# --- Do not remove these libs ---
from freqtrade.strategy import IStrategy
from pandas import DataFrame
import talib.abstract as ta
import numpy as np

# 1小时短线趋势追踪策略 V7 - EMA + MACD 综合趋势判断
# 使用 EMA8, EMA24, EMA72, EMA168 + MACD 识别趋势阶段和强度
# freqtrade backtesting -c ./user_data/Chan/config/EMA_Pattern.json --strategy EMA_Pattern --strategy-path ./user_data/Chan/strategies --timerange=20250101-
# freqtrade download-data -c ./user_data/Chan/config/EMA_Pattern.json -t 1h --pairs BTC/USDT:USDT --timerange=20250101-


class EMA_Pattern(IStrategy):
    """
    1小时K线短线趋势追踪策略 V7 - EMA + MACD
    
    核心逻辑:
    1. EMA多头排列 + MACD金叉确认 = 趋势启动（最佳入场）
    2. EMA发散 + MACD动能增加 = 趋势发展（可追涨）
    3. MACD动能减弱/顶背离 = 趋势成熟（减仓）
    4. EMA死叉 + MACD死叉 = 趋势结束（出场）
    """
    
    can_short: bool = True
    timeframe: str = "1h"
    process_only_new_candles: bool = True
    
    # EMA参数
    ema_fast: int = 8
    ema_short: int = 24
    ema_mid: int = 72
    ema_long: int = 168
    
    # 趋势强度阈值（放宽以增加交易机会）
    min_trend_strength: int = 65
    
    # ADX阈值
    min_adx: int = 25  # 有趋势就交易
    
    # ROI - V6最佳参数（无杠杆）
    minimal_roi = {
        "120": 0.02,   # 5天后 2%止盈
        "72": 0.03,    # 3天后 3%止盈
        "24": 0.05,    # 1天后 5%止盈
        "0": 0.08,     # 立即 8%止盈
    }
    
    # 固定止损
    stoploss: float = -0.05  # 5%止损
    
    # 追踪止损
    trailing_stop: bool = True
    trailing_stop_positive: float = 0.025  # 盈利2.5%后启动
    trailing_stop_positive_offset: float = 0.04  # 盈利4%后才触发
    trailing_only_offset_is_reached: bool = True
    
    # 禁用自定义止损（让追踪止损工作）
    use_custom_stoploss: bool = False
    
    # 只做多（做空效果差）
    can_short: bool = False
    
    startup_candle_count: int = 250

    def custom_stoploss(self, pair: str, trade, current_time, current_rate,
                        current_profit, after_fill, **kwargs) -> float:
        """
        动态止损 V3 - 保护利润但不过早出场：
        1. 盈利超过6%，止损移到盈利3%（锁定一半利润）
        2. 盈利超过3%，止损移到盈利1%
        3. 盈利超过1.5%，止损移到保本
        4. 长时间亏损才考虑缩紧止损
        """
        # 持仓时间（小时）
        trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
        
        # 盈利时动态止损 - 阶梯式保护利润
        if current_profit > 0.06:
            # 盈利超过6%，锁定3%利润
            return -0.03
        elif current_profit > 0.03:
            # 盈利超过3%，锁定1%利润
            return -0.02
        elif current_profit > 0.015:
            # 盈利超过1.5%，移到保本
            return -0.005
        
        # 长时间持仓亏损（超过96小时=4天），才缩紧止损
        if trade_duration > 96 and current_profit < -0.03:
            return -0.04  # 缩紧到4%
        
        # 默认使用配置的止损
        return self.stoploss

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if dataframe is None or dataframe.empty:
            return dataframe
        
        # ==================== EMA指标 ====================
        dataframe['ema8'] = ta.EMA(dataframe, timeperiod=self.ema_fast)
        dataframe['ema24'] = ta.EMA(dataframe, timeperiod=self.ema_short)
        dataframe['ema72'] = ta.EMA(dataframe, timeperiod=self.ema_mid)
        dataframe['ema168'] = ta.EMA(dataframe, timeperiod=self.ema_long)
        
