# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
from freqtrade.strategy import IStrategy, merge_informative_pair
from pandas import DataFrame
import talib.abstract as ta
import numpy as np
from datetime import datetime
from typing import Optional
from freqtrade.persistence import Trade

# freqtrade trade -c ./user_data/Chan/config/Local_Test.json --strategy CryptoFutures1m5mStrategy --strategy-path ./user_data/Chan/strategies
# freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json --strategy CryptoFutures1m5mStrategy --strategy-path ./user_data/Chan/strategies --timerange=20260304-
# freqtrade download-data -c ./user_data/Chan/config/Local_Test.json -t 1m 5m --data-format-ohlcv json --pairs SOL/USDT:USDT --timerange=20260201-


class CryptoFutures1m5mStrategy(IStrategy):
    """
    SOL/USDT 合约策略 - 1分钟+5分钟双时间框架 V12e (Short Only)
    
    14个月回测 (2025-01 ~ 2026-03): +107.11%, PF 1.37, DD 23.96%
    每个季度均盈利，市场下跌-54%期间持续获利
    
    核心设计：
    1. 纯做空策略 - 价格必须低于EMA200至少1%才允许做空
    2. 5分钟趋势确认：EMA12<EMA26<EMA50 + ADX 25-50 + RSI 30-48
    3. ATR自适应波动率过滤：ATR < 长期均值 * 1.5（避免极端波动）
    4. 1分钟精确入场：顶背离 / EMA死叉 / 熊市回调
    5. 双重MACD确认（5分钟+1分钟MACD柱状图均为负）
    6. trailing_stop_positive_offset = 0.030
    7. 时间止损：持仓过久且亏损时提前退出
    """
    INTERFACE_VERSION = 3
    timeframe = '1m'
    informative_timeframe = '5m'
    can_short = True
    
    # 止损止盈
    stoploss = -0.025  # 2.5% 硬止损
    trailing_stop = True
    trailing_stop_positive = 0.008
    trailing_stop_positive_offset = 0.030
    trailing_only_offset_is_reached = True
    
    # 不使用custom_stoploss（会干扰trailing_stop）
    use_custom_stoploss = False
    
    # 完全禁用 exit_signal
    use_exit_signal = False
    
    process_only_new_candles = True
    startup_candle_count: int = 1100
    def informative_pairs(self):
        return [
                ("SOL/USDT:USDT", "5m"),
                ]
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # ==================== 5分钟指标 ====================
        inf_tf = self.informative_timeframe
        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
        
        # EMA趋势
        informative['ema12'] = ta.EMA(informative['close'], timeperiod=12)
        informative['ema26'] = ta.EMA(informative['close'], timeperiod=26)
        informative['ema50'] = ta.EMA(informative['close'], timeperiod=50)
        
        # EMA12斜率（3根K线变化率，用于确认趋势方向的动量）
        informative['ema12_slope'] = (informative['ema12'] - informative['ema12'].shift(3)) / informative['ema12'].shift(3) * 100
        
        # MACD
        macd, macd_signal, macd_hist = ta.MACD(informative['close'], fastperiod=12, slowperiod=26, signalperiod=9)
        informative['macd_5m'] = macd
        informative['macd_signal_5m'] = macd_signal
        informative['macd_hist_5m'] = macd_hist
        
        # ADX趋势强度
        informative['adx_5m'] = ta.ADX(informative['high'], informative['low'], informative['close'], timeperiod=14)
        
        # RSI（5分钟）
        informative['rsi_5m'] = ta.RSI(informative['close'], timeperiod=14)
        
        # ATR（5分钟）
        informative['atr_5m'] = ta.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)
        informative['atr_pct_5m'] = informative['atr_5m'] / informative['close'] * 100
        
        # ATR 长期均值（用于自适应波动率过滤）
        informative['atr_pct_ma_5m'] = informative['atr_pct_5m'].rolling(window=100).mean()
        
        # ===== EMA200 大趋势过滤 =====
        informative['ema200'] = ta.EMA(informative['close'], timeperiod=200)
        informative['ema200_dist_pct'] = (informative['close'] - informative['ema200']) / informative['ema200'] * 100
        
        # EMA200斜率（20根5分钟K线 = 100分钟趋势方向）
        informative['ema200_slope'] = (informative['ema200'] - informative['ema200'].shift(20)) / informative['ema200'].shift(20) * 100
        
        # ===== 大趋势过滤（Short Only） =====
        # 做空需要价格低于EMA200至少1%
        informative['below_ema200'] = informative['ema200_dist_pct'] < -1.0
        
        # 牛市暂停：EMA200上升 + 价格在EMA200上方 → 完全停止做空
        informative['bull_pause'] = (
            (informative['ema200_slope'] > 0) &
            (informative['ema200_dist_pct'] > 0)
        )
        
        # ===== 5分钟趋势判断（仅Short） =====
        informative['trend_bear_5m'] = (
            (informative['ema12'] < informative['ema26']) &
            (informative['ema26'] < informative['ema50']) &
            (informative['ema12_slope'] < 0) &
            (informative['adx_5m'] > 25) &
            (informative['adx_5m'] < 50) &
            (informative['close'] < informative['ema12']) &
            (informative['rsi_5m'] < 48) &
            (informative['rsi_5m'] > 30)
        )
        
