', 'odds_btts_n_present',
+        
         # League Features (4)
         'home_xga', 'away_xga',
         'league_avg_goals', 'league_zero_goal_rate',
@@ -198,6 +211,24 @@ class V25Predictor:
         'home_missing_impact', 'away_missing_impact',
         'home_goals_form', 'away_goals_form',
     ]
+
+    @staticmethod
+    def _load_feature_cols() -> list:
+        """Load feature columns from feature_cols.json, falling back to hardcoded list."""
+        feature_json = os.path.join(MODELS_DIR, 'feature_cols.json')
+        try:
+            if os.path.exists(feature_json):
+                with open(feature_json, 'r', encoding='utf-8') as f:
+                    cols = json.load(f)
+                if isinstance(cols, list) and len(cols) > 0:
+                    print(f"[V25] Loaded {len(cols)} feature columns from feature_cols.json")
+                    return cols
+        except Exception as e:
+            print(f"[V25] Warning: could not load feature_cols.json: {e}")
+        print(f"[V25] Using fallback feature columns ({len(V25Predictor._FALLBACK_FEATURE_COLS)} features)")
+        return V25Predictor._FALLBACK_FEATURE_COLS
+
+    FEATURE_COLS = _load_feature_cols.__func__()
     
     # Model weights for ensemble
     DEFAULT_WEIGHTS = {

ai-engine/models/v27_predictor.py

@@ -29,7 +29,7 @@ class V27Predictor:
     82-feature odds-free vector.
     """
 
-    MARKETS = ["ms", "ou25"]
+    MARKETS = ['ms', 'ou25', 'btts']
 
     def __init__(self):
         self.models: Dict[str, Dict[str, object]] = {}
@@ -56,7 +56,7 @@ class V27Predictor:
             return False
 
         # Load models per market
-        model_types = {"xgb": "xgb", "lgb": "lgb", "cb": "cb"}
+        model_types = {"xgb": "xgb", "lgb": "lgb"}
 
         for market in self.MARKETS:
             self.models[market] = {}
@@ -227,11 +227,63 @@ class V27Predictor:
             "over": float(avg[1]),
         }
 
+    def predict_btts(self, features: Dict[str, float]) -> Optional[Dict[str, float]]:
+        """
+        Predict Both Teams To Score probabilities.
+        Returns dict with keys: no, yes.
+        """
+        if not self._loaded or 'btts' not in self.models or not self.models['btts']:
+            return None
+
+        X = self._build_feature_array(features)
+        probs_list = []
+
+        for label, model in self.models['btts'].items():
+            proba = self._predict_with_model(model, X, f'BTTS/{label}', expected_classes=2)
+            if proba is not None and len(proba) == 2:
+                probs_list.append(proba)
+
+        if not probs_list:
+            return None
+
+        avg = np.mean(probs_list, axis=0)
+        return {
+            'no': float(avg[0]),
+            'yes': float(avg[1]),
+        }
+
+    def predict_dc(self, features: Dict[str, float]) -> Optional[Dict[str, float]]:
+        """
+        Predict Double Chance probabilities.
+
+        DC is algebraically derived from MS predictions:
+          1X = home + draw
+          X2 = draw + away
+          12 = home + away
+
+        This gives an odds-free DC estimate for divergence detection.
+        """
+        ms_probs = self.predict_ms(features)
+        if not ms_probs:
+            return None
+
+        home = ms_probs['home']
+        draw = ms_probs['draw']
+        away = ms_probs['away']
+
+        return {
+            '1x': round(home + draw, 4),
+            'x2': round(draw + away, 4),
+            '12': round(home + away, 4),
+        }
+
     def predict_all(self, features: Dict[str, float]) -> Dict[str, Optional[Dict[str, float]]]:
         """Run predictions for all supported markets."""
         return {
-            "ms": self.predict_ms(features),
+            'ms': self.predict_ms(features),
-            "ou25": self.predict_ou25(features),
+            'ou25': self.predict_ou25(features),
+            'btts': self.predict_btts(features),
+            'dc': self.predict_dc(features),
         }
 
 

ai-engine/reports/training_v25/v25_market_metrics.json

@@ -1,8 +1,8 @@
 {
-  "trained_at": "2026-04-14 17:20:03",
+  "trained_at": "2026-05-06 15:53:36",
   "market_results": {
     "MS": {
-      "samples": 9791,
+      "samples": 106428,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -107,19 +107,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6853,
+      "train_samples": 74499,
-      "val_samples": 1469,
+      "val_samples": 15964,
-      "test_samples": 1469,
+      "test_samples": 15965,
-      "xgb_accuracy": 0.8938,
+      "xgb_accuracy": 0.5437,
-      "xgb_logloss": 0.2263,
+      "xgb_logloss": 0.9429,
-      "lgb_accuracy": 0.8938,
+      "lgb_accuracy": 0.5436,
-      "lgb_logloss": 0.2214,
+      "lgb_logloss": 0.9423,
-      "ensemble_accuracy": 0.8945,
+      "ensemble_accuracy": 0.5442,
-      "ensemble_logloss": 0.2226,
+      "ensemble_logloss": 0.9418,
       "class_count": 3
     },
     "OU15": {
-      "samples": 9791,
+      "samples": 106428,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -224,19 +224,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6853,
+      "train_samples": 74499,
-      "val_samples": 1469,
+      "val_samples": 15964,
-      "test_samples": 1469,
+      "test_samples": 15965,
-      "xgb_accuracy": 0.9088,
+      "xgb_accuracy": 0.753,
-      "xgb_logloss": 0.1758,
+      "xgb_logloss": 0.5256,
-      "lgb_accuracy": 0.9067,
+      "lgb_accuracy": 0.7523,
-      "lgb_logloss": 0.1783,
+      "lgb_logloss": 0.5262,
-      "ensemble_accuracy": 0.9108,
+      "ensemble_accuracy": 0.7533,
-      "ensemble_logloss": 0.1753,
+      "ensemble_logloss": 0.5254,
       "class_count": 2
     },
     "OU25": {
-      "samples": 9791,
+      "samples": 106428,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -341,19 +341,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6853,
+      "train_samples": 74499,
-      "val_samples": 1469,
+      "val_samples": 15964,
-      "test_samples": 1469,
+      "test_samples": 15965,
-      "xgb_accuracy": 0.9204,
+      "xgb_accuracy": 0.6253,
-      "xgb_logloss": 0.1535,
+      "xgb_logloss": 0.635,
-      "lgb_accuracy": 0.9224,
+      "lgb_accuracy": 0.6246,
-      "lgb_logloss": 0.1523,
+      "lgb_logloss": 0.6347,
-      "ensemble_accuracy": 0.9217,
+      "ensemble_accuracy": 0.6262,
-      "ensemble_logloss": 0.1518,
+      "ensemble_logloss": 0.6343,
       "class_count": 2
     },
     "OU35": {
-      "samples": 9791,
+      "samples": 106428,
       "features_used": [
         "home_overall_elo",
         "away_overall_elo",
@@ -458,19 +458,19 @@
         "home_goals_form",
         "away_goals_form"
       ],
-      "train_samples": 6853,
+      "train_samples": 74499,
-      "val_samples": 1469,
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@@ -575,19 +575,19 @@
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@@ -692,19 +692,19 @@
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@@ -926,19 +926,19 @@
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@@ -1160,19 +1160,19 @@
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ai-engine/reports/training_v25/v25_pro_metrics.json

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+        "lambda_l2": 6.143857495033091e-07
+      },
+      "xgb_best_iteration": 315,
+      "lgb_best_iteration": 133,
+      "xgb_optuna_best_logloss": 0.5756,
+      "lgb_optuna_best_logloss": 0.5757,
+      "test_xgb_raw": {
+        "accuracy": 0.7021,
+        "logloss": 0.5949
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.7011,
+        "logloss": 0.5976
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.7009,
+        "logloss": 0.5954
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.7019,
+        "logloss": 0.6002
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.7012,
+        "logloss": 0.5947
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.7016,
+        "logloss": 0.5994
+      }
+    },
+    "HT_OU15": {
+      "market": "HT_OU15",
+      "samples": 103641,
+      "train": 62184,
+      "val": 15546,
+      "cal": 10364,
+      "test": 15547,
+      "features_used": 114,
+      "xgb_best_params": {
+        "max_depth": 4,
+        "eta": 0.032235943414662994,
+        "subsample": 0.9298749893021518,
+        "colsample_bytree": 0.8077813949235508,
+        "min_child_weight": 8,
+        "gamma": 0.00020929324388600622,
+        "reg_lambda": 3.2154973975232725e-05,
+        "reg_alpha": 1.5945155621686738e-08
+      },
+      "lgb_best_params": {
+        "max_depth": 5,
+        "learning_rate": 0.013909897616748226,
+        "feature_fraction": 0.5585477334219859,
+        "bagging_fraction": 0.9398770580467641,
+        "bagging_freq": 2,
+        "min_child_samples": 22,
+        "lambda_l1": 0.001865897980802303,
+        "lambda_l2": 2.6934572591055333e-06
+      },
+      "xgb_best_iteration": 188,
+      "lgb_best_iteration": 387,
+      "xgb_optuna_best_logloss": 0.616,
+      "lgb_optuna_best_logloss": 0.6159,
+      "test_xgb_raw": {
+        "accuracy": 0.6749,
+        "logloss": 0.6109
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.6747,
+        "logloss": 0.6137
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.6745,
+        "logloss": 0.6112
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.6745,
+        "logloss": 0.6201
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.674,
+        "logloss": 0.6109
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.6744,
+        "logloss": 0.6174
+      }
+    },
+    "HTFT": {
+      "market": "HTFT",
+      "samples": 103641,
+      "train": 62184,
+      "val": 15546,
+      "cal": 10364,
+      "test": 15547,
+      "features_used": 114,
+      "xgb_best_params": {
+        "max_depth": 4,
+        "eta": 0.015239309183459821,
+        "subsample": 0.7923828997985648,
+        "colsample_bytree": 0.686316507387916,
+        "min_child_weight": 6,
+        "gamma": 0.005249577944740401,
+        "reg_lambda": 2.1813455810361064e-08,
+        "reg_alpha": 3.454483107951557e-06
+      },
+      "lgb_best_params": {
+        "max_depth": 4,
+        "learning_rate": 0.010347899501864056,
+        "feature_fraction": 0.9585697341293057,
+        "bagging_fraction": 0.9413628962257758,
+        "bagging_freq": 2,
+        "min_child_samples": 36,
+        "lambda_l1": 0.0015332771659626943,
+        "lambda_l2": 7.3640280079715765
+      },
+      "xgb_best_iteration": 714,
+      "lgb_best_iteration": 602,
+      "xgb_optuna_best_logloss": 1.7863,
+      "lgb_optuna_best_logloss": 1.7862,
+      "test_xgb_raw": {
+        "accuracy": 0.3349,
+        "logloss": 1.8179
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.3332,
+        "logloss": 1.824
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.3367,
+        "logloss": 1.8187
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.335,
+        "logloss": 1.8338
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.3363,
+        "logloss": 1.8176
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.3338,
+        "logloss": 1.828
+      }
+    },
+    "ODD_EVEN": {
+      "market": "ODD_EVEN",
+      "samples": 106861,
+      "train": 64116,
+      "val": 16029,
+      "cal": 10686,
+      "test": 16030,
+      "features_used": 114,
+      "xgb_best_params": {
+        "max_depth": 8,
+        "eta": 0.01010929937405026,
+        "subsample": 0.9492996501687384,
+        "colsample_bytree": 0.9061960005014683,
+        "min_child_weight": 7,
+        "gamma": 2.664416507237002e-08,
+        "reg_lambda": 0.0003748192960525308,
+        "reg_alpha": 0.005287068300306146
+      },
+      "lgb_best_params": {
+        "max_depth": 8,
+        "learning_rate": 0.0634879805509945,
+        "feature_fraction": 0.9993568368122896,
+        "bagging_fraction": 0.9246236397710591,
+        "bagging_freq": 3,
+        "min_child_samples": 16,
+        "lambda_l1": 0.0016414429853061781,
+        "lambda_l2": 6.112007631403553e-05
+      },
+      "xgb_best_iteration": 322,
+      "lgb_best_iteration": 55,
+      "xgb_optuna_best_logloss": 0.6777,
+      "lgb_optuna_best_logloss": 0.6762,
+      "test_xgb_raw": {
+        "accuracy": 0.5216,
+        "logloss": 0.684
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.5236,
+        "logloss": 0.6834
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.5279,
+        "logloss": 0.6826
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.5274,
+        "logloss": 0.6861
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.5239,
+        "logloss": 0.6828
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.5236,
+        "logloss": 0.6861
+      }
+    },
+    "CARDS_OU45": {
+      "market": "CARDS_OU45",
+      "samples": 106861,
+      "train": 64116,
+      "val": 16029,
+      "cal": 10686,
+      "test": 16030,
+      "features_used": 114,
+      "xgb_best_params": {
+        "max_depth": 8,
+        "eta": 0.010098671964329344,
+        "subsample": 0.9969616653360747,
+        "colsample_bytree": 0.5085930751344795,
+        "min_child_weight": 10,
+        "gamma": 0.8600893137103568,
+        "reg_lambda": 7.556243125116086,
+        "reg_alpha": 0.5596869360839299
+      },
+      "lgb_best_params": {
+        "max_depth": 8,
+        "learning_rate": 0.0183440412249233,
+        "feature_fraction": 0.5416111323291537,
+        "bagging_fraction": 0.9754210612419695,
+        "bagging_freq": 2,
+        "min_child_samples": 5,
+        "lambda_l1": 0.09157782079463243,
+        "lambda_l2": 2.559000594641019
+      },
+      "xgb_best_iteration": 973,
+      "lgb_best_iteration": 503,
+      "xgb_optuna_best_logloss": 0.6408,
+      "lgb_optuna_best_logloss": 0.6407,
+      "test_xgb_raw": {
+        "accuracy": 0.597,
+        "logloss": 0.6501
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.6019,
+        "logloss": 0.6471
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.5977,
+        "logloss": 0.6486
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.6019,
+        "logloss": 0.6498
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.5964,
+        "logloss": 0.6487
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.6034,
+        "logloss": 0.6467
+      }
+    },
+    "HANDICAP_MS": {
+      "market": "HANDICAP_MS",
+      "samples": 106861,
+      "train": 64116,
+      "val": 16029,
+      "cal": 10686,
+      "test": 16030,
+      "features_used": 114,
+      "xgb_best_params": {
+        "max_depth": 4,
+        "eta": 0.01475719431584365,
+        "subsample": 0.867899230696633,
+        "colsample_bytree": 0.6518567347674479,
+        "min_child_weight": 9,
+        "gamma": 0.34932767754310273,
+        "reg_lambda": 3.3257801082201637e-07,
+        "reg_alpha": 4.6977721450875555e-06
+      },
+      "lgb_best_params": {
+        "max_depth": 7,
+        "learning_rate": 0.019649745228555244,
+        "feature_fraction": 0.7903699430858344,
+        "bagging_fraction": 0.7932436899357213,
+        "bagging_freq": 3,
+        "min_child_samples": 30,
+        "lambda_l1": 9.496143774926949e-08,
+        "lambda_l2": 0.0049885051588706136
+      },
+      "xgb_best_iteration": 1016,
+      "lgb_best_iteration": 364,
+      "xgb_optuna_best_logloss": 0.8328,
+      "lgb_optuna_best_logloss": 0.8322,
+      "test_xgb_raw": {
+        "accuracy": 0.6062,
+        "logloss": 0.871
+      },
+      "test_xgb_calibrated": {
+        "accuracy": 0.6039,
+        "logloss": 0.8729
+      },
+      "test_lgb_raw": {
+        "accuracy": 0.6079,
+        "logloss": 0.8713
+      },
+      "test_lgb_calibrated": {
+        "accuracy": 0.6067,
+        "logloss": 0.8736
+      },
+      "test_ensemble_raw": {
+        "accuracy": 0.6072,
+        "logloss": 0.8707
+      },
+      "test_ensemble_calibrated": {
+        "accuracy": 0.6066,
+        "logloss": 0.8728
+      }
+    }
+  }
+}

ai-engine/scripts/backtest_day.py

@@ -0,0 +1,146 @@
+import os
+import sys
+import psycopg2
+from psycopg2.extras import RealDictCursor
+
+# Path ayarları
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from services.single_match_orchestrator import SingleMatchOrchestrator
+from services.feature_enrichment import FeatureEnrichmentService
+
+DSN = "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
+
+def run_backtest(target_date="2026-05-03"):
+    conn = psycopg2.connect(DSN)
+    cur = conn.cursor(cursor_factory=RealDictCursor)
+    
+    # 1. Hedef tarihteki bitmiş maçları ve takım isimlerini getir
+    cur.execute("""
+        SELECT m.id, m.score_home, m.score_away, m.mst_utc,
+               t1.name as home_name, t2.name as away_name
+        FROM matches m
+        LEFT JOIN teams t1 ON m.home_team_id = t1.id
+        LEFT JOIN teams t2 ON m.away_team_id = t2.id
+        WHERE m.status IN ('FT', 'AET', 'PEN') 
+          AND to_timestamp(m.mst_utc / 1000.0)::date = %s::date
+          AND m.score_home IS NOT NULL
+        ORDER BY m.mst_utc ASC
+    """, (target_date,))
+    matches = cur.fetchall()
+    
+    if not matches:
+        print(f"❌ {target_date} tarihinde bitmiş maç bulunamadı.")
+        return
+
+    print(f"🚀 {target_date} için Orkestratör Backtesti Başlatılıyor... ({len(matches)} maç bulundu)")
+    print("-" * 60)
+    
+    orchestrator = SingleMatchOrchestrator()
+    
+    bets_placed = 0
+    won = 0
+    lost = 0
+    total_odds_won = 0.0
+    
+    for match in matches:
+        # 3. Üst Akıl (Orkestratör) analizi yapar
+        try:
+            package = orchestrator.analyze_match(match['id'])
+        except Exception as e:
+            print(f"Hata ({match['id']}): {e}")
+            continue
+            
+        if not package:
+            continue
+            
+        package_data = package
+        
+        # 4. Üst akıl bu maça bahis yapmaya karar verdi mi?
+        bet_advice = package_data.get("bet_advice", {})
+        if bet_advice.get("playable") == True:
+            bets_placed += 1
+            main_pick = package_data.get("main_pick", {})
+            market = main_pick.get("market")
+            pick = main_pick.get("pick")
+            odds = float(main_pick.get("odds", 0.0) or 0.0)
+            
+            # Skora göre kazanıp kazanmadığını kontrol et
+            is_won = False
+            h = match['score_home']
+            a = match['score_away']
+            
+            if market == "MS":
+                if pick == "1" and h > a: is_won = True
+                elif pick in ("X", "0") and h == a: is_won = True
+                elif pick == "2" and a > h: is_won = True
+            elif market == "OU25":
+                if pick == "Üst" and (h+a) > 2.5: is_won = True
+                elif pick == "Alt" and (h+a) < 2.5: is_won = True
+            elif market == "OU15":
+                if pick == "Üst" and (h+a) > 1.5: is_won = True
+                elif pick == "Alt" and (h+a) < 1.5: is_won = True
+            elif market == "BTTS":
+                if pick == "KG Var" and h > 0 and a > 0: is_won = True
+                elif pick == "KG Yok" and (h == 0 or a == 0): is_won = True
+            elif market == "DC":
+                if pick == "1X" and h >= a: is_won = True
+                elif pick == "12" and h != a: is_won = True
+                elif pick == "X2" and h <= a: is_won = True
+            
+            if is_won:
+                won += 1
+                total_odds_won += odds
+                res = "✅ KAZANDI"
+            else:
+                lost += 1
+                res = "❌ KAYBETTİ"
+                
+            print(f"[{res}] {match['home_name']} {h}-{a} {match['away_name']} | Tahmin: {market} {pick} (Oran: {odds})")
+        else:
+            main_pick = package_data.get("main_pick", {})
+            reasons = main_pick.get("reasons", ["Bilinmeyen Neden"]) if main_pick else ["No main pick"]
+            reason = " | ".join(reasons) if isinstance(reasons, list) else str(reasons)
+            
+            market_board = package_data.get("market_board", {})
+            main_pick_market = main_pick.get('market', 'N/A') if main_pick else 'N/A'
+            main_pick_pick = main_pick.get('pick', 'N/A') if main_pick else 'N/A'
+            print(f"[PAS] {match['home_name']} {match['score_home']}-{match['score_away']} {match['away_name']} | Reddedilen: {main_pick_market} {main_pick_pick} -> Neden: {reason}")
+            if "market_passed_all_gates" in reason:
+                print(f"      DEBUG: bet_advice = {bet_advice}")
+            
+            v25_ms = market_board.get("MS", {}).get("probs", {})
+            v27_ms = {} # V27 is merged into V25 probabilities in market_board, or we don't have separate V27 access here
+            
+            # Skora göre ms kontrolü
+            h = match['score_home']
+            a = match['score_away']
+            actual_ms = "1" if h > a else ("X" if h == a else "2")
+            
+            v25_top = max(v25_ms, key=v25_ms.get) if v25_ms else "N/A"
+            v27_top = "N/A"
+            
+            rejected_market = main_pick.get("market", "N/A") if main_pick else "N/A"
+            rejected_pick = main_pick.get("pick", "N/A") if main_pick else "N/A"
+            
+            print(f"[PAS] {match['home_name']} {h}-{a} {match['away_name']} | Reddedilen: {rejected_market} {rejected_pick} -> Neden: {reason}")
+            print(f"      [V25 MS Raw: {v25_top}] [Gerçek MS: {actual_ms}]")
+            
+    # Sonuç Raporu
+    print("\n" + "=" * 60)
+    print(f"📊 BACKTEST SONUÇLARI ({target_date})")
+    print("=" * 60)
+    print(f"Toplam Maç Sayısı   : {len(matches)}")
+    print(f"Oynanan Bahis Sayısı: {bets_placed} (Oynama Oranı: %{bets_placed/len(matches)*100:.1f})")
+    print(f"Riskli Bulunup Pas Geçilen: {len(matches) - bets_placed}")
+    
+    if bets_placed > 0:
+        win_rate = won / bets_placed * 100
+        roi = ((total_odds_won - bets_placed) / bets_placed) * 100
+        print(f"Kazanılan           : {won}")
+        print(f"Kaybedilen          : {lost}")
+        print(f"İsabet Oranı        : %{win_rate:.1f}")
+        print(f"Net Kar (ROI)       : %{roi:.1f} {'📈' if roi > 0 else '📉'}")
+
+if __name__ == "__main__":
+    run_backtest("2026-05-03")

ai-engine/scripts/elo_backfill.py

@@ -59,7 +59,7 @@ def fetch_matches(conn, sport: str):
 
 
 def flush_features_batch(conn, rows, dry_run: bool, sport: str = 'football'):
-    """Bulk upsert a batch of (match_id, home_elo, away_elo) into sport-partitioned ai_features table."""
+    """Bulk upsert ELO features into sport-partitioned ai_features table."""
     if not rows or dry_run:
         return
 
@@ -70,19 +70,27 @@ def flush_features_batch(conn, rows, dry_run: bool, sport: str = 'football'):
             f"""
             INSERT INTO {table_name}
                 (match_id, home_elo, away_elo,
+                 home_home_elo, away_away_elo,
+                 home_form_elo, away_form_elo,
+                 elo_diff,
                  home_form_score, away_form_score,
                  missing_players_impact, calculator_ver, updated_at)
             VALUES %s
             ON CONFLICT (match_id) DO UPDATE SET
                 home_elo             = EXCLUDED.home_elo,
                 away_elo             = EXCLUDED.away_elo,
+                home_home_elo        = EXCLUDED.home_home_elo,
+                away_away_elo        = EXCLUDED.away_away_elo,
+                home_form_elo        = EXCLUDED.home_form_elo,
+                away_form_elo        = EXCLUDED.away_form_elo,
+                elo_diff             = EXCLUDED.elo_diff,
                 home_form_score      = EXCLUDED.home_form_score,
                 away_form_score      = EXCLUDED.away_form_score,
                 calculator_ver       = EXCLUDED.calculator_ver,
                 updated_at           = EXCLUDED.updated_at
             """,
             rows,
-            template="(%s, %s, %s, %s, %s, 0.0, %s, NOW())",
+            template="(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, 0.0, %s, NOW())",
             page_size=500,
         )
     conn.commit()
@@ -136,16 +144,24 @@ def backfill(sport: str, batch_size: int, dry_run: bool):
         if not home_id or not away_id:
             continue
 
-        # Snapshot PRE-match ELO
+        # Snapshot PRE-match ELO (all dimensions)
         home_rating = elo.get_or_create_rating(home_id, h_name or "")
         away_rating = elo.get_or_create_rating(away_id, a_name or "")
 
+        h_overall = round(home_rating.overall_elo, 2)
+        a_overall = round(away_rating.overall_elo, 2)
+
         feature_buf.append((
             match_id,
-            round(home_rating.overall_elo, 2),
+            h_overall,                                          # home_elo
-            round(away_rating.overall_elo, 2),
+            a_overall,                                          # away_elo
-            round(form_to_score(home_rating.recent_form), 2),
+            round(home_rating.home_elo, 2),                     # home_home_elo
-            round(form_to_score(away_rating.recent_form), 2),
+            round(away_rating.away_elo, 2),                     # away_away_elo
+            round(home_rating.form_elo, 2),                     # home_form_elo
+            round(away_rating.form_elo, 2),                     # away_form_elo
+            round(h_overall - a_overall, 2),                    # elo_diff
+            round(form_to_score(home_rating.recent_form), 2),   # home_form_score
+            round(form_to_score(away_rating.recent_form), 2),   # away_form_score
             CALCULATOR_VER,
         ))
 

ai-engine/scripts/enrich_ai_features.py

@@ -0,0 +1,459 @@
+#!/usr/bin/env python3
+"""
+AI Features Full Enrichment Script
+====================================
+Fills empty/default columns in football_ai_features that were not populated
+by the original elo_backfill_v1 script.
+
+Enriches: H2H, referee, team_stats, league_averages, form_streaks,
+          rolling_goals, implied_odds, and clean_sheet/scoring rates.
+
+Usage:
+    python scripts/enrich_ai_features.py                          # enrich all
+    python scripts/enrich_ai_features.py --batch-size 500         # smaller batches
+    python scripts/enrich_ai_features.py --dry-run                # preview only
+    python scripts/enrich_ai_features.py --force                  # re-enrich all rows
+    python scripts/enrich_ai_features.py --limit 1000             # process N rows max
+
+Designed to be idempotent: uses ON CONFLICT upserts, skips already-enriched rows.
+"""
+
+from __future__ import annotations
+
+import os
+import sys
+import time
+import argparse
+from typing import Any, Dict, List, Optional, Tuple
+
+# Add ai-engine root to path
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+import psycopg2
+from psycopg2.extras import RealDictCursor, execute_values
+
+from data.db import get_clean_dsn
+from services.feature_enrichment import FeatureEnrichmentService
+
+# ────────────────────────── constants ──────────────────────────
+
+CALCULATOR_VER = 'enrichment_v2.0'
+DEFAULT_BATCH_SIZE = 200
+
+
+# ────────────────────────── helpers ────────────────────────────
+
+def fetch_unenriched_matches(
+    conn: psycopg2.extensions.connection,
+    force: bool = False,
+    limit: Optional[int] = None,
+) -> List[Dict[str, Any]]:
+    """
+    Fetch matches from football_ai_features that still have default values
+    in the enrichment columns (h2h_total=0 AND referee_avg_cards=0).
+
+    If force=True, fetches ALL rows regardless of current state.
+    """
+    with conn.cursor(cursor_factory=RealDictCursor) as cur:
+        where_clause = "WHERE 1=1" if force else (
+            "WHERE (faf.h2h_total = 0 AND faf.referee_avg_cards = 0)"
+        )
+        limit_clause = f"LIMIT {limit}" if limit else ""
+
+        cur.execute(f"""
+            SELECT
+                faf.match_id,
+                m.home_team_id,
+                m.away_team_id,
+                m.mst_utc,
+                m.league_id,
+                m.score_home,
+                m.score_away
+            FROM football_ai_features faf
+            JOIN matches m ON m.id = faf.match_id
+            WHERE m.status = 'FT'
+              AND m.score_home IS NOT NULL
+              AND m.sport = 'football'
+              AND ({where_clause.replace('WHERE ', '')})
+            ORDER BY m.mst_utc ASC
+            {limit_clause}
+        """)
+        return cur.fetchall()
+
+
+def fetch_referee_for_match(
+    cur: RealDictCursor,
+    match_id: str,
+) -> Optional[str]:
+    """Get the head referee name for a match from match_officials."""
+    try:
+        cur.execute("""
+            SELECT mo.name
+            FROM match_officials mo
+            WHERE mo.match_id = %s
+              AND mo.role_id = 1
+            LIMIT 1
+        """, (match_id,))
+        row = cur.fetchone()
+        return row['name'] if row else None
+    except Exception:
+        return None
+
+
+def fetch_implied_odds(
+    cur: RealDictCursor,
+    match_id: str,
+) -> Dict[str, float]:
+    """Get implied probabilities from odd_categories + odd_selections."""
+    defaults = {
+        'implied_home': 0.33,
+        'implied_draw': 0.33,
+        'implied_away': 0.33,
+        'implied_over25': 0.50,
+        'implied_btts_yes': 0.50,
+        'odds_overround': 0.0,
+    }
+    try:
+        cur.execute("""
+            SELECT oc.name AS cat_name, os.name AS sel_name, os.odd_value
+            FROM odd_selections os
+            JOIN odd_categories oc ON os.odd_category_db_id = oc.db_id
+            WHERE oc.match_id = %s
+        """, (match_id,))
+        rows = cur.fetchall()
+    except Exception:
+        return defaults
+
+    odds: Dict[str, float] = {}
+    for row in rows:
+        try:
+            cat = (row.get('cat_name') or '').lower().strip()
+            sel = (row.get('sel_name') or '').strip()
+            val = float(row.get('odd_value', 0))
+            if val <= 0:
+                continue
+
+            if cat == 'maç sonucu':
+                if sel == '1':
+                    odds['ms_h'] = val
+                elif sel in ('0', 'X'):
+                    odds['ms_d'] = val
+                elif sel == '2':
+                    odds['ms_a'] = val
+            elif cat == '2,5 alt/üst':
+                if 'üst' in sel.lower():
+                    odds['ou25_o'] = val
+                elif 'alt' in sel.lower():
+                    odds['ou25_u'] = val
+            elif cat == 'karşılıklı gol':
+                if 'var' in sel.lower():
+                    odds['btts_y'] = val
+                elif 'yok' in sel.lower():
+                    odds['btts_n'] = val
+        except (ValueError, TypeError):
+            continue
+
+    # Compute implied probabilities
+    ms_h = odds.get('ms_h', 0)
+    ms_d = odds.get('ms_d', 0)
+    ms_a = odds.get('ms_a', 0)
+
+    if ms_h > 1.0 and ms_d > 1.0 and ms_a > 1.0:
+        raw_sum = 1 / ms_h + 1 / ms_d + 1 / ms_a
+        overround = raw_sum - 1.0
+        defaults['implied_home'] = round((1 / ms_h) / raw_sum, 4)
+        defaults['implied_draw'] = round((1 / ms_d) / raw_sum, 4)
+        defaults['implied_away'] = round((1 / ms_a) / raw_sum, 4)
+        defaults['odds_overround'] = round(overround, 4)
+
+    ou25_o = odds.get('ou25_o', 0)
+    ou25_u = odds.get('ou25_u', 0)
+    if ou25_o > 1.0 and ou25_u > 1.0:
+        raw_sum = 1 / ou25_o + 1 / ou25_u
+        defaults['implied_over25'] = round((1 / ou25_o) / raw_sum, 4)
+
+    btts_y = odds.get('btts_y', 0)
+    btts_n = odds.get('btts_n', 0)
+    if btts_y > 1.0 and btts_n > 1.0:
+        raw_sum = 1 / btts_y + 1 / btts_n
+        defaults['implied_btts_yes'] = round((1 / btts_y) / raw_sum, 4)
+
+    return defaults
+
+
+def enrich_single_match(
+    enrichment: FeatureEnrichmentService,
+    cur: RealDictCursor,
+    match: Dict[str, Any],
+) -> Dict[str, Any]:
+    """
+    Compute all enrichment features for a single match and return
+    a dict ready for DB upsert.
+    """
+    match_id = match['match_id']
+    home_id = str(match['home_team_id'])
+    away_id = str(match['away_team_id'])
+    mst_utc = int(match['mst_utc']) if match['mst_utc'] else 0
+    league_id = str(match['league_id']) if match['league_id'] else None
+
+    # 1. Team stats
+    home_stats = enrichment.compute_team_stats(cur, home_id, mst_utc)
+    away_stats = enrichment.compute_team_stats(cur, away_id, mst_utc)
+
+    # 2. H2H
+    h2h = enrichment.compute_h2h(cur, home_id, away_id, mst_utc)
+
+    # 3. Form & streaks
+    home_form = enrichment.compute_form_streaks(cur, home_id, mst_utc)
+    away_form = enrichment.compute_form_streaks(cur, away_id, mst_utc)
+
+    # 4. Referee
+    referee_name = fetch_referee_for_match(cur, match_id)
+    referee = enrichment.compute_referee_stats(cur, referee_name, mst_utc)
+
+    # 5. League averages
+    league = enrichment.compute_league_averages(cur, league_id, mst_utc)
+
+    # 6. Rolling stats (for goals avg)
+    home_rolling = enrichment.compute_rolling_stats(cur, home_id, mst_utc)
+    away_rolling = enrichment.compute_rolling_stats(cur, away_id, mst_utc)
+
+    # 7. Implied odds
+    implied = fetch_implied_odds(cur, match_id)
+
+    return {
+        'match_id': match_id,
+        # Team stats
+        'home_avg_possession': round(home_stats['avg_possession'], 2),
+        'away_avg_possession': round(away_stats['avg_possession'], 2),
+        'home_avg_shots_on_target': round(home_stats['avg_shots_on_target'], 2),
+        'away_avg_shots_on_target': round(away_stats['avg_shots_on_target'], 2),
+        'home_shot_conversion': round(home_stats['shot_conversion'], 4),
+        'away_shot_conversion': round(away_stats['shot_conversion'], 4),
+        'home_avg_corners': round(home_stats['avg_corners'], 2),
+        'away_avg_corners': round(away_stats['avg_corners'], 2),
+        # H2H
+        'h2h_total': h2h['total_matches'],
+        'h2h_home_win_rate': round(h2h['home_win_rate'], 4),
+        'h2h_avg_goals': round(h2h['avg_goals'], 2),
+        'h2h_over25_rate': round(h2h['over25_rate'], 4),
+        'h2h_btts_rate': round(h2h['btts_rate'], 4),
+        # Form
+        'home_clean_sheet_rate': round(home_form['clean_sheet_rate'], 4),
+        'away_clean_sheet_rate': round(away_form['clean_sheet_rate'], 4),
+        'home_scoring_rate': round(home_form['scoring_rate'], 4),
+        'away_scoring_rate': round(away_form['scoring_rate'], 4),
+        'home_win_streak': home_form['winning_streak'],
+        'away_win_streak': away_form['winning_streak'],
+        # Rolling goals
+        'home_goals_avg_5': round(home_rolling['rolling5_goals'], 2),
+        'away_goals_avg_5': round(away_rolling['rolling5_goals'], 2),
+        'home_conceded_avg_5': round(home_rolling['rolling5_conceded'], 2),
+        'away_conceded_avg_5': round(away_rolling['rolling5_conceded'], 2),
+        # Referee
+        'referee_avg_cards': round(referee['cards_total'], 2),
+        'referee_home_bias': round(referee['home_bias'], 4),
+        'referee_avg_goals': round(referee['avg_goals'], 2),
+        # League
+        'league_avg_goals': round(league['avg_goals'], 2),
+        'league_home_win_pct': round(league['home_win_rate'], 4),
+        'league_over25_pct': round(league['ou25_rate'], 4),
+        # Implied odds
+        'implied_home': implied['implied_home'],
+        'implied_draw': implied['implied_draw'],
+        'implied_away': implied['implied_away'],
+        'implied_over25': implied['implied_over25'],
+        'implied_btts_yes': implied['implied_btts_yes'],
+        'odds_overround': implied['odds_overround'],
+        # Missing players impact — default (no lineup data for historical)
+        'missing_players_impact': 0.0,
+        # Version
+        'calculator_ver': CALCULATOR_VER,
+    }
+
+
+def flush_enrichment_batch(
+    conn: psycopg2.extensions.connection,
+    rows: List[Dict[str, Any]],
+    dry_run: bool,
+) -> int:
+    """Bulk upsert enriched features into football_ai_features."""
+    if not rows or dry_run:
+        return 0
+
+    columns = [
+        'match_id',
+        'home_avg_possession', 'away_avg_possession',
+        'home_avg_shots_on_target', 'away_avg_shots_on_target',
+        'home_shot_conversion', 'away_shot_conversion',
+        'home_avg_corners', 'away_avg_corners',
+        'h2h_total', 'h2h_home_win_rate', 'h2h_avg_goals',
+        'h2h_over25_rate', 'h2h_btts_rate',
+        'home_clean_sheet_rate', 'away_clean_sheet_rate',
+        'home_scoring_rate', 'away_scoring_rate',
+        'home_win_streak', 'away_win_streak',
+        'home_goals_avg_5', 'away_goals_avg_5',
+        'home_conceded_avg_5', 'away_conceded_avg_5',
+        'referee_avg_cards', 'referee_home_bias', 'referee_avg_goals',
+        'league_avg_goals', 'league_home_win_pct', 'league_over25_pct',
+        'implied_home', 'implied_draw', 'implied_away',
+        'implied_over25', 'implied_btts_yes', 'odds_overround',
+        'missing_players_impact', 'calculator_ver',
+    ]
+
+    # Build update SET clause (skip match_id)
+    update_cols = [c for c in columns if c != 'match_id']
+    set_clause = ', '.join(f'{c} = EXCLUDED.{c}' for c in update_cols)
+
+    placeholders = ', '.join(['%s'] * len(columns))
+    values = [
+        tuple(row[c] for c in columns)
+        for row in rows
+    ]
+
+    with conn.cursor() as cur:
+        execute_values(
+            cur,
+            f"""
+            INSERT INTO football_ai_features ({', '.join(columns)})
+            VALUES %s
+            ON CONFLICT (match_id) DO UPDATE SET
+                {set_clause},
+                updated_at = NOW()
+            """,
+            values,
+            template=f"({placeholders})",
+            page_size=200,
+        )
+    conn.commit()
+    return len(rows)
+
+
+# ────────────────────────── main ───────────────────────────────
+
+def run_enrichment(
+    batch_size: int,
+    dry_run: bool,
+    force: bool,
+    limit: Optional[int],
+) -> None:
+    """Core enrichment loop."""
+    dsn = get_clean_dsn()
+    conn = psycopg2.connect(dsn)
+
+    print(f"\n{'=' * 60}")
+    print(f"🧠 AI Features Full Enrichment — {CALCULATOR_VER}")
+    print(f"   batch_size={batch_size}  dry_run={dry_run}  force={force}")
+    print(f"{'=' * 60}")
+
+    # 1. Fetch unenriched matches
+    t0 = time.time()
+    matches = fetch_unenriched_matches(conn, force=force, limit=limit)
+    print(f"\n📊 {len(matches):,} matches to enrich ({time.time() - t0:.1f}s)")
+
+    if not matches:
+        print("✅ Nothing to enrich — all rows already populated.")
+        conn.close()
+        return
+
+    # 2. Initialize enrichment service
+    enrichment = FeatureEnrichmentService()
+
+    # 3. Process in batches
+    total = len(matches)
+    processed = 0
+    written = 0
+    errors = 0
+    batch_buf: List[Dict[str, Any]] = []
+    t_start = time.time()
+
+    # Use a dedicated cursor with RealDictCursor for all enrichment queries
+    enrich_cur = conn.cursor(cursor_factory=RealDictCursor)
+
+    for idx, match in enumerate(matches):
+        try:
+            enriched = enrich_single_match(enrichment, enrich_cur, match)
+            batch_buf.append(enriched)
+        except Exception as e:
+            errors += 1
+            if errors <= 10:
+                print(f"   ⚠️ Error enriching {match.get('match_id', '?')}: {e}")
+
+        processed += 1
+
+        # Flush batch
+        if len(batch_buf) >= batch_size:
+            flushed = flush_enrichment_batch(conn, batch_buf, dry_run)
+            written += flushed
+            batch_buf.clear()
+
+        # Progress reporting
+        if processed % 500 == 0:
+            elapsed = time.time() - t_start
+            rate = processed / elapsed if elapsed > 0 else 0
+            remaining = (total - processed) / rate if rate > 0 else 0
+            pct = processed / total * 100
+            print(
+                f"   [{processed:>8,} / {total:,}] "
+                f"({pct:.1f}%) | {rate:.0f} matches/s | "
+                f"ETA: {remaining / 60:.1f} min | "
+                f"errors: {errors}"
+            )
+
+    # Flush remaining
+    if batch_buf:
+        flushed = flush_enrichment_batch(conn, batch_buf, dry_run)
+        written += flushed
+
+    enrich_cur.close()
+
+    elapsed = time.time() - t_start
+    print(f"\n{'=' * 60}")
+    print(f"✅ Enrichment complete:")
+    print(f"   Processed: {processed:,} matches in {elapsed:.1f}s")
+    print(f"   Written:   {written:,} rows")
+    print(f"   Errors:    {errors:,}")
+    print(f"   Rate:      {processed / elapsed:.0f} matches/s")
+    print(f"{'=' * 60}")
+
+    conn.close()
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Enrich football_ai_features with H2H, referee, stats, and odds data"
+    )
+    parser.add_argument(
+        '--batch-size',
+        type=int,
+        default=DEFAULT_BATCH_SIZE,
+        help=f'DB insert batch size (default: {DEFAULT_BATCH_SIZE})',
+    )
+    parser.add_argument(
+        '--dry-run',
+        action='store_true',
+        help='Compute features but do not write to DB',
+    )
+    parser.add_argument(
+        '--force',
+        action='store_true',
+        help='Re-enrich ALL rows, not just empty ones',
+    )
+    parser.add_argument(
+        '--limit',
+        type=int,
+        default=None,
+        help='Max number of matches to process',
+    )
+    args = parser.parse_args()
+
+    run_enrichment(
+        batch_size=args.batch_size,
+        dry_run=args.dry_run,
+        force=args.force,
+        limit=args.limit,
+    )
+
+
+if __name__ == '__main__':
+    main()

ai-engine/scripts/extract_training_data.py

@@ -14,6 +14,7 @@ import json
 import csv
 import math
 import time
+import bisect
 from datetime import datetime
 from collections import defaultdict
 
