docs: 添加 ablation 消融实验 + README 版本概览

README.md

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+# Beast Trader 策略仓库
+
+ETH/USDT 永续合约量化交易策略版本管理，基于 freqtrade + Binance。
+
+## 当前部署
+
+**v2.2d** — 三层趋势共振（D1+4H+1H），震荡市自动休眠，dry-run 运行中
+
+## 版本演进
+
+| 系列 | 版本范围 | 方向 | 状态 |
+|------|---------|------|------|
+| v0.x | v0.1 ~ v0.3 | 价格行为探索 | 已弃用 |
+| v1.x | v1.0 ~ v1.9 | 结构流策略迭代 | 已弃用 |
+| v2.x | v2.0 ~ v2.2d | 趋势跟踪（当前主线） | **v2.2d 运行中** |
+| v3.x | v3.0 ~ v3.2 | 震荡波段（Swing） | 已验证/备用 |
+| v4.x | v4.0 ~ v4.2 | 极简震荡 | 实验 |
+| Scalp | v1.8, v2.0 | 剥头皮 | 已弃用 |
+
+## 关键教训
+
+- v1.1~v1.8 Scalp：反向S/R交易 = 逆势接飞刀（0%胜率）
+- v2.3：参数调优不是方向（创建后10分钟删除）
+- v2.2b：当前最优回测基线（+4673%/+17%最大回撤）
+- v2.2d：D1趋势总闸门 — 震荡市不下单是保护机制不是bug
+
+## 铁律
+
+1. 只增不删 — 所有历史版本保留
+2. 版本归档 — 每个版本独立 commit
+3. 回测标准化 — 复用成功配置
+4. 主任不越俎代庖 — 方案设计归主任，代码编写归交易部

ablation/ablation_1.py

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+"""
+Structure Flow Strategy v2.1
+=======================
+变更记录：
+  v1.6 (2026-06-07): 最优基线 — +3659.63%, 190笔, 69.3% trailing胜率
+  v2.0 (2026-06-08): B1 入场延迟确认 — 方向正确但降频严重
+  v2.1 (2026-06-08): ===== D1: 趋势强度过滤 =====
+                      在4H级别评估趋势强度：最近2个Swing Point的间距变化。
+                      如果趋势在扩张（HH/HL间距增大），允许入场；
+                      如果趋势在收缩（HH/HL间距缩小或震荡），过滤信号。
+                      目的：只在趋势明确时交易，避免震荡市反复止损。
+"""
+
+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 StructureFlowStrategyV21_Abl1(IStrategy):
+    """
+    Ablation Variant 1: 移除条件 1
+
+    v2.1改动（相对于v1.6）：
+      在4H级别计算趋势强度：最近2个Swing High间距 + Swing Low间距的变化。
+      只有趋势在扩张（或至少不收缩）时才允许入场。
+    """
+
+    can_short = True
+    stoploss = -0.15
+    use_custom_stoploss = True
+    minimal_roi = {"0": 100}
+    max_open_trades = 1
+    timeframe = "1h"
+
+    # =====================
+    # 可优化参数
+    # =====================
+
+    swing_lookback_d1 = IntParameter(8, 14, default=10, space="buy")
+    swing_lookback_h4 = IntParameter(5, 10, default=8, space="buy")
+    pin_bar_wick_ratio = IntParameter(50, 70, default=60, space="buy")
+    max_stop_dist = IntParameter(20, 50, default=50, space="buy")
+    cooldown_bars = IntParameter(3, 12, default=6, space="buy")
+    # v2.1 新增：趋势强度最小扩张比例（x/100 = 0%~50%）
+    # 0 = 只要不收缩就行；越大要求趋势扩张越强
+    trend_strength_min = IntParameter(-50, 20, default=-20, space="buy")  # -20=允许SP轻微收缩, 最佳值
+
+    # =====================
+    # 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)
+        in_demand_zone = np.full(n, False)
+        in_supply_zone = np.full(n, False)
+
+        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:
+                nearest_support[i] = sl_prices[-1]
+            if sh_prices:
+                nearest_resistance[i] = sh_prices[-1]
+
+            c = close.iloc[i]
+            if not np.isnan(nearest_support[i]) and not np.isnan(nearest_resistance[i]):
+                zone_range = nearest_resistance[i] - nearest_support[i]
+                if zone_range > 0:
+                    pos_pct = (c - nearest_support[i]) / zone_range
+                    in_demand_zone[i] = pos_pct < 0.35
+                    in_supply_zone[i] = pos_pct > 0.65
+
+        return DataFrame({
+            "trend_up": trend_up_arr,
+            "trend_down": trend_down_arr,
+            "support": nearest_support,
+            "resistance": nearest_resistance,
+            "in_demand": in_demand_zone,
+            "in_supply": in_supply_zone,
+        }, 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 — D1 宏观结构
+    # ================================================================
+
+    @informative("1d")
+    def populate_indicators_1d(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        sh, sl = self._detect_swing_points(
+            dataframe["high"], dataframe["low"],
+            self.swing_lookback_d1.value,
+        )
+        structure = self._build_structure(
+            dataframe["high"], dataframe["low"], dataframe["close"],
+            sh, sl,
+        )
+        dataframe["trend_up"] = structure["trend_up"]
+        dataframe["trend_down"] = structure["trend_down"]
+        return dataframe
+
+    # ================================================================
+    #  信息时间框架 — 4H 中期结构
+    # ================================================================
+
+    @informative("4h")
+    def populate_indicators_4h(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        sh, sl = self._detect_swing_points(
+            dataframe["high"], dataframe["low"],
+            self.swing_lookback_h4.value,
+        )
+        structure = self._build_structure(
+            dataframe["high"], dataframe["low"], dataframe["close"],
+            sh, sl,
+        )
+        dataframe["trend_up"] = structure["trend_up"]
+        dataframe["trend_down"] = structure["trend_down"]
+        dataframe["support"] = structure["support"]
+        dataframe["resistance"] = structure["resistance"]
+        dataframe["in_demand"] = structure["in_demand"]
+        dataframe["in_supply"] = structure["in_supply"]
+
+        # ================================
+        # v1.6 活支撑/阻力检查（保留）
+        # ================================
+        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(3, min_periods=1).max() > 0
+
+        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(3, min_periods=1).max() > 0
+
+        # ================================
+        # v2.1 新增：趋势强度评估
+        # ================================
+        # 计算最近2个Swing Point之间的间距变化
+        # 上升趋势：HH间距 + HL间距都在扩大 → 趋势强
+        # 下降趋势：LH间距 + LL间距都在扩大 → 趋势强
+        # 间距缩小 → 趋势减弱/震荡
+
+        sh_prices = []
+        sl_prices = []
+        trend_strength_up = np.full(len(dataframe), np.nan)
+        trend_strength_down = np.full(len(dataframe), np.nan)
+
+        for i in range(len(dataframe)):
+            if pd.notna(sh.iloc[i]):
+                sh_prices.append(sh.iloc[i])
+                if len(sh_prices) > 4:
+                    sh_prices.pop(0)
+            if pd.notna(sl.iloc[i]):
+                sl_prices.append(sl.iloc[i])
+                if len(sl_prices) > 4:
+                    sl_prices.pop(0)
+
+            # 上升趋势强度：HH[-1] vs HH[-2], HL[-1] vs HL[-2]
+            if len(sh_prices) >= 2 and len(sl_prices) >= 2:
+                # HH间距：最近两个Swing High的差值百分比
+                hh_dist = (sh_prices[-1] - sh_prices[-2]) / sh_prices[-2] if sh_prices[-2] > 0 else 0
+                # HL间距：最近两个Swing Low的差值百分比
+                hl_dist = (sl_prices[-1] - sl_prices[-2]) / sl_prices[-2] if sl_prices[-2] > 0 else 0
+                # 上升趋势强度 = HH间距 + HL间距（都正=扩张，一正一负=不确定，都负=收缩）
+                trend_strength_up[i] = hh_dist + hl_dist
+
+                # 下降趋势强度（取反：间距缩小是负值）
+                trend_strength_down[i] = -(hh_dist + hl_dist)
+
+        dataframe["trend_strength_up"] = pd.Series(trend_strength_up, index=dataframe.index)
+        dataframe["trend_strength_down"] = pd.Series(trend_strength_down, index=dataframe.index)
+
+        # 趋势强度是否足够（扩张中）
+        min_strength = self.trend_strength_min.value / 100.0  # 0~0.30
+        dataframe["strong_uptrend"] = dataframe["trend_strength_up"] > min_strength
+        dataframe["strong_downtrend"] = dataframe["trend_strength_down"] > min_strength
+
+        return dataframe
+
+    # ================================================================
+    #  主时间框架 — 1H 指标
+    # ================================================================
+
+    def populate_indicators(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        """1H 级别：K线形态（零指标）。"""
+        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
+        dataframe["bullish_signal"] = bullish_pin | bullish_engulf
+        dataframe["bearish_signal"] = bearish_pin | bearish_engulf
+
+        # NaN 安全处理
+        bool_cols = [
+            "trend_up_1d", "trend_down_1d",
+            "trend_up_4h", "trend_down_4h",
+            "in_demand_4h", "in_supply_4h",
+            "support_alive_4h", "resistance_alive_4h",
+            "strong_uptrend_4h", "strong_downtrend_4h",
+            "bullish_signal", "bearish_signal",
+        ]
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].fillna(False)
+
+        return dataframe
+
+    # =====================
+    # 入场信号
+    # =====================
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        入场逻辑（1H 时间框架）。
+
+        v2.1 核心改动：D1 — 趋势强度过滤
+          做多额外条件：4H上升趋势在扩张（strong_uptrend_4h）
+          做空额外条件：4H下降趋势在扩张（strong_downtrend_4h）
+        """
+        max_dist = self.max_stop_dist.value / 100.0
+        cooldown = self.cooldown_bars.value
+
+        # NaN 安全处理
+        bool_cols = [
+            "trend_up_1d", "trend_down_1d",
+            "trend_up_4h", "trend_down_4h",
+            "in_demand_4h", "in_supply_4h",
+            "support_alive_4h", "resistance_alive_4h",
+            "strong_uptrend_4h", "strong_downtrend_4h",
+            "bullish_signal", "bearish_signal",
+        ]
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].fillna(False)
+
+        # ── 做多 ──
+        long_stop_dist = (dataframe["open"] - dataframe["support_4h"]) / dataframe["open"]
+
+        long_base = (
+            dataframe["in_demand_4h"]
+            & dataframe["bullish_signal"]
+            & (long_stop_dist <= max_dist)
+            & (long_stop_dist > 0.003)
+            & dataframe["support_alive_4h"]
+            # v2.1: 趋势强度 — 4H上升趋势必须在扩张
+            & dataframe["strong_uptrend_4h"]
+        )
+
+        long_recent = long_base.rolling(cooldown, min_periods=1).max().shift(1) == 0
+        dataframe.loc[long_base & long_recent, "enter_long"] = 1
+
+        # ── 做空 ──
+        short_stop_dist = (dataframe["resistance_4h"] - dataframe["open"]) / dataframe["open"]
+
+        short_base = (
+            dataframe["in_supply_4h"]
+            & dataframe["bearish_signal"]
+            & (short_stop_dist <= max_dist)
+            & (short_stop_dist > 0.003)
+            & dataframe["resistance_alive_4h"]
+            # v2.1: 趋势强度 — 4H下降趋势必须在扩张
+            & dataframe["strong_downtrend_4h"]
+        )
+
+        short_recent = short_base.rolling(cooldown, min_periods=1).max().shift(1) == 0
+        dataframe.loc[short_base & short_recent, "enter_short"] = 1
+
+        return dataframe
+
+    # =====================
+    # 出场信号
+    # =====================
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """出场逻辑 — 由结构反转触发。"""
+        exit_long = ~dataframe["trend_up_1d"].fillna(True)
+        dataframe.loc[exit_long, "exit_long"] = 1
+
+        exit_short = dataframe["trend_up_1d"].fillna(False)
+        dataframe.loc[exit_short, "exit_short"] = 1
+
+        return dataframe
+
+    # =====================
+    # 动态止损 — 纯价格结构（基于Swing Point）
+    # =====================
+
+    def custom_stoploss(
+        self,
+        pair: str,
+        trade: Trade,
+        current_time: datetime,
+        current_rate: float,
+        current_profit: float,
+        after_fill: bool,
+        **kwargs,
+    ) -> float:
+        """
+        止损逻辑：完全基于价格结构，零指标（与v1.6相同）。
+        """
+        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
+
+        last = dataframe.iloc[-1]
+
+        if not trade.is_short:
+            support = last.get("support_4h", np.nan)
+            if pd.isna(support) or support <= 0:
+                return -0.02
+            sl_price = support * 0.999
+            sl_ratio = (sl_price / current_rate) - 1.0
+            return max(sl_ratio, -0.15)
+        else:
+            resistance = last.get("resistance_4h", np.nan)
+            if pd.isna(resistance) or resistance <= 0:
+                return 0.02
+            sl_price = resistance * 1.001
+            sl_ratio = 1.0 - (sl_price / current_rate)
+            return min(sl_ratio, 0.15)
+
+    # =====================
+    # Plot config
+    # =====================
+
+    @staticmethod
+    def plot_config() -> dict:
+        return {
+            "main_plot": {
+                "support_4h": {"color": "green", "type": "line"},
+                "resistance_4h": {"color": "red", "type": "line"},
+            },
+            "subplots": {
+                "signals": {
+                    "bullish_pinbar": {"color": "green", "type": "scatter"},
+                    "bearish_pinbar": {"color": "red", "type": "scatter"},
+                },
+                "filters": {
+                    "support_alive_4h": {"color": "green", "type": "line"},
+                    "resistance_alive_4h": {"color": "red", "type": "line"},
+                    "strong_uptrend_4h": {"color": "blue", "type": "line"},
+                    "strong_downtrend_4h": {"color": "orange", "type": "line"},
+                },
+            },
+        }

