"""
Structure Flow Swing Strategy v4.1
==================================
15m 震荡波段 — 基于"碰壁验证"价格聚集度检测（短窗口版）

v4.1b (2026-06-10): 1H + 3K线验证（正收益但频率太低）
v4.1c (2026-06-10): 回到15m + 缩短lookback至24根(6h) + 保留min_rejections=2
"""

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


class StructureFlowSwingV41(IStrategy):
    """
    Structure Flow Swing Strategy v4.1
    15m 震荡区间波段交易 — 碰壁验证（短窗口）
    """

    can_short = True
    stoploss = -0.20
    use_custom_stoploss = True
    minimal_roi = {"0": 100}
    max_open_trades = 1
    timeframe = "15m"

    # =====================
    # 可优化参数
    # =====================
    lookback = IntParameter(8, 48, default=12, space="buy")           # 检测窗口：12根15m = 3h
    min_rejections = IntParameter(1, 4, default=1, space="buy")       # 碰壁验证1次即可
    rejection_window = IntParameter(1, 6, default=3, space="buy")     # 碰壁验证窗口
    zone_width_atr_mult = IntParameter(2, 6, default=4, space="buy")
    entry_zone_pct = IntParameter(2, 8, default=5, space="buy")
    take_profit_pct = IntParameter(50, 80, default=70, space="sell")

    # 固定参数
    breakout_bars = 2
    cooldown = 2                # 冷却 2 根15m（30分钟）

    # =====================
    # 工具：ATR计算
    # =====================

    @staticmethod
    def _calc_atr(high: pd.Series, low: pd.Series, close: pd.Series, period: int = 14) -> pd.Series:
        tr = pd.DataFrame({
            "hl": high - low,
            "hc": (high - close.shift(1)).abs(),
            "lc": (low - close.shift(1)).abs(),
        }).max(axis=1)
        return tr.rolling(period).mean()

    # =====================
    # 主时间框架 — 15m 指标
    # =====================

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        lookback = self.lookback.value
        rej_threshold = 0.005  # 边界 0.5% 范围内算"碰边"

        # ── 价格聚集范围 ──
        rolling_high = dataframe["high"].rolling(lookback).max()
        rolling_low = dataframe["low"].rolling(lookback).min()
        zone_width_raw = rolling_high - rolling_low

        dataframe["zone_high"] = rolling_high
        dataframe["zone_low"] = rolling_low
        dataframe["zone_width_raw"] = zone_width_raw
        dataframe["zone_width_pct"] = zone_width_raw / rolling_low

        # ATR
        dataframe["atr"] = self._calc_atr(dataframe["high"], dataframe["low"], dataframe["close"], 14)

        # ── 碰壁验证检测 ──
        # 支撑验证：价格到了低点附近 → 随后 rejection_window 根K线内有反弹
        near_low = dataframe["low"] <= rolling_low * (1 + rej_threshold)
        bounced_up = pd.Series(False, index=dataframe.index)
        for i in range(1, self.rejection_window.value + 1):
            future_close = dataframe["close"].shift(-i).fillna(dataframe["close"])
            bounced_up = bounced_up | (future_close > dataframe["close"])
        support_rejection = near_low & bounced_up
        dataframe["support_rejection_count"] = support_rejection.rolling(lookback).sum()

        # 阻力验证：价格到了高点附近 → 随后 rejection_window 根K线内有回落
        near_high = dataframe["high"] >= rolling_high * (1 - rej_threshold)
        bounced_down = pd.Series(False, index=dataframe.index)
        for i in range(1, self.rejection_window.value + 1):
            future_close = dataframe["close"].shift(-i).fillna(dataframe["close"])
            bounced_down = bounced_down | (future_close < dataframe["close"])
        resistance_rejection = near_high & bounced_down
        dataframe["resistance_rejection_count"] = resistance_rejection.rolling(lookback).sum()

        # ── 震荡判定 ──
        atr_mult = self.zone_width_atr_mult.value
        min_rej = self.min_rejections.value

        # 条件1：区间宽度合理（不超过 ATR × N）
        is_compact = zone_width_raw <= dataframe["atr"] * atr_mult

        # 条件2：上下边界都经过碰壁验证（各至少 min_rej 次）
        support_ok = dataframe["support_rejection_count"] >= min_rej
        resistance_ok = dataframe["resistance_rejection_count"] >= min_rej

        # 条件3：无突破
        no_break_high = True
        no_break_low = True
        for i in range(1, self.breakout_bars + 1):
            if i <= len(dataframe):
                no_break_high = no_break_high & (dataframe["close"].shift(i) <= rolling_high)
                no_break_low = no_break_low & (dataframe["close"].shift(i) >= rolling_low)

        is_ranging = is_compact & support_ok & resistance_ok & no_break_high & no_break_low
        dataframe["is_ranging"] = is_ranging

