import pandas as pd
import numpy as np
import os
import csv
import logging
from typing import List, Dict, Any, Optional, Tuple
from collections import defaultdict

from cycles.utils.storage import Storage


class ResultProcessor:
    """Handles processing, aggregation, and saving of backtest results"""
    
    def __init__(self, storage: Storage, logging_instance: Optional[logging.Logger] = None):
        """
        Initialize result processor
        
        Args:
            storage: Storage instance for file operations
            logging_instance: Optional logging instance
        """
        self.storage = storage
        self.logging = logging_instance

    def process_timeframe_results(
        self, 
        min1_df: pd.DataFrame, 
        df: pd.DataFrame, 
        stop_loss_pcts: List[float], 
        timeframe_name: str, 
        initial_usd: float, 
        debug: bool = False
    ) -> Tuple[List[Dict], List[Dict]]:
        """
        Process results for a single timeframe with multiple stop loss values
        
        Args:
            min1_df: 1-minute data DataFrame
            df: Resampled timeframe DataFrame
            stop_loss_pcts: List of stop loss percentages to test
            timeframe_name: Name of the timeframe (e.g., '1D', '6h')
            initial_usd: Initial USD amount
            debug: Whether to enable debug output
            
        Returns:
            Tuple of (results_rows, trade_rows)
        """
        from cycles.backtest import Backtest
        
        df = df.copy().reset_index(drop=True)
        results_rows = []
        trade_rows = []

        for stop_loss_pct in stop_loss_pcts:
            try:
                results = Backtest.run(
                    min1_df,
                    df,
                    initial_usd=initial_usd,
                    stop_loss_pct=stop_loss_pct,
                    debug=debug
                )
                
                # Calculate metrics
                metrics = self._calculate_metrics(results, initial_usd, stop_loss_pct, timeframe_name)
                results_rows.append(metrics)
                
                # Process trades
                trades = self._process_trades(results.get('trades', []), timeframe_name, stop_loss_pct)
                trade_rows.extend(trades)
                
                if self.logging:
                    self.logging.info(f"Timeframe: {timeframe_name}, Stop Loss: {stop_loss_pct}, Trades: {results['n_trades']}")
                
                if debug:
                    self._debug_output(results)
                    
            except Exception as e:
                error_msg = f"Error processing {timeframe_name} with stop loss {stop_loss_pct}: {e}"
                if self.logging:
                    self.logging.error(error_msg)
                raise RuntimeError(error_msg) from e

        return results_rows, trade_rows

    def _calculate_metrics(
        self, 
        results: Dict[str, Any], 
        initial_usd: float, 
        stop_loss_pct: float, 
        timeframe_name: str
    ) -> Dict[str, Any]:
        """Calculate performance metrics from backtest results"""
        trades = results.get('trades', [])
        n_trades = results["n_trades"]
        
        # Calculate win metrics
        winning_trades = [t for t in trades if t.get('exit') is not None and t['exit'] > t['entry']]
        n_winning_trades = len(winning_trades)
        win_rate = n_winning_trades / n_trades if n_trades > 0 else 0
        
        # Calculate profit metrics
        total_profit = sum(trade['profit_pct'] for trade in trades)
        total_loss = sum(-trade['profit_pct'] for trade in trades if trade['profit_pct'] < 0)
        avg_trade = total_profit / n_trades if n_trades > 0 else 0
        profit_ratio = total_profit / total_loss if total_loss > 0 else float('inf')
        
        # Calculate drawdown
        max_drawdown = self._calculate_max_drawdown(trades)
        
        # Calculate final USD
        final_usd = initial_usd
        for trade in trades:
            final_usd *= (1 + trade['profit_pct'])
            