ablation/ablation_2.py

@@ -0,0 +1,454 @@
+"""
+Structure Flow Strategy v2.1
+=======================
+变更记录：
+  v1.6 (2026-06-07): 最优基线 — +3659.63%, 190笔, 69.3% trailing胜率
+  v2.0 (2026-06-08): B1 入场延迟确认 — 方向正确但降频严重
+  v2.1 (2026-06-08): ===== D1: 趋势强度过滤 =====
+                      在4H级别评估趋势强度：最近2个Swing Point的间距变化。
+                      如果趋势在扩张（HH/HL间距增大），允许入场；
+                      如果趋势在收缩（HH/HL间距缩小或震荡），过滤信号。
+                      目的：只在趋势明确时交易，避免震荡市反复止损。
+"""
+
+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 StructureFlowStrategyV21_Abl2(IStrategy):
+    """
+    Ablation Variant 2: 移除条件 2
+
+    v2.1改动（相对于v1.6）：
+      在4H级别计算趋势强度：最近2个Swing High间距 + Swing Low间距的变化。
+      只有趋势在扩张（或至少不收缩）时才允许入场。
+    """
+
+    can_short = True
+    stoploss = -0.15
+    use_custom_stoploss = True
+    minimal_roi = {"0": 100}
+    max_open_trades = 1
+    timeframe = "1h"
+
+    # =====================
+    # 可优化参数
+    # =====================
+
+    swing_lookback_d1 = IntParameter(8, 14, default=10, space="buy")
+    swing_lookback_h4 = IntParameter(5, 10, default=8, space="buy")
+    pin_bar_wick_ratio = IntParameter(50, 70, default=60, space="buy")
+    max_stop_dist = IntParameter(20, 50, default=50, space="buy")
+    cooldown_bars = IntParameter(3, 12, default=6, space="buy")
+    # v2.1 新增：趋势强度最小扩张比例（x/100 = 0%~50%）
+    # 0 = 只要不收缩就行；越大要求趋势扩张越强
+    trend_strength_min = IntParameter(-50, 20, default=-20, space="buy")  # -20=允许SP轻微收缩, 最佳值
+
+    # =====================
+    # 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)
+        in_demand_zone = np.full(n, False)
+        in_supply_zone = np.full(n, False)
+
+        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:
+                nearest_support[i] = sl_prices[-1]
+            if sh_prices:
+                nearest_resistance[i] = sh_prices[-1]
+
+            c = close.iloc[i]
+            if not np.isnan(nearest_support[i]) and not np.isnan(nearest_resistance[i]):
+                zone_range = nearest_resistance[i] - nearest_support[i]
+                if zone_range > 0:
+                    pos_pct = (c - nearest_support[i]) / zone_range
+                    in_demand_zone[i] = pos_pct < 0.35
+                    in_supply_zone[i] = pos_pct > 0.65
+
+        return DataFrame({
+            "trend_up": trend_up_arr,
+            "trend_down": trend_down_arr,
+            "support": nearest_support,
+            "resistance": nearest_resistance,
+            "in_demand": in_demand_zone,
+            "in_supply": in_supply_zone,
+        }, 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 — D1 宏观结构
+    # ================================================================
+
+    @informative("1d")
+    def populate_indicators_1d(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        sh, sl = self._detect_swing_points(
+            dataframe["high"], dataframe["low"],
+            self.swing_lookback_d1.value,
+        )
+        structure = self._build_structure(
+            dataframe["high"], dataframe["low"], dataframe["close"],
+            sh, sl,
+        )
+        dataframe["trend_up"] = structure["trend_up"]
+        dataframe["trend_down"] = structure["trend_down"]
+        return dataframe
+
+    # ================================================================
+    #  信息时间框架 — 4H 中期结构
+    # ================================================================
+
+    @informative("4h")
+    def populate_indicators_4h(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        sh, sl = self._detect_swing_points(
+            dataframe["high"], dataframe["low"],
+            self.swing_lookback_h4.value,
+        )
+        structure = self._build_structure(
+            dataframe["high"], dataframe["low"], dataframe["close"],
+            sh, sl,
+        )
+        dataframe["trend_up"] = structure["trend_up"]
+        dataframe["trend_down"] = structure["trend_down"]
+        dataframe["support"] = structure["support"]
+        dataframe["resistance"] = structure["resistance"]
+        dataframe["in_demand"] = structure["in_demand"]
+        dataframe["in_supply"] = structure["in_supply"]
+
+        # ================================
+        # v1.6 活支撑/阻力检查（保留）
+        # ================================
+        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(3, min_periods=1).max() > 0
+
+        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(3, min_periods=1).max() > 0
+
+        # ================================
+        # v2.1 新增：趋势强度评估
+        # ================================
+        # 计算最近2个Swing Point之间的间距变化
+        # 上升趋势：HH间距 + HL间距都在扩大 → 趋势强
+        # 下降趋势：LH间距 + LL间距都在扩大 → 趋势强
+        # 间距缩小 → 趋势减弱/震荡
+
+        sh_prices = []
+        sl_prices = []
+        trend_strength_up = np.full(len(dataframe), np.nan)
+        trend_strength_down = np.full(len(dataframe), np.nan)
+
+        for i in range(len(dataframe)):
+            if pd.notna(sh.iloc[i]):
+                sh_prices.append(sh.iloc[i])
+                if len(sh_prices) > 4:
+                    sh_prices.pop(0)
+            if pd.notna(sl.iloc[i]):
+                sl_prices.append(sl.iloc[i])
+                if len(sl_prices) > 4:
+                    sl_prices.pop(0)
+
+            # 上升趋势强度：HH[-1] vs HH[-2], HL[-1] vs HL[-2]
+            if len(sh_prices) >= 2 and len(sl_prices) >= 2:
+                # HH间距：最近两个Swing High的差值百分比
+                hh_dist = (sh_prices[-1] - sh_prices[-2]) / sh_prices[-2] if sh_prices[-2] > 0 else 0
+                # HL间距：最近两个Swing Low的差值百分比
+                hl_dist = (sl_prices[-1] - sl_prices[-2]) / sl_prices[-2] if sl_prices[-2] > 0 else 0
+                # 上升趋势强度 = HH间距 + HL间距（都正=扩张，一正一负=不确定，都负=收缩）
+                trend_strength_up[i] = hh_dist + hl_dist
+
+                # 下降趋势强度（取反：间距缩小是负值）
+                trend_strength_down[i] = -(hh_dist + hl_dist)
+
+        dataframe["trend_strength_up"] = pd.Series(trend_strength_up, index=dataframe.index)
+        dataframe["trend_strength_down"] = pd.Series(trend_strength_down, index=dataframe.index)
+
+        # 趋势强度是否足够（扩张中）
+        min_strength = self.trend_strength_min.value / 100.0  # 0~0.30
+        dataframe["strong_uptrend"] = dataframe["trend_strength_up"] > min_strength
+        dataframe["strong_downtrend"] = dataframe["trend_strength_down"] > min_strength
+
+        return dataframe
+
+    # ================================================================
+    #  主时间框架 — 1H 指标
+    # ================================================================
+
+    def populate_indicators(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        """1H 级别：K线形态（零指标）。"""
+        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
+        dataframe["bullish_signal"] = bullish_pin | bullish_engulf
+        dataframe["bearish_signal"] = bearish_pin | bearish_engulf
+
+        # NaN 安全处理
+        bool_cols = [
+            "trend_up_1d", "trend_down_1d",
+            "trend_up_4h", "trend_down_4h",
+            "in_demand_4h", "in_supply_4h",
+            "support_alive_4h", "resistance_alive_4h",
+            "strong_uptrend_4h", "strong_downtrend_4h",
+            "bullish_signal", "bearish_signal",
+        ]
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].fillna(False)
+
+        return dataframe
+
+    # =====================
+    # 入场信号
+    # =====================
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        入场逻辑（1H 时间框架）。
+
+        v2.1 核心改动：D1 — 趋势强度过滤
+          做多额外条件：4H上升趋势在扩张（strong_uptrend_4h）
+          做空额外条件：4H下降趋势在扩张（strong_downtrend_4h）
+        """
+        max_dist = self.max_stop_dist.value / 100.0
+        cooldown = self.cooldown_bars.value
+
+        # NaN 安全处理
+        bool_cols = [
+            "trend_up_1d", "trend_down_1d",
+            "trend_up_4h", "trend_down_4h",
+            "in_demand_4h", "in_supply_4h",
+            "support_alive_4h", "resistance_alive_4h",
+            "strong_uptrend_4h", "strong_downtrend_4h",
+            "bullish_signal", "bearish_signal",
+        ]
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].fillna(False)
+
+        # ── 做多 ──
+        long_stop_dist = (dataframe["open"] - dataframe["support_4h"]) / dataframe["open"]
+
+        long_base = (
+            dataframe["trend_up_1d"]
+            & dataframe["bullish_signal"]
+            & (long_stop_dist <= max_dist)
+            & (long_stop_dist > 0.003)
+            & dataframe["support_alive_4h"]
+            # v2.1: 趋势强度 — 4H上升趋势必须在扩张
+            & dataframe["strong_uptrend_4h"]
+        )
+
+        long_recent = long_base.rolling(cooldown, min_periods=1).max().shift(1) == 0
+        dataframe.loc[long_base & long_recent, "enter_long"] = 1
+
+        # ── 做空 ──
+        short_stop_dist = (dataframe["resistance_4h"] - dataframe["open"]) / dataframe["open"]
+
+        short_base = (
+            dataframe["trend_down_1d"]
+            & dataframe["bearish_signal"]
+            & (short_stop_dist <= max_dist)
+            & (short_stop_dist > 0.003)
+            & dataframe["resistance_alive_4h"]
+            # v2.1: 趋势强度 — 4H下降趋势必须在扩张
+            & dataframe["strong_downtrend_4h"]
+        )
+
+        short_recent = short_base.rolling(cooldown, min_periods=1).max().shift(1) == 0
+        dataframe.loc[short_base & short_recent, "enter_short"] = 1
+
+        return dataframe
+
+    # =====================
+    # 出场信号
+    # =====================
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """出场逻辑 — 由结构反转触发。"""
+        exit_long = ~dataframe["trend_up_1d"].fillna(True)
+        dataframe.loc[exit_long, "exit_long"] = 1
+
+        exit_short = dataframe["trend_up_1d"].fillna(False)
+        dataframe.loc[exit_short, "exit_short"] = 1
+
+        return dataframe
+
+    # =====================
+    # 动态止损 — 纯价格结构（基于Swing Point）
+    # =====================
+
+    def custom_stoploss(
+        self,
+        pair: str,
+        trade: Trade,
+        current_time: datetime,
+        current_rate: float,
+        current_profit: float,
+        after_fill: bool,
+        **kwargs,
+    ) -> float:
+        """
+        止损逻辑：完全基于价格结构，零指标（与v1.6相同）。
+        """
+        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
+
+        last = dataframe.iloc[-1]
+
+        if not trade.is_short:
+            support = last.get("support_4h", np.nan)
+            if pd.isna(support) or support <= 0:
+                return -0.02
+            sl_price = support * 0.999
+            sl_ratio = (sl_price / current_rate) - 1.0
+            return max(sl_ratio, -0.15)
+        else:
+            resistance = last.get("resistance_4h", np.nan)
+            if pd.isna(resistance) or resistance <= 0:
+                return 0.02
+            sl_price = resistance * 1.001
+            sl_ratio = 1.0 - (sl_price / current_rate)
+            return min(sl_ratio, 0.15)
+
+    # =====================
+    # Plot config
+    # =====================
+
+    @staticmethod
+    def plot_config() -> dict:
+        return {
+            "main_plot": {
+                "support_4h": {"color": "green", "type": "line"},
+                "resistance_4h": {"color": "red", "type": "line"},
+            },
+            "subplots": {
+                "signals": {
+                    "bullish_pinbar": {"color": "green", "type": "scatter"},
+                    "bearish_pinbar": {"color": "red", "type": "scatter"},
+                },
+                "filters": {
+                    "support_alive_4h": {"color": "green", "type": "line"},
+                    "resistance_alive_4h": {"color": "red", "type": "line"},
+                    "strong_uptrend_4h": {"color": "blue", "type": "line"},
+                    "strong_downtrend_4h": {"color": "orange", "type": "line"},
+                },
+            },
+        }

ablation/ablation_3.py

@@ -0,0 +1,454 @@
+"""
+Structure Flow Strategy v2.1
+=======================
+变更记录：
+  v1.6 (2026-06-07): 最优基线 — +3659.63%, 190笔, 69.3% trailing胜率
+  v2.0 (2026-06-08): B1 入场延迟确认 — 方向正确但降频严重
+  v2.1 (2026-06-08): ===== D1: 趋势强度过滤 =====
+                      在4H级别评估趋势强度：最近2个Swing Point的间距变化。
+                      如果趋势在扩张（HH/HL间距增大），允许入场；
+                      如果趋势在收缩（HH/HL间距缩小或震荡），过滤信号。
+                      目的：只在趋势明确时交易，避免震荡市反复止损。
+"""
+
+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 StructureFlowStrategyV21_Abl3(IStrategy):
+    """
+    Ablation Variant 3: 移除条件 3
+
+    v2.1改动（相对于v1.6）：
+      在4H级别计算趋势强度：最近2个Swing High间距 + Swing Low间距的变化。
+      只有趋势在扩张（或至少不收缩）时才允许入场。
+    """
+
+    can_short = True
+    stoploss = -0.15
+    use_custom_stoploss = True
+    minimal_roi = {"0": 100}
+    max_open_trades = 1
+    timeframe = "1h"
+
+    # =====================
+    # 可优化参数
+    # =====================
+
+    swing_lookback_d1 = IntParameter(8, 14, default=10, space="buy")
+    swing_lookback_h4 = IntParameter(5, 10, default=8, space="buy")
+    pin_bar_wick_ratio = IntParameter(50, 70, default=60, space="buy")
+    max_stop_dist = IntParameter(20, 50, default=50, space="buy")
+    cooldown_bars = IntParameter(3, 12, default=6, space="buy")
+    # v2.1 新增：趋势强度最小扩张比例（x/100 = 0%~50%）
+    # 0 = 只要不收缩就行；越大要求趋势扩张越强
+    trend_strength_min = IntParameter(-50, 20, default=-20, space="buy")  # -20=允许SP轻微收缩, 最佳值
+
+    # =====================
+    # 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)
+        in_demand_zone = np.full(n, False)
+        in_supply_zone = np.full(n, False)
+
+        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:
+                nearest_support[i] = sl_prices[-1]
+            if sh_prices:
+                nearest_resistance[i] = sh_prices[-1]
+
+            c = close.iloc[i]
+            if not np.isnan(nearest_support[i]) and not np.isnan(nearest_resistance[i]):
+                zone_range = nearest_resistance[i] - nearest_support[i]
+                if zone_range > 0:
+                    pos_pct = (c - nearest_support[i]) / zone_range
+                    in_demand_zone[i] = pos_pct < 0.35
+                    in_supply_zone[i] = pos_pct > 0.65
+
+        return DataFrame({
+            "trend_up": trend_up_arr,
+            "trend_down": trend_down_arr,
+            "support": nearest_support,
+            "resistance": nearest_resistance,
+            "in_demand": in_demand_zone,
+            "in_supply": in_supply_zone,
+        }, 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 — D1 宏观结构
+    # ================================================================
+
+    @informative("1d")
+    def populate_indicators_1d(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        sh, sl = self._detect_swing_points(
+            dataframe["high"], dataframe["low"],
+            self.swing_lookback_d1.value,
+        )
+        structure = self._build_structure(
+            dataframe["high"], dataframe["low"], dataframe["close"],
+            sh, sl,
+        )
+        dataframe["trend_up"] = structure["trend_up"]
+        dataframe["trend_down"] = structure["trend_down"]
+        return dataframe
+
+    # ================================================================
+    #  信息时间框架 — 4H 中期结构
+    # ================================================================
+
+    @informative("4h")
+    def populate_indicators_4h(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        sh, sl = self._detect_swing_points(
+            dataframe["high"], dataframe["low"],
+            self.swing_lookback_h4.value,
+        )
+        structure = self._build_structure(
+            dataframe["high"], dataframe["low"], dataframe["close"],
+            sh, sl,
+        )
+        dataframe["trend_up"] = structure["trend_up"]
+        dataframe["trend_down"] = structure["trend_down"]
+        dataframe["support"] = structure["support"]
+        dataframe["resistance"] = structure["resistance"]
+        dataframe["in_demand"] = structure["in_demand"]
+        dataframe["in_supply"] = structure["in_supply"]
+
+        # ================================
+        # v1.6 活支撑/阻力检查（保留）
+        # ================================
+        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(3, min_periods=1).max() > 0
+
+        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(3, min_periods=1).max() > 0
+
+        # ================================
+        # v2.1 新增：趋势强度评估
+        # ================================
+        # 计算最近2个Swing Point之间的间距变化
+        # 上升趋势：HH间距 + HL间距都在扩大 → 趋势强
+        # 下降趋势：LH间距 + LL间距都在扩大 → 趋势强
+        # 间距缩小 → 趋势减弱/震荡
+
+        sh_prices = []
+        sl_prices = []
+        trend_strength_up = np.full(len(dataframe), np.nan)
+        trend_strength_down = np.full(len(dataframe), np.nan)
+
+        for i in range(len(dataframe)):
+            if pd.notna(sh.iloc[i]):
+                sh_prices.append(sh.iloc[i])
+                if len(sh_prices) > 4:
+                    sh_prices.pop(0)
+            if pd.notna(sl.iloc[i]):
+                sl_prices.append(sl.iloc[i])
+                if len(sl_prices) > 4:
+                    sl_prices.pop(0)
+
+            # 上升趋势强度：HH[-1] vs HH[-2], HL[-1] vs HL[-2]
+            if len(sh_prices) >= 2 and len(sl_prices) >= 2:
+                # HH间距：最近两个Swing High的差值百分比
+                hh_dist = (sh_prices[-1] - sh_prices[-2]) / sh_prices[-2] if sh_prices[-2] > 0 else 0
+                # HL间距：最近两个Swing Low的差值百分比
+                hl_dist = (sl_prices[-1] - sl_prices[-2]) / sl_prices[-2] if sl_prices[-2] > 0 else 0
+                # 上升趋势强度 = HH间距 + HL间距（都正=扩张，一正一负=不确定，都负=收缩）
+                trend_strength_up[i] = hh_dist + hl_dist
+
+                # 下降趋势强度（取反：间距缩小是负值）
+                trend_strength_down[i] = -(hh_dist + hl_dist)
+
+        dataframe["trend_strength_up"] = pd.Series(trend_strength_up, index=dataframe.index)
+        dataframe["trend_strength_down"] = pd.Series(trend_strength_down, index=dataframe.index)
+
+        # 趋势强度是否足够（扩张中）
+        min_strength = self.trend_strength_min.value / 100.0  # 0~0.30
+        dataframe["strong_uptrend"] = dataframe["trend_strength_up"] > min_strength
+        dataframe["strong_downtrend"] = dataframe["trend_strength_down"] > min_strength
+
+        return dataframe
+
+    # ================================================================
+    #  主时间框架 — 1H 指标
+    # ================================================================
+
+    def populate_indicators(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        """1H 级别：K线形态（零指标）。"""
+        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
+        dataframe["bullish_signal"] = bullish_pin | bullish_engulf
+        dataframe["bearish_signal"] = bearish_pin | bearish_engulf
+
+        # NaN 安全处理
+        bool_cols = [
+            "trend_up_1d", "trend_down_1d",
+            "trend_up_4h", "trend_down_4h",
+            "in_demand_4h", "in_supply_4h",
+            "support_alive_4h", "resistance_alive_4h",
+            "strong_uptrend_4h", "strong_downtrend_4h",
+            "bullish_signal", "bearish_signal",
+        ]
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].fillna(False)
+
+        return dataframe
+
+    # =====================
+    # 入场信号
+    # =====================
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        入场逻辑（1H 时间框架）。
+
+        v2.1 核心改动：D1 — 趋势强度过滤
+          做多额外条件：4H上升趋势在扩张（strong_uptrend_4h）
+          做空额外条件：4H下降趋势在扩张（strong_downtrend_4h）
+        """
+        max_dist = self.max_stop_dist.value / 100.0
+        cooldown = self.cooldown_bars.value
+
+        # NaN 安全处理
+        bool_cols = [
+            "trend_up_1d", "trend_down_1d",
+            "trend_up_4h", "trend_down_4h",
+            "in_demand_4h", "in_supply_4h",
+            "support_alive_4h", "resistance_alive_4h",
+            "strong_uptrend_4h", "strong_downtrend_4h",
+            "bullish_signal", "bearish_signal",
+        ]
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].fillna(False)
+
+        # ── 做多 ──
+        long_stop_dist = (dataframe["open"] - dataframe["support_4h"]) / dataframe["open"]
+
+        long_base = (
+            dataframe["trend_up_1d"]
+            & dataframe["in_demand_4h"]
+            & (long_stop_dist <= max_dist)
+            & (long_stop_dist > 0.003)
+            & dataframe["support_alive_4h"]
+            # v2.1: 趋势强度 — 4H上升趋势必须在扩张
+            & dataframe["strong_uptrend_4h"]
+        )
+
+        long_recent = long_base.rolling(cooldown, min_periods=1).max().shift(1) == 0
+        dataframe.loc[long_base & long_recent, "enter_long"] = 1
+
+        # ── 做空 ──
+        short_stop_dist = (dataframe["resistance_4h"] - dataframe["open"]) / dataframe["open"]
+
+        short_base = (
+            dataframe["trend_down_1d"]
+            & dataframe["in_supply_4h"]
+            & (short_stop_dist <= max_dist)
+            & (short_stop_dist > 0.003)
+            & dataframe["resistance_alive_4h"]
+            # v2.1: 趋势强度 — 4H下降趋势必须在扩张
+            & dataframe["strong_downtrend_4h"]
+        )
+
+        short_recent = short_base.rolling(cooldown, min_periods=1).max().shift(1) == 0
+        dataframe.loc[short_base & short_recent, "enter_short"] = 1
+
+        return dataframe
+
+    # =====================
+    # 出场信号
+    # =====================
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """出场逻辑 — 由结构反转触发。"""
+        exit_long = ~dataframe["trend_up_1d"].fillna(True)
+        dataframe.loc[exit_long, "exit_long"] = 1
+
+        exit_short = dataframe["trend_up_1d"].fillna(False)
+        dataframe.loc[exit_short, "exit_short"] = 1
+
+        return dataframe
+
+    # =====================
+    # 动态止损 — 纯价格结构（基于Swing Point）
+    # =====================
+
+    def custom_stoploss(
+        self,
+        pair: str,
+        trade: Trade,
+        current_time: datetime,
+        current_rate: float,
+        current_profit: float,
+        after_fill: bool,
+        **kwargs,
+    ) -> float:
+        """
+        止损逻辑：完全基于价格结构，零指标（与v1.6相同）。
+        """
+        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
+
+        last = dataframe.iloc[-1]
+
+        if not trade.is_short:
+            support = last.get("support_4h", np.nan)
+            if pd.isna(support) or support <= 0:
+                return -0.02
+            sl_price = support * 0.999
+            sl_ratio = (sl_price / current_rate) - 1.0
+            return max(sl_ratio, -0.15)
+        else:
+            resistance = last.get("resistance_4h", np.nan)
+            if pd.isna(resistance) or resistance <= 0:
+                return 0.02
+            sl_price = resistance * 1.001
+            sl_ratio = 1.0 - (sl_price / current_rate)
+            return min(sl_ratio, 0.15)
+
+    # =====================
+    # Plot config
+    # =====================
+
+    @staticmethod
+    def plot_config() -> dict:
+        return {
+            "main_plot": {
+                "support_4h": {"color": "green", "type": "line"},
+                "resistance_4h": {"color": "red", "type": "line"},
+            },
+            "subplots": {
+                "signals": {
+                    "bullish_pinbar": {"color": "green", "type": "scatter"},
+                    "bearish_pinbar": {"color": "red", "type": "scatter"},
+                },
+                "filters": {
+                    "support_alive_4h": {"color": "green", "type": "line"},
+                    "resistance_alive_4h": {"color": "red", "type": "line"},
+                    "strong_uptrend_4h": {"color": "blue", "type": "line"},
+                    "strong_downtrend_4h": {"color": "orange", "type": "line"},
+                },
+            },
+        }

