v0.2: 增加ATR动态止损 + 多时间框架结构分析

strategy.py

@@ -2,7 +2,7 @@
 多时间框架价格行为策略 — ETH/USDT 中低频交易
 ==============================================
 
-设计理念 (v0.3)：
+设计理念 (v0.2)：
 
 1. 反转大多会失败 → 不做反转预测，只做趋势延续。
    在 S/R 位入场不是赌反弹，是赌"回调结束、趋势恢复"。
@@ -16,7 +16,17 @@
 
 核心原则：只在大趋势方向上，在关键位置，等确认信号入场。
 
-版本：v0.3.0 — v0.2 回测后优化
+版本：v0.2.0 — 多时间框架重构
+回测日期：2026-06-07
+回测结果：1253笔 / 胜率17.4% / -0.36% / 平均持仓24min
+
+已知问题（诊断见 docs/backtest-pitfalls.md）：
+  1. 成交量 surge 计算了但未用于入场过滤 → 信号过多
+  2. 1H 只要求"非反向"而非"同向" → 过滤太弱
+  3. 止损太紧（保本0.5ATR/追踪1.0ATR） → 持仓仅24min
+  4. 缺少最低波动率过滤
+
+注意：以下属性在首次回测时缺失，后补（stoploss/use_custom_stoploss/minimal_roi/NaN清理）
 """
 
 from functools import reduce
@@ -30,17 +40,7 @@ from freqtrade.strategy import IStrategy, merge_informative_pair
 from freqtrade.strategy import IntParameter, DecimalParameter
 
 
-# ── 工具函数：Swing Point 检测 ──────────────────────────────────
-
-
 def detect_swing_points(df: DataFrame, window: int, col_high="high", col_low="low"):
-    """
-    在给定 DataFrame 上检测 Swing High / Swing Low。
-
-    返回添加了以下列的 DataFrame：
-      - is_swing_high / is_swing_low : bool
-      - last_swing_high / last_swing_low : float (前向填充)
-    """
     w = int(window)
     roll_max = df[col_high].rolling(window=w, center=True).max()
     roll_min = df[col_low].rolling(window=w, center=True).min()
@@ -63,24 +63,18 @@ def detect_swing_points(df: DataFrame, window: int, col_high="high", col_low="lo
 
 
 def detect_candle_patterns(df: DataFrame, pin_body_ratio=0.3, engulf_ratio=1.5):
-    """
-    K线形态检测。返回添加了形态布尔列的 DataFrame。
-    """
     body = abs(df["close"] - df["open"])
     c_range = df["high"] - df["low"]
     upper_wick = df["high"] - df[["open", "close"]].max(axis=1)
     lower_wick = df[["open", "close"]].min(axis=1) - df["low"]
     safe_range = c_range.replace(0, np.nan)
 
-    # 看涨 Pin Bar（锤子线）
     df["bullish_pinbar"] = (
         (body < pin_body_ratio * safe_range)
         & (lower_wick > 2 * body)
         & (lower_wick > upper_wick)
         & (df["close"] > df["open"])
     )
-
-    # 看跌 Pin Bar（射击之星）
     df["bearish_pinbar"] = (
         (body < pin_body_ratio * safe_range)
         & (upper_wick > 2 * body)
@@ -88,7 +82,6 @@ def detect_candle_patterns(df: DataFrame, pin_body_ratio=0.3, engulf_ratio=1.5):
         & (df["close"] < df["open"])
     )
 
-    # 看涨吞没
     prev_open = df["open"].shift(1)
     prev_close = df["close"].shift(1)
     prev_body = abs(prev_close - prev_open)
@@ -100,8 +93,6 @@ def detect_candle_patterns(df: DataFrame, pin_body_ratio=0.3, engulf_ratio=1.5):
         & (df["close"] > prev_open)
         & (body > engulf_ratio * prev_body)
     )
-
-    # 看跌吞没
     df["bearish_engulfing"] = (
         (prev_close > prev_open)
         & (df["close"] < df["open"])
@@ -113,28 +104,9 @@ def detect_candle_patterns(df: DataFrame, pin_body_ratio=0.3, engulf_ratio=1.5):
     return df
 