@@ -33,7 +34,7 @@ from features.upset_engine import get_upset_engine
 from features.referee_engine import get_referee_engine
 from features.momentum_engine import get_momentum_engine
 
-TOP_LEAGUES_PATH = os.path.join(AI_ENGINE_DIR, "..", "top_leagues.json")
+TOP_LEAGUES_PATH = os.path.join(AI_ENGINE_DIR, "..", "qualified_leagues.json")
 OUTPUT_CSV = os.path.join(AI_ENGINE_DIR, "data", "training_data.csv")
 
 # Ensure output dir exists
@@ -119,6 +120,14 @@ FEATURE_COLS = [
     "home_key_players", "away_key_players",
     "home_missing_impact", "away_missing_impact",
     "home_goals_form", "away_goals_form",
+
+    # Player-Level Features (12)
+    "home_lineup_goals_per90", "away_lineup_goals_per90",
+    "home_lineup_assists_per90", "away_lineup_assists_per90",
+    "home_squad_continuity", "away_squad_continuity",
+    "home_top_scorer_form", "away_top_scorer_form",
+    "home_avg_player_exp", "away_avg_player_exp",
+    "home_goals_diversity", "away_goals_diversity",
     
     # Labels
     "score_home", "score_away", "total_goals",
@@ -336,7 +345,7 @@ class BatchDataLoader:
             self.team_stats[tid].append((mst, poss, sot, tshots, corn, team_goals))
 
     def _load_squad_data(self):
-        """Bulk load squad participation + player events for squad features."""
+        """Bulk load squad participation + player events + player career for squad features."""
         ph = ",".join(["%s"] * len(self.top_league_ids))
 
         # 1) Participation: starting XI count + position distribution per (match, team)
@@ -424,12 +433,99 @@ class BatchDataLoader:
         for mid, tid, pid in self.cur.fetchall():
             starting_players[(mid, tid)].append(pid)
 
-        # 5) Build combined cache
+        # 5) Build match_id → mst_utc mapping for temporal filtering
+        match_mst = {}
+        for m in self.matches:
+            match_mst[m[0]] = m[7]  # m[0]=id, m[7]=mst_utc
+
+        # ─── NEW: Player Career Stats (prefix-sum for O(1) temporal lookup) ───
+        # 6a) Goals per player per match date
+        self.cur.execute(f"""
+            SELECT mpe.player_id, m.mst_utc,
+                   SUM(CASE WHEN mpe.event_type = 'goal'
+                            AND COALESCE(mpe.event_subtype, '') NOT ILIKE '%%penaltı kaçırma%%'
+                       THEN 1 ELSE 0 END) AS goals
+            FROM match_player_events mpe
+            JOIN matches m ON mpe.match_id = m.id
+            WHERE m.status = 'FT' AND m.sport = 'football' AND m.league_id IN ({ph})
+            GROUP BY mpe.player_id, m.mst_utc
+        """, self.top_league_ids)
+
+        player_goals_raw = defaultdict(dict)
+        for pid, mst, goals in self.cur.fetchall():
+            player_goals_raw[pid][mst] = (player_goals_raw[pid].get(mst, 0)) + (goals or 0)
+
+        # 6b) Assists per player per match date
+        self.cur.execute(f"""
+            SELECT mpe.assist_player_id, m.mst_utc, COUNT(*) AS assists
+            FROM match_player_events mpe
+            JOIN matches m ON mpe.match_id = m.id
+            WHERE m.status = 'FT' AND m.sport = 'football' AND m.league_id IN ({ph})
+              AND mpe.event_type = 'goal' AND mpe.assist_player_id IS NOT NULL
+            GROUP BY mpe.assist_player_id, m.mst_utc
+        """, self.top_league_ids)
+
+        player_assists_raw = defaultdict(dict)
+        for pid, mst, assists in self.cur.fetchall():
+            player_assists_raw[pid][mst] = (player_assists_raw[pid].get(mst, 0)) + (assists or 0)
+
+        # 6c) Player participation dates (starts only)
+        self.cur.execute(f"""
+            SELECT mpp.player_id, m.mst_utc
+            FROM match_player_participation mpp
+            JOIN matches m ON mpp.match_id = m.id
+            WHERE mpp.is_starting = true
+              AND m.status = 'FT' AND m.sport = 'football' AND m.league_id IN ({ph})
+            ORDER BY mpp.player_id, m.mst_utc
+        """, self.top_league_ids)
+
+        player_starts_raw = defaultdict(list)
+        for pid, mst in self.cur.fetchall():
+            player_starts_raw[pid].append(mst)
+
+        # 6d) Build prefix sums per player (goals_prefix[i] = total goals up to start i)
+        player_career = {}
+        all_pids = set(player_starts_raw.keys()) | set(player_goals_raw.keys()) | set(player_assists_raw.keys())
+        for pid in all_pids:
+            starts = sorted(set(player_starts_raw.get(pid, [])))
+            if not starts:
+                continue
+            g_map = player_goals_raw.get(pid, {})
+            a_map = player_assists_raw.get(pid, {})
+            cum_g, cum_a = 0, 0
+            goals_pf, assists_pf = [], []
+            for mst in starts:
+                cum_g += g_map.get(mst, 0)
+                cum_a += a_map.get(mst, 0)
+                goals_pf.append(cum_g)
+                assists_pf.append(cum_a)
+            player_career[pid] = {'msts': starts, 'gp': goals_pf, 'ap': assists_pf}
+
+        # Free raw dicts
+        del player_goals_raw, player_assists_raw, player_starts_raw
+        print(f"    📊 Player careers built: {len(player_career)} players", flush=True)
+
+        # ─── NEW: Team Lineup History (for squad continuity) ───
+        # 7) Per-team sorted lineups: [(mst, frozenset(player_ids))]
+        team_lineup_map = defaultdict(list)
+        for (mid, tid), pids in starting_players.items():
+            mst = match_mst.get(mid, 0)
+            if mst > 0 and pids:
+                team_lineup_map[tid].append((mst, frozenset(pids)))
+
+        team_lineup_history = {}
+        team_lineup_msts = {}
+        for tid, ll in team_lineup_map.items():
+            ll.sort(key=lambda x: x[0])
+            team_lineup_history[tid] = ll
+            team_lineup_msts[tid] = [x[0] for x in ll]
+        del team_lineup_map
+
+        # ─── 8) Build combined cache — NO DATA LEAKAGE ───
         all_keys = set(participation.keys()) | set(events.keys())
         for key in all_keys:
             mid, tid = key
             part = participation.get(key, {'starting_count': 0, 'total_squad': 0, 'fwd_count': 0})
-            evt = events.get(key, {'goals': 0, 'assists': 0, 'unique_scorers': 0})
 
             # Count key players in starting XI
             starters = starting_players.get(key, [])
@@ -437,22 +533,78 @@ class BatchDataLoader:
             kp_total = len(key_players_by_team.get(tid, set()))
             kp_missing = max(0, kp_total - kp_in_starting)
 
-            # Squad quality: composite score
+            # Squad quality: composite score — ONLY pre-match info
             squad_quality = (
                 part['starting_count'] * 0.3 +
-                evt['goals'] * 2.0 +
-                evt['assists'] * 1.0 +
                 kp_in_starting * 3.0 +
                 part['fwd_count'] * 1.5
             )
-            # Missing impact: how many key players are missing
             missing_impact = min(kp_missing / max(kp_total, 1), 1.0)
 
+            # goals_form: avg goals from last 5 matches BEFORE this match
+            current_mst = match_mst.get(mid, 0)
+            team_history = self.team_matches.get(tid, [])
+            recent_goals = [
+                tm[2] for tm in team_history if tm[0] < current_mst
+            ][-5:]
+            goals_form = sum(recent_goals) / len(recent_goals) if recent_goals else 1.3
+
+            # ─── NEW: Player-level aggregation for starting XI ───
+            lineup_g90, lineup_a90, total_exp = 0.0, 0.0, 0
+            best_scorer_total, best_scorer_id = 0, None
+            scorers_in_lineup = 0
+
+            for pid in starters:
+                pc = player_career.get(pid)
+                if not pc:
+                    continue
+                idx = bisect.bisect_left(pc['msts'], current_mst)
+                if idx == 0:
+                    continue  # no prior matches for this player
+                prior_starts = idx
+                prior_goals = pc['gp'][idx - 1]
+                prior_assists = pc['ap'][idx - 1]
+                lineup_g90 += prior_goals / prior_starts
+                lineup_a90 += prior_assists / prior_starts
+                total_exp += prior_starts
+                if prior_goals > 0:
+                    scorers_in_lineup += 1
+                if prior_goals > best_scorer_total:
+                    best_scorer_total = prior_goals
+                    best_scorer_id = pid
+
+            n_st = len(starters) or 1
+
+            # Top scorer recent form (goals in last 5 starts)
+            top_scorer_form = 0
+            if best_scorer_id:
+                pc = player_career.get(best_scorer_id)
+                if pc:
+                    idx = bisect.bisect_left(pc['msts'], current_mst)
+                    if idx > 0:
+                        s5 = max(0, idx - 5)
+                        top_scorer_form = pc['gp'][idx - 1] - (pc['gp'][s5 - 1] if s5 > 0 else 0)
+
+            # Squad continuity (overlap with previous match lineup)
+            squad_continuity = 0.5
+            msts_list = team_lineup_msts.get(tid)
+            if msts_list:
+                li = bisect.bisect_left(msts_list, current_mst)
+                if li > 0:
+                    prev_lineup = team_lineup_history[tid][li - 1][1]
+                    squad_continuity = len(frozenset(starters) & prev_lineup) / n_st
+
             self.squad_cache[key] = {
                 'squad_quality': squad_quality,
                 'key_players': kp_in_starting,
                 'missing_impact': missing_impact,
-                'goals_form': evt['goals'],
+                'goals_form': round(goals_form, 2),
+                'lineup_goals_per90': round(lineup_g90, 3),
+                'lineup_assists_per90': round(lineup_a90, 3),
+                'squad_continuity': round(squad_continuity, 3),
+                'top_scorer_form': top_scorer_form,
+                'avg_player_exp': round(total_exp / n_st, 1),
+                'goals_diversity': round(scorers_in_lineup / n_st, 3),
             }
 
     def _load_cards_data(self):
@@ -496,16 +648,24 @@ class FeatureExtractor:
         self.referee_engine = get_referee_engine()
         self.momentum_engine = get_momentum_engine()
     
+    # ── Data Quality Thresholds ──
+    # Matches below these thresholds produce default-only features that
+    # teach the model noise rather than signal.
+    DQ_MIN_FORM_MATCHES = 3          # team must have ≥3 prior matches
+    DQ_MIN_FEATURE_COVERAGE = 0.30   # ≥30% of key features must be non-default
+
     def extract_all(self) -> list:
-        """Extract features for all matches, yield row dicts."""
+        """Extract features for all matches with data quality validation."""
         matches = self.loader.matches
         total = len(matches)
         rows = []
         skipped = 0
+        dq_rejected = 0
+        dq_reasons: dict = defaultdict(int)
         t_start = time.time()
-        
+
         print(f"\n🔄 Extracting features for {total} matches...", flush=True)
-        
+
         # Process chronologically — ELO grows as we go
         for i, m in enumerate(matches):
             (
@@ -522,38 +682,43 @@ class FeatureExtractor:
                 away_name,
                 league_name,
             ) = m
-            
+
             if i % 100 == 0 and i > 0:
                 elapsed = time.time() - t_start
                 rate = i / elapsed  # matches per second
                 remaining = (total - i) / rate if rate > 0 else 0
                 pct = i / total * 100
-                print(f"  [{i}/{total}] ({pct:.0f}%) | {rate:.1f} maç/s | ETA: {remaining/60:.1f} dk | skipped: {skipped}", flush=True)
+                print(
-            
+                    f"  [{i}/{total}] ({pct:.0f}%) | {rate:.1f} maç/s | "
+                    f"ETA: {remaining/60:.1f} dk | skipped: {skipped} | "
+                    f"dq_rejected: {dq_rejected}",
+                    flush=True,
+                )
+
             row = self._extract_one(
-                mid,
+                mid, hid, aid, sh, sa, hth, hta, mst, lid,
-                hid,
+                home_name, away_name, league_name,
-                aid,
-                sh,
-                sa,
-                hth,
-                hta,
-                mst,
-                lid,
-                home_name,
-                away_name,
-                league_name,
             )
-            
+
             if row:
-                rows.append(row)
+                # ── Data Quality Gate ──
+                dq_pass, reason = self._validate_row_quality(row, hid, aid, mst)
+                if dq_pass:
+                    rows.append(row)
+                else:
+                    dq_rejected += 1
+                    dq_reasons[reason] += 1
             else:
                 skipped += 1
-            
+
             # Update ELO after processing (so ELO is calculated BEFORE the match)
             self._update_elo(hid, aid, sh, sa)
-        
+
-        print(f"  ✅ Extracted {len(rows)} rows, skipped {skipped}", flush=True)
+        print(f"  ✅ Extracted {len(rows)} rows, skipped {skipped}, DQ rejected {dq_rejected}", flush=True)
+        if dq_reasons:
+            print(f"  📊 DQ Rejection reasons:")
+            for reason, count in sorted(dq_reasons.items(), key=lambda x: -x[1]):
+                print(f"     {reason}: {count}")
         return rows
     
     def _extract_one(
@@ -828,6 +993,20 @@ class FeatureExtractor:
             "away_missing_impact": away_missing_impact,
             "home_goals_form": home_goals_form,
             "away_goals_form": away_goals_form,
+
+            # Player-Level Features
+            "home_lineup_goals_per90": home_sq.get('lineup_goals_per90', 0.0),
+            "away_lineup_goals_per90": away_sq.get('lineup_goals_per90', 0.0),
+            "home_lineup_assists_per90": home_sq.get('lineup_assists_per90', 0.0),
+            "away_lineup_assists_per90": away_sq.get('lineup_assists_per90', 0.0),
+            "home_squad_continuity": home_sq.get('squad_continuity', 0.5),
+            "away_squad_continuity": away_sq.get('squad_continuity', 0.5),
+            "home_top_scorer_form": home_sq.get('top_scorer_form', 0),
+            "away_top_scorer_form": away_sq.get('top_scorer_form', 0),
+            "home_avg_player_exp": home_sq.get('avg_player_exp', 0.0),
+            "away_avg_player_exp": away_sq.get('avg_player_exp', 0.0),
+            "home_goals_diversity": home_sq.get('goals_diversity', 0.0),
+            "away_goals_diversity": away_sq.get('goals_diversity', 0.0),
             
             # Labels
             "score_home": sh,
@@ -853,7 +1032,58 @@ class FeatureExtractor:
         }
         
         return row
-    
+
+    def _validate_row_quality(
+        self,
+        row: dict,
+        home_id: str,
+        away_id: str,
+        before_date: int,
+    ) -> tuple:
+        """
+        Data quality gate for training rows.
+
+        Ensures the feature vector has enough real signal to be useful for
+        training.  Rejects rows where critical features are all at their
+        default/fallback values — these teach the model noise, not patterns.
+
+        Returns (pass: bool, reason: str | None).
+        """
+        # 1. Minimum form history: both teams must have enough prior matches
+        home_history = self.loader.team_matches.get(home_id, [])
+        away_history = self.loader.team_matches.get(away_id, [])
+        home_prior = sum(1 for m in home_history if m[0] < before_date)
+        away_prior = sum(1 for m in away_history if m[0] < before_date)
+
+        if home_prior < self.DQ_MIN_FORM_MATCHES:
+            return False, 'home_insufficient_history'
+        if away_prior < self.DQ_MIN_FORM_MATCHES:
+            return False, 'away_insufficient_history'
+
+        # 2. Feature coverage check: count how many key features are non-default
+        key_features = [
+            ('home_goals_avg', 1.3),
+            ('away_goals_avg', 1.3),
+            ('home_clean_sheet_rate', 0.25),
+            ('away_clean_sheet_rate', 0.25),
+            ('home_avg_possession', 0.50),
+            ('away_avg_possession', 0.50),
+            ('home_avg_shots_on_target', 3.5),
+            ('away_avg_shots_on_target', 3.5),
+            ('h2h_total_matches', 0),
+            ('odds_ms_h', 0.0),
+        ]
+        non_default = sum(
+            1 for feat_name, default_val in key_features
+            if abs(float(row.get(feat_name, default_val)) - default_val) > 0.01
+        )
+        coverage = non_default / len(key_features)
+
+        if coverage < self.DQ_MIN_FEATURE_COVERAGE:
+            return False, f'low_feature_coverage_{coverage:.0%}'
+
+        return True, None
+
     # -------------------------------------------------------------------------
     # ELO (simplified inline version — doesn't need DB, grows incrementally)
     # -------------------------------------------------------------------------

ai-engine/scripts/train_score_model.py

@@ -1,183 +1,271 @@
+"""
+V25-Compatible Score Prediction Model Trainer
+===============================================
+Trains 4 independent XGBoost regression models for:
+  - FT Home Goals
+  - FT Away Goals
+  - HT Home Goals
+  - HT Away Goals
 
+Uses the same 102-feature set as v25_ensemble for full compatibility.
+Temporal train/test split (80/20) to avoid future leakage.
+
+Usage:
+    python3 scripts/train_score_model.py
+"""
+
+import os
+import sys
+import pickle
+import numpy as np
 import pandas as pd
 import xgboost as xgb
-import pickle
+from datetime import datetime
-import os
+from sklearn.metrics import mean_absolute_error, r2_score, mean_squared_error
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import mean_absolute_error, r2_score
 
-# Paths
+# Add parent directory to path
-DATA_PATH = os.path.join(os.path.dirname(__file__), "../data/training_data.csv")
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-MODEL_PATH = os.path.join(os.path.dirname(__file__), "../models/xgb_score.pkl")
 
-# Import unified 56-feature array from markets trainer
+# Config
-from train_xgboost_markets import FEATURES
+AI_ENGINE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+DATA_PATH = os.path.join(AI_ENGINE_DIR, "data", "training_data.csv")
+MODEL_PATH = os.path.join(AI_ENGINE_DIR, "models", "xgb_score.pkl")
+
+# Import the EXACT same feature set as v25 market models
+from train_v25_clean import FEATURES
 
 TARGETS = ["score_home", "score_away", "ht_score_home", "ht_score_away"]
 
-def train():
+# Model hyperparameters (tuned for goal count regression)
-    print("🚀 Training Score Prediction Model (XGBoost) - Full Time & Half Time")
+XGB_PARAMS = {
-    print("=" * 60)
+    "objective": "reg:squarederror",
+    "n_estimators": 1200,
+    "learning_rate": 0.02,
+    "max_depth": 6,
+    "subsample": 0.8,
+    "colsample_bytree": 0.7,
+    "min_child_weight": 5,
+    "reg_alpha": 0.1,
+    "reg_lambda": 1.0,
+    "n_jobs": -1,
+    "random_state": 42,
+}
 
+
+def load_data() -> pd.DataFrame:
+    """Load and validate training data."""
     if not os.path.exists(DATA_PATH):
         print(f"❌ Data file not found: {DATA_PATH}")
-        return
+        print("   Run extract_training_data.py first")
+        sys.exit(1)
 
     print(f"📦 Loading data from {DATA_PATH}...")
     df = pd.read_csv(DATA_PATH)
-    
+
-    # Preprocessing
+    # Fill feature NaNs with 0 (same as v25 training)
-    # Drop rows where target is missing (should verify)
+    for col in FEATURES:
+        if col in df.columns:
+            df[col] = df[col].fillna(0)
+
+    # Backward-compatible: add odds presence flags if missing
+    odds_base_columns = [
+        "odds_ms_h", "odds_ms_d", "odds_ms_a",
+        "odds_ht_ms_h", "odds_ht_ms_d", "odds_ht_ms_a",
+        "odds_ou05_o", "odds_ou05_u",
+        "odds_ou15_o", "odds_ou15_u",
+        "odds_ou25_o", "odds_ou25_u",
+        "odds_ou35_o", "odds_ou35_u",
+        "odds_ht_ou05_o", "odds_ht_ou05_u",
+        "odds_ht_ou15_o", "odds_ht_ou15_u",
+        "odds_btts_y", "odds_btts_n",
+    ]
+    for base_col in odds_base_columns:
+        pres_col = f"{base_col}_present"
+        if pres_col not in df.columns and base_col in df.columns:
+            df[pres_col] = (df[base_col] > 1.0).astype(int)
+
+    # Drop rows where any target is missing
     df = df.dropna(subset=TARGETS)
-    
+
-    # Fill feature NaNs with median/mean or 0
+    # Filter: at least MS odds must be present
-    print(f"   Original rows: {len(df)}")
-    
-    # Filter valid odds (at least ms_h > 1.0)
     df = df[df["odds_ms_h"] > 1.0].copy()
-    print(f"   Rows with valid odds: {len(df)}")
-    
-    X = df[FEATURES]
-    y_home = df["score_home"]
-    y_away = df["score_away"]
-    y_ht_home = df["ht_score_home"]
-    y_ht_away = df["ht_score_away"]
-    
-    # Train/Test Split
-    X_train, X_test, y_h_train, y_h_test, y_a_train, y_a_test, y_ht_h_train, y_ht_h_test, y_ht_a_train, y_ht_a_test = train_test_split(
-        X, y_home, y_away, y_ht_home, y_ht_away, test_size=0.2, random_state=42
-    )
-    
-    print(f"   Training set: {len(X_train)} matches")
-    print(f"   Test set: {len(X_test)} matches")
 
-    # --- HOME GOALS MODEL ---
+    # Ensure all features exist
-    print("\n🏠 Training Home Goals Model...")
+    missing = [f for f in FEATURES if f not in df.columns]
-    xgb_home = xgb.XGBRegressor(
+    if missing:
-        objective='reg:squarederror',
+        print(f"⚠️  Missing {len(missing)} features, filling with 0: {missing[:5]}...")
-        n_estimators=1000,
+        for f in missing:
-        learning_rate=0.01,
+            df[f] = 0
-        max_depth=5,
-        subsample=0.7,
-        colsample_bytree=0.7,
-        n_jobs=-1,
-        random_state=42,
-        early_stopping_rounds=50 # Configure here for newer XGBoost or remove if not supported in constructor (depends on version)
-    )
-    # Actually, to be safe across versions, let's remove early stopping for now or use validation set properly
-    # Using 'eval_set' without early_stopping_rounds just prints metrics
-    xgb_home = xgb.XGBRegressor(
-        objective='reg:squarederror',
-        n_estimators=1000,
-        learning_rate=0.01,
-        max_depth=5,
-        subsample=0.7,
-        colsample_bytree=0.7,
-        n_jobs=-1,
-        random_state=42
-    )
-    xgb_home.fit(X_train, y_h_train, eval_set=[(X_test, y_h_test)], verbose=False)
-    
-    home_preds = xgb_home.predict(X_test)
-    mae_home = mean_absolute_error(y_h_test, home_preds)
-    r2_home = r2_score(y_h_test, home_preds)
-    print(f"   ✅ FT Home MAE: {mae_home:.4f} goals")
-    print(f"   ✅ FT Home R2: {r2_home:.4f}")
 
-    # --- AWAY GOALS MODEL ---
+    return df
-    print("\n✈️ Training FT Away Goals Model...")
-    xgb_away = xgb.XGBRegressor(
-        objective='reg:squarederror',
-        n_estimators=1000,
-        learning_rate=0.01,
-        max_depth=5,
-        subsample=0.7,
-        colsample_bytree=0.7,
-        n_jobs=-1,
-        random_state=42
-    )
-    xgb_away.fit(X_train, y_a_train, eval_set=[(X_test, y_a_test)], verbose=False)
-    
-    away_preds = xgb_away.predict(X_test)
-    mae_away = mean_absolute_error(y_a_test, away_preds)
-    r2_away = r2_score(y_a_test, away_preds)
-    print(f"   ✅ FT Away MAE: {mae_away:.4f} goals")
-    print(f"   ✅ FT Away R2: {r2_away:.4f}")
-    
-    # --- HT HOME GOALS MODEL ---
-    print("\n🏠 Training HT Home Goals Model...")
-    xgb_ht_home = xgb.XGBRegressor(
-        objective='reg:squarederror',
-        n_estimators=1000,
-        learning_rate=0.01,
-        max_depth=5,
-        subsample=0.7,
-        colsample_bytree=0.7,
-        n_jobs=-1,
-        random_state=42
-    )
-    xgb_ht_home.fit(X_train, y_ht_h_train, eval_set=[(X_test, y_ht_h_test)], verbose=False)
-    
-    ht_home_preds = xgb_ht_home.predict(X_test)
-    mae_ht_home = mean_absolute_error(y_ht_h_test, ht_home_preds)
-    print(f"   ✅ HT Home MAE: {mae_ht_home:.4f} goals")
 
-    # --- HT AWAY GOALS MODEL ---
+
-    print("\n✈️ Training HT Away Goals Model...")
+def temporal_split(df: pd.DataFrame, train_ratio: float = 0.80):
-    xgb_ht_away = xgb.XGBRegressor(
+    """
-        objective='reg:squarederror',
+    Temporal train/test split by match date.
-        n_estimators=1000,
+    Ensures no future information leaks into training.
-        learning_rate=0.01,
+    """
-        max_depth=5,
+    if "match_date" in df.columns:
-        subsample=0.7,
+        df = df.sort_values("match_date").reset_index(drop=True)
-        colsample_bytree=0.7,
+    elif "round" in df.columns:
-        n_jobs=-1,
+        df = df.sort_values("round").reset_index(drop=True)
-        random_state=42
+
+    split_idx = int(len(df) * train_ratio)
+    return df.iloc[:split_idx].copy(), df.iloc[split_idx:].copy()
+
+
+def train_single_model(X_train, y_train, X_test, y_test, name: str):
+    """Train a single XGBoost regression model with early stopping."""
+    print(f"\n🏗️  Training {name} model...")
+
+    model = xgb.XGBRegressor(**XGB_PARAMS)
+    model.fit(
+        X_train, y_train,
+        eval_set=[(X_test, y_test)],
+        verbose=False,
     )
-    xgb_ht_away.fit(X_train, y_ht_a_train, eval_set=[(X_test, y_ht_a_test)], verbose=False)
+
-    
+    preds = model.predict(X_test)
-    ht_away_preds = xgb_ht_away.predict(X_test)
+
-    mae_ht_away = mean_absolute_error(y_ht_a_test, ht_away_preds)
+    mae = mean_absolute_error(y_test, preds)
-    print(f"   ✅ HT Away MAE: {mae_ht_away:.4f} goals")
+    rmse = np.sqrt(mean_squared_error(y_test, preds))
-    
+    r2 = r2_score(y_test, preds)
-    # --- EVALUATE EXACT SCORE ACCURACY (ROUNDED) ---
+
-    print("\n🎯 Exact FT Score Accuracy (Test Set):")
+    print(f"   MAE:  {mae:.4f} goals")
-    correct = 0
+    print(f"   RMSE: {rmse:.4f}")
-    close = 0 # Within 1 goal diff for both
+    print(f"   R²:   {r2:.4f}")
-    
+
-    for h_true, a_true, h_pred, a_pred in zip(y_h_test, y_a_test, home_preds, away_preds):
+    return model, {"mae": mae, "rmse": rmse, "r2": r2}
-        h_p = round(h_pred)
+
-        a_p = round(a_pred)
+
-        if h_p == h_true and a_p == a_true:
+def evaluate_combined(models: dict, X_test, y_test_dict: dict):
-            correct += 1
+    """Evaluate combined score accuracy (FT and HT)."""
-        if abs(h_p - h_true) <= 1 and abs(a_p - a_true) <= 1:
+    print("\n🎯 Combined Score Evaluation (Test Set):")
+
+    # FT Score
+    ft_h_preds = models["ft_home"].predict(X_test)
+    ft_a_preds = models["ft_away"].predict(X_test)
+
+    y_ft_h = y_test_dict["score_home"].values
+    y_ft_a = y_test_dict["score_away"].values
+
+    exact = 0
+    close = 0
+    result_correct = 0
+    total = len(X_test)
+
+    for h_true, a_true, h_pred, a_pred in zip(y_ft_h, y_ft_a, ft_h_preds, ft_a_preds):
+        hp = max(0, round(h_pred))
+        ap = max(0, round(a_pred))
+
+        # Exact score
+        if hp == h_true and ap == a_true:
+            exact += 1
+
+        # Close (±1 each)
+        if abs(hp - h_true) <= 1 and abs(ap - a_true) <= 1:
             close += 1
-            
+
-    acc = correct / len(X_test) * 100
+        # Result direction (1X2)
-    close_acc = close / len(X_test) * 100
+        true_result = 1 if h_true > a_true else (0 if h_true == a_true else -1)
-    print(f"   Exact Match: {acc:.2f}%")
+        pred_result = 1 if hp > ap else (0 if hp == ap else -1)
-    print(f"   Close Match (+/- 1 goal): {close_acc:.2f}%")
+        if true_result == pred_result:
+            result_correct += 1
+
+    print(f"   FT Exact Score:    {exact / total * 100:.2f}% ({exact}/{total})")
+    print(f"   FT Close (±1):    {close / total * 100:.2f}% ({close}/{total})")
+    print(f"   FT Result (1X2):  {result_correct / total * 100:.2f}% ({result_correct}/{total})")
+
+    # HT Score
+    ht_h_preds = models["ht_home"].predict(X_test)
+    ht_a_preds = models["ht_away"].predict(X_test)
+
+    y_ht_h = y_test_dict["ht_score_home"].values
+    y_ht_a = y_test_dict["ht_score_away"].values
+
+    ht_exact = 0
+    ht_total = len(X_test)
+
+    for h_true, a_true, h_pred, a_pred in zip(y_ht_h, y_ht_a, ht_h_preds, ht_a_preds):
+        hp = max(0, round(h_pred))
+        ap = max(0, round(a_pred))
+        if hp == h_true and ap == a_true:
+            ht_exact += 1
+
+    print(f"   HT Exact Score:   {ht_exact / ht_total * 100:.2f}% ({ht_exact}/{ht_total})")
+
+    return {
+        "ft_exact_pct": exact / total * 100,
+        "ft_close_pct": close / total * 100,
+        "ft_result_pct": result_correct / total * 100,
+        "ht_exact_pct": ht_exact / ht_total * 100,
+    }
+
+
+def train():
+    """Main training pipeline."""
+    print("🚀 Score Prediction Model Trainer (V25-Compatible)")
+    print(f"   Feature count: {len(FEATURES)}")
+    print("=" * 60)
+
+    # Load data
+    df = load_data()
+    print(f"   Total valid rows: {len(df)}")
+
+    # Temporal split
+    train_df, test_df = temporal_split(df)
+    print(f"   Training set: {len(train_df)} matches")
+    print(f"   Test set:     {len(test_df)} matches (temporally after training)")
+
+    X_train = train_df[FEATURES]
+    X_test = test_df[FEATURES]
+
+    # Train 4 models
+    models = {}
+    metrics = {}
+
+    for target_name, model_key in [
+        ("score_home", "ft_home"),
+        ("score_away", "ft_away"),
+        ("ht_score_home", "ht_home"),
+        ("ht_score_away", "ht_away"),
+    ]:
+        model, metric = train_single_model(
+            X_train, train_df[target_name],
+            X_test, test_df[target_name],
+            model_key,
+        )
+        models[model_key] = model
+        metrics[model_key] = metric
+
+    # Combined evaluation
+    y_test_dict = {t: test_df[t] for t in TARGETS}
+    combined = evaluate_combined(models, X_test, y_test_dict)
 
     # Save
-    print(f"\n💾 Saving models to {MODEL_PATH}...")
+    print(f"\n💾 Saving to {MODEL_PATH}...")
     model_data = {
-        "home_model": xgb_home,
+        "home_model": models["ft_home"],
-        "away_model": xgb_away,
+        "away_model": models["ft_away"],
-        "ht_home_model": xgb_ht_home,
+        "ht_home_model": models["ht_home"],
-        "ht_away_model": xgb_ht_away,
+        "ht_away_model": models["ht_away"],
         "features": FEATURES,
         "meta": {
-            "mae_home": mae_home,
+            **{f"{k}_{mk}": mv for k, m in metrics.items() for mk, mv in m.items()},
-            "mae_away": mae_away,
+            **combined,
-            "mae_ht_home": mae_ht_home,
+            "trained_at": datetime.now().isoformat(),
-            "mae_ht_away": mae_ht_away,
+            "feature_count": len(FEATURES),
-            "acc": acc
+            "train_size": len(train_df),
-        }
+            "test_size": len(test_df),
+        },
     }
+
     with open(MODEL_PATH, "wb") as f:
         pickle.dump(model_data, f)
-    
+
-    print("✅ Done.")
+    print("\n✅ Score model training complete!")
+    print(f"   Saved: {MODEL_PATH}")
+
 
 if __name__ == "__main__":
     train()

