# Scalping Strategy
import logging
import numpy as np
from typing import List, Dict, Optional
from dataclasses import dataclass
from datetime import datetime

logger = logging.getLogger(__name__)

@dataclass
class ScalpingSignal:
    symbol: str
    side: str  # 'buy' or 'sell'
    confidence: float
    entry_price: float
    stop_loss: float
    take_profit: float
    timeframe: str
    reason: str

class ScalpingStrategy:
    """
    High-frequency scalping strategy.
    Targets small price movements with tight risk management.
    """
    
    def __init__(self, config: dict):
        self.config = config
        self.timeframes = config.get('timeframes', ['1m', '5m'])
        self.min_spread = config.get('min_spread', 0.001)  # 0.1%
        self.max_hold_time = config.get('max_hold_time', 300)  # 5 minutes
        self.risk_reward_ratio = config.get('risk_reward_ratio', 1.5)
        self.enabled = config.get('enabled', False)
        
        logger.info(f"ScalpingStrategy initialized: timeframes={self.timeframes}")
    
    async def generate_signals(self, market_data: dict) -> List[ScalpingSignal]:
        """Generate scalping signals"""
        if not self.enabled:
            return []
        
        signals = []
        
        for symbol, data in market_data.items():
            try:
                # Order book imbalance signal
                ob_signal = self._check_orderbook_imbalance(data)
                if ob_signal:
                    signals.append(ob_signal)
                
                # Price action signal
                pa_signal = self._check_price_action(data)
                if pa_signal:
                    signals.append(pa_signal)
                
                # Momentum burst signal
                mb_signal = self._check_momentum_burst(data)
                if mb_signal:
                    signals.append(mb_signal)
                    
            except Exception as e:
                logger.error(f"Error generating scalping signal for {symbol}: {e}")
        
        return signals
    
    def _check_orderbook_imbalance(self, data: dict) -> Optional[ScalpingSignal]:
        """Check for order book imbalance"""
        if 'bid' not in data or 'ask' not in data:
            return None
        
        bid = data.get('bid', 0)
        ask = data.get('ask', 0)
        spread = (ask - bid) / ((ask + bid) / 2)
        
        # If spread is tight, check for imbalance
        if spread < self.min_spread:
            return None
        
        # Placeholder for order book depth analysis
        # In production, analyze bid/ask volume imbalance
        
        return None
    
    def _check_price_action(self, data: dict) -> Optional[ScalpingSignal]:
        """Check for price action patterns"""
        # Look for quick reversals or breakouts
        # Requires OHLCV history
        
        return None
    
    def _check_momentum_burst(self, data: dict) -> Optional[ScalpingSignal]:
        """Check for momentum burst patterns"""
        # Look for sudden volume + price movement
        
        return None
    
    def calculate_position_size(self, account_value: float, risk_per_trade: float = 0.001) -> float:
        """Calculate position size for scalping"""
        # Very small positions, frequent trades
        return account_value * risk_per_trade
