feat: Multi-Pair Divergence Selection Strategy

- Extend regime detection to top 10 cryptocurrencies (45 pairs)
- Dynamic pair selection based on divergence score (|z_score| * probability)
- Universal ML model trained on all pairs
- Correlation-based filtering to avoid redundant positions
- Funding rate integration from OKX for all 10 assets
- ATR-based dynamic stop-loss and take-profit
- Walk-forward training with 70/30 split

Performance: +35.69% return (vs +28.66% baseline), 63.6% win rate

strategies/factory.py

@@ -37,6 +37,7 @@ def _build_registry() -> dict[str, StrategyConfig]:
     from strategies.examples import MaCrossStrategy, RsiStrategy
     from strategies.supertrend import MetaSupertrendStrategy
     from strategies.regime_strategy import RegimeReversionStrategy
+    from strategies.multi_pair import MultiPairDivergenceStrategy, MultiPairConfig
     
     return {
         "rsi": StrategyConfig(
@@ -98,6 +99,18 @@ def _build_registry() -> dict[str, StrategyConfig]:
                 'stop_loss': [0.04, 0.06, 0.08],
                 'funding_threshold': [0.005, 0.01, 0.02]
             }
+        ),
+        "multi_pair": StrategyConfig(
+            strategy_class=MultiPairDivergenceStrategy,
+            default_params={
+                # Multi-pair divergence strategy uses config object
+                # Parameters passed here will override MultiPairConfig defaults
+            },
+            grid_params={
+                'z_entry_threshold': [0.8, 1.0, 1.2],
+                'prob_threshold': [0.4, 0.5, 0.6],
+                'correlation_threshold': [0.75, 0.85, 0.95]
+            }
         )
     }