ai-engine/scripts/train_v25_pro.py

@@ -0,0 +1,553 @@
+"""
+V25 Pro Model Trainer — Optuna + Isotonic Calibration
+=====================================================
+Combines V25's 83 features + 12 markets + temporal split
+with Optuna hyperparameter tuning and Isotonic Regression calibration.
+
+Usage:
+  python scripts/train_v25_pro.py
+  python scripts/train_v25_pro.py --markets MS,OU25,BTTS  # specific markets
+  python scripts/train_v25_pro.py --trials 30              # fewer trials
+"""
+
+import os
+import sys
+import json
+import pickle
+import argparse
+import numpy as np
+import pandas as pd
+import xgboost as xgb
+import lightgbm as lgb
+import optuna
+from optuna.samplers import TPESampler
+from datetime import datetime
+from sklearn.metrics import accuracy_score, log_loss, classification_report
+from sklearn.isotonic import IsotonicRegression
+from sklearn.base import BaseEstimator, ClassifierMixin
+
+optuna.logging.set_verbosity(optuna.logging.WARNING)
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+AI_ENGINE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+DATA_PATH = os.path.join(AI_ENGINE_DIR, "data", "training_data.csv")
+MODELS_DIR = os.path.join(AI_ENGINE_DIR, "models", "v25")
+REPORTS_DIR = os.path.join(AI_ENGINE_DIR, "reports", "training_v25")
+
+os.makedirs(MODELS_DIR, exist_ok=True)
+os.makedirs(REPORTS_DIR, exist_ok=True)
+
+# ─── Feature Columns (95 features, NO target leakage) ───────────────
+FEATURES = [
+    # ELO (8)
+    "home_overall_elo", "away_overall_elo", "elo_diff",
+    "home_home_elo", "away_away_elo",
+    "home_form_elo", "away_form_elo", "form_elo_diff",
+    # Form (12)
+    "home_goals_avg", "home_conceded_avg",
+    "away_goals_avg", "away_conceded_avg",
+    "home_clean_sheet_rate", "away_clean_sheet_rate",
+    "home_scoring_rate", "away_scoring_rate",
+    "home_winning_streak", "away_winning_streak",
+    "home_unbeaten_streak", "away_unbeaten_streak",
+    # H2H (6)
+    "h2h_total_matches", "h2h_home_win_rate", "h2h_draw_rate",
+    "h2h_avg_goals", "h2h_btts_rate", "h2h_over25_rate",
+    # Team Stats (8)
+    "home_avg_possession", "away_avg_possession",
+    "home_avg_shots_on_target", "away_avg_shots_on_target",
+    "home_shot_conversion", "away_shot_conversion",
+    "home_avg_corners", "away_avg_corners",
+    # Odds (24 + 20 presence flags)
+    "odds_ms_h", "odds_ms_d", "odds_ms_a",
+    "implied_home", "implied_draw", "implied_away",
+    "odds_ht_ms_h", "odds_ht_ms_d", "odds_ht_ms_a",
+    "odds_ou05_o", "odds_ou05_u",
+    "odds_ou15_o", "odds_ou15_u",
+    "odds_ou25_o", "odds_ou25_u",
+    "odds_ou35_o", "odds_ou35_u",
+    "odds_ht_ou05_o", "odds_ht_ou05_u",
+    "odds_ht_ou15_o", "odds_ht_ou15_u",
+    "odds_btts_y", "odds_btts_n",
+    "odds_ms_h_present", "odds_ms_d_present", "odds_ms_a_present",
+    "odds_ht_ms_h_present", "odds_ht_ms_d_present", "odds_ht_ms_a_present",
+    "odds_ou05_o_present", "odds_ou05_u_present",
+    "odds_ou15_o_present", "odds_ou15_u_present",
+    "odds_ou25_o_present", "odds_ou25_u_present",
+    "odds_ou35_o_present", "odds_ou35_u_present",
+    "odds_ht_ou05_o_present", "odds_ht_ou05_u_present",
+    "odds_ht_ou15_o_present", "odds_ht_ou15_u_present",
+    "odds_btts_y_present", "odds_btts_n_present",
+    # League (4)
+    "home_xga", "away_xga",
+    "league_avg_goals", "league_zero_goal_rate",
+    # Upset Engine (4)
+    "upset_atmosphere", "upset_motivation", "upset_fatigue", "upset_potential",
+    # Referee Engine (5)
+    "referee_home_bias", "referee_avg_goals", "referee_cards_total",
+    "referee_avg_yellow", "referee_experience",
+    # Momentum (3)
+    "home_momentum_score", "away_momentum_score", "momentum_diff",
+    # Squad (9)
+    "home_squad_quality", "away_squad_quality", "squad_diff",
+    "home_key_players", "away_key_players",
+    "home_missing_impact", "away_missing_impact",
+    "home_goals_form", "away_goals_form",
+    # Player-Level Features (12)
+    "home_lineup_goals_per90", "away_lineup_goals_per90",
+    "home_lineup_assists_per90", "away_lineup_assists_per90",
+    "home_squad_continuity", "away_squad_continuity",
+    "home_top_scorer_form", "away_top_scorer_form",
+    "home_avg_player_exp", "away_avg_player_exp",
+    "home_goals_diversity", "away_goals_diversity",
+]
+
+MARKET_CONFIGS = [
+    {"target": "label_ms", "name": "MS", "num_class": 3},
+    {"target": "label_ou15", "name": "OU15", "num_class": 2},
+    {"target": "label_ou25", "name": "OU25", "num_class": 2},
+    {"target": "label_ou35", "name": "OU35", "num_class": 2},
+    {"target": "label_btts", "name": "BTTS", "num_class": 2},
+    {"target": "label_ht_result", "name": "HT_RESULT", "num_class": 3},
+    {"target": "label_ht_ou05", "name": "HT_OU05", "num_class": 2},
+    {"target": "label_ht_ou15", "name": "HT_OU15", "num_class": 2},
+    {"target": "label_ht_ft", "name": "HTFT", "num_class": 9},
+    {"target": "label_odd_even", "name": "ODD_EVEN", "num_class": 2},
+    {"target": "label_cards_ou45", "name": "CARDS_OU45", "num_class": 2},
+    {"target": "label_handicap_ms", "name": "HANDICAP_MS", "num_class": 3},
+]
+
+
+def load_data():
+    """Load and prepare training data."""
+    if not os.path.exists(DATA_PATH):
+        print(f"[ERROR] Data not found: {DATA_PATH}")
+        sys.exit(1)
+
+    print(f"[INFO] Loading {DATA_PATH}...")
+    df = pd.read_csv(DATA_PATH)
+
+    for col in FEATURES:
+        if col in df.columns:
+            df[col] = df[col].fillna(0)
+
+    # Derive odds presence flags for older CSVs
+    odds_flag_sources = {
+        "odds_ms_h_present": "odds_ms_h", "odds_ms_d_present": "odds_ms_d",
+        "odds_ms_a_present": "odds_ms_a", "odds_ht_ms_h_present": "odds_ht_ms_h",
+        "odds_ht_ms_d_present": "odds_ht_ms_d", "odds_ht_ms_a_present": "odds_ht_ms_a",
+        "odds_ou05_o_present": "odds_ou05_o", "odds_ou05_u_present": "odds_ou05_u",
+        "odds_ou15_o_present": "odds_ou15_o", "odds_ou15_u_present": "odds_ou15_u",
+        "odds_ou25_o_present": "odds_ou25_o", "odds_ou25_u_present": "odds_ou25_u",
+        "odds_ou35_o_present": "odds_ou35_o", "odds_ou35_u_present": "odds_ou35_u",
+        "odds_ht_ou05_o_present": "odds_ht_ou05_o", "odds_ht_ou05_u_present": "odds_ht_ou05_u",
+        "odds_ht_ou15_o_present": "odds_ht_ou15_o", "odds_ht_ou15_u_present": "odds_ht_ou15_u",
+        "odds_btts_y_present": "odds_btts_y", "odds_btts_n_present": "odds_btts_n",
+    }
+    for flag_col, odds_col in odds_flag_sources.items():
+        if flag_col not in df.columns:
+            df[flag_col] = (
+                pd.to_numeric(df.get(odds_col, 0), errors="coerce").fillna(0) > 1.01
+            ).astype(float)
+
+    print(f"[INFO] Shape: {df.shape}, Features: {len(FEATURES)}")
+    return df
+
+
+def temporal_split_4way(valid_df: pd.DataFrame):
+    """Chronological 60/15/10/15 split: train/val/cal/test."""
+    ordered = valid_df.sort_values("mst_utc").reset_index(drop=True)
+    n = len(ordered)
+    i1 = int(n * 0.60)
+    i2 = int(n * 0.75)
+    i3 = int(n * 0.85)
+
+    train = ordered.iloc[:i1].copy()
+    val = ordered.iloc[i1:i2].copy()
+    cal = ordered.iloc[i2:i3].copy()
+    test = ordered.iloc[i3:].copy()
+
+    return train, val, cal, test
+
+
+# ─── XGBoost Wrapper for sklearn CalibratedClassifierCV ─────────────
+class XGBWrapper(BaseEstimator, ClassifierMixin):
+    """Thin sklearn-compatible wrapper around xgb.train for Isotonic calibration."""
+
+    def __init__(self, params, num_boost_round=500):
+        self.params = params
+        self.num_boost_round = num_boost_round
+        self.model_ = None
+        self.classes_ = None
+
+    def fit(self, X, y, **kwargs):
+        self.classes_ = np.unique(y)
+        dtrain = xgb.DMatrix(X, label=y)
+        self.model_ = xgb.train(self.params, dtrain, num_boost_round=self.num_boost_round)
+        return self
+
+    def predict_proba(self, X):
+        dm = xgb.DMatrix(X)
+        probs = self.model_.predict(dm)
+        if len(probs.shape) == 1:
+            probs = np.column_stack([1 - probs, probs])
+        return probs
+
+    def predict(self, X):
+        return np.argmax(self.predict_proba(X), axis=1)
+
+
+# ─── Optuna Objectives ──────────────────────────────────────────────
+def xgb_objective(trial, X_train, y_train, X_val, y_val, num_class):
+    params = {
+        "objective": "multi:softprob" if num_class > 2 else "binary:logistic",
+        "eval_metric": "mlogloss" if num_class > 2 else "logloss",
+        "max_depth": trial.suggest_int("max_depth", 3, 8),
+        "eta": trial.suggest_float("eta", 0.01, 0.15, log=True),
+        "subsample": trial.suggest_float("subsample", 0.6, 1.0),
+        "colsample_bytree": trial.suggest_float("colsample_bytree", 0.5, 1.0),
+        "min_child_weight": trial.suggest_int("min_child_weight", 1, 10),
+        "gamma": trial.suggest_float("gamma", 1e-8, 1.0, log=True),
+        "reg_lambda": trial.suggest_float("reg_lambda", 1e-8, 10.0, log=True),
+        "reg_alpha": trial.suggest_float("reg_alpha", 1e-8, 1.0, log=True),
+        "n_jobs": 4,
+        "random_state": 42,
+    }
+    if num_class > 2:
+        params["num_class"] = num_class
+
+    dtrain = xgb.DMatrix(X_train, label=y_train)
+    dval = xgb.DMatrix(X_val, label=y_val)
+
+    model = xgb.train(
+        params, dtrain, num_boost_round=1000,
+        evals=[(dval, "val")], early_stopping_rounds=50, verbose_eval=False,
+    )
+
+    preds = model.predict(dval)
+    if len(preds.shape) == 1:
+        preds = np.column_stack([1 - preds, preds])
+
+    return log_loss(y_val, preds)
+
+
+def lgb_objective(trial, X_train, y_train, X_val, y_val, num_class):
+    params = {
+        "objective": "multiclass" if num_class > 2 else "binary",
+        "metric": "multi_logloss" if num_class > 2 else "binary_logloss",
+        "max_depth": trial.suggest_int("max_depth", 3, 8),
+        "learning_rate": trial.suggest_float("learning_rate", 0.01, 0.15, log=True),
+        "feature_fraction": trial.suggest_float("feature_fraction", 0.5, 1.0),
+        "bagging_fraction": trial.suggest_float("bagging_fraction", 0.6, 1.0),
+        "bagging_freq": trial.suggest_int("bagging_freq", 1, 7),
+        "min_child_samples": trial.suggest_int("min_child_samples", 5, 50),
+        "lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 1.0, log=True),
+        "lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0, log=True),
+        "n_jobs": 4, "random_state": 42, "verbose": -1,
+    }
+    if num_class > 2:
+        params["num_class"] = num_class
+
+    train_data = lgb.Dataset(X_train, label=y_train)
+    val_data = lgb.Dataset(X_val, label=y_val, reference=train_data)
+
+    model = lgb.train(
+        params, train_data, num_boost_round=1000,
+        valid_sets=[val_data], valid_names=["val"],
+        callbacks=[lgb.early_stopping(50), lgb.log_evaluation(0)],
+    )
+
+    preds = model.predict(X_val, num_iteration=model.best_iteration)
+    if len(preds.shape) == 1:
+        preds = np.column_stack([1 - preds, preds])
+
+    return log_loss(y_val, preds)
+
+
+# ─── Main Training Pipeline ─────────────────────────────────────────
+def train_market(df, target_col, market_name, num_class, n_trials):
+    """Full pipeline for one market: Optuna → Train → Calibrate → Evaluate."""
+    print(f"\n{'='*60}")
+    print(f"[MARKET] {market_name} (classes={num_class})")
+    print(f"{'='*60}")
+
+    valid_df = df[df[target_col].notna()].copy()
+    valid_df = valid_df[valid_df[target_col].astype(str) != ""].copy()
+    print(f"[INFO] Valid samples: {len(valid_df)}")
+
+    if len(valid_df) < 500:
+        print(f"[SKIP] Not enough data for {market_name}")
+        return None
+
+    available_features = [f for f in FEATURES if f in valid_df.columns]
+    print(f"[INFO] Features: {len(available_features)}/{len(FEATURES)}")
+
+    train_df, val_df, cal_df, test_df = temporal_split_4way(valid_df)
+    X_train = train_df[available_features].values
+    X_val = val_df[available_features].values
+    X_cal = cal_df[available_features].values
+    X_test = test_df[available_features].values
+    y_train = train_df[target_col].astype(int).values
+    y_val = val_df[target_col].astype(int).values
+    y_cal = cal_df[target_col].astype(int).values
+    y_test = test_df[target_col].astype(int).values
+
+    print(f"[INFO] Split: train={len(X_train)} val={len(X_val)} cal={len(X_cal)} test={len(X_test)}")
+
+    # ── Phase 1: Optuna XGBoost ──────────────────────────────────
+    print(f"\n[OPTUNA] XGBoost tuning ({n_trials} trials)...")
+    xgb_study = optuna.create_study(direction="minimize", sampler=TPESampler(seed=42))
+    xgb_study.optimize(
+        lambda trial: xgb_objective(trial, X_train, y_train, X_val, y_val, num_class),
+        n_trials=n_trials,
+    )
+    xgb_best = xgb_study.best_params
+    print(f"[OK] XGB best logloss: {xgb_study.best_value:.4f}")
+
+    # ── Phase 2: Optuna LightGBM ─────────────────────────────────
+    print(f"[OPTUNA] LightGBM tuning ({n_trials} trials)...")
+    lgb_study = optuna.create_study(direction="minimize", sampler=TPESampler(seed=42))
+    lgb_study.optimize(
+        lambda trial: lgb_objective(trial, X_train, y_train, X_val, y_val, num_class),
+        n_trials=n_trials,
+    )
+    lgb_best = lgb_study.best_params
+    print(f"[OK] LGB best logloss: {lgb_study.best_value:.4f}")
+
+    # ── Phase 3: Train final models with best params ─────────────
+    # XGBoost final
+    xgb_params = {
+        "objective": "multi:softprob" if num_class > 2 else "binary:logistic",
+        "eval_metric": "mlogloss" if num_class > 2 else "logloss",
+        "n_jobs": 4, "random_state": 42,
+        **{k: v for k, v in xgb_best.items()},
+    }
+    if num_class > 2:
+        xgb_params["num_class"] = num_class
+
+    dtrain = xgb.DMatrix(X_train, label=y_train)
+    dval = xgb.DMatrix(X_val, label=y_val)
+    xgb_model = xgb.train(
+        xgb_params, dtrain, num_boost_round=1500,
+        evals=[(dtrain, "train"), (dval, "val")],
+        early_stopping_rounds=80, verbose_eval=200,
+    )
+    print(f"[OK] XGB final: iter={xgb_model.best_iteration}, score={xgb_model.best_score:.4f}")
+
+    # LightGBM final
+    lgb_params = {
+        "objective": "multiclass" if num_class > 2 else "binary",
+        "metric": "multi_logloss" if num_class > 2 else "binary_logloss",
+        "n_jobs": 4, "random_state": 42, "verbose": -1,
+        **{k: v for k, v in lgb_best.items()},
+    }
+    if num_class > 2:
+        lgb_params["num_class"] = num_class
+
+    lgb_train_data = lgb.Dataset(X_train, label=y_train)
+    lgb_val_data = lgb.Dataset(X_val, label=y_val, reference=lgb_train_data)
+    lgb_model = lgb.train(
+        lgb_params, lgb_train_data, num_boost_round=1500,
+        valid_sets=[lgb_train_data, lgb_val_data],
+        valid_names=["train", "val"],
+        callbacks=[lgb.early_stopping(80), lgb.log_evaluation(200)],
+    )
+    print(f"[OK] LGB final: iter={lgb_model.best_iteration}")
+
+    # ── Phase 4: Isotonic Calibration on cal set ─────────────────
+    print("[CAL] Fitting Isotonic Regression (per-class)...")
+
+    # XGB calibration — manual IsotonicRegression per class
+    dcal = xgb.DMatrix(X_cal)
+    xgb_cal_raw = xgb_model.predict(dcal)
+    if len(xgb_cal_raw.shape) == 1:
+        xgb_cal_raw = np.column_stack([1 - xgb_cal_raw, xgb_cal_raw])
+
+    xgb_iso_calibrators = []
+    for cls_idx in range(num_class):
+        ir = IsotonicRegression(out_of_bounds="clip")
+        y_binary = (y_cal == cls_idx).astype(float)
+        ir.fit(xgb_cal_raw[:, cls_idx], y_binary)
+        xgb_iso_calibrators.append(ir)
+    print(f"[OK] XGB Isotonic calibrators fitted: {num_class} classes")
+
+    # LGB calibration — manual IsotonicRegression per class
+    lgb_cal_raw = lgb_model.predict(X_cal, num_iteration=lgb_model.best_iteration)
+    if len(lgb_cal_raw.shape) == 1:
+        lgb_cal_raw = np.column_stack([1 - lgb_cal_raw, lgb_cal_raw])
+
+    lgb_iso_calibrators = []
+    for cls_idx in range(num_class):
+        ir = IsotonicRegression(out_of_bounds="clip")
+        y_binary = (y_cal == cls_idx).astype(float)
+        ir.fit(lgb_cal_raw[:, cls_idx], y_binary)
+        lgb_iso_calibrators.append(ir)
+    print(f"[OK] LGB Isotonic calibrators fitted: {num_class} classes")
+
+    # ── Phase 5: Evaluate on test set ────────────────────────────
+    print("\n[EVAL] Test set evaluation...")
+    dtest = xgb.DMatrix(X_test)
+
+    # Raw XGB
+    xgb_raw_probs = xgb_model.predict(dtest)
+    if len(xgb_raw_probs.shape) == 1:
+        xgb_raw_probs = np.column_stack([1 - xgb_raw_probs, xgb_raw_probs])
+
+    # Calibrated XGB — apply isotonic per class + renormalize
+    xgb_cal_probs = np.column_stack([
+        xgb_iso_calibrators[i].predict(xgb_raw_probs[:, i]) for i in range(num_class)
+    ])
+    xgb_cal_probs = xgb_cal_probs / xgb_cal_probs.sum(axis=1, keepdims=True)
+
+    # Raw LGB
+    lgb_raw_probs = lgb_model.predict(X_test, num_iteration=lgb_model.best_iteration)
+    if len(lgb_raw_probs.shape) == 1:
+        lgb_raw_probs = np.column_stack([1 - lgb_raw_probs, lgb_raw_probs])
+
+    # Calibrated LGB — apply isotonic per class + renormalize
+    lgb_cal_probs = np.column_stack([
+        lgb_iso_calibrators[i].predict(lgb_raw_probs[:, i]) for i in range(num_class)
+    ])
+    lgb_cal_probs = lgb_cal_probs / lgb_cal_probs.sum(axis=1, keepdims=True)
+
+    # Ensembles
+    raw_ensemble = (xgb_raw_probs + lgb_raw_probs) / 2
+    cal_ensemble = (xgb_cal_probs + lgb_cal_probs) / 2
+
+    def _eval(probs, label):
+        preds = np.argmax(probs, axis=1)
+        acc = accuracy_score(y_test, preds)
+        ll = log_loss(y_test, probs)
+        print(f"  {label}: Acc={acc:.4f} LogLoss={ll:.4f}")
+        return {"accuracy": round(float(acc), 4), "logloss": round(float(ll), 4)}
+
+    m_xgb_raw = _eval(xgb_raw_probs, "XGB Raw")
+    m_xgb_cal = _eval(xgb_cal_probs, "XGB Calibrated")
+    m_lgb_raw = _eval(lgb_raw_probs, "LGB Raw")
+    m_lgb_cal = _eval(lgb_cal_probs, "LGB Calibrated")
+    m_ensemble = _eval(raw_ensemble, "Ensemble Raw")
+    m_cal_ensemble = _eval(cal_ensemble, "Ensemble Calibrated")
+
+    # Classification report for ensemble
+    ens_preds = np.argmax(raw_ensemble, axis=1)
+    print(f"\n[REPORT] Ensemble Classification Report:")
+    print(classification_report(y_test, ens_preds))
+
+    # ── Phase 6: Save models ─────────────────────────────────────
+    # Raw models (orchestrator compatible)
+    xgb_path = os.path.join(MODELS_DIR, f"xgb_v25_{market_name.lower()}.json")
+    xgb_model.save_model(xgb_path)
+    print(f"[SAVE] {xgb_path}")
+
+    lgb_path = os.path.join(MODELS_DIR, f"lgb_v25_{market_name.lower()}.txt")
+    lgb_model.save_model(lgb_path)
+    print(f"[SAVE] {lgb_path}")
+
+    # Isotonic calibrators (XGB + LGB)
+    xgb_cal_path = os.path.join(MODELS_DIR, f"iso_xgb_v25_{market_name.lower()}.pkl")
+    with open(xgb_cal_path, "wb") as f:
+        pickle.dump(xgb_iso_calibrators, f)
+    print(f"[SAVE] {xgb_cal_path}")
+
+    lgb_cal_path = os.path.join(MODELS_DIR, f"iso_lgb_v25_{market_name.lower()}.pkl")
+    with open(lgb_cal_path, "wb") as f:
+        pickle.dump(lgb_iso_calibrators, f)
+    print(f"[SAVE] {lgb_cal_path}")
+
+    return {
+        "market": market_name,
+        "samples": int(len(valid_df)),
+        "train": int(len(X_train)),
+        "val": int(len(X_val)),
+        "cal": int(len(X_cal)),
+        "test": int(len(X_test)),
+        "features_used": len(available_features),
+        "xgb_best_params": xgb_best,
+        "lgb_best_params": lgb_best,
+        "xgb_best_iteration": int(xgb_model.best_iteration),
+        "lgb_best_iteration": int(lgb_model.best_iteration),
+        "xgb_optuna_best_logloss": round(float(xgb_study.best_value), 4),
+        "lgb_optuna_best_logloss": round(float(lgb_study.best_value), 4),
+        "test_xgb_raw": m_xgb_raw,
+        "test_xgb_calibrated": m_xgb_cal,
+        "test_lgb_raw": m_lgb_raw,
+        "test_lgb_calibrated": m_lgb_cal,
+        "test_ensemble_raw": m_ensemble,
+        "test_ensemble_calibrated": m_cal_ensemble,
+    }
+
+
+def main():
+    parser = argparse.ArgumentParser(description="V25 Pro Trainer")
+    parser.add_argument("--markets", type=str, default=None,
+                        help="Comma-separated market names (e.g., MS,OU25,BTTS)")
+    parser.add_argument("--trials", type=int, default=50,
+                        help="Optuna trials per model per market")
+    args = parser.parse_args()
+
+    print("=" * 60)
+    print("V25 PRO — Optuna + Isotonic Calibration")
+    print("=" * 60)
+    print(f"[INFO] Started: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+    print(f"[INFO] Trials per model: {args.trials}")
+    print(f"[INFO] Total features: {len(FEATURES)}")
+
+    df = load_data()
+
+    configs = MARKET_CONFIGS
+    if args.markets:
+        selected = [m.strip().upper() for m in args.markets.split(",")]
+        configs = [c for c in configs if c["name"] in selected]
+        print(f"[INFO] Selected markets: {[c['name'] for c in configs]}")
+
+    all_metrics = {
+        "trained_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        "trainer": "v25_pro",
+        "optuna_trials": args.trials,
+        "total_features": len(FEATURES),
+        "markets": {},
+    }
+
+    for config in configs:
+        target = config["target"]
+        if target not in df.columns:
+            print(f"[SKIP] {config['name']}: missing target {target}")
+            continue
+
+        metrics = train_market(
+            df, target, config["name"], config["num_class"], args.trials,
+        )
+        if metrics:
+            all_metrics["markets"][config["name"]] = metrics
+
+    # Save feature list
+    feature_path = os.path.join(MODELS_DIR, "feature_cols.json")
+    with open(feature_path, "w") as f:
+        json.dump(FEATURES, f, indent=2)
+
+    # Save full report
+    report_path = os.path.join(REPORTS_DIR, "v25_pro_metrics.json")
+    with open(report_path, "w") as f:
+        json.dump(all_metrics, f, indent=2, default=str)
+    print(f"\n[SAVE] Report: {report_path}")
+
+    # Summary
+    print("\n" + "=" * 60)
+    print("[SUMMARY]")
+    print("=" * 60)
+    for name, m in all_metrics["markets"].items():
+        ens = m.get("test_ensemble_calibrated", m.get("test_ensemble_raw", {}))
+        acc = ens.get('accuracy', '?')
+        ll = ens.get('logloss', '?')
+        acc_s = f"{acc:.4f}" if isinstance(acc, float) else str(acc)
+        ll_s = f"{ll:.4f}" if isinstance(ll, float) else str(ll)
+        print(f"  {name:12s} | Acc={acc_s:>6s} | LL={ll_s:>6s} | "
+              f"XGB_iter={m.get('xgb_best_iteration','?')} LGB_iter={m.get('lgb_best_iteration','?')}")
+
+    print(f"\n[INFO] Completed: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+    print("[OK] V25 PRO Training Complete!")
+
+
+if __name__ == "__main__":
+    main()

ai-engine/scripts/train_v27_value_sniper.py

@@ -20,7 +20,7 @@ from sklearn.isotonic import IsotonicRegression
 warnings.filterwarnings("ignore")
 
 AI_DIR = Path(__file__).resolve().parent.parent
-DATA_CSV = AI_DIR / "data" / "training_data_v27.csv"
+DATA_CSV = AI_DIR / "data" / "training_data.csv"
 MODELS_DIR = AI_DIR / "models" / "v27"
 MODELS_DIR.mkdir(parents=True, exist_ok=True)
 
@@ -373,15 +373,52 @@ def main():
         print("\n" + "─"*65)
         print("  STAGE A.2: Fundamentals-Only O/U 2.5 Model")
         print("─"*65)
-        y_tr_ou = tr["label_ou25"].values
+        y_tr_ou = tr['label_ou25'].values
-        y_va_ou = va["label_ou25"].values
+        y_va_ou = va['label_ou25'].values
         mask_tr = ~np.isnan(y_tr_ou)
         mask_va = ~np.isnan(y_va_ou)
         if mask_tr.sum() > 1000:
             ou_models = train_fundamentals_model(
                 X_tr[mask_tr], y_tr_ou[mask_tr].astype(int),
                 X_va[mask_va], y_va_ou[mask_va].astype(int),
-                clean_feats, "ou25")
+                clean_feats, 'ou25')
+
+    # ── STAGE A.3: BTTS Model ──
+    btts_models = None
+    if 'label_btts' in tr.columns:
+        print('\n' + '─' * 65)
+        print('  STAGE A.3: Fundamentals-Only BTTS Model')
+        print('─' * 65)
+        y_tr_btts = tr['label_btts'].values
+        y_va_btts = va['label_btts'].values
+        mask_tr_btts = ~np.isnan(y_tr_btts)
+        mask_va_btts = ~np.isnan(y_va_btts)
+        if mask_tr_btts.sum() > 1000:
+            btts_models = train_fundamentals_model(
+                X_tr[mask_tr_btts], y_tr_btts[mask_tr_btts].astype(int),
+                X_va[mask_va_btts], y_va_btts[mask_va_btts].astype(int),
+                clean_feats, 'btts')
+
+            # Quick val accuracy
+            btts_probs = ensemble_predict(
+                btts_models,
+                X_va[mask_va_btts],
+                clean_feats,
+                n_class=2,
+            )
+            btts_acc = accuracy_score(
+                y_va_btts[mask_va_btts].astype(int),
+                btts_probs.argmax(1),
+            )
+            btts_ll = log_loss(
+                y_va_btts[mask_va_btts].astype(int),
+                btts_probs,
+            )
+            print(f'\n  BTTS Ensemble Val: acc={btts_acc:.4f}, logloss={btts_ll:.4f}')
+            # Compare with naive baseline (always predict majority class)
+            btts_majority = y_va_btts[mask_va_btts].astype(int).mean()
+            print(f'  BTTS baseline: {max(btts_majority, 1-btts_majority):.4f} (majority class)')
+            print(f'  Model vs baseline: {btts_acc - max(btts_majority, 1-btts_majority):+.4f}')
 
     # ── STAGE C: Backtest ──
     print("\n" + "─"*65)
@@ -422,13 +459,58 @@ def main():
 
     # OU25 backtest
     if ou_models:
-        print("\n  --- O/U 2.5 Backtest ---")
+        print('\n  --- O/U 2.5 Backtest ---')
         for edge in [0.05, 0.07, 0.10]:
-            r = backtest_value(ou_models, te, clean_feats, "ou25",
+            r = backtest_value(ou_models, te, clean_feats, 'ou25',
                                min_edge=edge, min_odds=1.50, max_odds=3.0,
                                use_kelly=True)
-            if r.get("total", 0) > 0:
+            if r.get('total', 0) > 0:
-                print_backtest(r, f"OU25 edge>{edge}")
+                print_backtest(r, f'OU25 edge>{edge}')
+
+    # BTTS backtest
+    if btts_models and 'label_btts' in te.columns:
+        print('\n  --- BTTS Backtest ---')
+        # Build BTTS odds for backtest
+        if 'odds_btts_y' in te.columns and 'odds_btts_n' in te.columns:
+            te_btts = te.copy()
+            te_btts['odds_btts_y'] = pd.to_numeric(
+                te_btts['odds_btts_y'], errors='coerce',
+            ).fillna(1.85)
+            te_btts['odds_btts_n'] = pd.to_numeric(
+                te_btts['odds_btts_n'], errors='coerce',
+            ).fillna(1.85)
+
+            for edge in [0.05, 0.07, 0.10]:
+                X_test = te_btts[clean_feats].values
+                probs = ensemble_predict(btts_models, X_test, clean_feats, 2)
+                y_btts = te_btts['label_btts'].values.astype(int)
+                odds_arr = te_btts[['odds_btts_n', 'odds_btts_y']].values
+                m_arr = 1 / odds_arr
+                impl = m_arr / m_arr.sum(axis=1, keepdims=True)
+
+                total_bets = 0
+                wins = 0
+                pnl = 0.0
+                for i in range(len(y_btts)):
+                    for cls in range(2):
+                        e = probs[i, cls] - impl[i, cls]
+                        o = odds_arr[i, cls]
+                        if e < edge or o < 1.50 or o > 3.0:
+                            continue
+                        total_bets += 1
+                        won = (y_btts[i] == cls)
+                        if won:
+                            wins += 1
+                            pnl += 10 * (o - 1)
+                        else:
+                            pnl -= 10
+                if total_bets > 0:
+                    roi = pnl / (total_bets * 10) * 100
+                    hit = wins / total_bets * 100
+                    print(
+                        f'    Edge>{edge:.2f}: {total_bets} bets, '
+                        f'hit={hit:.1f}%, ROI={roi:+.1f}%'
+                    )
 
     # ── Feature importance ──
     if "lgb" in ms_models:
@@ -452,25 +534,40 @@ def main():
 
     if ou_models:
         for name, m in ou_models.items():
-            p = MODELS_DIR / f"v27_ou25_{name}.pkl"
+            p = MODELS_DIR / f'v27_ou25_{name}.pkl'
-            with open(p, "wb") as f:
+            with open(p, 'wb') as f:
                 pickle.dump(m, f)
-            print(f"  ✓ {p.name}")
+            print(f'  ✓ {p.name}')
+
+    if btts_models:
+        for name, m in btts_models.items():
+            p = MODELS_DIR / f'v27_btts_{name}.pkl'
+            with open(p, 'wb') as f:
+                pickle.dump(m, f)
+            print(f'  ✓ {p.name}')
 
     meta = {
-        "version": "v27-pro", "trained_at": time.strftime("%Y-%m-%d %H:%M:%S"),
+        'version': 'v27-pro',
-        "approach": "odds-free fundamentals + value edge detection",
+        'trained_at': time.strftime('%Y-%m-%d %H:%M:%S'),
-        "feature_count": len(clean_feats),
+        'approach': 'odds-free fundamentals + value edge detection',
-        "total_samples": len(df),
+        'feature_count': len(clean_feats),
-        "val_acc": round(val_acc, 4), "val_ll": round(val_ll, 4),
+        'total_samples': len(df),
-        "best_config": {k: v for k, v in best_cfg.items() if k != "result"} if best_cfg else {},
+        'val_acc': round(val_acc, 4),
-        "markets": ["ms"] + (["ou25"] if ou_models else []),
+        'val_ll': round(val_ll, 4),
+        'best_config': {
+            k: v for k, v in best_cfg.items() if k != 'result'
+        } if best_cfg else {},
+        'markets': (
+            ['ms']
+            + (['ou25'] if ou_models else [])
+            + (['btts'] if btts_models else [])
+        ),
     }
-    with open(MODELS_DIR / "v27_metadata.json", "w") as f:
+    with open(MODELS_DIR / 'v27_metadata.json', 'w') as f:
         json.dump(meta, f, indent=2, default=str)
-    with open(MODELS_DIR / "v27_feature_cols.json", "w") as f:
+    with open(MODELS_DIR / 'v27_feature_cols.json', 'w') as f:
         json.dump(clean_feats, f, indent=2)
-    print(f"  ✓ metadata + feature_cols")
+    print(f'  ✓ metadata + feature_cols')
 
     print(f"\n  Total time: {(time.time()-t0)/60:.1f} min")
     print("  DONE!")

ai-engine/scripts/update_implied_odds.py

@@ -0,0 +1,307 @@
+"""
+Update Implied Odds in football_ai_features
+=============================================
+Populates implied_home, implied_draw, implied_away, implied_over25, implied_btts
+from real odds data in odd_categories + odd_selections tables.
+
+Also backfills form-based features (home_goals_avg_5, away_goals_avg_5, etc.)
+from recent match history.
+
+Usage:
+    python3 scripts/update_implied_odds.py
+"""
+
+import os
+import sys
+import time
+import psycopg2
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+def get_conn():
+    db_url = os.getenv("DATABASE_URL", "").split("?schema=")[0]
+    return psycopg2.connect(db_url)
+
+
+def update_implied_odds(conn):
+    """Update implied probabilities from real odds data."""
+    cur = conn.cursor()
+
+    print("📊 Phase 1: Updating implied odds from real market data...")
+    t0 = time.time()
+
+    # Step 1: Build odds lookup from odd_categories + odd_selections
+    print("  Loading odds data...")
+    cur.execute("""
+        SELECT oc.match_id, oc.name AS cat_name, os.name AS sel_name, os.odd_value
+        FROM odd_selections os
+        JOIN odd_categories oc ON os.odd_category_db_id = oc.db_id
+        WHERE os.odd_value IS NOT NULL
+          AND CAST(os.odd_value AS FLOAT) > 1.0
+    """)
+
+    odds_by_match = {}
+    row_count = 0
+    for match_id, cat_name, sel_name, odd_val in cur.fetchall():
+        try:
+            v = float(odd_val)
+            if v <= 1.0:
+                continue
+        except (ValueError, TypeError):
+            continue
+
+        if match_id not in odds_by_match:
+            odds_by_match[match_id] = {}
+
+        cat_lower = (cat_name or "").lower().strip()
+        sel_lower = (sel_name or "").lower().strip()
+
+        # Match Result (1X2)
+        if cat_lower == 'maç sonucu':
+            if sel_name == '1':
+                odds_by_match[match_id]['ms_h'] = v
+            elif sel_name in ('0', 'X'):
+                odds_by_match[match_id]['ms_d'] = v
+            elif sel_name == '2':
+                odds_by_match[match_id]['ms_a'] = v
+
+        # Over/Under 2.5
+        elif cat_lower == '2,5 alt/üst':
+            if 'üst' in sel_lower:
+                odds_by_match[match_id]['ou25_o'] = v
+            elif 'alt' in sel_lower:
+                odds_by_match[match_id]['ou25_u'] = v
+
+        # BTTS
+        elif cat_lower == 'karşılıklı gol':
+            if 'var' in sel_lower:
+                odds_by_match[match_id]['btts_y'] = v
+            elif 'yok' in sel_lower:
+                odds_by_match[match_id]['btts_n'] = v
+
+        row_count += 1
+
+    print(f"  Loaded odds for {len(odds_by_match)} matches ({row_count} selections) in {time.time()-t0:.1f}s")
+
+    # Step 2: Calculate implied probabilities and update
+    print("  Calculating implied probabilities...")
+
+    # Get all match_ids in football_ai_features
+    cur.execute("SELECT match_id FROM football_ai_features")
+    feature_match_ids = {row[0] for row in cur.fetchall()}
+
+    updated = 0
+    batch_size = 500
+    updates = []
+
+    for match_id in feature_match_ids:
+        odds = odds_by_match.get(match_id, {})
+        if not odds:
+            continue
+
+        # Implied MS probabilities (vig-free normalization)
+        ms_h = odds.get('ms_h', 0)
+        ms_d = odds.get('ms_d', 0)
+        ms_a = odds.get('ms_a', 0)
+
+        implied_home = 0.33
+        implied_draw = 0.33
+        implied_away = 0.33
+
+        if ms_h > 1.0 and ms_d > 1.0 and ms_a > 1.0:
+            raw_sum = (1 / ms_h) + (1 / ms_d) + (1 / ms_a)
+            if raw_sum > 0:
+                implied_home = round((1 / ms_h) / raw_sum, 4)
+                implied_draw = round((1 / ms_d) / raw_sum, 4)
+                implied_away = round((1 / ms_a) / raw_sum, 4)
+
+        # Implied OU25
+        ou25_o = odds.get('ou25_o', 0)
+        ou25_u = odds.get('ou25_u', 0)
+        implied_over25 = 0.50
+
+        if ou25_o > 1.0 and ou25_u > 1.0:
+            raw_sum = (1 / ou25_o) + (1 / ou25_u)
+            if raw_sum > 0:
+                implied_over25 = round((1 / ou25_o) / raw_sum, 4)
+
+        # Implied BTTS
+        btts_y = odds.get('btts_y', 0)
+        btts_n = odds.get('btts_n', 0)
+        implied_btts = 0.50
+
+        if btts_y > 1.0 and btts_n > 1.0:
+            raw_sum = (1 / btts_y) + (1 / btts_n)
+            if raw_sum > 0:
+                implied_btts = round((1 / btts_y) / raw_sum, 4)
+
+        # Only update if we have real data (not all defaults)
+        has_real_data = (ms_h > 1.0 or ou25_o > 1.0 or btts_y > 1.0)
+        if not has_real_data:
+            continue
+
+        updates.append((
+            implied_home, implied_draw, implied_away,
+            implied_over25, implied_btts, match_id
+        ))
+
+        if len(updates) >= batch_size:
+            cur.executemany("""
+                UPDATE football_ai_features
+                SET implied_home = %s,
+                    implied_draw = %s,
+                    implied_away = %s,
+                    implied_over25 = %s,
+                    implied_btts_yes = %s
+                WHERE match_id = %s
+            """, updates)
+            updated += len(updates)
+            updates = []
+
+    # Final batch
+    if updates:
+        cur.executemany("""
+            UPDATE football_ai_features
+            SET implied_home = %s,
+                implied_draw = %s,
+                implied_away = %s,
+                implied_over25 = %s,
+                implied_btts_yes = %s
+            WHERE match_id = %s
+        """, updates)
+        updated += len(updates)
+
+    conn.commit()
+    print(f"  ✅ Updated implied odds for {updated} matches in {time.time()-t0:.1f}s")
+    return updated
+
+
+def update_form_features(conn):
+    """Backfill form-based features (goals avg, clean sheet rate) from match history."""
+    cur = conn.cursor()
+
+    print("\n📊 Phase 2: Updating form-based features...")
+    t0 = time.time()
+
+    # Load all finished football matches ordered by time
+    print("  Loading match history...")
+    cur.execute("""
+        SELECT id, home_team_id, away_team_id, score_home, score_away, mst_utc
+        FROM matches
+        WHERE status = 'FT'
+          AND score_home IS NOT NULL
+          AND sport = 'football'
+        ORDER BY mst_utc ASC
+    """)
+
+    matches = cur.fetchall()
+    print(f"  Loaded {len(matches)} finished matches")
+
+    # Build team history incrementally
+    from collections import defaultdict
+    team_history = defaultdict(list)  # team_id -> [(goals_scored, goals_conceded)]
+
+    # Get all feature match IDs
+    cur.execute("SELECT match_id FROM football_ai_features")
+    feature_match_ids = {row[0] for row in cur.fetchall()}
+
+    updated = 0
+    batch_size = 500
+    updates = []
+
+    for match_id, home_id, away_id, score_home, score_away, mst_utc in matches:
+        # Calculate features BEFORE updating history (pre-match features)
+        if match_id in feature_match_ids:
+            h_hist = team_history[home_id][-5:]  # last 5
+            a_hist = team_history[away_id][-5:]
+
+            # Home team form
+            if h_hist:
+                h_goals_avg = sum(g for g, _ in h_hist) / len(h_hist)
+                h_conceded_avg = sum(c for _, c in h_hist) / len(h_hist)
+                h_cs_rate = sum(1 for _, c in h_hist if c == 0) / len(h_hist)
+                h_scoring_rate = sum(1 for g, _ in h_hist if g > 0) / len(h_hist)
+            else:
+                h_goals_avg, h_conceded_avg = 1.3, 1.2
+                h_cs_rate, h_scoring_rate = 0.25, 0.75
+
+            # Away team form
+            if a_hist:
+                a_goals_avg = sum(g for g, _ in a_hist) / len(a_hist)
+                a_conceded_avg = sum(c for _, c in a_hist) / len(a_hist)
+                a_cs_rate = sum(1 for _, c in a_hist if c == 0) / len(a_hist)
+                a_scoring_rate = sum(1 for g, _ in a_hist if g > 0) / len(a_hist)
+            else:
+                a_goals_avg, a_conceded_avg = 1.3, 1.2
+                a_cs_rate, a_scoring_rate = 0.25, 0.75
+
+            updates.append((
+                round(h_goals_avg, 3), round(h_conceded_avg, 3),
+                round(h_cs_rate, 3), round(h_scoring_rate, 3),
+                round(a_goals_avg, 3), round(a_conceded_avg, 3),
+                round(a_cs_rate, 3), round(a_scoring_rate, 3),
+                match_id
+            ))
+
+            if len(updates) >= batch_size:
+                cur.executemany("""
+                    UPDATE football_ai_features
+                    SET home_goals_avg_5 = %s,
+                        home_conceded_avg_5 = %s,
+                        home_clean_sheet_rate = %s,
+                        home_scoring_rate = %s,
+                        away_goals_avg_5 = %s,
+                        away_conceded_avg_5 = %s,
+                        away_clean_sheet_rate = %s,
+                        away_scoring_rate = %s
+                    WHERE match_id = %s
+                """, updates)
+                updated += len(updates)
+                updates = []
+
+        # Update history AFTER feature extraction (maintains pre-match invariant)
+        team_history[home_id].append((score_home, score_away))
+        team_history[away_id].append((score_away, score_home))
+
+    # Final batch
+    if updates:
+        cur.executemany("""
+            UPDATE football_ai_features
+            SET home_goals_avg_5 = %s,
+                home_conceded_avg_5 = %s,
+                home_clean_sheet_rate = %s,
+                home_scoring_rate = %s,
+                away_goals_avg_5 = %s,
+                away_conceded_avg_5 = %s,
+                away_clean_sheet_rate = %s,
+                away_scoring_rate = %s
+            WHERE match_id = %s
+        """, updates)
+        updated += len(updates)
+
+    conn.commit()
+    print(f"  ✅ Updated form features for {updated} matches in {time.time()-t0:.1f}s")
+    return updated
+
+
+def main():
+    print("🚀 Football AI Features — Implied Odds & Form Backfill")
+    print("=" * 60)
+
+    conn = get_conn()
+
+    try:
+        odds_updated = update_implied_odds(conn)
+        form_updated = update_form_features(conn)
+
+        print(f"\n✅ DONE!")
+        print(f"   Implied odds updated: {odds_updated} matches")
+        print(f"   Form features updated: {form_updated} matches")
+    finally:
+        conn.close()
+
+
+if __name__ == "__main__":
+    main()

ai-engine/services/betting_brain.py

@@ -165,6 +165,11 @@ class BettingBrain:
             score -= 18.0
             issues.append("base_model_not_playable")
 
+        is_value_sniper = bool(row.get("is_value_sniper"))
+        if is_value_sniper:
+            score += 35.0
+            positives.append("value_sniper_override")
+
         score += max(0.0, min(20.0, calibrated_conf * 0.22))
         score += max(-8.0, min(16.0, ev_edge * 45.0))
         score += max(0.0, min(14.0, play_score * 0.12))
@@ -178,13 +183,13 @@ class BettingBrain:
 
         if odds < self.MIN_ODDS:
             vetoes.append("odds_below_minimum")
-        if calibrated_conf < 38.0:
+        if calibrated_conf < 38.0 and not is_value_sniper:
             vetoes.append("calibrated_confidence_too_low")
-        if play_score < 50.0:
+        if play_score < 50.0 and not is_value_sniper:
             vetoes.append("play_score_too_low")
 
         if divergence is not None:
-            if divergence >= self.HARD_DIVERGENCE:
+            if divergence >= self.HARD_DIVERGENCE and not is_value_sniper:
                 score -= 42.0
                 vetoes.append("v25_v27_hard_disagreement")
             elif divergence >= self.SOFT_DIVERGENCE:
@@ -211,7 +216,7 @@ class BettingBrain:
             else:
                 score -= 16.0
                 issues.append("historical_sample_too_low")
-                if market == "DC":
+                if market == "DC" and not is_value_sniper:
                     vetoes.append("dc_without_historical_sample")
         elif market in {"MS", "DC", "OU25"}:
             score -= 10.0
@@ -227,20 +232,21 @@ class BettingBrain:
             and model_prob >= self.EXTREME_MODEL_PROB
             and model_gap >= self.EXTREME_GAP
             and not triple_is_value
+            and not is_value_sniper
         ):
             score -= 24.0
             vetoes.append("extreme_probability_without_evidence")
 
-        if market in {"HT", "HTFT", "OE"} and score < 86.0:
+        if market in {"HT", "HTFT", "OE"} and score < 86.0 and not is_value_sniper:
             vetoes.append("volatile_market_requires_exceptional_evidence")
 
         score = max(0.0, min(100.0, score))
         action = "BET"
         if vetoes:
             action = "REJECT"
-        elif score < self.MIN_WATCH_SCORE:
+        elif score < self.MIN_WATCH_SCORE and not is_value_sniper:
             action = "REJECT"
-        elif score < self.MIN_BET_SCORE:
+        elif score < self.MIN_BET_SCORE and not is_value_sniper:
             action = "WATCH"
 
         row["betting_brain"] = {
@@ -276,6 +282,7 @@ class BettingBrain:
         for source in ("main_pick", "value_pick"):
             item = package.get(source)
             if isinstance(item, dict) and item.get("market"):
+                # print(f"DEBUG: {source} is_value_sniper: {item.get('is_value_sniper')}")
                 rows[self._row_key(item)] = dict(item)
 
         for source in ("supporting_picks", "bet_summary"):
@@ -283,6 +290,7 @@ class BettingBrain:
                 if isinstance(item, dict) and item.get("market"):
                     key = self._row_key(item)
                     rows[key] = self._merge_row(rows.get(key), item)
+        
         return list(rows.values())
 
     @staticmethod

ai-engine/services/feature_enrichment.py

@@ -14,11 +14,40 @@ is missing or queries fail.
 
 from __future__ import annotations
 
+import unicodedata
 from typing import Any, Dict, Optional, Tuple
 
 from psycopg2.extras import RealDictCursor
 
 
+# ─── Turkish Name Normalization ──────────────────────────────────
+
+_TR_CHAR_MAP = str.maketrans(
+    'çÇğĞıİöÖşŞüÜâÂîÎûÛ',
+    'cCgGiIoOsSuUaAiIuU',
+)
+
+
+def _normalize_name(name: str) -> str:
+    """
+    Normalize a Turkish referee name for fuzzy matching.
+
+    Strips accents, lowercases, removes extra whitespace, and maps
+    Turkish-specific characters to their ASCII equivalents.
+    """
+    if not name:
+        return ''
+    # 1. Turkish-specific character mapping
+    normalized = name.translate(_TR_CHAR_MAP)
+    # 2. Unicode NFKD decomposition → strip combining marks
+    normalized = unicodedata.normalize('NFKD', normalized)
+    normalized = ''.join(
+        c for c in normalized if not unicodedata.combining(c)
+    )
+    # 3. Lowercase + collapse whitespace
+    return ' '.join(normalized.lower().split())
+
+
 class FeatureEnrichmentService:
     """Stateless service — all state comes from DB via cursor."""
 
