"""
Structure Flow Scalp — 震荡市剥头皮策略
==========================================
基于Al Brooks价格行为学：
- 在已识别的震荡区间内，支撑位做多、阻力位做空
- 15m级别支撑/阻力决定交易区间，5m级别入场
- 100x全仓杠杆，每次10%仓位
- 区间高度40%止盈，15m支撑/阻力外侧0.3%止损

变更记录：
   v1 (2026-06-10): 初版，基于v2.2b核心逻辑重构
   v1.1 (2026-06-10): 支撑阻力从4H改为15m
   v1.2 (2026-06-10): 去掉4H趋势强度判断（冗余）；启用100x全仓杠杆，10%仓位
   v1.3 (2026-06-10): 代码审查修复——移除populate_exit_trend死循环，NaN安全，杠杆上限
   v1.4 (2026-06-10): EMA动态S/R + 入场锁定S/R——止损止盈使用入场时的锁定值，不追最新
   v1.5 (2026-06-10): 扩展入场信号 + 追踪止损保护 + 延长活S/R窗口
   v1.6 (2026-06-10): 止损改为ATR动态计算——绑入场价，不绑支撑位；追踪改为ATR×0.5自适应
"""

from datetime import datetime
import numpy as np
import pandas as pd
from pandas import DataFrame
from freqtrade.strategy import IStrategy, IntParameter, informative
from freqtrade.persistence import Trade


class StructureFlowScalp(IStrategy):
    """
    震荡市剥头皮策略 — 5m框架，100x全仓杠杆。
    去掉4H趋势强度判断——15m支撑阻力本身就是最好的过滤器。
    """

    can_short = True
    stoploss = -0.15
    use_custom_stoploss = True
    use_custom_exit = True
    minimal_roi = {"0": 100}
    max_open_trades = 1
    timeframe = "5m"

    # =====================
    # 杠杆设置 - 全仓 100x
    # =====================

    def leverage(self, pair: str, current_time: datetime, current_rate: float,
                 proposed_leverage: float, max_leverage: float, side: str,
                 **kwargs) -> float:
        """返回固定 100x 杠杆，不超过交易所允许的最大值"""
        return min(100.0, max_leverage)

    # =====================
    # 工具：查找入场K线（锁定S/R用）
    # =====================

    def _get_entry_row(self, dataframe: DataFrame, trade: Trade) -> pd.Series | None:
        """
        从 dataframe 中找到入场 trade 对应的 K 线行。
        兼容 live/dry_run（DatetimeIndex）和 backtesting（RangeIndex + date 列）两种模式。
        """
        if 'date' in dataframe.columns:
            # Backtesting 模式：dataframe 有 date 列，index 是 int
            entry_mask = pd.to_datetime(dataframe['date']) <= trade.open_date
            if not entry_mask.any():
                return None
            return dataframe[entry_mask].iloc[-1]
        else:
            # Live/Dry-run 模式：index 是 DatetimeIndex
            try:
                entry_idx = dataframe.index.get_indexer([trade.open_date], method="pad")
                if entry_idx[0] < 0 or entry_idx[0] >= len(dataframe):
                    return None
                return dataframe.iloc[entry_idx[0]]
            except (TypeError, ValueError):
                return None

    # =====================
    # 可优化参数
    # =====================

    # 15m支撑阻力计算窗口
    swing_lookback_15m = IntParameter(5, 15, default=10, space="buy")
    pin_bar_wick_ratio = IntParameter(50, 70, default=60, space="buy")
    cooldown_bars = IntParameter(2, 8, default=3, space="buy")

    # 区间高度止盈比例（%）
    profit_zone_pct = IntParameter(20, 60, default=40, space="buy")

    # =====================
    # 工具：Swing Point 检测
    # =====================

    @staticmethod
    def _detect_swing_points(
        high: pd.Series,
        low: pd.Series,
        window: int = 5,
    ) -> tuple[pd.Series, pd.Series]:
        n = len(high)
        sh = pd.Series(np.nan, index=high.index, dtype=float)
        sl = pd.Series(np.nan, index=low.index, dtype=float)

        for i in range(window, n - window):
            if high.iloc[i] > high.iloc[i - window : i].max() and high.iloc[i] > high.iloc[i + 1 : i + window + 1].max():
                sh.iloc[i] = high.iloc[i]
            if low.iloc[i] < low.iloc[i - window : i].min() and low.iloc[i] < low.iloc[i + 1 : i + window + 1].min():
                sl.iloc[i] = low.iloc[i]

