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

from freqtrade.strategy import (
    IStrategy,
    Trade,
    Order,
    PairLocks,
    informative,  # @informative decorator
    # Hyperopt Parameters
    BooleanParameter,
    CategoricalParameter,
    DecimalParameter,
    IntParameter,
    RealParameter,
    # timeframe helpers
    timeframe_to_minutes,
    timeframe_to_next_date,
    timeframe_to_prev_date,
    timeframe_to_seconds,
    timeframe_to_msecs,
)

import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib


class ProfitSafeStrategy(IStrategy):
    """
    优化利润的安全策略 - 调整止损+提高盈亏比+趋势过滤
    
    主要改进：
    1. 止损从4%放宽到6%，减少过早止损
    2. ROI从2%提高到3-4%，提高盈亏比
    3. 加入MA趋势过滤，只做多头趋势
    4. 分批止盈，拉高整体收益
    5. 增加持仓时间，避免过早止损
    """
    
    INTERFACE_VERSION = 3

    # 策略时间框架
    timeframe = "15m"

    # 是否可以做空
    can_short: bool = False

    # ROI设置 - 提高盈亏比
    minimal_roi = {
        "1440": 0.02,  # 24小时后2%收益
        "720": 0.03,   # 12小时后3%收益
        "480": 0.04,   # 8小时后4%收益
        "360": 0.05,   # 6小时后5%收益
        "240": 0.06,   # 4小时后6%收益
        "180": 0.07,   # 3小时后7%收益
        "120": 0.08,   # 2小时后8%收益
        "60": 0.10,    # 1小时后10%收益
        "0": 0.12      # 立即12%收益
    }

    # 止损设置 - 从4%放宽到6%
    stoploss = -0.06  # 6%止损（放宽）

    # 追踪止损 - 更积极的利润保护
    trailing_stop = True
    trailing_stop_positive = 0.03  # 3%开始追踪
    trailing_stop_positive_offset = 0.04  # 4%偏移
    trailing_only_offset_is_reached = True

    # 只处理新K线
    process_only_new_candles = True

    # 策略参数
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    # 策略启动所需的K线数量
    startup_candle_count: int = 50

    # 订单类型
    order_types = {
        "entry": "limit",
        "exit": "limit",
        "stoploss": "market",
        "stoploss_on_exchange": False
    }

    # 订单时间
    order_time_in_force = {
        "entry": "GTC",
        "exit": "GTC"
    }

    def informative_pairs(self):
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        计算15分钟时间框架的指标，增加趋势过滤
        """
        # RSI
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
        
        # 多条MA - 用于趋势过滤
        dataframe["ema_20"] = ta.EMA(dataframe, timeperiod=20)
        dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50)
        dataframe["sma_100"] = ta.SMA(dataframe, timeperiod=100)  # 长期趋势
        
        # MACD
        macd = ta.MACD(dataframe)
        dataframe["macd"] = macd["macd"]
        dataframe["macdsignal"] = macd["macdsignal"]
        dataframe["macdhist"] = macd["macdhist"]
        
        # ADX - 趋势强度指标
        dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
        
        # 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_percent"] = (
            (dataframe["close"] - dataframe["bb_lowerband"]) /
            (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
        )
        
        # ATR - 用于动态止损
        dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)
        dataframe["atr_percent"] = dataframe["atr"] / dataframe["close"]
        
        # 成交量指标
        dataframe["volume_sma"] = dataframe["volume"].rolling(window=20).mean()
        dataframe["volume_ratio"] = dataframe["volume"] / dataframe["volume_sma"]
        
        # 价格动量
        dataframe["momentum_3"] = dataframe["close"] / dataframe["close"].shift(3) - 1
        dataframe["momentum_5"] = dataframe["close"] / dataframe["close"].shift(5) - 1
        
        # 趋势强度指标
        dataframe["trend_strength"] = (
            (dataframe["ema_20"] > dataframe["ema_50"]).astype(int) +
            (dataframe["ema_50"] > dataframe["sma_100"]).astype(int) +
            (dataframe["close"] > dataframe["ema_20"]).astype(int) +
            (dataframe["macd"] > dataframe["macdsignal"]).astype(int)
        )
        
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        入场条件 - 趋势过滤+确保能开单
        """
        # 基础条件（宽松）
        base_conditions = (
            (dataframe["volume"] > 0) &
            (dataframe["rsi"].notna()) &
            (dataframe["volume_ratio"] >= 0.3)  # 成交量不低于平均的30%
        )
        
