v1.4: 进一步精简入场条件 + 止损收紧

strategy.py

@@ -1,19 +1,17 @@
 """
-Structure Flow Strategy v1.3
+Structure Flow Strategy v1.4
 =======================
 变更记录：
-  v1.0 (2026-06-07): 纯价格结构策略，D1定方向→4H定位→1H入场，支撑/阻力来自Swing Point
+  v1.0 (2026-06-07): 纯价格结构策略，D1定方向→4H定位→1H入场
-  v1.1 (2026-06-07): 修复freqtrade 2026.2 Binance futures bug(use_order_book:true)，
+  v1.1 (2026-06-07): 1H futures，结构止损，首次回测成功(+61.52%)
-                      硬止损改为结构失效点，首次futures回测成功(+61.52%)
+  v1.2 (2026-06-07): Entry Candle止损，bug导致50笔硬止损全亏
-  v1.2 (2026-06-07): 尝试Entry Candle止损（入场K线低点/高点），增加时间止损
+  v1.3 (2026-06-07): ATR动态止损，结果-63.72%，胜率20.2%
-                      结果：50笔硬止损全亏，Entry Candle查找有bug，return None退回到25%宽止损
+  v1.4 (2026-06-07): ===== 回归纯价格结构止损 =====
-  v1.3 (2026-06-07): ===== 重写 custom_stoploss =====
+                      - 完全移除ATR（违背价格行为内核）
-                      弃用脆弱的Entry Candle查找，改用ATR动态止损：
+                      - 止损 = support_4h(resistance_4h) ± 缓冲
-                      - 初始止损：入场价 ± 1.0 ATR（紧，快速认输）
+                      - support_4h随新Swing Low自动更新 → 天然追踪止损
-                      - 盈利>1%：移动止损至保本线
+                      - 新增入场过滤：止损距离>3%则跳过（赔率太差）
-                      - 盈利>2%：ATR追踪止损，锁定利润
+                      核心哲学：止损必须在价格结构位，不在指标计算结果
-                      - 硬止损安全网：-5%（stoploss属性）
-                      核心哲学：预估错误的交易，早早认输止损离场，不硬扛单
 """
 
 from datetime import datetime
@@ -24,32 +22,21 @@ from freqtrade.strategy import IStrategy, IntParameter, informative
 from freqtrade.persistence import Trade
 
 
-class StructureFlowStrategyV13(IStrategy):
+class StructureFlowStrategyV14(IStrategy):
     """
-    Structure Flow Strategy v1.3
+    Structure Flow Strategy v1.4 — 纯价格结构，零指标
 
-    核心逻辑：
+    止损逻辑（v1.4重写，完全移除ATR）：
-      D1 定宏观方向（HH/HL 上升，LH/LL 下降）
+      - 做多止损 = support_4h - 0.1%缓冲
-      ↓
+      - 做空止损 = resistance_4h + 0.1%缓冲
-      4H 定位结构位（Swing Point → 支撑/阻力区域）
+      - support_4h / resistance_4h 随时间更新 → 天然追踪止损
-      ↓
+      - 硬止损安全网：-5%（stoploss属性）
-      1H 找入场时机（K线形态 + 在结构区域内）
-
-    止损逻辑（v1.3重写）：
-      - 初始止损：入场价 ± 1.0 ATR（紧，快速认输）
-      - 盈利 > 1%：移动止损至保本（open_rate ± 0.1%）
-      - 盈利 > 2%：ATR追踪止损（current_rate ∓ 1.0 ATR）
-      - 硬止损安全网：-5%（防止极端行情）
     """
 
-    # =====================
-    # 基础属性
-    # =====================
-
     can_short = True
-    stoploss = -0.05  # 硬止损安全网 5%，实际由 custom_stoploss 动态管理
+    stoploss = -0.05
     use_custom_stoploss = True
-    minimal_roi = {"0": 100}  # 不设时间止盈，靠移动止损出场
+    minimal_roi = {"0": 100}
     max_open_trades = 1
     timeframe = "1h"
 