        # Calculate fees
        total_fees_usd = sum(trade.get('fee_usd', 0) for trade in trades)
        
        return {
            "timeframe": timeframe_name,
            "stop_loss_pct": stop_loss_pct,
            "n_trades": n_trades,
            "n_stop_loss": sum(1 for trade in trades if trade.get('type') == 'STOP'),
            "win_rate": win_rate,
            "max_drawdown": max_drawdown,
            "avg_trade": avg_trade,
            "total_profit": total_profit,
            "total_loss": total_loss,
            "profit_ratio": profit_ratio,
            "initial_usd": initial_usd,
            "final_usd": final_usd,
            "total_fees_usd": total_fees_usd,
        }

    def _calculate_max_drawdown(self, trades: List[Dict]) -> float:
        """Calculate maximum drawdown from trade sequence"""
        cumulative_profit = 0
        max_drawdown = 0
        peak = 0

        for trade in trades:
            cumulative_profit += trade['profit_pct']
            if cumulative_profit > peak:
                peak = cumulative_profit
            drawdown = peak - cumulative_profit
            if drawdown > max_drawdown:
                max_drawdown = drawdown

        return max_drawdown

    def _process_trades(
        self, 
        trades: List[Dict], 
        timeframe_name: str, 
        stop_loss_pct: float
    ) -> List[Dict]:
        """Process individual trades with metadata"""
        processed_trades = []
        
        for trade in trades:
            processed_trade = {
                "timeframe": timeframe_name,
                "stop_loss_pct": stop_loss_pct,
                "entry_time": trade.get("entry_time"),
                "exit_time": trade.get("exit_time"),
                "entry_price": trade.get("entry"),
                "exit_price": trade.get("exit"),
                "profit_pct": trade.get("profit_pct"),
                "type": trade.get("type"),
                "fee_usd": trade.get("fee_usd"),
            }
            processed_trades.append(processed_trade)
            
        return processed_trades

    def _debug_output(self, results: Dict[str, Any]) -> None:
        """Output debug information for backtest results"""
        trades = results.get('trades', [])
        
        # Print stop loss trades
        stop_loss_trades = [t for t in trades if t.get('type') == 'STOP']
        if stop_loss_trades:
            print("Stop Loss Trades:")
            for trade in stop_loss_trades:
                print(trade)
        
        # Print large loss trades
        large_loss_trades = [t for t in trades if t.get('profit_pct', 0) < -0.09]
        if large_loss_trades:
            print("Large Loss Trades:")
            for trade in large_loss_trades:
                print("Large loss trade:", trade)

    def aggregate_results(self, all_results: List[Dict]) -> List[Dict]:
        """
        Aggregate results per stop_loss_pct and timeframe
        
        Args:
            all_results: List of result dictionaries from all timeframes
            
        Returns:
            List of aggregated summary rows
        """
        grouped = defaultdict(list)
        for row in all_results:
            key = (row['timeframe'], row['stop_loss_pct'])
            grouped[key].append(row)

        summary_rows = []
        for (timeframe, stop_loss_pct), rows in grouped.items():
            summary = self._aggregate_group(rows, timeframe, stop_loss_pct)
            summary_rows.append(summary)
            
        return summary_rows

    def _aggregate_group(self, rows: List[Dict], timeframe: str, stop_loss_pct: float) -> Dict:
        """Aggregate a group of rows with the same timeframe and stop loss"""
        total_trades = sum(r['n_trades'] for r in rows)
        total_stop_loss = sum(r['n_stop_loss'] for r in rows)
        
        # Calculate averages
        avg_win_rate = np.mean([r['win_rate'] for r in rows])
        avg_max_drawdown = np.mean([r['max_drawdown'] for r in rows])
        avg_avg_trade = np.mean([r['avg_trade'] for r in rows])
        avg_profit_ratio = np.mean([r['profit_ratio'] for r in rows])
        