        # ==================== MACD指标 ====================
        macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
        dataframe['macd'] = macd['macd']
        dataframe['macd_signal'] = macd['macdsignal']
        dataframe['macd_hist'] = macd['macdhist']
        
        # MACD辅助指标
        dataframe['macd_hist_change'] = dataframe['macd_hist'] - dataframe['macd_hist'].shift(1)
        dataframe['macd_hist_ma'] = dataframe['macd_hist'].rolling(5).mean()
        
        # ADX趋势强度
        dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
        
        # ATR波动率
        dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
        dataframe['atr_pct'] = dataframe['atr'] / dataframe['close'] * 100
        
        # 成交量
        dataframe['volume_ma'] = dataframe['volume'].rolling(20).mean()
        dataframe['volume_ratio'] = dataframe['volume'] / dataframe['volume_ma']
        
        # ==================== 均线排列 ====================
        dataframe['bull_align'] = (
            (dataframe['ema8'] > dataframe['ema24']) &
            (dataframe['ema24'] > dataframe['ema72']) &
            (dataframe['ema72'] > dataframe['ema168'])
        )
        
        dataframe['bear_align'] = (
            (dataframe['ema8'] < dataframe['ema24']) &
            (dataframe['ema24'] < dataframe['ema72']) &
            (dataframe['ema72'] < dataframe['ema168'])
        )
        
        # 简化排列（短中期）
        dataframe['bull_align_short'] = (
            (dataframe['ema8'] > dataframe['ema24']) &
            (dataframe['ema24'] > dataframe['ema72'])
        )
        
        dataframe['bear_align_short'] = (
            (dataframe['ema8'] < dataframe['ema24']) &
            (dataframe['ema24'] < dataframe['ema72'])
        )
        
        # ==================== 均线斜率 ====================
        lookback = 5
        dataframe['slope_ema8'] = (dataframe['ema8'] - dataframe['ema8'].shift(lookback)) / dataframe['ema8'].shift(lookback) * 100
        dataframe['slope_ema24'] = (dataframe['ema24'] - dataframe['ema24'].shift(lookback)) / dataframe['ema24'].shift(lookback) * 100
        dataframe['slope_ema72'] = (dataframe['ema72'] - dataframe['ema72'].shift(lookback)) / dataframe['ema72'].shift(lookback) * 100
        dataframe['slope_ema168'] = (dataframe['ema168'] - dataframe['ema168'].shift(lookback)) / dataframe['ema168'].shift(lookback) * 100
        
        dataframe['slope_bull_confirm'] = (
            (dataframe['slope_ema8'] > 0) &
            (dataframe['slope_ema24'] > 0) &
            (dataframe['slope_ema72'] > 0)
        )
        
        dataframe['slope_bear_confirm'] = (
            (dataframe['slope_ema8'] < 0) &
            (dataframe['slope_ema24'] < 0) &
            (dataframe['slope_ema72'] < 0)
        )
        
        # ==================== 均线间距 ====================
        dataframe['spread_total'] = (dataframe['ema8'] - dataframe['ema168']) / dataframe['ema168'] * 100
        dataframe['spread_short'] = (dataframe['ema8'] - dataframe['ema24']) / dataframe['ema24'] * 100
        dataframe['spread_change'] = dataframe['spread_total'] - dataframe['spread_total'].shift(lookback)
        dataframe['spread_ma'] = dataframe['spread_total'].rolling(20).mean()
        
        spread_abs = dataframe['spread_total'].abs()
        dataframe['spread_pct_30'] = spread_abs.rolling(50, min_periods=20).quantile(0.3)
        dataframe['spread_pct_70'] = spread_abs.rolling(50, min_periods=20).quantile(0.7)
        
        # ==================== MACD交叉信号 ====================
        # MACD金叉（MACD线上穿信号线）
        dataframe['macd_golden_cross'] = (
            (dataframe['macd'] > dataframe['macd_signal']) &
            (dataframe['macd'].shift(1) <= dataframe['macd_signal'].shift(1))
        )
        
