diff --git a/MALKHUT/malkhut/training/selector.py b/MALKHUT/malkhut/training/selector.py
index 4c4024f..ef49dbd 100644
--- a/MALKHUT/malkhut/training/selector.py
+++ b/MALKHUT/malkhut/training/selector.py
@@ -169,15 +169,18 @@ class RegimeStrategyScore:
 
 class PerformanceMatrix:
     """
-    Tracks strategy performance across regimes.
+    Tracks strategy performance across regimes AND venues.
 
-    Matrix: (regime, strategy_id) → RegimeStrategyScore
+    Matrix: (regime, strategy_id, venue) → RegimeStrategyScore
 
-    Used by the selector to choose the best strategy for current conditions.
+    Enables:
+      - Per-venue best strategy: get_best(regime, venue='bingx')
+      - Cross-venue comparison: get_venue_comparison(regime, strategy_id)
+      - Venue-agnostic fallback: get_best(regime) scans all venues
     """
 
     def __init__(self) -> None:
-        self._scores: Dict[Tuple[MarketRegime, str], RegimeStrategyScore] = {}
+        self._scores: Dict[Tuple[MarketRegime, str, str], RegimeStrategyScore] = {}
         self._strategy_regime_history: Dict[str, List[MarketRegime]] = defaultdict(list)
 
     def record(
@@ -190,12 +193,11 @@ class PerformanceMatrix:
         adverse_fill_ratio: float = 0.0,
         venue: str = "bingx",
     ) -> None:
-        """Record a strategy's performance in a regime (optionally venue-tagged)."""
-        key = (regime, strategy_id)
+        """Record a strategy's performance in a regime on a specific venue."""
+        key = (regime, strategy_id, venue)
         existing = self._scores.get(key)
 
         if existing:
-            # Exponential moving average
             alpha = 0.3
             new_score = alpha * score + (1 - alpha) * existing.score
             new_episodes = existing.episodes + 1
@@ -218,9 +220,9 @@ class PerformanceMatrix:
             avg_drawdown_bps=new_dd,
             avg_adverse_fill_ratio=new_adverse,
             last_updated_ns=time.time_ns(),
-            confidence=min(1.0, new_episodes / 10.0),  # confidence grows with evidence
+            confidence=min(1.0, new_episodes / 10.0),
             support_count=new_episodes,
-            distance_to_nearest=0.0,  # computed lazily on query
+            distance_to_nearest=0.0,
         )
         self._strategy_regime_history[strategy_id].append(regime)
 
@@ -228,23 +230,52 @@ class PerformanceMatrix:
         self,
         regime: MarketRegime,
         exclude: Optional[set[str]] = None,
+        venue: Optional[str] = None,
     ) -> Optional[str]:
-        """Get the best strategy for a given regime."""
+        """Get the best strategy for a given regime, optionally filtered by venue."""
         exclude = exclude or set()
         candidates = [
             (key[1], score.score)
             for key, score in self._scores.items()
             if key[0] == regime and key[1] not in exclude
+            and (venue is None or key[2] == venue)
         ]
         if not candidates:
             return None
         return max(candidates, key=lambda x: x[1])[0]
 
-    def get_scores_for_regime(self, regime: MarketRegime) -> List[RegimeStrategyScore]:
-        """Get all strategy scores for a regime, sorted by score."""
-        scores = [s for s in self._scores.values() if s.regime == regime]
+    def get_scores_for_regime(
+        self,
+        regime: MarketRegime,
+        venue: Optional[str] = None,
+    ) -> List[RegimeStrategyScore]:
+        """Get all strategy scores for a regime, optionally filtered by venue."""
+        scores = [
+            s for s in self._scores.values()
+            if s.regime == regime
+            and (venue is None or self._venue_of(s) == venue)
+        ]
         return sorted(scores, key=lambda s: s.score, reverse=True)
 
+    def _venue_of(self, score: RegimeStrategyScore) -> str:
+        """Reverse-lookup venue from key. Returns 'bingx' if not found."""
+        for key in self._scores:
+            if self._scores[key] is score:
+                return key[2]
+        return "bingx"
+
+    def get_venue_comparison(
+        self,
+        regime: MarketRegime,
+        strategy_id: str,
+    ) -> Dict[str, float]:
+        """Compare a strategy's performance across venues for a given regime."""
+        result: Dict[str, float] = {}
+        for (reg, strat, venue), score in self._scores.items():
+            if reg == regime and strat == strategy_id:
+                result[venue] = score.score
+        return result
+
     def get_regimes_for_strategy(self, strategy_id: str) -> List[MarketRegime]:
         """Get all regimes where a strategy has been tested."""
         return list(set(key[0] for key in self._scores if key[1] == strategy_id))
@@ -376,6 +407,7 @@ class StrategySelector:
         pnl_bps: float = 0.0,
         drawdown_bps: float = 0.0,
         adverse_fill_ratio: float = 0.0,
+        venue: str = "bingx",
     ) -> None:
         """Record a strategy's outcome for learning."""
         self._matrix.record(
@@ -385,6 +417,7 @@ class StrategySelector:
             pnl_bps=pnl_bps,
             drawdown_bps=drawdown_bps,
             adverse_fill_ratio=adverse_fill_ratio,
+            venue=venue,
         )
 
     @property