        return sh, sl

    # =====================
    # 工具：结构分析
    # =====================

    def _build_structure(
        self,
        high: pd.Series,
        low: pd.Series,
        close: pd.Series,
        swing_high: pd.Series,
        swing_low: pd.Series,
    ) -> DataFrame:
        n = len(high)

        trend_up_arr = np.full(n, False)
        trend_down_arr = np.full(n, False)
        nearest_support = np.full(n, np.nan)
        nearest_resistance = np.full(n, np.nan)

        sh_prices = []
        sl_prices = []

        for i in range(n):
            if pd.notna(swing_high.iloc[i]):
                sh_prices.append(swing_high.iloc[i])
                if len(sh_prices) > 4:
                    sh_prices.pop(0)

            if pd.notna(swing_low.iloc[i]):
                sl_prices.append(swing_low.iloc[i])
                if len(sl_prices) > 4:
                    sl_prices.pop(0)

            if len(sh_prices) >= 2 and len(sl_prices) >= 2:
                if sh_prices[-1] > sh_prices[-2] and sl_prices[-1] > sl_prices[-2]:
                    trend_up_arr[i] = True
                elif sh_prices[-1] < sh_prices[-2] and sl_prices[-1] < sl_prices[-2]:
                    trend_down_arr[i] = True
                elif i > 0:
                    trend_up_arr[i] = trend_up_arr[i - 1]
                    trend_down_arr[i] = trend_down_arr[i - 1]
            elif i > 0:
                trend_up_arr[i] = trend_up_arr[i - 1]
                trend_down_arr[i] = trend_down_arr[i - 1]

            if sl_prices:
                # EMA平滑：不取最后一个，而是对最近swing lows做指数加权
                # alpha=0.3，每个新swing point向它移动30%，有"惯性"不跳变
                ema_s = sl_prices[0]
                for p in sl_prices[1:]:
                    ema_s = 0.3 * p + 0.7 * ema_s
                nearest_support[i] = ema_s
            if sh_prices:
                ema_r = sh_prices[0]
                for p in sh_prices[1:]:
                    ema_r = 0.3 * p + 0.7 * ema_r
                nearest_resistance[i] = ema_r

        return DataFrame({
            "trend_up": trend_up_arr,
            "trend_down": trend_down_arr,
            "support": nearest_support,
            "resistance": nearest_resistance,
        }, index=high.index)

    # =====================
    # 工具：K线形态检测
    # =====================

    @staticmethod
    def _detect_candle_patterns(
        open_: pd.Series,
        high: pd.Series,
        low: pd.Series,
        close: pd.Series,
        pin_bar_wick_ratio: float = 0.6,
    ) -> tuple[pd.Series, pd.Series, pd.Series, pd.Series]:
        body = (close - open_).abs()
        total_range = (high - low).replace(0, 0.0001)

        upper_wick = high - close.where(close > open_, open_)
        lower_wick = open_.where(close > open_, close) - low
        is_pin = (upper_wick + lower_wick) / total_range > pin_bar_wick_ratio

        bullish_pin = is_pin & (close > open_) & (lower_wick > upper_wick)
        bearish_pin = is_pin & (close < open_) & (upper_wick > lower_wick)

        prev_open = open_.shift(1)
        prev_close = close.shift(1)
        bullish_engulf = (close > prev_open) & (open_ < prev_close) & (close > open_)
        bearish_engulf = (close < prev_open) & (open_ > prev_close) & (close < open_)

        return bullish_pin, bearish_pin, bullish_engulf, bearish_engulf

    # ================================================================
    #  信息时间框架 — 15m 短期支撑阻力（核心过滤器）
    # ================================================================

    @informative("15m")
    def populate_indicators_15m(
        self, dataframe: DataFrame, metadata: dict
    ) -> DataFrame:
        sh, sl = self._detect_swing_points(
            dataframe["high"], dataframe["low"],
            self.swing_lookback_15m.value,
        )
        structure = self._build_structure(
            dataframe["high"], dataframe["low"], dataframe["close"],
            sh, sl,
        )
        dataframe["support"] = structure["support"]
        dataframe["resistance"] = structure["resistance"]

        # ── 活支撑检查（15根15m ≈ 3.75小时，震荡市中支撑可长期有效）──
        touched_support = (
            (dataframe["low"] <= dataframe["support"] * 1.005) &
            (dataframe["low"] >= dataframe["support"] * 0.995)
        )
        held_support = dataframe["close"] > dataframe["support"]
        support_tested_and_held = touched_support & held_support
        dataframe["support_alive"] = support_tested_and_held.rolling(15, min_periods=1).max() > 0

