# --- Do not remove these libs ---
from freqtrade.strategy import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, pandas
import freqtrade.vendor.qtpylib.indicators as qtpylib

# --------------------------------
from technical.util import resample_to_interval, resampled_merge
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from datetime import datetime, timedelta, timezone
from freqtrade.persistence import Trade, Order
from typing import Optional

import logging
logger = logging.getLogger(__name__)

### Now you can use logger.info('asfd') to log

# freqtrade trade -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL_2 --strategy-path ./user_data/Chan/strategies
# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL_2 --strategy-path ./user_data/Chan/strategies --timerange=20250309-
# freqtrade download-data -c ./user_data/Chan/config/ChanLun_SOL.json -t 1m --pairs SOL/USDT:USDT --timerange=20250501-
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss --strategy ChanLun_SOL_2 --strategy-path ./user_data/strategies -c ./user_data/ChanLun_SOL.json -e 200 --timerange=20250101-20250215

# sudo docker compose run --rm chan_btc backtesting -c ./user_data/Chan.json --strategy Chan_SOL_2 --strategy-path ./user_data/strategies --timerange=20250101-
# sudo docker compose run --rm chan_btc download-data -c ./user_data/Chan.json --pairs SOL/USDT:USDT -t 1m --timerange 20240101-
# sudo docker compose run --rm chan_btc trade -c ./user_data/Chan.json --strategy Chan_SOL_2 --strategy-path ./user_data/strategies

class ChanLun_SOL_2(IStrategy):
    INTERFACE_VERSION: int = 3
    
    # 优化的ROI设置 - 更快速获利
    minimal_roi = {
        "0": 0.012,    # 立即获利1.2%
        "5": 0.01,     # 5分钟后获利1%
        "15": 0.007,   # 15分钟后获利0.7%
        "30": 0.005    # 30分钟后获利0.5%
    }
    
    can_short = True
    stoploss = -0.007  # 降低止损为0.7%
    
    # 追踪止损设置 - 更积极的追踪止损
    trailing_stop = True
    trailing_stop_positive = 0.003  # 0.3%
    trailing_stop_positive_offset = 0.005  # 0.5%
    trailing_only_offset_is_reached = True
    
    # 时间周期
    timeframe = '5m'
    informative_timeframe = '1h'
    startup_candle_count = 200
    
    # 只做空头策略
    only_short = True
    
    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
        return informative_pairs
    
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # 获取更高时间周期的数据
        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe)
        
        # === 高时间周期指标 ===
        # 三均线系统
        informative['ema50'] = ta.EMA(informative, timeperiod=50)
        informative['ema100'] = ta.EMA(informative, timeperiod=100)
        informative['ema200'] = ta.SMA(informative, timeperiod=200)  # 使用SMA作为长期趋势
        
        # 趋势方向
        informative['uptrend'] = (
            (informative['ema50'] > informative['ema100']) & 
            (informative['ema100'] > informative['ema200']) &
            (informative['close'] > informative['ema50'])
        ).astype(int)
        
        informative['downtrend'] = (
            (informative['ema50'] < informative['ema100']) & 
            (informative['ema100'] < informative['ema200']) &
            (informative['close'] < informative['ema50'])
        ).astype(int)
        
        # 强下降趋势
        informative['strong_downtrend'] = (
            (informative['ema50'] < informative['ema100']) & 
            (informative['ema100'] < informative['ema200']) &
            (informative['close'] < informative['ema50']) &
            (informative['ema50'].shift(3) < informative['ema50'])  # 确认EMA50下降
        ).astype(int)
        
        # 添加高时间周期的ADX指标
        informative['adx'] = ta.ADX(informative, timeperiod=14)
        
        # 添加高时间周期的波动率
        informative['atr'] = ta.ATR(informative, timeperiod=14)
        informative['atr_percent'] = (informative['atr'] / informative['close']) * 100
        
        # 高时间周期RSI
        informative['rsi'] = ta.RSI(informative, timeperiod=14)
        
        # 将informative数据帧中的列重命名，以便在合并后区分
        for col in informative.columns:
            if col not in ['date', 'open', 'high', 'low', 'close', 'volume']:
                informative[f"{col}_{self.informative_timeframe}"] = informative[col]
        