@@ -60,6 +47,8 @@ class StructureFlowStrategyV13(IStrategy):
     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")
+    # 最大可接受止损距离（超过则跳过入场）
+    max_stop_dist = IntParameter(20, 50, default=30, space="buy")
 
     # =====================
     # 工具：Swing Point 检测
@@ -71,7 +60,6 @@ class StructureFlowStrategyV13(IStrategy):
         low: pd.Series,
         window: int = 5,
     ) -> tuple[pd.Series, pd.Series]:
-        """检测 Swing High / Swing Low。"""
         n = len(high)
         sh = pd.Series(np.nan, index=high.index, dtype=float)
         sl = pd.Series(np.nan, index=low.index, dtype=float)
@@ -96,7 +84,6 @@ class StructureFlowStrategyV13(IStrategy):
         swing_high: pd.Series,
         swing_low: pd.Series,
     ) -> DataFrame:
-        """从 Swing Points 构建市场结构信息。"""
         n = len(high)
 
         trend_up_arr = np.full(n, False)
@@ -110,40 +97,33 @@ class StructureFlowStrategyV13(IStrategy):
         sl_prices = []
 
         for i in range(n):
-            if swing_high.iloc[i] and not np.isnan(high.iloc[i]):
+            if pd.notna(swing_high.iloc[i]):
-                sh_prices.append(high.iloc[i])
+                sh_prices.append(swing_high.iloc[i])
                 if len(sh_prices) > 4:
                     sh_prices.pop(0)
 
-            if swing_low.iloc[i] and not np.isnan(low.iloc[i]):
+            if pd.notna(swing_low.iloc[i]):
-                sl_prices.append(low.iloc[i])
+                sl_prices.append(swing_low.iloc[i])
                 if len(sl_prices) > 4:
                     sl_prices.pop(0)
 
-            # 趋势判断
             if len(sh_prices) >= 2 and len(sl_prices) >= 2:
-                latest_sh, prev_sh = sh_prices[-1], sh_prices[-2]
+                if sh_prices[-1] > sh_prices[-2] and sl_prices[-1] > sl_prices[-2]:
-                latest_sl, prev_sl = sl_prices[-1], sl_prices[-2]
-
-                if latest_sh > prev_sh and latest_sl > prev_sl:
                     trend_up_arr[i] = True
-                elif latest_sh < prev_sh and latest_sl < prev_sl:
+                elif sh_prices[-1] < sh_prices[-2] and sl_prices[-1] < sl_prices[-2]:
                     trend_down_arr[i] = True
-                else:
+                elif i > 0:
-                    # 沿用上一根K线的状态
+                    trend_up_arr[i] = trend_up_arr[i - 1]
-                    trend_up_arr[i] = trend_up_arr[i - 1] if i > 0 else False
+                    trend_down_arr[i] = trend_down_arr[i - 1]
-                    trend_down_arr[i] = trend_down_arr[i - 1] if i > 0 else False
             elif i > 0:
                 trend_up_arr[i] = trend_up_arr[i - 1]
                 trend_down_arr[i] = trend_down_arr[i - 1]
 
-            # 支撑/阻力
             if sl_prices:
                 nearest_support[i] = sl_prices[-1]
             if sh_prices:
                 nearest_resistance[i] = sh_prices[-1]
 
-            # 需求/供给区域
             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]
@@ -173,7 +153,6 @@ class StructureFlowStrategyV13(IStrategy):
         close: pd.Series,
         pin_bar_wick_ratio: float = 0.6,
     ) -> tuple[pd.Series, pd.Series, pd.Series, pd.Series]:
-        """检测 Pin Bar 和 Engulfing 形态。"""
         body = (close - open_).abs()
         total_range = (high - low).replace(0, 0.0001)
 