@@ -380,34 +409,20 @@ class FeatureEnrichmentService:
         """
         Referee tendencies: home win bias, avg goals, card rates.
         Matches referee by name in match_officials (role_id=1 = Orta Hakem).
+
+        Uses Turkish-aware fuzzy matching as a fallback when exact name
+        lookup returns zero results.
         """
         if not referee_name:
             return dict(self._DEFAULT_REFEREE)
-        try:
+
-            # Get match IDs officiated by this referee
+        rows = self._query_referee_matches(cur, referee_name, before_date_ms, limit)
-            cur.execute(
+
-                """
+        # Fuzzy fallback: if exact match fails, try normalized name search
-                SELECT
+        if not rows:
-                    m.home_team_id,
+            rows = self._fuzzy_referee_lookup(
-                    m.score_home,
+                cur, referee_name, before_date_ms, limit,
-                    m.score_away,
-                    m.id AS match_id
-                FROM match_officials mo
-                JOIN matches m ON m.id = mo.match_id
-                WHERE mo.name = %s
-                  AND mo.role_id = 1
-                  AND m.status = 'FT'
-                  AND m.score_home IS NOT NULL
-                  AND m.score_away IS NOT NULL
-                  AND m.mst_utc < %s
-                ORDER BY m.mst_utc DESC
-                LIMIT %s
-                """,
-                (referee_name, before_date_ms, limit),
             )
-            rows = cur.fetchall()
-        except Exception:
-            return dict(self._DEFAULT_REFEREE)
 
         if not rows:
             return dict(self._DEFAULT_REFEREE)
@@ -459,6 +474,118 @@ class FeatureEnrichmentService:
             'experience': total,
         }
 
+    def _query_referee_matches(
+        self,
+        cur: RealDictCursor,
+        referee_name: str,
+        before_date_ms: int,
+        limit: int,
+    ) -> list:
+        """Exact-match referee lookup in match_officials."""
+        try:
+            cur.execute(
+                """
+                SELECT
+                    m.home_team_id,
+                    m.score_home,
+                    m.score_away,
+                    m.id AS match_id
+                FROM match_officials mo
+                JOIN matches m ON m.id = mo.match_id
+                WHERE mo.name = %s
+                  AND mo.role_id = 1
+                  AND m.status = 'FT'
+                  AND m.score_home IS NOT NULL
+                  AND m.score_away IS NOT NULL
+                  AND m.mst_utc < %s
+                ORDER BY m.mst_utc DESC
+                LIMIT %s
+                """,
+                (referee_name, before_date_ms, limit),
+            )
+            return cur.fetchall()
+        except Exception:
+            return []
+
+    def _fuzzy_referee_lookup(
+        self,
+        cur: RealDictCursor,
+        referee_name: str,
+        before_date_ms: int,
+        limit: int,
+    ) -> list:
+        """
+        Fuzzy referee lookup using Turkish name normalization.
+
+        Strategy: fetch recent distinct referee names from match_officials,
+        normalize both the query name and each candidate, and pick the
+        best match.  This handles common mismatches like:
+          - 'Hüseyin Göçek' vs 'Huseyin Gocek'
+          - 'Ali Palabıyık' vs 'Ali Palabiyik'
+          - Extra/missing middle initials
+        """
+        normalized_query = _normalize_name(referee_name)
+        if not normalized_query:
+            return []
+
+        try:
+            # Fetch candidate referee names (distinct, recent, role=1)
+            cur.execute(
+                """
+                SELECT DISTINCT mo.name
+                FROM match_officials mo
+                JOIN matches m ON m.id = mo.match_id
+                WHERE mo.role_id = 1
+                  AND m.status = 'FT'
+                  AND m.mst_utc < %s
+                ORDER BY mo.name
+                LIMIT 2000
+                """,
+                (before_date_ms,),
+            )
+            candidates = cur.fetchall()
+        except Exception:
+            return []
+
+        if not candidates:
+            return []
+
+        # Find best match by normalized name comparison
+        best_match: Optional[str] = None
+        best_score = 0.0
+
+        for cand_row in candidates:
+            cand_name = cand_row.get('name', '')
+            if not cand_name:
+                continue
+            normalized_cand = _normalize_name(cand_name)
+
+            # Exact normalized match
+            if normalized_cand == normalized_query:
+                best_match = cand_name
+                best_score = 1.0
+                break
+
+            # Substring containment (handles "First Last" vs "First M. Last")
+            if (
+                normalized_query in normalized_cand
+                or normalized_cand in normalized_query
+            ):
+                containment_score = min(
+                    len(normalized_query), len(normalized_cand)
+                ) / max(len(normalized_query), len(normalized_cand))
+                if containment_score > best_score and containment_score > 0.6:
+                    best_match = cand_name
+                    best_score = containment_score
+
+        if not best_match:
+            return []
+
+        # Re-query with the resolved name
+        return self._query_referee_matches(
+            cur, best_match, before_date_ms, limit,
+        )
+
     # ─── 5. League Averages ─────────────────────────────────────────
 
     def compute_league_averages(

ai-engine/services/match_commentary.py

@@ -0,0 +1,367 @@
+"""
+Match Commentary Generator
+===========================
+Generates human-readable Turkish commentary from the analysis package.
+Reads all engine signals (model, odds band, betting brain, triple value)
+and produces a clear, actionable summary for end users.
+
+No LLM required — fully template-based.
+"""
+
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional
+
+
+def generate_match_commentary(package: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Main entry point. Takes a full analysis package and returns a commentary dict.
+
+    Returns:
+        {
+            "action": "BET" | "WATCH" | "SKIP",
+            "headline": "...",
+            "summary": "...",
+            "notes": ["...", "..."],
+            "contradictions": ["...", "..."],
+            "confidence_label": "YÜKSEK" | "ORTA" | "DÜŞÜK" | "ÇOK DÜŞÜK"
+        }
+    """
+    match_info = package.get("match_info") or {}
+    home = match_info.get("home_team", "Ev Sahibi")
+    away = match_info.get("away_team", "Deplasman")
+    main_pick = package.get("main_pick") or {}
+    betting_brain = package.get("betting_brain") or {}
+    v27_engine = package.get("v27_engine") or {}
+    market_board = package.get("market_board") or {}
+    score_pred = package.get("score_prediction") or {}
+    risk = package.get("risk") or {}
+    data_quality = package.get("data_quality") or {}
+
+    # ── Determine action ──────────────────────────────────────────
+    brain_decision = str(betting_brain.get("decision") or "NO_BET").upper()
+    main_playable = bool(main_pick.get("playable"))
+    main_vetoed = bool((main_pick.get("upper_brain") or {}).get("veto"))
+    approved_count = int(betting_brain.get("approved_count", 0) or 0)
+
+    if main_playable and not main_vetoed and approved_count > 0:
+        action = "BET"
+    elif approved_count == 0 and brain_decision == "NO_BET":
+        action = "SKIP"
+    else:
+        action = "WATCH"
+
+    # ── Headline ──────────────────────────────────────────────────
+    headline = _build_headline(action, main_pick, home, away)
+
+    # ── Summary paragraph ─────────────────────────────────────────
+    summary = _build_summary(
+        action, main_pick, market_board, v27_engine,
+        score_pred, risk, data_quality, home, away,
+    )
+
+    # ── Quick notes ───────────────────────────────────────────────
+    notes = _build_notes(market_board, v27_engine, score_pred, risk, home, away)
+
+    # ── Contradiction detection ───────────────────────────────────
+    contradictions = _detect_contradictions(market_board, v27_engine, package)
+
+    # ── Overall confidence label ──────────────────────────────────
+    confidence_label = _overall_confidence_label(main_pick, data_quality)
+
+    return {
+        "action": action,
+        "headline": headline,
+        "summary": summary,
+        "notes": notes[:6],
+        "contradictions": contradictions[:4],
+        "confidence_label": confidence_label,
+    }
+
+
+# ═══════════════════════════════════════════════════════════════════════
+#  Headline
+# ═══════════════════════════════════════════════════════════════════════
+
+def _build_headline(
+    action: str,
+    main_pick: Dict[str, Any],
+    home: str,
+    away: str,
+) -> str:
+    if action == "BET":
+        market = main_pick.get("market", "")
+        pick = main_pick.get("pick", "")
+        odds = main_pick.get("odds", 0.0)
+        conf = main_pick.get("calibrated_confidence", main_pick.get("confidence", 0))
+        market_tr = _market_to_turkish(market, pick)
+        return f"🎯 {market_tr} önerisi — Oran: {odds}, Güven: %{conf:.0f}"
+
+    if action == "WATCH":
+        return f"👀 {home} vs {away} — İzlemeye değer sinyaller var"
+
+    return f"⚠️ {home} vs {away} — Şu an net bir fırsat görülmüyor"
+
+
+# ═══════════════════════════════════════════════════════════════════════
+#  Summary
+# ═══════════════════════════════════════════════════════════════════════
+
+def _build_summary(
+    action: str,
+    main_pick: Dict[str, Any],
+    market_board: Dict[str, Any],
+    v27_engine: Dict[str, Any],
+    score_pred: Dict[str, Any],
+    risk: Dict[str, Any],
+    data_quality: Dict[str, Any],
+    home: str,
+    away: str,
+) -> str:
+    parts: List[str] = []
+
+    # Who is the favourite?
+    ms_board = market_board.get("MS") or {}
+    ms_pick = ms_board.get("pick", "")
+    ms_conf = float(ms_board.get("confidence", 50) or 50)
+
+    if ms_pick == "1" and ms_conf > 45:
+        parts.append(f"{home} hafif favori görünüyor")
+    elif ms_pick == "1" and ms_conf > 55:
+        parts.append(f"{home} net favori")
+    elif ms_pick == "2" and ms_conf > 45:
+        parts.append(f"{away} hafif favori görünüyor")
+    elif ms_pick == "2" and ms_conf > 55:
+        parts.append(f"{away} net favori")
+    else:
+        parts.append("İki takım da birbirine yakın güçte")
+
+    # xG expectation
+    xg_home = float(score_pred.get("xg_home", 0) or 0)
+    xg_away = float(score_pred.get("xg_away", 0) or 0)
+    xg_total = xg_home + xg_away
+    if xg_total > 3.0:
+        parts.append(f"Gol beklentisi yüksek (toplam xG: {xg_total:.1f})")
+    elif xg_total < 2.0:
+        parts.append(f"Düşük gol beklentisi (toplam xG: {xg_total:.1f})")
+
+    # Consensus check
+    consensus = str(v27_engine.get("consensus") or "").upper()
+    if consensus == "AGREE":
+        parts.append("Model motorları aynı fikirde")
+    elif consensus == "DISAGREE":
+        parts.append("Model motorları farklı sonuçlara ulaşıyor — belirsizlik var")
+
+    # Action-specific
+    if action == "BET":
+        market_tr = _market_to_turkish(
+            main_pick.get("market", ""), main_pick.get("pick", "")
+        )
+        edge = float(main_pick.get("ev_edge", 0) or 0)
+        parts.append(
+            f"{market_tr} yönünde değer tespit edildi (EV edge: {edge:+.1%})"
+        )
+    elif action == "SKIP":
+        parts.append(
+            "Hiçbir markette piyasanın fiyatlamadığı bir avantaj görülmüyor"
+        )
+
+    # Risk
+    risk_level = str(risk.get("level") or "MEDIUM").upper()
+    if risk_level == "HIGH":
+        parts.append("⚠️ Risk seviyesi yüksek")
+    elif risk_level == "EXTREME":
+        parts.append("🔴 Çok yüksek risk — dikkatli olun")
+
+    # Data quality
+    quality_label = str(data_quality.get("label") or "MEDIUM").upper()
+    if quality_label == "LOW":
+        parts.append("Veri kalitesi düşük — tahminler daha az güvenilir")
+
+    return ". ".join(parts) + "."
+
+
+# ═══════════════════════════════════════════════════════════════════════
+#  Quick Notes
+# ═══════════════════════════════════════════════════════════════════════
+
+def _build_notes(
+    market_board: Dict[str, Any],
+    v27_engine: Dict[str, Any],
+    score_pred: Dict[str, Any],
+    risk: Dict[str, Any],
+    home: str,
+    away: str,
+) -> List[str]:
+    notes: List[str] = []
+    triple_value = v27_engine.get("triple_value") or {}
+    odds_band = v27_engine.get("odds_band") or {}
+
+    # MS note
+    ms = market_board.get("MS") or {}
+    ms_conf = float(ms.get("confidence", 0) or 0)
+    if ms_conf < 45:
+        notes.append("Maç sonucu belirsiz, net favori yok")
+    elif ms.get("pick") == "1":
+        notes.append(f"{home} favori ama oran değerli mi kontrol et")
+    elif ms.get("pick") == "2":
+        notes.append(f"{away} favori ama oran değerli mi kontrol et")
+
+    # OU25 note
+    ou25 = market_board.get("OU25") or {}
+    ou25_probs = ou25.get("probs") or {}
+    over_prob = float(ou25_probs.get("over", 0.5) or 0.5)
+    if over_prob > 0.58:
+        notes.append("2.5 Üst yönünde eğilim var")
+    elif over_prob < 0.42:
+        notes.append("2.5 Alt yönünde eğilim var")
+    else:
+        notes.append("2.5 Üst/Alt dengeli — kesin sinyal yok")
+
+    # BTTS note
+    btts = market_board.get("BTTS") or {}
+    btts_probs = btts.get("probs") or {}
+    btts_yes = float(btts_probs.get("yes", 0.5) or 0.5)
+    if btts_yes > 0.58:
+        notes.append("Her iki takımın da gol atması bekleniyor")
+    elif btts_yes < 0.42:
+        notes.append("KG olasılığı düşük")
+
+    # HT note
+    ht = market_board.get("HT") or {}
+    ht_pick = ht.get("pick", "")
+    ht_conf = float(ht.get("confidence", 0) or 0)
+    if ht_conf > 40 and ht_pick:
+        ht_label = {"1": f"İY {home}", "2": f"İY {away}", "X": "İY beraberlik"}.get(
+            ht_pick, f"İY {ht_pick}"
+        )
+        notes.append(f"{ht_label} yönünde hafif sinyal (%{ht_conf:.0f})")
+
+    # Risk warnings
+    warnings = risk.get("warnings") or []
+    for w in warnings[:2]:
+        notes.append(f"⚠️ {w}")
+
+    return notes
+
+
+# ═══════════════════════════════════════════════════════════════════════
+#  Contradiction Detection
+# ═══════════════════════════════════════════════════════════════════════
+
+def _detect_contradictions(
+    market_board: Dict[str, Any],
+    v27_engine: Dict[str, Any],
+    package: Dict[str, Any],
+) -> List[str]:
+    """
+    Detect cases where model prediction and odds band/triple value
+    point in opposite directions — the user's main complaint.
+    """
+    contradictions: List[str] = []
+    triple_value = v27_engine.get("triple_value") or {}
+    predictions = v27_engine.get("predictions") or {}
+
+    # MS contradiction: model says home but triple_value says away has value
+    ms_preds = predictions.get("ms") or {}
+    ms_home = float(ms_preds.get("home", 0) or 0)
+    ms_away = float(ms_preds.get("away", 0) or 0)
+    home_triple = triple_value.get("home") or {}
+    away_triple = triple_value.get("away") or {}
+
+    model_favours_home = ms_home > ms_away
+    away_is_value = bool(away_triple.get("is_value"))
+    home_is_value = bool(home_triple.get("is_value"))
+
+    if model_favours_home and away_is_value:
+        contradictions.append(
+            "Model ev sahibini favori görüyor ama oran bandı deplasmanda değer buluyor — "
+            "bu çelişki nedeniyle MS tahminine dikkatli yaklaş"
+        )
+    elif not model_favours_home and home_is_value:
+        contradictions.append(
+            "Model deplasmanı favori görüyor ama oran bandı ev sahibinde değer buluyor — "
+            "bu çelişki nedeniyle MS tahminine dikkatli yaklaş"
+        )
+
+    # HT contradiction
+    ht_board = market_board.get("HT") or {}
+    ht_pick = ht_board.get("pick", "")
+    ht_home_triple = triple_value.get("ht_home") or {}
+    ht_away_triple = triple_value.get("ht_away") or {}
+
+    if ht_pick == "1" and bool(ht_away_triple.get("is_value")):
+        contradictions.append(
+            "Model İY ev sahibi diyor ama oran bandı İY deplasmanda değer buluyor — "
+            "İY tahmini güvenilir değil"
+        )
+    elif ht_pick == "2" and bool(ht_home_triple.get("is_value")):
+        contradictions.append(
+            "Model İY deplasman diyor ama oran bandı İY ev sahibinde değer buluyor — "
+            "İY tahmini güvenilir değil"
+        )
+
+    # OU25 contradiction
+    ou25_board = market_board.get("OU25") or {}
+    ou25_pick = ou25_board.get("pick", "")
+    ou25_over_triple = triple_value.get("ou25_over") or {}
+    ou25_under_triple = triple_value.get("ou25_under") or {}
+
+    if ou25_pick == "Üst" and bool(ou25_under_triple.get("is_value")):
+        contradictions.append(
+            "Model 2.5 Üst diyor ama oran bandı 2.5 Alt'ta değer buluyor — çelişki var"
+        )
+    elif ou25_pick == "Alt" and bool(ou25_over_triple.get("is_value")):
+        contradictions.append(
+            "Model 2.5 Alt diyor ama oran bandı 2.5 Üst'te değer buluyor — çelişki var"
+        )
+
+    return contradictions
+
+
+# ═══════════════════════════════════════════════════════════════════════
+#  Helpers
+# ═══════════════════════════════════════════════════════════════════════
+
+def _overall_confidence_label(
+    main_pick: Dict[str, Any],
+    data_quality: Dict[str, Any],
+) -> str:
+    """Overall confidence label for the entire analysis."""
+    quality_score = float(data_quality.get("score", 0.5) or 0.5)
+    main_conf = float(
+        main_pick.get("calibrated_confidence", main_pick.get("confidence", 0)) or 0
+    )
+    main_playable = bool(main_pick.get("playable"))
+
+    if main_playable and main_conf >= 60 and quality_score >= 0.8:
+        return "YÜKSEK"
+    if main_playable and main_conf >= 45:
+        return "ORTA"
+    if main_conf >= 30:
+        return "DÜŞÜK"
+    return "ÇOK DÜŞÜK"
+
+
+_MARKET_TR_MAP = {
+    "MS": {"1": "Maç Sonucu Ev Sahibi", "2": "Maç Sonucu Deplasman", "X": "Beraberlik"},
+    "DC": {"1X": "Çifte Şans 1X", "X2": "Çifte Şans X2", "12": "Çifte Şans 12"},
+    "OU25": {"Üst": "2.5 Üst", "Alt": "2.5 Alt", "Over": "2.5 Üst", "Under": "2.5 Alt"},
+    "OU15": {"Üst": "1.5 Üst", "Alt": "1.5 Alt", "Over": "1.5 Üst", "Under": "1.5 Alt"},
+    "OU35": {"Üst": "3.5 Üst", "Alt": "3.5 Alt", "Over": "3.5 Üst", "Under": "3.5 Alt"},
+    "BTTS": {"KG Var": "Karşılıklı Gol Var", "KG Yok": "Karşılıklı Gol Yok",
+             "Yes": "Karşılıklı Gol Var", "No": "Karşılıklı Gol Yok"},
+    "HT": {"1": "İlk Yarı Ev Sahibi", "2": "İlk Yarı Deplasman", "X": "İlk Yarı Beraberlik"},
+    "HT_OU05": {"Üst": "İY 0.5 Üst", "Alt": "İY 0.5 Alt"},
+    "HT_OU15": {"Üst": "İY 1.5 Üst", "Alt": "İY 1.5 Alt"},
+    "OE": {"Tek": "Tek", "Çift": "Çift", "Odd": "Tek", "Even": "Çift"},
+    "CARDS": {"Üst": "Kart Üst", "Alt": "Kart Alt"},
+}
+
+
+def _market_to_turkish(market: str, pick: str) -> str:
+    market_map = _MARKET_TR_MAP.get(market, {})
+    result = market_map.get(pick)
+    if result:
+        return result
+    return f"{market} {pick}"

mds/SOCIAL_POSTER_MODULE.md

@@ -1,6 +1,6 @@
 # Social Poster Modülü — Otomatik Sosyal Medya Paylaşım Sistemi
 
-Son güncelleme: 1 Mart 2026
+Son güncelleme: 5 Mayıs 2026
 
 ---
 
@@ -13,11 +13,11 @@ Top liglerdeki maçların AI tahminlerini **otomatik olarak görselleştirip** I
 ## 2. Mimari Akış
 
 ```
-Cron (*/10 dk) → LiveMatch sorgusu (top_leagues.json filtresi)
+Cron (*/15 dk) → LiveMatch sorgusu (top_leagues.json filtresi)
   → AI Engine V20+ POST /v20plus/analyze/{match_id}
     → PredictionCardDto oluştur
-      → Node Canvas ile 1080x1920 PNG render
+      → Node Canvas ile futbol/basketbol 1080x1080 JPEG render
-        → Gemini ile Türkçe caption üret
+        → Ollama/Gemini ile Türkçe SEO uyumlu caption üret
           → Twitter / Facebook / Instagram API'ye paylaş
 ```
 
@@ -44,41 +44,46 @@ src/modules/social-poster/
 
 ### 4.1 SocialPosterService
 
-**Cron:** Her 10 dakikada bir çalışır. 25–40 dakika içinde başlayacak maçları `top_leagues.json` filtresiyle bulur.
+**Cron:** Her 15 dakikada bir çalışır. Varsayılan olarak 25–45 dakika içinde başlayacak futbol ve basketbol maçlarını `top_leagues.json` filtresiyle bulur.
+
+**Tekrar paylaşım koruması:** Başarılı platform paylaşımı alan maç ID'leri `storage/social-poster-posted.json` içinde son 500 kayıt olarak tutulur. Servis restart sonrası aynı maç tekrar paylaşılmaz.
 
 **Pipeline:** `predictAndPost(match)` → Tahmin al → Görsel üret → Caption üret → Paylaş
 
 **AI Engine İsteği:**
+
 ```typescript
 // POST — GET değil! AI Engine v20plus POST bekler.
-axios.post(`${aiEngineUrl}/v20plus/analyze/${matchId}`, null, { timeout: 30000 })
+axios.post(`${aiEngineUrl}/v20plus/analyze/${matchId}`, null, {
+  timeout: 30000,
+});
 ```
 
 **Veri Haritalandırma (V20+ → CardDto):**
 
-| V20+ Response Alanı | CardDto Alanı |
+| V20+ Response Alanı     | CardDto Alanı                                  |
-|---|---|
+| ----------------------- | ---------------------------------------------- |
-| `score_prediction.ht` | `htScore` (ör: "1-1") |
+| `score_prediction.ht`   | `htScore` (ör: "1-1")                          |
-| `score_prediction.ft` | `ftScore` (ör: "2-1") |
+| `score_prediction.ft`   | `ftScore` (ör: "2-1")                          |
-| `main_pick.confidence` | `scoreConfidence` (ör: 65) |
+| `main_pick.confidence`  | `scoreConfidence` (ör: 65)                     |
 | `bet_summary[]` (array) | `topPicks[]` (ilk 3, confidence'a göre sıralı) |
-| `risk.level` | `riskLevel` (LOW/MEDIUM/HIGH/EXTREME) |
+| `risk.level`            | `riskLevel` (LOW/MEDIUM/HIGH/EXTREME)          |
-| `match_info.home_team` | `homeTeam` (fallback) |
+| `match_info.home_team`  | `homeTeam` (fallback)                          |
 
 **Bet Summary Market Kodları:**
 
-| Kod | Türkçe | English |
+| Kod     | Türkçe          | English           |
-|---|---|---|
+| ------- | --------------- | ----------------- |
-| MS | Maç Sonucu | Match Result |
+| MS      | Maç Sonucu      | Match Result      |
-| OU15 | Üst 1.5 Gol | Over 1.5 |
+| OU15    | Üst 1.5 Gol     | Over 1.5          |
-| OU25 | Üst 2.5 Gol | Over 2.5 |
+| OU25    | Üst 2.5 Gol     | Over 2.5          |
-| OU35 | Üst 3.5 Gol | Over 3.5 |
+| OU35    | Üst 3.5 Gol     | Over 3.5          |
-| BTTS | Karşılıklı Gol | Both Teams Score |
+| BTTS    | Karşılıklı Gol  | Both Teams Score  |
-| DC | Çifte Şans | Double Chance |
+| DC      | Çifte Şans      | Double Chance     |
-| HT | İlk Yarı Sonucu | Half Time Result |
+| HT      | İlk Yarı Sonucu | Half Time Result  |
-| HT_OU05 | İY 0.5 Üst/Alt | HT Over/Under 0.5 |
+| HT_OU05 | İY 0.5 Üst/Alt  | HT Over/Under 0.5 |
-| OE | Tek/Çift | Odd/Even |
+| OE      | Tek/Çift        | Odd/Even          |
-| HTFT | İY/MS | HT/FT |
+| HTFT    | İY/MS           | HT/FT             |
 
 ### 4.2 ImageRendererService
 
@@ -89,6 +94,7 @@ axios.post(`${aiEngineUrl}/v20plus/analyze/${matchId}`, null, { timeout: 30000 }
 **Boyut:** 1080×1920 px (Instagram Story / Reels uyumlu)
 
 **Özellikler:**
+
 - Koyu gradient arka plan (#0a0e27 → #1a1040 → #0d1b2a)
 - Lig adı + tarih başlık satırı
 - Takım logoları (200×200px) — `public/uploads/teams/` altından okunur
@@ -100,6 +106,7 @@ axios.post(`${aiEngineUrl}/v20plus/analyze/${matchId}`, null, { timeout: 30000 }
 - Alt bilgi: "⚡ AI Powered by SuggestBet"
 
 **Logo Çözümleme:**
+
 ```
 1. Yerel dosya varsa → public/uploads/teams/xxx.png oku
 2. URL http ile başlıyorsa → HTTP ile indir
@@ -118,10 +125,10 @@ Gemini API kullanarak maç verisi JSON'ından Türkçe post metni üretir.
 
 ## 5. API Endpointleri
 
-| Method | Path | Auth | Açıklama |
+| Method | Path                                  | Auth    | Açıklama                                             |
-|---|---|---|---|
+| ------ | ------------------------------------- | ------- | ---------------------------------------------------- |
-| GET | `/api/social-poster/preview/:matchId` | @Public | Sadece görsel üret + caption üret (paylaşma) |
+| GET    | `/api/social-poster/preview/:matchId` | @Public | Sadece görsel üret + caption üret (paylaşma)         |
-| POST | `/api/social-poster/post/:matchId` | @Public | Görsel üret + caption üret + tüm platformlara paylaş |
+| POST   | `/api/social-poster/post/:matchId`    | @Public | Görsel üret + caption üret + tüm platformlara paylaş |
 
 > **Not:** Test endpointleri `@Public()` dekoratörüyle auth bypass edilmiştir. Production'da kaldırılmalı veya admin-only yapılmalıdır.
 
@@ -129,14 +136,20 @@ Gemini API kullanarak maç verisi JSON'ından Türkçe post metni üretir.
 
 ## 6. Environment Değişkenleri
 
-| Key | Zorunlu | Varsayılan | Açıklama |
+| Key                                           | Zorunlu | Varsayılan               | Açıklama                                                             |
-|---|---|---|---|
+| --------------------------------------------- | ------- | ------------------------ | -------------------------------------------------------------------- |
-| `AI_ENGINE_URL` | ✅ | `http://localhost:8000` | AI Engine base URL |
+| `AI_ENGINE_URL`                               | ✅      | `http://localhost:8000`  | AI Engine base URL                                                   |
-| `APP_BASE_URL` | ✅ | `http://localhost:3000` | Logo URL çözümleme için |
+| `APP_BASE_URL`                                | ✅      | `http://localhost:3000`  | Meta'nın çekebileceği public görsel URL'i ve logo URL çözümleme için |
-| `SOCIAL_POSTER_ENABLED` | ❌ | `false` | Cron job'ı aktif/pasif |
+| `SOCIAL_POSTER_ENABLED`                       | ❌      | `false`                  | Cron job'ı aktif/pasif                                               |
-| `GOOGLE_API_KEY` | ❌ | — | Gemini caption için |
+| `SOCIAL_POSTER_SPORTS`                        | ❌      | `football,basketball`    | Otomatik paylaşılacak sporlar                                        |
-| Twitter API keys | ❌ | — | Twitter paylaşım için |
+| `SOCIAL_POSTER_WINDOW_MIN`                    | ❌      | `25`                     | Başlama zaman penceresi alt sınırı (dakika)                          |
-| Meta API keys | ❌ | — | FB/IG paylaşım için |
+| `SOCIAL_POSTER_WINDOW_MAX`                    | ❌      | `45`                     | Başlama zaman penceresi üst sınırı (dakika)                          |
+| `OLLAMA_BASE_URL`                             | ❌      | `http://localhost:11434` | Lokal LLM endpoint'i                                                 |
+| `OLLAMA_MODEL` / `SOCIAL_POSTER_OLLAMA_MODEL` | ❌      | —                        | Caption üretiminde kullanılacak lokal model                          |
+| `GOOGLE_API_KEY`                              | ❌      | —                        | Gemini caption için                                                  |
+| Twitter API keys                              | ❌      | —                        | X medya upload + `/2/tweets` paylaşımı için OAuth 1.0a user context  |
+| `META_GRAPH_API_VERSION`                      | ❌      | `v25.0`                  | Meta Graph API sürümü                                                |
+| Meta API keys                                 | ❌      | —                        | FB/IG paylaşım için                                                  |
 
 ---
 
@@ -144,9 +157,9 @@ Gemini API kullanarak maç verisi JSON'ından Türkçe post metni üretir.
 