        # ── 活阻力检查（15根窗口）──
        touched_resistance = (
            (dataframe["high"] >= dataframe["resistance"] * 0.995) &
            (dataframe["high"] <= dataframe["resistance"] * 1.005)
        )
        held_resistance = dataframe["close"] < dataframe["resistance"]
        resistance_tested_and_held = touched_resistance & held_resistance
        dataframe["resistance_alive"] = resistance_tested_and_held.rolling(15, min_periods=1).max() > 0

        # 区间高度（用于止盈计算）
        dataframe["zone_height"] = (dataframe["resistance"] - dataframe["support"]).fillna(0)

        return dataframe

    # ================================================================
    #  主时间框架 — 5m 指标
    # ================================================================

    def populate_indicators(
        self, dataframe: DataFrame, metadata: dict
    ) -> DataFrame:
        """5m级别：ATR + K线形态 + 信号整合。"""

        # ── ATR(14) — 用于动态止损，根据市场波动自适应 ──
        high = dataframe["high"]
        low = dataframe["low"]
        close = dataframe["close"]
        prev_close = close.shift(1)
        tr = pd.concat([
            high - low,
            (high - prev_close).abs(),
            (low - prev_close).abs(),
        ], axis=1).max(axis=1)
        dataframe["atr"] = tr.rolling(14).mean()

        bullish_pin, bearish_pin, bullish_engulf, bearish_engulf = (
            self._detect_candle_patterns(
                dataframe["open"],
                dataframe["high"],
                dataframe["low"],
                dataframe["close"],
                self.pin_bar_wick_ratio.value / 100.0,
            )
        )
        dataframe["bullish_pinbar"] = bullish_pin
        dataframe["bearish_pinbar"] = bearish_pin
        dataframe["bullish_engulfing"] = bullish_engulf
        dataframe["bearish_engulfing"] = bearish_engulf

        # ── 扩展信号：长下影线（比pinbar更宽松，只要下影线>总范围50%） ──
        total_range = (dataframe["high"] - dataframe["low"]).replace(0, 0.0001)
        body = (dataframe["close"] - dataframe["open"]).abs()
        # 下影线 = min(open, close) - low
        lower_wick = (
            dataframe[["open", "close"]].min(axis=1) - dataframe["low"]
        )
        # 上影线 = high - max(open, close)
        upper_wick = (
            dataframe["high"] - dataframe[["open", "close"]].max(axis=1)
        )
        # 长下影线：下影线>总范围50% 且 下影线>上影线
        long_lower_wick = (
            (lower_wick / total_range > 0.5) &
            (lower_wick > upper_wick)
        )
        dataframe["long_lower_wick"] = long_lower_wick

        # ── 扩展信号：支撑位附近的强力反弹阳线 ──
        # 条件：价格在支撑0.5%范围内 + 阳线 + 实体>0.2%
        if "support_15m" in dataframe.columns:
            near_support = (
                (dataframe["low"] <= dataframe["support_15m"] * 1.005) &
                (dataframe["low"] >= dataframe["support_15m"] * 0.995)
            )
            is_bullish = dataframe["close"] > dataframe["open"]
            body_pct = body / dataframe["open"]
            strong_recovery = near_support & is_bullish & (body_pct > 0.002)
        else:
            strong_recovery = pd.Series(False, index=dataframe.index)
        dataframe["strong_recovery"] = strong_recovery

        # ── 综合止跌/止涨信号（扩展后） ──
        dataframe["bullish_signal"] = (
            bullish_pin | bullish_engulf | long_lower_wick | strong_recovery
        )
        dataframe["bearish_signal"] = (
            bearish_pin | bearish_engulf
        )
        # 做空对称：阻力位附近的强力下跌阴线
        if "resistance_15m" in dataframe.columns:
            near_resistance = (
                (dataframe["high"] >= dataframe["resistance_15m"] * 0.995) &
                (dataframe["high"] <= dataframe["resistance_15m"] * 1.005)
            )
            is_bearish = dataframe["close"] < dataframe["open"]
            body_pct = body / dataframe["open"]
            strong_rejection = near_resistance & is_bearish & (body_pct > 0.002)
        else:
            strong_rejection = pd.Series(False, index=dataframe.index)
        dataframe["strong_rejection"] = strong_rejection
        dataframe["bearish_signal"] = (
            bearish_pin | bearish_engulf | strong_rejection
        )

        # NaN 安全处理
        bool_cols = [
            "support_alive_15m", "resistance_alive_15m",
            "bullish_signal", "bearish_signal",
        ]
        for col in bool_cols:
            if col in dataframe.columns:
                dataframe[col] = dataframe[col].fillna(False)

