# source: https://raw.githubusercontent.com/Yohalloo/freqtrade/9ac43149e8f5c35fd0deb19d8ef7270443639caa/user_data/strategies/BearPullbackShortBearV4.py
# -*- coding: utf-8 -*-
"""
Github_Yohalloo_freqtrade__BearPullbackShortBearV4__20251122_100212 - 熊市回调做空策略（强化 Regime + 反弹强度过滤）

在 V3 的基础上增加：
- pullback_strength（反弹强度）过滤，只做“反弹足够强”的高概率做空：
  pullback_strength = close / rolling_low(N) - 1
- 通过可调参数 pullback_min 控制最小反弹幅度（默认 5%）。

整体结构：
- 只在“真正的熊市 Regime”做空：
  - EMA20 < EMA50 < EMA200
  - close < EMA200
  - MACD < 0
  - ADX > adx_threshold
  - 以上条件连续 regime_persistence 根 K 成立才视为熊市
- 熊市中：布林上轨突破 + RSI 过热 + pullback_strength > 阈值 才允许开空；
- 止损：ATR * mult + 全局上限（1%~2.2%）+ soft_stop（短期走反 -1% 认错）；
- 止盈：超卖信号 + 盈利 >= 3% / 6% 分两档；
- 移动止盈：3%、5%、8%、12% 四档动态止盈。
"""

from datetime import datetime

import numpy as np
import talib.abstract as ta
from pandas import DataFrame

from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter
from freqtrade.persistence import Trade


class Github_Yohalloo_freqtrade__BearPullbackShortBearV4__20251122_100212(IStrategy):
    """熊市回调做空策略（Regime + 反弹强度双过滤）"""

    INTERFACE_VERSION = 3

    timeframe = "1h"
    can_short = True

    # 兜底硬止损（真正逻辑由 custom_exit / custom_stoploss 接管）
    stoploss = -0.15

    # ROI 完全交给 custom_exit
    minimal_roi = {"0": 100}

    # === 参数区（后续可用 hyperopt 调优） ===
    # 判趋势用 ADX 阈值（熊市要求适中，不必极强）
    adx_threshold = IntParameter(15, 30, default=20, space="buy")

    # 入场过热条件：RSI 阈值（熊市反弹一般 55~70 区间）
    rsi_entry = IntParameter(55, 75, default=60, space="buy")

    # 布林带标准差
    bb_std = DecimalParameter(1.8, 2.6, default=2.0, space="buy")

    # 超时（小时）：超过这个时间，盈利或小亏就平仓
    timeout_hours = IntParameter(12, 48, default=24, space="sell")

    # ATR 止损倍数（最大允许亏损 = min(ATR_pct * mult, cap)）
    atr_mult = DecimalParameter(2.0, 4.0, default=3.0, space="sell")

    # Regime 持续性要求：熊市条件至少连续多少根 K 后才算真正进入熊市
    regime_persistence = IntParameter(12, 72, default=24, space="buy")

    # 反弹强度过滤：最小 pullback_strength（例如 0.05 = 5%）
    pullback_min = DecimalParameter(0.03, 0.12, default=0.05, space="buy")

    # === 保护机制 ===
    @property
    def protections(self):
        return [
            {"method": "CooldownPeriod", "stop_duration_candles": 1},
            {
                "method": "StoplossGuard",
                "lookback_period_candles": 24,
                "trade_limit": 5,
                "stop_duration_candles": 6,
                "only_per_pair": False,
            },
        ]

    # === 指标计算 ===
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # 布林带
        bb = ta.BBANDS(
            dataframe,
            timeperiod=20,
            nbdevup=float(self.bb_std.value),
            nbdevdn=float(self.bb_std.value),
        )
        dataframe["bb_upper"] = bb["upperband"]
        dataframe["bb_middle"] = bb["middleband"]
        dataframe["bb_lower"] = bb["lowerband"]

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

        # EMA 趋势线
        dataframe["ema_20"] = ta.EMA(dataframe, timeperiod=20)
        dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50)
        dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200)

        # MACD（用于确认长期处于 0 轴下方）
        macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
        dataframe["macd"] = macd["macd"]
        dataframe["macdsignal"] = macd["macdsignal"]
        dataframe["macdhist"] = macd["macdhist"]

        # ATR 用于动态止损
        dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)

        # --- 熊市 Regime 检测 ---
        adx_thr = int(self.adx_threshold.value)
        pers = int(self.regime_persistence.value)

        # 基础熊市条件：价格与均线空头排列 + MACD < 0
        base_bear = (
            (dataframe["close"] < dataframe["ema_200"])
            & (dataframe["ema_20"] < dataframe["ema_50"])
            & (dataframe["ema_50"] < dataframe["ema_200"])
            & (dataframe["macd"] < 0)
        )

        # 趋势强度：ADX > 阈值
        strong_trend = dataframe["adx"] > adx_thr

        # 原始熊市判定（未考虑持续性）
        raw_bear = base_bear & strong_trend
        dataframe["bear_raw"] = np.where(raw_bear, 1, 0)

        # 持续性过滤：要求 raw_bear 连续 pers 根 K 都为 True
        if pers > 1:
            bear_regime = (
                raw_bear.astype("int").rolling(window=pers, min_periods=pers).sum()
                == pers
            )
        else:
            bear_regime = raw_bear

        dataframe["bear_regime"] = np.where(bear_regime, 1, 0)