        # ── 价格在区间内的位置 ──
        denom = rolling_high - rolling_low
        dataframe["zone_position"] = np.where(
            denom > 0,
            (dataframe["close"] - rolling_low) / denom,
            np.nan,
        )

        dataframe["dist_to_low"] = np.where(
            rolling_low > 0,
            (dataframe["close"] - rolling_low) / dataframe["close"],
            np.nan,
        )
        dataframe["dist_to_high"] = np.where(
            rolling_high > 0,
            (rolling_high - dataframe["close"]) / dataframe["close"],
            np.nan,
        )

        # ── 填充 ──
        for col in ["is_ranging", "zone_position", "dist_to_low", "dist_to_high"]:
            if col in dataframe.columns:
                dataframe[col] = dataframe[col].fillna(False if col == "is_ranging" else 999)

        return dataframe

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

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        entry_zone = self.entry_zone_pct.value / 1000.0

        if "is_ranging" not in dataframe.columns:
            dataframe["is_ranging"] = False

        # ── 做多：震荡中，价格靠近下边界 ──
        long_conds = (
            dataframe["is_ranging"]
            & (dataframe["dist_to_low"] < entry_zone)
            & (dataframe["dist_to_low"] > 0)
        )
        long_recent = long_conds.rolling(self.cooldown, min_periods=1).max().shift(1) == 0
        dataframe.loc[long_conds & long_recent, "enter_long"] = 1

        # ── 做空：震荡中，价格靠近上边界 ──
        short_conds = (
            dataframe["is_ranging"]
            & (dataframe["dist_to_high"] < entry_zone)
            & (dataframe["dist_to_high"] > 0)
        )
        short_recent = short_conds.rolling(self.cooldown, min_periods=1).max().shift(1) == 0
        dataframe.loc[short_conds & short_recent, "enter_short"] = 1

        return dataframe

    # ================================================================
    # 出场信号
    # ================================================================

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        return dataframe

    # ================================================================
    # 自定义止损：固定入场价下方-3%（真固定，不追踪）
    # ================================================================

    def custom_stoploss(
        self,
        pair: str,
        trade: Trade,
        current_time: datetime,
        current_rate: float,
        current_profit: float,
        after_fill: bool,
        **kwargs,
    ) -> float:
        # 固定止损 = 入场价下方3%，不随当前价格移动
        # 不用current_rate，用trade.open_rate确保锚点固定
        if not trade.is_short:
            return max((trade.open_rate * 0.98 / current_rate) - 1.0, -0.20)
        else:
            return min(1.0 - (trade.open_rate * 1.02 / current_rate), 0.20)

    # ================================================================
    # 自定义止盈：区间高度 × TP%
    # ================================================================

    def custom_exit(
        self,
        pair: str,
        trade: Trade,
        current_time: datetime,
        current_rate: float,
        current_profit: float,
        **kwargs,
    ) -> str | None:
        tp_pct = self.take_profit_pct.value / 100.0

        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        if dataframe is None or len(dataframe) == 0:
            return None

        last = dataframe.iloc[-1]
        z_low = last.get("zone_low", np.nan)
        z_high = last.get("zone_high", np.nan)

        if pd.notna(z_low) and pd.notna(z_high) and z_high > z_low:
            base = z_low if not trade.is_short else z_high
            zone_height = (z_high - z_low) / base
            if current_profit >= zone_height * tp_pct:
                return "take_profit"

        return None

    # ================================================================
    # 自定义退出：锁定入场边界 + 收盘确认止损 + 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:
        tp_pct = self.take_profit_pct.value / 100.0

        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        if dataframe is None or len(dataframe) == 0:
            return None

        last = dataframe.iloc[-1]

        # ── 锁定入场时的区间边界 ──
        open_time = trade.open_date_utc.replace(tzinfo=None)
        time_diff = (dataframe["date"] - open_time).abs()
        open_idx = time_diff.idxmin()
        open_row = dataframe.iloc[open_idx]

        z_low_open = open_row.get("zone_low", np.nan)
        z_high_open = open_row.get("zone_high", np.nan)

        # ── 结构止损：收盘价确认跌破入场时的边界 ──
        if not trade.is_short:
            if pd.notna(z_low_open) and z_low_open > 0 and last["close"] < z_low_open:
                return "stop_loss"  # 收盘跌破支撑
        else:
            if pd.notna(z_high_open) and z_high_open > 0 and last["close"] > z_high_open:
                return "stop_loss"  # 收盘涨破阻力

        # ── 止盈：区间高度 × TP%（也用锁定边界） ──
        if pd.notna(z_low_open) and pd.notna(z_high_open) and z_high_open > z_low_open:
            base = z_low_open if not trade.is_short else z_high_open
            zone_height = (z_high_open - z_low_open) / base
            if current_profit >= zone_height * tp_pct:
                return "take_profit"

        return None

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

    @staticmethod
    def plot_config() -> dict:
        return {
            "main_plot": {
                "zone_high": {"color": "red", "type": "line"},
                "zone_low": {"color": "green", "type": "line"},
            },
            "subplots": {
                "rejections": {
                    "support_rejection_count": {"color": "green", "type": "line"},
                    "resistance_rejection_count": {"color": "red", "type": "line"},
                },
                "zone": {
                    "is_ranging": {"color": "blue", "type": "line"},
                },
            },
        }