        # ATR fillna（前14根无ATR值用均值填补）
        if "atr" in dataframe.columns:
            atr_mean = dataframe["atr"].mean()
            dataframe["atr"] = dataframe["atr"].fillna(atr_mean)

        return dataframe

    # =====================
    # 入场信号
    # =====================

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        入场逻辑（5m 时间框架）。

        不做4H趋势判断——15m支撑阻力本身就是过滤器：
        - 趋势强时价格直接突破15m S/R，不会在支撑/阻力附近停留
        - 在支撑/阻力附近停留 = 震荡市

        入场条件（3个，去掉了冗余的4H趋势判断）：
        - 做多：价格贴近15m支撑 + 支撑有效 + K线止跌信号
        - 做空：价格贴近15m阻力 + 阻力有效 + K线止涨信号

        出场只依赖 custom_stoploss 和 custom_exit，不需要 D1 结构反转退出。
        （去掉 populate_exit_trend：震荡市入场 → D1 非上升趋势 → 立即出场 的死循环）
        """
        cooldown = self.cooldown_bars.value

        # NaN 安全处理 — 如果 15m informative 列还没对齐，直接跳过本根 K 线
        required_cols = ["support_15m", "resistance_15m",
                         "support_alive_15m", "resistance_alive_15m"]
        for col in required_cols:
            if col not in dataframe.columns:
                return dataframe  # 数据尚未就绪，跳过

        for col in ["bullish_signal", "bearish_signal",
                     "support_alive_15m", "resistance_alive_15m"]:
            dataframe[col] = dataframe[col].fillna(False)

        # ── 做多 ──
        # 条件：价格贴近15m支撑（0.5%范围内）- 使用 low 而非 open
        # 因为支撑测试看的是价格是否到达支撑位，不是开盘在哪
        near_support = (
            (dataframe["low"] <= dataframe["support_15m"] * 1.005) &
            (dataframe["low"] >= dataframe["support_15m"] * 0.995)
        )

        long_conditions = (
            near_support
            & dataframe["support_alive_15m"]
            & dataframe["bullish_signal"]
        )

        long_recent = long_conditions.rolling(cooldown, min_periods=1).max().shift(1) == 0
        dataframe.loc[long_conditions & long_recent, "enter_long"] = 1

        # ── 做空 ──
        # 条件：价格贴近15m阻力（0.5%范围内）- 使用 high 而非 open
        near_resistance = (
            (dataframe["high"] >= dataframe["resistance_15m"] * 0.995) &
            (dataframe["high"] <= dataframe["resistance_15m"] * 1.005)
        )

        short_conditions = (
            near_resistance
            & dataframe["resistance_alive_15m"]
            & dataframe["bearish_signal"]
        )

        short_recent = short_conditions.rolling(cooldown, min_periods=1).max().shift(1) == 0
        dataframe.loc[short_conditions & short_recent, "enter_short"] = 1

        return dataframe

    # =====================
    # exit_trend（freqtrade 2025.11 要求必须实现，即使 use_custom_exit=True）
    # =====================

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """退出逻辑完全由 custom_stoploss + custom_exit 管理。"""
        return dataframe

    # =====================
    # 动态止损 — 入场价 - ATR×2.0（基于市场波动，非固定比例）
    # =====================

    def custom_stoploss(
        self,
        pair: str,
        trade: Trade,
        current_time: datetime,
        current_rate: float,
        current_profit: float,
        after_fill: bool,
        **kwargs,
    ) -> float:
        """
        止损锚定入场价，宽度根据市场波动（ATR）动态计算，而非固定比例。

        核心逻辑：
        - 做多止损 = entry_price - ATR_5m × 2.0
        - 做空止损 = entry_price + ATR_5m × 2.0
        - ATR值从入场时的K线锁定，持仓期间不漂移

        为什么用ATR不用固定比例：
        - ATR自动适应市场：波动大时止损放宽免误扫，波动小时收紧控风险
        - 固定比例是拍脑袋，ATR是算出来的

        追踪保护（v1.6 ATR自适应版）：
        - 利润达止盈目标50%：上移到保本（入场价）
        - 利润达止盈目标80%：启动ATR×0.5窄追踪
        """
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        if dataframe is None or len(dataframe) == 0:
            return -0.02 if not trade.is_short else 0.02

        # 查找入场时的 K 线，锁定当时的 ATR 值
        entry_row = self._get_entry_row(dataframe, trade)
        if entry_row is None:
            return -0.02 if not trade.is_short else 0.02