        # Calculate final USD and fees
        final_usd = np.mean([r.get('final_usd', r.get('initial_usd', 0)) for r in rows])
        total_fees_usd = np.mean([r.get('total_fees_usd', 0) for r in rows])
        initial_usd = rows[0].get('initial_usd', 0) if rows else 0

        return {
            "timeframe": timeframe,
            "stop_loss_pct": stop_loss_pct,
            "n_trades": total_trades,
            "n_stop_loss": total_stop_loss,
            "win_rate": avg_win_rate,
            "max_drawdown": avg_max_drawdown,
            "avg_trade": avg_avg_trade,
            "profit_ratio": avg_profit_ratio,
            "initial_usd": initial_usd,
            "final_usd": final_usd,
            "total_fees_usd": total_fees_usd,
        }

    def save_trade_file(self, trades: List[Dict], timeframe: str, stop_loss_pct: float) -> None:
        """
        Save individual trade file with summary header
        
        Args:
            trades: List of trades for this combination
            timeframe: Timeframe name
            stop_loss_pct: Stop loss percentage
        """
        if not trades:
            return
            
        try:
            # Generate filename
            sl_percent = int(round(stop_loss_pct * 100))
            trades_filename = os.path.join(self.storage.results_dir, f"trades_{timeframe}_ST{sl_percent}pct.csv")
            
            # Prepare summary from first trade
            sample_trade = trades[0]
            summary_fields = ["timeframe", "stop_loss_pct", "n_trades", "win_rate"]
            summary_values = [timeframe, stop_loss_pct, len(trades), "calculated_elsewhere"]
            
            # Write file with header and trades
            trades_fieldnames = ["entry_time", "exit_time", "entry_price", "exit_price", "profit_pct", "type", "fee_usd"]
            
            with open(trades_filename, "w", newline="") as f:
                # Write summary header
                f.write("\t".join(summary_fields) + "\n")
                f.write("\t".join(str(v) for v in summary_values) + "\n")
                
                # Write trades
                writer = csv.DictWriter(f, fieldnames=trades_fieldnames)
                writer.writeheader()
                for trade in trades:
                    writer.writerow({k: trade.get(k, "") for k in trades_fieldnames})
                    
            if self.logging:
                self.logging.info(f"Trades saved to {trades_filename}")
                
        except Exception as e:
            error_msg = f"Failed to save trades file for {timeframe}_ST{int(round(stop_loss_pct * 100))}pct: {e}"
            if self.logging:
                self.logging.error(error_msg)
            raise RuntimeError(error_msg) from e

    def save_backtest_results(
        self, 
        results: List[Dict], 
        metadata_lines: List[str], 
        timestamp: str
    ) -> str:
        """
        Save aggregated backtest results to CSV file
        
        Args:
            results: List of aggregated result dictionaries
            metadata_lines: List of metadata strings
            timestamp: Timestamp for filename
            
        Returns:
            Path to saved file
        """
        try:
            filename = f"{timestamp}_backtest.csv"
            fieldnames = [
                "timeframe", "stop_loss_pct", "n_trades", "n_stop_loss", "win_rate",
                "max_drawdown", "avg_trade", "profit_ratio", "final_usd", "total_fees_usd"
            ]
            
            filepath = self.storage.write_backtest_results(filename, fieldnames, results, metadata_lines)
            
            if self.logging:
                self.logging.info(f"Backtest results saved to {filepath}")
                
            return filepath
            
        except Exception as e:
            error_msg = f"Failed to save backtest results: {e}"
            if self.logging:
                self.logging.error(error_msg)
            raise RuntimeError(error_msg) from e

    def get_price_info(self, data_df: pd.DataFrame, date: str) -> Tuple[Optional[str], Optional[float]]:
        """
        Get nearest price information for a given date
        
        Args:
            data_df: DataFrame with price data
            date: Target date string
            
        Returns:
            Tuple of (nearest_time, price) or (None, None) if no data
        """
        try:
            if len(data_df) == 0:
                return None, None
                
            target_ts = pd.to_datetime(date)
            nearest_idx = data_df.index.get_indexer([target_ts], method='nearest')[0]
            nearest_time = data_df.index[nearest_idx]
            price = data_df.iloc[nearest_idx]['close']
            
            return str(nearest_time), float(price)
            
        except Exception as e:
            if self.logging:
                self.logging.warning(f"Could not get price info for {date}: {e}")
            return None, None