ablation/ablation_4.py

@@ -0,0 +1,454 @@
+"""
+Structure Flow Strategy v2.1
+=======================
+变更记录：
+  v1.6 (2026-06-07): 最优基线 — +3659.63%, 190笔, 69.3% trailing胜率
+  v2.0 (2026-06-08): B1 入场延迟确认 — 方向正确但降频严重
+  v2.1 (2026-06-08): ===== D1: 趋势强度过滤 =====
+                      在4H级别评估趋势强度：最近2个Swing Point的间距变化。
+                      如果趋势在扩张（HH/HL间距增大），允许入场；
+                      如果趋势在收缩（HH/HL间距缩小或震荡），过滤信号。
+                      目的：只在趋势明确时交易，避免震荡市反复止损。
+"""
+
+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 StructureFlowStrategyV21_Abl4(IStrategy):
+    """
+    Ablation Variant 4: 移除条件 4
+
+    v2.1改动（相对于v1.6）：
+      在4H级别计算趋势强度：最近2个Swing High间距 + Swing Low间距的变化。
+      只有趋势在扩张（或至少不收缩）时才允许入场。
+    """
+
+    can_short = True
+    stoploss = -0.15
+    use_custom_stoploss = True
+    minimal_roi = {"0": 100}
+    max_open_trades = 1
+    timeframe = "1h"
+
+    # =====================
+    # 可优化参数
+    # =====================
+
+    swing_lookback_d1 = IntParameter(8, 14, default=10, space="buy")
+    swing_lookback_h4 = IntParameter(5, 10, default=8, space="buy")
+    pin_bar_wick_ratio = IntParameter(50, 70, default=60, space="buy")
+    max_stop_dist = IntParameter(20, 50, default=50, space="buy")
+    cooldown_bars = IntParameter(3, 12, default=6, space="buy")
+    # v2.1 新增：趋势强度最小扩张比例（x/100 = 0%~50%）
+    # 0 = 只要不收缩就行；越大要求趋势扩张越强
+    trend_strength_min = IntParameter(-50, 20, default=-20, space="buy")  # -20=允许SP轻微收缩, 最佳值
+
+    # =====================
+    # 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)
+        in_demand_zone = np.full(n, False)
+        in_supply_zone = np.full(n, False)
+
+        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:
+                nearest_support[i] = sl_prices[-1]
+            if sh_prices:
+                nearest_resistance[i] = sh_prices[-1]
+
+            c = close.iloc[i]
+            if not np.isnan(nearest_support[i]) and not np.isnan(nearest_resistance[i]):
+                zone_range = nearest_resistance[i] - nearest_support[i]
+                if zone_range > 0:
+                    pos_pct = (c - nearest_support[i]) / zone_range
+                    in_demand_zone[i] = pos_pct < 0.35
+                    in_supply_zone[i] = pos_pct > 0.65
+
+        return DataFrame({
+            "trend_up": trend_up_arr,
+            "trend_down": trend_down_arr,
+            "support": nearest_support,
+            "resistance": nearest_resistance,
+            "in_demand": in_demand_zone,
+            "in_supply": in_supply_zone,
+        }, 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 — D1 宏观结构
+    # ================================================================
+
+    @informative("1d")
+    def populate_indicators_1d(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        sh, sl = self._detect_swing_points(
+            dataframe["high"], dataframe["low"],
+            self.swing_lookback_d1.value,
+        )
+        structure = self._build_structure(
+            dataframe["high"], dataframe["low"], dataframe["close"],
+            sh, sl,
+        )
+        dataframe["trend_up"] = structure["trend_up"]
+        dataframe["trend_down"] = structure["trend_down"]
+        return dataframe
+
+    # ================================================================
+    #  信息时间框架 — 4H 中期结构
+    # ================================================================
+
+    @informative("4h")
+    def populate_indicators_4h(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        sh, sl = self._detect_swing_points(
+            dataframe["high"], dataframe["low"],
+            self.swing_lookback_h4.value,
+        )
+        structure = self._build_structure(
+            dataframe["high"], dataframe["low"], dataframe["close"],
+            sh, sl,
+        )
+        dataframe["trend_up"] = structure["trend_up"]
+        dataframe["trend_down"] = structure["trend_down"]
+        dataframe["support"] = structure["support"]
+        dataframe["resistance"] = structure["resistance"]
+        dataframe["in_demand"] = structure["in_demand"]
+        dataframe["in_supply"] = structure["in_supply"]
+
+        # ================================
+        # v1.6 活支撑/阻力检查（保留）
+        # ================================
+        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(3, min_periods=1).max() > 0
+
+        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(3, min_periods=1).max() > 0
+
+        # ================================
+        # v2.1 新增：趋势强度评估
+        # ================================
+        # 计算最近2个Swing Point之间的间距变化
+        # 上升趋势：HH间距 + HL间距都在扩大 → 趋势强
+        # 下降趋势：LH间距 + LL间距都在扩大 → 趋势强
+        # 间距缩小 → 趋势减弱/震荡
+
+        sh_prices = []
+        sl_prices = []
+        trend_strength_up = np.full(len(dataframe), np.nan)
+        trend_strength_down = np.full(len(dataframe), np.nan)
+
+        for i in range(len(dataframe)):
+            if pd.notna(sh.iloc[i]):
+                sh_prices.append(sh.iloc[i])
+                if len(sh_prices) > 4:
+                    sh_prices.pop(0)
+            if pd.notna(sl.iloc[i]):
+                sl_prices.append(sl.iloc[i])
+                if len(sl_prices) > 4:
+                    sl_prices.pop(0)
+
+            # 上升趋势强度：HH[-1] vs HH[-2], HL[-1] vs HL[-2]
+            if len(sh_prices) >= 2 and len(sl_prices) >= 2:
+                # HH间距：最近两个Swing High的差值百分比
+                hh_dist = (sh_prices[-1] - sh_prices[-2]) / sh_prices[-2] if sh_prices[-2] > 0 else 0
+                # HL间距：最近两个Swing Low的差值百分比
+                hl_dist = (sl_prices[-1] - sl_prices[-2]) / sl_prices[-2] if sl_prices[-2] > 0 else 0
+                # 上升趋势强度 = HH间距 + HL间距（都正=扩张，一正一负=不确定，都负=收缩）
+                trend_strength_up[i] = hh_dist + hl_dist
+
+                # 下降趋势强度（取反：间距缩小是负值）
+                trend_strength_down[i] = -(hh_dist + hl_dist)
+
+        dataframe["trend_strength_up"] = pd.Series(trend_strength_up, index=dataframe.index)
+        dataframe["trend_strength_down"] = pd.Series(trend_strength_down, index=dataframe.index)
+
+        # 趋势强度是否足够（扩张中）
+        min_strength = self.trend_strength_min.value / 100.0  # 0~0.30
+        dataframe["strong_uptrend"] = dataframe["trend_strength_up"] > min_strength
+        dataframe["strong_downtrend"] = dataframe["trend_strength_down"] > min_strength
+
+        return dataframe
+
+    # ================================================================
+    #  主时间框架 — 1H 指标
+    # ================================================================
+
+    def populate_indicators(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        """1H 级别：K线形态（零指标）。"""
+        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
+        dataframe["bullish_signal"] = bullish_pin | bullish_engulf
+        dataframe["bearish_signal"] = bearish_pin | bearish_engulf
+
+        # NaN 安全处理
+        bool_cols = [
+            "trend_up_1d", "trend_down_1d",
+            "trend_up_4h", "trend_down_4h",
+            "in_demand_4h", "in_supply_4h",
+            "support_alive_4h", "resistance_alive_4h",
+            "strong_uptrend_4h", "strong_downtrend_4h",
+            "bullish_signal", "bearish_signal",
+        ]
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].fillna(False)
+
+        return dataframe
+
+    # =====================
+    # 入场信号
+    # =====================
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        入场逻辑（1H 时间框架）。
+
+        v2.1 核心改动：D1 — 趋势强度过滤
+          做多额外条件：4H上升趋势在扩张（strong_uptrend_4h）
+          做空额外条件：4H下降趋势在扩张（strong_downtrend_4h）
+        """
+        max_dist = self.max_stop_dist.value / 100.0
+        cooldown = self.cooldown_bars.value
+
+        # NaN 安全处理
+        bool_cols = [
+            "trend_up_1d", "trend_down_1d",
+            "trend_up_4h", "trend_down_4h",
+            "in_demand_4h", "in_supply_4h",
+            "support_alive_4h", "resistance_alive_4h",
+            "strong_uptrend_4h", "strong_downtrend_4h",
+            "bullish_signal", "bearish_signal",
+        ]
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].fillna(False)
+
+        # ── 做多 ──
+        long_stop_dist = (dataframe["open"] - dataframe["support_4h"]) / dataframe["open"]
+
+        long_base = (
+            dataframe["trend_up_1d"]
+            & dataframe["in_demand_4h"]
+            & dataframe["bullish_signal"]
+            & (long_stop_dist > 0.003)
+            & dataframe["support_alive_4h"]
+            # v2.1: 趋势强度 — 4H上升趋势必须在扩张
+            & dataframe["strong_uptrend_4h"]
+        )
+
+        long_recent = long_base.rolling(cooldown, min_periods=1).max().shift(1) == 0
+        dataframe.loc[long_base & long_recent, "enter_long"] = 1
+
+        # ── 做空 ──
+        short_stop_dist = (dataframe["resistance_4h"] - dataframe["open"]) / dataframe["open"]
+
+        short_base = (
+            dataframe["trend_down_1d"]
+            & dataframe["in_supply_4h"]
+            & dataframe["bearish_signal"]
+            & (short_stop_dist > 0.003)
+            & dataframe["resistance_alive_4h"]
+            # v2.1: 趋势强度 — 4H下降趋势必须在扩张
+            & dataframe["strong_downtrend_4h"]
+        )
+
+        short_recent = short_base.rolling(cooldown, min_periods=1).max().shift(1) == 0
+        dataframe.loc[short_base & short_recent, "enter_short"] = 1
+
+        return dataframe
+
+    # =====================
+    # 出场信号
+    # =====================
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """出场逻辑 — 由结构反转触发。"""
+        exit_long = ~dataframe["trend_up_1d"].fillna(True)
+        dataframe.loc[exit_long, "exit_long"] = 1
+
+        exit_short = dataframe["trend_up_1d"].fillna(False)
+        dataframe.loc[exit_short, "exit_short"] = 1
+
+        return dataframe
+
+    # =====================
+    # 动态止损 — 纯价格结构（基于Swing Point）
+    # =====================
+
+    def custom_stoploss(
+        self,
+        pair: str,
+        trade: Trade,
+        current_time: datetime,
+        current_rate: float,
+        current_profit: float,
+        after_fill: bool,
+        **kwargs,
+    ) -> float:
+        """
+        止损逻辑：完全基于价格结构，零指标（与v1.6相同）。
+        """
+        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
+
+        last = dataframe.iloc[-1]
+
+        if not trade.is_short:
+            support = last.get("support_4h", np.nan)
+            if pd.isna(support) or support <= 0:
+                return -0.02
+            sl_price = support * 0.999
+            sl_ratio = (sl_price / current_rate) - 1.0
+            return max(sl_ratio, -0.15)
+        else:
+            resistance = last.get("resistance_4h", np.nan)
+            if pd.isna(resistance) or resistance <= 0:
+                return 0.02
+            sl_price = resistance * 1.001
+            sl_ratio = 1.0 - (sl_price / current_rate)
+            return min(sl_ratio, 0.15)
+
+    # =====================
+    # Plot config
+    # =====================
+
+    @staticmethod
+    def plot_config() -> dict:
+        return {
+            "main_plot": {
+                "support_4h": {"color": "green", "type": "line"},
+                "resistance_4h": {"color": "red", "type": "line"},
+            },
+            "subplots": {
+                "signals": {
+                    "bullish_pinbar": {"color": "green", "type": "scatter"},
+                    "bearish_pinbar": {"color": "red", "type": "scatter"},
+                },
+                "filters": {
+                    "support_alive_4h": {"color": "green", "type": "line"},
+                    "resistance_alive_4h": {"color": "red", "type": "line"},
+                    "strong_uptrend_4h": {"color": "blue", "type": "line"},
+                    "strong_downtrend_4h": {"color": "orange", "type": "line"},
+                },
+            },
+        }

ablation/ablation_5.py

@@ -0,0 +1,454 @@
+"""
+Structure Flow Strategy v2.1
+=======================
+变更记录：
+  v1.6 (2026-06-07): 最优基线 — +3659.63%, 190笔, 69.3% trailing胜率
+  v2.0 (2026-06-08): B1 入场延迟确认 — 方向正确但降频严重
+  v2.1 (2026-06-08): ===== D1: 趋势强度过滤 =====
+                      在4H级别评估趋势强度：最近2个Swing Point的间距变化。
+                      如果趋势在扩张（HH/HL间距增大），允许入场；
+                      如果趋势在收缩（HH/HL间距缩小或震荡），过滤信号。
+                      目的：只在趋势明确时交易，避免震荡市反复止损。
+"""
+
+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 StructureFlowStrategyV21_Abl5(IStrategy):
+    """
+    Ablation Variant 5: 移除条件 5
+
+    v2.1改动（相对于v1.6）：
+      在4H级别计算趋势强度：最近2个Swing High间距 + Swing Low间距的变化。
+      只有趋势在扩张（或至少不收缩）时才允许入场。
+    """
+
+    can_short = True
+    stoploss = -0.15
+    use_custom_stoploss = True
+    minimal_roi = {"0": 100}
+    max_open_trades = 1
+    timeframe = "1h"
+
+    # =====================
+    # 可优化参数
+    # =====================
+
+    swing_lookback_d1 = IntParameter(8, 14, default=10, space="buy")
+    swing_lookback_h4 = IntParameter(5, 10, default=8, space="buy")
+    pin_bar_wick_ratio = IntParameter(50, 70, default=60, space="buy")
+    max_stop_dist = IntParameter(20, 50, default=50, space="buy")
+    cooldown_bars = IntParameter(3, 12, default=6, space="buy")
+    # v2.1 新增：趋势强度最小扩张比例（x/100 = 0%~50%）
+    # 0 = 只要不收缩就行；越大要求趋势扩张越强
+    trend_strength_min = IntParameter(-50, 20, default=-20, space="buy")  # -20=允许SP轻微收缩, 最佳值
+
+    # =====================
+    # 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)
+        in_demand_zone = np.full(n, False)
+        in_supply_zone = np.full(n, False)
+
+        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:
+                nearest_support[i] = sl_prices[-1]
+            if sh_prices:
+                nearest_resistance[i] = sh_prices[-1]
+
+            c = close.iloc[i]
+            if not np.isnan(nearest_support[i]) and not np.isnan(nearest_resistance[i]):
+                zone_range = nearest_resistance[i] - nearest_support[i]
+                if zone_range > 0:
+                    pos_pct = (c - nearest_support[i]) / zone_range
+                    in_demand_zone[i] = pos_pct < 0.35
+                    in_supply_zone[i] = pos_pct > 0.65
+
+        return DataFrame({
+            "trend_up": trend_up_arr,
+            "trend_down": trend_down_arr,
+            "support": nearest_support,
+            "resistance": nearest_resistance,
+            "in_demand": in_demand_zone,
+            "in_supply": in_supply_zone,
+        }, 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 — D1 宏观结构
+    # ================================================================
+
+    @informative("1d")
+    def populate_indicators_1d(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        sh, sl = self._detect_swing_points(
+            dataframe["high"], dataframe["low"],
+            self.swing_lookback_d1.value,
+        )
+        structure = self._build_structure(
+            dataframe["high"], dataframe["low"], dataframe["close"],
+            sh, sl,
+        )
+        dataframe["trend_up"] = structure["trend_up"]
+        dataframe["trend_down"] = structure["trend_down"]
+        return dataframe
+
+    # ================================================================
+    #  信息时间框架 — 4H 中期结构
+    # ================================================================
+
+    @informative("4h")
+    def populate_indicators_4h(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        sh, sl = self._detect_swing_points(
+            dataframe["high"], dataframe["low"],
+            self.swing_lookback_h4.value,
+        )
+        structure = self._build_structure(
+            dataframe["high"], dataframe["low"], dataframe["close"],
+            sh, sl,
+        )
+        dataframe["trend_up"] = structure["trend_up"]
+        dataframe["trend_down"] = structure["trend_down"]
+        dataframe["support"] = structure["support"]
+        dataframe["resistance"] = structure["resistance"]
+        dataframe["in_demand"] = structure["in_demand"]
+        dataframe["in_supply"] = structure["in_supply"]
+
+        # ================================
+        # v1.6 活支撑/阻力检查（保留）
+        # ================================
+        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(3, min_periods=1).max() > 0
+
+        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(3, min_periods=1).max() > 0
+
+        # ================================
+        # v2.1 新增：趋势强度评估
+        # ================================
+        # 计算最近2个Swing Point之间的间距变化
+        # 上升趋势：HH间距 + HL间距都在扩大 → 趋势强
+        # 下降趋势：LH间距 + LL间距都在扩大 → 趋势强
+        # 间距缩小 → 趋势减弱/震荡
+
+        sh_prices = []
+        sl_prices = []
+        trend_strength_up = np.full(len(dataframe), np.nan)
+        trend_strength_down = np.full(len(dataframe), np.nan)
+
+        for i in range(len(dataframe)):
+            if pd.notna(sh.iloc[i]):
+                sh_prices.append(sh.iloc[i])
+                if len(sh_prices) > 4:
+                    sh_prices.pop(0)
+            if pd.notna(sl.iloc[i]):
+                sl_prices.append(sl.iloc[i])
+                if len(sl_prices) > 4:
+                    sl_prices.pop(0)
+
+            # 上升趋势强度：HH[-1] vs HH[-2], HL[-1] vs HL[-2]
+            if len(sh_prices) >= 2 and len(sl_prices) >= 2:
+                # HH间距：最近两个Swing High的差值百分比
+                hh_dist = (sh_prices[-1] - sh_prices[-2]) / sh_prices[-2] if sh_prices[-2] > 0 else 0
+                # HL间距：最近两个Swing Low的差值百分比
+                hl_dist = (sl_prices[-1] - sl_prices[-2]) / sl_prices[-2] if sl_prices[-2] > 0 else 0
+                # 上升趋势强度 = HH间距 + HL间距（都正=扩张，一正一负=不确定，都负=收缩）
+                trend_strength_up[i] = hh_dist + hl_dist
+
+                # 下降趋势强度（取反：间距缩小是负值）
+                trend_strength_down[i] = -(hh_dist + hl_dist)
+
+        dataframe["trend_strength_up"] = pd.Series(trend_strength_up, index=dataframe.index)
+        dataframe["trend_strength_down"] = pd.Series(trend_strength_down, index=dataframe.index)
+
+        # 趋势强度是否足够（扩张中）
+        min_strength = self.trend_strength_min.value / 100.0  # 0~0.30
+        dataframe["strong_uptrend"] = dataframe["trend_strength_up"] > min_strength
+        dataframe["strong_downtrend"] = dataframe["trend_strength_down"] > min_strength
+
+        return dataframe
+
+    # ================================================================
+    #  主时间框架 — 1H 指标
+    # ================================================================
+
+    def populate_indicators(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        """1H 级别：K线形态（零指标）。"""
+        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
+        dataframe["bullish_signal"] = bullish_pin | bullish_engulf
+        dataframe["bearish_signal"] = bearish_pin | bearish_engulf
+
+        # NaN 安全处理
+        bool_cols = [
+            "trend_up_1d", "trend_down_1d",
+            "trend_up_4h", "trend_down_4h",
+            "in_demand_4h", "in_supply_4h",
+            "support_alive_4h", "resistance_alive_4h",
+            "strong_uptrend_4h", "strong_downtrend_4h",
+            "bullish_signal", "bearish_signal",
+        ]
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].fillna(False)
+
+        return dataframe
+
+    # =====================
+    # 入场信号
+    # =====================
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        入场逻辑（1H 时间框架）。
+
+        v2.1 核心改动：D1 — 趋势强度过滤
+          做多额外条件：4H上升趋势在扩张（strong_uptrend_4h）
+          做空额外条件：4H下降趋势在扩张（strong_downtrend_4h）
+        """
+        max_dist = self.max_stop_dist.value / 100.0
+        cooldown = self.cooldown_bars.value
+
+        # NaN 安全处理
+        bool_cols = [
+            "trend_up_1d", "trend_down_1d",
+            "trend_up_4h", "trend_down_4h",
+            "in_demand_4h", "in_supply_4h",
+            "support_alive_4h", "resistance_alive_4h",
+            "strong_uptrend_4h", "strong_downtrend_4h",
+            "bullish_signal", "bearish_signal",
+        ]
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].fillna(False)
+
+        # ── 做多 ──
+        long_stop_dist = (dataframe["open"] - dataframe["support_4h"]) / dataframe["open"]
+
+        long_base = (
+            dataframe["trend_up_1d"]
+            & dataframe["in_demand_4h"]
+            & dataframe["bullish_signal"]
+            & (long_stop_dist <= max_dist)
+            & dataframe["support_alive_4h"]
+            # v2.1: 趋势强度 — 4H上升趋势必须在扩张
+            & dataframe["strong_uptrend_4h"]
+        )
+
+        long_recent = long_base.rolling(cooldown, min_periods=1).max().shift(1) == 0
+        dataframe.loc[long_base & long_recent, "enter_long"] = 1
+
+        # ── 做空 ──
+        short_stop_dist = (dataframe["resistance_4h"] - dataframe["open"]) / dataframe["open"]
+
+        short_base = (
+            dataframe["trend_down_1d"]
+            & dataframe["in_supply_4h"]
+            & dataframe["bearish_signal"]
+            & (short_stop_dist <= max_dist)
+            & dataframe["resistance_alive_4h"]
+            # v2.1: 趋势强度 — 4H下降趋势必须在扩张
+            & dataframe["strong_downtrend_4h"]
+        )
+
+        short_recent = short_base.rolling(cooldown, min_periods=1).max().shift(1) == 0
+        dataframe.loc[short_base & short_recent, "enter_short"] = 1
+
+        return dataframe
+
+    # =====================
+    # 出场信号
+    # =====================
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """出场逻辑 — 由结构反转触发。"""
+        exit_long = ~dataframe["trend_up_1d"].fillna(True)
+        dataframe.loc[exit_long, "exit_long"] = 1
+
+        exit_short = dataframe["trend_up_1d"].fillna(False)
+        dataframe.loc[exit_short, "exit_short"] = 1
+
+        return dataframe
+
+    # =====================
+    # 动态止损 — 纯价格结构（基于Swing Point）
+    # =====================
+
+    def custom_stoploss(
+        self,
+        pair: str,
+        trade: Trade,
+        current_time: datetime,
+        current_rate: float,
+        current_profit: float,
+        after_fill: bool,
+        **kwargs,
+    ) -> float:
+        """
+        止损逻辑：完全基于价格结构，零指标（与v1.6相同）。
+        """
+        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
+
+        last = dataframe.iloc[-1]
+
+        if not trade.is_short:
+            support = last.get("support_4h", np.nan)
+            if pd.isna(support) or support <= 0:
+                return -0.02
+            sl_price = support * 0.999
+            sl_ratio = (sl_price / current_rate) - 1.0
+            return max(sl_ratio, -0.15)
+        else:
+            resistance = last.get("resistance_4h", np.nan)
+            if pd.isna(resistance) or resistance <= 0:
+                return 0.02
+            sl_price = resistance * 1.001
+            sl_ratio = 1.0 - (sl_price / current_rate)
+            return min(sl_ratio, 0.15)
+
+    # =====================
+    # Plot config
+    # =====================
+
+    @staticmethod
+    def plot_config() -> dict:
+        return {
+            "main_plot": {
+                "support_4h": {"color": "green", "type": "line"},
+                "resistance_4h": {"color": "red", "type": "line"},
+            },
+            "subplots": {
+                "signals": {
+                    "bullish_pinbar": {"color": "green", "type": "scatter"},
+                    "bearish_pinbar": {"color": "red", "type": "scatter"},
+                },
+                "filters": {
+                    "support_alive_4h": {"color": "green", "type": "line"},
+                    "resistance_alive_4h": {"color": "red", "type": "line"},
+                    "strong_uptrend_4h": {"color": "blue", "type": "line"},
+                    "strong_downtrend_4h": {"color": "orange", "type": "line"},
+                },
+            },
+        }