        # 锁定入场时的 ATR 值，用于全程止损/追踪计算（不追最新，防止漂移）
        atr_value = entry_row.get("atr", np.nan)
        if pd.isna(atr_value) or atr_value <= 0:
            return -0.02 if not trade.is_short else 0.02

        if not trade.is_short:
            # 做多：止损 = 入场价 - ATR × 2.0
            base_sl_price = trade.open_rate - (atr_value * 2.0)
            base_sl = (base_sl_price / trade.open_rate) - 1.0
            base_sl = max(base_sl, -0.15)

            # 追踪保护：需要入场行计算止盈目标
            support = entry_row.get("support_15m", np.nan)
            resistance = entry_row.get("resistance_15m", np.nan)
            if (not pd.isna(support) and not pd.isna(resistance)
                    and resistance > support and current_profit > 0):
                zone_height = resistance - support
                tp_target = (zone_height * self.profit_zone_pct.value / 100.0) / trade.open_rate

                if current_profit >= tp_target * 0.8:
                    # 利润达止盈80%：ATR自适应窄追踪
                    trail_price = current_rate - (atr_value * 0.5)
                    trail_ratio = (trail_price / trade.open_rate) - 1.0
                    return max(trail_ratio, base_sl)
                elif current_profit >= tp_target * 0.5:
                    # 利润达止盈50%：保本
                    return max(0.0, base_sl)

            return base_sl
        else:
            # 做空：止损 = 入场价 + ATR × 2.0
            base_sl_price = trade.open_rate + (atr_value * 2.0)
            base_sl = 1.0 - (base_sl_price / trade.open_rate)
            base_sl = min(base_sl, 0.15)

            # 追踪保护（做空对称）
            support = entry_row.get("support_15m", np.nan)
            resistance = entry_row.get("resistance_15m", np.nan)
            if (not pd.isna(support) and not pd.isna(resistance)
                    and resistance > support and current_profit > 0):
                zone_height = resistance - support
                tp_target = (zone_height * self.profit_zone_pct.value / 100.0) / trade.open_rate

                if current_profit >= tp_target * 0.8:
                    # ATR自适应窄追踪（做空对称）
                    trail_price = current_rate + (atr_value * 0.5)
                    trail_ratio = (trail_price / trade.open_rate) - 1.0
                    return min(trail_ratio, base_sl)
                elif current_profit >= tp_target * 0.5:
                    # 保本
                    return min(0.0, base_sl)

            return base_sl

    # =====================
    # 区间高度止盈
    # =====================

    def custom_exit(
        self,
        pair: str,
        trade: Trade,
        current_time: datetime,
        current_rate: float,
        current_profit: float,
        **kwargs,
    ) -> str | None:
        """
        当利润达到入场时锁定的15m区间高度的设定比例时止盈。

        使用入场时锁定的S/R值计算区间高度（zone_height），而非最新的值：
        - 入场后如果区间收缩，止盈目标不会跟着变小
        - 让入场时确定的止盈逻辑"钉死"
        - profit_zone_pct 默认40%，即锁定区间高度的40%
        """
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        if dataframe is None or len(dataframe) == 0:
            return None

        # 查找入场时的 K 线，锁定当时的 S/R 值
        entry_row = self._get_entry_row(dataframe, trade)
        if entry_row is None:
            return None

        support = entry_row.get("support_15m", np.nan)
        resistance = entry_row.get("resistance_15m", np.nan)

        if pd.isna(support) or pd.isna(resistance) or resistance <= support:
            return None

        # 用锁定的区间高度计算止盈目标（不随市场漂移）
        locked_zone_height = resistance - support
        target_pct = (locked_zone_height * self.profit_zone_pct.value / 100.0) / trade.open_rate

        if current_profit >= target_pct:
            return "zone_tp"

        return None

    # =====================
    # Plot config
    # =====================

    @staticmethod
    def plot_config() -> dict:
        return {
            "main_plot": {
                "support_15m": {"color": "green", "type": "line"},
                "resistance_15m": {"color": "red", "type": "line"},
            },
            "subplots": {
                "signals": {
                    "bullish_pinbar": {"color": "green", "type": "scatter"},
                    "bearish_pinbar": {"color": "red", "type": "scatter"},
                    "bullish_signal": {"color": "lime", "type": "scatter"},
                    "bearish_signal": {"color": "orange", "type": "scatter"},
                },
                "filters": {
                    "support_alive_15m": {"color": "green", "type": "line"},
                    "resistance_alive_15m": {"color": "red", "type": "line"},
                },
            },
        }