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/multi_pair/pair_scanner.py

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+"""
+Pair Scanner for Multi-Pair Divergence Strategy.
+
+Generates all possible pairs from asset universe and checks tradeability.
+"""
+from dataclasses import dataclass
+from itertools import combinations
+from typing import Optional
+
+import ccxt
+
+from engine.logging_config import get_logger
+from .config import MultiPairConfig
+
+logger = get_logger(__name__)
+
+
+@dataclass
+class TradingPair:
+    """
+    Represents a tradeable pair for spread analysis.
+    
+    Attributes:
+        base_asset: First asset in the pair (numerator)
+        quote_asset: Second asset in the pair (denominator)
+        pair_id: Unique identifier for the pair
+        is_direct: Whether pair can be traded directly on exchange
+        exchange_symbol: Symbol for direct trading (if available)
+    """
+    base_asset: str
+    quote_asset: str
+    pair_id: str
+    is_direct: bool = False
+    exchange_symbol: Optional[str] = None
+    
+    @property
+    def name(self) -> str:
+        """Human-readable pair name."""
+        return f"{self.base_asset}/{self.quote_asset}"
+    
+    def __hash__(self):
+        return hash(self.pair_id)
+    
+    def __eq__(self, other):
+        if not isinstance(other, TradingPair):
+            return False
+        return self.pair_id == other.pair_id
+
+
+class PairScanner:
+    """
+    Scans and generates tradeable pairs from asset universe.
+    
+    Checks OKX for directly tradeable cross-pairs and generates
+    synthetic pairs via USDT for others.
+    """
+    
+    def __init__(self, config: MultiPairConfig):
+        self.config = config
+        self.exchange: Optional[ccxt.Exchange] = None
+        self._available_markets: set[str] = set()
+    
+    def _init_exchange(self) -> None:
+        """Initialize exchange connection for market lookup."""
+        if self.exchange is None:
+            exchange_class = getattr(ccxt, self.config.exchange_id)
+            self.exchange = exchange_class({'enableRateLimit': True})
+            self.exchange.load_markets()
+            self._available_markets = set(self.exchange.symbols)
+            logger.info(
+                "Loaded %d markets from %s",
+                len(self._available_markets), 
+                self.config.exchange_id
+            )
+    
+    def generate_pairs(self, check_exchange: bool = True) -> list[TradingPair]:
+        """
+        Generate all unique pairs from asset universe.
+        
+        Args:
+            check_exchange: Whether to check OKX for direct trading
+            
+        Returns:
+            List of TradingPair objects
+        """
+        if check_exchange:
+            self._init_exchange()
+        
+        pairs = []
+        assets = self.config.assets
+        
+        for base, quote in combinations(assets, 2):
+            pair_id = f"{base}__{quote}"
+            
+            # Check if directly tradeable as cross-pair on OKX
+            is_direct = False
+            exchange_symbol = None
+            
+            if check_exchange:
+                # Check perpetual cross-pair (e.g., ETH/BTC:BTC)
+                # OKX perpetuals are typically quoted in USDT
+                # Cross-pairs like ETH/BTC are less common
+                cross_symbol = f"{base.replace('-USDT', '')}/{quote.replace('-USDT', '')}:USDT"
+                if cross_symbol in self._available_markets:
+                    is_direct = True
+                    exchange_symbol = cross_symbol
+            
+            pair = TradingPair(
+                base_asset=base,
+                quote_asset=quote,
+                pair_id=pair_id,
+                is_direct=is_direct,
+                exchange_symbol=exchange_symbol
+            )
+            pairs.append(pair)
+        
+        # Log summary
+        direct_count = sum(1 for p in pairs if p.is_direct)
+        logger.info(
+            "Generated %d pairs: %d direct, %d synthetic",
+            len(pairs), direct_count, len(pairs) - direct_count
+        )
+        
+        return pairs
+    
+    def get_required_symbols(self, pairs: list[TradingPair]) -> list[str]:
+        """
+        Get list of symbols needed to calculate all pair spreads.
+        
+        For synthetic pairs, we need both USDT pairs.
+        For direct pairs, we still load USDT pairs for simplicity.
+        
+        Args:
+            pairs: List of trading pairs
+            
+        Returns:
+            List of unique symbols to load (e.g., ['BTC-USDT', 'ETH-USDT'])
+        """
+        symbols = set()
+        for pair in pairs:
+            symbols.add(pair.base_asset)
+            symbols.add(pair.quote_asset)
+        return list(symbols)
+    
+    def filter_by_assets(
+        self, 
+        pairs: list[TradingPair], 
+        exclude_assets: list[str]
+    ) -> list[TradingPair]:
+        """
+        Filter pairs that contain any of the excluded assets.
+        
+        Args:
+            pairs: List of trading pairs
+            exclude_assets: Assets to exclude
+            
+        Returns:
+            Filtered list of pairs
+        """
+        if not exclude_assets:
+            return pairs
+        
+        exclude_set = set(exclude_assets)
+        return [
+            p for p in pairs
+            if p.base_asset not in exclude_set 
+            and p.quote_asset not in exclude_set
+        ]