@@ -191,25 +170,6 @@ class StructureFlowStrategyV13(IStrategy):
 
         return bullish_pin, bearish_pin, bullish_engulf, bearish_engulf
 
-    # =====================
-    # 工具：ATR 计算
-    # =====================
-
-    @staticmethod
-    def _calc_atr(
-        high: pd.Series,
-        low: pd.Series,
-        close: pd.Series,
-        period: int = 14,
-    ) -> pd.Series:
-        """计算 ATR。"""
-        prev_close = close.shift(1)
-        tr = pd.concat(
-            [high - low, (high - prev_close).abs(), (low - prev_close).abs()],
-            axis=1,
-        ).max(axis=1)
-        return tr.rolling(period).mean()
-
     # ================================================================
     #  信息时间框架 — D1 宏观结构
     # ================================================================
@@ -252,11 +212,6 @@ class StructureFlowStrategyV13(IStrategy):
         dataframe["resistance"] = structure["resistance"]
         dataframe["in_demand"] = structure["in_demand"]
         dataframe["in_supply"] = structure["in_supply"]
-
-        # 4H ATR（保留，可能用于未来优化）
-        dataframe["atr"] = self._calc_atr(
-            dataframe["high"], dataframe["low"], dataframe["close"], period=14
-        )
         return dataframe
 
     # ================================================================
@@ -266,7 +221,7 @@ class StructureFlowStrategyV13(IStrategy):
     def populate_indicators(
         self, dataframe: DataFrame, metadata: dict
     ) -> DataFrame:
-        """1H 级别：K线形态 + ATR。"""
+        """1H 级别：K线形态（零指标）。"""
         bullish_pin, bearish_pin, bullish_engulf, bearish_engulf = (
             self._detect_candle_patterns(
                 dataframe["open"],
@@ -283,11 +238,6 @@ class StructureFlowStrategyV13(IStrategy):
         dataframe["bullish_signal"] = bullish_pin | bullish_engulf
         dataframe["bearish_signal"] = bearish_pin | bearish_engulf
 
-        # 1H ATR（用于动态止损）
-        dataframe["atr_1h"] = self._calc_atr(
-            dataframe["high"], dataframe["low"], dataframe["close"], period=14
-        )
-
         # NaN 安全处理
         bool_cols = [
             "trend_up_1d", "trend_down_1d",
@@ -310,15 +260,19 @@ class StructureFlowStrategyV13(IStrategy):
         入场逻辑（1H 时间框架）。
 
         做多条件:
-          1. D1 上升结构（trend_up_1d）
+          1. D1 上升结构（trend_up_1d）— 宏观方向
-          2. 4H 下半区 / 需求区域（in_demand_4h）
+          2. 4H 需求区域（in_demand_4h）— 在支撑附近
           3. 1H 看涨 K 线形态（bullish_signal）
+          4. 止损距离 ≤ max_stop_dist% — 赔率过滤
 
         做空条件:
           1. D1 下降结构（trend_down_1d）
-          2. 4H 上半区 / 供给区域（in_supply_4h）
+          2. 4H 供给区域（in_supply_4h）
           3. 1H 看跌 K 线形态（bearish_signal）
+          4. 止损距离 ≤ max_stop_dist%
         """
+        max_dist = self.max_stop_dist.value / 100.0
+
         # NaN 安全处理
         bool_cols = [
             "trend_up_1d", "trend_down_1d",
@@ -330,19 +284,29 @@ class StructureFlowStrategyV13(IStrategy):
             if col in dataframe.columns:
                 dataframe[col] = dataframe[col].fillna(False)
 
-        # 做多
+        # ── 做多 ──
+        # 止损距离 = (入场价 - support_4h) / 入场价
+        # support_4h 已 ffilled，取当前值
+        long_stop_dist = (dataframe["open"] - dataframe["support_4h"]) / dataframe["open"]
+
         long_conditions = (
             dataframe["trend_up_1d"]
             & dataframe["in_demand_4h"]
             & dataframe["bullish_signal"]
+            & (long_stop_dist <= max_dist)
+            & (long_stop_dist > 0.003)  # 至少0.3%距离（避免support就在眼前）
         )
         dataframe.loc[long_conditions, "enter_long"] = 1
 