        # MACD死叉
        dataframe['macd_death_cross'] = (
            (dataframe['macd'] < dataframe['macd_signal']) &
            (dataframe['macd'].shift(1) >= dataframe['macd_signal'].shift(1))
        )
        
        # MACD零轴上方金叉（更强信号）
        dataframe['macd_strong_golden'] = dataframe['macd_golden_cross'] & (dataframe['macd'] > 0)
        
        # ==================== MACD动能判断 ====================
        # 柱状图动能增加（多头）
        dataframe['macd_momentum_up'] = (
            (dataframe['macd_hist'] > 0) &
            (dataframe['macd_hist'] > dataframe['macd_hist'].shift(1)) &
            (dataframe['macd_hist'].shift(1) > dataframe['macd_hist'].shift(2))
        )
        
        # 柱状图动能增加（空头）
        dataframe['macd_momentum_down'] = (
            (dataframe['macd_hist'] < 0) &
            (dataframe['macd_hist'] < dataframe['macd_hist'].shift(1)) &
            (dataframe['macd_hist'].shift(1) < dataframe['macd_hist'].shift(2))
        )
        
        # 柱状图动能减弱（顶部信号）
        dataframe['macd_momentum_weakening'] = (
            (dataframe['macd_hist'] > 0) &
            (dataframe['macd_hist'] < dataframe['macd_hist'].shift(1)) &
            (dataframe['macd_hist'].shift(1) < dataframe['macd_hist'].shift(2))
        )
        
        # ==================== 趋势阶段（EMA + MACD综合判断）====================
        # 趋势启动期：EMA开始排列 + MACD金叉 + 柱状图正向增长
        dataframe['trend_start_bull'] = (
            dataframe['bull_align_short'] &  # 至少短中期排列
            (dataframe['macd'] > dataframe['macd_signal']) &  # MACD金叉状态
            (dataframe['macd_hist'] > 0) &  # 柱状图为正
            (dataframe['macd_hist'] > dataframe['macd_hist'].shift(1)) &  # 动能增加
            (spread_abs < dataframe['spread_pct_70'])  # 发散度不是最大
        )
        
        # 趋势发展期：完整排列 + MACD在零轴上方 + 持续放量
        dataframe['trend_develop_bull'] = (
            dataframe['bull_align'] &  # 完整4线排列
            (dataframe['macd'] > 0) &  # MACD在零轴上方
            (dataframe['macd'] > dataframe['macd_signal']) &  # 金叉状态
            dataframe['slope_bull_confirm'] &  # 斜率确认
            (dataframe['spread_change'] > 0)  # 发散度扩大
        )
        
        # 趋势成熟期：发散度大 + MACD动能减弱
        dataframe['trend_mature_bull'] = (
            dataframe['bull_align'] &
            (dataframe['macd'] > 0) &
            dataframe['macd_momentum_weakening'] &  # 动能减弱
            (spread_abs > dataframe['spread_pct_70'])  # 发散度很大
        )
        
        # 趋势结束：EMA开始死叉 + MACD死叉
        dataframe['trend_end_bull'] = (
            (dataframe['ema8'] < dataframe['ema24']) &  # EMA8死叉EMA24
            (dataframe['macd'] < dataframe['macd_signal']) &  # MACD死叉
            (dataframe['macd_hist'] < 0)  # 柱状图转负
        )
        
        # 空头阶段（镜像）
        dataframe['trend_start_bear'] = (
            dataframe['bear_align_short'] &
            (dataframe['macd'] < dataframe['macd_signal']) &
            (dataframe['macd_hist'] < 0) &
            (dataframe['macd_hist'] < dataframe['macd_hist'].shift(1)) &
            (spread_abs < dataframe['spread_pct_70'])
        )
        
        dataframe['trend_develop_bear'] = (
            dataframe['bear_align'] &
            (dataframe['macd'] < 0) &
            (dataframe['macd'] < dataframe['macd_signal']) &
            dataframe['slope_bear_confirm'] &
            (dataframe['spread_change'] < 0)
        )
        
        dataframe['trend_end_bear'] = (
            (dataframe['ema8'] > dataframe['ema24']) &
            (dataframe['macd'] > dataframe['macd_signal']) &
            (dataframe['macd_hist'] > 0)
        )
        