ablation/ablation_6.py

@@ -0,0 +1,454 @@
+"""
+Structure Flow Strategy v2.1
+=======================
+变更记录：
+  v1.6 (2026-06-07): 最优基线 — +3659.63%, 190笔, 69.3% trailing胜率
+  v2.0 (2026-06-08): B1 入场延迟确认 — 方向正确但降频严重
+  v2.1 (2026-06-08): ===== D1: 趋势强度过滤 =====
+                      在4H级别评估趋势强度：最近2个Swing Point的间距变化。
+                      如果趋势在扩张（HH/HL间距增大），允许入场；
+                      如果趋势在收缩（HH/HL间距缩小或震荡），过滤信号。
+                      目的：只在趋势明确时交易，避免震荡市反复止损。
+"""
+
+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 StructureFlowStrategyV21_Abl6(IStrategy):
+    """
+    Ablation Variant 6: 移除条件 6
+
+    v2.1改动（相对于v1.6）：
+      在4H级别计算趋势强度：最近2个Swing High间距 + Swing Low间距的变化。
+      只有趋势在扩张（或至少不收缩）时才允许入场。
+    """
+
+    can_short = True
+    stoploss = -0.15
+    use_custom_stoploss = True
+    minimal_roi = {"0": 100}
+    max_open_trades = 1
+    timeframe = "1h"
+
+    # =====================
+    # 可优化参数
+    # =====================
+
+    swing_lookback_d1 = IntParameter(8, 14, default=10, space="buy")
+    swing_lookback_h4 = IntParameter(5, 10, default=8, space="buy")
+    pin_bar_wick_ratio = IntParameter(50, 70, default=60, space="buy")
+    max_stop_dist = IntParameter(20, 50, default=50, space="buy")
+    cooldown_bars = IntParameter(3, 12, default=6, space="buy")
+    # v2.1 新增：趋势强度最小扩张比例（x/100 = 0%~50%）
+    # 0 = 只要不收缩就行；越大要求趋势扩张越强
+    trend_strength_min = IntParameter(-50, 20, default=-20, space="buy")  # -20=允许SP轻微收缩, 最佳值
+
+    # =====================
+    # 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)
+        in_demand_zone = np.full(n, False)
+        in_supply_zone = np.full(n, False)
+
+        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:
+                nearest_support[i] = sl_prices[-1]
+            if sh_prices:
+                nearest_resistance[i] = sh_prices[-1]
+
+            c = close.iloc[i]
+            if not np.isnan(nearest_support[i]) and not np.isnan(nearest_resistance[i]):
+                zone_range = nearest_resistance[i] - nearest_support[i]
+                if zone_range > 0:
+                    pos_pct = (c - nearest_support[i]) / zone_range
+                    in_demand_zone[i] = pos_pct < 0.35
+                    in_supply_zone[i] = pos_pct > 0.65
+
+        return DataFrame({
+            "trend_up": trend_up_arr,
+            "trend_down": trend_down_arr,
+            "support": nearest_support,
+            "resistance": nearest_resistance,
+            "in_demand": in_demand_zone,
+            "in_supply": in_supply_zone,
+        }, 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 — D1 宏观结构
+    # ================================================================
+
+    @informative("1d")
+    def populate_indicators_1d(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        sh, sl = self._detect_swing_points(
+            dataframe["high"], dataframe["low"],
+            self.swing_lookback_d1.value,
+        )
+        structure = self._build_structure(
+            dataframe["high"], dataframe["low"], dataframe["close"],
+            sh, sl,
+        )
+        dataframe["trend_up"] = structure["trend_up"]
+        dataframe["trend_down"] = structure["trend_down"]
+        return dataframe
+
+    # ================================================================
+    #  信息时间框架 — 4H 中期结构
+    # ================================================================
+
+    @informative("4h")
+    def populate_indicators_4h(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        sh, sl = self._detect_swing_points(
+            dataframe["high"], dataframe["low"],
+            self.swing_lookback_h4.value,
+        )
+        structure = self._build_structure(
+            dataframe["high"], dataframe["low"], dataframe["close"],
+            sh, sl,
+        )
+        dataframe["trend_up"] = structure["trend_up"]
+        dataframe["trend_down"] = structure["trend_down"]
+        dataframe["support"] = structure["support"]
+        dataframe["resistance"] = structure["resistance"]
+        dataframe["in_demand"] = structure["in_demand"]
+        dataframe["in_supply"] = structure["in_supply"]
+
+        # ================================
+        # v1.6 活支撑/阻力检查（保留）
+        # ================================
+        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(3, min_periods=1).max() > 0
+
+        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(3, min_periods=1).max() > 0
+
+        # ================================
+        # v2.1 新增：趋势强度评估
+        # ================================
+        # 计算最近2个Swing Point之间的间距变化
+        # 上升趋势：HH间距 + HL间距都在扩大 → 趋势强
+        # 下降趋势：LH间距 + LL间距都在扩大 → 趋势强
+        # 间距缩小 → 趋势减弱/震荡
+
+        sh_prices = []
+        sl_prices = []
+        trend_strength_up = np.full(len(dataframe), np.nan)
+        trend_strength_down = np.full(len(dataframe), np.nan)
+
+        for i in range(len(dataframe)):
+            if pd.notna(sh.iloc[i]):
+                sh_prices.append(sh.iloc[i])
+                if len(sh_prices) > 4:
+                    sh_prices.pop(0)
+            if pd.notna(sl.iloc[i]):
+                sl_prices.append(sl.iloc[i])
+                if len(sl_prices) > 4:
+                    sl_prices.pop(0)
+
+            # 上升趋势强度：HH[-1] vs HH[-2], HL[-1] vs HL[-2]
+            if len(sh_prices) >= 2 and len(sl_prices) >= 2:
+                # HH间距：最近两个Swing High的差值百分比
+                hh_dist = (sh_prices[-1] - sh_prices[-2]) / sh_prices[-2] if sh_prices[-2] > 0 else 0
+                # HL间距：最近两个Swing Low的差值百分比
+                hl_dist = (sl_prices[-1] - sl_prices[-2]) / sl_prices[-2] if sl_prices[-2] > 0 else 0
+                # 上升趋势强度 = HH间距 + HL间距（都正=扩张，一正一负=不确定，都负=收缩）
+                trend_strength_up[i] = hh_dist + hl_dist
+
+                # 下降趋势强度（取反：间距缩小是负值）
+                trend_strength_down[i] = -(hh_dist + hl_dist)
+
+        dataframe["trend_strength_up"] = pd.Series(trend_strength_up, index=dataframe.index)
+        dataframe["trend_strength_down"] = pd.Series(trend_strength_down, index=dataframe.index)
+
+        # 趋势强度是否足够（扩张中）
+        min_strength = self.trend_strength_min.value / 100.0  # 0~0.30
+        dataframe["strong_uptrend"] = dataframe["trend_strength_up"] > min_strength
+        dataframe["strong_downtrend"] = dataframe["trend_strength_down"] > min_strength
+
+        return dataframe
+
+    # ================================================================
+    #  主时间框架 — 1H 指标
+    # ================================================================
+
+    def populate_indicators(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        """1H 级别：K线形态（零指标）。"""
+        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
+        dataframe["bullish_signal"] = bullish_pin | bullish_engulf
+        dataframe["bearish_signal"] = bearish_pin | bearish_engulf
+
+        # NaN 安全处理
+        bool_cols = [
+            "trend_up_1d", "trend_down_1d",
+            "trend_up_4h", "trend_down_4h",
+            "in_demand_4h", "in_supply_4h",
+            "support_alive_4h", "resistance_alive_4h",
+            "strong_uptrend_4h", "strong_downtrend_4h",
+            "bullish_signal", "bearish_signal",
+        ]
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].fillna(False)
+
+        return dataframe
+
+    # =====================
+    # 入场信号
+    # =====================
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        入场逻辑（1H 时间框架）。
+
+        v2.1 核心改动：D1 — 趋势强度过滤
+          做多额外条件：4H上升趋势在扩张（strong_uptrend_4h）
+          做空额外条件：4H下降趋势在扩张（strong_downtrend_4h）
+        """
+        max_dist = self.max_stop_dist.value / 100.0
+        cooldown = self.cooldown_bars.value
+
+        # NaN 安全处理
+        bool_cols = [
+            "trend_up_1d", "trend_down_1d",
+            "trend_up_4h", "trend_down_4h",
+            "in_demand_4h", "in_supply_4h",
+            "support_alive_4h", "resistance_alive_4h",
+            "strong_uptrend_4h", "strong_downtrend_4h",
+            "bullish_signal", "bearish_signal",
+        ]
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].fillna(False)
+
+        # ── 做多 ──
+        long_stop_dist = (dataframe["open"] - dataframe["support_4h"]) / dataframe["open"]
+
+        long_base = (
+            dataframe["trend_up_1d"]
+            & dataframe["in_demand_4h"]
+            & dataframe["bullish_signal"]
+            & (long_stop_dist <= max_dist)
+            & (long_stop_dist > 0.003)
+            # v2.1: 趋势强度 — 4H上升趋势必须在扩张
+            & dataframe["strong_uptrend_4h"]
+        )
+
+        long_recent = long_base.rolling(cooldown, min_periods=1).max().shift(1) == 0
+        dataframe.loc[long_base & long_recent, "enter_long"] = 1
+
+        # ── 做空 ──
+        short_stop_dist = (dataframe["resistance_4h"] - dataframe["open"]) / dataframe["open"]
+
+        short_base = (
+            dataframe["trend_down_1d"]
+            & dataframe["in_supply_4h"]
+            & dataframe["bearish_signal"]
+            & (short_stop_dist <= max_dist)
+            & (short_stop_dist > 0.003)
+            # v2.1: 趋势强度 — 4H下降趋势必须在扩张
+            & dataframe["strong_downtrend_4h"]
+        )
+
+        short_recent = short_base.rolling(cooldown, min_periods=1).max().shift(1) == 0
+        dataframe.loc[short_base & short_recent, "enter_short"] = 1
+
+        return dataframe
+
+    # =====================
+    # 出场信号
+    # =====================
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """出场逻辑 — 由结构反转触发。"""
+        exit_long = ~dataframe["trend_up_1d"].fillna(True)
+        dataframe.loc[exit_long, "exit_long"] = 1
+
+        exit_short = dataframe["trend_up_1d"].fillna(False)
+        dataframe.loc[exit_short, "exit_short"] = 1
+
+        return dataframe
+
+    # =====================
+    # 动态止损 — 纯价格结构（基于Swing Point）
+    # =====================
+
+    def custom_stoploss(
+        self,
+        pair: str,
+        trade: Trade,
+        current_time: datetime,
+        current_rate: float,
+        current_profit: float,
+        after_fill: bool,
+        **kwargs,
+    ) -> float:
+        """
+        止损逻辑：完全基于价格结构，零指标（与v1.6相同）。
+        """
+        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
+
+        last = dataframe.iloc[-1]
+
+        if not trade.is_short:
+            support = last.get("support_4h", np.nan)
+            if pd.isna(support) or support <= 0:
+                return -0.02
+            sl_price = support * 0.999
+            sl_ratio = (sl_price / current_rate) - 1.0
+            return max(sl_ratio, -0.15)
+        else:
+            resistance = last.get("resistance_4h", np.nan)
+            if pd.isna(resistance) or resistance <= 0:
+                return 0.02
+            sl_price = resistance * 1.001
+            sl_ratio = 1.0 - (sl_price / current_rate)
+            return min(sl_ratio, 0.15)
+
+    # =====================
+    # Plot config
+    # =====================
+
+    @staticmethod
+    def plot_config() -> dict:
+        return {
+            "main_plot": {
+                "support_4h": {"color": "green", "type": "line"},
+                "resistance_4h": {"color": "red", "type": "line"},
+            },
+            "subplots": {
+                "signals": {
+                    "bullish_pinbar": {"color": "green", "type": "scatter"},
+                    "bearish_pinbar": {"color": "red", "type": "scatter"},
+                },
+                "filters": {
+                    "support_alive_4h": {"color": "green", "type": "line"},
+                    "resistance_alive_4h": {"color": "red", "type": "line"},
+                    "strong_uptrend_4h": {"color": "blue", "type": "line"},
+                    "strong_downtrend_4h": {"color": "orange", "type": "line"},
+                },
+            },
+        }

