"""
Freqtrade Execution Strategy for ads-anomaly-detection
======================================================

PURE EXECUTION LAYER - NO INTERNAL LOGIC

This strategy acts as a minimal execution wrapper that:
1. Receives pre-tagged trading decisions from ads-anomaly-detection
2. Executes those decisions within Freqtrade's framework
3. Contains NO internal trading logic, indicators, or filters
4. All intelligence comes from the external strategy/memory system

Trading Decision Sources:
- Random trades (testing)
- Test trades (validation)
- Confirmed anomaly trades (production)

Usage: Paper trading mode recommended for initial deployment
"""

import logging
import redis
import json
from typing import Optional, Dict, Any
from datetime import datetime, timezone
from pandas import DataFrame

# Note: Freqtrade imports will be available when running in Freqtrade environment
# For development/testing, these imports may show as unresolved

try:
    from freqtrade.strategy import IStrategy
except ImportError:
    # Fallback for development environment
    class IStrategy:
        pass

logger = logging.getLogger(__name__)


class AdsExecutionStrategy(IStrategy):
    """
    Minimal Freqtrade execution strategy for ads-anomaly-detection system.
    
    This strategy is intentionally thin - it's the trigger, not the brain.
    All trading logic resides in the ads-anomaly-detection memory system.
    """
    
    # Strategy metadata
    INTERFACE_VERSION: int = 3
    
    # Minimal timeframe - we don't analyze, just execute
    timeframe = '1m'
    
    # Minimal startup candles - we don't need history
    startup_candle_count: int = 1
    
    # Risk management (external system should handle this, but Freqtrade requires it)
    stoploss = -0.10  # 10% safety net
    
    # No trailing stop - external system controls exits
    trailing_stop = False
    
    # ROI table - disabled, external system controls exits
    minimal_roi = {
        "0": 100  # Never sell based on ROI, external system decides
    }
    
    # External system connection
    redis_client = None
    
    def __init__(self, config: dict) -> None:
        super().__init__(config)
        self.setup_external_connection()
        
    def setup_external_connection(self):
        """Initialize connection to ads-anomaly-detection system via Redis"""
        try:
            self.redis_client = redis.Redis(
                host=self.config.get('redis_host', 'localhost'),
                port=self.config.get('redis_port', 6379),
                db=self.config.get('redis_db', 0),
                decode_responses=True
            )
            # Test connection
            self.redis_client.ping()
            logger.info("✅ Connected to ads-anomaly-detection system via Redis")
        except Exception as e:
            logger.error(f"❌ Failed to connect to ads-anomaly-detection system: {e}")
            self.redis_client = None
    
    def get_external_signal(self, pair: str) -> Optional[Dict[str, Any]]:
        """
        Retrieve trading signal from ads-anomaly-detection system
        
        Expected signal format:
        {
            "action": "buy" | "sell" | "hold",
            "pair": "BTC/USDT",
            "signal_type": "random" | "test" | "anomaly",
            "confidence": 0.0-1.0,
            "timestamp": "2025-08-06T00:30:00Z",
            "metadata": {
                "anomaly_severity": "critical",
                "detector": "statistical_detector",
                "reason": "spike_detected"
            }
        }
        """
        if not self.redis_client:
            return None
            
        try:
            # Check for signals for this pair
            signal_key = f"freqtrade_signal:{pair}"
            signal_data = self.redis_client.get(signal_key)
            
            if signal_data and isinstance(signal_data, str):
                signal = json.loads(signal_data)
                
                # Remove consumed signal
                self.redis_client.delete(signal_key)
                
                logger.info(f"📡 Received external signal for {pair}: {signal}")
                return signal
                
        except Exception as e:
            logger.error(f"❌ Error retrieving external signal for {pair}: {e}")
            
        return None
    
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Minimal indicators - we don't analyze, just need basic data structure
        The external system provides all analysis
        """
        # Only add timestamp for external system reference
        dataframe['timestamp'] = dataframe['date'].astype(str)
        
        # Add basic volume indicator for Freqtrade compatibility (simple moving average)
        dataframe['volume_sma'] = dataframe['volume'].rolling(window=1).mean()
        
        return dataframe
    
    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Entry logic - purely based on external signals from ads-anomaly-detection
        NO internal analysis, indicators, or filters
        """
        pair = metadata['pair']
        
        # Initialize entry signals
        dataframe['enter_long'] = False
        dataframe['enter_short'] = False
        dataframe['enter_tag'] = ''
        