-        # 做空
+        # ── 做空 ──
+        short_stop_dist = (dataframe["resistance_4h"] - dataframe["open"]) / dataframe["open"]
+
         short_conditions = (
             dataframe["trend_down_1d"]
             & dataframe["in_supply_4h"]
             & dataframe["bearish_signal"]
+            & (short_stop_dist <= max_dist)
+            & (short_stop_dist > 0.003)
         )
         dataframe.loc[short_conditions, "enter_short"] = 1
 
@@ -363,7 +327,7 @@ class StructureFlowStrategyV13(IStrategy):
         return dataframe
 
     # =====================
-    # 动态止损 — v1.3 重写
+    # 动态止损 — v1.4 重写：纯价格结构
     # =====================
 
     def custom_stoploss(
@@ -377,63 +341,57 @@ class StructureFlowStrategyV13(IStrategy):
         **kwargs,
     ) -> float:
         """
-        v1.3 止损逻辑（完全重写）：
+        v1.4 止损逻辑：完全基于价格结构，零指标。
 
-        核心哲学：「预估错误的交易，早早认输止损离场，而不要硬扛单」
+        止损位：
+          做多 → support_4h - 0.1%缓冲（最近4H Swing Low下方）
+          做空 → resistance_4h + 0.1%缓冲（最近4H Swing High上方）
 
-        三阶段止损：
+        support_4h / resistance_4h 随新Swing Point自动更新，
+        天然形成追踪止损效果。
 
-        阶段一（无盈利或微盈利 < 1%）：
+        永不返回 None，始终返回显式止损比率。
-          止损 = 入场价 ± 1.0 ATR
+        最终截断在 -5% / +5% 安全网内。
-          → 距离近，价格稍有不利变动就止损，快速认输
-
-        阶段二（盈利 1% ~ 2%）：
-          止损移动至保本线（open_rate ± 0.1%）
-          → 这笔交易已经不亏了，卸下心理负担
-
-        阶段三（盈利 > 2%）：
-          追踪止损 = current_rate ∓ 1.0 ATR
-          → 价格回调超过1ATR才出场，给趋势足够的呼吸空间
-
-        参数说明：
-          - ATR 来自当前1H K线的 atr_1h 值
-          - 如果 ATR 为 NaN，fallback 到 2% 固定止损
-          - 最终返回的止损比率不会超过 -5%（硬止损安全网）
         """
         dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
         if dataframe is None or len(dataframe) == 0:
+            # 极端情况：返回2%固定止损
             return -0.02 if not trade.is_short else 0.02
 
-        last_candle = dataframe.iloc[-1]
+        last = dataframe.iloc[-1]
-        atr = last_candle.get("atr_1h", np.nan)
-
-        if pd.isna(atr) or atr <= 0:
-            atr = current_rate * 0.02
-        else:
-            atr = float(atr)
-
-        open_rate = trade.open_rate
 
         if not trade.is_short:
-            # ── 做多 ──
+            support = last.get("support_4h", np.nan)
-            if current_profit <= 0.01:
+            if pd.isna(support) or support <= 0:
-                sl_price = open_rate - atr * 1.0
+                return -0.02  # fallback
-            elif current_profit <= 0.02:
+            # 止损 = support_4h 下方 0.1%
-                sl_price = open_rate * 0.999
+            sl_price = support * 0.999
-            else:
-                sl_price = current_rate - atr * 1.0
-
             sl_ratio = (sl_price / current_rate) - 1.0
             return max(sl_ratio, -0.05)
-
         else:
-            # ── 做空 ──
+            resistance = last.get("resistance_4h", np.nan)
-            if current_profit <= 0.01:
+            if pd.isna(resistance) or resistance <= 0:
-                sl_price = open_rate + atr * 1.0
+                return 0.02  # fallback
-            elif current_profit <= 0.02:
+            # 止损 = resistance_4h 上方 0.1%
-                sl_price = open_rate * 1.001
+            sl_price = resistance * 1.001
-            else:
-                sl_price = current_rate + atr * 1.0
-
             sl_ratio = 1.0 - (sl_price / current_rate)
             return min(sl_ratio, 0.05)
+
+    # =====================
+    # 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"},
+                },
+            },
+        }