ablation/ablation_7.py

@@ -0,0 +1,454 @@
+"""
+Structure Flow Strategy v2.1
+=======================
+变更记录：
+  v1.6 (2026-06-07): 最优基线 — +3659.63%, 190笔, 69.3% trailing胜率
+  v2.0 (2026-06-08): B1 入场延迟确认 — 方向正确但降频严重
+  v2.1 (2026-06-08): ===== D1: 趋势强度过滤 =====
+                      在4H级别评估趋势强度：最近2个Swing Point的间距变化。
+                      如果趋势在扩张（HH/HL间距增大），允许入场；
+                      如果趋势在收缩（HH/HL间距缩小或震荡），过滤信号。
+                      目的：只在趋势明确时交易，避免震荡市反复止损。
+"""
+
+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 StructureFlowStrategyV21_Abl7(IStrategy):
+    """
+    Ablation Variant 7: 移除条件 7
+
+    v2.1改动（相对于v1.6）：
+      在4H级别计算趋势强度：最近2个Swing High间距 + Swing Low间距的变化。
+      只有趋势在扩张（或至少不收缩）时才允许入场。
+    """
+
+    can_short = True
+    stoploss = -0.15
+    use_custom_stoploss = True
+    minimal_roi = {"0": 100}
+    max_open_trades = 1
+    timeframe = "1h"
+
+    # =====================
+    # 可优化参数
+    # =====================
+
+    swing_lookback_d1 = IntParameter(8, 14, default=10, space="buy")
+    swing_lookback_h4 = IntParameter(5, 10, default=8, space="buy")
+    pin_bar_wick_ratio = IntParameter(50, 70, default=60, space="buy")
+    max_stop_dist = IntParameter(20, 50, default=50, space="buy")
+    cooldown_bars = IntParameter(3, 12, default=6, space="buy")
+    # v2.1 新增：趋势强度最小扩张比例（x/100 = 0%~50%）
+    # 0 = 只要不收缩就行；越大要求趋势扩张越强
+    trend_strength_min = IntParameter(-50, 20, default=-20, space="buy")  # -20=允许SP轻微收缩, 最佳值
+
+    # =====================
+    # 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)
+        in_demand_zone = np.full(n, False)
+        in_supply_zone = np.full(n, False)
+
+        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:
+                nearest_support[i] = sl_prices[-1]
+            if sh_prices:
+                nearest_resistance[i] = sh_prices[-1]
+
+            c = close.iloc[i]
+            if not np.isnan(nearest_support[i]) and not np.isnan(nearest_resistance[i]):
+                zone_range = nearest_resistance[i] - nearest_support[i]
+                if zone_range > 0:
+                    pos_pct = (c - nearest_support[i]) / zone_range
+                    in_demand_zone[i] = pos_pct < 0.35
+                    in_supply_zone[i] = pos_pct > 0.65
+
+        return DataFrame({
+            "trend_up": trend_up_arr,
+            "trend_down": trend_down_arr,
+            "support": nearest_support,
+            "resistance": nearest_resistance,
+            "in_demand": in_demand_zone,
+            "in_supply": in_supply_zone,
+        }, 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 — D1 宏观结构
+    # ================================================================
+
+    @informative("1d")
+    def populate_indicators_1d(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        sh, sl = self._detect_swing_points(
+            dataframe["high"], dataframe["low"],
+            self.swing_lookback_d1.value,
+        )
+        structure = self._build_structure(
+            dataframe["high"], dataframe["low"], dataframe["close"],
+            sh, sl,
+        )
+        dataframe["trend_up"] = structure["trend_up"]
+        dataframe["trend_down"] = structure["trend_down"]
+        return dataframe
+
+    # ================================================================
+    #  信息时间框架 — 4H 中期结构
+    # ================================================================
+
+    @informative("4h")
+    def populate_indicators_4h(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        sh, sl = self._detect_swing_points(
+            dataframe["high"], dataframe["low"],
+            self.swing_lookback_h4.value,
+        )
+        structure = self._build_structure(
+            dataframe["high"], dataframe["low"], dataframe["close"],
+            sh, sl,
+        )
+        dataframe["trend_up"] = structure["trend_up"]
+        dataframe["trend_down"] = structure["trend_down"]
+        dataframe["support"] = structure["support"]
+        dataframe["resistance"] = structure["resistance"]
+        dataframe["in_demand"] = structure["in_demand"]
+        dataframe["in_supply"] = structure["in_supply"]
+
+        # ================================
+        # v1.6 活支撑/阻力检查（保留）
+        # ================================
+        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(3, min_periods=1).max() > 0
+
+        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(3, min_periods=1).max() > 0
+
+        # ================================
+        # v2.1 新增：趋势强度评估
+        # ================================
+        # 计算最近2个Swing Point之间的间距变化
+        # 上升趋势：HH间距 + HL间距都在扩大 → 趋势强
+        # 下降趋势：LH间距 + LL间距都在扩大 → 趋势强
+        # 间距缩小 → 趋势减弱/震荡
+
+        sh_prices = []
+        sl_prices = []
+        trend_strength_up = np.full(len(dataframe), np.nan)
+        trend_strength_down = np.full(len(dataframe), np.nan)
+
+        for i in range(len(dataframe)):
+            if pd.notna(sh.iloc[i]):
+                sh_prices.append(sh.iloc[i])
+                if len(sh_prices) > 4:
+                    sh_prices.pop(0)
+            if pd.notna(sl.iloc[i]):
+                sl_prices.append(sl.iloc[i])
+                if len(sl_prices) > 4:
+                    sl_prices.pop(0)
+
+            # 上升趋势强度：HH[-1] vs HH[-2], HL[-1] vs HL[-2]
+            if len(sh_prices) >= 2 and len(sl_prices) >= 2:
+                # HH间距：最近两个Swing High的差值百分比
+                hh_dist = (sh_prices[-1] - sh_prices[-2]) / sh_prices[-2] if sh_prices[-2] > 0 else 0
+                # HL间距：最近两个Swing Low的差值百分比
+                hl_dist = (sl_prices[-1] - sl_prices[-2]) / sl_prices[-2] if sl_prices[-2] > 0 else 0
+                # 上升趋势强度 = HH间距 + HL间距（都正=扩张，一正一负=不确定，都负=收缩）
+                trend_strength_up[i] = hh_dist + hl_dist
+
+                # 下降趋势强度（取反：间距缩小是负值）
+                trend_strength_down[i] = -(hh_dist + hl_dist)
+
+        dataframe["trend_strength_up"] = pd.Series(trend_strength_up, index=dataframe.index)
+        dataframe["trend_strength_down"] = pd.Series(trend_strength_down, index=dataframe.index)
+
+        # 趋势强度是否足够（扩张中）
+        min_strength = self.trend_strength_min.value / 100.0  # 0~0.30
+        dataframe["strong_uptrend"] = dataframe["trend_strength_up"] > min_strength
+        dataframe["strong_downtrend"] = dataframe["trend_strength_down"] > min_strength
+
+        return dataframe
+
+    # ================================================================
+    #  主时间框架 — 1H 指标
+    # ================================================================
+
+    def populate_indicators(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        """1H 级别：K线形态（零指标）。"""
+        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
+        dataframe["bullish_signal"] = bullish_pin | bullish_engulf
+        dataframe["bearish_signal"] = bearish_pin | bearish_engulf
+
+        # NaN 安全处理
+        bool_cols = [
+            "trend_up_1d", "trend_down_1d",
+            "trend_up_4h", "trend_down_4h",
+            "in_demand_4h", "in_supply_4h",
+            "support_alive_4h", "resistance_alive_4h",
+            "strong_uptrend_4h", "strong_downtrend_4h",
+            "bullish_signal", "bearish_signal",
+        ]
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].fillna(False)
+
+        return dataframe
+
+    # =====================
+    # 入场信号
+    # =====================
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        入场逻辑（1H 时间框架）。
+
+        v2.1 核心改动：D1 — 趋势强度过滤
+          做多额外条件：4H上升趋势在扩张（strong_uptrend_4h）
+          做空额外条件：4H下降趋势在扩张（strong_downtrend_4h）
+        """
+        max_dist = self.max_stop_dist.value / 100.0
+        cooldown = self.cooldown_bars.value
+
+        # NaN 安全处理
+        bool_cols = [
+            "trend_up_1d", "trend_down_1d",
+            "trend_up_4h", "trend_down_4h",
+            "in_demand_4h", "in_supply_4h",
+            "support_alive_4h", "resistance_alive_4h",
+            "strong_uptrend_4h", "strong_downtrend_4h",
+            "bullish_signal", "bearish_signal",
+        ]
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].fillna(False)
+
+        # ── 做多 ──
+        long_stop_dist = (dataframe["open"] - dataframe["support_4h"]) / dataframe["open"]
+
+        long_base = (
+            dataframe["trend_up_1d"]
+            & dataframe["in_demand_4h"]
+            & dataframe["bullish_signal"]
+            & (long_stop_dist <= max_dist)
+            & (long_stop_dist > 0.003)
+            & dataframe["support_alive_4h"]
+            # v2.1: 趋势强度 — 4H上升趋势必须在扩张
+        )
+
+        long_recent = long_base.rolling(cooldown, min_periods=1).max().shift(1) == 0
+        dataframe.loc[long_base & long_recent, "enter_long"] = 1
+
+        # ── 做空 ──
+        short_stop_dist = (dataframe["resistance_4h"] - dataframe["open"]) / dataframe["open"]
+
+        short_base = (
+            dataframe["trend_down_1d"]
+            & dataframe["in_supply_4h"]
+            & dataframe["bearish_signal"]
+            & (short_stop_dist <= max_dist)
+            & (short_stop_dist > 0.003)
+            & dataframe["resistance_alive_4h"]
+            # v2.1: 趋势强度 — 4H下降趋势必须在扩张
+        )
+
+        short_recent = short_base.rolling(cooldown, min_periods=1).max().shift(1) == 0
+        dataframe.loc[short_base & short_recent, "enter_short"] = 1
+
+        return dataframe
+
+    # =====================
+    # 出场信号
+    # =====================
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """出场逻辑 — 由结构反转触发。"""
+        exit_long = ~dataframe["trend_up_1d"].fillna(True)
+        dataframe.loc[exit_long, "exit_long"] = 1
+
+        exit_short = dataframe["trend_up_1d"].fillna(False)
+        dataframe.loc[exit_short, "exit_short"] = 1
+
+        return dataframe
+
+    # =====================
+    # 动态止损 — 纯价格结构（基于Swing Point）
+    # =====================
+
+    def custom_stoploss(
+        self,
+        pair: str,
+        trade: Trade,
+        current_time: datetime,
+        current_rate: float,
+        current_profit: float,
+        after_fill: bool,
+        **kwargs,
+    ) -> float:
+        """
+        止损逻辑：完全基于价格结构，零指标（与v1.6相同）。
+        """
+        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
+
+        last = dataframe.iloc[-1]
+
+        if not trade.is_short:
+            support = last.get("support_4h", np.nan)
+            if pd.isna(support) or support <= 0:
+                return -0.02
+            sl_price = support * 0.999
+            sl_ratio = (sl_price / current_rate) - 1.0
+            return max(sl_ratio, -0.15)
+        else:
+            resistance = last.get("resistance_4h", np.nan)
+            if pd.isna(resistance) or resistance <= 0:
+                return 0.02
+            sl_price = resistance * 1.001
+            sl_ratio = 1.0 - (sl_price / current_rate)
+            return min(sl_ratio, 0.15)
+
+    # =====================
+    # Plot config
+    # =====================
+
+    @staticmethod
+    def plot_config() -> dict:
+        return {
+            "main_plot": {
+                "support_4h": {"color": "green", "type": "line"},
+                "resistance_4h": {"color": "red", "type": "line"},
+            },
+            "subplots": {
+                "signals": {
+                    "bullish_pinbar": {"color": "green", "type": "scatter"},
+                    "bearish_pinbar": {"color": "red", "type": "scatter"},
+                },
+                "filters": {
+                    "support_alive_4h": {"color": "green", "type": "line"},
+                    "resistance_alive_4h": {"color": "red", "type": "line"},
+                    "strong_uptrend_4h": {"color": "blue", "type": "line"},
+                    "strong_downtrend_4h": {"color": "orange", "type": "line"},
+                },
+            },
+        }

ablation/ablation_8.py

@@ -0,0 +1,456 @@
+"""
+Structure Flow Strategy v2.1
+=======================
+变更记录：
+  v1.6 (2026-06-07): 最优基线 — +3659.63%, 190笔, 69.3% trailing胜率
+  v2.0 (2026-06-08): B1 入场延迟确认 — 方向正确但降频严重
+  v2.1 (2026-06-08): ===== D1: 趋势强度过滤 =====
+                      在4H级别评估趋势强度：最近2个Swing Point的间距变化。
+                      如果趋势在扩张（HH/HL间距增大），允许入场；
+                      如果趋势在收缩（HH/HL间距缩小或震荡），过滤信号。
+                      目的：只在趋势明确时交易，避免震荡市反复止损。
+"""
+
+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 StructureFlowStrategyV21_Abl8(IStrategy):
+    """
+    Ablation Variant 8: 移除条件 8
+
+    v2.1改动（相对于v1.6）：
+      在4H级别计算趋势强度：最近2个Swing High间距 + Swing Low间距的变化。
+      只有趋势在扩张（或至少不收缩）时才允许入场。
+    """
+
+    can_short = True
+    stoploss = -0.15
+    use_custom_stoploss = True
+    minimal_roi = {"0": 100}
+    max_open_trades = 1
+    timeframe = "1h"
+
+    # =====================
+    # 可优化参数
+    # =====================
+
+    swing_lookback_d1 = IntParameter(8, 14, default=10, space="buy")
+    swing_lookback_h4 = IntParameter(5, 10, default=8, space="buy")
+    pin_bar_wick_ratio = IntParameter(50, 70, default=60, space="buy")
+    max_stop_dist = IntParameter(20, 50, default=50, space="buy")
+    cooldown_bars = IntParameter(3, 12, default=6, space="buy")
+    # v2.1 新增：趋势强度最小扩张比例（x/100 = 0%~50%）
+    # 0 = 只要不收缩就行；越大要求趋势扩张越强
+    trend_strength_min = IntParameter(-50, 20, default=-20, space="buy")  # -20=允许SP轻微收缩, 最佳值
+
+    # =====================
+    # 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)
+        in_demand_zone = np.full(n, False)
+        in_supply_zone = np.full(n, False)
+
+        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:
+                nearest_support[i] = sl_prices[-1]
+            if sh_prices:
+                nearest_resistance[i] = sh_prices[-1]
+
+            c = close.iloc[i]
+            if not np.isnan(nearest_support[i]) and not np.isnan(nearest_resistance[i]):
+                zone_range = nearest_resistance[i] - nearest_support[i]
+                if zone_range > 0:
+                    pos_pct = (c - nearest_support[i]) / zone_range
+                    in_demand_zone[i] = pos_pct < 0.35
+                    in_supply_zone[i] = pos_pct > 0.65
+
+        return DataFrame({
+            "trend_up": trend_up_arr,
+            "trend_down": trend_down_arr,
+            "support": nearest_support,
+            "resistance": nearest_resistance,
+            "in_demand": in_demand_zone,
+            "in_supply": in_supply_zone,
+        }, 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 — D1 宏观结构
+    # ================================================================
+
+    @informative("1d")
+    def populate_indicators_1d(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        sh, sl = self._detect_swing_points(
+            dataframe["high"], dataframe["low"],
+            self.swing_lookback_d1.value,
+        )
+        structure = self._build_structure(
+            dataframe["high"], dataframe["low"], dataframe["close"],
+            sh, sl,
+        )
+        dataframe["trend_up"] = structure["trend_up"]
+        dataframe["trend_down"] = structure["trend_down"]
+        return dataframe
+
+    # ================================================================
+    #  信息时间框架 — 4H 中期结构
+    # ================================================================
+
+    @informative("4h")
+    def populate_indicators_4h(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        sh, sl = self._detect_swing_points(
+            dataframe["high"], dataframe["low"],
+            self.swing_lookback_h4.value,
+        )
+        structure = self._build_structure(
+            dataframe["high"], dataframe["low"], dataframe["close"],
+            sh, sl,
+        )
+        dataframe["trend_up"] = structure["trend_up"]
+        dataframe["trend_down"] = structure["trend_down"]
+        dataframe["support"] = structure["support"]
+        dataframe["resistance"] = structure["resistance"]
+        dataframe["in_demand"] = structure["in_demand"]
+        dataframe["in_supply"] = structure["in_supply"]
+
+        # ================================
+        # v1.6 活支撑/阻力检查（保留）
+        # ================================
+        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(3, min_periods=1).max() > 0
+
+        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(3, min_periods=1).max() > 0
+
+        # ================================
+        # v2.1 新增：趋势强度评估
+        # ================================
+        # 计算最近2个Swing Point之间的间距变化
+        # 上升趋势：HH间距 + HL间距都在扩大 → 趋势强
+        # 下降趋势：LH间距 + LL间距都在扩大 → 趋势强
+        # 间距缩小 → 趋势减弱/震荡
+
+        sh_prices = []
+        sl_prices = []
+        trend_strength_up = np.full(len(dataframe), np.nan)
+        trend_strength_down = np.full(len(dataframe), np.nan)
+
+        for i in range(len(dataframe)):
+            if pd.notna(sh.iloc[i]):
+                sh_prices.append(sh.iloc[i])
+                if len(sh_prices) > 4:
+                    sh_prices.pop(0)
+            if pd.notna(sl.iloc[i]):
+                sl_prices.append(sl.iloc[i])
+                if len(sl_prices) > 4:
+                    sl_prices.pop(0)
+
+            # 上升趋势强度：HH[-1] vs HH[-2], HL[-1] vs HL[-2]
+            if len(sh_prices) >= 2 and len(sl_prices) >= 2:
+                # HH间距：最近两个Swing High的差值百分比
+                hh_dist = (sh_prices[-1] - sh_prices[-2]) / sh_prices[-2] if sh_prices[-2] > 0 else 0
+                # HL间距：最近两个Swing Low的差值百分比
+                hl_dist = (sl_prices[-1] - sl_prices[-2]) / sl_prices[-2] if sl_prices[-2] > 0 else 0
+                # 上升趋势强度 = HH间距 + HL间距（都正=扩张，一正一负=不确定，都负=收缩）
+                trend_strength_up[i] = hh_dist + hl_dist
+
+                # 下降趋势强度（取反：间距缩小是负值）
+                trend_strength_down[i] = -(hh_dist + hl_dist)
+
+        dataframe["trend_strength_up"] = pd.Series(trend_strength_up, index=dataframe.index)
+        dataframe["trend_strength_down"] = pd.Series(trend_strength_down, index=dataframe.index)
+
+        # 趋势强度是否足够（扩张中）
+        min_strength = self.trend_strength_min.value / 100.0  # 0~0.30
+        dataframe["strong_uptrend"] = dataframe["trend_strength_up"] > min_strength
+        dataframe["strong_downtrend"] = dataframe["trend_strength_down"] > min_strength
+
+        return dataframe
+
+    # ================================================================
+    #  主时间框架 — 1H 指标
+    # ================================================================
+
+    def populate_indicators(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        """1H 级别：K线形态（零指标）。"""
+        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
+        dataframe["bullish_signal"] = bullish_pin | bullish_engulf
+        dataframe["bearish_signal"] = bearish_pin | bearish_engulf
+
+        # NaN 安全处理
+        bool_cols = [
+            "trend_up_1d", "trend_down_1d",
+            "trend_up_4h", "trend_down_4h",
+            "in_demand_4h", "in_supply_4h",
+            "support_alive_4h", "resistance_alive_4h",
+            "strong_uptrend_4h", "strong_downtrend_4h",
+            "bullish_signal", "bearish_signal",
+        ]
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].fillna(False)
+
+        return dataframe
+
+    # =====================
+    # 入场信号
+    # =====================
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        入场逻辑（1H 时间框架）。
+
+        v2.1 核心改动：D1 — 趋势强度过滤
+          做多额外条件：4H上升趋势在扩张（strong_uptrend_4h）
+          做空额外条件：4H下降趋势在扩张（strong_downtrend_4h）
+        """
+        max_dist = self.max_stop_dist.value / 100.0
+        cooldown = self.cooldown_bars.value
+
+        # NaN 安全处理
+        bool_cols = [
+            "trend_up_1d", "trend_down_1d",
+            "trend_up_4h", "trend_down_4h",
+            "in_demand_4h", "in_supply_4h",
+            "support_alive_4h", "resistance_alive_4h",
+            "strong_uptrend_4h", "strong_downtrend_4h",
+            "bullish_signal", "bearish_signal",
+        ]
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].fillna(False)
+
+        # ── 做多 ──
+        long_stop_dist = (dataframe["open"] - dataframe["support_4h"]) / dataframe["open"]
+
+        long_base = (
+            dataframe["trend_up_1d"]
+            & dataframe["in_demand_4h"]
+            & dataframe["bullish_signal"]
+            & (long_stop_dist <= max_dist)
+            & (long_stop_dist > 0.003)
+            & dataframe["support_alive_4h"]
+            # v2.1: 趋势强度 — 4H上升趋势必须在扩张
+            & dataframe["strong_uptrend_4h"]
+        )
+
+        long_recent = True  # cooldown removed
+        dataframe.loc[long_base & long_recent, "enter_long"] = 1
+
+        # ── 做空 ──
+        short_stop_dist = (dataframe["resistance_4h"] - dataframe["open"]) / dataframe["open"]
+
+        short_base = (
+            dataframe["trend_down_1d"]
+            & dataframe["in_supply_4h"]
+            & dataframe["bearish_signal"]
+            & (short_stop_dist <= max_dist)
+            & (short_stop_dist > 0.003)
+            & dataframe["resistance_alive_4h"]
+            # v2.1: 趋势强度 — 4H下降趋势必须在扩张
+            & dataframe["strong_downtrend_4h"]
+        )
+
+        short_recent = True  # cooldown removed
+        dataframe.loc[short_base & short_recent, "enter_short"] = 1
+
+        return dataframe
+
+    # =====================
+    # 出场信号
+    # =====================
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """出场逻辑 — 由结构反转触发。"""
+        exit_long = ~dataframe["trend_up_1d"].fillna(True)
+        dataframe.loc[exit_long, "exit_long"] = 1
+
+        exit_short = dataframe["trend_up_1d"].fillna(False)
+        dataframe.loc[exit_short, "exit_short"] = 1
+
+        return dataframe
+
+    # =====================
+    # 动态止损 — 纯价格结构（基于Swing Point）
+    # =====================
+
+    def custom_stoploss(
+        self,
+        pair: str,
+        trade: Trade,
+        current_time: datetime,
+        current_rate: float,
+        current_profit: float,
+        after_fill: bool,
+        **kwargs,
+    ) -> float:
+        """
+        止损逻辑：完全基于价格结构，零指标（与v1.6相同）。
+        """
+        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
+
+        last = dataframe.iloc[-1]
+
+        if not trade.is_short:
+            support = last.get("support_4h", np.nan)
+            if pd.isna(support) or support <= 0:
+                return -0.02
+            sl_price = support * 0.999
+            sl_ratio = (sl_price / current_rate) - 1.0
+            return max(sl_ratio, -0.15)
+        else:
+            resistance = last.get("resistance_4h", np.nan)
+            if pd.isna(resistance) or resistance <= 0:
+                return 0.02
+            sl_price = resistance * 1.001
+            sl_ratio = 1.0 - (sl_price / current_rate)
+            return min(sl_ratio, 0.15)
+
+    # =====================
+    # Plot config
+    # =====================
+
+    @staticmethod
+    def plot_config() -> dict:
+        return {
+            "main_plot": {
+                "support_4h": {"color": "green", "type": "line"},
+                "resistance_4h": {"color": "red", "type": "line"},
+            },
+            "subplots": {
+                "signals": {
+                    "bullish_pinbar": {"color": "green", "type": "scatter"},
+                    "bearish_pinbar": {"color": "red", "type": "scatter"},
+                },
+                "filters": {
+                    "support_alive_4h": {"color": "green", "type": "line"},
+                    "resistance_alive_4h": {"color": "red", "type": "line"},
+                    "strong_uptrend_4h": {"color": "blue", "type": "line"},
+                    "strong_downtrend_4h": {"color": "orange", "type": "line"},
+                },
+            },
+        }