 
-# ── 策略类 ──────────────────────────────────────────────────────
+class PriceActionStrategy(IStrategy):
-
-
-class PriceActionStrategyV03(IStrategy):
-    """
-    多时间框架价格行为策略 — D1 定方向 → 1H 找结构 → 5M 抓时机。
-
-    v0.3 相比 v0.2 的核心改进：
-      - 成交量确认由"计算但未使用"→ 成为入场必要条件
-      - 1H 趋势要求从"非反向"→ 必须同向
-      - S/R 接近阈值从 3.0% → 1.5%
-      - 移动止损更宽：初始 2.0 ATR / 保本 1.5 ATR / 追踪 2.0 ATR
-      - 新增最低 ATR 波动率过滤
-      - 出场增加 1H 趋势反转条件
-
-    适用：ETH/USDT 永续合约，Binance，5M 主时间框架。
-    """
-
     INTERFACE_VERSION = 3
 
-    # ── 基础设置 ──────────────────────────────────────────────
-
     timeframe = "5m"
     can_short = True
     max_open_trades = 1
@@ -142,42 +114,25 @@ class PriceActionStrategyV03(IStrategy):
     process_only_new_candles = True
     use_exit_signal = True
 
-    # ── 运行时强制属性（回测配置补齐） ─────────────────────────
+    stoploss = -0.10            # [回测补] 首次缺失
-    stoploss = -0.15
+    use_custom_stoploss = True  # [回测补] 首次缺失
-    use_custom_stoploss = True
+    minimal_roi = {"0": 100}    # [回测补] 首次缺失
-    minimal_roi = {"0": 100}
 
-    # ── 可优化参数 ────────────────────────────────────────────
-
-    # -- 日线（宏观）--
     ema_fast_daily = IntParameter(10, 30, default=20, space="buy")
     ema_slow_daily = IntParameter(40, 80, default=50, space="buy")
     swing_window_daily = IntParameter(3, 10, default=5, space="buy")
 
-    # -- 1H（中期结构）--
     ema_fast_h1 = IntParameter(10, 30, default=20, space="buy")
     ema_slow_h1 = IntParameter(40, 80, default=50, space="buy")
     swing_window_h1 = IntParameter(3, 10, default=5, space="buy")
 
-    # -- ATR 止损 --
     atr_period = IntParameter(10, 28, default=14, space="buy")
-    atr_stop_multiplier = DecimalParameter(1.5, 3.0, default=2.0, space="sell")
+    atr_stop_multiplier = DecimalParameter(1.0, 3.0, default=1.5, space="sell")
 
-    # -- K线形态 --
     pin_bar_body_ratio = DecimalParameter(0.15, 0.40, default=0.30, space="buy")
     engulfing_body_ratio = DecimalParameter(1.2, 3.0, default=1.5, space="buy")
-
-    # -- 成交量 --
     volume_surge_multiplier = DecimalParameter(1.2, 3.0, default=1.5, space="buy")
 
-    # -- S/R 接近阈值 --
-    sr_proximity_pct = DecimalParameter(0.5, 3.0, default=1.5, space="buy")
-
-    # -- ATR 最低波动率 --
-    min_atr_ratio = DecimalParameter(0.3, 1.0, default=0.5, space="buy")
-
-    # ── 多时间框架声明 ────────────────────────────────────────
-
     def informative_pairs(self):
         pairs = self.dp.current_whitelist()
         informative_pairs = []
@@ -186,29 +141,13 @@ class PriceActionStrategyV03(IStrategy):
             informative_pairs.append((pair, "1d"))
         return informative_pairs
 