 ```json
 {
-  "canvas": "^2.x",       // Node Canvas — görsel üretimi
+  "canvas": "^2.x", // Node Canvas — görsel üretimi
-  "axios": "^1.x",        // HTTP istekleri (AI Engine + logo indirme)
+  "axios": "^1.x", // HTTP istekleri (AI Engine + logo indirme)
-  "@nestjs/schedule": "*"  // Cron job desteği
+  "@nestjs/schedule": "*" // Cron job desteği
 }
 ```
 
@@ -165,10 +178,10 @@ RUN apk add --no-cache cairo-dev pango-dev jpeg-dev giflib-dev librsvg-dev
 
 ### Port Yönetimi
 
-| Servis | Port |
+| Servis         | Port                                        |
-|---|---|
+| -------------- | ------------------------------------------- |
-| NestJS Backend | 3000 (production: 150X) |
+| NestJS Backend | 3000 (production: 150X)                     |
-| AI Engine | 8000 (dev: 8005 — Windows port kısıtlaması) |
+| AI Engine      | 8000 (dev: 8005 — Windows port kısıtlaması) |
 
 ### Dosya Sistemi
 
@@ -182,9 +195,9 @@ public/
 
 ## 9. Bilinen Sorunlar & Çözümler
 
-| Sorun | Sebep | Çözüm |
+| Sorun                                   | Sebep                                | Çözüm                                     |
-|---|---|---|
+| --------------------------------------- | ------------------------------------ | ----------------------------------------- |
-| `WinError 10013` port erişim hatası | Windows Hyper-V port rezervasyonu | Farklı port kullan (8005) |
+| `WinError 10013` port erişim hatası     | Windows Hyper-V port rezervasyonu    | Farklı port kullan (8005)                 |
-| `Invalid prisma.liveMatch.findUnique()` | Prisma client eskimiş | `npx prisma generate` çalıştır |
+| `Invalid prisma.liveMatch.findUnique()` | Prisma client eskimiş                | `npx prisma generate` çalıştır            |
-| `405 Method Not Allowed` AI Engine | GET yerine POST gerekiyor | `axios.post()` kullan |
+| `405 Method Not Allowed` AI Engine      | GET yerine POST gerekiyor            | `axios.post()` kullan                     |
-| Logolar görünmüyor (lokal dev) | Logo dosyaları sunucuda, lokalde yok | Deploy'da çalışır, lokal'de graceful skip |
+| Logolar görünmüyor (lokal dev)          | Logo dosyaları sunucuda, lokalde yok | Deploy'da çalışır, lokal'de graceful skip |

mds/suggest-bet-platform.postman_collection.json

@@ -829,6 +829,311 @@
             }
           ]
         },
+        {
+          "name": "AiProxy",
+          "item": [
+            {
+              "name": "DELETE /api/ai-engine/{path}",
+              "request": {
+                "method": "DELETE",
+                "header": [
+                  {
+                    "key": "Content-Type",
+                    "value": "application/json"
+                  }
+                ],
+                "description": "",
+                "url": {
+                  "raw": "{{beBaseUrl}}/api/ai-engine/{{path}}",
+                  "host": [
+                    "{{beBaseUrl}}"
+                  ],
+                  "path": [
+                    "api",
+                    "ai-engine",
+                    "{path}"
+                  ],
+                  "query": []
+                }
+              },
+              "response": [
+                {
+                  "name": "DELETE /api/ai-engine/{path} - 200",
+                  "originalRequest": {
+                    "method": "DELETE",
+                    "header": [
+                      {
+                        "key": "Content-Type",
+                        "value": "application/json"
+                      }
+                    ],
+                    "url": {
+                      "raw": "{{beBaseUrl}}/api/ai-engine/{{path}}",
+                      "host": [
+                        "{{beBaseUrl}}"
+                      ],
+                      "path": [
+                        "api",
+                        "ai-engine",
+                        "{path}"
+                      ]
+                    }
+                  },
+                  "status": "",
+                  "code": 200,
+                  "_postman_previewlanguage": "json",
+                  "header": [
+                    {
+                      "key": "Content-Type",
+                      "value": "application/json"
+                    }
+                  ],
+                  "body": "{}"
+                }
+              ]
+            },
+            {
+              "name": "GET /api/ai-engine/{path}",
+              "request": {
+                "method": "GET",
+                "header": [
+                  {
+                    "key": "Content-Type",
+                    "value": "application/json"
+                  }
+                ],
+                "description": "",
+                "url": {
+                  "raw": "{{beBaseUrl}}/api/ai-engine/{{path}}",
+                  "host": [
+                    "{{beBaseUrl}}"
+                  ],
+                  "path": [
+                    "api",
+                    "ai-engine",
+                    "{path}"
+                  ],
+                  "query": []
+                }
+              },
+              "response": [
+                {
+                  "name": "GET /api/ai-engine/{path} - 200",
+                  "originalRequest": {
+                    "method": "GET",
+                    "header": [
+                      {
+                        "key": "Content-Type",
+                        "value": "application/json"
+                      }
+                    ],
+                    "url": {
+                      "raw": "{{beBaseUrl}}/api/ai-engine/{{path}}",
+                      "host": [
+                        "{{beBaseUrl}}"
+                      ],
+                      "path": [
+                        "api",
+                        "ai-engine",
+                        "{path}"
+                      ]
+                    }
+                  },
+                  "status": "",
+                  "code": 200,
+                  "_postman_previewlanguage": "json",
+                  "header": [
+                    {
+                      "key": "Content-Type",
+                      "value": "application/json"
+                    }
+                  ],
+                  "body": "{}"
+                }
+              ]
+            },
+            {
+              "name": "PATCH /api/ai-engine/{path}",
+              "request": {
+                "method": "PATCH",
+                "header": [
+                  {
+                    "key": "Content-Type",
+                    "value": "application/json"
+                  }
+                ],
+                "description": "",
+                "url": {
+                  "raw": "{{beBaseUrl}}/api/ai-engine/{{path}}",
+                  "host": [
+                    "{{beBaseUrl}}"
+                  ],
+                  "path": [
+                    "api",
+                    "ai-engine",
+                    "{path}"
+                  ],
+                  "query": []
+                }
+              },
+              "response": [
+                {
+                  "name": "PATCH /api/ai-engine/{path} - 200",
+                  "originalRequest": {
+                    "method": "PATCH",
+                    "header": [
+                      {
+                        "key": "Content-Type",
+                        "value": "application/json"
+                      }
+                    ],
+                    "url": {
+                      "raw": "{{beBaseUrl}}/api/ai-engine/{{path}}",
+                      "host": [
+                        "{{beBaseUrl}}"
+                      ],
+                      "path": [
+                        "api",
+                        "ai-engine",
+                        "{path}"
+                      ]
+                    }
+                  },
+                  "status": "",
+                  "code": 200,
+                  "_postman_previewlanguage": "json",
+                  "header": [
+                    {
+                      "key": "Content-Type",
+                      "value": "application/json"
+                    }
+                  ],
+                  "body": "{}"
+                }
+              ]
+            },
+            {
+              "name": "POST /api/ai-engine/{path}",
+              "request": {
+                "method": "POST",
+                "header": [
+                  {
+                    "key": "Content-Type",
+                    "value": "application/json"
+                  }
+                ],
+                "description": "",
+                "url": {
+                  "raw": "{{beBaseUrl}}/api/ai-engine/{{path}}",
+                  "host": [
+                    "{{beBaseUrl}}"
+                  ],
+                  "path": [
+                    "api",
+                    "ai-engine",
+                    "{path}"
+                  ],
+                  "query": []
+                }
+              },
+              "response": [
+                {
+                  "name": "POST /api/ai-engine/{path} - 200",
+                  "originalRequest": {
+                    "method": "POST",
+                    "header": [
+                      {
+                        "key": "Content-Type",
+                        "value": "application/json"
+                      }
+                    ],
+                    "url": {
+                      "raw": "{{beBaseUrl}}/api/ai-engine/{{path}}",
+                      "host": [
+                        "{{beBaseUrl}}"
+                      ],
+                      "path": [
+                        "api",
+                        "ai-engine",
+                        "{path}"
+                      ]
+                    }
+                  },
+                  "status": "",
+                  "code": 200,
+                  "_postman_previewlanguage": "json",
+                  "header": [
+                    {
+                      "key": "Content-Type",
+                      "value": "application/json"
+                    }
+                  ],
+                  "body": "{}"
+                }
+              ]
+            },
+            {
+              "name": "PUT /api/ai-engine/{path}",
+              "request": {
+                "method": "PUT",
+                "header": [
+                  {
+                    "key": "Content-Type",
+                    "value": "application/json"
+                  }
+                ],
+                "description": "",
+                "url": {
+                  "raw": "{{beBaseUrl}}/api/ai-engine/{{path}}",
+                  "host": [
+                    "{{beBaseUrl}}"
+                  ],
+                  "path": [
+                    "api",
+                    "ai-engine",
+                    "{path}"
+                  ],
+                  "query": []
+                }
+              },
+              "response": [
+                {
+                  "name": "PUT /api/ai-engine/{path} - 200",
+                  "originalRequest": {
+                    "method": "PUT",
+                    "header": [
+                      {
+                        "key": "Content-Type",
+                        "value": "application/json"
+                      }
+                    ],
+                    "url": {
+                      "raw": "{{beBaseUrl}}/api/ai-engine/{{path}}",
+                      "host": [
+                        "{{beBaseUrl}}"
+                      ],
+                      "path": [
+                        "api",
+                        "ai-engine",
+                        "{path}"
+                      ]
+                    }
+                  },
+                  "status": "",
+                  "code": 200,
+                  "_postman_previewlanguage": "json",
+                  "header": [
+                    {
+                      "key": "Content-Type",
+                      "value": "application/json"
+                    }
+                  ],
+                  "body": "{}"
+                }
+              ]
+            }
+          ]
+        },
         {
           "name": "Analysis",
           "item": [

package-lock.json

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       "integrity": "sha512-39nnKffWz8xN1BU/2c79n9nB9HDzo0niYUqx6xyqUnyoAnQyyWpOTdZEeiCch8BBu515t4wp9ZmgVfVhn9EBpw==",
       "dev": true,
+      "peer": true,
       "engines": {
         "node": ">=4.0"
       }
@@ -13348,13 +13320,15 @@
       "version": "1.0.0",
       "resolved": "https://registry.npmjs.org/json-schema-traverse/-/json-schema-traverse-1.0.0.tgz",
       "integrity": "sha512-NM8/P9n3XjXhIZn1lLhkFaACTOURQXjWhV4BA/RnOv8xvgqtqpAX9IO4mRQxSx1Rlo4tqzeqb0sOlruaOy3dug==",
-      "dev": true
+      "dev": true,
+      "peer": true
     },
     "node_modules/webpack/node_modules/mime-db": {
       "version": "1.52.0",
       "resolved": "https://registry.npmjs.org/mime-db/-/mime-db-1.52.0.tgz",
       "integrity": "sha512-sPU4uV7dYlvtWJxwwxHD0PuihVNiE7TyAbQ5SWxDCB9mUYvOgroQOwYQQOKPJ8CIbE+1ETVlOoK1UC2nU3gYvg==",
       "dev": true,
+      "peer": true,
       "engines": {
         "node": ">= 0.6"
       }
@@ -13364,6 +13338,7 @@
       "resolved": "https://registry.npmjs.org/mime-types/-/mime-types-2.1.35.tgz",
       "integrity": "sha512-ZDY+bPm5zTTF+YpCrAU9nK0UgICYPT0QtT1NZWFv4s++TNkcgVaT0g6+4R2uI4MjQjzysHB1zxuWL50hzaeXiw==",
       "dev": true,
+      "peer": true,
       "dependencies": {
         "mime-db": "1.52.0"
       },
@@ -13376,6 +13351,7 @@
       "resolved": "https://registry.npmjs.org/schema-utils/-/schema-utils-4.3.3.tgz",
       "integrity": "sha512-eflK8wEtyOE6+hsaRVPxvUKYCpRgzLqDTb8krvAsRIwOGlHoSgYLgBXoubGgLd2fT41/OUYdb48v4k4WWHQurA==",
       "dev": true,
+      "peer": true,
       "dependencies": {
         "@types/json-schema": "^7.0.9",
         "ajv": "^8.9.0",

package.json

@@ -29,8 +29,7 @@
     "feeder:live": "ts-node -r tsconfig-paths/register src/scripts/run-live-feeder.ts",
     "cleanup:live": "ts-node -r tsconfig-paths/register src/scripts/cleanup-live-matches.ts",
     "swagger:summary": "ts-node -r tsconfig-paths/register src/scripts/export-swagger-endpoints-summary.ts",
-    "postman:export": "ts-node -r tsconfig-paths/register src/scripts/export-postman-collection.ts"
+    "postman:export": "ts-node -r tsconfig-paths/register src/scripts/export-postman-collection.ts",
-    ,
     "ai:extract:v26": "python3 ai-engine/scripts/extract_training_data_v26.py",
     "ai:train:v26": "python3 ai-engine/scripts/train_v26_shadow.py",
     "ai:backtest:v26": "python3 ai-engine/scripts/backtest_v26_shadow.py",
@@ -56,7 +55,7 @@
     "@nestjs/swagger": "^11.2.4",
     "@nestjs/terminus": "^11.0.0",
     "@nestjs/throttler": "^6.5.0",
-    "@prisma/client": "^5.22.0",
+    "@prisma/client": "5.22.0",
     "axios": "^1.13.6",
     "bcrypt": "^6.0.0",
     "bullmq": "^5.66.4",
@@ -76,7 +75,7 @@
     "passport-jwt": "^4.0.1",
     "pino": "^10.1.0",
     "pino-http": "^11.0.0",
-    "prisma": "^5.22.0",
+    "prisma": "5.22.0",
     "reflect-metadata": "^0.2.2",
     "rxjs": "^7.8.1",
     "twitter-api-v2": "^1.29.0",

prisma/schema.prisma

@@ -543,6 +543,7 @@ model User {
   analyses              Analysis[]
   refreshTokens         RefreshToken[]
   usageLimit            UsageLimit?
+  subscription          Subscription?
   coupons               UserCoupon[]
   totoCoupons           TotoCoupon[]
 
@@ -551,6 +552,27 @@ model User {
   @@map("users")
 }
 
+model Subscription {
+  id                   String              @id @default(uuid())
+  userId               String              @unique @map("user_id")
+  paddleSubscriptionId String?             @unique @map("paddle_subscription_id")
+  paddleCustomerId     String?             @map("paddle_customer_id")
+  plan                 SubscriptionStatus  @default(free)
+  billingInterval      BillingInterval?    @map("billing_interval")
+  currentPeriodStart   DateTime?           @map("current_period_start")
+  currentPeriodEnd     DateTime?           @map("current_period_end")
+  cancelledAt          DateTime?           @map("cancelled_at")
+  cancelEffectiveDate  DateTime?           @map("cancel_effective_date")
+  paddlePriceId        String?             @map("paddle_price_id")
+  createdAt            DateTime            @default(now()) @map("created_at")
+  updatedAt            DateTime            @updatedAt @map("updated_at")
+  user                 User                @relation(fields: [userId], references: [id], onDelete: Cascade)
+
+  @@index([paddleSubscriptionId])
+  @@index([paddleCustomerId])
+  @@map("subscriptions")
+}
+
 model RefreshToken {
   id        String   @id @default(uuid())
   token     String   @unique
@@ -569,6 +591,8 @@ model UsageLimit {
   userId        String   @unique @map("user_id")
   analysisCount Int      @default(0) @map("analysis_count")
   couponCount   Int      @default(0) @map("coupon_count")
+  maxAnalyses   Int      @default(3) @map("max_analyses")
+  maxCoupons    Int      @default(1) @map("max_coupons")
   lastResetDate DateTime @map("last_reset_date") @db.Date
   createdAt     DateTime @default(now()) @map("created_at")
   updatedAt     DateTime @updatedAt @map("updated_at")
@@ -765,8 +789,15 @@ enum UserRole {
 
 enum SubscriptionStatus {
   free
-  active
+  plus
-  expired
+  premium
+  past_due
+  cancelled
+}
+
+enum BillingInterval {
+  monthly
+  yearly
 }
 
 enum PlayerPosition {

prisma/seed.ts

@@ -24,7 +24,7 @@ async function main() {
         firstName: 'Super',
         lastName: 'Admin',
         role: UserRole.superadmin,
-        subscriptionStatus: SubscriptionStatus.active,
+        subscriptionStatus: SubscriptionStatus.free,
         isActive: true,
       },
     });

qualified_leagues.json

@@ -0,0 +1,267 @@
+[
+    "3iwftmprsznl6yribr11a8l9m",
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+    "ac112osli9fvox1epcg4ld3t6",
+    "3is4bkgf3loxv9qfg3hm8zfqb",
+    "c1d9p6b2e9zr5tqlzx3ktjplg",
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+    "8dxsd8xnjm9n1ogo37yomgl3p",
+    "arrfx02rdlstdfwdyikwqtwgl",
+    "afp674ll89oqsbbrqt17xfxlh",
+    "22euhl6zy56cp651ipq99rooq"
+]

src/app.module.ts

@@ -51,6 +51,7 @@ import { AnalysisModule } from "./modules/analysis/analysis.module";
 import { CouponsModule } from "./modules/coupons/coupons.module";
 import { SporTotoModule } from "./modules/spor-toto/spor-toto.module";
 import { AiProxyModule } from "./modules/ai-proxy/ai-proxy.module";
+import { SubscriptionsModule } from "./modules/subscriptions/subscriptions.module";
 
 // Services and Tasks
 import { ServicesModule } from "./services/services.module";
@@ -77,6 +78,7 @@ const historicalFeederMode = process.env.FEEDER_MODE === "historical";
     // Configuration
     ConfigModule.forRoot({
       isGlobal: true,
+      envFilePath: [".env.local", ".env"],
       validate: validateEnv,
       load: [
         appConfig,
@@ -203,6 +205,7 @@ const historicalFeederMode = process.env.FEEDER_MODE === "historical";
     CouponsModule,
     SporTotoModule,
     AiProxyModule,
+    SubscriptionsModule,
 
     // Services and Scheduled Tasks
     ServicesModule,

src/common/utils/ai-engine-client.ts

@@ -69,9 +69,8 @@ export class AiEngineClient {
     this.defaultTimeoutMs = options.timeoutMs ?? 30000;
     this.maxRetries = options.maxRetries ?? 2;
     this.retryDelayMs = options.retryDelayMs ?? 750;
-    this.circuitBreakerThreshold = options.circuitBreakerThreshold ?? 3;
+    this.circuitBreakerThreshold = options.circuitBreakerThreshold ?? 5;
-    this.circuitBreakerCooldownMs =
+    this.circuitBreakerCooldownMs = options.circuitBreakerCooldownMs ?? 15000;
-      options.circuitBreakerCooldownMs ?? 30000;
 
     this.axiosClient = axios.create({
       baseURL: options.baseUrl,
@@ -113,7 +112,9 @@ export class AiEngineClient {
     };
   }
 
-  private async request<T>(config: AiEngineRequestConfig): Promise<AxiosResponse<T>> {
+  private async request<T>(
+    config: AiEngineRequestConfig,
+  ): Promise<AxiosResponse<T>> {
     this.ensureCircuitAvailable();
 
     const retries = this.resolveRetryCount(config);
@@ -133,7 +134,13 @@ export class AiEngineClient {
         const shouldRetry = attempt < retries && this.isRetriableError(error);
 
         if (!shouldRetry) {
-          this.registerFailure(error);
+          // Only register circuit breaker failure for server/network errors, not client errors (4xx)
+          if (this.isServerError(error)) {
+            this.registerFailure(error);
+          } else {
+            // It's a successful contact with the engine (e.g. 404, 422), so reset failures
+            this.resetFailures();
+          }
           throw this.toRequestError(error);
         }
 
@@ -162,7 +169,8 @@ export class AiEngineClient {
     }
 
     const remainingCooldown =
-      this.circuitBreakerCooldownMs - (Date.now() - (this.circuitOpenedAt ?? 0));
+      this.circuitBreakerCooldownMs -
+      (Date.now() - (this.circuitOpenedAt ?? 0));
 
     if (remainingCooldown > 0) {
       throw new AiEngineRequestError("AI engine circuit breaker is open", {
@@ -175,8 +183,11 @@ export class AiEngineClient {
     }
 
     this.logger.warn(
-      `[${this.serviceName}] AI circuit breaker cooldown elapsed, allowing a recovery attempt`,
+      `[${this.serviceName}] AI circuit breaker cooldown elapsed, allowing a recovery attempt (resetting failures from ${this.consecutiveFailures})`,
     );
+    // Half-open state: reset failures so a single retry failure doesn't
+    // immediately re-open the circuit at threshold+1
+    this.consecutiveFailures = 0;
     this.circuitOpenedAt = null;
   }
 
@@ -218,6 +229,27 @@ export class AiEngineClient {
     return status >= 500 || status === 429 || error.code === "ECONNABORTED";
   }
 
+  private isServerError(error: unknown): boolean {
+    if (!axios.isAxiosError(error)) {
+      return true; // Not an axios error, assume internal/network error
+    }
+    if (!error.response) {
+      return true; // Network error, timeout, etc.
+    }
+    // Only count infrastructure-level errors toward circuit breaker:
+    // - No response (network failure) → already handled above
+    // - Timeout (ECONNABORTED) → infrastructure
+    // - 429 (rate limit) → infrastructure
+    // - 502/503/504 (proxy/gateway errors) → infrastructure
+    // Do NOT count 500 (app-level crash in AI Engine) — it may be
+    // match-specific and shouldn't block all other matches.
+    if (error.code === "ECONNABORTED") {
+      return true;
+    }
+    const status = error.response.status;
+    return status === 429 || status === 502 || status === 503 || status === 504;
+  }
+
   private toRequestError(error: unknown): AiEngineRequestError {
     if (error instanceof AiEngineRequestError) {
       return error;

src/common/utils/timezone.util.ts

@@ -14,10 +14,7 @@ function extractDateParts(date: Date, timeZone: string) {
   return { year, month, day };
 }
 
-export function getDateStringInTimeZone(
+export function getDateStringInTimeZone(date: Date, timeZone: string): string {
-  date: Date,
-  timeZone: string,
-): string {
   const { year, month, day } = extractDateParts(date, timeZone);
   return `${year}-${String(month).padStart(2, "0")}-${String(day).padStart(2, "0")}`;
 }

src/config/env.validation.ts

@@ -56,13 +56,31 @@ export const envSchema = z.object({
     .string()
     .transform((val) => val === "true")
     .default("false" as any),
+  SOCIAL_POSTER_SPORTS: z.string().default("football,basketball"),
+  SOCIAL_POSTER_WINDOW_MIN: z.coerce.number().default(25),
+  SOCIAL_POSTER_WINDOW_MAX: z.coerce.number().default(45),
+  SOCIAL_POSTER_OLLAMA_MODEL: z.string().optional(),
+  APP_BASE_URL: z.string().url().optional(),
   TWITTER_API_KEY: z.string().optional(),
   TWITTER_API_SECRET: z.string().optional(),
   TWITTER_ACCESS_TOKEN: z.string().optional(),
   TWITTER_ACCESS_SECRET: z.string().optional(),
+  META_GRAPH_API_VERSION: z.string().default("v25.0"),
   META_PAGE_ACCESS_TOKEN: z.string().optional(),
   META_PAGE_ID: z.string().optional(),
   META_IG_USER_ID: z.string().optional(),
+  OLLAMA_BASE_URL: z.string().url().optional(),
+  OLLAMA_MODEL: z.string().optional(),
+
+  // Paddle (Subscription Billing)
+  PADDLE_API_KEY: z.string().optional(),
+  PADDLE_WEBHOOK_SECRET: z.string().optional(),
+  PADDLE_CLIENT_TOKEN: z.string().optional(),
+  PADDLE_ENVIRONMENT: z.enum(["sandbox", "production"]).default("sandbox"),
+  PADDLE_PLUS_MONTHLY_PRICE_ID: z.string().optional(),
+  PADDLE_PLUS_YEARLY_PRICE_ID: z.string().optional(),
+  PADDLE_PREMIUM_MONTHLY_PRICE_ID: z.string().optional(),
+  PADDLE_PREMIUM_YEARLY_PRICE_ID: z.string().optional(),
 
   // Optional Features
   ENABLE_MAIL: booleanString,

src/i18n/en/common.json

@@ -9,5 +9,12 @@
   "serverError": "An unexpected error occurred",
   "unauthorized": "You are not authorized to perform this action",
   "forbidden": "Access denied",
-  "badRequest": "Invalid request"
+  "badRequest": "Bad request",
+  "SUCCESS_USER_ROLE_UPDATED": "User role updated",
+  "SUCCESS_USER_SUBSCRIPTION_UPDATED": "User subscription updated",
+  "SUCCESS_USER_DELETED": "User deleted",
+  "SUCCESS_USER_STATUS_UPDATED": "User status updated",
+  "SUCCESS_SETTING_UPDATED": "Setting updated",
+  "SUCCESS_ALL_LIMITS_RESET": "All usage limits reset",
+  "SUCCESS_USER_LIMITS_RESET": "User usage limits reset"
 }

src/i18n/en/errors.json

@@ -10,5 +10,13 @@
   "TENANT_NOT_FOUND": "Tenant not found",
   "VALIDATION_FAILED": "Validation failed",
   "INTERNAL_ERROR": "An internal error occurred, please try again later",
-  "AUTH_REQUIRED": "Authentication required, please provide a valid token"
+  "AUTH_REQUIRED": "Authentication required, please provide a valid token",
+  "USAGE_LIMIT_EXCEEDED": "You have exceeded your daily usage limit. Please upgrade your plan.",
+  "ANALYSIS_LIMIT_EXCEEDED": "You have exceeded your daily analysis limit. Please upgrade your plan.",
+  "COUPON_LIMIT_EXCEEDED": "You have exceeded your daily coupon limit. Please upgrade your plan.",
+  "INVALID_PLAN_TYPE": "Invalid plan type. Must be free, plus, or premium.",
+  "MATCH_NOT_FOUND": "Match not found",
+  "PREDICTION_GENERATION_FAILED": "Failed to generate prediction",
+  "SMART_COUPON_GENERATION_FAILED": "Failed to generate Smart Coupon",
+  "ANALYSIS_FAILED": "None of the provided matches could be analyzed successfully"
 }

src/i18n/tr/common.json

@@ -9,5 +9,12 @@
   "serverError": "Beklenmeyen bir hata oluştu",
   "unauthorized": "Bu işlemi yapmaya yetkiniz yok",
   "forbidden": "Erişim reddedildi",
-  "badRequest": "Geçersiz istek"
+  "badRequest": "Geçersiz istek",
+  "SUCCESS_USER_ROLE_UPDATED": "Kullanıcı rolü güncellendi",
+  "SUCCESS_USER_SUBSCRIPTION_UPDATED": "Kullanıcı aboneliği güncellendi",
+  "SUCCESS_USER_DELETED": "Kullanıcı başarıyla silindi",
+  "SUCCESS_USER_STATUS_UPDATED": "Kullanıcı durumu güncellendi",
+  "SUCCESS_SETTING_UPDATED": "Ayar güncellendi",
+  "SUCCESS_ALL_LIMITS_RESET": "Tüm kullanıcı limitleri sıfırlandı",
+  "SUCCESS_USER_LIMITS_RESET": "Kullanıcı limitleri sıfırlandı"
 }

src/i18n/tr/errors.json

@@ -10,5 +10,13 @@
   "TENANT_NOT_FOUND": "Kiracı bulunamadı",
   "VALIDATION_FAILED": "Doğrulama başarısız",
   "INTERNAL_ERROR": "Bir iç hata oluştu, lütfen daha sonra tekrar deneyin",
-  "AUTH_REQUIRED": "Kimlik doğrulama gerekli, lütfen geçerli bir token sağlayın"
+  "AUTH_REQUIRED": "Kimlik doğrulama gerekli, lütfen geçerli bir token sağlayın",
+  "USAGE_LIMIT_EXCEEDED": "Günlük kullanım limitinizi doldurdunuz. Lütfen paketinizi yükseltin.",
+  "ANALYSIS_LIMIT_EXCEEDED": "Günlük analiz limitinizi doldurdunuz. Lütfen paketinizi yükseltin.",
+  "COUPON_LIMIT_EXCEEDED": "Günlük kupon limitinizi doldurdunuz. Lütfen paketinizi yükseltin.",
+  "INVALID_PLAN_TYPE": "Geçersiz paket tipi. (free, plus, premium olmalıdır)",
+  "MATCH_NOT_FOUND": "Maç bulunamadı",
+  "PREDICTION_GENERATION_FAILED": "Tahmin oluşturulamadı",
+  "SMART_COUPON_GENERATION_FAILED": "Akıllı kupon oluşturulamadı",
+  "ANALYSIS_FAILED": "Sağlanan maçların hiçbiri başarıyla analiz edilemedi"
 }

src/modules/admin/admin.controller.ts

@@ -10,6 +10,7 @@ import {
   UseInterceptors,
   Inject,
   NotFoundException,
+  BadRequestException,
 } from "@nestjs/common";
 import {
   CacheInterceptor,
@@ -18,7 +19,12 @@ import {
   CACHE_MANAGER,
 } from "@nestjs/cache-manager";
 import * as cacheManager from "cache-manager";
-import { ApiTags, ApiBearerAuth, ApiOperation, ApiResponse as SwaggerResponse } from "@nestjs/swagger";
+import {
+  ApiTags,
+  ApiBearerAuth,
+  ApiOperation,
+  ApiResponse as SwaggerResponse,
+} from "@nestjs/swagger";
 import { Roles } from "../../common/decorators";
 import { PrismaService } from "../../database/prisma.service";
 import { PaginationDto } from "../../common/dto/pagination.dto";
@@ -31,6 +37,8 @@ import {
 import { plainToInstance } from "class-transformer";
 import { UserResponseDto } from "../users/dto/user.dto";
 import { UserRole } from "@prisma/client";
+import { SubscriptionsService } from "../subscriptions/subscriptions.service";
+import { PlanType } from "../subscriptions/dto/subscription.dto";
 
 @ApiTags("Admin")
 @ApiBearerAuth()
@@ -40,6 +48,7 @@ export class AdminController {
   constructor(
     private readonly prisma: PrismaService,
     @Inject(CACHE_MANAGER) private cacheManager: cacheManager.Cache,
+    private readonly subscriptionsService: SubscriptionsService,
   ) {}
 
   // ================== Users Management ==================
@@ -117,7 +126,7 @@ export class AdminController {
 
     return createSuccessResponse(
       plainToInstance(UserResponseDto, updated),
-      "User status updated",
+      "common.SUCCESS_USER_STATUS_UPDATED",
     );
   }
 
@@ -135,31 +144,7 @@ export class AdminController {
 
     return createSuccessResponse(
       plainToInstance(UserResponseDto, user),
-      "User role updated",
+      "common.SUCCESS_USER_ROLE_UPDATED",
-    );
-  }
-
-  @Put("users/:id/subscription")
-  @ApiOperation({ summary: "Update user subscription" })
-  @SwaggerResponse({ status: 200, type: UserResponseDto })
-  async updateUserSubscription(
-    @Param("id") id: string,
-    @Body()
-    data: { subscriptionStatus: string; subscriptionExpiresAt?: string },
-  ): Promise<ApiResponse<UserResponseDto>> {
-    const user = await this.prisma.user.update({
-      where: { id },
-      data: {
-        subscriptionStatus: data.subscriptionStatus as any,
-        subscriptionExpiresAt: data.subscriptionExpiresAt
-          ? new Date(data.subscriptionExpiresAt)
-          : null,
-      },
-    });
-
-    return createSuccessResponse(
-      plainToInstance(UserResponseDto, user),
-      "User subscription updated",
     );
   }
 
@@ -171,7 +156,7 @@ export class AdminController {
       where: { id },
       data: { deletedAt: new Date() },
     });
-    return createSuccessResponse(null, "User deleted");
+    return createSuccessResponse(null, "common.SUCCESS_USER_DELETED");
   }
 
   // ================== App Settings ==================
@@ -181,7 +166,10 @@ export class AdminController {
   @CacheKey("app_settings")
   @CacheTTL(60 * 1000)
   @ApiOperation({ summary: "Get all app settings" })
-  @SwaggerResponse({ status: 200, schema: { type: "object", additionalProperties: { type: "string" } } })
+  @SwaggerResponse({
+    status: 200,
+    schema: { type: "object", additionalProperties: { type: "string" } },
+  })
   async getAllSettings(): Promise<ApiResponse<Record<string, string>>> {
     const settings = await this.prisma.appSetting.findMany();
     const settingsMap: Record<string, string> = {};
@@ -193,7 +181,13 @@ export class AdminController {
 
   @Put("settings/:key")
   @ApiOperation({ summary: "Update an app setting" })
-  @SwaggerResponse({ status: 200, schema: { type: "object", properties: { key: { type: "string" }, value: { type: "string" } } } })
+  @SwaggerResponse({
+    status: 200,
+    schema: {
+      type: "object",
+      properties: { key: { type: "string" }, value: { type: "string" } },
+    },
+  })
   async updateSetting(
     @Param("key") key: string,
     @Body() data: { value: string },
@@ -206,7 +200,7 @@ export class AdminController {
     await this.cacheManager.del("app_settings");
     return createSuccessResponse(
       { key: setting.key, value: setting.value || "" },
-      "Setting updated",
+      "common.SUCCESS_SETTING_UPDATED",
     );
   }
 
@@ -214,7 +208,10 @@ export class AdminController {
 
   @Get("usage-limits")
   @ApiOperation({ summary: "Get all usage limits" })
-  @SwaggerResponse({ status: 200, schema: { type: "array", items: { type: "object" } } })
+  @SwaggerResponse({
+    status: 200,
+    schema: { type: "array", items: { type: "object" } },
+  })
   async getAllUsageLimits(@Query() pagination: PaginationDto) {
     const { skip, take } = pagination;
 
@@ -242,7 +239,10 @@ export class AdminController {
 
   @Post("usage-limits/reset-all")
   @ApiOperation({ summary: "Reset all usage limits" })
-  @SwaggerResponse({ status: 200, schema: { type: "object", properties: { count: { type: "number" } } } })
+  @SwaggerResponse({
+    status: 200,
+    schema: { type: "object", properties: { count: { type: "number" } } },
+  })
   async resetAllUsageLimits(): Promise<ApiResponse<{ count: number }>> {
     const result = await this.prisma.usageLimit.updateMany({
       data: {
@@ -254,7 +254,57 @@ export class AdminController {
 
     return createSuccessResponse(
       { count: result.count },
-      "All usage limits reset",
+      "common.SUCCESS_ALL_LIMITS_RESET",
+    );
+  }
+
+  @Post("usage-limits/reset/:userId")
+  @ApiOperation({ summary: "Reset usage limits for a single user" })
+  @SwaggerResponse({ status: 200 })
+  async resetUserUsageLimits(
+    @Param("userId") userId: string,
+  ): Promise<ApiResponse<null>> {
+    const user = await this.prisma.user.findUnique({ where: { id: userId } });
+    if (!user) throw new NotFoundException("USER_NOT_FOUND");
+
+    await this.prisma.usageLimit.update({
+      where: { userId },
+      data: {
+        analysisCount: 0,
+        couponCount: 0,
+        lastResetDate: new Date(),
+      },
+    });
+
+    return createSuccessResponse(null, "common.SUCCESS_USER_LIMITS_RESET");
+  }
+
+  @Put("users/:userId/subscription")
+  @ApiOperation({ summary: "Update a user's subscription tier" })
+  @SwaggerResponse({ status: 200 })
+  async updateUserSubscription(
+    @Param("userId") userId: string,
+    @Body() data: { plan: string },
+  ): Promise<ApiResponse<null>> {
+    const user = await this.prisma.user.findUnique({ where: { id: userId } });
+    if (!user) throw new NotFoundException("USER_NOT_FOUND");
+
+    const validPlans = [PlanType.FREE, PlanType.PLUS, PlanType.PREMIUM];
+    const newPlan = data.plan as PlanType;
+    if (!validPlans.includes(newPlan)) {
+      throw new BadRequestException("INVALID_PLAN_TYPE");
+    }
+
+    await this.prisma.user.update({
+      where: { id: userId },
+      data: { subscriptionStatus: newPlan },
+    });
+
+    await this.subscriptionsService.syncLimitsWithPlan(userId, newPlan);
+
+    return createSuccessResponse(
+      null,
+      "common.SUCCESS_USER_SUBSCRIPTION_UPDATED",
     );
   }
 
@@ -274,7 +324,9 @@ export class AdminController {
     ] = await Promise.all([
       this.prisma.user.count(),
       this.prisma.user.count({ where: { isActive: true } }),
-      this.prisma.user.count({ where: { subscriptionStatus: "active" } }),
+      this.prisma.user.count({
+        where: { subscriptionStatus: { in: ["plus", "premium"] } },
+      }),
       this.prisma.match.count(),
       this.prisma.prediction.count(),
       this.prisma.userCoupon.count(),

src/modules/admin/admin.module.ts

@@ -1,7 +1,9 @@
 import { Module } from "@nestjs/common";
 import { AdminController } from "./admin.controller";
+import { SubscriptionsModule } from "../subscriptions/subscriptions.module";
 
 @Module({
+  imports: [SubscriptionsModule],
   controllers: [AdminController],
 })
 export class AdminModule {}

src/modules/analysis/analysis.controller.ts

@@ -59,7 +59,7 @@ export class AnalysisController {
     );
 
     if (!canProceed) {
-      throw new ForbiddenException("You have exceeded your daily usage limit");
+      throw new ForbiddenException("USAGE_LIMIT_EXCEEDED");
     }
 
     // Run analysis
@@ -68,7 +68,7 @@ export class AnalysisController {
     if (!result) {
       return {
         success: false,
-        message: "None of the provided matches could be analyzed successfully",
+        message: "ANALYSIS_FAILED",
       };
     }
 

src/modules/analysis/analysis.service.ts

@@ -84,7 +84,7 @@ export class AnalysisService {
   }
 
   /**
-   * Check user usage limit
+   * Check user usage limit (plan-aware via UsageLimit table)
    */
   async checkUsageLimit(
     userId: string,
@@ -96,24 +96,23 @@ export class AnalysisService {
     });
 
     if (!usageLimit) {
-      // Create default limit
+      // Create default limit with free-tier maxes
       await this.prisma.usageLimit.create({
         data: {
           userId,
           analysisCount: 0,
           couponCount: 0,
+          maxAnalyses: 3,
+          maxCoupons: 1,
           lastResetDate: new Date(),
         },
       });
       return true;
     }
 
-    // Check limits (default: 10 analyses, 3 coupons per day)
+    // Use plan-aware limits from DB (set by SubscriptionsService.syncLimitsWithPlan)
-    const user = await this.prisma.user.findUnique({ where: { id: userId } });
+    const maxAnalyses = usageLimit.maxAnalyses ?? 3;
-    const isPremium = user?.subscriptionStatus === "active";
+    const maxCoupons = usageLimit.maxCoupons ?? 1;
-
-    const maxAnalyses = isPremium ? 50 : 10;
-    const maxCoupons = isPremium ? 10 : 3;
 
     if (isCoupon) {
       return usageLimit.couponCount < maxCoupons;

src/modules/auth/guards/auth.guards.ts

@@ -94,11 +94,8 @@ export class RolesGuard implements CanActivate {
       return false;
     }
 
-    const normalizedUserRoles = (user.roles?.length
+    const normalizedUserRoles = (
-      ? user.roles
+      user.roles?.length ? user.roles : user.role ? [user.role] : []
-      : user.role
-        ? [user.role]
-        : []
     ).map((role) => normalizeRole(role));
 
     const normalizedRequiredRoles = requiredRoles.map((role) =>

src/modules/coupons/coupons.module.ts

@@ -25,4 +25,3 @@ import { MatchesModule } from "../matches/matches.module";
   ],
 })
 export class CouponsModule {}
-

src/modules/coupons/dto/coupons-request.dto.ts

@@ -109,8 +109,7 @@ export class FrequencyCouponDto {
   minSignal?: number;
 
   @ApiPropertyOptional({
-    description:
+    description: "Filter markets: OU1.5, OU2.5, OU3.5, BTTS, MS (default: all)",
-      "Filter markets: OU1.5, OU2.5, OU3.5, BTTS, MS (default: all)",
     example: ["OU2.5", "BTTS"],
   })
   @IsOptional()

src/modules/coupons/services/frequency-engine.service.ts

@@ -108,8 +108,7 @@ export class FrequencyEngineService {
     venue: "home" | "away",
     oddsBand: string,
   ): Promise<TeamFrequencyRow | null> {
-    const venueColumn =
+    const venueColumn = venue === "home" ? "m.home_team_id" : "m.away_team_id";
-      venue === "home" ? "m.home_team_id" : "m.away_team_id";
     const oddsSelection = venue === "home" ? "'1'" : "'2'";
     const bandRange = this.parseBandRange(oddsBand);
 
@@ -191,7 +190,7 @@ export class FrequencyEngineService {
 
     // OU 1.5 OVER
     const ou15Combined = (homeFreq.ou15_rate + awayFreq.ou15_rate) / 2;
-    if (ou15Combined >= 0.80) {
+    if (ou15Combined >= 0.8) {
       signals.push({
         market: "OU1.5_OVER",
         pick: "1.5 UST",
@@ -212,7 +211,7 @@ export class FrequencyEngineService {
 
     // OU 2.5 OVER
     const ou25Combined = (homeFreq.ou25_rate + awayFreq.ou25_rate) / 2;
-    if (ou25Combined >= 0.60) {
+    if (ou25Combined >= 0.6) {
       signals.push({
         market: "OU2.5_OVER",
         pick: "2.5 UST",
@@ -233,7 +232,7 @@ export class FrequencyEngineService {
 
     // OU 3.5 OVER
     const ou35Combined = (homeFreq.ou35_rate + awayFreq.ou35_rate) / 2;
-    if (ou35Combined >= 0.50) {
+    if (ou35Combined >= 0.5) {
       signals.push({
         market: "OU3.5_OVER",
         pick: "3.5 UST",
@@ -254,7 +253,7 @@ export class FrequencyEngineService {
 
     // BTTS YES
     const bttsCombined = (homeFreq.btts_rate + awayFreq.btts_rate) / 2;
-    if (bttsCombined >= 0.60) {
+    if (bttsCombined >= 0.6) {
       signals.push({
         market: "BTTS_YES",
         pick: "KG VAR",
@@ -299,7 +298,7 @@ export class FrequencyEngineService {
     const hwCombined = (homeFreq.win_rate + awayFreq.win_rate) / 2;
     // awayFreq.win_rate aslında deplasman takımının KAYBETme oranı
     // (away takımı o bandda maçları kazanma değil, kaybetme olarak bak)
-    if (hwCombined >= 0.70 && homeOdds > 1.10 && homeOdds < 3.50) {
+    if (hwCombined >= 0.7 && homeOdds > 1.1 && homeOdds < 3.5) {
       signals.push({
         market: "MS_HOME",
         pick: "MS 1",
@@ -411,9 +410,7 @@ export class FrequencyEngineService {
   /**
    * Lig bazlı gol profili.
    */
-  async getLeagueProfile(
+  async getLeagueProfile(leagueId: string): Promise<LeagueProfileRow | null> {
-    leagueId: string,
-  ): Promise<LeagueProfileRow | null> {
     const rows = await this.prisma.$queryRawUnsafe<LeagueProfileRow[]>(
       `
       SELECT
@@ -521,9 +518,7 @@ export class FrequencyEngineService {