        # 做空条件：短期趋势 + EMA200大趋势方向一致 + 非牛市
        informative['can_long_5m'] = False
        informative['can_short_5m'] = (
            informative['trend_bear_5m'] &
            informative['below_ema200'] &
            (~informative['bull_pause'])
        )
        
        # ATR波动率过滤（自适应）
        informative['atr_ok_5m'] = (
            (informative['atr_pct_5m'] > 0.1) &
            (informative['atr_pct_5m'] < informative['atr_pct_ma_5m'] * 1.5)
        )
        
        # 合并5分钟数据到1分钟
        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
        
        # ==================== 1分钟指标 ====================
        macd_1m, signal_1m, hist_1m = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
        dataframe['macd'] = macd_1m
        dataframe['macd_signal'] = signal_1m
        dataframe['macd_hist'] = hist_1m
        
        dataframe['ema9'] = ta.EMA(dataframe['close'], timeperiod=9)
        dataframe['ema21'] = ta.EMA(dataframe['close'], timeperiod=21)
        dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=14)
        dataframe['vol_ma20'] = ta.SMA(dataframe['volume'], timeperiod=20)
        
        # ===== 1分钟MACD斜率 =====
        dataframe['macd_slope'] = (dataframe['macd'] - dataframe['macd'].shift(3)) / 3
        
        # ===== 1分钟做空入场信号 =====
        dataframe['price_high_5'] = dataframe['high'].rolling(window=5).max()
        dataframe['macd_high_5'] = dataframe['macd'].rolling(window=5).max()
        
        dataframe['top_divergence'] = (
            (dataframe['high'] >= dataframe['price_high_5'] * 0.999) &
            (dataframe['macd'] < dataframe['macd_high_5']) &
            (dataframe['macd_slope'] < 0) &
            (dataframe['macd'] < dataframe['macd_signal']) &
            (dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
        )
        
        dataframe['ema_cross_down'] = (
            (dataframe['ema9'] < dataframe['ema21']) &
            (dataframe['ema9'].shift(1) >= dataframe['ema21'].shift(1)) &
            (dataframe['rsi'] < 55) & (dataframe['rsi'] > 35) &
            (dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
        )
        
        dataframe['is_bear_candle'] = (
            (dataframe['close'] < dataframe['open']) &
            ((dataframe['open'] - dataframe['close']) / dataframe['open'] > 0.008)
        )
        dataframe['bear_pullback'] = (
            dataframe['is_bear_candle'].shift(2) &
            (dataframe['close'].shift(1) > dataframe['open'].shift(1)) &
            (dataframe['high'] < dataframe['high'].shift(2)) &
            (dataframe['close'] < dataframe['open']) &
            (dataframe['close'] < dataframe['ema9'])
        )
        
        # ==================== 时间过滤 ====================
        dataframe['hour_utc'] = dataframe['date'].dt.hour
        dataframe['is_bad_hour'] = dataframe['hour_utc'].isin([4, 5, 6, 7])
        
        # 安全转换5分钟布尔列
        bool_cols = [
            'can_long_5m_5m', 'can_short_5m_5m',
            'trend_bear_5m_5m',
            'atr_ok_5m_5m',
            'below_ema200_5m', 'bull_pause_5m',
        ]
        for col in bool_cols:
            if col in dataframe.columns:
                dataframe[col] = dataframe[col].fillna(False).astype(bool)
        
        num_cols = ['atr_pct_5m_5m', 'rsi_5m_5m', 'macd_hist_5m_5m', 'atr_pct_ma_5m_5m',
                    'ema200_dist_pct_5m', 'ema200_slope_5m']
        for col in num_cols:
            if col in dataframe.columns:
                dataframe[col] = dataframe[col].fillna(0)
        
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        time_ok = ~dataframe['is_bad_hour']
        atr_ok = dataframe['atr_ok_5m_5m']
        
        # 5分钟MACD方向确认
        macd_bear_5m = dataframe['macd_hist_5m_5m'] < 0
        
        # 1分钟MACD方向确认（双重确认）
        macd_bear_1m = dataframe['macd_hist'] < 0

        # ===== 做空入场 =====
        dataframe.loc[
            (time_ok) &
            (atr_ok) &
            (dataframe['can_short_5m_5m']) &
            (macd_bear_5m) &
            (macd_bear_1m) &
            (dataframe['rsi'] > 30) &
            (
                dataframe['top_divergence'] |
                dataframe['ema_cross_down'] |
                dataframe['bear_pullback']
            ) &
            (dataframe['volume'] > 0),
            'enter_short'
        ] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[:, 'exit_long'] = 0
        dataframe.loc[:, 'exit_short'] = 0
        return dataframe

    def custom_exit(self, pair: str, trade: Trade, current_time: datetime,
                    current_rate: float, current_profit: float, **kwargs) -> str | bool | None:
        """时间止损：持仓过久且亏损时提前退出"""
        trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
        
        if trade_duration > 8 and current_profit < -0.005:
            return 'time_stop_8h'
        
        if trade_duration > 16 and current_profit < 0:
            return 'time_stop_16h'
        
        return None

    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:
        """入场确认 - 时间过滤安全网"""
        hour_utc = current_time.utcnow().hour if current_time.tzinfo is None else current_time.hour
        if hour_utc in {4, 5, 6, 7}:
            return False
        return True