# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np
import pandas as pd
from pandas import DataFrame
from datetime import datetime
from typing import Optional, Union

from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
                                IntParameter, merge_informative_pair, stoploss_from_absolute,
                                stoploss_from_open)
from freqtrade.strategy.interface import IStrategy
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib


class TrendStrategy(IStrategy):
    """
    趋势策略 - 基于EMA和RSI的趋势跟踪策略
    
    策略逻辑：
    买入条件：
    1. EMA多头排列 + 短期趋势加速：EMA10 > EMA20 且 EMA10继续上行
    2. 双周期RSI深度超卖反弹：6周期RSI和12周期RSI都进入超卖区域
    
    卖出条件：
    1. EMA10连续下跌（动量走弱）
    2. 空头趋势加速（EMA价差达到近期最大）
    """

    # Strategy interface version - allow new iterations of the strategy
    INTERFACE_VERSION = 3

    # Optimal timeframe for the strategy
    timeframe = '1d'

    # Can this strategy go short?
    can_short: bool = False

    # Minimal ROI designed for the strategy.
    # minimal_roi = {
    #     "0": 0.1  # 10% ROI，让止损和卖出条件来控制退出
    # }

    # Optimal stoploss designed for the strategy
    stoploss = -0.05  # 5% 止损

    # Trailing stoploss
    trailing_stop = False
    trailing_stop_positive = None
    trailing_stop_positive_offset = 0.0
    trailing_only_offset_is_reached = False

    # Run "populate_indicators" only for new candle
    process_only_new_candles = False

    # These values can be overridden in the config
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 30

    # Optional order type mapping
    order_types = {
        'entry': 'limit',
        'exit': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }

    # Optional order time in force
    order_time_in_force = {
        'entry': 'gtc',
        'exit': 'gtc'
    }

    # 策略参数
    ema_short_window = IntParameter(8, 15, default=10, space="buy")
    ema_long_window = IntParameter(15, 25, default=20, space="buy")
    rsi_short_window = IntParameter(4, 8, default=6, space="buy")
    rsi_long_window = IntParameter(10, 15, default=12, space="buy")
    
    # RSI超卖阈值
    rsi_short_oversold = DecimalParameter(15.0, 25.0, default=21.0, space="buy")
    rsi_long_oversold = DecimalParameter(20.0, 30.0, default=26.25, space="buy")
    
    # 趋势检查窗口
    trend_check_window = IntParameter(3, 7, default=3, space="buy")
    momentum_check_window = IntParameter(3, 7, default=5, space="sell")
    diff_check_window = IntParameter(4, 8, default=6, space="sell")

    def informative_pairs(self):
        """
        Define additional, informative pair/interval combinations to be cached from the exchange.
        These pairs will automatically be available for use in the `populate_indicators` method.
        """
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Adds several different TA indicators to the given DataFrame

        Performance Note: For the best performance be frugal on the number of indicators
        you are using. Let uncomment only the indicator you are using in your strategies
        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
        :param dataframe: Dataframe with data from the exchange
        :param metadata: Additional information, like the currently traded pair
        :return: a Dataframe with all mandatory indicators for the strategy
        """

        # EMA指标
        dataframe['ema_short'] = ta.EMA(dataframe, timeperiod=self.ema_short_window.value)
        dataframe['ema_long'] = ta.EMA(dataframe, timeperiod=self.ema_long_window.value)
        
        # RSI指标
        dataframe['rsi_short'] = ta.RSI(dataframe, timeperiod=self.rsi_short_window.value)
        dataframe['rsi_long'] = ta.RSI(dataframe, timeperiod=self.rsi_long_window.value)
        
        # 计算EMA价差（用于卖出条件）
        dataframe['ema_diff'] = dataframe['ema_long'] - dataframe['ema_short']
        
        # 计算EMA短期趋势（用于买入条件）
        dataframe['ema_short_rising'] = dataframe['ema_short'] > dataframe['ema_short'].shift(1)
        dataframe['ema_short_rising_2'] = dataframe['ema_short'].shift(1) > dataframe['ema_short'].shift(2)
        
        # 计算RSI超卖条件
        dataframe['rsi_short_oversold'] = (
            dataframe['rsi_short'].shift(1).rolling(window=self.trend_check_window.value).max() < self.rsi_short_oversold.value
        )
        dataframe['rsi_long_oversold'] = (
            dataframe['rsi_long'].shift(1).rolling(window=self.trend_check_window.value).max() < self.rsi_long_oversold.value
        )
        
        # 计算EMA10连续下跌条件（用于卖出）
        dataframe['ema_short_falling'] = (
            dataframe['ema_short'] < dataframe['ema_short'].shift(1).rolling(window=self.momentum_check_window.value).min()
        )
        
        # 计算价差扩大条件（用于卖出）
        dataframe['ema_diff_expanding'] = (
            dataframe['ema_diff'] == dataframe['ema_diff'].rolling(window=self.diff_check_window.value).max()
        )

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the entry signal for the given dataframe
        :param dataframe: DataFrame populated with indicators
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with entry columns populated
        """
        
        # 条件1：EMA多头排列 + 短期趋势加速
        ema_bullish_cross = (
            (dataframe['ema_short'] > dataframe['ema_long']) &
            (dataframe['ema_short_rising'] & dataframe['ema_short_rising_2'])
        )
        
        # 条件2：双周期RSI深度超卖反弹
        rsi_oversold_bounce = (
            dataframe['rsi_short_oversold'] &
            dataframe['rsi_long_oversold']
        )
        
        # 合并买入条件：任一条件满足即产生买入信号
        dataframe.loc[
            ema_bullish_cross | rsi_oversold_bounce,
            'enter_long'
        ] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the exit signal for the given dataframe
        :param dataframe: DataFrame populated with indicators
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with exit columns populated
        """
        
        # 卖出条件：EMA10连续下跌 + 空头趋势加速
        sell_condition = (
            dataframe['ema_short_falling'] &
            dataframe['ema_diff_expanding']
        )
        
        dataframe.loc[
            sell_condition,
            'exit_long'
        ] = 1

        return dataframe

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:
        """
        自定义止损逻辑
        这里保持原有的5%止损，但可以根据需要添加更复杂的止损逻辑
        """
        return self.stoploss