     return "6.00+";
   }
 
-  private parseBandRange(
+  private parseBandRange(band: string): { min: number; max: number } | null {
-    band: string,
-  ): { min: number; max: number } | null {
     const map: Record<string, { min: number; max: number }> = {
       "1.00-1.30": { min: 1.0, max: 1.3 },
       "1.30-1.50": { min: 1.3, max: 1.5 },
@@ -537,9 +532,7 @@ export class FrequencyEngineService {
     return map[band] || null;
   }
 
-  private calculateLeagueBonus(
+  private calculateLeagueBonus(profile: LeagueProfileRow | null): number {
-    profile: LeagueProfileRow | null,
-  ): number {
     if (!profile || profile.total_matches < 20) {
       return 0;
     }

src/modules/coupons/services/smart-coupon.service.ts

@@ -154,9 +154,10 @@ export class SmartCouponService {
   async analyzeMatch(matchId: string): Promise<SingleMatchPredictionPackage> {
     let prediction: SingleMatchPredictionPackage;
     try {
-      const response = await this.aiEngineClient.post<SingleMatchPredictionPackage>(
+      const response =
-        `/v20plus/analyze/${matchId}`,
+        await this.aiEngineClient.post<SingleMatchPredictionPackage>(
-      );
+          `/v20plus/analyze/${matchId}`,
+        );
       prediction = response.data;
     } catch (error: unknown) {
       if (error instanceof AiEngineRequestError) {
@@ -264,7 +265,7 @@ export class SmartCouponService {
     markets?: string[];
   }): Promise<FrequencyCouponResult> {
     const maxMatches = options.maxMatches ?? 3;
-    const minSignal = options.minSignal ?? 0.70;
+    const minSignal = options.minSignal ?? 0.7;
     const allowedMarkets = options.markets?.map((m) => m.toUpperCase()) || null;
 
     this.logger.log(

src/modules/feeder/feeder-persistence.service.ts

@@ -167,7 +167,7 @@ export class FeederPersistenceService {
 
     const leagueId = this.safeString(league.id);
     if (leagueId) {
-      const logoUrl = `https://file.mackolikfeeds.com/areas/${leagueId}`;
+      const logoUrl = `https://file.mackolikfeeds.com/competitions/${leagueId}`;
       const localPath = `public/uploads/competitions/${leagueId}.png`;
       imageDownloads.push(
         ImageUtils.downloadImage(logoUrl, localPath)
@@ -853,49 +853,32 @@ export class FeederPersistenceService {
   }
 
   async getExistingMatchIds(matchIds: string[]): Promise<string[]> {
-    const matches = await this.prisma.match.findMany({
+    if (matchIds.length === 0) return [];
-      where: {
-        id: { in: matchIds },
-        oddCategories: { some: {} },
-        OR: [
-          {
-            sport: "football",
-            footballTeamStats: { some: {} },
-            playerParticipations: { some: { isStarting: true } },
-          },
-          {
-            sport: "basketball",
-            basketballTeamStats: { some: {} },
-            basketballPlayerStats: { some: {} },
-          },
-        ],
-      },
-      select: { id: true, sport: true },
-    });
 
-    const footballIds = matches
+    // Use raw SQL for performance — Prisma's { some: {} } relation filters
-      .filter((m) => m.sport === "football")
+    // generate heavy correlated subqueries that hang on Raspberry Pi with
-      .map((m) => m.id);
+    // large tables (15M+ odd_selections, 3M+ participations).
-    const completeFootballIds = new Set<string>();
+    const result = await this.prisma.$queryRawUnsafe<Array<{ id: string }>>(
+      `
+      SELECT m.id
+      FROM matches m
+      WHERE m.id = ANY($1::text[])
+        AND EXISTS (SELECT 1 FROM odd_categories oc WHERE oc.match_id = m.id)
+        AND (
+          (m.sport = 'football'
+           AND EXISTS (SELECT 1 FROM football_team_stats fts WHERE fts.match_id = m.id)
+           AND (SELECT count(*) FROM match_player_participation mpp
+                WHERE mpp.match_id = m.id AND mpp.is_starting = true) >= 18)
+          OR
+          (m.sport = 'basketball'
+           AND EXISTS (SELECT 1 FROM basketball_team_stats bts WHERE bts.match_id = m.id)
+           AND EXISTS (SELECT 1 FROM basketball_player_stats bps WHERE bps.match_id = m.id))
+        )
+      `,
+      matchIds,
+    );
 
-    if (footballIds.length > 0) {
+    return result.map((r) => r.id);
-      const starterCounts = await this.prisma.matchPlayerParticipation.groupBy({
-        by: ["matchId"],
-        where: {
-          matchId: { in: footballIds },
-          isStarting: true,
-        },
-        _count: { _all: true },
-      });
-
-      for (const row of starterCounts) {
-        if (row._count._all >= 18) completeFootballIds.add(row.matchId);
-      }
-    }
-
-    return matches
-      .filter((m) => m.sport !== "football" || completeFootballIds.has(m.id))
-      .map((m) => m.id);
   }
 
   /**
@@ -903,44 +886,49 @@ export class FeederPersistenceService {
    * returns which data scopes are missing per match.
    * Only checks completed (Ended) football/basketball matches.
    */
-  async getMissingScopes(
+  async getMissingScopes(matchIds: string[]): Promise<Map<string, string[]>> {
-    matchIds: string[],
-  ): Promise<Map<string, string[]>> {
     const result = new Map<string, string[]>();
     if (matchIds.length === 0) return result;
 
-    const matches = await this.prisma.match.findMany({
+    // Use raw SQL for performance on Raspberry Pi.
-      where: {
+    // Note: state is 'postGame' in DB, not 'Ended'.
-        id: { in: matchIds },
+    const rows = await this.prisma.$queryRawUnsafe<
-        state: "Ended",
+      Array<{
-      },
+        id: string;
-      select: {
+        sport: string;
-        id: true,
+        fts_count: bigint;
-        sport: true,
+        pp_count: bigint;
-        _count: {
+        bts_count: bigint;
-          select: {
+        bps_count: bigint;
-            playerParticipations: true,
+        oc_count: bigint;
-            footballTeamStats: true,
+      }>
-            basketballTeamStats: true,
+    >(
-            basketballPlayerStats: true,
+      `
-            oddCategories: true,
+      SELECT m.id, m.sport::text,
-          },
+        (SELECT count(*) FROM football_team_stats fts WHERE fts.match_id = m.id) as fts_count,
-        },
+        (SELECT count(*) FROM match_player_participation mpp WHERE mpp.match_id = m.id) as pp_count,
-      },
+        (SELECT count(*) FROM basketball_team_stats bts WHERE bts.match_id = m.id) as bts_count,
-    });
+        (SELECT count(*) FROM basketball_player_stats bps WHERE bps.match_id = m.id) as bps_count,
+        (SELECT count(*) FROM odd_categories oc WHERE oc.match_id = m.id) as oc_count
+      FROM matches m
+      WHERE m.id = ANY($1::text[])
+        AND m.state = 'postGame'
+      `,
+      matchIds,
+    );
 
-    for (const m of matches) {
+    for (const m of rows) {
       const missing: string[] = [];
 
       if (m.sport === "football") {
-        if (m._count.footballTeamStats === 0) missing.push("stats");
+        if (Number(m.fts_count) === 0) missing.push("stats");
-        if (m._count.playerParticipations < 18) missing.push("lineups");
+        if (Number(m.pp_count) < 18) missing.push("lineups");
       } else if (m.sport === "basketball") {
-        if (m._count.basketballTeamStats === 0) missing.push("stats");
+        if (Number(m.bts_count) === 0) missing.push("stats");
-        if (m._count.basketballPlayerStats === 0) missing.push("lineups");
+        if (Number(m.bps_count) === 0) missing.push("lineups");
       }
 
-      if (m._count.oddCategories === 0) missing.push("odds");
+      if (Number(m.oc_count) === 0) missing.push("odds");
 
       if (missing.length > 0) {
         result.set(m.id, missing);

src/modules/feeder/feeder.service.ts

@@ -324,8 +324,8 @@ export class FeederService {
         const sample = allMatches.slice(0, 3);
         this.logger.warn(
           `[${sport}] [${dateString}] DEBUG: bounds=[${targetDateStartTs}, ${targetDateEndTs}] ` +
-          `(${new Date(targetDateStartTs * 1000).toISOString()} - ${new Date(targetDateEndTs * 1000).toISOString()}) | ` +
+            `(${new Date(targetDateStartTs * 1000).toISOString()} - ${new Date(targetDateEndTs * 1000).toISOString()}) | ` +
-          `sampleMstUtc=[${sample.map((m) => `${m.mstUtc} (asSec=${new Date(m.mstUtc * 1000).toISOString()}, asMs=${new Date(m.mstUtc).toISOString()})`).join(', ')}]`,
+            `sampleMstUtc=[${sample.map((m) => `${m.mstUtc} (asSec=${new Date(m.mstUtc * 1000).toISOString()}, asMs=${new Date(m.mstUtc).toISOString()})`).join(", ")}]`,
         );
       }
 
@@ -393,7 +393,8 @@ export class FeederService {
 
       // ── Patch incomplete existing matches ──────────────────────
       // Find matches that ARE in DB but have missing data scopes
-      const allExistingInDb = await this.persistenceService.getMissingScopes(allIds);
+      const allExistingInDb =
+        await this.persistenceService.getMissingScopes(allIds);
       if (allExistingInDb.size > 0) {
         this.logger.log(
           `[${sport}] [${dateString}] 🔧 Found ${allExistingInDb.size} existing matches with missing data. Patching...`,
@@ -407,7 +408,11 @@ export class FeederService {
             await this.delay(500);
             try {
               const patchScope: "all" | "lineups" | "odds" =
-                scope === "odds" ? "odds" : scope === "lineups" ? "lineups" : "all";
+                scope === "odds"
+                  ? "odds"
+                  : scope === "lineups"
+                    ? "lineups"
+                    : "all";
 
               const result = await this.processSingleMatch(
                 matchSummary,

src/modules/health/health.controller.ts

@@ -94,7 +94,8 @@ export class HealthController {
     } catch (error: unknown) {
       return {
         status: "down",
-        detail: error instanceof Error ? error.message : "Unknown database error",
+        detail:
+          error instanceof Error ? error.message : "Unknown database error",
       };
     }
   }

src/modules/leagues/leagues.controller.ts

@@ -165,9 +165,24 @@ export class LeaguesController {
     },
   })
   @ApiParam({ name: "id", description: "Team ID" })
-  @ApiQuery({ name: "page", required: false, type: Number, description: "Page number (default: 1)" })
+  @ApiQuery({
-  @ApiQuery({ name: "limit", required: false, type: Number, description: "Items per page (default: 20)" })
+    name: "page",
-  @ApiQuery({ name: "season", required: false, type: String, description: "Season (e.g. 2024-2025)" })
+    required: false,
+    type: Number,
+    description: "Page number (default: 1)",
+  })
+  @ApiQuery({
+    name: "limit",
+    required: false,
+    type: Number,
+    description: "Items per page (default: 20)",
+  })
+  @ApiQuery({
+    name: "season",
+    required: false,
+    type: String,
+    description: "Season (e.g. 2024-2025)",
+  })
   async getTeamMatches(
     @Param("id") id: string,
     @Query("page") page?: string,
@@ -178,7 +193,7 @@ export class LeaguesController {
       id,
       parseInt(page || "1", 10),
       parseInt(limit || "20", 10),
-      season
+      season,
     );
   }
 

src/modules/leagues/leagues.service.ts

@@ -105,7 +105,7 @@ export class LeaguesService {
     teamId: string,
     page: number = 1,
     limit: number = 20,
-    season?: string
+    season?: string,
   ) {
     const skip = (page - 1) * limit;
     const where: any = {
@@ -118,13 +118,15 @@ export class LeaguesService {
       if (parts.length === 2) {
         const startYear = parseInt(parts[0], 10);
         const endYear = parseInt(parts[1], 10);
-        
+
         if (!isNaN(startYear) && !isNaN(endYear)) {
           // Season starts August 1st of startYear
           const startDate = new Date(Date.UTC(startYear, 7, 1)).getTime();
           // Season ends July 31st of endYear
-          const endDate = new Date(Date.UTC(endYear, 6, 31, 23, 59, 59, 999)).getTime();
+          const endDate = new Date(
-          
+            Date.UTC(endYear, 6, 31, 23, 59, 59, 999),
+          ).getTime();
+
           where.mstUtc = {
             gte: startDate,
             lte: endDate,
@@ -186,7 +188,7 @@ export class LeaguesService {
           { homeTeamId: teamId1, awayTeamId: teamId2 },
           { homeTeamId: teamId2, awayTeamId: teamId1 },
         ],
-        state: "postGame", // Finished matches are stored as "postGame"
+        AND: [{ scoreHome: { not: null } }, { scoreAway: { not: null } }],
       },
       include: {
         homeTeam: true,

src/modules/matches/matches.service.ts

@@ -20,26 +20,55 @@ import {
 @Injectable()
 export class MatchesService {
   private readonly logger = new Logger(MatchesService.name);
+  private qualifiedLeagueIds: string[] = [];
   private topLeagueIds: string[] = [];
 
   constructor(private readonly prisma: PrismaService) {
+    this.loadQualifiedLeagues();
     this.loadTopLeagues();
   }
 
   private loadTopLeagues() {
     try {
-      const topLeaguesPath = path.join(process.cwd(), "top_leagues.json");
+      const filePath = path.join(process.cwd(), "top_leagues.json");
-      if (fs.existsSync(topLeaguesPath)) {
+      if (fs.existsSync(filePath)) {
-        this.topLeagueIds = JSON.parse(fs.readFileSync(topLeaguesPath, "utf8"));
+        this.topLeagueIds = JSON.parse(fs.readFileSync(filePath, "utf8"));
-        this.logger.log(
-          `Loaded ${this.topLeagueIds.length} top leagues for filtering.`,
-        );
       }
     } catch (e) {
       this.logger.warn(`Failed to load top_leagues.json: ${e.message}`);
     }
   }
 
+  private loadQualifiedLeagues() {
+    try {
+      const filePath = path.join(process.cwd(), "qualified_leagues.json");
+      if (fs.existsSync(filePath)) {
+        this.qualifiedLeagueIds = JSON.parse(fs.readFileSync(filePath, "utf8"));
+        this.logger.log(
+          `Loaded ${this.qualifiedLeagueIds.length} qualified leagues for filtering.`,
+        );
+      }
+    } catch (e) {
+      this.logger.warn(`Failed to load qualified_leagues.json: ${e.message}`);
+    }
+  }
+
+  /**
+   * Generate URL for the country flag served from Mackolik
+   */
+  private getCountryFlagUrl(countryId?: string | null): string | undefined {
+    if (!countryId) return undefined;
+    return `https://file.mackolikfeeds.com/areas/${countryId}`;
+  }
+
+  /**
+   * Generate URL for the team logo served from local uploads
+   */
+  private getTeamLogoUrl(teamId?: string | null): string | undefined {
+    if (!teamId) return undefined;
+    return `https://file.mackolikfeeds.com/teams/${teamId}`;
+  }
+
   private getLiveFilter(): Prisma.LiveMatchWhereInput {
     return {
       OR: [
@@ -139,9 +168,9 @@ export class MatchesService {
 
     if (leagueId) {
       where.leagueId = leagueId;
-    } else if (status === "LIVE" && this.topLeagueIds.length > 0) {
+    } else if (this.qualifiedLeagueIds.length > 0) {
-      // Filter live matches by top leagues by default if no leagueId is provided
+      // Only show matches from qualified leagues (leagues with historical data for AI analysis)
-      where.leagueId = { in: this.topLeagueIds };
+      where.leagueId = { in: this.qualifiedLeagueIds };
     }
 
     if (status === "LIVE") {
@@ -298,7 +327,9 @@ export class MatchesService {
           country: {
             id: match.league?.country?.id || "",
             name: match.league?.country?.name || "",
-            flagUrl: match.league?.country?.flagUrl || undefined,
+            flagUrl:
+              match.league?.country?.flagUrl ||
+              this.getCountryFlagUrl(match.league?.country?.id),
           },
           sport: sport,
           matches: [],
@@ -353,11 +384,11 @@ export class MatchesService {
         htScoreAway: undefined,
         homeTeamName: match.homeTeam?.name || "Unknown",
         homeTeamLogo: match.homeTeamId
-          ? `https://file.mackolikfeeds.com/teams/${match.homeTeamId}`
+          ? this.getTeamLogoUrl(match.homeTeamId)
           : undefined,
         awayTeamName: match.awayTeam?.name || "Unknown",
         awayTeamLogo: match.awayTeamId
-          ? `https://file.mackolikfeeds.com/teams/${match.awayTeamId}`
+          ? this.getTeamLogoUrl(match.awayTeamId)
           : undefined,
         leagueName: match.league?.name,
         countryName: match.league?.country?.name,
@@ -365,48 +396,62 @@ export class MatchesService {
       });
     }
 
-    return Array.from(leaguesMap.values());
+    return Array.from(leaguesMap.values()).sort((a, b) => {
+      const aIdx = this.topLeagueIds.indexOf(a.id);
+      const bIdx = this.topLeagueIds.indexOf(b.id);
+      const aPriority = aIdx === -1 ? 999 : aIdx;
+      const bPriority = bIdx === -1 ? 999 : bIdx;
+
+      if (aPriority !== bPriority) return aPriority - bPriority;
+      return (a.name || "").localeCompare(b.name || "");
+    });
   }
 
   /**
    * Get active leagues with match counts
    */
   async getActiveLeagues(sport: Sport): Promise<ActiveLeagueDto[]> {
+    // Start of today in UTC — same reference point as findMatches browse filter
+    const today = new Date();
+    today.setUTCHours(0, 0, 0, 0);
+    const todayMs = BigInt(today.getTime());
+
+    // Build finished statuses/states for exclusion (mirroring getBrowseFilter logic)
+    const finishedStatuses = FINISHED_STATUS_VALUES_FOR_DB;
+    const finishedStates = FINISHED_STATE_VALUES_FOR_DB;
+    const liveStatuses = LIVE_STATUS_VALUES_FOR_DB;
+    const liveStates = LIVE_STATE_VALUES_FOR_DB;
+
     // Use raw query for complex aggregation
+    // Filter: (mstUtc >= today AND NOT finished) OR is currently live
     const leagues = await this.prisma.$queryRaw<any[]>`
       SELECT 
         l.id, l.name, l.code, 
+        c.id as country_id,
         c.name as country_name, 
         c.flag_url as country_flag,
         COUNT(lm.id)::int as match_count,
-        COUNT(CASE WHEN lm.status IN ('LIVE', '1H', '2H', 'HT', '1Q', '2Q', '3Q', '4Q', 'Playing', 'Half Time') 
+        COUNT(CASE WHEN lm.status IN (${Prisma.join(liveStatuses)})
-                   OR lm.state IN ('live', 'firsthalf', 'secondhalf') THEN 1 END)::int as live_count
+                   OR lm.state IN (${Prisma.join(liveStates)}) THEN 1 END)::int as live_count
       FROM live_matches lm
       JOIN leagues l ON lm.league_id = l.id
       LEFT JOIN countries c ON l.country_id = c.id
       WHERE lm.sport = ${sport}
-      ${this.topLeagueIds.length > 0 ? Prisma.sql`AND l.id IN (${Prisma.join(this.topLeagueIds)})` : Prisma.empty}
+      ${this.qualifiedLeagueIds.length > 0 ? Prisma.sql`AND l.id IN (${Prisma.join(this.qualifiedLeagueIds)})` : Prisma.empty}
-      GROUP BY l.id, l.name, l.code, c.name, c.flag_url
+      AND (
+        (lm.mst_utc >= ${todayMs} AND lm.status NOT IN (${Prisma.join(finishedStatuses)}) AND COALESCE(lm.state, '') NOT IN (${Prisma.join(finishedStates)}))
+        OR lm.status IN (${Prisma.join(liveStatuses)})
+        OR lm.state IN (${Prisma.join(liveStates)})
+      )
+      GROUP BY l.id, l.name, l.code, c.id, c.name, c.flag_url
       ORDER BY l.name ASC
     `;
 
-    // Priority sorting (Mackolik style)
-    const PRIORITY = [
-      "Trendyol Süper Lig",
-      "Süper Lig",
-      "Trendyol 1. Lig",
-      "1. Lig",
-      "Premier Lig",
-      "LaLiga",
-      "Serie A",
-      "Bundesliga",
-      "Ligue 1",
-    ];
-
     return leagues
+      .filter((l) => l.match_count > 0)
       .sort((a, b) => {
-        const aIdx = PRIORITY.findIndex((p) => a.name?.includes(p));
+        const aIdx = this.topLeagueIds.indexOf(a.id);
-        const bIdx = PRIORITY.findIndex((p) => b.name?.includes(p));
+        const bIdx = this.topLeagueIds.indexOf(b.id);
 
         const aPriority = aIdx === -1 ? 999 : aIdx;
         const bPriority = bIdx === -1 ? 999 : bIdx;
@@ -419,7 +464,7 @@ export class MatchesService {
         name: l.name,
         code: l.code,
         countryName: l.country_name,
-        countryFlag: l.country_flag,
+        countryFlag: l.country_flag || this.getCountryFlagUrl(l.country_id),
         matchCount: l.match_count,
         liveCount: l.live_count,
       }));
@@ -458,13 +503,9 @@ export class MatchesService {
         scoreAway: m.scoreAway,
         status: m.status,
         homeTeamName: m.homeTeam?.name,
-        homeTeamLogo: m.homeTeamId
+        homeTeamLogo: m.homeTeamId ? this.getTeamLogoUrl(m.homeTeamId) : null,
-          ? `https://file.mackolikfeeds.com/teams/${m.homeTeamId}`
-          : null,
         awayTeamName: m.awayTeam?.name,
-        awayTeamLogo: m.awayTeamId
+        awayTeamLogo: m.awayTeamId ? this.getTeamLogoUrl(m.awayTeamId) : null,
-          ? `https://file.mackolikfeeds.com/teams/${m.awayTeamId}`
-          : null,
         leagueName: m.league?.name,
         countryName: m.league?.country?.name,
       })),
@@ -587,12 +628,19 @@ export class MatchesService {
           // Fill missing relations with empty arrays
           teamStats: [],
           playerParticipations: (() => {
-            const parsed: Array<{ teamId: string; isStarting: boolean; shirtNumber: string | number | null; position: string | null; player: { id: string; name: string } }> = [];
+            const parsed: Array<{
-            const canTrustFeedLineups = displayStatus === "LIVE" || displayStatus === "Finished";
+              teamId: string;
+              isStarting: boolean;
+              shirtNumber: string | number | null;
+              position: string | null;
+              player: { id: string; name: string };
+            }> = [];
+            const canTrustFeedLineups =
+              displayStatus === "LIVE" || displayStatus === "Finished";
             if (!canTrustFeedLineups) {
               return parsed;
             }
-            if (liveMatch.lineups && typeof liveMatch.lineups === 'object') {
+            if (liveMatch.lineups && typeof liveMatch.lineups === "object") {
               const lu = liveMatch.lineups as Record<string, any>;
               const addPlayers = (teamLu: any, teamId: string | null) => {
                 if (!teamLu || !teamId) return;
@@ -603,7 +651,11 @@ export class MatchesService {
                       isStarting: true,
                       shirtNumber: p.shirtNumber || p.number,
                       position: p.position || p.pos,
-                      player: { id: p.personId || p.id || p.playerId || 'unknown', name: p.matchName || p.name || p.playerName || 'Bilinmiyor' }
+                      player: {
+                        id: p.personId || p.id || p.playerId || "unknown",
+                        name:
+                          p.matchName || p.name || p.playerName || "Bilinmiyor",
+                      },
                     });
                   });
                 }
@@ -614,7 +666,11 @@ export class MatchesService {
                       isStarting: false,
                       shirtNumber: p.shirtNumber || p.number,
                       position: p.position || p.pos,
-                      player: { id: p.personId || p.id || p.playerId || 'unknown', name: p.matchName || p.name || p.playerName || 'Bilinmiyor' }
+                      player: {
+                        id: p.personId || p.id || p.playerId || "unknown",
+                        name:
+                          p.matchName || p.name || p.playerName || "Bilinmiyor",
+                      },
                     });
                   });
                 }
@@ -641,7 +697,8 @@ export class MatchesService {
       scoreHome: match.scoreHome,
       scoreAway: match.scoreAway,
     });
-    const canTrustStoredLineups = this.canTrustStoredLineups(detailDisplayStatus);
+    const canTrustStoredLineups =
+      this.canTrustStoredLineups(detailDisplayStatus);
 
     if (Array.isArray(match.playerParticipations)) {
       if (!canTrustStoredLineups) {
@@ -745,10 +802,19 @@ export class MatchesService {
       teamStats: normalizedTeamStats,
       mstUtc: Number(match.mstUtc),
       date: match.date || new Date(Number(match.mstUtc)),
-      // Ensure score is in expected format (nested object for frontend if needed, but frontend seems to use match.score.home in some places and match.scoreHome in others.
-      // The match-detail-content uses match.score.home. Match entity has scoreHome/scoreAway fields.
-      // Let's ensure compatibility.
       score: match.score || { home: match.scoreHome, away: match.scoreAway },
+      homeTeam: {
+        ...match.homeTeam,
+        logo: match.homeTeamId
+          ? this.getTeamLogoUrl(match.homeTeamId)
+          : match.homeTeam?.logoUrl || null,
+      },
+      awayTeam: {
+        ...match.awayTeam,
+        logo: match.awayTeamId
+          ? this.getTeamLogoUrl(match.awayTeamId)
+          : match.awayTeam?.logoUrl || null,
+      },
       stats: {
         home: this.normalizeTeamStat(homeStat, match.sport),
         away: this.normalizeTeamStat(awayStat, match.sport),
@@ -865,9 +931,7 @@ export class MatchesService {
 
     if (!rows.length) return [];
 
-    const latestMst = Math.max(
+    const latestMst = Math.max(...rows.map((row) => Number(row.mstUtc || 0)));
-      ...rows.map((row) => Number(row.mstUtc || 0)),
-    );
     const ageDays =
       latestMst > 0
         ? (beforeDateMs - latestMst) / (24 * 60 * 60 * 1000)
@@ -901,8 +965,7 @@ export class MatchesService {
 
       const rank = matchOrder.get(String(row.matchId)) ?? matchLimit;
       const recencyWeight = Math.max(1, matchLimit - rank);
-      const score =
+      const score = recencyWeight + (rank === 0 ? 3 : rank === 1 ? 1.5 : 0);
-        recencyWeight + (rank === 0 ? 3 : rank === 1 ? 1.5 : 0);
       const existing = playerMap.get(playerId);
 
       if (!existing) {
@@ -996,9 +1059,7 @@ export class MatchesService {
   private canTrustStoredLineups(displayStatus?: string): boolean {
     const normalized = String(displayStatus || "").toLowerCase();
     return (
-      normalized === "live" ||
+      normalized === "live" || normalized === "finished" || normalized === "ft"
-      normalized === "finished" ||
-      normalized === "ft"
     );
   }
 }

src/modules/predictions/dto/index.ts

@@ -148,6 +148,27 @@ export class MatchPickDto {
   @ApiProperty({ required: false, default: 0 })
   implied_prob?: number;
 
+  @ApiProperty({ required: false, default: 0 })
+  model_probability?: number;
+
+  @ApiProperty({ required: false, default: 0 })
+  model_edge?: number;
+
+  @ApiProperty({ required: false, default: 0 })
+  calibrated_probability?: number;
+
+  @ApiProperty({ required: false, default: 0 })
+  odds_band_probability?: number;
+
+  @ApiProperty({ required: false, default: 0 })
+  odds_band_sample?: number;
+
+  @ApiProperty({ required: false, default: 0 })
+  odds_band_edge?: number;
+
+  @ApiProperty({ required: false, default: false })
+  odds_band_aligned?: boolean;
+
   @ApiProperty()
   play_score: number;
 
@@ -171,6 +192,9 @@ export class MatchPickDto {
     enum: ["CORE", "VALUE", "LEAN", "LONGSHOT", "PASS"],
   })
   signal_tier?: SignalTier;
+
+  @ApiProperty({ required: false, default: false })
+  is_guaranteed?: boolean;
 }
 
 export class MatchBetAdviceDto {
@@ -227,6 +251,27 @@ export class MatchBetSummaryItemDto {
   @ApiProperty({ required: false, default: 0 })
   implied_prob?: number;
 
+  @ApiProperty({ required: false, default: 0 })
+  model_probability?: number;
+
+  @ApiProperty({ required: false, default: 0 })
+  model_edge?: number;
+
+  @ApiProperty({ required: false, default: 0 })
+  calibrated_probability?: number;
+
+  @ApiProperty({ required: false, default: 0 })
+  odds_band_probability?: number;
+
+  @ApiProperty({ required: false, default: 0 })
+  odds_band_sample?: number;
+
+  @ApiProperty({ required: false, default: 0 })
+  odds_band_edge?: number;
+
+  @ApiProperty({ required: false, default: false })
+  odds_band_aligned?: boolean;
+
   @ApiProperty({ required: false, default: 0 })
   odds?: number;
 

src/modules/predictions/predictions.controller.ts

@@ -21,12 +21,17 @@ import {
   GeneratePredictionDto,
   SmartCouponRequestDto,
 } from "./dto/predictions-request.dto";
-import { Public } from "src/common/decorators";
+import { CurrentUser } from "src/common/decorators";
+import { AnalysisService } from "../analysis/analysis.service";
+import { ForbiddenException } from "@nestjs/common";
 
 @ApiTags("Predictions")
 @Controller("predictions")
 export class PredictionsController {
-  constructor(private readonly predictionsService: PredictionsService) {}
+  constructor(
+    private readonly predictionsService: PredictionsService,
+    private readonly analysisService: AnalysisService,
+  ) {}
 
   /**
    * GET /predictions/health
@@ -93,7 +98,6 @@ export class PredictionsController {
    * Get prediction for a specific match
    */
   @Get(":matchId")
-  @Public()
   @ApiOperation({ summary: "Get prediction for a specific match" })
   @ApiParam({ name: "matchId", description: "Match ID" })
   @ApiResponse({
@@ -103,11 +107,23 @@ export class PredictionsController {
     type: MatchPredictionDto,
   })
   @ApiResponse({ status: 404, description: "Match not found" })
+  @ApiResponse({ status: 403, description: "Daily limit exceeded" })
   async getPrediction(
     @Param("matchId") matchId: string,
+    @CurrentUser() user: any,
   ): Promise<MatchPredictionDto> {
+    const canProceed = await this.analysisService.checkUsageLimit(
+      user.id,
+      false,
+      1,
+    );
+    if (!canProceed) {
+      throw new ForbiddenException("ANALYSIS_LIMIT_EXCEEDED");
+    }
+
     const cached = await this.predictionsService.getCachedPrediction(matchId);
     if (cached) {
+      await this.analysisService.recordUsage(user.id, false);
       return cached;
     }
 
@@ -115,9 +131,10 @@ export class PredictionsController {
     const prediction = await this.predictionsService.getPredictionById(matchId);
 
     if (!prediction) {
-      throw new NotFoundException(`Match not found: ${matchId}`);
+      throw new NotFoundException("MATCH_NOT_FOUND");
     }
 
+    await this.analysisService.recordUsage(user.id, false);
     return prediction;
   }
 
@@ -129,17 +146,29 @@ export class PredictionsController {
   @HttpCode(HttpStatus.OK)
   @ApiOperation({ summary: "Generate prediction with provided match data" })
   @ApiResponse({ status: 200, type: MatchPredictionDto })
+  @ApiResponse({ status: 403, description: "Daily limit exceeded" })
   async generatePrediction(
+    @CurrentUser() user: any,
     @Body() dto: GeneratePredictionDto,
   ): Promise<MatchPredictionDto> {
+    const canProceed = await this.analysisService.checkUsageLimit(
+      user.id,
+      false,
+      1,
+    );
+    if (!canProceed) {
+      throw new ForbiddenException("ANALYSIS_LIMIT_EXCEEDED");
+    }
+
     const prediction = await this.predictionsService.getPredictionWithData({
       matchId: dto.matchId,
     });
 
     if (!prediction) {
-      throw new NotFoundException("Failed to generate prediction");
+      throw new NotFoundException("PREDICTION_GENERATION_FAILED");
     }
 
+    await this.analysisService.recordUsage(user.id, false);
     return prediction;
   }
 
@@ -157,7 +186,20 @@ export class PredictionsController {
     description: "Smart coupon generated successfully",
     schema: { type: "object" },
   })
-  async generateSmartCoupon(@Body() dto: SmartCouponRequestDto): Promise<any> {
+  @ApiResponse({ status: 403, description: "Daily limit exceeded" })
+  async generateSmartCoupon(
+    @CurrentUser() user: any,
+    @Body() dto: SmartCouponRequestDto,
+  ): Promise<any> {
+    const canProceed = await this.analysisService.checkUsageLimit(
+      user.id,
+      true,
+      dto.matchIds?.length || 1,
+    );
+    if (!canProceed) {
+      throw new ForbiddenException("COUPON_LIMIT_EXCEEDED");
+    }
+
     const coupon = await this.predictionsService.getSmartCoupon(
       dto.matchIds,
       dto.strategy || "BALANCED",
@@ -168,9 +210,10 @@ export class PredictionsController {
     );
 
     if (!coupon) {
-      throw new NotFoundException("Failed to generate Smart Coupon");
+      throw new NotFoundException("SMART_COUPON_GENERATION_FAILED");
     }
 
+    await this.analysisService.recordUsage(user.id, true);
     return coupon;
   }
 }

src/modules/predictions/predictions.module.ts

@@ -10,6 +10,7 @@ import { PredictionsQueue } from "./queues/predictions.queue";
 import { PredictionsProcessor } from "./queues/predictions.processor";
 import { PREDICTIONS_QUEUE } from "./queues/predictions.types";
 import { FeederModule } from "../feeder/feeder.module";
+import { AnalysisModule } from "../analysis/analysis.module";
 
 const redisEnabled = process.env.REDIS_ENABLED === "true";
 
@@ -25,6 +26,7 @@ const redisEnabled = process.env.REDIS_ENABLED === "true";
       : []),
     MatchesModule,
     FeederModule,
+    AnalysisModule,
   ],
   controllers: [PredictionsController],
   providers: [

src/modules/predictions/predictions.service.ts

@@ -21,6 +21,10 @@ import {
 } from "./dto";
 import { Prisma } from "@prisma/client";
 import { FeederService } from "../feeder/feeder.service";
+import {
+  isMatchCompleted,
+  isMatchLive,
+} from "../../common/utils/match-status.util";
 import * as fs from "node:fs";
 import * as path from "node:path";
 import {
@@ -49,14 +53,14 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
   private queueEvents: QueueEvents | null = null;
   private readonly aiEngineUrl: string;
   private readonly aiEngineClient: AiEngineClient;
-  private readonly topLeagueIds = new Set<string>();
+  private readonly qualifiedLeagueIds = new Set<string>();
   private readonly reasonTranslations: Record<string, string> = {
     confidence_below_threshold: "Güven eşiğin altında",
     confidence_interval_too_wide: "Güven aralığı çok geniş",
     confidence_interval_too_wide_for_main_pick:
       "Ana seçim için güven aralığı çok geniş",
     confidence_band_low: "Güven bandı düşük",
-    playable_edge_found: "Oynanabilir avantaj bulundu",
+    playable_edge_found: "Model avantaj sinyali bulundu",
     market_signal_dominant: "Piyasa sinyali baskın",
     team_form_signal_dominant: "Takım formuna dayalı sinyaller çok baskın",
     lineup_signal_strong: "İlk on bir sinyali güçlü",
@@ -73,7 +77,12 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
     limited_data_confidence: "Veri kısıtlı olduğu için güven sınırlı",
     data_quality_issue: "Veri kalitesi sorunu var",
     high_risk_low_data_quality: "Risk yüksek, veri kalitesi düşük",
-    insufficient_play_score: "Oynanabilirlik puanı yetersiz",
+    insufficient_play_score: "Model sinyali yetersiz",
+    odds_band_confirms_value: "Tarihsel oran bandı değeri doğruluyor",
+    odds_band_sample_too_low: "Tarihsel oran bandı örneklemi yetersiz",
+    odds_band_missing_probability: "Tarihsel oran bandı olasılığı yok",
+    odds_band_unavailable: "Tarihsel oran bandı kullanılamıyor",
+    odds_band_not_aligned: "Model ve tarihsel oran bandı aynı yönde değil",
     no_bet_conditions_met: "Bahis koşulları oluşmadı",
     market_passed_all_gates: "Market tüm güvenlik kontrollerini geçti",
     no_ev_edge_minimum_stake:
@@ -125,10 +134,7 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
     private readonly feederService: FeederService,
     @Optional() private readonly predictionsQueue?: PredictionsQueue,
   ) {
-    this.aiEngineUrl = this.configService.get(
+    this.aiEngineUrl = this.resolveAiEngineUrl();
-      "AI_ENGINE_URL",
-      "http://localhost:8000",
-    );
     this.aiEngineClient = new AiEngineClient({
       baseUrl: this.aiEngineUrl,
       logger: this.logger,
@@ -137,7 +143,7 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
       maxRetries: 2,
       retryDelayMs: 750,
     });
-    this.topLeagueIds = this.loadTopLeagueIds();
+    this.qualifiedLeagueIds = this.loadQualifiedLeagueIds();
   }
 
   onModuleInit() {
@@ -155,6 +161,11 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
     }
   }
 
+  private predictionMemCache = new Map<
+    string,
+    { timestamp: number; payload: MatchPredictionDto }
+  >();
+
   async onModuleDestroy() {
     if (this.queueEvents) {
       await this.queueEvents.close();
@@ -177,8 +188,7 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
       return {
         status: response.data?.status || "healthy",
         modelLoaded: response.data?.model_loaded ?? true,
-        predictionServiceReady:
+        predictionServiceReady: response.data?.prediction_service_ready ?? true,
-          response.data?.prediction_service_ready ?? true,
         aiEngineReachable: true,
         circuitState: circuit.state,
         consecutiveFailures: circuit.consecutiveFailures,
@@ -238,10 +248,21 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
       const status = requestError.status;
       const detail = requestError.detail || requestError.message;
 
+      // ── Cooldown fallback cascade: memCache → DB stored → DB cached → wait & retry ──
       if (
         status === HttpStatus.SERVICE_UNAVAILABLE &&
         this.hasCooldown(detail)
       ) {
+        // 1) In-memory cache (10min TTL)
+        const memCached = this.predictionMemCache.get(matchId);
+        if (memCached && Date.now() - memCached.timestamp < 10 * 60 * 1000) {
+          this.logger.warn(
+            `AI Engine cooldown for ${matchId}; returning mem-cached prediction`,
+          );
+          return memCached.payload;
+        }
+
+        // 2) DB stored prediction (no TTL filter)
         const storedPrediction = await this.getStoredPrediction(matchId);
         if (storedPrediction) {
           this.logger.warn(
@@ -249,13 +270,46 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
           );
           return this.enrichPredictionResponse(storedPrediction, matchContext);
         }
+
+        // 3) DB cached prediction (with model version check)
+        const cachedPrediction = await this.getCachedPrediction(matchId);
+        if (cachedPrediction) {
+          this.logger.warn(
+            `AI Engine cooldown for ${matchId}; returning cached prediction`,
+          );
+          return this.enrichPredictionResponse(cachedPrediction, matchContext);
+        }
+
+        // 4) No cached data at all — return null gracefully
+        this.logger.warn(
+          `AI Engine cooldown for ${matchId}; no cached data available — returning null gracefully`,
+        );
+        return null;
+      }
+
+      // ── Non-cooldown errors (e.g. AI Engine 500 for this match) ──
+      // Try DB fallback before giving up
+      const storedFallback = await this.getStoredPrediction(matchId);
+      if (storedFallback) {
+        this.logger.warn(
+          `AI Engine failed for ${matchId} (status=${status}); returning stored prediction as fallback`,
+        );
+        return this.enrichPredictionResponse(storedFallback, matchContext);
+      }
+
+      const cachedFallback = await this.getCachedPrediction(matchId);
+      if (cachedFallback) {
+        this.logger.warn(
+          `AI Engine failed for ${matchId} (status=${status}); returning cached prediction as fallback`,
+        );
+        return this.enrichPredictionResponse(cachedFallback, matchContext);
       }
 
       this.logger.error(
         `Direct AI Engine call failed for ${matchId}: status=${status}, detail=${JSON.stringify(detail)}`,
       );
 
-      // Forward AI Engine's actual error
+      // Forward AI Engine's actual error for client-meaningful statuses
       if (status === 404) {
         throw new HttpException(
           `Match not found in AI Engine: ${matchId}`,
@@ -268,10 +322,13 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
           HttpStatus.UNPROCESSABLE_ENTITY,
         );
       }
-      throw new HttpException(
+
-        `AI Engine error: ${typeof detail === "string" ? detail : JSON.stringify(detail)}`,
+      // For server errors (500, 503 etc.) return null instead of throwing
-        status || HttpStatus.SERVICE_UNAVAILABLE,
+      // This prevents the user from seeing raw 503 errors
+      this.logger.warn(
+        `AI Engine server error for ${matchId}; returning null gracefully instead of ${status}`,
       );
+      return null;
     }
   }
 
@@ -330,37 +387,95 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
             match_date_ms: Number(p.match.mstUtc) * 1000,
             league: p.match.league?.name || "",
             league_id: p.match.leagueId,
-            is_top_league: this.topLeagueIds.has(p.match.leagueId ?? ""),
+            is_top_league: this.qualifiedLeagueIds.has(p.match.leagueId ?? ""),
           },
         } as unknown as MatchPredictionDto;
       }),
     };
   }
 
-  private loadTopLeagueIds(): Set<string> {
+  private loadQualifiedLeagueIds(): Set<string> {
     try {
-      const topLeaguesPath = path.join(process.cwd(), "top_leagues.json");
+      const filePath = path.join(process.cwd(), "qualified_leagues.json");
-      if (!fs.existsSync(topLeaguesPath)) {
+      if (!fs.existsSync(filePath)) {
+        this.logger.warn(
+          "qualified_leagues.json not found — all leagues allowed",
+        );
         return new Set<string>();
       }
 
-      const raw = JSON.parse(fs.readFileSync(topLeaguesPath, "utf8"));
+      const raw = JSON.parse(fs.readFileSync(filePath, "utf8"));
       if (!Array.isArray(raw)) {
         return new Set<string>();
       }
 
-      return new Set(
+      const ids = new Set(
         raw
           .map((value) => String(value).trim())
           .filter((value) => value.length > 0),
       );
+      this.logger.log(`Loaded ${ids.size} qualified league IDs`);
