# -*- coding: utf-8 -*-
"""
双均线策略实现
当短期均线上穿长期均线时买入，当短期均线下穿长期均线时卖出
"""

import pandas as pd
import numpy as np
import logging
from typing import Dict, List, Tuple, Optional
from .base_strategy import BaseStrategy

class DualMovingAverageStrategy(BaseStrategy):
    """双均线策略实现"""
    
    def __init__(self, config: Dict):
        super().__init__()
        # 使用主日志记录器，确保日志能被正确记录到文件
        self.logger = logging.getLogger('backtrader')
        
        # 策略参数
        self.short_period = config.get('short_period', 5)  # 短期均线周期
        self.long_period = config.get('long_period', 20)   # 长期均线周期
        self.trade_quantity = config.get('trade_quantity', 100)  # 每次交易数量
        
        # 保存生成的信号
        self.signals = None
        
        self.logger.info(f'双均线策略初始化完成: 短期周期={self.short_period}, 长期周期={self.long_period}')
    
    def initialize(self, data):
        """
        初始化策略，处理数据，计算指标等
        
        Args:
            data: 股票数据DataFrame
        """
        # 初始化信号数据
        self.signals = None
        
        # 计算移动平均线
        self._calculate_moving_averages(data)
    
    def generate_signals(self, data):
        """
        生成交易信号
        
        Args:
            data: 股票数据DataFrame
            
        Returns:
            pandas.DataFrame: 添加了信号列的DataFrame
        """
        if data.empty or len(data) < self.long_period:
            return pd.DataFrame()
        
        # 确保数据按日期从旧到新排序
        data = data.sort_index()
        
        # 计算移动平均线
        self._calculate_moving_averages(data)
        
        signals = pd.DataFrame(index=data.index)
        signals['signal'] = 0  # 0:无信号, 1:买入信号, -1:卖出信号
        signals['reason'] = ''  # 信号原因
        signals['short_ma'] = data[f'sma{self.short_period}']
        signals['long_ma'] = data[f'sma{self.long_period}']
        signals['strength'] = 0.0  # 信号强度
        
        # 生成交易信号
        for i in range(1, len(data)):
            # 获取当前和前一天的均线值
            short_ma_current = data[f'sma{self.short_period}'].iloc[i]
            long_ma_current = data[f'sma{self.long_period}'].iloc[i]
            short_ma_prev = data[f'sma{self.short_period}'].iloc[i-1]
            long_ma_prev = data[f'sma{self.long_period}'].iloc[i-1]
            
            signal = 0
            strength = 0.0
            reason = ''
            
            # 短期均线上穿长期均线 - 买入信号
            if short_ma_prev <= long_ma_prev and short_ma_current > long_ma_current:
                signal = 1
                strength = abs((short_ma_current - long_ma_current) / long_ma_current * 100)
                reason = f'短期均线上穿长期均线 ({short_ma_current:.2f} > {long_ma_current:.2f})'
            
            # 短期均线下穿长期均线 - 卖出信号
            elif short_ma_prev >= long_ma_prev and short_ma_current < long_ma_current:
                signal = -1
                strength = abs((long_ma_current - short_ma_current) / long_ma_current * 100)
                reason = f'短期均线下穿长期均线 ({short_ma_current:.2f} < {long_ma_current:.2f})'
            
            # 记录信号
            signals.loc[data.index[i], 'signal'] = signal
            signals.loc[data.index[i], 'strength'] = strength
            signals.loc[data.index[i], 'reason'] = reason
        
        # 保存信号
        self.signals = signals
        return signals
    
    def on_signal(self, date, signal, price):
        """
        处理交易信号
        
        Args:
            date: 日期
            signal: 信号类型 (1=买入, 0=持有, -1=卖出)
            price: 价格
            
        Returns:
            dict: 交易指令 {action: 'buy'|'sell'|'hold', quantity: int}
        """
        if signal == 1:
            # 买入信号
            self.logger.info(f'{date} 买入信号: 价格={price:.2f}, 数量={self.trade_quantity}')
            return {'action': 'buy', 'quantity': self.trade_quantity}
        elif signal == -1:
            # 卖出信号
            self.logger.info(f'{date} 卖出信号: 价格={price:.2f}, 数量={self.trade_quantity}')
            return {'action': 'sell', 'quantity': self.trade_quantity}
        else:
            # 持有
            return {'action': 'hold', 'quantity': 0}
    
    def _calculate_moving_averages(self, data):
        """
        计算移动平均线
        
        Args:
            data: 股票数据DataFrame
        """
        # 计算短期移动平均线
        data[f'sma{self.short_period}'] = data['close'].rolling(window=self.short_period).mean()
        
        # 计算长期移动平均线
        data[f'sma{self.long_period}'] = data['close'].rolling(window=self.long_period).mean()
        
        self.logger.debug(f'已计算移动平均线: SMA{self.short_period} 和 SMA{self.long_period}')