ablation/ablation_all_removed.py

@@ -0,0 +1,442 @@
+"""
+Structure Flow Strategy v2.1
+=======================
+变更记录：
+  v1.6 (2026-06-07): 最优基线 — +3659.63%, 190笔, 69.3% trailing胜率
+  v2.0 (2026-06-08): B1 入场延迟确认 — 方向正确但降频严重
+  v2.1 (2026-06-08): ===== D1: 趋势强度过滤 =====
+                      在4H级别评估趋势强度：最近2个Swing Point的间距变化。
+                      如果趋势在扩张（HH/HL间距增大），允许入场；
+                      如果趋势在收缩（HH/HL间距缩小或震荡），过滤信号。
+                      目的：只在趋势明确时交易，避免震荡市反复止损。
+"""
+
+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 StructureFlowStrategyV21_Ablall(IStrategy):
+    """
+    Ablation Variant all: 移除条件 1,2,3,4,5,6,7,8
+
+    v2.1改动（相对于v1.6）：
+      在4H级别计算趋势强度：最近2个Swing High间距 + Swing Low间距的变化。
+      只有趋势在扩张（或至少不收缩）时才允许入场。
+    """
+
+    can_short = True
+    stoploss = -0.15
+    use_custom_stoploss = True
+    minimal_roi = {"0": 100}
+    max_open_trades = 1
+    timeframe = "1h"
+
+    # =====================
+    # 可优化参数
+    # =====================
+
+    swing_lookback_d1 = IntParameter(8, 14, default=10, space="buy")
+    swing_lookback_h4 = IntParameter(5, 10, default=8, space="buy")
+    pin_bar_wick_ratio = IntParameter(50, 70, default=60, space="buy")
+    max_stop_dist = IntParameter(20, 50, default=50, space="buy")
+    cooldown_bars = IntParameter(3, 12, default=6, space="buy")
+    # v2.1 新增：趋势强度最小扩张比例（x/100 = 0%~50%）
+    # 0 = 只要不收缩就行；越大要求趋势扩张越强
+    trend_strength_min = IntParameter(-50, 20, default=-20, space="buy")  # -20=允许SP轻微收缩, 最佳值
+
+    # =====================
+    # 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)
+        in_demand_zone = np.full(n, False)
+        in_supply_zone = np.full(n, False)
+
+        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:
+                nearest_support[i] = sl_prices[-1]
+            if sh_prices:
+                nearest_resistance[i] = sh_prices[-1]
+
+            c = close.iloc[i]
+            if not np.isnan(nearest_support[i]) and not np.isnan(nearest_resistance[i]):
+                zone_range = nearest_resistance[i] - nearest_support[i]
+                if zone_range > 0:
+                    pos_pct = (c - nearest_support[i]) / zone_range
+                    in_demand_zone[i] = pos_pct < 0.35
+                    in_supply_zone[i] = pos_pct > 0.65
+
+        return DataFrame({
+            "trend_up": trend_up_arr,
+            "trend_down": trend_down_arr,
+            "support": nearest_support,
+            "resistance": nearest_resistance,
+            "in_demand": in_demand_zone,
+            "in_supply": in_supply_zone,
+        }, 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 — D1 宏观结构
+    # ================================================================
+
+    @informative("1d")
+    def populate_indicators_1d(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        sh, sl = self._detect_swing_points(
+            dataframe["high"], dataframe["low"],
+            self.swing_lookback_d1.value,
+        )
+        structure = self._build_structure(
+            dataframe["high"], dataframe["low"], dataframe["close"],
+            sh, sl,
+        )
+        dataframe["trend_up"] = structure["trend_up"]
+        dataframe["trend_down"] = structure["trend_down"]
+        return dataframe
+
+    # ================================================================
+    #  信息时间框架 — 4H 中期结构
+    # ================================================================
+
+    @informative("4h")
+    def populate_indicators_4h(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        sh, sl = self._detect_swing_points(
+            dataframe["high"], dataframe["low"],
+            self.swing_lookback_h4.value,
+        )
+        structure = self._build_structure(
+            dataframe["high"], dataframe["low"], dataframe["close"],
+            sh, sl,
+        )
+        dataframe["trend_up"] = structure["trend_up"]
+        dataframe["trend_down"] = structure["trend_down"]
+        dataframe["support"] = structure["support"]
+        dataframe["resistance"] = structure["resistance"]
+        dataframe["in_demand"] = structure["in_demand"]
+        dataframe["in_supply"] = structure["in_supply"]
+
+        # ================================
+        # v1.6 活支撑/阻力检查（保留）
+        # ================================
+        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(3, min_periods=1).max() > 0
+
+        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(3, min_periods=1).max() > 0
+
+        # ================================
+        # v2.1 新增：趋势强度评估
+        # ================================
+        # 计算最近2个Swing Point之间的间距变化
+        # 上升趋势：HH间距 + HL间距都在扩大 → 趋势强
+        # 下降趋势：LH间距 + LL间距都在扩大 → 趋势强
+        # 间距缩小 → 趋势减弱/震荡
+
+        sh_prices = []
+        sl_prices = []
+        trend_strength_up = np.full(len(dataframe), np.nan)
+        trend_strength_down = np.full(len(dataframe), np.nan)
+
+        for i in range(len(dataframe)):
+            if pd.notna(sh.iloc[i]):
+                sh_prices.append(sh.iloc[i])
+                if len(sh_prices) > 4:
+                    sh_prices.pop(0)
+            if pd.notna(sl.iloc[i]):
+                sl_prices.append(sl.iloc[i])
+                if len(sl_prices) > 4:
+                    sl_prices.pop(0)
+
+            # 上升趋势强度：HH[-1] vs HH[-2], HL[-1] vs HL[-2]
+            if len(sh_prices) >= 2 and len(sl_prices) >= 2:
+                # HH间距：最近两个Swing High的差值百分比
+                hh_dist = (sh_prices[-1] - sh_prices[-2]) / sh_prices[-2] if sh_prices[-2] > 0 else 0
+                # HL间距：最近两个Swing Low的差值百分比
+                hl_dist = (sl_prices[-1] - sl_prices[-2]) / sl_prices[-2] if sl_prices[-2] > 0 else 0
+                # 上升趋势强度 = HH间距 + HL间距（都正=扩张，一正一负=不确定，都负=收缩）
+                trend_strength_up[i] = hh_dist + hl_dist
+
+                # 下降趋势强度（取反：间距缩小是负值）
+                trend_strength_down[i] = -(hh_dist + hl_dist)
+
+        dataframe["trend_strength_up"] = pd.Series(trend_strength_up, index=dataframe.index)
+        dataframe["trend_strength_down"] = pd.Series(trend_strength_down, index=dataframe.index)
+
+        # 趋势强度是否足够（扩张中）
+        min_strength = self.trend_strength_min.value / 100.0  # 0~0.30
+        dataframe["strong_uptrend"] = dataframe["trend_strength_up"] > min_strength
+        dataframe["strong_downtrend"] = dataframe["trend_strength_down"] > min_strength
+
+        return dataframe
+
+    # ================================================================
+    #  主时间框架 — 1H 指标
+    # ================================================================
+
+    def populate_indicators(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        """1H 级别：K线形态（零指标）。"""
+        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
+        dataframe["bullish_signal"] = bullish_pin | bullish_engulf
+        dataframe["bearish_signal"] = bearish_pin | bearish_engulf
+
+        # NaN 安全处理
+        bool_cols = [
+            "trend_up_1d", "trend_down_1d",
+            "trend_up_4h", "trend_down_4h",
+            "in_demand_4h", "in_supply_4h",
+            "support_alive_4h", "resistance_alive_4h",
+            "strong_uptrend_4h", "strong_downtrend_4h",
+            "bullish_signal", "bearish_signal",
+        ]
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].fillna(False)
+
+        return dataframe
+
+    # =====================
+    # 入场信号
+    # =====================
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        入场逻辑（1H 时间框架）。
+
+        v2.1 核心改动：D1 — 趋势强度过滤
+          做多额外条件：4H上升趋势在扩张（strong_uptrend_4h）
+          做空额外条件：4H下降趋势在扩张（strong_downtrend_4h）
+        """
+        max_dist = self.max_stop_dist.value / 100.0
+        cooldown = self.cooldown_bars.value
+
+        # NaN 安全处理
+        bool_cols = [
+            "trend_up_1d", "trend_down_1d",
+            "trend_up_4h", "trend_down_4h",
+            "in_demand_4h", "in_supply_4h",
+            "support_alive_4h", "resistance_alive_4h",
+            "strong_uptrend_4h", "strong_downtrend_4h",
+            "bullish_signal", "bearish_signal",
+        ]
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].fillna(False)
+
+        # ── 做多 ──
+        long_stop_dist = (dataframe["open"] - dataframe["support_4h"]) / dataframe["open"]
+
+        long_base = (
+            # v2.1: 趋势强度 — 4H上升趋势必须在扩张
+        )
+
+        long_recent = True  # cooldown removed
+        dataframe.loc[long_base & long_recent, "enter_long"] = 1
+
+        # ── 做空 ──
+        short_stop_dist = (dataframe["resistance_4h"] - dataframe["open"]) / dataframe["open"]
+
+        short_base = (
+            # v2.1: 趋势强度 — 4H下降趋势必须在扩张
+        )
+
+        short_recent = True  # cooldown removed
+        dataframe.loc[short_base & short_recent, "enter_short"] = 1
+
+        return dataframe
+
+    # =====================
+    # 出场信号
+    # =====================
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """出场逻辑 — 由结构反转触发。"""
+        exit_long = ~dataframe["trend_up_1d"].fillna(True)
+        dataframe.loc[exit_long, "exit_long"] = 1
+
+        exit_short = dataframe["trend_up_1d"].fillna(False)
+        dataframe.loc[exit_short, "exit_short"] = 1
+
+        return dataframe
+
+    # =====================
+    # 动态止损 — 纯价格结构（基于Swing Point）
+    # =====================
+
+    def custom_stoploss(
+        self,
+        pair: str,
+        trade: Trade,
+        current_time: datetime,
+        current_rate: float,
+        current_profit: float,
+        after_fill: bool,
+        **kwargs,
+    ) -> float:
+        """
+        止损逻辑：完全基于价格结构，零指标（与v1.6相同）。
+        """
+        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
+
+        last = dataframe.iloc[-1]
+
+        if not trade.is_short:
+            support = last.get("support_4h", np.nan)
+            if pd.isna(support) or support <= 0:
+                return -0.02
+            sl_price = support * 0.999
+            sl_ratio = (sl_price / current_rate) - 1.0
+            return max(sl_ratio, -0.15)
+        else:
+            resistance = last.get("resistance_4h", np.nan)
+            if pd.isna(resistance) or resistance <= 0:
+                return 0.02
+            sl_price = resistance * 1.001
+            sl_ratio = 1.0 - (sl_price / current_rate)
+            return min(sl_ratio, 0.15)
+
+    # =====================
+    # Plot config
+    # =====================
+
+    @staticmethod
+    def plot_config() -> dict:
+        return {
+            "main_plot": {
+                "support_4h": {"color": "green", "type": "line"},
+                "resistance_4h": {"color": "red", "type": "line"},
+            },
+            "subplots": {
+                "signals": {
+                    "bullish_pinbar": {"color": "green", "type": "scatter"},
+                    "bearish_pinbar": {"color": "red", "type": "scatter"},
+                },
+                "filters": {
+                    "support_alive_4h": {"color": "green", "type": "line"},
+                    "resistance_alive_4h": {"color": "red", "type": "line"},
+                    "strong_uptrend_4h": {"color": "blue", "type": "line"},
+                    "strong_downtrend_4h": {"color": "orange", "type": "line"},
+                },
+            },
+        }