        # 删除原始列，只保留重命名后的列和必要的日期、OHLCV列
        for col in list(informative.columns):
            if col not in ['date', 'open', 'high', 'low', 'close', 'volume'] and not col.endswith(f"_{self.informative_timeframe}"):
                del informative[col]
        
        # 打印列名以便调试
        logger.info(f"Informative columns after renaming: {informative.columns.tolist()}")
        
        # 合并数据 - 使用正确的参数
        dataframe = resampled_merge(dataframe, informative, self.informative_timeframe)
        
        # 打印合并后的列名以便调试
        logger.info(f"Dataframe columns after merge: {dataframe.columns.tolist()}")
        
        # === 主时间周期指标 ===
        # 布林带
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe['bb_width'] = ((bollinger['upper'] - bollinger['lower']) / bollinger['mid'])
        
        # 动量指标
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['mfi'] = ta.MFI(dataframe, timeperiod=14)
        
        # MACD
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']
        
        # 均线
        dataframe['ema9'] = ta.EMA(dataframe, timeperiod=9)
        dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200)
        
        # 成交量
        dataframe['volume_mean'] = dataframe['volume'].rolling(window=20).mean()
        dataframe['volume_ratio'] = dataframe['volume'] / dataframe['volume_mean']
        
        # 波动率
        dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
        
        # ADX - 趋势强度指标
        dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
        
        # 价格突破
        dataframe['upper_break'] = (
            (dataframe['close'] > dataframe['bb_upperband']) & 
            (dataframe['close'].shift() <= dataframe['bb_upperband'].shift())
        ).astype(int)
        
        dataframe['lower_break'] = (
            (dataframe['close'] < dataframe['bb_lowerband']) & 
            (dataframe['close'].shift() >= dataframe['bb_lowerband'].shift())
        ).astype(int)
        
        # 均线交叉
        dataframe['ema_cross_up'] = (
            (dataframe['ema9'] > dataframe['ema21']) & 
            (dataframe['ema9'].shift() <= dataframe['ema21'].shift())
        ).astype(int)
        
        dataframe['ema_cross_down'] = (
            (dataframe['ema9'] < dataframe['ema21']) & 
            (dataframe['ema9'].shift() >= dataframe['ema21'].shift())
        ).astype(int)
        
        # 超买超卖区域
        dataframe['rsi_oversold'] = (dataframe['rsi'] < 30).astype(int)
        dataframe['rsi_overbought'] = (dataframe['rsi'] > 70).astype(int)
        
        # 价格与均线的关系
        dataframe['price_above_ema50'] = (dataframe['close'] > dataframe['ema50']).astype(int)
        dataframe['price_below_ema50'] = (dataframe['close'] < dataframe['ema50']).astype(int)
        
        # 趋势强度
        dataframe['strong_trend'] = (dataframe['adx'] > 25).astype(int)
        
        # 添加蜡烛图形态识别
        dataframe['doji'] = ta.CDLDOJI(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])
        dataframe['engulfing'] = ta.CDLENGULFING(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])
        dataframe['hammer'] = ta.CDLHAMMER(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])
        dataframe['shooting_star'] = ta.CDLSHOOTINGSTAR(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])
        
        # 价格动量
        dataframe['momentum'] = dataframe['close'] - dataframe['close'].shift(5)
        
        return dataframe
    
    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # 检查列名是否存在
        downtrend_col = 'resample_60_downtrend_1h'
        strong_downtrend_col = 'resample_60_strong_downtrend_1h'
        adx_col = 'resample_60_adx_1h'
        rsi_col = 'resample_60_rsi_1h'
        
        # 如果列名不存在，使用替代方案
        for col, default_value in [
            (downtrend_col, 0),
            (strong_downtrend_col, 0),
            (adx_col, 25),
            (rsi_col, 50)
        ]:
            if col not in dataframe.columns:
                logger.warning(f"Column {col} not found in dataframe. Creating with default value {default_value}.")
                dataframe[col] = default_value
        