        # --- 反弹强度：当前价相对过去 N 根最低价的反弹比例 ---
        # 使用 low 的 rolling min 更稳健
        lookback = 20
        rolling_low = dataframe["low"].rolling(lookback).min()
        dataframe["pullback_strength"] = (dataframe["close"] / rolling_low - 1.0).clip(
            lower=-0.5, upper=1.0
        )

        # 预备列
        if "enter_tag" not in dataframe.columns:
            dataframe["enter_tag"] = ""
        if "exit_short" not in dataframe.columns:
            dataframe["exit_short"] = 0

        return dataframe

    # === 入场：熊市中的“强反弹 + 过热” ===
    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # 只做首次突破上轨的K线，避免上轨附近连环抄顶
        cross_upper = (
            (dataframe["close"] > dataframe["bb_upper"])
            & (dataframe["close"].shift(1) <= dataframe["bb_upper"].shift(1))
        )

        rsi_thr = int(self.rsi_entry.value)
        pb_min = float(self.pullback_min.value)

        cond = (
            (dataframe["bear_regime"] == 1)
            & cross_upper
            & (dataframe["rsi"] > rsi_thr)
            & (dataframe["pullback_strength"] > pb_min)
            & (dataframe["volume"] > 0)
        )

        dataframe.loc[cond, ["enter_short", "enter_tag"]] = (1, "bear_strong")

        return dataframe

    # === 出场信号基准（超卖位置） ===
    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """给出一个“超卖”信号，供 custom_exit 使用：
        价格跌破布林下轨 或 RSI < 30。
        """
        if "exit_short" not in dataframe.columns:
            dataframe["exit_short"] = 0

        oversold = (
            (dataframe["close"] < dataframe["bb_lower"]) | (dataframe["rsi"] < 30)
        ) & (dataframe["volume"] > 0)

        dataframe.loc[oversold, "exit_short"] = 1
        return dataframe

    # === 工具：ATR 动态止损（返回最大允许亏损百分比，正数） ===
    def _calc_max_loss_pct(self, candle) -> float:
        atr = float(candle.get("atr", 0) or 0)
        close = float(candle.get("close", 0) or 1)
        atr_pct = atr / close if close > 0 else 0.01

        mult = float(self.atr_mult.value)
        raw = atr_pct * mult

        # 根据 enter_tag 设置不同的上限（预留扩展位，当前只用 bear_strong）
        enter_tag = candle.get("enter_tag", "") or "bear_strong"

        if enter_tag == "bear_strong":
            cap = 0.022  # 强熊市允许稍宽
        else:
            cap = 0.018  # 预留：弱熊市更窄

        floor = 0.010  # 至少 1%
        return max(floor, min(raw, cap))

    # === 核心：自定义出场逻辑 ===
    def custom_exit(
        self,
        pair: str,
        trade: Trade,
        current_time: datetime,
        current_rate: float,
        current_profit: float,
        **kwargs,
    ):
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        try:
            df = dataframe.loc[dataframe["date"] <= current_time]
            last = df.iloc[-1] if not df.empty else dataframe.iloc[-1]
        except Exception:
            last = dataframe.iloc[-1]

        is_bear = int(last.get("bear_regime", 0)) == 1
        exit_signal = int(last.get("exit_short", 0))
        max_loss_pct = self._calc_max_loss_pct(last)

        # 交易存活时长（小时）
        duration_hrs = (current_time - trade.open_date_utc).total_seconds() / 3600
        timeout = int(self.timeout_hours.value)

        # 1) 硬止损：动态 ATR 止损
        if current_profit <= -max_loss_pct:
            return "bear_stop_loss"

        # 2) soft_stop：开仓后不久就走反，-1% 附近直接认错
        #    避免拖到最大止损
        if is_bear and duration_hrs < 6:  # 开仓 6 小时内
            if current_profit <= -0.01:
                return "soft_stop"

        # 3) Regime Flip：不再是熊市时，盈利或小亏就走
        if not is_bear:
            if current_profit > -0.005:  # -0.5% 以内
                return "regime_flip"
            # 更深亏损交由硬止损处理

        # 4) 正常熊市中，根据盈利和超卖信号出场
        if exit_signal == 1:
            # 第二目标：盈利 >= 6%
            if current_profit >= 0.06:
                return "tp2_exit"
            # 第一目标：盈利 >= 3%
            if current_profit >= 0.03:
                return "tp1_exit"

        # 5) 超时处理：超过 timeout，小亏/小赢也离场
        if duration_hrs > timeout:
            tag = f"timeout_exit_({timeout}h)"
            if current_profit > 0:
                return tag
            if current_profit > -0.005:
                return tag

        # 其它情况不强制退出
        return None

    # === 动态移动止盈：在大盈利时保护仓位 ===
    def custom_stoploss(
        self,
        pair: str,
        trade: Trade,
        current_time: datetime,
        current_rate: float,
        current_profit: float,
        **kwargs,
    ) -> float:
        """根据浮盈动态上调止损价位：
        - 盈利 > 3%  -> 锁定 0.5%
        - 盈利 > 5%  -> 锁定 2%
        - 盈利 > 8%  -> 锁定 4%
        - 盈利 > 12% -> 锁定 6%
        其它情况返回 1，让 custom_exit / 兜底止损处理。
        """

        if current_profit > 0.12:
            return 0.06
        if current_profit > 0.08:
            return 0.04
        if current_profit > 0.05:
            return 0.02
        if current_profit > 0.03:
            return 0.005

        return 1