+      return ids;
     } catch (error) {
       const message = error instanceof Error ? error.message : String(error);
-      this.logger.warn(`Failed to load top_leagues.json: ${message}`);
+      this.logger.warn(`Failed to load qualified_leagues.json: ${message}`);
       return new Set<string>();
     }
   }
 
+  private resolveAiEngineUrl(): string {
+    const configuredUrl = this.configService.get(
+      "AI_ENGINE_URL",
+      "http://localhost:8000",
+    );
+    const localEnvUrl = this.readLocalEnvValue("AI_ENGINE_URL");
+
+    if (
+      process.env.NODE_ENV !== "production" &&
+      localEnvUrl &&
+      localEnvUrl !== configuredUrl &&
+      this.isLocalhostUrl(configuredUrl) &&
+      this.isLocalhostUrl(localEnvUrl)
+    ) {
+      this.logger.warn(
+        `AI_ENGINE_URL inherited from parent process (${configuredUrl}) differs from .env.local (${localEnvUrl}); using .env.local for local development`,
+      );
+      return localEnvUrl;
+    }
+
+    return configuredUrl;
+  }
+
+  private readLocalEnvValue(key: string): string | null {
+    const filePath = path.join(process.cwd(), ".env.local");
+    if (!fs.existsSync(filePath)) {
+      return null;
+    }
+
+    const line = fs
+      .readFileSync(filePath, "utf8")
+      .split(/\r?\n/u)
+      .find((entry) => entry.trim().startsWith(`${key}=`));
+
+    if (!line) {
+      return null;
+    }
+
+    return line
+      .slice(line.indexOf("=") + 1)
+      .trim()
+      .replace(/^['"]|['"]$/gu, "");
+  }
+
+  private isLocalhostUrl(value: string): boolean {
+    try {
+      const url = new URL(value);
+      return ["localhost", "127.0.0.1", "::1"].includes(url.hostname);
+    } catch {
+      return false;
+    }
+  }
+
   private async getMatchContext(matchId: string): Promise<MatchContext> {
     const match = await this.prisma.match.findUnique({
       where: { id: matchId },
@@ -370,7 +485,7 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
     if (match) {
       return {
         leagueId: match.leagueId ?? null,
-        isTopLeague: this.topLeagueIds.has(match.leagueId ?? ""),
+        isTopLeague: this.qualifiedLeagueIds.has(match.leagueId ?? ""),
       };
     }
 
@@ -381,7 +496,7 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
 
     return {
       leagueId: liveMatch?.leagueId ?? null,
-      isTopLeague: this.topLeagueIds.has(liveMatch?.leagueId ?? ""),
+      isTopLeague: this.qualifiedLeagueIds.has(liveMatch?.leagueId ?? ""),
     };
   }
 
@@ -645,6 +760,7 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
       ),
       confidence_interval: interval,
       signal_tier: this.classifySignalTier(record, interval),
+      is_guaranteed: false,
     };
   }
 
@@ -731,20 +847,27 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
       return this.reasonTranslations[normalized];
     }
 
-    const evMatch = normalized.match(/^ev_edge_([+\-][\d.]+%)_grade_(\w)$/);
+    const evMatch = normalized.match(/^ev_edge_([-+][\d.]+%)_grade_(\w)$/);
     if (evMatch) {
-      return `Beklenen avantaj ${evMatch[1]} (Not ${evMatch[2]})`;
+      return `Teorik avantaj sinyali: Not ${evMatch[2]}`;
     }
 
     const negativeEdgeMatch = normalized.match(
-      /^negative_model_edge_([+\-]?[\d.]+)$/,
+      /^negative_model_edge_([-+]?[\d.]+)$/,
     );
     if (negativeEdgeMatch) {
       return `Model avantajı negatif (${negativeEdgeMatch[1]})`;
     }
 
+    const bandNoValueMatch = normalized.match(
+      /^odds_band_no_value_([-+]?[\d.]+)$/,
+    );
+    if (bandNoValueMatch) {
+      return `Tarihsel oran bandı değeri doğrulamadı (${bandNoValueMatch[1]})`;
+    }
+
     const edgeThresholdMatch = normalized.match(
-      /^below_market_edge_threshold_([+\-]?[\d.]+)$/,
+      /^below_market_edge_threshold_([-+]?[\d.]+)$/,
     );
     if (edgeThresholdMatch) {
       return `Piyasa avantaj eşiğinin altında (${edgeThresholdMatch[1]})`;
@@ -1071,10 +1194,11 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
 
     // Direct HTTP mode
     try {
-      const response = await this.aiEngineClient.post(
+      const response = await this.aiEngineClient.post("/smart-coupon", {
-        "/smart-coupon",
+        match_ids: matchIds,
-        { match_ids: matchIds, strategy, ...options },
+        strategy,
-      );
+        ...options,
+      });
       return response.data;
     } catch (error: unknown) {
       const message =
@@ -1130,8 +1254,26 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
   }
 
   async cachePrediction(matchId: string, prediction: MatchPredictionDto) {
+    this.predictionMemCache.set(matchId, {
+      timestamp: Date.now(),
+      payload: prediction,
+    });
+    if (this.predictionMemCache.size > 500) {
+      const firstKey = this.predictionMemCache.keys().next().value;
+      if (firstKey) this.predictionMemCache.delete(firstKey);
+    }
+
     const payload = prediction as unknown as Prisma.InputJsonObject;
     try {
+      const existsInMatch = await this.prisma.match.findUnique({
+        where: { id: matchId },
+        select: { id: true },
+      });
+
+      if (!existsInMatch) {
+        return;
+      }
+
       await this.prisma.prediction.upsert({
         where: { matchId },
         update: {
@@ -1151,6 +1293,16 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
   async getCachedPrediction(
     matchId: string,
   ): Promise<MatchPredictionDto | null> {
+    const memCached = this.predictionMemCache.get(matchId);
+    if (memCached) {
+      if (Date.now() - memCached.timestamp < 10 * 60 * 1000) {
+        // 10 mins TTL
+        return memCached.payload;
+      } else {
+        this.predictionMemCache.delete(matchId);
+      }
+    }
+
     const prediction = await this.prisma.prediction.findUnique({
       where: { matchId },
     });
@@ -1201,6 +1353,25 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
     return false;
   }
 
+  private extractCooldownMs(detail: unknown): number {
+    if (
+      detail &&
+      typeof detail === "object" &&
+      "cooldownRemainingMs" in detail
+    ) {
+      return (
+        Number((detail as Record<string, unknown>).cooldownRemainingMs) || 0
+      );
+    }
+
+    if (typeof detail === "string") {
+      const match = detail.match(/cooldownRemainingMs[":\s]+(\d+)/);
+      return match ? Number(match[1]) : 0;
+    }
+
+    return 0;
+  }
+
   private async ensureSmartCouponDataReady(matchIds: string[]): Promise<void> {
     const uniqueMatchIds = [...new Set(matchIds.filter((id) => !!id))];
     if (uniqueMatchIds.length === 0) {
@@ -1216,32 +1387,38 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
   }
 
   private async ensurePredictionDataReady(matchId: string): Promise<void> {
-    const [liveMatch, persistedMatch, oddCategoryCount] = await Promise.all([
+    const [liveMatch, persistedMatch, oddCategoryCount, lineupCount] =
-      this.prisma.liveMatch.findUnique({
+      await Promise.all([
-        where: { id: matchId },
+        this.prisma.liveMatch.findUnique({
-        select: {
+          where: { id: matchId },
-          id: true,
+          select: {
-          odds: true,
+            id: true,
-          state: true,
+            odds: true,
-          status: true,
+            state: true,
-          scoreHome: true,
+            status: true,
-          scoreAway: true,
+            scoreHome: true,
-        },
+            scoreAway: true,
-      }),
+            leagueId: true,
-      this.prisma.match.findUnique({
+          },
-        where: { id: matchId },
+        }),
-        select: {
+        this.prisma.match.findUnique({
-          id: true,
+          where: { id: matchId },
-          state: true,
+          select: {
-          status: true,
+            id: true,
-          scoreHome: true,
+            state: true,
-          scoreAway: true,
+            status: true,
-        },
+            scoreHome: true,
-      }),
+            scoreAway: true,
-      this.prisma.oddCategory.count({
+            leagueId: true,
-        where: { matchId },
+          },
-      }),
+        }),
-    ]);
+        this.prisma.oddCategory.count({
+          where: { matchId },
+        }),
+        this.prisma.matchPlayerParticipation.count({
+          where: { matchId },
+        }),
+      ]);
 
     const hasLiveOdds =
       !!liveMatch?.odds &&
@@ -1257,27 +1434,68 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
       );
     }
 
+    // League qualification gate: reject predictions for leagues without
+    // sufficient historical training data (odds + lineups + stats)
+    const leagueId = liveMatch?.leagueId || persistedMatch?.leagueId;
+    if (
+      this.qualifiedLeagueIds.size > 0 &&
+      (!leagueId || !this.qualifiedLeagueIds.has(leagueId))
+    ) {
+      throw new HttpException(
+        `Bu lig için yeterli geçmiş veri bulunmuyor. Tahmin yapılamaz.`,
+        HttpStatus.UNPROCESSABLE_ENTITY,
+      );
+    }
+
     const state = liveMatch?.state || persistedMatch?.state;
     const status = liveMatch?.status || persistedMatch?.status;
     const scoreHome = liveMatch?.scoreHome ?? persistedMatch?.scoreHome;
     const scoreAway = liveMatch?.scoreAway ?? persistedMatch?.scoreAway;
-    const hasScores =
-      scoreHome !== null &&
-      scoreHome !== undefined &&
-      scoreAway !== null &&
-      scoreAway !== undefined;
 
-    const isFinished =
+    const isFinished = isMatchCompleted({
-      hasScores ||
+      state: state ?? null,
-      state === "MS" ||
+      status: status ?? null,
-      state === "postGame" ||
+      scoreHome,
-      ["Finished", "Played", "FT", "AET", "PEN", "Ended"].includes(
+      scoreAway,
-        status as string,
+    });
-      );
+
+    const isLive = isMatchLive({
+      state: state ?? null,
+      status: status ?? null,
+    });
 
     const hasOdds = hasLiveOdds || oddCategoryCount > 0;
 
-    if (hasOdds || isFinished) {
+    if (hasOdds || isFinished || isLive) {
+      // ── Lineup guard: fetch lineups if missing before analysis ──
+      // A proper football lineup has at least 11 starting players (22 total
+      // with subs). If we have fewer than 11 participation records, the
+      // lineup data is likely missing — attempt to fetch it from source.
+      if (lineupCount < 11) {
+        this.logger.log(
+          `[${matchId}] ⚠️ Lineups missing (${lineupCount} players in DB). Fetching from source before analysis...`,
+        );
+        try {
+          const refreshResult = await this.feederService.refreshMatch(
+            matchId,
+            "lineups",
+          );
+          if (refreshResult.success) {
+            this.logger.log(
+              `[${matchId}] ✅ Lineups fetched successfully before analysis`,
+            );
+          } else {
+            this.logger.warn(
+              `[${matchId}] ⚠️ Lineup fetch returned failure — proceeding with existing data. Error: ${refreshResult.error ?? "unknown"}`,
+            );
+          }
+        } catch (err: unknown) {
+          const message = err instanceof Error ? err.message : String(err);
+          this.logger.warn(
+            `[${matchId}] ⚠️ Lineup fetch exception — proceeding with existing data. ${message}`,
+          );
+        }
+      }
       return;
     }
 
@@ -1315,7 +1533,9 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
       );
     } catch (error) {
       const message = error instanceof Error ? error.message : String(error);
-      this.logger.warn(`Prediction run audit skipped for ${matchId}: ${message}`);
+      this.logger.warn(
+        `Prediction run audit skipped for ${matchId}: ${message}`,
+      );
     }
   }
 
@@ -1363,8 +1583,15 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
           pick: item.pick,
           playable: item.playable,
           bet_grade: item.bet_grade,
+          odds: item.odds,
+          model_edge: item.model_edge,
+          calibrated_probability: item.calibrated_probability,
           calibrated_confidence: item.calibrated_confidence,
           ev_edge: item.ev_edge ?? 0,
+          odds_band_probability: item.odds_band_probability,
+          odds_band_sample: item.odds_band_sample,
+          odds_band_edge: item.odds_band_edge,
+          odds_band_aligned: item.odds_band_aligned,
           stake_units: item.stake_units,
         }))
       : [];
@@ -1380,8 +1607,15 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
             pick: payload.main_pick.pick,
             playable: payload.main_pick.playable,
             bet_grade: payload.main_pick.bet_grade,
+            odds: payload.main_pick.odds,
+            model_edge: payload.main_pick.model_edge,
+            calibrated_probability: payload.main_pick.calibrated_probability,
             calibrated_confidence: payload.main_pick.calibrated_confidence,
             ev_edge: payload.main_pick.ev_edge ?? 0,
+            odds_band_probability: payload.main_pick.odds_band_probability,
+            odds_band_sample: payload.main_pick.odds_band_sample,
+            odds_band_edge: payload.main_pick.odds_band_edge,
+            odds_band_aligned: payload.main_pick.odds_band_aligned,
             stake_units: payload.main_pick.stake_units,
           }
         : null,
@@ -1391,14 +1625,15 @@ export class PredictionsService implements OnModuleInit, OnModuleDestroy {
             pick: payload.value_pick.pick,
             playable: payload.value_pick.playable,
             bet_grade: payload.value_pick.bet_grade,
+            odds: payload.value_pick.odds,
+            model_edge: payload.value_pick.model_edge,
             calibrated_confidence: payload.value_pick.calibrated_confidence,
             ev_edge: payload.value_pick.ev_edge ?? 0,
           }
         : null,
       bet_advice: {
         playable: payload.bet_advice?.playable ?? false,
-        suggested_stake_units:
+        suggested_stake_units: payload.bet_advice?.suggested_stake_units ?? 0,
-          payload.bet_advice?.suggested_stake_units ?? 0,
         reason: payload.bet_advice?.reason ?? null,
       },
       top_summary: topSummary,

src/modules/social-poster/caption-generator.service.ts

@@ -1,6 +1,8 @@
 import { Injectable, Logger } from "@nestjs/common";
+import { ConfigService } from "@nestjs/config";
 import { GeminiService } from "../gemini/gemini.service";
 import { PredictionCardDto } from "./dto/prediction-card.dto";
+import axios from "axios";
 
 const SYSTEM_PROMPT = `Sen profesyonel bir spor analisti ve sosyal medya içerik üreticisisin.
 Verilen maç tahmin verisini kullanarak kısa, etkili ve ilgi çekici sosyal medya postları yazıyorsun.
@@ -11,6 +13,7 @@ KURALLAR:
 - Emoji kullan ama abartma (2-4 emoji yeterli)
 - Skor tahminini vurgula
 - Güven yüzdesini belirt
+- Lig, ülke, takım adları ve ana tahminleri SEO için doğal şekilde geçir
 - İlgili hashtag'leri ekle (#PremierLeague, #SüperLig vb.)
 - KESİNLİKLE "kesin kazanır", "garanti" gibi ifadeler KULLANMA
 - "Tahminimiz", "Beklentimiz", "Analizimiz" gibi ifadeler kullan
@@ -20,13 +23,31 @@ KURALLAR:
 @Injectable()
 export class CaptionGeneratorService {
   private readonly logger = new Logger(CaptionGeneratorService.name);
+  private readonly ollamaBaseUrl: string;
+  private readonly ollamaModel: string;
 
-  constructor(private readonly geminiService: GeminiService) {}
+  constructor(
+    private readonly geminiService: GeminiService,
+    private readonly configService: ConfigService,
+  ) {
+    this.ollamaBaseUrl =
+      this.configService.get<string>("OLLAMA_BASE_URL") ||
+      "http://localhost:11434";
+    this.ollamaModel =
+      this.configService.get<string>("OLLAMA_MODEL") ||
+      this.configService.get<string>("SOCIAL_POSTER_OLLAMA_MODEL") ||
+      "";
+  }
 
   /**
    * Generate a social media caption for a match prediction using Gemini AI.
    */
   async generateCaption(card: PredictionCardDto): Promise<string> {
+    if (this.ollamaModel) {
+      const caption = await this.generateWithOllama(card);
+      if (caption) return caption;
+    }
+
     if (!this.geminiService.isAvailable()) {
       this.logger.warn("Gemini not available, using template caption");
       return this.generateFallbackCaption(card);
@@ -53,6 +74,39 @@ export class CaptionGeneratorService {
     }
   }
 
+  private async generateWithOllama(card: PredictionCardDto): Promise<string> {
+    const prompt = `${SYSTEM_PROMPT}
+
+${this.buildPrompt(card)}`;
+
+    try {
+      const response = await axios.post(
+        `${this.ollamaBaseUrl.replace(/\/$/, "")}/api/generate`,
+        {
+          model: this.ollamaModel,
+          prompt,
+          stream: false,
+          options: {
+            temperature: 0.7,
+            num_predict: 260,
+          },
+        },
+        { timeout: 20000 },
+      );
+
+      const text = String(response.data?.response || "").trim();
+      if (!text) return "";
+
+      this.logger.log(
+        `Ollama caption generated for ${card.homeTeam} vs ${card.awayTeam}`,
+      );
+      return this.ensureHashtags(text, card);
+    } catch (error) {
+      this.logger.warn(`Ollama caption generation failed: ${error.message}`);
+      return "";
+    }
+  }
+
   private buildPrompt(card: PredictionCardDto): string {
     const topPicksText = card.topPicks
       .map(
@@ -64,9 +118,11 @@ export class CaptionGeneratorService {
     return `Aşağıdaki maç tahmin verisini kullanarak bir sosyal medya postu oluştur:
 
 MAÇ: ${card.homeTeam} vs ${card.awayTeam}
+SPOR: ${card.sport === "basketball" ? "Basketbol" : "Futbol"}
 LİG: ${card.leagueName}
+ÜLKE/BÖLGE: ${card.countryName || "-"}
 TARİH: ${card.matchDate}
-İLK YARI SKOR TAHMİNİ: ${card.htScore}
+${card.sport === "basketball" ? "İLK DEVRE" : "İLK YARI"} SKOR TAHMİNİ: ${card.htScore}
 MAÇ SONU SKOR TAHMİNİ: ${card.ftScore}
 SKOR GÜVEN: %${card.scoreConfidence}
 RİSK SEVİYESİ: ${card.riskLevel}
@@ -85,7 +141,8 @@ Sadece post metnini yaz, başka hiçbir şey ekleme.`;
         .replace(/[^a-zA-Z0-9üöçşğıİÜÖÇŞĞ]/g, "");
       const homeTag = card.homeTeam.replace(/\s+/g, "");
       const awayTag = card.awayTeam.replace(/\s+/g, "");
-      text += `\n\n#${leagueTag} #${homeTag} #${awayTag}`;
+      const sportTag = card.sport === "basketball" ? "Basketbol" : "Futbol";
+      text += `\n\n#${leagueTag} #${homeTag} #${awayTag} #${sportTag}`;
     }
     return text.trim();
   }
@@ -99,11 +156,14 @@ Sadece post metnini yaz, başka hiçbir şey ekleme.`;
       .replace(/\s+/g, "")
       .replace(/[^a-zA-Z0-9üöçşğıİÜÖÇŞĞ]/g, "");
 
-    return `⚡ ${card.homeTeam} vs ${card.awayTeam}
+    const sportLabel = card.sport === "basketball" ? "Basketbol" : "Futbol";
-🎯 Tahminimiz: ${card.ftScore} (İY: ${card.htScore})
+    const halfLabel = card.sport === "basketball" ? "İD" : "İY";
+
+    return `⚡ ${card.leagueName}${card.countryName ? ` (${card.countryName})` : ""}: ${card.homeTeam} vs ${card.awayTeam}
+🎯 ${sportLabel} tahminimiz: ${card.ftScore} (${halfLabel}: ${card.htScore})
 📊 Güven: %${card.scoreConfidence}
 ${topPick ? `🔥 ${topPick.market}: ${topPick.pick} (%${topPick.confidence})` : ""}
 
-#${leagueTag} #SuggestBet #Bahis`.trim();
+#${leagueTag} #${sportLabel} #MaçTahmini #iddaai`.trim();
   }
 }

src/modules/social-poster/dto/prediction-card.dto.ts

@@ -21,12 +21,15 @@ export interface TopPick {
 export interface PredictionCardDto {
   // ─── Match Info ───
   matchId: string;
+  sport: "football" | "basketball";
   homeTeam: string;
   awayTeam: string;
   homeLogo: string;
   awayLogo: string;
   leagueName: string;
   leagueLogo?: string;
+  countryName?: string;
+  countryFlag?: string;
   /** Formatted date, e.g. "01 Mar 2026 - 21:00" */
   matchDate: string;
 

src/modules/social-poster/meta.service.ts

@@ -10,13 +10,16 @@ export class MetaService {
   private readonly pageId: string;
   private readonly igUserId: string;
   private readonly isEnabled: boolean;
-  private readonly graphApiBase = "https://graph.facebook.com/v21.0";
+  private readonly graphApiBase: string;
 
   constructor(private readonly configService: ConfigService) {
     this.pageAccessToken =
       this.configService.get<string>("META_PAGE_ACCESS_TOKEN") || "";
     this.pageId = this.configService.get<string>("META_PAGE_ID") || "";
     this.igUserId = this.configService.get<string>("META_IG_USER_ID") || "";
+    const graphVersion =
+      this.configService.get<string>("META_GRAPH_API_VERSION") || "v25.0";
+    this.graphApiBase = `https://graph.facebook.com/${graphVersion}`;
 
     this.isEnabled = !!(this.pageAccessToken && this.pageId);
 
@@ -63,11 +66,12 @@ export class MetaService {
         {
           url: imageUrl,
           message,
+          published: true,
           access_token: this.pageAccessToken,
         },
       );
 
-      const postId = response.data?.id;
+      const postId = response.data?.post_id || response.data?.id;
       this.logger.log(`✅ Facebook post published: ${postId}`);
       return postId || null;
     } catch (error) {
@@ -109,6 +113,7 @@ export class MetaService {
         {
           image_url: imageUrl,
           caption,
+          alt_text: this.buildAltText(caption),
           access_token: this.pageAccessToken,
         },
       );
@@ -156,7 +161,7 @@ export class MetaService {
           `${this.graphApiBase}/${containerId}`,
           {
             params: {
-              fields: "status_code",
+              fields: "status_code,status",
               access_token: this.pageAccessToken,
             },
           },
@@ -177,4 +182,12 @@ export class MetaService {
 
     this.logger.warn("Container wait timed out, attempting publish anyway");
   }
+
+  private buildAltText(caption: string): string {
+    return caption
+      .replace(/#[^\s#]+/g, "")
+      .replace(/\s+/g, " ")
+      .trim()
+      .slice(0, 900);
+  }
 }

src/modules/social-poster/social-poster.service.ts

@@ -19,6 +19,11 @@ import {
 // Top leagues loaded once
 
 const TOP_LEAGUES_PATH = path.join(process.cwd(), "top_leagues.json");
+const POSTED_STATE_PATH = path.join(
+  process.cwd(),
+  "storage",
+  "social-poster-posted.json",
+);
 
 @Injectable()
 export class SocialPosterService {
@@ -26,6 +31,9 @@ export class SocialPosterService {
   private readonly aiEngineUrl: string;
   private readonly appBaseUrl: string;
   private readonly isEnabled: boolean;
+  private readonly sports: string[];
+  private readonly windowMinMinutes: number;
+  private readonly windowMaxMinutes: number;
   private readonly postedMatchIds = new Set<string>();
   private topLeagueIds: Set<string> = new Set();
 
@@ -44,8 +52,22 @@ export class SocialPosterService {
       this.configService.get<string>("APP_BASE_URL") || "http://localhost:3000";
     this.isEnabled =
       this.configService.get<string>("SOCIAL_POSTER_ENABLED") === "true";
+    this.sports = (
+      this.configService.get<string>("SOCIAL_POSTER_SPORTS") ||
+      "football,basketball"
+    )
+      .split(",")
+      .map((sport) => sport.trim())
+      .filter(Boolean);
+    this.windowMinMinutes = Number(
+      this.configService.get<string>("SOCIAL_POSTER_WINDOW_MIN") || 25,
+    );
+    this.windowMaxMinutes = Number(
+      this.configService.get<string>("SOCIAL_POSTER_WINDOW_MAX") || 45,
+    );
 
     this.loadTopLeagues();
+    this.loadPostedState();
   }
 
   private loadTopLeagues() {
@@ -59,16 +81,45 @@ export class SocialPosterService {
     }
   }
 
+  private loadPostedState() {
+    try {
+      const data = fs.readFileSync(POSTED_STATE_PATH, "utf-8");
+      const ids = JSON.parse(data);
+      if (Array.isArray(ids)) {
+        ids.forEach((id) => this.postedMatchIds.add(String(id)));
+      }
+      this.logger.log(
+        `✅ Loaded ${this.postedMatchIds.size} posted social match IDs`,
+      );
+    } catch {
+      this.logger.warn("⚠️ No social poster state file found yet");
+    }
+  }
+
+  private savePostedState() {
+    const dir = path.dirname(POSTED_STATE_PATH);
+    if (!fs.existsSync(dir)) {
+      fs.mkdirSync(dir, { recursive: true });
+    }
+    fs.writeFileSync(
+      POSTED_STATE_PATH,
+      JSON.stringify(Array.from(this.postedMatchIds).slice(-500), null, 2),
+    );
+  }
+
   /**
-   * Cron: Every 10 minutes, check for upcoming matches.
+   * Cron: Every 15 minutes, check for upcoming matches.
    * Posts predictions 30 minutes before kickoff.
    */
-  @Cron("*/10 * * * *")
+  @Cron("*/15 * * * *")
   async checkAndPostUpcomingMatches() {
     if (!this.isEnabled) return;
 
     try {
-      const matches = await this.getUpcomingMatches(25, 40); // 25-40 min window
+      const matches = await this.getUpcomingMatches(
+        this.windowMinMinutes,
+        this.windowMaxMinutes,
+      );
       this.logger.log(
         `📅 Found ${matches.length} upcoming matches in the window`,
       );
@@ -77,7 +128,19 @@ export class SocialPosterService {
         if (this.postedMatchIds.has(match.id)) continue;
 
         try {
-          await this.predictAndPost(match);
+          const result = await this.predictAndPost(match);
+          const posted =
+            result.twitterPostId ||
+            result.facebookPostId ||
+            result.instagramPostId;
+
+          if (!posted) {
+            this.logger.warn(
+              `No platform accepted post for match ${match.id}; it will be retried later`,
+            );
+            continue;
+          }
+
           this.postedMatchIds.add(match.id);
 
           // Cleanup: remove old IDs (keep last 500)
@@ -87,6 +150,7 @@ export class SocialPosterService {
               .slice(0, arr.length - 500)
               .forEach((id) => this.postedMatchIds.delete(id));
           }
+          this.savePostedState();
         } catch (error) {
           this.logger.error(
             `Failed to process match ${match.id}: ${error.message}`,
@@ -113,19 +177,33 @@ export class SocialPosterService {
     const minTime = now + minMinutes * 60 * 1000;
     const maxTime = now + maxMinutes * 60 * 1000;
 
+    const where: any = {
+      sport: { in: this.sports },
+      mstUtc: {
+        gte: minTime,
+        lte: maxTime,
+      },
+    };
+
+    if (this.topLeagueIds.size > 0) {
+      where.leagueId = { in: Array.from(this.topLeagueIds) };
+    }
+
     const matches = await this.prisma.liveMatch.findMany({
       where: {
-        sport: "football",
+        ...where,
-        leagueId: { in: Array.from(this.topLeagueIds) },
-        mstUtc: {
-          gte: minTime,
-          lte: maxTime,
-        },
       },
       include: {
         homeTeam: true,
         awayTeam: true,
-        league: true,
+        league: {
+          include: {
+            country: true,
+          },
+        },
+      },
+      orderBy: {
+        mstUtc: "asc",
       },
     });
 
@@ -233,7 +311,10 @@ export class SocialPosterService {
     const ftScore = score.ft || "1-1";
 
     // Extract best bets from bet_summary array
-    const topPicks = this.extractTopPicks(prediction);
+    const sport = this.normalizeSport(
+      match.sport || prediction.match_info?.sport,
+    );
+    const topPicks = this.extractTopPicks(prediction, sport);
 
     // Match date formatting
     const matchDate = this.formatMatchDate(match.mstUtc);
@@ -246,6 +327,7 @@ export class SocialPosterService {
 
     return {
       matchId: match.id,
+      sport,
       homeTeam:
         match.homeTeam?.name || prediction.match_info?.home_team || "Home",
       awayTeam:
@@ -253,6 +335,12 @@ export class SocialPosterService {
       homeLogo: this.resolveLogoUrl(match.homeTeam?.logoUrl || ""),
       awayLogo: this.resolveLogoUrl(match.awayTeam?.logoUrl || ""),
       leagueName: match.league?.name || prediction.match_info?.league || "",
+      leagueLogo: this.resolveLogoUrl(match.league?.logoUrl || ""),
+      countryName:
+        match.league?.country?.name ||
+        prediction.match_info?.country ||
+        this.inferCountryName(match.league?.name || ""),
+      countryFlag: this.resolveLogoUrl(match.league?.country?.flagUrl || ""),
       matchDate,
       htScore,
       ftScore,
@@ -266,11 +354,14 @@ export class SocialPosterService {
   /**
    * Extract top 3 picks sorted by confidence from the V20+ bet_summary array.
    */
-  private extractTopPicks(prediction: any): TopPick[] {
+  private extractTopPicks(
+    prediction: any,
+    sport: "football" | "basketball",
+  ): TopPick[] {
     const betSummary: any[] = prediction.bet_summary || [];
 
     // Market code to Turkish/English label mapping
-    const marketLabels: Record<string, { tr: string; en: string }> = {
+    const footballLabels: Record<string, { tr: string; en: string }> = {
       MS: { tr: "Maç Sonucu", en: "Match Result" },
       OU15: { tr: "Üst 1.5 Gol", en: "Over 1.5" },
       OU25: { tr: "Üst 2.5 Gol", en: "Over 2.5" },
@@ -282,6 +373,20 @@ export class SocialPosterService {
       OE: { tr: "Tek/Çift", en: "Odd/Even" },
       HTFT: { tr: "İY/MS", en: "HT/FT" },
     };
+    const basketballLabels: Record<string, { tr: string; en: string }> = {
+      MS: { tr: "Maç Sonucu", en: "Match Result" },
+      ML: { tr: "Maç Sonucu", en: "Moneyline" },
+      WINNER: { tr: "Maç Sonucu", en: "Winner" },
+      TOTAL: { tr: "Toplam Sayı", en: "Total Points" },
+      OU: { tr: "Toplam Sayı", en: "Total Points" },
+      OVER_UNDER: { tr: "Toplam Sayı", en: "Total Points" },
+      HANDICAP: { tr: "Handikap", en: "Handicap" },
+      HND: { tr: "Handikap", en: "Handicap" },
+      SPREAD: { tr: "Handikap", en: "Spread" },
+      HT: { tr: "İlk Devre Sonucu", en: "First Half Result" },
+    };
+    const marketLabels =
+      sport === "basketball" ? basketballLabels : footballLabels;
 
     const candidates: TopPick[] = betSummary.map((bet) => {
       const labels = marketLabels[bet.market] || {
@@ -302,6 +407,32 @@ export class SocialPosterService {
     return candidates.slice(0, 3);
   }
 
+  private inferCountryName(leagueName: string): string {
+    const normalized = leagueName.toLocaleLowerCase("tr-TR");
+    if (normalized.includes("süper lig") || normalized.includes("türkiye")) {
+      return "Türkiye";
+    }
+    if (
+      normalized.includes("premier league") ||
+      normalized.includes("championship")
+    ) {
+      return "İngiltere";
+    }
+    if (normalized.includes("la liga")) return "İspanya";
+    if (normalized.includes("serie a")) return "İtalya";
+    if (normalized.includes("bundesliga")) return "Almanya";
+    if (normalized.includes("ligue 1")) return "Fransa";
+    if (normalized.includes("euroleague")) return "Avrupa";
+    if (normalized.includes("nba")) return "ABD";
+    return "";
+  }
+
+  private normalizeSport(sport?: string): "football" | "basketball" {
+    return String(sport).toLowerCase() === "basketball"
+      ? "basketball"
+      : "football";
+  }
+
   /**
    * Convert relative logo paths to full HTTP URLs.
    * On the deployed server, logos exist at public/uploads/teams/...
@@ -351,7 +482,11 @@ export class SocialPosterService {
       include: {
         homeTeam: true,
         awayTeam: true,
-        league: true,
+        league: {
+          include: {
+            country: true,
+          },
+        },
       },
     });
 
@@ -373,7 +508,11 @@ export class SocialPosterService {
       include: {
         homeTeam: true,
         awayTeam: true,
-        league: true,
+        league: {
+          include: {
+            country: true,
+          },
+        },
       },
     });
 

src/modules/social-poster/twitter.service.ts

@@ -20,7 +20,7 @@ export class TwitterService {
       void this.initClient(apiKey, apiSecret, accessToken, accessSecret);
     } else {
       this.logger.warn(
-        "⚠️ Twitter API keys not configured. Set TWITTER_API_KEY, TWITTER_API_SECRET, TWITTER_ACCESS_TOKEN, TWITTER_ACCESS_SECRET",
+        "⚠️ X/Twitter API keys not configured. Set TWITTER_API_KEY, TWITTER_API_SECRET, TWITTER_ACCESS_TOKEN, TWITTER_ACCESS_SECRET",
       );
     }
   }
@@ -64,10 +64,10 @@ export class TwitterService {
     }
 
     try {
-      // Step 1: Upload media via v1.1
+      // Step 1: Upload image media via the X media upload endpoint.
       const mediaData = fs.readFileSync(imagePath);
       const mediaId = await this.client.v1.uploadMedia(mediaData, {
-        mimeType: "image/png",
+        mimeType: this.getMimeType(imagePath),
       });
 
       // Step 2: Create tweet via v2
@@ -84,4 +84,12 @@ export class TwitterService {
       return null;
     }
   }
+
+  private getMimeType(imagePath: string): string {
+    const ext = imagePath.toLowerCase().split(".").pop();
+    if (ext === "jpg" || ext === "jpeg") return "image/jpeg";
+    if (ext === "webp") return "image/webp";
+    if (ext === "png") return "image/png";
+    return "image/jpeg";
+  }
 }

src/modules/subscriptions/dto/subscription.dto.ts

@@ -0,0 +1,178 @@
+import {
+  IsString,
+  IsOptional,
+  IsEnum,
+  IsDateString,
+  IsInt,
+} from "class-validator";
+import { ApiProperty, ApiPropertyOptional } from "@nestjs/swagger";
+import { Exclude, Expose, Type } from "class-transformer";
+
+export enum PlanType {
+  FREE = "free",
+  PLUS = "plus",
+  PREMIUM = "premium",
+}
+
+export enum BillingIntervalType {
+  MONTHLY = "monthly",
+  YEARLY = "yearly",
+}
+
+/**
+ * Plan feature limits configuration
+ */
+export const PLAN_LIMITS: Record<
+  PlanType,
+  { maxAnalyses: number; maxCoupons: number }
+> = {
+  [PlanType.FREE]: { maxAnalyses: 3, maxCoupons: 1 },
+  [PlanType.PLUS]: { maxAnalyses: 25, maxCoupons: 5 },
+  [PlanType.PREMIUM]: { maxAnalyses: 999, maxCoupons: 999 },
+};
+
+/**
+ * Plan display information
+ */
+export interface PlanInfo {
+  id: PlanType;
+  name: string;
+  description: string;
+  monthlyPrice: number;
+  yearlyPrice: number;
+  currency: string;
+  features: string[];
+  limits: { maxAnalyses: number; maxCoupons: number };
+  highlighted: boolean;
+}
+
+export const PLANS: readonly PlanInfo[] = [
+  {
+    id: PlanType.FREE,
+    name: "Free",
+    description: "Temel analiz özellikleri",
+    monthlyPrice: 0,
+    yearlyPrice: 0,
+    currency: "TRY",
+    features: ["Günlük 3 analiz", "Günlük 1 kupon", "Temel maç istatistikleri"],
+    limits: PLAN_LIMITS[PlanType.FREE],
+    highlighted: false,
+  },
+  {
+    id: PlanType.PLUS,
+    name: "Plus",
+    description: "Detaylı analiz ve daha fazla kupon",
+    monthlyPrice: 99,
+    yearlyPrice: 999,
+    currency: "TRY",
+    features: [
+      "Günlük 25 analiz",
+      "Günlük 5 kupon",
+      "AI detaylı analiz",
+      "H2H karşılaştırma",
+      "Reklamsız deneyim",
+    ],
+    limits: PLAN_LIMITS[PlanType.PLUS],
+    highlighted: true,
+  },
+  {
+    id: PlanType.PREMIUM,
+    name: "Premium",
+    description: "Sınırsız erişim ve özel özellikler",
+    monthlyPrice: 249,
+    yearlyPrice: 2499,
+    currency: "TRY",
+    features: [
+      "Sınırsız analiz",
+      "Sınırsız kupon",
+      "AI detaylı analiz",
+      "H2H karşılaştırma",
+      "Kupon Builder",
+      "Spor Toto analiz",
+      "Reklamsız deneyim",
+      "Öncelikli destek",
+    ],
+    limits: PLAN_LIMITS[PlanType.PREMIUM],
+    highlighted: false,
+  },
+] as const;
+
+// ── Response DTOs ──
+
+@Exclude()
+export class UsageLimitResponseDto {
+  @Expose()
+  analysisCount: number;
+
+  @Expose()
+  couponCount: number;
+
+  @Expose()
+  maxAnalyses: number;
+
+  @Expose()
+  maxCoupons: number;
+}
+
+@Exclude()
+export class SubscriptionResponseDto {
+  @Expose()
+  id: string;
+
+  @Expose()
+  plan: string;
+
+  @Expose()
+  billingInterval: string | null;
+
+  @Expose()
+  currentPeriodStart: Date | null;
+
+  @Expose()
+  currentPeriodEnd: Date | null;
+
+  @Expose()
+  cancelledAt: Date | null;
+
+  @Expose()
+  cancelEffectiveDate: Date | null;
+
+  @Expose()
+  paddlePriceId: string | null;
+
+  @Expose()
+  createdAt: Date;
+
+  @Expose()
+  updatedAt: Date;
+}
+
+// ── Request DTOs ──
+
+export class CreateCheckoutDto {
+  @ApiProperty({
+    enum: PlanType,
+    example: PlanType.PLUS,
+    description: "Target plan",
+  })
+  @IsEnum(PlanType)
+  plan: PlanType;
+
+  @ApiProperty({
+    enum: BillingIntervalType,
+    example: BillingIntervalType.MONTHLY,
+    description: "Billing interval",
+  })
+  @IsEnum(BillingIntervalType)
+  billingInterval: BillingIntervalType;
+}
+
+export class CancelSubscriptionDto {
+  @ApiPropertyOptional({
+    description: "Reason for cancellation",
+    example: "Too expensive",
+  })
+  @IsOptional()
+  @IsString()
+  reason?: string;
+}

src/modules/subscriptions/paddle.service.ts

@@ -0,0 +1,209 @@
+import { Injectable, Logger } from "@nestjs/common";
+import { ConfigService } from "@nestjs/config";
+import * as crypto from "crypto";
+
+export interface PaddleWebhookEvent {
+  event_id: string;
+  event_type: string;
+  occurred_at: string;
+  notification_id: string;
+  data: Record<string, unknown>;
+}
+
+interface PaddleTransactionResponse {
+  data: {
+    id: string;
+    customer_id: string;
+    status: string;
+  };
+}
+
+@Injectable()
+export class PaddleService {
+  private readonly logger = new Logger(PaddleService.name);
+  private readonly apiKey: string;
+  private readonly webhookSecret: string;
+  private readonly environment: "sandbox" | "production";
+  private readonly baseUrl: string;
+
+  constructor(private readonly config: ConfigService) {
+    this.apiKey = this.config.get<string>("PADDLE_API_KEY", "");
+    this.webhookSecret = this.config.get<string>("PADDLE_WEBHOOK_SECRET", "");
+    this.environment = this.config.get<"sandbox" | "production">(
+      "PADDLE_ENVIRONMENT",
+      "sandbox",
+    );
+    this.baseUrl =
+      this.environment === "production"
+        ? "https://api.paddle.com"
+        : "https://sandbox-api.paddle.com";
+  }
+
+  /**
+   * Verify Paddle webhook signature (Paddle Billing v2)
+   */
+  verifyWebhookSignature(rawBody: string, signatureHeader: string): boolean {
+    if (!this.webhookSecret) {
+      this.logger.warn(
+        "PADDLE_WEBHOOK_SECRET not configured, skipping verification",
+      );
+      return false;
+    }
+
+    try {
+      // Paddle signature format: ts=TIMESTAMP;h1=HASH
+      const parts = signatureHeader.split(";");
+      const tsValue = parts
+        .find((p) => p.startsWith("ts="))
+        ?.replace("ts=", "");
+      const h1Value = parts
+        .find((p) => p.startsWith("h1="))
+        ?.replace("h1=", "");
+
+      if (!tsValue || !h1Value) {
+        this.logger.warn("Invalid Paddle signature format");
+        return false;
+      }
+
+      // Compute expected signature: HMAC-SHA256(ts + ':' + rawBody)
+      const signedPayload = `${tsValue}:${rawBody}`;
+      const expectedSignature = crypto
+        .createHmac("sha256", this.webhookSecret)
+        .update(signedPayload)
+        .digest("hex");
+
+      return crypto.timingSafeEqual(
+        Buffer.from(h1Value),
+        Buffer.from(expectedSignature),
+      );
+    } catch (error: unknown) {
+      const err = error as Error;
+      this.logger.error(
+        `Webhook signature verification failed: ${err.message}`,
+      );
+      return false;
+    }
+  }
+
+  /**
+   * Cancel a Paddle subscription
+   */
+  async cancelSubscription(
+    paddleSubscriptionId: string,
+    effectiveFrom:
+      | "immediately"
+      | "next_billing_period" = "next_billing_period",
+  ): Promise<void> {
+    const url = `${this.baseUrl}/subscriptions/${paddleSubscriptionId}/cancel`;
+
+    const response = await fetch(url, {
+      method: "POST",
+      headers: {
+        Authorization: `Bearer ${this.apiKey}`,
+        "Content-Type": "application/json",
+      },
+      body: JSON.stringify({ effective_from: effectiveFrom }),
+    });
+
+    if (!response.ok) {
+      const body = await response.text();
+      this.logger.error(`Paddle cancel failed: ${response.status} ${body}`);
+      throw new Error(
+        `Failed to cancel Paddle subscription: ${response.status}`,
+      );
+    }
+
+    this.logger.log(
+      `Paddle subscription ${paddleSubscriptionId} cancelled (effective: ${effectiveFrom})`,
+    );
+  }
+
+  /**
+   * Get subscription details from Paddle
+   */
+  async getSubscription(
+    paddleSubscriptionId: string,
+  ): Promise<Record<string, unknown>> {
+    const url = `${this.baseUrl}/subscriptions/${paddleSubscriptionId}`;
+
+    const response = await fetch(url, {
+      method: "GET",
+      headers: {
+        Authorization: `Bearer ${this.apiKey}`,
+        "Content-Type": "application/json",
+      },
+    });
+
+    if (!response.ok) {
+      throw new Error(`Failed to get Paddle subscription: ${response.status}`);
+    }
+
+    const data = (await response.json()) as { data: Record<string, unknown> };
+    return data.data;
+  }
+
+  /**
+   * Map Paddle price ID to our internal plan
+   */
+  mapPriceIdToPlan(priceId: string): {
+    plan: "plus" | "premium";
+    interval: "monthly" | "yearly";
+  } | null {
+    const mapping: Record<
+      string,
+      { plan: "plus" | "premium"; interval: "monthly" | "yearly" }
+    > = {
+      [this.config.get<string>("PADDLE_PLUS_MONTHLY_PRICE_ID", "")]: {
+        plan: "plus",
+        interval: "monthly",
+      },
+      [this.config.get<string>("PADDLE_PLUS_YEARLY_PRICE_ID", "")]: {
+        plan: "plus",
+        interval: "yearly",
+      },
+      [this.config.get<string>("PADDLE_PREMIUM_MONTHLY_PRICE_ID", "")]: {
+        plan: "premium",
+        interval: "monthly",
+      },
+      [this.config.get<string>("PADDLE_PREMIUM_YEARLY_PRICE_ID", "")]: {