-    # ── 指标计算 ──────────────────────────────────────────────
-
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        """
+        # Layer 1: D1
-        三层时间框架的指标计算流水线：
-
-        Layer 1 — D1：宏观趋势方向
-        Layer 2 — 1H：S/R 区域 + 中期结构
-        Layer 3 — 5M：入场信号 + K线形态
-        """
-
-        # ============================================================
-        # Layer 1: 日线 —— 宏观方向
-        # ============================================================
-
         daily = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe="1d")
-
         if not daily.empty:
             daily["ema_fast"] = ta.EMA(daily, timeperiod=self.ema_fast_daily.value)
             daily["ema_slow"] = ta.EMA(daily, timeperiod=self.ema_slow_daily.value)
-
             daily = detect_swing_points(daily, self.swing_window_daily.value)
-
             daily["trend_up"] = (
                 (daily["ema_fast"] > daily["ema_slow"])
                 & (daily["close"] > daily["ema_fast"])
@@ -219,28 +158,18 @@ class PriceActionStrategyV03(IStrategy):
             )
         else:
             daily = dataframe.copy()
-            for col in [
+            for col in ["ema_fast", "ema_slow", "is_swing_high", "is_swing_low",
-                "ema_fast", "ema_slow", "is_swing_high", "is_swing_low",
+                        "last_swing_high", "last_swing_low", "trend_up", "trend_down"]:
-                "last_swing_high", "last_swing_low", "trend_up", "trend_down",
-            ]:
                 daily[col] = np.nan
 
-        dataframe = merge_informative_pair(
+        dataframe = merge_informative_pair(dataframe, daily, self.timeframe, "1d", ffill=True)
-            dataframe, daily, self.timeframe, "1d", ffill=True,
-        )
-
-        # ============================================================
-        # Layer 2: 1H —— 中期结构 + S/R 区域
-        # ============================================================
 
+        # Layer 2: 1H
         hourly = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe="1h")
-
         if not hourly.empty:
             hourly["ema_fast"] = ta.EMA(hourly, timeperiod=self.ema_fast_h1.value)
             hourly["ema_slow"] = ta.EMA(hourly, timeperiod=self.ema_slow_h1.value)
-
             hourly = detect_swing_points(hourly, self.swing_window_h1.value)
-
             hourly["trend_up"] = (
                 (hourly["ema_fast"] > hourly["ema_slow"])
                 & (hourly["close"] > hourly["ema_fast"])
@@ -251,53 +180,33 @@ class PriceActionStrategyV03(IStrategy):
             )
         else:
             hourly = dataframe.copy()
-            for col in [
+            for col in ["ema_fast", "ema_slow", "is_swing_high", "is_swing_low",
-                "ema_fast", "ema_slow", "is_swing_high", "is_swing_low",
+                        "last_swing_high", "last_swing_low", "trend_up", "trend_down"]:
-                "last_swing_high", "last_swing_low", "trend_up", "trend_down",
-            ]:
                 hourly[col] = np.nan
 
-        dataframe = merge_informative_pair(
+        dataframe = merge_informative_pair(dataframe, hourly, self.timeframe, "1h", ffill=True)
-            dataframe, hourly, self.timeframe, "1h", ffill=True,
-        )
 
-        # ============================================================
+        # Layer 3: 5M
-        # Layer 3: 5M —— 入场执行信号
-        # ============================================================
-
-        # ATR
         dataframe["atr"] = ta.ATR(dataframe, timeperiod=self.atr_period.value)
-        dataframe["atr_ratio"] = (
+        dataframe["atr_ratio"] = dataframe["atr"] / dataframe["atr"].rolling(20).mean()
-            dataframe["atr"] / dataframe["atr"].rolling(20).mean()
-        )
-
-        # 5M EMA
         dataframe["ema_20_5m"] = ta.EMA(dataframe, timeperiod=20)
 
-        # K线形态
         dataframe = detect_candle_patterns(
             dataframe,
             pin_body_ratio=self.pin_bar_body_ratio.value,
             engulf_ratio=self.engulfing_body_ratio.value,
         )
 
-        # 成交量确认
         dataframe["volume_ma20"] = dataframe["volume"].rolling(20).mean()
         dataframe["volume_surge"] = (
-            dataframe["volume"]
+            dataframe["volume"] > self.volume_surge_multiplier.value * dataframe["volume_ma20"]
-            > self.volume_surge_multiplier.value * dataframe["volume_ma20"]
         )
 
-        # ============================================================
-        # S/R 距离
-        # ============================================================
-
         support = dataframe["last_swing_low_1h"]
         resistance = dataframe["last_swing_high_1h"]
-
         dataframe["dist_to_support_pct"] = np.where(
             support > 0,
-            (dataframe["close"] - support) / support * 100,
+            (dataframe["close"] - support) / dataframe["close"] * 100,
             np.nan,
         )
         dataframe["dist_to_resistance_pct"] = np.where(
@@ -306,24 +215,11 @@ class PriceActionStrategyV03(IStrategy):
             np.nan,
         )
 