        # ==================== 趋势强度计算 (0-100) ====================
        # 1. 排列得分 (0-25)
        bull_align_score = (
            (dataframe['ema8'] > dataframe['ema24']).astype(int) +
            (dataframe['ema24'] > dataframe['ema72']).astype(int) +
            (dataframe['ema72'] > dataframe['ema168']).astype(int) +
            (dataframe['close'] > dataframe['ema8']).astype(int)
        ) * 6.25
        
        bear_align_score = (
            (dataframe['ema8'] < dataframe['ema24']).astype(int) +
            (dataframe['ema24'] < dataframe['ema72']).astype(int) +
            (dataframe['ema72'] < dataframe['ema168']).astype(int) +
            (dataframe['close'] < dataframe['ema8']).astype(int)
        ) * 6.25
        
        dataframe['align_score'] = np.maximum(bull_align_score, bear_align_score)
        
        # 2. 斜率得分 (0-25)
        dataframe['slope_score'] = np.clip(np.abs(dataframe['slope_ema24']) * 10, 0, 25)
        
        # 3. MACD动能得分 (0-25) - 替换原来的间距得分
        macd_hist_norm = dataframe['macd_hist'].abs() / dataframe['close'] * 1000
        dataframe['macd_score'] = np.clip(macd_hist_norm * 5, 0, 25)
        
        # 4. ADX得分 (0-25)
        dataframe['adx_score'] = np.clip(dataframe['adx'] - 15, 0, 25)
        
        # 综合强度
        dataframe['trend_strength'] = (
            dataframe['align_score'] +
            dataframe['slope_score'] +
            dataframe['macd_score'].fillna(12.5) +
            dataframe['adx_score']
        )
        
        # ==================== 入场信号 ====================
        # EMA金叉
        dataframe['ema8_cross_ema24_up'] = (
            (dataframe['ema8'] > dataframe['ema24']) &
            (dataframe['ema8'].shift(1) <= dataframe['ema24'].shift(1))
        )
        
        dataframe['ema8_cross_ema24_down'] = (
            (dataframe['ema8'] < dataframe['ema24']) &
            (dataframe['ema8'].shift(1) >= dataframe['ema24'].shift(1))
        )
        
        # 回踩EMA24反弹
        dataframe['pullback_buy'] = (
            (dataframe['low'].shift(1) <= dataframe['ema24'].shift(1) * 1.005) &
            (dataframe['close'] > dataframe['ema8']) &
            (dataframe['close'] > dataframe['open']) &
            (dataframe['macd'] > dataframe['macd_signal'])  # MACD确认
        )
        
        # 突破前高
        dataframe['high_break'] = dataframe['close'] > dataframe['high'].rolling(24).max().shift(1)
        dataframe['low_break'] = dataframe['close'] < dataframe['low'].rolling(24).min().shift(1)
        
        # ==================== 大周期趋势过滤 ====================
        dataframe['big_trend_bull'] = (
            (dataframe['ema168'] > dataframe['ema168'].shift(24)) &
            (dataframe['close'] > dataframe['ema168'])
        )
        
        dataframe['big_trend_bear'] = (
            (dataframe['ema168'] < dataframe['ema168'].shift(24)) &
            (dataframe['close'] < dataframe['ema168'])
        )
        
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if dataframe is None or dataframe.empty:
            return dataframe
        