        # Get external signal
        signal = self.get_external_signal(pair)
        
        if signal and signal.get('action') == 'buy':
            # Execute buy signal from external system
            current_idx = len(dataframe) - 1
            dataframe.loc[current_idx, 'enter_long'] = True
            
            # Tag with signal type and metadata
            signal_type = signal.get('signal_type', 'unknown')
            confidence = signal.get('confidence', 0)
            tag = f"{signal_type}_buy_conf_{confidence:.2f}"
            
            dataframe.loc[current_idx, 'enter_tag'] = tag
            
            logger.info(f"🚀 EXECUTING BUY for {pair} - Signal: {signal_type} (confidence: {confidence})")
            
            # Log metadata for tracking
            metadata_info = signal.get('metadata', {})
            if metadata_info:
                logger.info(f"📊 Trade metadata: {metadata_info}")
        
        return dataframe
    
    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Exit logic - purely based on external signals from ads-anomaly-detection
        NO internal analysis, indicators, or filters
        """
        pair = metadata['pair']
        
        # Initialize exit signals
        dataframe['exit_long'] = False
        dataframe['exit_short'] = False
        dataframe['exit_tag'] = ''
        
        # Get external signal
        signal = self.get_external_signal(pair)
        
        if signal and signal.get('action') == 'sell':
            # Execute sell signal from external system
            current_idx = len(dataframe) - 1
            dataframe.loc[current_idx, 'exit_long'] = True
            
            # Tag with signal type and metadata
            signal_type = signal.get('signal_type', 'unknown')
            confidence = signal.get('confidence', 0)
            tag = f"{signal_type}_sell_conf_{confidence:.2f}"
            
            dataframe.loc[current_idx, 'exit_tag'] = tag
            
            logger.info(f"🛑 EXECUTING SELL for {pair} - Signal: {signal_type} (confidence: {confidence})")
            
            # Log metadata for tracking
            metadata_info = signal.get('metadata', {})
            if metadata_info:
                logger.info(f"📊 Trade metadata: {metadata_info}")
        
        return dataframe
    
    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
                          time_in_force: str, current_time: datetime, entry_tag: Optional[str],
                          side: str, **kwargs) -> bool:
        """
        Final confirmation before trade execution
        Can be used by external system for last-minute validation
        """
        logger.info(f"🔍 Trade confirmation requested:")
        logger.info(f"   Pair: {pair}")
        logger.info(f"   Side: {side}")
        logger.info(f"   Amount: {amount}")
        logger.info(f"   Rate: {rate}")
        logger.info(f"   Tag: {entry_tag}")
        
        # Could check with external system for final confirmation here
        # For now, always confirm (external system already decided)
        return True
    
    def confirm_trade_exit(self, pair: str, trade, order_type: str, amount: float,
                         rate: float, time_in_force: str, exit_reason: str,
                         current_time: datetime, **kwargs) -> bool:
        """
        Final confirmation before trade exit
        Can be used by external system for last-minute validation
        """
        logger.info(f"🔍 Exit confirmation requested:")
        logger.info(f"   Pair: {pair}")
        logger.info(f"   Exit reason: {exit_reason}")
        logger.info(f"   Amount: {amount}")
        logger.info(f"   Rate: {rate}")
        
        # Could check with external system for final confirmation here
        # For now, always confirm (external system already decided)
        return True
    
    def custom_stoploss(self, pair: str, trade, current_time: datetime,
                       current_rate: float, current_profit: float, **kwargs) -> float:
        """
        Custom stoploss - can be controlled by external system
        """
        if not self.redis_client:
            return self.stoploss
            
        try:
            # Check if external system wants to adjust stoploss
            stoploss_key = f"freqtrade_stoploss:{pair}"
            custom_sl = self.redis_client.get(stoploss_key)
            
            if custom_sl and isinstance(custom_sl, str):
                return float(custom_sl)
                
        except Exception as e:
            logger.error(f"❌ Error getting custom stoploss: {e}")
            
        return self.stoploss
    
    def check_entry_timeout(self, pair: str, trade, order, current_time: datetime, **kwargs) -> bool:
        """Entry timeout - external system can control this"""
        # Default timeout handling
        return False
    
    def check_exit_timeout(self, pair: str, trade, order, current_time: datetime, **kwargs) -> bool:
        """Exit timeout - external system can control this"""
        # Default timeout handling  
        return False
    
    def informative_pairs(self):
        """No additional pairs needed - we're execution only"""
        return []