-        # ============================================================
+        # NaN 清理 [回测补]
-        # v0.3 新增：连续确认（避免单根假突破）
-        # ============================================================
-
-        dataframe["bullish_pattern_prev"] = dataframe["bullish_pinbar"].shift(1) | dataframe["bullish_engulfing"].shift(1)
-        dataframe["bearish_pattern_prev"] = dataframe["bearish_pinbar"].shift(1) | dataframe["bearish_engulfing"].shift(1)
-
-        # ============================================================
-        # NaN 清理：多时间框架合并后布尔列前部有 NaN
-        # ============================================================
-
         bool_cols = [
-            "trend_up_1d", "trend_down_1d",
+            "trend_up_1d", "trend_down_1d", "trend_up_1h", "trend_down_1h",
-            "trend_up_1h", "trend_down_1h",
             "bullish_pinbar", "bearish_pinbar",
-            "bullish_engulfing", "bearish_engulfing",
+            "bullish_engulfing", "bearish_engulfing", "volume_surge",
-            "volume_surge",
-            "bullish_pattern_prev", "bearish_pattern_prev",
         ]
         for col in bool_cols:
             if col in dataframe.columns:
@@ -331,178 +227,46 @@ class PriceActionStrategyV03(IStrategy):
 
         return dataframe
 
-    # ── 入场信号 ──────────────────────────────────────────────
-
     def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        """
+        daily_bullish = dataframe["trend_up_1d"] & (dataframe["close"] > dataframe["ema_fast_1d"])
-        入场逻辑 —— 四层确认（v0.3 强化版）：
+        daily_bearish = dataframe["trend_down_1d"] & (dataframe["close"] < dataframe["ema_fast_1d"])
 
-        做多：
+        h1_not_bearish = ~dataframe["trend_down_1h"]
-          D1: 上升趋势
+        price_near_support = (dataframe["dist_to_support_pct"] < 3.0) & (dataframe["dist_to_support_pct"] > 0)
-          1H: 也必须上升趋势（v0.2 只要求"非下降"→ v0.3 要求同向）
-          5M: 价格在支撑附近(<1.5%) + 看涨形态 + 成交量放大 + 连续确认
-          风控: ATR 波动率充足（不在沉闷市场中交易）
-        """
 
-        # ── 宏观环境 ──
+        h1_not_bullish = ~dataframe["trend_up_1h"]
-
+        price_near_resistance = (dataframe["dist_to_resistance_pct"] < 3.0) & (dataframe["dist_to_resistance_pct"] > 0)
-        daily_bullish = (
-            dataframe["trend_up_1d"]
-            & (dataframe["close"] > dataframe["ema_fast_1d"])
-        )
-        daily_bearish = (
-            dataframe["trend_down_1d"]
-            & (dataframe["close"] < dataframe["ema_fast_1d"])
-        )
-
-        # ── 1H 中期条件 ──
-
-        # v0.3 改动：从 "h1_not_bearish" 升级为 "h1_bullish"（必须同向）
-        h1_bullish = dataframe["trend_up_1h"] & (dataframe["close"] > dataframe["ema_fast_1h"])
-        h1_bearish = dataframe["trend_down_1h"] & (dataframe["close"] < dataframe["ema_fast_1h"])
-
-        sr_pct = self.sr_proximity_pct.value
-
-        price_near_support = (
-            (dataframe["dist_to_support_pct"] < sr_pct)
-            & (dataframe["dist_to_support_pct"] > 0)
-        )
-        price_near_resistance = (
-            (dataframe["dist_to_resistance_pct"] < sr_pct)
-            & (dataframe["dist_to_resistance_pct"] > 0)
-        )
-
-        # ── 5M 入场形态 ──
 
         bullish_pattern = dataframe["bullish_pinbar"] | dataframe["bullish_engulfing"]
         bearish_pattern = dataframe["bearish_pinbar"] | dataframe["bearish_engulfing"]
-
-        # ── v0.3 新增过滤 ──
-
-        # 成交量必选
-        volume_ok = dataframe["volume_surge"]
-
-        # 最低波动率：ATR 不能太小（市场太沉闷不做）
-        sufficient_volatility = dataframe["atr_ratio"] >= self.min_atr_ratio.value
-
-        # 避免极端波动
         normal_vol = dataframe["atr_ratio"] < 2.0
 
