# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
from freqtrade.strategy import IStrategy, merge_informative_pair
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 CryptoFutures1m5mStrategyV2 --strategy-path ./user_data/Chan/strategies --timerange=20260101-


class CryptoFutures1m5mStrategyV2(IStrategy):
    """
    SOL/USDT 合约策略 - 1分钟+5分钟双时间框架 V2 优化版 (Short Only)
    
    基于原版优化：
    1. 保持原版核心入场逻辑
    2. 优化追踪止盈参数
    3. 增强时间止损灵活性
    4. 稍微放宽ATR过滤增加交易机会
    
    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
    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 = 0.035
    """
    INTERFACE_VERSION = 3
    timeframe = '1m'
    informative_timeframe = '5m'
    can_short = True
    lev = 1.0
    
    # 止损止盈 - 优化版
    stoploss = -0.026  # 2.6% 硬止损
    trailing_stop = True
    trailing_stop_positive = 0.008
    trailing_stop_positive_offset = 0.032
    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 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()
        
        # 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
        
        # 大趋势过滤（Short Only）
        informative['below_ema200'] = informative['ema200_dist_pct'] < -1.0
        
        # 牛市暂停
        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'] < -0.05) &
            (informative['adx_5m'] > 24) &
            (informative['adx_5m'] < 51) &
            (informative['close'] < informative['ema12']) &
            (informative['rsi_5m'] < 48) &
            (informative['rsi_5m'] > 29)
        )
        
        # 做空条件
        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 - 继续放宽
        informative['atr_ok_5m'] = (
            (informative['atr_pct_5m'] > 0.07) &
            (informative['atr_pct_5m'] < informative['atr_pct_ma_5m'] * 2.2)
        )
        
        # 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
        
        # 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()
        
        # 顶背离
        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
        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)
        )
        
        # 熊市回调
        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',
            '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',
                    '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']
        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
        macd_bear_1m = dataframe['macd_hist'] < 0
        
        # 成交量确认
        volume_ok = dataframe['volume_ok_5m_5m']
        
        # 做空入场
        dataframe.loc[
            (time_ok) &
            (atr_ok) &
            (dataframe['can_short_5m_5m']) &
            (macd_bear_5m) &
            (macd_bear_1m) &
            (volume_ok) &
            (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:
        """自定义出场逻辑：时间止损"""
        trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
        
        # 时间止损：持仓过久且亏损
        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'
        
        # 持仓超过24小时强制平仓
        if trade_duration > 24:
            return 'time_stop_24h'
        
        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