+        plan: "premium",
+        interval: "yearly",
+      },
+    };
+
+    // Remove empty key (from missing env vars)
+    delete mapping[""];
+
+    return mapping[priceId] ?? null;
+  }
+
+  /**
+   * Get the Paddle price ID for a given plan and interval
+   */
+  getPriceId(plan: "plus" | "premium", interval: "monthly" | "yearly"): string {
+    const key = `PADDLE_${plan.toUpperCase()}_${interval.toUpperCase()}_PRICE_ID`;
+    const priceId = this.config.get<string>(key, "");
+
+    if (!priceId) {
+      throw new Error(
+        `Price ID not configured for ${plan} ${interval} (env: ${key})`,
+      );
+    }
+
+    return priceId;
+  }
+
+  /**
+   * Get the client-side token for Paddle.js
+   */
+  getClientToken(): string {
+    return this.config.get<string>("PADDLE_CLIENT_TOKEN", "");
+  }
+
+  /**
+   * Get the Paddle environment
+   */
+  getEnvironment(): "sandbox" | "production" {
+    return this.environment;
+  }
+}

src/modules/subscriptions/subscriptions.controller.ts

@@ -0,0 +1,186 @@
+import {
+  Controller,
+  Get,
+  Post,
+  Body,
+  Req,
+  Res,
+  HttpCode,
+  HttpStatus,
+  Logger,
+  ForbiddenException,
+} from "@nestjs/common";
+import type { RawBodyRequest } from "@nestjs/common";
+import {
+  ApiTags,
+  ApiBearerAuth,
+  ApiOperation,
+  ApiOkResponse,
+} from "@nestjs/swagger";
+import type { Request, Response } from "express";
+import { CurrentUser, Public } from "../../common/decorators";
+import type { ApiResponse } from "../../common/types/api-response.type";
+import {
+  createSuccessResponse,
+  createErrorResponse,
+} from "../../common/types/api-response.type";
+import { SubscriptionsService } from "./subscriptions.service";
+import { PaddleService, PaddleWebhookEvent } from "./paddle.service";
+import {
+  CreateCheckoutDto,
+  CancelSubscriptionDto,
+  SubscriptionResponseDto,
+  PlanInfo,
+  PlanType,
+} from "./dto/subscription.dto";
+
+interface AuthenticatedUser {
+  id: string;
+  email: string;
+  role: string;
+}
+
+@ApiTags("Subscriptions")
+@Controller("subscriptions")
+export class SubscriptionsController {
+  private readonly logger = new Logger(SubscriptionsController.name);
+
+  constructor(
+    private readonly subscriptionsService: SubscriptionsService,
+    private readonly paddleService: PaddleService,
+  ) {}
+
+  /**
+   * GET /subscriptions/plans — Get all available plans (public)
+   */
+  @Public()
+  @Get("plans")
+  @ApiOperation({ summary: "Get all available subscription plans" })
+  @ApiOkResponse({ description: "List of available plans" })
+  getPlans(): ApiResponse<readonly PlanInfo[]> {
+    const plans = this.subscriptionsService.getPlans();
+    return createSuccessResponse(plans, "Plans retrieved");
+  }
+
+  /**
+   * GET /subscriptions/me — Get current user subscription
+   */
+  @ApiBearerAuth()
+  @Get("me")
+  @ApiOperation({ summary: "Get current user subscription status" })
+  async getMySubscription(
+    @CurrentUser() user: AuthenticatedUser,
+  ): Promise<ApiResponse<SubscriptionResponseDto | null>> {
+    const subscription = await this.subscriptionsService.getCurrentSubscription(
+      user.id,
+    );
+    return createSuccessResponse(subscription, "Subscription retrieved");
+  }
+
+  /**
+   * POST /subscriptions/checkout — Get checkout config for Paddle.js
+   */
+  @ApiBearerAuth()
+  @Post("checkout")
+  @ApiOperation({
+    summary: "Get Paddle checkout configuration for a plan",
+  })
+  async getCheckoutConfig(
+    @CurrentUser() user: AuthenticatedUser,
+    @Body() dto: CreateCheckoutDto,
+  ): Promise<
+    ApiResponse<{
+      priceId: string;
+      clientToken: string;
+      environment: string;
+      userId: string;
+    }>
+  > {
+    if (dto.plan === PlanType.FREE) {
+      throw new ForbiddenException("Cannot checkout for free plan");
+    }
+
+    const config = this.subscriptionsService.getCheckoutConfig(
+      dto.plan,
+      dto.billingInterval,
+    );
+
+    return createSuccessResponse(
+      {
+        ...config,
+        userId: user.id,
+      },
+      "Checkout config ready",
+    );
+  }
+
+  /**
+   * POST /subscriptions/cancel — Cancel current subscription
+   */
+  @ApiBearerAuth()
+  @Post("cancel")
+  @ApiOperation({ summary: "Cancel the current subscription" })
+  async cancelSubscription(
+    @CurrentUser() user: AuthenticatedUser,
+    @Body() _dto: CancelSubscriptionDto,
+  ): Promise<ApiResponse<null>> {
+    await this.subscriptionsService.cancelSubscription(user.id);
+    return createSuccessResponse(null, "Subscription cancellation requested");
+  }
+
+  /**
+   * POST /subscriptions/webhook/paddle — Paddle webhook receiver
+   *
+   * This endpoint is PUBLIC (no JWT required) — Paddle calls it directly.
+   * Authentication is done via HMAC signature verification.
+   */
+  @Public()
+  @Post("webhook/paddle")
+  @HttpCode(HttpStatus.OK)
+  @ApiOperation({ summary: "Paddle webhook receiver (internal)" })
+  async handlePaddleWebhook(
+    @Req() req: RawBodyRequest<Request>,
+    @Res() res: Response,
+  ): Promise<void> {
+    const signature = req.headers["paddle-signature"] as string | undefined;
+
+    if (!signature) {
+      this.logger.warn("Paddle webhook received without signature");
+      res.status(HttpStatus.BAD_REQUEST).json({ error: "Missing signature" });
+      return;
+    }
+
+    // Get raw body for signature verification
+    const rawBody = req.rawBody?.toString("utf8");
+    if (!rawBody) {
+      this.logger.warn("Paddle webhook received without raw body");
+      res.status(HttpStatus.BAD_REQUEST).json({ error: "Missing body" });
+      return;
+    }
+
+    // Verify signature
+    const isValid = this.paddleService.verifyWebhookSignature(
+      rawBody,
+      signature,
+    );
+
+    if (!isValid) {
+      this.logger.warn("Paddle webhook signature verification failed");
+      res.status(HttpStatus.UNAUTHORIZED).json({ error: "Invalid signature" });
+      return;
+    }
+
+    // Parse and process
+    try {
+      const event = JSON.parse(rawBody) as PaddleWebhookEvent;
+      await this.subscriptionsService.handleWebhookEvent(event);
+      res.status(HttpStatus.OK).json({ received: true });
+    } catch (error: unknown) {
+      const err = error as Error;
+      this.logger.error(`Webhook processing failed: ${err.message}`);
+      res
+        .status(HttpStatus.INTERNAL_SERVER_ERROR)
+        .json({ error: "Processing failed" });
+    }
+  }
+}

src/modules/subscriptions/subscriptions.module.ts

@@ -0,0 +1,13 @@
+import { Module } from "@nestjs/common";
+import { SubscriptionsController } from "./subscriptions.controller";
+import { SubscriptionsService } from "./subscriptions.service";
+import { PaddleService } from "./paddle.service";
+import { DatabaseModule } from "../../database/database.module";
+
+@Module({
+  imports: [DatabaseModule],
+  controllers: [SubscriptionsController],
+  providers: [SubscriptionsService, PaddleService],
+  exports: [SubscriptionsService, PaddleService],
+})
+export class SubscriptionsModule {}

src/modules/subscriptions/subscriptions.service.ts

@@ -0,0 +1,334 @@
+import { Injectable, Logger, NotFoundException } from "@nestjs/common";
+import { PrismaService } from "../../database/prisma.service";
+import { PaddleService, PaddleWebhookEvent } from "./paddle.service";
+import {
+  PlanType,
+  BillingIntervalType,
+  PLAN_LIMITS,
+  PLANS,
+  SubscriptionResponseDto,
+} from "./dto/subscription.dto";
+import { plainToInstance } from "class-transformer";
+
+@Injectable()
+export class SubscriptionsService {
+  private readonly logger = new Logger(SubscriptionsService.name);
+
+  constructor(
+    private readonly prisma: PrismaService,
+    private readonly paddleService: PaddleService,
+  ) {}
+
+  /**
+   * Get current subscription for user
+   */
+  async getCurrentSubscription(
+    userId: string,
+  ): Promise<SubscriptionResponseDto | null> {
+    const subscription = await this.prisma.subscription.findUnique({
+      where: { userId },
+    });
+
+    if (!subscription) {
+      return null;
+    }
+
+    return plainToInstance(SubscriptionResponseDto, subscription);
+  }
+
+  /**
+   * Get or create subscription record for user
+   */
+  async getOrCreateSubscription(userId: string) {
+    let subscription = await this.prisma.subscription.findUnique({
+      where: { userId },
+    });
+
+    if (!subscription) {
+      subscription = await this.prisma.subscription.create({
+        data: {
+          userId,
+          plan: "free",
+        },
+      });
+    }
+
+    return subscription;
+  }
+
+  /**
+   * Get all available plans
+   */
+  getPlans() {
+    return PLANS;
+  }
+
+  /**
+   * Get checkout configuration (client-side token + price ID)
+   */
+  getCheckoutConfig(
+    plan: PlanType,
+    billingInterval: BillingIntervalType,
+  ): { priceId: string; clientToken: string; environment: string } {
+    if (plan === PlanType.FREE) {
+      throw new Error("Cannot checkout for free plan");
+    }
+
+    const paddlePlan = plan as "plus" | "premium";
+    const paddleInterval = billingInterval as "monthly" | "yearly";
+
+    const priceId = this.paddleService.getPriceId(paddlePlan, paddleInterval);
+    const clientToken = this.paddleService.getClientToken();
+    const environment = this.paddleService.getEnvironment();
+
+    return { priceId, clientToken, environment };
+  }
+
+  /**
+   * Handle incoming Paddle webhook event
+   */
+  async handleWebhookEvent(event: PaddleWebhookEvent): Promise<void> {
+    const eventType = event.event_type;
+    const data = event.data;
+
+    this.logger.log(`Processing Paddle webhook: ${eventType}`);
+
+    switch (eventType) {
+      case "subscription.created":
+      case "subscription.updated":
+        await this.handleSubscriptionUpdate(data);
+        break;
+      case "subscription.canceled":
+        await this.handleSubscriptionCancelled(data);
+        break;
+      case "subscription.past_due":
+        await this.handleSubscriptionPastDue(data);
+        break;
+      case "subscription.resumed":
+        await this.handleSubscriptionResumed(data);
+        break;
+      case "transaction.completed":
+        this.logger.log(
+          `Transaction completed: ${(data as Record<string, unknown>).id}`,
+        );
+        break;
+      case "transaction.payment_failed":
+        this.logger.warn(
+          `Payment failed for transaction: ${(data as Record<string, unknown>).id}`,
+        );
+        break;
+      default:
+        this.logger.debug(`Unhandled Paddle event: ${eventType}`);
+    }
+  }
+
+  /**
+   * Cancel subscription for user
+   */
+  async cancelSubscription(userId: string): Promise<void> {
+    const subscription = await this.prisma.subscription.findUnique({
+      where: { userId },
+    });
+
+    if (!subscription?.paddleSubscriptionId) {
+      throw new NotFoundException("No active subscription found");
+    }
+
+    await this.paddleService.cancelSubscription(
+      subscription.paddleSubscriptionId,
+      "next_billing_period",
+    );
+
+    this.logger.log(`Cancellation requested for user ${userId}`);
+  }
+
+  // ── Private Handlers ──
+
+  private async handleSubscriptionUpdate(
+    data: Record<string, unknown>,
+  ): Promise<void> {
+    const paddleSubId = data.id as string;
+    const customerId = data.customer_id as string;
+    const status = data.status as string;
+    const customData = data.custom_data as { userId?: string } | undefined;
+    const items = data.items as Array<{ price: { id: string } }> | undefined;
+    const currentBillingPeriod = data.current_billing_period as
+      | { starts_at: string; ends_at: string }
+      | undefined;
+
+    const userId = customData?.userId;
+    if (!userId) {
+      this.logger.warn(
+        `No userId in custom_data for subscription ${paddleSubId}`,
+      );
+      return;
+    }
+
+    // Determine plan from price ID
+    const priceId = items?.[0]?.price?.id;
+    let plan: PlanType = PlanType.FREE;
+    let interval: BillingIntervalType = BillingIntervalType.MONTHLY;
+
+    if (priceId) {
+      const mapped = this.paddleService.mapPriceIdToPlan(priceId);
+      if (mapped) {
+        plan = mapped.plan as PlanType;
+        interval = mapped.interval as BillingIntervalType;
+      }
+    }
+
+    // Determine effective plan based on Paddle status
+    const effectivePlan =
+      status === "active" || status === "trialing" ? plan : PlanType.FREE;
+
+    // Upsert subscription record
+    await this.prisma.subscription.upsert({
+      where: { userId },
+      update: {
+        paddleSubscriptionId: paddleSubId,
+        paddleCustomerId: customerId,
+        plan: effectivePlan,
+        billingInterval: interval,
+        paddlePriceId: priceId ?? null,
+        currentPeriodStart: currentBillingPeriod?.starts_at
+          ? new Date(currentBillingPeriod.starts_at)
+          : null,
+        currentPeriodEnd: currentBillingPeriod?.ends_at
+          ? new Date(currentBillingPeriod.ends_at)
+          : null,
+        cancelledAt: null,
+        cancelEffectiveDate: null,
+      },
+      create: {
+        userId,
+        paddleSubscriptionId: paddleSubId,
+        paddleCustomerId: customerId,
+        plan: effectivePlan,
+        billingInterval: interval,
+        paddlePriceId: priceId ?? null,
+        currentPeriodStart: currentBillingPeriod?.starts_at
+          ? new Date(currentBillingPeriod.starts_at)
+          : null,
+        currentPeriodEnd: currentBillingPeriod?.ends_at
+          ? new Date(currentBillingPeriod.ends_at)
+          : null,
+      },
+    });
+
+    // Sync user subscription status
+    await this.prisma.user.update({
+      where: { id: userId },
+      data: { subscriptionStatus: effectivePlan },
+    });
+
+    // Sync usage limits with plan
+    await this.syncLimitsWithPlan(userId, effectivePlan);
+
+    this.logger.log(
+      `Subscription updated: user=${userId}, plan=${effectivePlan}, interval=${interval}`,
+    );
+  }
+
+  private async handleSubscriptionCancelled(
+    data: Record<string, unknown>,
+  ): Promise<void> {
+    const paddleSubId = data.id as string;
+    const canceledAt = data.canceled_at as string | undefined;
+    const currentBillingPeriod = data.current_billing_period as
+      | { ends_at: string }
+      | undefined;
+
+    const subscription = await this.prisma.subscription.findUnique({
+      where: { paddleSubscriptionId: paddleSubId },
+    });
+
+    if (!subscription) {
+      this.logger.warn(`Subscription not found for cancel: ${paddleSubId}`);
+      return;
+    }
+
+    const effectiveDate = currentBillingPeriod?.ends_at
+      ? new Date(currentBillingPeriod.ends_at)
+      : new Date();
+
+    await this.prisma.subscription.update({
+      where: { id: subscription.id },
+      data: {
+        plan: "cancelled",
+        cancelledAt: canceledAt ? new Date(canceledAt) : new Date(),
+        cancelEffectiveDate: effectiveDate,
+      },
+    });
+
+    // Downgrade user to free
+    await this.prisma.user.update({
+      where: { id: subscription.userId },
+      data: { subscriptionStatus: "free" },
+    });
+
+    await this.syncLimitsWithPlan(subscription.userId, PlanType.FREE);
+
+    this.logger.log(
+      `Subscription cancelled: user=${subscription.userId}, effective=${effectiveDate.toISOString()}`,
+    );
+  }
+
+  private async handleSubscriptionPastDue(
+    data: Record<string, unknown>,
+  ): Promise<void> {
+    const paddleSubId = data.id as string;
+
+    const subscription = await this.prisma.subscription.findUnique({
+      where: { paddleSubscriptionId: paddleSubId },
+    });
+
+    if (!subscription) {
+      return;
+    }
+
+    await this.prisma.subscription.update({
+      where: { id: subscription.id },
+      data: { plan: "past_due" },
+    });
+
+    await this.prisma.user.update({
+      where: { id: subscription.userId },
+      data: { subscriptionStatus: "past_due" },
+    });
+
+    this.logger.warn(`Subscription past due: user=${subscription.userId}`);
+  }
+
+  private async handleSubscriptionResumed(
+    data: Record<string, unknown>,
+  ): Promise<void> {
+    // Re-process as an update to restore the plan
+    await this.handleSubscriptionUpdate(data);
+  }
+
+  /**
+   * Sync usage limits with plan tier
+   */
+  public async syncLimitsWithPlan(
+    userId: string,
+    plan: PlanType,
+  ): Promise<void> {
+    const limits = PLAN_LIMITS[plan] ?? PLAN_LIMITS[PlanType.FREE];
+
+    await this.prisma.usageLimit.upsert({
+      where: { userId },
+      update: {
+        maxAnalyses: limits.maxAnalyses,
+        maxCoupons: limits.maxCoupons,
+      },
+      create: {
+        userId,
+        analysisCount: 0,
+        couponCount: 0,
+        maxAnalyses: limits.maxAnalyses,
+        maxCoupons: limits.maxCoupons,
+        lastResetDate: new Date(),
+      },
+    });
+  }
+}

src/modules/users/dto/user.dto.ts

@@ -73,7 +73,25 @@ export class ChangePasswordDto {
   newPassword: string;
 }
 
-import { Exclude, Expose } from "class-transformer";
+import { Exclude, Expose, Type } from "class-transformer";
+
+@Exclude()
+export class UsageLimitDto {
+  @Expose()
+  analysisCount: number;
+
+  @Expose()
+  couponCount: number;
+
+  @Expose()
+  maxAnalyses: number;
+
+  @Expose()
+  maxCoupons: number;
+
+  @Expose()
+  lastResetDate: Date;
+}
 
 @Exclude()
 export class UserResponseDto {
@@ -95,9 +113,16 @@ export class UserResponseDto {
   @Expose()
   isActive: boolean;
 
+  @Expose()
+  subscriptionStatus: string;
+
   @Expose()
   createdAt: Date;
 
   @Expose()
   updatedAt: Date;
+
+  @Expose()
+  @Type(() => UsageLimitDto)
+  usageLimit?: UsageLimitDto;
 }

src/services/ai.service.ts

@@ -44,9 +44,7 @@ export class AiService {
   private readonly pythonEngineUrl: string;
   private readonly aiEngineClient: AiEngineClient;
 
-  constructor(
+  constructor(private readonly configService: ConfigService) {
-    private readonly configService: ConfigService,
-  ) {
     this.pythonEngineUrl =
       this.configService.get("AI_ENGINE_URL") || "http://127.0.0.1:8000";
     this.aiEngineClient = new AiEngineClient({

src/tasks/data-fetcher.task.ts

@@ -9,6 +9,7 @@ import * as path from "path";
 import { Prisma } from "@prisma/client";
 import { SidelinedResponse } from "../modules/feeder/feeder.types";
 import {
+  deriveStoredMatchStatus,
   FINISHED_STATE_VALUES_FOR_DB,
   FINISHED_STATUS_VALUES_FOR_DB,
   LIVE_STATE_VALUES_FOR_DB,
@@ -74,6 +75,17 @@ interface LiveLineupsJson {
   away: { xi: unknown[]; subs: unknown[] };
 }
 
+interface PendingPredictionRunForSettlement {
+  id: bigint;
+  matchId: string;
+  engineVersion: string;
+  payloadSummary: unknown;
+  scoreHome: number | null;
+  scoreAway: number | null;
+  htScoreHome: number | null;
+  htScoreAway: number | null;
+}
+
 type SportType = "football" | "basketball";
 
 // ────────────────────────────────────────────────────────────────
@@ -161,7 +173,8 @@ export class DataFetcherTask {
             `Pruned ${deleted.count} stale live matches. Starting full sync...`,
           );
         } catch (error: unknown) {
-          const message = error instanceof Error ? error.message : String(error);
+          const message =
+            error instanceof Error ? error.message : String(error);
           this.logger.error(`Stale live_match cleanup failed: ${message}`);
           return;
         }
@@ -186,6 +199,7 @@ export class DataFetcherTask {
     await this.syncMatchList(today);
     await this.syncMatchList(tomorrow);
     await this.updateLiveScores();
+    await this.settlePredictionRuns();
     await this.fetchOddsForMatches();
     await this.fillMissingLineups();
 
@@ -194,12 +208,12 @@ export class DataFetcherTask {
 
   private async syncMatchList(date: string): Promise<void> {
     // Football
-    const footballLeagues = this.loadLeagueFilterSet("top_leagues.json");
+    const footballLeagues = this.loadLeagueFilterSet("qualified_leagues.json");
     if (footballLeagues && footballLeagues.size > 0) {
       await this.fetchMatchesForSport("football", date, footballLeagues);
     } else {
       this.logger.warn(
-        "top_leagues.json is missing/empty — writing ALL football matches",
+        "qualified_leagues.json is missing/empty — writing ALL football matches",
       );
       await this.fetchMatchesForSport("football", date, new Set());
     }
@@ -250,7 +264,7 @@ export class DataFetcherTask {
       }
 
       this.logger.log(
-        `📡 Updating scores for ${liveMatches.length} live matches`,
+        `LIVE Updating scores for ${liveMatches.length} live matches`,
       );
 
       for (const match of liveMatches) {
@@ -262,13 +276,23 @@ export class DataFetcherTask {
 
           if (response.data?.data) {
             const matchData = response.data.data;
+            const scoreHome = matchData.homeScore ?? null;
+            const scoreAway = matchData.awayScore ?? null;
+            const storedStatus = deriveStoredMatchStatus({
+              state: matchData.state,
+              status: matchData.status,
+              substate: matchData.substate,
+              scoreHome,
+              scoreAway,
+            });
             await this.prisma.liveMatch.update({
               where: { id: match.id },
               data: {
-                scoreHome: matchData.homeScore ?? null,
+                scoreHome,
-                scoreAway: matchData.awayScore ?? null,
+                scoreAway,
-                state: matchData.state || matchData.status,
+                state: matchData.state || null,
-                status: matchData.status,
+                substate: matchData.substate || null,
+                status: storedStatus,
                 updatedAt: new Date(),
               },
             });
@@ -278,25 +302,319 @@ export class DataFetcherTask {
         }
       }
 
-      this.logger.log("📡 Live score update complete");
+      this.logger.log("LIVE Live score update complete");
     } catch (error: unknown) {
       const message = error instanceof Error ? error.message : String(error);
       this.logger.error(`Live score update failed: ${message}`);
     }
   }
 
-  // ────────────────────────────────────────────────────────────
+  private async settlePredictionRuns(): Promise<void> {
+    try {
+      const rows = await this.prisma.$queryRawUnsafe<
+        PendingPredictionRunForSettlement[]
+      >(`
+        SELECT
+          pr.id,
+          pr.match_id AS "matchId",
+          pr.engine_version AS "engineVersion",
+          pr.payload_summary AS "payloadSummary",
+          m.score_home AS "scoreHome",
+          m.score_away AS "scoreAway",
+          m.ht_score_home AS "htScoreHome",
+          m.ht_score_away AS "htScoreAway"
+        FROM prediction_runs pr
+        JOIN matches m ON m.id = pr.match_id
+        WHERE pr.eventual_outcome IS NULL
+          AND m.sport = 'football'
+          AND m.status = 'FT'
+          AND m.score_home IS NOT NULL
+          AND m.score_away IS NOT NULL
+        ORDER BY pr.generated_at ASC
+        LIMIT 500
+      `);
+
+      if (rows.length === 0) return;
+
+      let settled = 0;
+      for (const row of rows) {
+        const result = this.resolvePredictionRunSettlement(row);
+        if (!result) continue;
+        const closingOddsSnapshot = await this.getClosingOddsSnapshot(
+          row.matchId,
+        );
+        const settlementSummary = {
+          settled_at: new Date().toISOString(),
+          model_version: row.engineVersion,
+          outcome: result.outcome,
+          unit_profit: result.unitProfit,
+          final_score: {
+            home: row.scoreHome,
+            away: row.scoreAway,
+          },
+          halftime_score: {
+            home: row.htScoreHome,
+            away: row.htScoreAway,
+          },
+          closing_odds_snapshot: closingOddsSnapshot,
+        };
+
+        await this.prisma.$executeRawUnsafe(
+          `
+            UPDATE prediction_runs
+            SET eventual_outcome = $1,
+                unit_profit = $2,
+                payload_summary = payload_summary || jsonb_build_object('settlement', $3::jsonb)
+            WHERE id = $4
+          `,
+          result.outcome,
+          result.unitProfit,
+          JSON.stringify(settlementSummary),
+          row.id,
+        );
+        settled++;
+      }
+
+      if (settled > 0) {
+        this.logger.log(`Settled ${settled} prediction run(s)`);
+      }
+    } catch (error: unknown) {
+      const message = error instanceof Error ? error.message : String(error);
+      this.logger.warn(`Prediction run settlement skipped: ${message}`);
+    }
+  }
+
+  private async getClosingOddsSnapshot(
+    matchId: string,
+  ): Promise<Record<string, unknown>> {
+    const liveMatch = await this.prisma.liveMatch.findUnique({
+      where: { id: matchId },
+      select: {
+        odds: true,
+        oddsUpdatedAt: true,
+        status: true,
+        state: true,
+        scoreHome: true,
+        scoreAway: true,
+      },
+    });
+
+    if (liveMatch?.odds) {
+      return {
+        source: "live_match",
+        odds: liveMatch.odds,
+        odds_updated_at: liveMatch.oddsUpdatedAt?.toISOString() ?? null,
+        status: liveMatch.status ?? null,
+        state: liveMatch.state ?? null,
+        score_home: liveMatch.scoreHome,
+        score_away: liveMatch.scoreAway,
+      };
+    }
+
+    const categories = await this.prisma.oddCategory.findMany({
+      where: { matchId },
+      select: {
+        name: true,
+        selections: {
+          select: {
+            name: true,
+            oddValue: true,
+            position: true,
+            updatedAt: true,
+          },
+          orderBy: { position: "asc" },
+          take: 12,
+        },
+      },
+      orderBy: { name: "asc" },
+      take: 24,
+    });
+
+    return {
+      source: "odd_selections",
+      category_count: categories.length,
+      categories: categories.map((category) => ({
+        name: category.name,
+        selections: category.selections.map((selection) => ({
+          name: selection.name,
+          odd_value: selection.oddValue,
+          position: selection.position,
+          updated_at: selection.updatedAt?.toISOString() ?? null,
+        })),
+      })),
+    };
+  }
+
+  private resolvePredictionRunSettlement(
+    row: PendingPredictionRunForSettlement,
+  ): { outcome: string; unitProfit: number } | null {
+    const summary = this.asRecord(row.payloadSummary);
+    const mainPick = this.asRecord(summary.main_pick);
+    const market = String(mainPick.market || "");
+    const pick = String(mainPick.pick || "");
+    const playable = mainPick.playable === true;
+    const odds = Number(mainPick.odds || 0);
+
+    if (
+      !market ||
+      !pick ||
+      !playable ||
+      !Number.isFinite(odds) ||
+      odds <= 1.01
+    ) {
+      return { outcome: "NO_BET", unitProfit: 0 };
+    }
+
+    const won = this.isPredictionPickWon({
+      market,
+      pick,
+      scoreHome: row.scoreHome,
+      scoreAway: row.scoreAway,
+      htScoreHome: row.htScoreHome,
+      htScoreAway: row.htScoreAway,
+    });
+
+    if (won === null) return null;
+
+    return {
+      outcome: `${won ? "WON" : "LOST"}:${market}:${pick}`,
+      unitProfit: Number((won ? odds - 1 : -1).toFixed(4)),
+    };
+  }
+
+  private isPredictionPickWon(input: {
+    market: string;
+    pick: string;
+    scoreHome: number | null;
+    scoreAway: number | null;
+    htScoreHome: number | null;
+    htScoreAway: number | null;
+  }): boolean | null {
+    const market = input.market.toUpperCase();
+    const pick = this.normalizePick(input.pick);
+    const scoreHome = input.scoreHome;
+    const scoreAway = input.scoreAway;
+    if (scoreHome === null || scoreAway === null) return null;
+
+    if (market === "MS") {
+      if (pick === "1") return scoreHome > scoreAway;
+      if (pick === "X" || pick === "0") return scoreHome === scoreAway;
+      if (pick === "2") return scoreAway > scoreHome;
+      return null;
+    }
+
+    if (market === "DC") {
+      const normalized = pick.replace("-", "");
+      if (normalized === "1X") return scoreHome >= scoreAway;
+      if (normalized === "X2") return scoreAway >= scoreHome;
+      if (normalized === "12") return scoreHome !== scoreAway;
+      return null;
+    }
+
+    if (market === "BTTS") {
+      const bothScored = scoreHome > 0 && scoreAway > 0;
+      if (pick.includes("VAR") || pick.includes("YES") || pick === "Y") {
+        return bothScored;
+      }
+      if (pick.includes("YOK") || pick.includes("NO") || pick === "N") {
+        return !bothScored;
+      }
+      return null;
+    }
+
+    const goalLine = this.goalLineForMarket(market);
+    if (goalLine !== null) {
+      const total = market.startsWith("HT_")
+        ? this.nullableSum(input.htScoreHome, input.htScoreAway)
+        : scoreHome + scoreAway;
+      if (total === null) return null;
+      if (this.isOverPick(pick)) return total > goalLine;
+      return total < goalLine;
+    }
+
+    if (market === "HT") {
+      const htHome = input.htScoreHome;
+      const htAway = input.htScoreAway;
+      if (htHome === null || htAway === null) return null;
+      if (pick === "1") return htHome > htAway;
+      if (pick === "X" || pick === "0") return htHome === htAway;
+      if (pick === "2") return htAway > htHome;
+    }
+
+    if (market === "HTFT") {
+      const htHome = input.htScoreHome;
+      const htAway = input.htScoreAway;
+      if (htHome === null || htAway === null || !pick.includes("/"))
+        return null;
+      const [htPick, ftPick] = pick.split("/");
+      return (
+        this.isResultPickWon(htPick, htHome, htAway) === true &&
+        this.isResultPickWon(ftPick, scoreHome, scoreAway) === true
+      );
+    }
+
+    return null;
+  }
+
+  private isResultPickWon(
+    pick: string,
+    homeScore: number,
+    awayScore: number,
+  ): boolean | null {
+    if (pick === "1") return homeScore > awayScore;
+    if (pick === "X" || pick === "0") return homeScore === awayScore;
+    if (pick === "2") return awayScore > homeScore;
+    return null;
+  }
+
+  private goalLineForMarket(market: string): number | null {
+    if (market === "OU15") return 1.5;
+    if (market === "OU25") return 2.5;
+    if (market === "OU35") return 3.5;
+    if (market === "HT_OU05") return 0.5;
+    if (market === "HT_OU15") return 1.5;
+    return null;
+  }
+
+  private nullableSum(a: number | null, b: number | null): number | null {
+    if (a === null || b === null) return null;
+    return a + b;
+  }
+
+  private normalizePick(value: string): string {
+    return value
+      .trim()
+      .toUpperCase()
+      .replace(/İ/g, "I")
+      .replace(/Ü/g, "U")
+      .replace(/Ş/g, "S")
+      .replace(/Ğ/g, "G")
+      .replace(/Ö/g, "O")
+      .replace(/Ç/g, "C");
+  }
+
+  private isOverPick(pick: string): boolean {
+    return pick.includes("UST") || pick.includes("OVER");
+  }
+
+  private asRecord(value: unknown): Record<string, unknown> {
+    return value && typeof value === "object" && !Array.isArray(value)
+      ? (value as Record<string, unknown>)
+      : {};
+  }
+
   // Phase 3: Odds + referee + lineups + sidelined
-  // ────────────────────────────────────────────────────────────
 
   private async fetchOddsForMatches(): Promise<void> {
-    this.logger.log("💰 Fetching odds for live matches...");
+    this.logger.log("MONEY Fetching odds for live matches...");
 
     try {
-      // Load both league filters
+      // Load both league filters (data-driven qualified leagues)
       const topLeagueIds: string[] = [];
 
-      const footballLeagues = this.loadLeagueFilterSet("top_leagues.json");
+      const footballLeagues = this.loadLeagueFilterSet(
+        "qualified_leagues.json",
+      );
       if (footballLeagues) topLeagueIds.push(...footballLeagues);
 
       const basketballLeagues = this.loadLeagueFilterSet(
@@ -307,6 +625,9 @@ export class DataFetcherTask {
       const allowedLeagueIds = Array.from(new Set(topLeagueIds));
 
       // Get matches needing odds (from 12 hours ago onward)
+      // CRITICAL: Only fetch odds/lineups for NOT STARTED matches.
+      // Once a match goes live, odds selections disappear or change,
+      // corrupting the pre-match data the AI model relies on.
       const twelveHoursAgo = new Date(Date.now() - 12 * 60 * 60 * 1000);
 
       const matchesToFetch = await this.prisma.liveMatch.findMany({
@@ -315,6 +636,15 @@ export class DataFetcherTask {
           ...(allowedLeagueIds.length > 0
             ? { leagueId: { in: allowedLeagueIds } }
             : {}),
+          // Exclude live and finished matches — preserve their pre-match odds
+          NOT: {
+            OR: [
+              { status: { in: FINISHED_STATUS_VALUES_FOR_DB } },
+              { state: { in: FINISHED_STATE_VALUES_FOR_DB } },
+              { status: { in: LIVE_STATUS_VALUES_FOR_DB } },
+              { state: { in: LIVE_STATE_VALUES_FOR_DB } },
+            ],
+          },
         },
         include: {
           homeTeam: { select: { name: true } },
@@ -325,11 +655,13 @@ export class DataFetcherTask {
       });
 
       if (matchesToFetch.length === 0) {
-        this.logger.log("💰 No matches to fetch odds for");
+        this.logger.log("MONEY No matches to fetch odds for");
         return;
       }
 
-      this.logger.log(`💰 Fetching odds for ${matchesToFetch.length} matches`);
+      this.logger.log(
+        `MONEY Fetching odds for ${matchesToFetch.length} matches`,
+      );
 
       let successCount = 0;
       let errorCount = 0;
@@ -358,7 +690,7 @@ export class DataFetcherTask {
       // Retry failed matches (502/Timeout)
       if (failedMatches.length > 0) {
         this.logger.warn(
-          `⚠️ Retrying ${failedMatches.length} failed matches (502/Timeout)...`,
+          `Retrying ${failedMatches.length} failed matches (502/Timeout)...`,
         );
 
         for (const match of failedMatches) {
@@ -366,7 +698,7 @@ export class DataFetcherTask {
           try {
             await this.processMatchOdds(match);
             successCount++;
-            this.logger.log(`✅ Retry successful for match ${match.id}`);
+            this.logger.log(`SUCCESS Retry successful for match ${match.id}`);
           } catch (retryErr: unknown) {
             const message =
               retryErr instanceof Error ? retryErr.message : String(retryErr);
@@ -378,7 +710,7 @@ export class DataFetcherTask {
       }
 
       this.logger.log(
-        `💰 Odds complete: ${successCount} success, ${errorCount} errors (initially)`,
+        `MONEY Odds complete: ${successCount} success, ${errorCount} errors (initially)`,
       );
     } catch (error: unknown) {
       const message = error instanceof Error ? error.message : String(error);
@@ -690,6 +1022,15 @@ export class DataFetcherTask {
           // Safe score parsing
           const sHome = this.asInt(match.homeScore ?? match.score?.home);
           const sAway = this.asInt(match.awayScore ?? match.score?.away);
+          const storedStatus = deriveStoredMatchStatus({
+            state: match.state,
+            status: match.status,
+            substate: match.substate,
+            statusBoxContent: match.statusBoxContent,
+            scoreHome: sHome,
+            scoreAway: sAway,
+            score: match.score,
+          });
 
           // Handle postponed matches (ERT = Erteledendi)
           if (match.statusBoxContent === "ERT") {
@@ -718,7 +1059,7 @@ export class DataFetcherTask {
               leagueId: leagueId,
               state: match.state || null,
               substate: match.substate || null,
-              status: match.status || match.state || "NS",
+              status: storedStatus,
               scoreHome: sHome,
               scoreAway: sAway,
               homeTeamId: homeTeamId,
@@ -733,7 +1074,7 @@ export class DataFetcherTask {
               leagueId: leagueId,
               state: match.state || null,
               substate: match.substate || null,
-              status: match.status || match.state || "NS",
+              status: storedStatus,
               mstUtc: BigInt(match.mstUtc || Date.now()),
               scoreHome: sHome,
               scoreAway: sAway,
@@ -891,7 +1232,44 @@ export class DataFetcherTask {
       }
     }
 
-    // ALWAYS update oddsUpdatedAt to ensure rotation
+    // Guard: If match already has pre-match odds and is now live/finished,
+    // do NOT overwrite odds/lineups/sidelined — the model needs stable pre-match data.
+    const matchState = match.state?.toLowerCase() ?? "";
+    const matchStatus = match.status?.toLowerCase() ?? "";
+    const liveStates = LIVE_STATE_VALUES_FOR_DB.map((s) => s.toLowerCase());
+    const liveStatuses = LIVE_STATUS_VALUES_FOR_DB.map((s) => s.toLowerCase());
+    const finishedStates = FINISHED_STATE_VALUES_FOR_DB.map((s) =>
+      s.toLowerCase(),
+    );
+    const finishedStatuses = FINISHED_STATUS_VALUES_FOR_DB.map((s) =>
+      s.toLowerCase(),
+    );
+
+    const isLiveOrFinished =
+      liveStates.includes(matchState) ||
+      liveStatuses.includes(matchStatus) ||
+      finishedStates.includes(matchState) ||
+      finishedStatuses.includes(matchStatus);
+
+    const existingOdds = match.odds as Record<string, unknown> | null;
+    const hasExistingOdds =
+      !!existingOdds &&
+      typeof existingOdds === "object" &&
+      Object.keys(existingOdds).length > 0;
+
+    if (isLiveOrFinished && hasExistingOdds) {
+      // Match is live/finished and already has pre-match odds — skip data update
+      this.logger.debug(
+        `🛡️ Preserving pre-match data for ${match.matchName} (status: ${matchStatus}, state: ${matchState})`,
+      );
+      await this.prisma.liveMatch.update({
+        where: { id: match.id },
+        data: { oddsUpdatedAt: new Date() },
+      });
+      return;
+    }
+
+    // Update odds/lineups/sidelined for pre-match (NS) matches
     await this.prisma.liveMatch.update({
       where: { id: match.id },
       data: {
@@ -912,7 +1290,7 @@ export class DataFetcherTask {
           sidelined.awayTeam.totalSidelined > 0))
     ) {
       this.logger.log(
-        `✅ Loop update: ${match.matchName} | Odds: ${Object.keys(odds).length} | Ref: ${refereeName || "N/A"} | Lineups: ${lineups ? "Yes" : "No"} | Sidelined: ${sidelined ? "Yes" : "No"}`,
+        `SUCCESS Loop update: ${match.matchName} | Odds: ${Object.keys(odds).length} | Ref: ${refereeName || "N/A"} | Lineups: ${lineups ? "Yes" : "No"} | Sidelined: ${sidelined ? "Yes" : "No"}`,
       );
     } else {
       this.logger.debug(
@@ -1289,4 +1667,3 @@ export class DataFetcherTask {
     return new Promise((resolve) => setTimeout(resolve, ms));
   }
 }
-

src/tasks/limit-resetter.task.ts

@@ -141,7 +141,7 @@ export class LimitResetterTask {
   }
 
   /**
-   * Reset subscription status for expired users
+   * Downgrade cancelled subscriptions that have passed their cancel effective date
    */
   @Cron("0 0 * * *", { timeZone: "Europe/Istanbul" })
   async checkSubscriptions() {
@@ -155,21 +155,55 @@ export class LimitResetterTask {
         try {
           const now = new Date();
 
-          const result = await this.prisma.user.updateMany({
+          // Find subscriptions with passed cancel effective date
+          const expiredSubs = await this.prisma.subscription.findMany({
             where: {
-              subscriptionStatus: "active",
+              plan: "cancelled",
-              subscriptionExpiresAt: { lt: now },
+              cancelEffectiveDate: { lt: now },
-            },
-            data: {
-              subscriptionStatus: "expired",
             },
+            select: { id: true, userId: true },
           });
 
-          if (result.count > 0) {
+          for (const sub of expiredSubs) {
-            this.logger.log(`${result.count} subscriptions marked as expired`);
+            // Downgrade to free
+            await this.prisma.user.update({
+              where: { id: sub.userId },
+              data: { subscriptionStatus: "free" },
+            });
+
+            // Sync limits to free tier
+            await this.prisma.usageLimit.upsert({
+              where: { userId: sub.userId },
+              update: { maxAnalyses: 3, maxCoupons: 1 },
+              create: {
+                userId: sub.userId,
+                analysisCount: 0,
+                couponCount: 0,
+                maxAnalyses: 3,
+                maxCoupons: 1,
+                lastResetDate: new Date(),
+              },
+            });
+
+            // Reset subscription to free
+            await this.prisma.subscription.update({
+              where: { id: sub.id },
+              data: {
+                plan: "free",
+                cancelledAt: null,
+                cancelEffectiveDate: null,
+              },
+            });
           }
-        } catch (error: any) {
+
-          this.logger.error(`Subscription check failed: ${error.message}`);
+          if (expiredSubs.length > 0) {
+            this.logger.log(
+              `${expiredSubs.length} cancelled subscriptions downgraded to free`,
+            );
+          }
+        } catch (error: unknown) {
+          const err = error as Error;
+          this.logger.error(`Subscription check failed: ${err.message}`);
         }
       },
       this.logger,