-        # 连续确认：当前和前一根 K 线都有看涨/看跌信号，减少假突破
+        conditions_long = [daily_bullish, h1_not_bearish, price_near_support, bullish_pattern, normal_vol]
-        consecutive_bullish = bullish_pattern & dataframe["bullish_pattern_prev"]
+        conditions_short = [daily_bearish, h1_not_bullish, price_near_resistance, bearish_pattern, normal_vol]
-        consecutive_bearish = bearish_pattern & dataframe["bearish_pattern_prev"]
-
-        # ============================================================
-        # 做多条件（严格过滤）
-        # ============================================================
-
-        conditions_long = [
-            daily_bullish,
-            h1_bullish,               # v0.3: 1H 必须同向上升
-            price_near_support,
-            bullish_pattern,
-            volume_ok,                # v0.3: 成交量必选
-            sufficient_volatility,    # v0.3: 最低波动率
-            normal_vol,
-        ]
-
-        # ============================================================
-        # 做空条件（严格过滤）
-        # ============================================================
-
-        conditions_short = [
-            daily_bearish,
-            h1_bearish,               # v0.3: 1H 必须同向下降
-            price_near_resistance,
-            bearish_pattern,
-            volume_ok,
-            sufficient_volatility,
-            normal_vol,
-        ]
-
-        # ── 写入信号 ──
 
         if conditions_long:
-            dataframe.loc[
+            dataframe.loc[reduce(lambda a, b: a & b, conditions_long), "enter_long"] = 1
-                reduce(lambda a, b: a & b, conditions_long),
-                "enter_long",
-            ] = 1
-
         if conditions_short:
-            dataframe.loc[
+            dataframe.loc[reduce(lambda a, b: a & b, conditions_short), "enter_short"] = 1
-                reduce(lambda a, b: a & b, conditions_short),
-                "enter_short",
-            ] = 1
 
         return dataframe
 
-    # ── 出场信号 ──────────────────────────────────────────────
-
     def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        """
-        信号出场（v0.3 增强）：
-
-        主要出场仍由 custom_stoploss 的移动止损处理。
-        这里追加结构破坏级别的强制离场。
-        """
-
-        # ── 多头离场 ──
-
         daily_no_longer_bullish = ~dataframe["trend_up_1d"]
-        h1_no_longer_bullish = ~dataframe["trend_up_1h"]  # v0.3 新增
-
-        conditions_exit_long = [
-            daily_no_longer_bullish,
-            h1_no_longer_bullish,
-        ]
-
-        # ── 空头离场 ──
-
         daily_no_longer_bearish = ~dataframe["trend_down_1d"]
-        h1_no_longer_bearish = ~dataframe["trend_down_1h"]  # v0.3 新增
 
-        conditions_exit_short = [
+        conditions_exit_long = [daily_no_longer_bullish]
-            daily_no_longer_bearish,
+        conditions_exit_short = [daily_no_longer_bearish]
-            h1_no_longer_bearish,
-        ]
-
-        # ── 写入 ──
 
         if conditions_exit_long:
-            dataframe.loc[
+            dataframe.loc[reduce(lambda a, b: a | b, conditions_exit_long), "exit_long"] = 1
-                reduce(lambda a, b: a | b, conditions_exit_long),
-                "exit_long",
-            ] = 1
-
         if conditions_exit_short:
-            dataframe.loc[
+            dataframe.loc[reduce(lambda a, b: a | b, conditions_exit_short), "exit_short"] = 1
-                reduce(lambda a, b: a | b, conditions_exit_short),
-                "exit_short",
-            ] = 1
 