ablation/v2_1_baseline.py

@@ -0,0 +1,456 @@
+"""
+Structure Flow Strategy v2.1
+=======================
+变更记录：
+  v1.6 (2026-06-07): 最优基线 — +3659.63%, 190笔, 69.3% trailing胜率
+  v2.0 (2026-06-08): B1 入场延迟确认 — 方向正确但降频严重
+  v2.1 (2026-06-08): ===== D1: 趋势强度过滤 =====
+                      在4H级别评估趋势强度：最近2个Swing Point的间距变化。
+                      如果趋势在扩张（HH/HL间距增大），允许入场；
+                      如果趋势在收缩（HH/HL间距缩小或震荡），过滤信号。
+                      目的：只在趋势明确时交易，避免震荡市反复止损。
+"""
+
+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 StructureFlowStrategyV21(IStrategy):
+    """
+    Structure Flow Strategy v2.1 — D1: 趋势强度过滤
+
+    v2.1改动（相对于v1.6）：
+      在4H级别计算趋势强度：最近2个Swing High间距 + Swing Low间距的变化。
+      只有趋势在扩张（或至少不收缩）时才允许入场。
+    """
+
+    can_short = True
+    stoploss = -0.15
+    use_custom_stoploss = True
+    minimal_roi = {"0": 100}
+    max_open_trades = 1
+    timeframe = "1h"
+
+    # =====================
+    # 可优化参数
+    # =====================
+
+    swing_lookback_d1 = IntParameter(8, 14, default=10, space="buy")
+    swing_lookback_h4 = IntParameter(5, 10, default=8, space="buy")
+    pin_bar_wick_ratio = IntParameter(50, 70, default=60, space="buy")
+    max_stop_dist = IntParameter(20, 50, default=50, space="buy")
+    cooldown_bars = IntParameter(3, 12, default=6, space="buy")
+    # v2.1 新增：趋势强度最小扩张比例（x/100 = 0%~50%）
+    # 0 = 只要不收缩就行；越大要求趋势扩张越强
+    trend_strength_min = IntParameter(-50, 20, default=-20, space="buy")  # -20=允许SP轻微收缩, 最佳值
+
+    # =====================
+    # 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)
+        in_demand_zone = np.full(n, False)
+        in_supply_zone = np.full(n, False)
+
+        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:
+                nearest_support[i] = sl_prices[-1]
+            if sh_prices:
+                nearest_resistance[i] = sh_prices[-1]
+
+            c = close.iloc[i]
+            if not np.isnan(nearest_support[i]) and not np.isnan(nearest_resistance[i]):
+                zone_range = nearest_resistance[i] - nearest_support[i]
+                if zone_range > 0:
+                    pos_pct = (c - nearest_support[i]) / zone_range
+                    in_demand_zone[i] = pos_pct < 0.35
+                    in_supply_zone[i] = pos_pct > 0.65
+
+        return DataFrame({
+            "trend_up": trend_up_arr,
+            "trend_down": trend_down_arr,
+            "support": nearest_support,
+            "resistance": nearest_resistance,
+            "in_demand": in_demand_zone,
+            "in_supply": in_supply_zone,
+        }, 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 — D1 宏观结构
+    # ================================================================
+
+    @informative("1d")
+    def populate_indicators_1d(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        sh, sl = self._detect_swing_points(
+            dataframe["high"], dataframe["low"],
+            self.swing_lookback_d1.value,
+        )
+        structure = self._build_structure(
+            dataframe["high"], dataframe["low"], dataframe["close"],
+            sh, sl,
+        )
+        dataframe["trend_up"] = structure["trend_up"]
+        dataframe["trend_down"] = structure["trend_down"]
+        return dataframe
+
+    # ================================================================
+    #  信息时间框架 — 4H 中期结构
+    # ================================================================
+
+    @informative("4h")
+    def populate_indicators_4h(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        sh, sl = self._detect_swing_points(
+            dataframe["high"], dataframe["low"],
+            self.swing_lookback_h4.value,
+        )
+        structure = self._build_structure(
+            dataframe["high"], dataframe["low"], dataframe["close"],
+            sh, sl,
+        )
+        dataframe["trend_up"] = structure["trend_up"]
+        dataframe["trend_down"] = structure["trend_down"]
+        dataframe["support"] = structure["support"]
+        dataframe["resistance"] = structure["resistance"]
+        dataframe["in_demand"] = structure["in_demand"]
+        dataframe["in_supply"] = structure["in_supply"]
+
+        # ================================
+        # v1.6 活支撑/阻力检查（保留）
+        # ================================
+        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(3, min_periods=1).max() > 0
+
+        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(3, min_periods=1).max() > 0
+
+        # ================================
+        # v2.1 新增：趋势强度评估
+        # ================================
+        # 计算最近2个Swing Point之间的间距变化
+        # 上升趋势：HH间距 + HL间距都在扩大 → 趋势强
+        # 下降趋势：LH间距 + LL间距都在扩大 → 趋势强
+        # 间距缩小 → 趋势减弱/震荡
+
+        sh_prices = []
+        sl_prices = []
+        trend_strength_up = np.full(len(dataframe), np.nan)
+        trend_strength_down = np.full(len(dataframe), np.nan)
+
+        for i in range(len(dataframe)):
+            if pd.notna(sh.iloc[i]):
+                sh_prices.append(sh.iloc[i])
+                if len(sh_prices) > 4:
+                    sh_prices.pop(0)
+            if pd.notna(sl.iloc[i]):
+                sl_prices.append(sl.iloc[i])
+                if len(sl_prices) > 4:
+                    sl_prices.pop(0)
+
+            # 上升趋势强度：HH[-1] vs HH[-2], HL[-1] vs HL[-2]
+            if len(sh_prices) >= 2 and len(sl_prices) >= 2:
+                # HH间距：最近两个Swing High的差值百分比
+                hh_dist = (sh_prices[-1] - sh_prices[-2]) / sh_prices[-2] if sh_prices[-2] > 0 else 0
+                # HL间距：最近两个Swing Low的差值百分比
+                hl_dist = (sl_prices[-1] - sl_prices[-2]) / sl_prices[-2] if sl_prices[-2] > 0 else 0
+                # 上升趋势强度 = HH间距 + HL间距（都正=扩张，一正一负=不确定，都负=收缩）
+                trend_strength_up[i] = hh_dist + hl_dist
+
+                # 下降趋势强度（取反：间距缩小是负值）
+                trend_strength_down[i] = -(hh_dist + hl_dist)
+
+        dataframe["trend_strength_up"] = pd.Series(trend_strength_up, index=dataframe.index)
+        dataframe["trend_strength_down"] = pd.Series(trend_strength_down, index=dataframe.index)
+
+        # 趋势强度是否足够（扩张中）
+        min_strength = self.trend_strength_min.value / 100.0  # 0~0.30
+        dataframe["strong_uptrend"] = dataframe["trend_strength_up"] > min_strength
+        dataframe["strong_downtrend"] = dataframe["trend_strength_down"] > min_strength
+
+        return dataframe
+
+    # ================================================================
+    #  主时间框架 — 1H 指标
+    # ================================================================
+
+    def populate_indicators(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        """1H 级别：K线形态（零指标）。"""
+        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
+        dataframe["bullish_signal"] = bullish_pin | bullish_engulf
+        dataframe["bearish_signal"] = bearish_pin | bearish_engulf
+
+        # NaN 安全处理
+        bool_cols = [
+            "trend_up_1d", "trend_down_1d",
+            "trend_up_4h", "trend_down_4h",
+            "in_demand_4h", "in_supply_4h",
+            "support_alive_4h", "resistance_alive_4h",
+            "strong_uptrend_4h", "strong_downtrend_4h",
+            "bullish_signal", "bearish_signal",
+        ]
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].fillna(False)
+
+        return dataframe
+
+    # =====================
+    # 入场信号
+    # =====================
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        入场逻辑（1H 时间框架）。
+
+        v2.1 核心改动：D1 — 趋势强度过滤
+          做多额外条件：4H上升趋势在扩张（strong_uptrend_4h）
+          做空额外条件：4H下降趋势在扩张（strong_downtrend_4h）
+        """
+        max_dist = self.max_stop_dist.value / 100.0
+        cooldown = self.cooldown_bars.value
+
+        # NaN 安全处理
+        bool_cols = [
+            "trend_up_1d", "trend_down_1d",
+            "trend_up_4h", "trend_down_4h",
+            "in_demand_4h", "in_supply_4h",
+            "support_alive_4h", "resistance_alive_4h",
+            "strong_uptrend_4h", "strong_downtrend_4h",
+            "bullish_signal", "bearish_signal",
+        ]
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].fillna(False)
+
+        # ── 做多 ──
+        long_stop_dist = (dataframe["open"] - dataframe["support_4h"]) / dataframe["open"]
+
+        long_base = (
+            dataframe["trend_up_1d"]
+            & dataframe["in_demand_4h"]
+            & dataframe["bullish_signal"]
+            & (long_stop_dist <= max_dist)
+            & (long_stop_dist > 0.003)
+            & dataframe["support_alive_4h"]
+            # v2.1: 趋势强度 — 4H上升趋势必须在扩张
+            & dataframe["strong_uptrend_4h"]
+        )
+
+        long_recent = long_base.rolling(cooldown, min_periods=1).max().shift(1) == 0
+        dataframe.loc[long_base & long_recent, "enter_long"] = 1
+
+        # ── 做空 ──
+        short_stop_dist = (dataframe["resistance_4h"] - dataframe["open"]) / dataframe["open"]
+
+        short_base = (
+            dataframe["trend_down_1d"]
+            & dataframe["in_supply_4h"]
+            & dataframe["bearish_signal"]
+            & (short_stop_dist <= max_dist)
+            & (short_stop_dist > 0.003)
+            & dataframe["resistance_alive_4h"]
+            # v2.1: 趋势强度 — 4H下降趋势必须在扩张
+            & dataframe["strong_downtrend_4h"]
+        )
+
+        short_recent = short_base.rolling(cooldown, min_periods=1).max().shift(1) == 0
+        dataframe.loc[short_base & short_recent, "enter_short"] = 1
+
+        return dataframe
+
+    # =====================
+    # 出场信号
+    # =====================
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """出场逻辑 — 由结构反转触发。"""
+        exit_long = ~dataframe["trend_up_1d"].fillna(True)
+        dataframe.loc[exit_long, "exit_long"] = 1
+
+        exit_short = dataframe["trend_up_1d"].fillna(False)
+        dataframe.loc[exit_short, "exit_short"] = 1
+
+        return dataframe
+
+    # =====================
+    # 动态止损 — 纯价格结构（基于Swing Point）
+    # =====================
+
+    def custom_stoploss(
+        self,
+        pair: str,
+        trade: Trade,
+        current_time: datetime,
+        current_rate: float,
+        current_profit: float,
+        after_fill: bool,
+        **kwargs,
+    ) -> float:
+        """
+        止损逻辑：完全基于价格结构，零指标（与v1.6相同）。
+        """
+        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
+
+        last = dataframe.iloc[-1]
+
+        if not trade.is_short:
+            support = last.get("support_4h", np.nan)
+            if pd.isna(support) or support <= 0:
+                return -0.02
+            sl_price = support * 0.999
+            sl_ratio = (sl_price / current_rate) - 1.0
+            return max(sl_ratio, -0.15)
+        else:
+            resistance = last.get("resistance_4h", np.nan)
+            if pd.isna(resistance) or resistance <= 0:
+                return 0.02
+            sl_price = resistance * 1.001
+            sl_ratio = 1.0 - (sl_price / current_rate)
+            return min(sl_ratio, 0.15)
+
+    # =====================
+    # Plot config
+    # =====================
+
+    @staticmethod
+    def plot_config() -> dict:
+        return {
+            "main_plot": {
+                "support_4h": {"color": "green", "type": "line"},
+                "resistance_4h": {"color": "red", "type": "line"},
+            },
+            "subplots": {
+                "signals": {
+                    "bullish_pinbar": {"color": "green", "type": "scatter"},
+                    "bearish_pinbar": {"color": "red", "type": "scatter"},
+                },
+                "filters": {
+                    "support_alive_4h": {"color": "green", "type": "line"},
+                    "resistance_alive_4h": {"color": "red", "type": "line"},
+                    "strong_uptrend_4h": {"color": "blue", "type": "line"},
+                    "strong_downtrend_4h": {"color": "orange", "type": "line"},
+                },
+            },
+        }

strategy.py

@@ -1,314 +1,269 @@
-"""
-Structure Flow Swing Strategy v3.1
-==================================
-波段交易策略 — 基于4H震荡区间，保守参数 v2
+# structure_flow_momentum_scalp.py
+# 顺趋势剥头皮策略 v2.0
+#
+# 核心思路：不再在S/R处做反向交易接飞刀，而是顺趋势方向，等回调后入场。
+#
+# ┌─────────────────────────────────────────────────────────────┐
+# │  15m趋势方向判断（EMA20 vs EMA50）                           │
+# │         ↓                                                    │
+# │  上升趋势 → 只等5m回调到EMA20/支撑附近 → 止跌信号 → 做多     │
+# │  下降趋势 → 只等5m反弹到EMA20/阻力附近 → 止涨信号 → 做空     │
+# │         ↓                                                    │
+# │  止损：ATR×1.0 | 止盈：ATR×1.5 | 时间止损：60分钟           │
+# └─────────────────────────────────────────────────────────────┘
+#
+# v2.0 (2026-06-10): 初始版本，完全重写
 
-v3.1 改动（基于v3.0诊断结果）：
-  1. 双边测试 AND→OR：在10根K线内测试过支撑 OR 阻力即可（不需两者都测过）
-  2. 区间稳定性 15%→25%：放宽波动容忍度
-  3. 入场范围 2%→3%：增加候选信号密度
-  4. 冷却期 3根→1根：减少过渡过滤
-
-保留：纯震荡定位、ATR×1.5止损、区间70%止盈、D1趋势过滤
-
-预期：年交易量从9笔 → 50-80笔（约1-2单/周）
-
-版本历史：
-  v3.0 (2026-06-10): 初版，基于冯总波段交易新思路
-  v3.1 (2026-06-10): 降低条件门槛，提升交易频率
-"""
-
-from datetime import datetime
-import numpy as np
-import pandas as pd
+from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter, informative
 from pandas import DataFrame
-from freqtrade.strategy import IStrategy, IntParameter, informative
+import pandas as pd
+import numpy as np
+from datetime import datetime
 from freqtrade.persistence import Trade
 
 
-class StructureFlowSwingV31(IStrategy):
+class StructureFlowMomentumScalp(IStrategy):
     """
-    Structure Flow Swing Strategy v3.1
-    4H震荡区间波段交易 — 放宽震荡判定
+    顺趋势剥头皮策略 v2.0
+
+    核心逻辑：
+    - 15m EMA趋势方向过滤，只做顺趋势方向的单
+    - 5m 回调到EMA20或S/R支撑/阻力区域时，等待K线信号确认后入场
+    - 止损 ATR×1.0，止盈 ATR×1.5，时间止损 60 分钟
+    - 不做方向猜测，不吃鱼头鱼尾，只吃回调结束那一小段
     """
 
+    # ── 时间框架 ──
+    timeframe = "5m"
+
+    # ── 交易参数 ──
     can_short = True
-    stoploss = -0.20
-    use_custom_stoploss = True
-    minimal_roi = {"0": 100}
     max_open_trades = 1
-    timeframe = "4h"
+    stake_amount = "unlimited"
+    use_custom_stoploss = True
+    use_exit_signal = False  # 出场完全由 custom_stoploss + custom_exit 管理
 
-    # =====================
-    # 可优化参数（放宽后默认值）
-    # =====================
-    swing_lookback = IntParameter(4, 8, default=5, space="buy")
-    zone_stability_threshold = IntParameter(15, 40, default=25, space="buy")  # v3.1: 15→25↑
-    entry_zone_pct = IntParameter(1, 5, default=3, space="buy")               # v3.1: 2→3↑
-    atr_stop_mult = IntParameter(10, 25, default=15, space="buy")
-    take_profit_pct = IntParameter(50, 80, default=70, space="sell")
+    # ── 合约参数 ──
+    margin_mode = "cross"
+    trading_mode = "futures"
 
-    # 固定参数
-    zone_touch_lookback = 10
-    breakout_bars = 2
+    # ── 可优化参数 ──
+    # 趋势检测
+    trend_ema_period = IntParameter(10, 30, default=20, space="buy")
+    # 回调确认幅度
+    pullback_deviation = DecimalParameter(0.2, 1.0, default=0.5, decimals=1, space="buy")
+    # 入场冷却期
+    cooldown_bars = IntParameter(2, 8, default=3, space="buy")
+    # K线形态灵敏度
+    pin_bar_wick_ratio = IntParameter(50, 80, default=60, space="buy")
+    # 止损ATR倍数
+    atr_mult_stop = DecimalParameter(0.8, 2.0, default=1.0, decimals=1, space="sell")
+    # 止盈ATR倍数
+    atr_mult_tp = DecimalParameter(1.0, 3.0, default=1.5, decimals=1, space="sell")
 
-    # =====================
-    # 工具：Swing Point 检测
-    # =====================
+    # ── 常数 ──
+    time_stop_minutes = 60  # 最大持仓时间
 
-    @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
+    # ── 保护性止损 ──
+    stoploss = -0.10  # 硬止损 10%
 
-    # =====================
-    # 工具：区间震荡检测
-    # =====================
+    # ================================================================
+    #  杠杆
+    # ================================================================
 
-    def _detect_range(
-        self,
-        sh: pd.Series,
-        sl: pd.Series,
-        high: pd.Series,
-        low: pd.Series,
-        close: pd.Series,
+    def leverage(
+        self, pair: str, current_time: datetime, current_rate: float,
+        proposed_leverage: float, max_leverage: float, side: str,
+        **kwargs,
+    ) -> float:
+        """20x 杠杆起步，验证胜率后再上量"""
+        return min(20.0, max_leverage)
+
+    # ================================================================
+    #  信息时间框架 — 15m 趋势判断 + S/R
+    # ================================================================
+
+    @informative("15m")
+    def populate_indicators_15m(
+        self, dataframe: DataFrame, metadata: dict
     ) -> DataFrame:
-        n = len(high)
-        is_ranging = np.full(n, False)
-        support_arr = np.full(n, np.nan)
-        resistance_arr = np.full(n, np.nan)
-        zone_width_arr = np.full(n, np.nan)
+        """15m级别：EMA趋势方向 + swing point S/R。"""
 
-        sh_prices = []
-        sl_prices = []
+        # ── EMA 趋势方向 ──
+        ema_period = self.trend_ema_period.value
+        dataframe["ema_fast"] = dataframe["close"].ewm(span=ema_period, adjust=False).mean()
+        dataframe["ema_slow"] = dataframe["close"].ewm(span=ema_period * 2.5, adjust=False).mean()
 
-        for i in range(n):
-            if pd.notna(sh.iloc[i]):
-                sh_prices.append(sh.iloc[i])
-                if len(sh_prices) > 5:
-                    sh_prices.pop(0)
-            if pd.notna(sl.iloc[i]):
-                sl_prices.append(sl.iloc[i])
-                if len(sl_prices) > 5:
-                    sl_prices.pop(0)
+        dataframe["trend_up"] = dataframe["ema_fast"] > dataframe["ema_slow"]
+        dataframe["trend_down"] = dataframe["ema_fast"] < dataframe["ema_slow"]
 
-            if len(sh_prices) < 3 or len(sl_prices) < 3:
-                continue
+        # ── Swing Point 支撑/阻力 ──
+        high = dataframe["high"].tolist()
+        low = dataframe["low"].tolist()
+        close = dataframe["close"].tolist()
 
-            current_sh = sh_prices[-1]
-            current_sl = sl_prices[-1]
-
-            if current_sh <= current_sl:
-                continue
-
-            zone_width = (current_sh - current_sl) / current_sl
-            support_arr[i] = current_sl
-            resistance_arr[i] = current_sh
-            zone_width_arr[i] = zone_width
-
-            # 条件1：区间宽度稳定性
-            widths = []
-            for j in range(min(len(sh_prices), len(sl_prices)) - 1, -1, -1):
-                w = (sh_prices[j] - sl_prices[j]) / sl_prices[j]
-                widths.append(w)
-                if len(widths) >= 3:
-                    break
-
-            if len(widths) >= 3:
-                mean_width = np.mean(widths)
-                if mean_width > 0:
-                    max_dev = max(abs(w - mean_width) / mean_width for w in widths)
-                    stability_threshold = self.zone_stability_threshold.value / 100.0
-                    is_stable = max_dev <= stability_threshold
-                else:
-                    is_stable = False
-            else:
-                is_stable = False
-
-            if not is_stable:
-                continue
-
-            # 条件2：价格测试过边界 — v3.1: AND→OR
-            # 只需要测试过支撑或阻力之一，不需要两者都测过
-            start_idx = max(0, i - self.zone_touch_lookback)
-            support_zone_upper = current_sl * 1.01
-            touched_support = any(
-                low.iloc[j] <= support_zone_upper
-                for j in range(start_idx, i + 1)
-            )
-            resistance_zone_lower = current_sh * 0.99
-            touched_resistance = any(
-                high.iloc[j] >= resistance_zone_lower
-                for j in range(start_idx, i + 1)
-            )
-
-            # v3.1: AND → OR
-            if not (touched_support or touched_resistance):
-                continue
-
-            # 条件3：无突破
-            consecutive_outside = 0
-            for j in range(i, max(0, i - self.breakout_bars) - 1, -1):
-                if close.iloc[j] > current_sh or close.iloc[j] < current_sl:
-                    consecutive_outside += 1
-                else:
-                    break
-
-            if consecutive_outside >= self.breakout_bars:
-                continue
-
-            is_ranging[i] = True
-
-        return DataFrame({
-            "is_ranging": is_ranging,
-            "support": support_arr,
-            "resistance": resistance_arr,
-            "zone_width": zone_width_arr,
-        }, index=high.index)
-
-    # =====================
-    # 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()
-
-    # ================================================================
-    # D1 信息时间框架 — 宏观趋势参考
-    # ================================================================
-
-    @informative("1d")
-    def populate_indicators_1d(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        sh, sl = self._detect_swing_points(
-            dataframe["high"], dataframe["low"], window=5
-        )
-        sh_vals = sh.dropna()
-        sl_vals = sl.dropna()
-
-        is_uptrend = pd.Series(False, index=dataframe.index)
-        is_downtrend = pd.Series(False, index=dataframe.index)
-
-        if len(sh_vals) >= 2 and len(sl_vals) >= 2:
-            if sh_vals.iloc[-1] > sh_vals.iloc[-2] and sl_vals.iloc[-1] > sl_vals.iloc[-2]:
-                is_uptrend[:] = True
-            elif sh_vals.iloc[-1] < sh_vals.iloc[-2] and sl_vals.iloc[-1] < sl_vals.iloc[-2]:
-                is_downtrend[:] = True
-
-        dataframe["d1_uptrend"] = is_uptrend
-        dataframe["d1_downtrend"] = is_downtrend
-        return dataframe
-
-    # ================================================================
-    # 主时间框架 — 4H 指标
-    # ================================================================
-
-    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        sh, sl = self._detect_swing_points(
-            dataframe["high"], dataframe["low"],
-            self.swing_lookback.value,
+        sh, sl = self._detect_swing_points(high, low, window=5)
+        trend_up_arr, trend_down_arr, support_arr, resistance_arr = self._build_structure(
+            high, low, close, sh, sl,
         )
 
