"""
波动率上升确认的趋势跟踪策略（Grok 方案 #3）。

核心逻辑：只在趋势明确（EMA9/21 金叉死叉）且波动率上升时跟随趋势，
用波动率过滤低波动环境下的假突破；移动止损让利润奔跑。

适合：强趋势 + 波动率扩张阶段。
参数未经 hyperopt 优化，dry-run 验证用。
"""

from datetime import datetime

import talib.abstract as ta
from pandas import DataFrame

from freqtrade.strategy import IStrategy


class RisingVolTrend(IStrategy):
    INTERFACE_VERSION = 3

    timeframe = "5m"
    can_short = True

    # 趋势策略：让利润奔跑，靠 trailing 保护
    minimal_roi = {"0": 0.06, "240": 0.03, "720": 0.01}
    stoploss = -0.02

    trailing_stop = True
    trailing_stop_positive = 0.02
    trailing_stop_positive_offset = 0.03
    trailing_only_offset_is_reached = True

    startup_candle_count = 60

    ema_fast = 9
    ema_slow = 21
    atr_period = 14
    atr_confirm_period = 10

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe["ema_fast"] = ta.EMA(dataframe, timeperiod=self.ema_fast)
        dataframe["ema_slow"] = ta.EMA(dataframe, timeperiod=self.ema_slow)
        dataframe["atr"] = ta.ATR(dataframe, timeperiod=self.atr_period)
        dataframe["atr_sma"] = dataframe["atr"].rolling(self.atr_confirm_period).mean()
        dataframe["rising_vol"] = dataframe["atr"] > dataframe["atr_sma"]
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (dataframe["ema_fast"] > dataframe["ema_slow"])
            & (dataframe["close"] > dataframe["ema_fast"])
            & dataframe["rising_vol"]
            & (dataframe["volume"] > 0),
            "enter_long",
        ] = 1

        dataframe.loc[
            (dataframe["ema_fast"] < dataframe["ema_slow"])
            & (dataframe["close"] < dataframe["ema_fast"])
            & dataframe["rising_vol"]
            & (dataframe["volume"] > 0),
            "enter_short",
        ] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # EMA 反向交叉离场（趋势结束信号）
        dataframe.loc[
            (dataframe["ema_fast"] < dataframe["ema_slow"]) & (dataframe["volume"] > 0),
            "exit_long",
        ] = 1
        dataframe.loc[
            (dataframe["ema_fast"] > dataframe["ema_slow"]) & (dataframe["volume"] > 0),
            "exit_short",
        ] = 1
        return dataframe

    def leverage(
        self,
        pair: str,
        current_time: datetime,
        current_rate: float,
        proposed_leverage: float,
        max_leverage: float,
        entry_tag: str | None,
        side: str,
        **kwargs,
    ) -> float:
        return 2.0