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

# 抑制 pandas FutureWarning 关于 fillna 的隐式降级警告
warnings.filterwarnings('ignore', category=FutureWarning, message='.*Downcasting object dtype arrays.*')
pd.set_option('future.no_silent_downcasting', True)

# freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json --strategy CryptoFutures1m5mStrategyV4 --strategy-path ./user_data/Chan/strategies --timerange=20250101-


class CryptoFutures1m5mStrategyV4(IStrategy):
    """
    SOL/USDT 合约策略 - 1分钟+5分钟双时间框架 V4 多空完全分离版
    
    基于V3优化：
    1. 多空参数完全分离
    2. 分别优化做多做空的风险参数
    
    核心设计：
    1. 5分钟趋势确认 + 1分钟精确入场
    2. ATR自适应波动率过滤
    3. 多空trailing参数分离
    """
    INTERFACE_VERSION = 3
    timeframe = '1m'
    informative_timeframe = '5m'
    can_short = True
    can_long = True
    lev = 1.0
    
    # ==================== 多空分离参数 ====================
    
    # 做多止损 (更宽松，因为牛市回调幅度大)
    stoploss_long = -0.035
    
    # 做空止损 (相对紧凑，熊市反弹快)
    stoploss_short = -0.025
    
    # 统一下跌止损（取两者较宽松值）
    stoploss = -0.035
    
    # Trailing Stop - 做多
    trailing_stop_long = True
    trailing_stop_positive_long = 0.006
    trailing_stop_positive_offset_long = 0.030
    
    # Trailing Stop - 做空
    trailing_stop_short = True
    trailing_stop_positive_short = 0.010
    trailing_stop_positive_offset_short = 0.038
    
    # 统一设置
    trailing_stop = True
    trailing_stop_positive = 0.008
    trailing_stop_positive_offset = 0.035
    trailing_only_offset_is_reached = True
    
    # 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 get_stoploss(self, side: str, trade: Optional[Trade] = None, current_rate: float = 0, 
                     current_time: datetime = None, after_fill: bool = False, **kwargs) -> float:
        """动态获取多空不同的止损"""
        if side == "long":
            return self.stoploss_long
        elif side == "short":
            return self.stoploss_short
        return self.stoploss
    
    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）
        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
        informative['atr_pct_ma_5m'] = informative['atr_pct_5m'].rolling(window=100).mean()
        
        # ATR 短期均值 (用于做空过滤 - 更严格)
        informative['atr_pct_ma_short_5m'] = informative['atr_pct_5m'].rolling(window=20).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
        informative['ema200_slope'] = (informative['ema200'] - informative['ema200'].shift(20)) / informative['ema200'].shift(20) * 100
        
        # ==================== 多空趋势判断 ====================
        
        # 5分钟趋势判断 - 做多 (Bull)
        informative['trend_bull_5m'] = (
            (informative['ema12'] > informative['ema26']) &
            (informative['ema26'] > informative['ema50']) &
            (informative['ema12_slope'] > 0.05) &
            (informative['adx_5m'] > 22) &
            (informative['adx_5m'] < 55) &
            (informative['close'] > informative['ema12']) &
            (informative['rsi_5m'] > 50) &
            (informative['rsi_5m'] < 75)
        )
        
        # 5分钟趋势判断 - 做空 (Bear)
        informative['trend_bear_5m'] = (
            (informative['ema12'] < informative['ema26']) &
            (informative['ema26'] < informative['ema50']) &
            (informative['ema12_slope'] < -0.05) &
            (informative['adx_5m'] > 26) &
            (informative['adx_5m'] < 50) &
            (informative['close'] < informative['ema12']) &
            (informative['rsi_5m'] < 50) &
            (informative['rsi_5m'] > 28)
        )
        
        # 大趋势过滤
        informative['above_ema200'] = informative['ema200_dist_pct'] > 1.0
        informative['below_ema200'] = informative['ema200_dist_pct'] < -1.0
        
        # 牛市/熊市环境
        informative['bull_market'] = (
            (informative['ema200_slope'] > 0) &
            (informative['ema200_dist_pct'] > 0)
        )
        informative['bear_market'] = (
            (informative['ema200_slope'] < 0) &
            (informative['ema200_dist_pct'] < 0)
        )
        
        # 做多条件
        informative['can_long_5m'] = (
            informative['trend_bull_5m'] &
            informative['above_ema200']
        )
        
        # 做空条件
        informative['can_short_5m'] = (
            informative['trend_bear_5m'] &
            informative['below_ema200']
        )
        
        # ==================== 多空分离的ATR过滤 ====================
        
        # 做多ATR过滤 - 允许更大波动（牛市波动大）
        informative['atr_ok_long_5m'] = (
            (informative['atr_pct_5m'] > 0.08) &
            (informative['atr_pct_5m'] < informative['atr_pct_ma_5m'] * 2.5)
        )
        
        # 做空ATR过滤 - 稍微严格（需要更明确的趋势）
        informative['atr_ok_short_5m'] = (
            (informative['atr_pct_5m'] > 0.06) &
            (informative['atr_pct_5m'] < informative['atr_pct_ma_short_5m'] * 2.0)
        )
        
        # 成交量确认
        informative['volume_ma_5m'] = ta.SMA(informative['volume'], timeperiod=20)
        informative['volume_ok_5m'] = informative['volume'] > informative['volume_ma_5m'] * 0.75
        