-        range_info = self._detect_range(sh, sl, dataframe["high"], dataframe["low"], dataframe["close"])
-        dataframe["is_ranging"] = range_info["is_ranging"]
-        dataframe["range_support"] = range_info["support"]
-        dataframe["range_resistance"] = range_info["resistance"]
-        dataframe["zone_width_pct"] = range_info["zone_width"]
-
-        dataframe["atr"] = self._calc_atr(dataframe["high"], dataframe["low"], dataframe["close"], 14)
-
-        # 价格在区间内的位置
-        denom = dataframe["range_resistance"] - dataframe["range_support"]
-        dataframe["zone_position"] = np.where(
-            denom > 0,
-            (dataframe["close"] - dataframe["range_support"]) / denom,
-            np.nan,
-        )
-
-        # 距离边界百分比
-        dataframe["dist_to_support"] = np.where(
-            dataframe["range_support"] > 0,
-            (dataframe["close"] - dataframe["range_support"]) / dataframe["close"],
-            np.nan,
-        )
-        dataframe["dist_to_resistance"] = np.where(
-            dataframe["range_resistance"] > 0,
-            (dataframe["range_resistance"] - dataframe["close"]) / dataframe["close"],
-            np.nan,
-        )
-
-        for col in ["is_ranging", "zone_position", "dist_to_support", "dist_to_resistance"]:
-            if col in dataframe.columns:
-                dataframe[col] = dataframe[col].fillna(False if col == "is_ranging" else 999)
+        dataframe["trend_up_sp"] = trend_up_arr
+        dataframe["trend_down_sp"] = trend_down_arr
+        # EMA平滑S/R（避免跳变）
+        dataframe["support"] = self._ema_smooth(support_arr, alpha=0.3)
+        dataframe["resistance"] = self._ema_smooth(resistance_arr, alpha=0.3)
 
         return dataframe
 
     # ================================================================
-    # 入场信号 — v3.1: 冷却期 3→1
+    #  主框架 — 5m 级别指标
     # ================================================================
 
-    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        entry_zone = self.entry_zone_pct.value / 100.0
+    def populate_indicators(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        """5m级别：ATR + K线形态 + EMA趋势整合。"""
 
-        d1_downtrend_col = "d1_downtrend_1d"
-        d1_uptrend_col = "d1_uptrend_1d"
+        # ── ATR(14) ──
+        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()
+        atr_mean = dataframe["atr"].mean()
+        dataframe["atr"] = dataframe["atr"].fillna(atr_mean)
 
-        for col in ["is_ranging", d1_uptrend_col, d1_downtrend_col]:
+        # ── K线形态 ──
+        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_signal"] = bullish_pin | bullish_engulf
+        dataframe["bearish_signal"] = bearish_pin | bearish_engulf
+
+        # ── 5m EMA（用于短期拉回确认） ──
+        dataframe["ema5"] = close.ewm(span=5, adjust=False).mean()
+        dataframe["ema8"] = close.ewm(span=8, adjust=False).mean()
+
+        # ── 布尔列NaN填充 ──
+        for col in ["bullish_signal", "bearish_signal"]:
+            dataframe[col] = dataframe[col].fillna(False)
+
+        return dataframe
+
+    # ================================================================
+    #  入场逻辑
+    # ================================================================
+
+    def populate_entry_trend(
+        self, dataframe: DataFrame, metadata: dict
+    ) -> DataFrame:
+        """
+        入场逻辑。
+
+        只做顺趋势回调入场，不做S/R反向交易：
+
+        做多条件：
+        1. 15m 上升趋势（EMA_fast > EMA_slow）
+        2. 5m 价格回调到15m EMA_fast 或 支撑位附近
+        3. 5m K线止跌信号（pinbar/engulfing）
+
+        做空条件（对称）：
+        1. 15m 下降趋势
+        2. 5m 价格反弹到15m EMA_fast 或 阻力位附近
+        3. 5m K线止涨信号
+        """
+        cooldown = self.cooldown_bars.value
+        dev = self.pullback_deviation.value / 100.0  # 0.5% → 0.005
+
+        # ── 必要列检查 ──
+        required = [
+            "ema_fast_15m", "trend_up_15m", "trend_down_15m",
+            "support_15m", "resistance_15m",
+        ]
+        for col in required:
+            if col not in dataframe.columns:
+                return dataframe
+
+        # ── 布尔列填充 ──
+        for col in [
+            "bullish_signal", "bearish_signal",
+            "trend_up_15m", "trend_down_15m",
+        ]:
             if col in dataframe.columns:
                 dataframe[col] = dataframe[col].fillna(False)
-            else:
-                dataframe[col] = False
 
-        # ── 做多：震荡市中，价格靠近支撑位 ──
-        long_conds = (
-            dataframe["is_ranging"]
-            & (dataframe["dist_to_support"] <= entry_zone)
-            & (dataframe["dist_to_support"] > 0)
-            & (~dataframe[d1_downtrend_col])
+        # ═══════════════════════════════════════════════════════════
+        #  做多：上升趋势 + 回调到EMA/支撑 + 止跌信号
+        # ═══════════════════════════════════════════════════════════
+
+        # 条件1：15m 上升趋势
+        trend_up = dataframe["trend_up_15m"]
+
+        # 条件2：价格在EMA20或支撑位附近（回调到顺趋势的支撑区）
+        near_ema = (
+            (dataframe["low"] <= dataframe["ema_fast_15m"] * (1.0 + dev * 0.5)) &
+            (dataframe["low"] >= dataframe["ema_fast_15m"] * (1.0 - dev * 2.0))
         )
-
-        cooldown = 1  # v3.1: 3→1
-        long_recent = long_conds.rolling(cooldown, min_periods=1).max().shift(1) == 0
-        dataframe.loc[long_conds & long_recent, "enter_long"] = 1
-
-        # ── 做空：震荡市中，价格靠近阻力位 ──
-        short_conds = (
-            dataframe["is_ranging"]
-            & (dataframe["dist_to_resistance"] <= entry_zone)
-            & (dataframe["dist_to_resistance"] > 0)
-            & (~dataframe[d1_uptrend_col])
+        near_support = (
+            (dataframe["low"] <= dataframe["support_15m"] * (1.0 + dev)) &
+            (dataframe["low"] >= dataframe["support_15m"] * (1.0 - dev))
         )
+        pullback_long = near_ema | near_support
 
-        short_recent = short_conds.rolling(cooldown, min_periods=1).max().shift(1) == 0
-        dataframe.loc[short_conds & short_recent, "enter_short"] = 1
+        # 条件3：K线止跌信号
+        signal_long = dataframe["bullish_signal"]
+
+        # 综合入场
+        enter_long = trend_up & pullback_long & signal_long
+        long_recent = enter_long.rolling(cooldown, min_periods=1).max().shift(1) == 0
+        dataframe.loc[enter_long & long_recent, "enter_long"] = 1
+
+        # ═══════════════════════════════════════════════════════════
+        #  做空：下降趋势 + 反弹到EMA/阻力 + 止涨信号
+        # ═══════════════════════════════════════════════════════════
+
+        # 条件1：15m 下降趋势
+        trend_down = dataframe["trend_down_15m"]
+
+        # 条件2：价格在EMA20或阻力位附近（反弹到顺趋势的阻力区）
+        near_ema_short = (
+            (dataframe["high"] >= dataframe["ema_fast_15m"] * (1.0 - dev * 0.5)) &
+            (dataframe["high"] <= dataframe["ema_fast_15m"] * (1.0 + dev * 2.0))
+        )
+        near_resistance = (
+            (dataframe["high"] >= dataframe["resistance_15m"] * (1.0 - dev)) &
+            (dataframe["high"] <= dataframe["resistance_15m"] * (1.0 + dev))
+        )
+        pullback_short = near_ema_short | near_resistance
+
+        # 条件3：K线止涨信号
+        signal_short = dataframe["bearish_signal"]
+
+        # 综合入场
+        enter_short = trend_down & pullback_short & signal_short
+        short_recent = enter_short.rolling(cooldown, min_periods=1).max().shift(1) == 0
+        dataframe.loc[enter_short & short_recent, "enter_short"] = 1
 
         return dataframe
 
     # ================================================================
-    # 出场信号
+    #  exit_trend（freqtrade 2025.11 强制要求，即使 use_exit_signal=False）
     # ================================================================
 
     def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """出场完全由 custom_stoploss + custom_exit 管理。"""
         return dataframe
 
     # ================================================================
-    # 自定义止损：支撑/阻力外侧，ATR*1.5 缓冲
+    #  出场 — 止损（ATR动态）
     # ================================================================
 
     def custom_stoploss(
@@ -321,44 +276,39 @@ class StructureFlowSwingV31(IStrategy):
         after_fill: bool,
         **kwargs,
     ) -> float:
+        """
+        止损 = 入场价 ± ATR × atr_mult_stop
+
+        - ATR值从入场K线锁定，持仓期间不变
+        - 做多：entry_price - (locked_atr × mult)
+        - 做空：entry_price + (locked_atr × mult)
+        - 配20x杠杆，ATR×1.0 ≈ 对应约 $3.7 止损（当前5m ATR~$3.74）
+        """
         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
 
-        last = dataframe.iloc[-1]
-        atr_mult = self.atr_stop_mult.value / 10.0
+        entry_row = self._get_entry_row(dataframe, trade)
+        if entry_row is None:
+            return -0.02 if not trade.is_short else 0.02
+
+        atr = entry_row.get("atr", np.nan)
+        if pd.isna(atr) or atr <= 0:
+            return -0.02 if not trade.is_short else 0.02
+
+        mult = self.atr_mult_stop.value
 
         if not trade.is_short:
-            support = last.get("range_support", np.nan)
-            atr = last.get("atr", np.nan)
-
-            if pd.isna(support) or support <= 0:
-                return -0.02
-
-            if pd.notna(atr) and atr > 0:
-                sl_price = support - atr * atr_mult
-            else:
-                sl_price = support * 0.985
-
-            sl_ratio = (sl_price / current_rate) - 1.0
-            return max(sl_ratio, -0.20)
+            sl_price = trade.open_rate - (atr * mult)
+            sl_ratio = (sl_price / trade.open_rate) - 1.0
+            return max(sl_ratio, -self.stoploss)
         else:
-            resistance = last.get("range_resistance", np.nan)
-            atr = last.get("atr", np.nan)
-
-            if pd.isna(resistance) or resistance <= 0:
-                return 0.02
-
-            if pd.notna(atr) and atr > 0:
-                sl_price = resistance + atr * atr_mult
-            else:
-                sl_price = resistance * 1.015
-
-            sl_ratio = 1.0 - (sl_price / current_rate)
-            return min(sl_ratio, 0.20)
+            sl_price = trade.open_rate + (atr * mult)
+            sl_ratio = 1.0 - (sl_price / trade.open_rate)
+            return min(sl_ratio, self.stoploss)
 
     # ================================================================
-    # 自定义止盈：区间70%
+    #  出场 — 止盈（ATR动态）+ 时间止损
     # ================================================================
 
     def custom_exit(
@@ -370,54 +320,196 @@ class StructureFlowSwingV31(IStrategy):
         current_profit: float,
         **kwargs,
     ) -> str | None:
-        tp_pct = self.take_profit_pct.value / 100.0
-
+        """
+        出场逻辑：
+        1. ATR止盈：利润达到入场时锁定的 ATR × atr_mult_tp → 止盈
+        2. 时间止损：持仓超过 time_stop_minutes → 强制出场
+        """
         dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
         if dataframe is None or len(dataframe) == 0:
             return None
 
-        last = dataframe.iloc[-1]
+        entry_row = self._get_entry_row(dataframe, trade)
+        if entry_row is None:
+            return None
 
-        if not trade.is_short:
-            support = last.get("range_support", np.nan)
-            resistance = last.get("range_resistance", np.nan)
+        atr = entry_row.get("atr", np.nan)
+        if pd.isna(atr) or atr <= 0:
+            return None
 
-            if pd.notna(support) and pd.notna(resistance) and resistance > support:
-                zone_height = (resistance - support) / support
-                tp_target = zone_height * tp_pct
-                if current_profit >= tp_target:
-                    return "take_profit"
-        else:
-            support = last.get("range_support", np.nan)
-            resistance = last.get("range_resistance", np.nan)
+        # 1. ATR 止盈
+        tp_mult = self.atr_mult_tp.value
+        tp_ratio = (atr * tp_mult) / trade.open_rate
 
-            if pd.notna(support) and pd.notna(resistance) and resistance > support:
-                zone_height = (resistance - support) / resistance
-                tp_target = zone_height * tp_pct
-                if current_profit >= tp_target:
-                    return "take_profit"
+        if current_profit >= tp_ratio:
+            return "atr_tp"
+
+        # 2. 时间止损
+        elapsed = (current_time - trade.open_date).total_seconds() / 60.0
+        if elapsed >= self.time_stop_minutes:
+            return "time_stop"
 
         return None
 
     # ================================================================
-    # Plot config
+    #  工具函数
     # ================================================================
 
-    @staticmethod
-    def plot_config() -> dict:
-        return {
-            "main_plot": {
-                "range_support": {"color": "green", "type": "line"},
-                "range_resistance": {"color": "red", "type": "line"},
-            },
-            "subplots": {
-                "range": {
-                    "is_ranging": {"color": "blue", "type": "line"},
-                    "zone_width_pct": {"color": "purple", "type": "line"},
-                },
-                "position": {
-                    "dist_to_support": {"color": "green", "type": "line"},
-                    "dist_to_resistance": {"color": "red", "type": "line"},
-                },
-            },
-        }
+    def _detect_swing_points(
+        self, highs: list, lows: list, window: int = 5
+    ):
+        """
+        Swing High / Swing Low 检测。
+
+        当一根K线的最高价高于其两侧window根K线的最高价时，标记为Swing High。
+        Swing Low同理。
+        """
+        n = len(highs)
+        swing_high = [np.nan] * n
+        swing_low = [np.nan] * n
+
+        for i in range(window, n - window):
+            # Swing High
+            is_high = True
+            for j in range(i - window, i + window + 1):
+                if j == i:
+                    continue
+                if highs[j] >= highs[i]:
+                    is_high = False
+                    break
+            if is_high:
+                swing_high[i] = highs[i]
+
+            # Swing Low
+            is_low = True
+            for j in range(i - window, i + window + 1):
+                if j == i:
+                    continue
+                if lows[j] <= lows[i]:
+                    is_low = False
+                    break
+            if is_low:
+                swing_low[i] = lows[i]
+
+        return swing_high, swing_low
+
+    def _build_structure(
+        self, highs: list, lows: list, closes: list,
+        swing_high: list, swing_low: list,
+    ):
+        """构建趋势结构和支撑/阻力位。"""
+        n = len(highs)
+        trend_up = [False] * n
+        trend_down = [False] * n
+        support = [np.nan] * n
+        resistance = [np.nan] * n
+
+        # 用最近4个swing point的位置判断
+        last_sh_idx = -1
+        last_sl_idx = -1
+        prev_sh = []
+        prev_sl = []
+
+        for i in range(n):
+            if not np.isnan(swing_high[i]):
+                prev_sh.append(swing_high[i])
+                last_sh_idx = i
+                if len(prev_sh) > 4:
+                    prev_sh.pop(0)
+
+            if not np.isnan(swing_low[i]):
+                prev_sl.append(swing_low[i])
+                last_sl_idx = i
+                if len(prev_sl) > 4:
+                    prev_sl.pop(0)
+
+            # 趋势判断：最新的HH > 次新的HH = 上升趋势中的higher high
+            if len(prev_sh) >= 2 and prev_sh[-1] > prev_sh[-2]:
+                trend_up[i] = True
+
+            # 趋势判断：最新的LL < 次新的LL = 下降趋势中的lower low
+            if len(prev_sl) >= 2 and prev_sl[-1] < prev_sl[-2]:
+                trend_down[i] = True
+
+            # 支撑 = 最近的有效Swing Low（EMA平滑后在调用侧处理）
+            if prev_sl:
+                support[i] = prev_sl[-1]
+            if prev_sh:
+                resistance[i] = prev_sh[-1]
+
+        return trend_up, trend_down, support, resistance
+
+    def _ema_smooth(self, values: list, alpha: float = 0.3):
+        """对数组做EMA平滑，避免跳变。"""
+        result = [np.nan] * len(values)
+        ema = None
+        for i, v in enumerate(values):
+            if pd.isna(v) or v is None:
+                if ema is not None:
+                    result[i] = ema
+                continue
+            if ema is None:
+                ema = v
+            else:
+                ema = alpha * v + (1 - alpha) * ema
+            result[i] = ema
+        return np.array(result)
+
+    def _detect_candle_patterns(
+        self, opens, highs, lows, closes, wick_ratio=0.6,
+    ):
+        """检测K线形态：pinbar（锤子线/射击星）和吞没形态。"""
+        n = len(opens)
+        bullish_pin = [False] * n
+        bearish_pin = [False] * n
+        bullish_engulf = [False] * n
+        bearish_engulf = [False] * n
+
+        for i in range(n):
+            o, h, l, c = opens[i], highs[i], lows[i], closes[i]
+            total_range = h - l if h > l else 0.001
+
+            is_bullish = c > o
+            is_bearish = c < o
+
+            body = abs(c - o)
+            upper_wick = h - max(c, o)
+            lower_wick = min(c, o) - l
+
+            # Pinbar：影线 > total_range × wick_ratio
+            if is_bullish and lower_wick / total_range > wick_ratio:
+                bullish_pin[i] = True
+            if is_bearish and upper_wick / total_range > wick_ratio:
+                bearish_pin[i] = True
+
+            # 吞没形态
+            if i > 0:
+                prev_o = opens[i - 1]
+                prev_c = closes[i - 1]
+                if is_bullish and c > prev_o and o < prev_c:
+                    bullish_engulf[i] = True
+                if is_bearish and c < prev_o and o > prev_c:
+                    bearish_engulf[i] = True
+
+        return (
+            pd.Series(bullish_pin),
+            pd.Series(bearish_pin),
+            pd.Series(bullish_engulf),
+            pd.Series(bearish_engulf),
+        )
+
+    def _get_entry_row(self, dataframe: DataFrame, trade: Trade):
+        """查找入场K线行，兼容live/backtesting两种模式。"""
+        if "date" in dataframe.columns:
+            entry_mask = pd.to_datetime(dataframe["date"]) <= trade.open_date
+            if not entry_mask.any():
+                return None
+            return dataframe[entry_mask].iloc[-1]
+        else:
+            try:
+                idx = dataframe.index.get_indexer([trade.open_date], method="pad")
+                if idx[0] < 0 or idx[0] >= len(dataframe):
+                    return None
+                return dataframe.iloc[idx[0]]
+            except (TypeError, ValueError):
+                return None