         return dataframe
 
-    # ── 动态移动止损 ──────────────────────────────────────────
+    def custom_stoploss(self, pair, trade, current_time, current_rate, current_profit,
-
+                        after_fill, **kwargs) -> Optional[float]:
-    def custom_stoploss(
-        self,
-        pair: str,
-        trade,
-        current_time,
-        current_rate: float,
-        current_profit: float,
-        after_fill: bool,
-        **kwargs,
-    ) -> Optional[float]:
-        """
-        v0.3 宽止损设计 —— 给趋势呼吸空间：
-
-        阶段1（利润 < 1.5 ATR）：初始止损 ATR × 2.0
-        阶段2（利润 1.5~3.0 ATR）：保本
-        阶段3（利润 > 3.0 ATR）：追踪止损 ATR × 2.0
-
-        v0.2 参考：初始 1.5 / 保本 0.5 / 追踪 1.0
-        """
         dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
         if dataframe.empty:
             return None
@@ -514,68 +278,37 @@ class PriceActionStrategyV03(IStrategy):
 
         if trade.is_short:
             profit_ratio = -current_profit
-
+            if profit_ratio > atr_ratio * 2.0:
-            if profit_ratio > atr_ratio * 3.0:
+                return -atr_ratio * 1.0
-                return -atr_ratio * 2.0
+            elif profit_ratio > atr_ratio * 0.5:
-            elif profit_ratio > atr_ratio * 1.5:
                 return 0
             else:
                 return -atr_ratio * self.atr_stop_multiplier.value
         else:
-            if current_profit > atr_ratio * 3.0:
+            if current_profit > atr_ratio * 2.0:
-                return -atr_ratio * 2.0
+                return -atr_ratio * 1.0
-            elif current_profit > atr_ratio * 1.5:
+            elif current_profit > atr_ratio * 0.5:
                 return 0
             else:
                 return -atr_ratio * self.atr_stop_multiplier.value
 
-    # ── 自定义出场（结构破坏） ────────────────────────────────
+    def custom_exit(self, pair, trade, current_time, current_rate, current_profit,
-
+                    **kwargs) -> Optional[str]:
-    def custom_exit(
-        self,
-        pair: str,
-        trade,
-        current_time,
-        current_rate: float,
-        current_profit: float,
-        **kwargs,
-    ) -> Optional[str]:
-        """
-        结构层面出场：D1 或 1H 趋势反转 → 立刻离场。
-        """
         dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
         if dataframe.empty:
             return None
 
         last_candle = dataframe.iloc[-1]
-
         if trade.is_short:
-            if last_candle.get("trend_up_1d", False) or last_candle.get("trend_up_1h", False):
+            if last_candle.get("trend_up_1d", False):
-                return "trend_reversed"
+                return "daily_trend_reversed"
         else:
-            if last_candle.get("trend_down_1d", False) or last_candle.get("trend_down_1h", False):
+            if last_candle.get("trend_down_1d", False):
-                return "trend_reversed"
+                return "daily_trend_reversed"
-
         return None
 
-    # ── 仓位管理 ──────────────────────────────────────────────
+    def custom_stake_amount(self, pair, current_time, current_rate, proposed_stake,
-
+                            min_stake, max_stake, leverage, entry_tag, side, **kwargs) -> float:
-    def custom_stake_amount(
-        self,
-        pair: str,
-        current_time,
-        current_rate: float,
-        proposed_stake: float,
-        min_stake: Optional[float],
-        max_stake: float,
-        leverage: float,
-        entry_tag: Optional[str],
-        side: str,
-        **kwargs,
-    ) -> float:
-        """
-        固定风险仓位管理：每次交易风险 = 账户的 1%。
-        """
         dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
         if dataframe.empty:
             return min_stake or proposed_stake
@@ -589,24 +322,10 @@ class PriceActionStrategyV03(IStrategy):
 
         position_size = risk_amount / stop_distance if stop_distance > 0 else proposed_stake
         position_size = min(position_size, max_stake or float("inf"))
-
         if min_stake and position_size < min_stake:
             return 0
-
         return position_size
 
-    # ── 最终入场确认 ──────────────────────────────────────────
+    def confirm_trade_entry(self, pair, order_type, amount, rate, time_in_force,
-
+                            current_time, entry_tag, side, **kwargs) -> bool:
-    def confirm_trade_entry(
-        self,
-        pair: str,
-        order_type: str,
-        amount: float,
-        rate: float,
-        time_in_force: str,
-        current_time,
-        entry_tag: Optional[str],
-        side: str,
-        **kwargs,
-    ) -> bool:
         return True