        # MA趋势过滤 - 只做多头趋势
        trend_filter = (
            (dataframe["ema_20"] > dataframe["ema_50"]) &  # EMA20在EMA50之上
            (dataframe["ema_50"] > dataframe["sma_100"])   # EMA50在SMA100之上
        )
        
        # RSI信号（宽松）
        rsi_signal = (
            (dataframe["rsi"] < 50) &  # RSI低于50
            (dataframe["rsi"] > dataframe["rsi"].shift(1))  # RSI上升
        )
        
        # 趋势确认（宽松）
        trend_confirm = (
            (dataframe["close"] > dataframe["ema_20"] * 0.95)  # 价格在EMA20附近（5%容忍度）
        )
        
        # 动量确认（宽松）
        momentum_confirm = (
            (dataframe["momentum_3"] > -0.03)  # 3周期内跌幅不超过3%
        )
        
        # 布林带位置（宽松）
        bb_position = (
            (dataframe["bb_percent"] > 0.05)  # 不在布林带下轨附近
        )
        
        # 综合入场条件
        dataframe.loc[
            base_conditions &
            trend_filter &
            rsi_signal &
            trend_confirm &
            momentum_confirm &
            bb_position,
            "enter_long"
        ] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        出场条件 - 基于趋势转弱
        """
        # 基础条件
        base_conditions = (
            (dataframe["volume"] > 0) &
            (dataframe["rsi"].notna())
        )
        
        # 趋势转弱
        trend_weak = (
            (dataframe["ema_20"] < dataframe["ema_50"]) |  # EMA20跌破EMA50
            (dataframe["ema_50"] < dataframe["sma_100"])   # EMA50跌破SMA100
        )
        
        # RSI超买
        rsi_overbought = (
            (dataframe["rsi"] > 75) &  # RSI超买
            (dataframe["rsi"] < dataframe["rsi"].shift(1))  # RSI下降
        )
        
        # 价格跌破关键支撑
        price_breakdown = (
            (dataframe["close"] < dataframe["ema_20"] * 0.95)  # 价格跌破EMA20（5%容忍度）
        )
        
        # 综合出场条件
        dataframe.loc[
            base_conditions &
            (trend_weak | rsi_overbought | price_breakdown),
            "exit_long"
        ] = 1

        return dataframe

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                       current_rate: float, current_profit: float, **kwargs) -> float:
        """
        动态止损 - 从6%开始，根据盈利情况调整
        """
        # 基于盈利情况的止损调整
        if current_profit > 0.08:  # 盈利超过8%
            return -0.03  # 3%止损（保护大部分利润）
        elif current_profit > 0.06:  # 盈利超过6%
            return -0.04  # 4%止损
        elif current_profit > 0.04:  # 盈利超过4%
            return -0.05  # 5%止损
        elif current_profit > 0.02:  # 盈利超过2%
            return -0.055  # 5.5%止损
        
        # 基础止损
        return self.stoploss  # -0.06 (6%止损)

    def custom_exit(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float,
                   current_profit: float, **kwargs) -> Optional[Union[str, bool]]:
        """
        自定义退出逻辑 - 分批止盈
        """
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        if len(dataframe) == 0:
            return None
            
        last_candle = dataframe.iloc[-1].squeeze()
        
        # 分批止盈逻辑
        if current_profit > 0.08:  # 8%全部止盈
            return "full_profit_exit"
        elif current_profit > 0.06:  # 6%部分止盈
            # 检查是否应该部分止盈
            if (last_candle.get("rsi", 50) > 70 or 
                last_candle.get("close", 0) < last_candle.get("ema_20", 0)):
                return "partial_profit_exit"
        
        # 如果趋势转弱且有利润，立即退出
        if (current_profit > 0.03 and 
            (last_candle.get("ema_20", 0) < last_candle.get("ema_50", 0) or
             last_candle.get("ema_50", 0) < last_candle.get("sma_100", 0))):
            return "trend_weak_exit"
        
        # 如果RSI极度超买且有足够利润，立即退出
        if (last_candle.get("rsi", 50) > 80 and 
            current_profit > 0.04):
            return "rsi_extreme_exit"
        
        # 如果持仓时间过长且有利润，退出
        if (current_profit > 0.02 and 
            (current_time - trade.open_date_utc).total_seconds() > 43200):  # 12小时
            return "time_profit_exit"
        
        return None