From bd293d2f5a21b78157b808c180302063b1dce1e3 Mon Sep 17 00:00:00 2001 From: Beast Trader Date: Sun, 7 Jun 2026 22:35:00 +0800 Subject: [PATCH] =?UTF-8?q?v0.2:=20=E5=A2=9E=E5=8A=A0ATR=E5=8A=A8=E6=80=81?= =?UTF-8?q?=E6=AD=A2=E6=8D=9F=20+=20=E5=A4=9A=E6=97=B6=E9=97=B4=E6=A1=86?= =?UTF-8?q?=E6=9E=B6=E7=BB=93=E6=9E=84=E5=88=86=E6=9E=90?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- strategy.py | 409 ++++++++-------------------------------------------- 1 file changed, 64 insertions(+), 345 deletions(-) diff --git a/strategy.py b/strategy.py index 83b9c1d..a312f17 100644 --- a/strategy.py +++ b/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 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。 - """ - +class PriceActionStrategy(IStrategy): 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 = -0.15 - use_custom_stoploss = True - minimal_roi = {"0": 100} + stoploss = -0.10 # [回测补] 首次缺失 + use_custom_stoploss = True # [回测补] 首次缺失 + minimal_roi = {"0": 100} # [回测补] 首次缺失 - # ── 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: - """ - 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 - # ============================================================ - + # Layer 1: D1 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 [ - "ema_fast", "ema_slow", "is_swing_high", "is_swing_low", - "last_swing_high", "last_swing_low", "trend_up", "trend_down", - ]: + for col in ["ema_fast", "ema_slow", "is_swing_high", "is_swing_low", + "last_swing_high", "last_swing_low", "trend_up", "trend_down"]: daily[col] = np.nan - dataframe = merge_informative_pair( - dataframe, daily, self.timeframe, "1d", ffill=True, - ) - - # ============================================================ - # Layer 2: 1H —— 中期结构 + S/R 区域 - # ============================================================ + dataframe = merge_informative_pair(dataframe, daily, self.timeframe, "1d", ffill=True) + # 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 [ - "ema_fast", "ema_slow", "is_swing_high", "is_swing_low", - "last_swing_high", "last_swing_low", "trend_up", "trend_down", - ]: + for col in ["ema_fast", "ema_slow", "is_swing_high", "is_swing_low", + "last_swing_high", "last_swing_low", "trend_up", "trend_down"]: hourly[col] = np.nan - dataframe = merge_informative_pair( - dataframe, hourly, self.timeframe, "1h", ffill=True, - ) + dataframe = merge_informative_pair(dataframe, hourly, self.timeframe, "1h", ffill=True) - # ============================================================ - # Layer 3: 5M —— 入场执行信号 - # ============================================================ - - # ATR + # Layer 3: 5M dataframe["atr"] = ta.ATR(dataframe, timeperiod=self.atr_period.value) - dataframe["atr_ratio"] = ( - dataframe["atr"] / dataframe["atr"].rolling(20).mean() - ) - - # 5M EMA + dataframe["atr_ratio"] = dataframe["atr"] / dataframe["atr"].rolling(20).mean() 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"] - > self.volume_surge_multiplier.value * dataframe["volume_ma20"] + dataframe["volume"] > 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, ) - # ============================================================ - # 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 - # ============================================================ - + # NaN 清理 [回测补] bool_cols = [ - "trend_up_1d", "trend_down_1d", - "trend_up_1h", "trend_down_1h", + "trend_up_1d", "trend_down_1d", "trend_up_1h", "trend_down_1h", "bullish_pinbar", "bearish_pinbar", - "bullish_engulfing", "bearish_engulfing", - "volume_surge", - "bullish_pattern_prev", "bearish_pattern_prev", + "bullish_engulfing", "bearish_engulfing", "volume_surge", ] 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: - """ - 入场逻辑 —— 四层确认（v0.3 强化版）： + daily_bullish = dataframe["trend_up_1d"] & (dataframe["close"] > dataframe["ema_fast_1d"]) + daily_bearish = dataframe["trend_down_1d"] & (dataframe["close"] < dataframe["ema_fast_1d"]) - 做多： - D1: 上升趋势 - 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） - """ + h1_not_bearish = ~dataframe["trend_down_1h"] + price_near_support = (dataframe["dist_to_support_pct"] < 3.0) & (dataframe["dist_to_support_pct"] > 0) - # ── 宏观环境 ── - - 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 ── + h1_not_bullish = ~dataframe["trend_up_1h"] + price_near_resistance = (dataframe["dist_to_resistance_pct"] < 3.0) & (dataframe["dist_to_resistance_pct"] > 0) 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 - consecutive_bullish = bullish_pattern & dataframe["bullish_pattern_prev"] - 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 ── + conditions_long = [daily_bullish, h1_not_bearish, price_near_support, bullish_pattern, normal_vol] + conditions_short = [daily_bearish, h1_not_bullish, price_near_resistance, bearish_pattern, normal_vol] if conditions_long: - dataframe.loc[ - reduce(lambda a, b: a & b, conditions_long), - "enter_long", - ] = 1 - + dataframe.loc[reduce(lambda a, b: a & b, conditions_long), "enter_long"] = 1 if conditions_short: - dataframe.loc[ - reduce(lambda a, b: a & b, conditions_short), - "enter_short", - ] = 1 + dataframe.loc[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 = [ - daily_no_longer_bearish, - h1_no_longer_bearish, - ] - - # ── 写入 ── + conditions_exit_long = [daily_no_longer_bullish] + conditions_exit_short = [daily_no_longer_bearish] if conditions_exit_long: - dataframe.loc[ - reduce(lambda a, b: a | b, conditions_exit_long), - "exit_long", - ] = 1 - + dataframe.loc[reduce(lambda a, b: a | b, conditions_exit_long), "exit_long"] = 1 if conditions_exit_short: - dataframe.loc[ - reduce(lambda a, b: a | b, conditions_exit_short), - "exit_short", - ] = 1 + dataframe.loc[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: 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 - """ + def custom_stoploss(self, pair, trade, current_time, current_rate, current_profit, + after_fill, **kwargs) -> Optional[float]: 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 * 3.0: - return -atr_ratio * 2.0 - elif profit_ratio > atr_ratio * 1.5: + if profit_ratio > atr_ratio * 2.0: + return -atr_ratio * 1.0 + elif profit_ratio > atr_ratio * 0.5: return 0 else: return -atr_ratio * self.atr_stop_multiplier.value else: - if current_profit > atr_ratio * 3.0: - return -atr_ratio * 2.0 - elif current_profit > atr_ratio * 1.5: + if current_profit > atr_ratio * 2.0: + return -atr_ratio * 1.0 + 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: 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。 - """ + def custom_exit(self, pair, trade, current_time, current_rate, current_profit, + **kwargs) -> Optional[str]: 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): - return "trend_reversed" + if last_candle.get("trend_up_1d", False): + return "daily_trend_reversed" else: - if last_candle.get("trend_down_1d", False) or last_candle.get("trend_down_1h", False): - return "trend_reversed" - + if last_candle.get("trend_down_1d", False): + return "daily_trend_reversed" return None - # ── 仓位管理 ────────────────────────────────────────────── - - 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%。 - """ + def custom_stake_amount(self, pair, current_time, current_rate, proposed_stake, + min_stake, max_stake, leverage, entry_tag, side, **kwargs) -> float: 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: str, - order_type: str, - amount: float, - rate: float, - time_in_force: str, - current_time, - entry_tag: Optional[str], - side: str, - **kwargs, - ) -> bool: + def confirm_trade_entry(self, pair, order_type, amount, rate, time_in_force, + current_time, entry_tag, side, **kwargs) -> bool: return True