"""
Configuration for Multi-Pair Divergence Strategy.
"""
from dataclasses import dataclass, field


@dataclass
class MultiPairConfig:
    """
    Configuration parameters for multi-pair divergence strategy.
    
    Attributes:
        assets: List of asset symbols to analyze (top 10 by market cap)
        z_window: Rolling window for Z-Score calculation (hours)
        z_entry_threshold: Minimum |Z-Score| to consider for entry
        prob_threshold: Minimum ML probability to consider for entry
        correlation_threshold: Max correlation to allow between pairs
        correlation_window: Rolling window for correlation (hours)
        atr_period: ATR lookback period for dynamic stops
        sl_atr_multiplier: Stop-loss as multiple of ATR
        tp_atr_multiplier: Take-profit as multiple of ATR
        train_ratio: Walk-forward train/test split ratio
        horizon: Look-ahead horizon for target calculation (hours)
        profit_target: Minimum profit threshold for target labels
        funding_threshold: Funding rate threshold for filtering
    """
    # Asset Universe
    assets: list[str] = field(default_factory=lambda: [
        "BTC-USDT", "ETH-USDT", "SOL-USDT", "XRP-USDT", "BNB-USDT",
        "DOGE-USDT", "ADA-USDT", "AVAX-USDT", "LINK-USDT", "DOT-USDT"
    ])
    
    # Z-Score Thresholds
    z_window: int = 24
    z_entry_threshold: float = 1.0
    
    # ML Thresholds
    prob_threshold: float = 0.5
    train_ratio: float = 0.7
    horizon: int = 102
    profit_target: float = 0.005
    
    # Correlation Filtering
    correlation_threshold: float = 0.85
    correlation_window: int = 168  # 7 days in hours
    
    # Risk Management - ATR-Based Stops
    # SL/TP are calculated as multiples of ATR
    # Mean ATR for crypto is ~0.6% per hour, so:
    # - 10x ATR = ~6% SL (matches previous fixed 6%)
    # - 8x ATR = ~5% TP (matches previous fixed 5%)
    atr_period: int = 14  # ATR lookback period (hours for 1h timeframe)
    sl_atr_multiplier: float = 10.0  # Stop-loss = entry +/- (ATR * multiplier)
    tp_atr_multiplier: float = 8.0  # Take-profit = entry +/- (ATR * multiplier)
    
    # Fallback fixed percentages (used if ATR is unavailable)
    base_sl_pct: float = 0.06
    base_tp_pct: float = 0.05
    
    # ATR bounds to prevent extreme stops
    min_sl_pct: float = 0.02  # Minimum 2% stop-loss
    max_sl_pct: float = 0.10  # Maximum 10% stop-loss
    min_tp_pct: float = 0.02  # Minimum 2% take-profit
    max_tp_pct: float = 0.15  # Maximum 15% take-profit
    
    volatility_window: int = 24
    
    # Funding Rate Filter
    # OKX funding rates are typically 0.0001 (0.01%) per 8h
    # Extreme funding is > 0.0005 (0.05%) which indicates crowded trade
    funding_threshold: float = 0.0005  # 0.05% - filter extreme funding
    
    # Trade Management
    # Note: Setting min_hold_bars=0 and z_exit_threshold=0 gives best results
    # The mean-reversion exit at Z=0 is the primary profit driver
    min_hold_bars: int = 0  # Disabled - let mean reversion drive exits
    switch_threshold: float = 999.0  # Disabled - don't switch mid-trade
    cooldown_bars: int = 0  # Disabled - enter when signal appears
    z_exit_threshold: float = 0.0  # Exit at Z=0 (mean reversion complete)
    
    # Exchange
    exchange_id: str = "okx"
    timeframe: str = "1h"
    
    def get_pair_count(self) -> int:
        """Calculate number of unique pairs from asset list."""
        n = len(self.assets)
        return n * (n - 1) // 2