        # 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
        
        # ==================== 做空信号 ====================
        dataframe['price_high_5'] = dataframe['high'].rolling(window=5).max()
        dataframe['macd_high_5'] = dataframe['macd'].rolling(window=5).max()
        
        # 顶背离 (做空)
        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)
        )
        
        # EMA死叉 (做空)
        dataframe['ema_cross_down'] = (
            (dataframe['ema9'] < dataframe['ema21']) &
            (dataframe['ema9'].shift(1) >= dataframe['ema21'].shift(1)) &
            (dataframe['rsi'] < 58) &
            (dataframe['rsi'] > 35) &
            (dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
        )
        
        # 熊市回调 (做空)
        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'])
        )
        
        # ==================== 做多信号 ====================
        dataframe['price_low_5'] = dataframe['low'].rolling(window=5).min()
        dataframe['macd_low_5'] = dataframe['macd'].rolling(window=5).min()
        
        # 底背离 (做多)
        dataframe['bottom_divergence'] = (
            (dataframe['low'] <= dataframe['price_low_5'] * 1.001) &
            (dataframe['macd'] > dataframe['macd_low_5']) &
            (dataframe['macd_slope'] > 0) &
            (dataframe['macd'] > dataframe['macd_signal']) &
            (dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
        )
        
        # EMA金叉 (做多)
        dataframe['ema_cross_up'] = (
            (dataframe['ema9'] > dataframe['ema21']) &
            (dataframe['ema9'].shift(1) <= dataframe['ema21'].shift(1)) &
            (dataframe['rsi'] > 42) &
            (dataframe['rsi'] < 72) &
            (dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
        )
        
        # 牛市回调 (做多)
        dataframe['is_bull_candle'] = (
            (dataframe['close'] > dataframe['open']) &
            ((dataframe['close'] - dataframe['open']) / dataframe['open'] > 0.008)
        )
        dataframe['bull_pullback'] = (
            dataframe['is_bull_candle'].shift(2) &
            (dataframe['close'].shift(1) < dataframe['open'].shift(1)) &
            (dataframe['low'] > dataframe['low'].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_bull_5m_5m', 'trend_bear_5m_5m',
            'atr_ok_long_5m_5m', 'atr_ok_short_5m_5m',
            'above_ema200_5m', 'below_ema200_5m',
            'bull_market_5m', 'bear_market_5m',
            'volume_ok_5m_5m',
        ]
        for col in bool_cols:
            if col in dataframe.columns:
                dataframe[col] = dataframe[col].astype(bool).fillna(False)
        
        num_cols = ['atr_pct_5m_5m', 'rsi_5m_5m', 'macd_hist_5m_5m', 
                    'atr_pct_ma_5m_5m', 'atr_pct_ma_short_5m_5m',
                    'ema200_dist_pct_5m', 'ema200_slope_5m']
        for col in num_cols:
            if col in dataframe.columns:
                dataframe[col] = dataframe[col].astype(float).fillna(0.0)
        
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        time_ok = ~dataframe['is_bad_hour']
        volume_ok = dataframe['volume_ok_5m_5m']
        
        # ========== 做空入场 ==========
        atr_ok_short = dataframe['atr_ok_short_5m_5m']
        macd_bear_5m = dataframe['macd_hist_5m_5m'] < 0
        macd_bear_1m = dataframe['macd_hist'] < 0
        
        dataframe.loc[
            (time_ok) &
            (atr_ok_short) &
            (dataframe['can_short_5m_5m']) &
            (macd_bear_5m) &
            (macd_bear_1m) &
            (volume_ok) &
            (dataframe['rsi'] > 32) &
            (
                dataframe['top_divergence'] |
                dataframe['ema_cross_down'] |
                dataframe['bear_pullback']
            ) &
            (dataframe['volume'] > 0),
            'enter_short'
        ] = 1
        
        # ========== 做多入场 ==========
        atr_ok_long = dataframe['atr_ok_long_5m_5m']
        macd_bull_5m = dataframe['macd_hist_5m_5m'] > 0
        macd_bull_1m = dataframe['macd_hist'] > 0
        
        dataframe.loc[
            (time_ok) &
            (atr_ok_long) &
            (dataframe['can_long_5m_5m']) &
            (macd_bull_5m) &
            (macd_bull_1m) &
            (volume_ok) &
            (dataframe['rsi'] < 72) &
            (dataframe['rsi'] > 38) &
            (
                dataframe['bottom_divergence'] |
                dataframe['ema_cross_up'] |
                dataframe['bull_pullback']
            ) &
            (dataframe['volume'] > 0),
            'enter_long'
        ] = 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:
        """自定义出场逻辑 - 多空不同的时间止损"""
        trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
        
        # ==================== 做空时间止损 (更激进) ====================
        if trade.trade_direction == 'short':
            if trade_duration > 6 and current_profit < -0.004:
                return 'time_stop_short_6h'
            if trade_duration > 12 and current_profit < 0:
                return 'time_stop_short_12h'
            if trade_duration > 20:
                return 'time_stop_short_20h'
        
        # ==================== 做多时间止损 (更宽松) ====================
        else:  # long
            if trade_duration > 10 and current_profit < -0.006:
                return 'time_stop_long_10h'
            if trade_duration > 20 and current_profit < 0:
                return 'time_stop_long_20h'
            if trade_duration > 30:
                return 'time_stop_long_30h'
        
        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

    def leverage(self, pair: str, current_time: datetime, current_rate: float,
                proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
                **kwargs) -> float:
        return self.lev