        # 禁用多头入场
        dataframe['enter_long'] = 0
        
        # 空头入场条件 - 专注于空头策略
        short_conditions = (
            # 高时间周期处于下降趋势
            (dataframe[downtrend_col] > 0) &
            
            # 趋势强度确认
            (dataframe[adx_col] > 25) &
            
            # 条件1: 价格突破上轨后回落 + 成交量确认
            (
                (dataframe['upper_break'].rolling(window=5).sum() > 0) &  # 最近5根K线内有突破上轨
                (dataframe['close'] < dataframe['close'].shift(2)) &  # 价格开始下跌
                (dataframe['close'] < dataframe['ema9']) &  # 价格在短期均线下方
                (dataframe['volume_ratio'] > 1.3) &  # 成交量放大
                (dataframe['rsi'] < 70) &  # RSI不在极度超买区
                (dataframe['rsi'] > 40) &  # RSI不在超卖区
                (dataframe[rsi_col] < 60)  # 高时间周期RSI不过高
            ) |
            
            # 条件2: 均线死叉 + RSI超买回落 + 趋势确认
            (
                (dataframe['ema_cross_down'] > 0) &  # 均线死叉
                (dataframe['rsi'] > 55) &  # RSI相对较高
                (dataframe['rsi'] < dataframe['rsi'].shift(3)) &  # RSI下降
                (dataframe['volume_ratio'] > 1.2) &  # 成交量放大
                (dataframe['adx'] > 20) &  # ADX显示有一定趋势强度
                ((dataframe['shooting_star'] > 0) | (dataframe['engulfing'] < 0))  # 流星线或看跌吞没形态
            ) |
            
            # 条件3: 价格在高点回落 + 强趋势
            (
                (dataframe['close'] < dataframe['high'].shift()) &
                (dataframe['high'].shift() > dataframe['high'].shift(2)) &
                (dataframe['close'] < dataframe['ema21']) &
                (dataframe['adx'] > 30) &
                (dataframe['rsi'] < dataframe['rsi'].shift()) &
                (dataframe['rsi'].shift() > 65) &
                (dataframe['volume_ratio'] > 1.0)
            ) |
            
            # 条件4: 强下降趋势确认
            (
                (dataframe[strong_downtrend_col] > 0) &
                (dataframe['close'] < dataframe['ema21']) &
                (dataframe['close'] < dataframe['close'].shift(3)) &
                (dataframe['momentum'] < 0) &
                (dataframe['volume_ratio'] > 1.1) &
                (dataframe['adx'] > 25)
            )
        )
        
        dataframe.loc[short_conditions, 'enter_short'] = 1
        dataframe.loc[short_conditions, 'enter_tag'] = 'chan_sol_short'
        
        return dataframe
    
    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # 禁用多头出场
        dataframe['exit_long'] = 0
        
        # 空头出场条件 - 更精确的出场
        short_exit_conditions = (
            # 条件1: 趋势反转信号
            (
                (dataframe['ema_cross_up'] > 0) &  # 均线金叉
                (dataframe['volume_ratio'] > 1.0)  # 成交量确认
            ) |
            
            # 条件2: 价格突破中期均线
            (
                (dataframe['close'] > dataframe['ema21']) &
                (dataframe['close'].shift() < dataframe['ema21'].shift()) &  # 确认是刚刚突破
                (dataframe['volume_ratio'] > 1.2)  # 成交量确认
            ) |
            
            # 条件3: 超卖信号
            (
                (dataframe['rsi'] < 30) &  # RSI超卖
                (dataframe['close'] < dataframe['bb_lowerband'])  # 价格突破下轨
            ) |
            
            # 条件4: 动量减弱
            (
                (dataframe['rsi'] < 35) &
                (dataframe['rsi'] > dataframe['rsi'].shift()) &
                (dataframe['rsi'].shift() > dataframe['rsi'].shift(2)) &  # RSI连续两根K线上升
                (dataframe['momentum'] > 0)  # 价格动量转为正
            ) |
            
            # 条件5: 锤子线形态 (潜在反转信号)
            (
                (dataframe['hammer'] > 0) &
                (dataframe['volume_ratio'] > 1.3)
            )
        )
        
        dataframe.loc[short_exit_conditions, 'exit_short'] = 1
        dataframe.loc[short_exit_conditions, 'exit_tag'] = 'chan_sol_short_exit'
        
        return dataframe
    
    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:
        """
        在进入交易前进行额外的确认
        """
        # 只做空头交易
        if side == "sell" and entry_tag == "chan_sol_short":
            return True
        return False
    
    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 1.0
    
    def get_ticker_indicator(self):
        return int(self.timeframe[:-1])