        # ==================== 做多入场1：趋势启动（EMA排列 + MACD确认）====================
        # 核心条件：EMA开始多头排列 + MACD金叉 + 动能增加
        dataframe.loc[
            (
                dataframe['bull_align_short'] &  # 至少短中期排列（EMA8>24>72）
                (dataframe['macd'] > dataframe['macd_signal']) &  # MACD金叉
                (dataframe['macd_hist'] > 0) &  # 柱状图为正
                (dataframe['macd_hist'] > dataframe['macd_hist'].shift(1)) &  # 动能增加
                (dataframe['close'] > dataframe['ema24']) &  # 价格在EMA24上方
                (dataframe['adx'] > 20) &  # 有趋势
                (dataframe['slope_ema8'] > 0) &  # EMA8向上
                (dataframe['volume_ratio'] > 0.7)
            ),
            ["enter_long", "enter_tag"],
        ] = (1, "trend_start")
        
        # ==================== 做多入场2：强势突破（突破前高 + MACD确认）====================
        dataframe.loc[
            (
                dataframe['bull_align'] &  # 完整排列
                dataframe['high_break'] &  # 突破前高
                (dataframe['macd'] > dataframe['macd_signal']) &  # MACD金叉状态
                (dataframe['adx'] > self.min_adx) &  # 强趋势
                (dataframe['trend_strength'] >= self.min_trend_strength - 10) &
                (dataframe['volume_ratio'] > 0.9)
            ),
            ["enter_long", "enter_tag"],
        ] = (1, "bull_breakout")
        
        # ==================== 做多入场3：回踩反弹（趋势中回调）====================
        dataframe.loc[
            (
                dataframe['bull_align'] &  # 完整排列
                (dataframe['low'].shift(1) <= dataframe['ema24'].shift(1) * 1.01) &  # 回踩EMA24附近
                (dataframe['close'] > dataframe['open']) &  # 阳线
                (dataframe['close'] > dataframe['ema8']) &  # 收盘价回到EMA8上方
                (dataframe['macd'] > 0) &  # MACD在零轴上方
                (dataframe['macd'] > dataframe['macd_signal']) &  # 金叉状态
                (dataframe['trend_strength'] >= self.min_trend_strength - 15)
            ),
            ["enter_long", "enter_tag"],
        ] = (1, "bull_pullback")
        
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if dataframe is None or dataframe.empty:
            return dataframe
        
        # ==================== 多头出场1：趋势结束（EMA死叉 + MACD死叉）====================
        dataframe.loc[
            dataframe['trend_end_bull'] &  # EMA8死叉 + MACD死叉 + 柱状图转负
            (dataframe['close'] < dataframe['ema24']),  # 价格确认跌破EMA24
            ["exit_long", "exit_tag"],
        ] = (1, "trend_end")
        
        # ==================== 多头出场2：趋势反转（完全转为空头）====================
        dataframe.loc[
            dataframe['bear_align'] &  # 完全空头排列
            (dataframe['macd'] < dataframe['macd_signal']) &  # MACD死叉
            (dataframe['macd_hist'] < 0) &  # 柱状图为负
            (dataframe['close'] < dataframe['ema72']) &  # 价格跌破EMA72
            (dataframe['close'].shift(1) < dataframe['ema72'].shift(1)),  # 连续2根确认
            ["exit_long", "exit_tag"],
        ] = (1, "trend_reverse")
        
        # ==================== 空头出场1：趋势结束 ====================
        dataframe.loc[
            dataframe['trend_end_bear'] &
            (dataframe['close'] > dataframe['ema24']),
            ["exit_short", "exit_tag"],
        ] = (1, "trend_end")
        
        # ==================== 空头出场2：趋势反转 ====================
        dataframe.loc[
            dataframe['bull_align'] &
            (dataframe['macd'] > dataframe['macd_signal']) &
            (dataframe['macd_hist'] > 0) &
            (dataframe['close'] > dataframe['ema72']) &
            (dataframe['close'].shift(1) > dataframe['ema72'].shift(1)),
            ["exit_short", "exit_tag"],
        ] = (1, "trend_reverse")
        
        return dataframe

    def get_ticker_indicator(self):
        return int(self.timeframe[:-1])