"""
SOL5mStrategyV6 - 趋势跟随策略 V6（基于V5改进）

核心改进：
    - 去掉trend_reversal退出（V5中54笔全亏 -611 USDT）
    - 完全依赖 ROI + trailing_stop + stoploss 管理退出
    - 更激进的trailing：1.5%盈利后激活，0.6%回撤
    - 保持V5的入场逻辑（EMA排列 + 回调入场 + RSI + MACD + ADX）
"""

from datetime import datetime
from typing import Optional

import talib.abstract as ta
from pandas import DataFrame

from freqtrade.persistence import Trade
from freqtrade.strategy import IStrategy


class SOL5mStrategyV6(IStrategy):
    INTERFACE_VERSION: int = 3

    timeframe = "15m"
    can_short = True
    startup_candle_count: int = 200

    # 止损
    stoploss = -0.025
    use_custom_stoploss = False

    # 更激进的trailing stop
    trailing_stop = True
    trailing_stop_positive = 0.006   # 0.6% 回撤止盈
    trailing_stop_positive_offset = 0.015  # 1.5% 盈利后激活
    trailing_only_offset_is_reached = True

    # ROI
    minimal_roi = {
        "0": 0.04,       # 4%直接止盈
        "60": 0.03,      # 1小时后 3%
        "180": 0.02,     # 3小时后 2%
        "360": 0.01,     # 6小时后 1%
        "720": 0.005,    # 12小时后 0.5%
        "1440": 0,       # 24小时后保本
    }

    order_types = {
        "entry": "market",
        "exit": "market",
        "stoploss": "market",
        "stoploss_on_exchange": False,
    }

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # EMA趋势
        dataframe["ema20"] = ta.EMA(dataframe, timeperiod=20)
        dataframe["ema50"] = ta.EMA(dataframe, timeperiod=50)
        dataframe["ema100"] = ta.EMA(dataframe, timeperiod=100)

        # RSI
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)

        # MACD
        macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
        dataframe["macd"] = macd["macd"]
        dataframe["macdsignal"] = macd["macdsignal"]
        dataframe["macdhist"] = macd["macdhist"]

        # ADX (趋势强度)
        dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # 做多条件（与V5相同）
        dataframe.loc[
            (dataframe["ema20"] > dataframe["ema50"])
            & (dataframe["ema50"] > dataframe["ema100"])
            & (dataframe["close"] <= dataframe["ema20"] * 1.005)
            & (dataframe["close"] >= dataframe["ema50"])
            & (dataframe["rsi"] > 40)
            & (dataframe["rsi"] < 65)
            & (dataframe["macdhist"] > 0)
            & (dataframe["adx"] > 20),
            ["enter_long", "enter_tag"],
        ] = (1, "trend_pullback_long")

        # 做空条件（与V5相同）
        dataframe.loc[
            (dataframe["ema20"] < dataframe["ema50"])
            & (dataframe["ema50"] < dataframe["ema100"])
            & (dataframe["close"] >= dataframe["ema20"] * 0.995)
            & (dataframe["close"] <= dataframe["ema50"])
            & (dataframe["rsi"] < 60)
            & (dataframe["rsi"] > 35)
            & (dataframe["macdhist"] < 0)
            & (dataframe["adx"] > 20),
            ["enter_short", "enter_tag"],
        ] = (1, "trend_pullback_short")

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # 不使用信号退出，完全依赖 ROI + trailing + stoploss
        return dataframe

    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