""" PriceActionStrategy - 纯价格行为策略 核心原则： 零指标。不用 EMA、RSI、MACD、ATR 或任何计算指标。 只看 K线本身（Open/High/Low/Close/Volume）。 价格行为判断方法： 1. 市场结构（趋势）：用 Swing High / Swing Low 判断 - 上升趋势 = Higher High + Higher Low - 下降趋势 = Lower High + Lower Low 2. 入场信号：纯K线形态 - Pin Bar（锤子线/射击之星） - 吞没形态（Engulfing） - Inside Bar 突破 3. 出场：用前一个 Swing High/Low 作为止盈目标 4. 止损：放在信号K线的另一端 使用时间框架： - 5m（主时间框架）：入场/出场 + 结构判断 """ import numpy as np from pandas import DataFrame from freqtrade.strategy import IStrategy class PriceActionStrategy(IStrategy): """ 纯价格行为策略 - 零指标 """ INTERFACE_VERSION = 3 # === 基础配置 === timeframe = "5m" can_short = True stoploss = -0.03 # 3% 硬止损安全网 trailing_stop = False use_custom_stoploss = False startup_candle_count: int = 100 # 不用 ROI 自动止盈，让价格行为决定出场 minimal_roi = {} order_types = { "entry": "market", "exit": "market", "stoploss": "market", "stoploss_on_exchange": False, } use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # === 参数 === swing_lookback = 10 # Swing High/Low 回看K线数 min_body_ratio = 0.55 # 最小实体占比（实体/全幅） pin_shadow_ratio = 2.5 # Pin Bar 影线至少是实体的 N 倍 engulf_body_ratio = 1.2 # 吞没K线实体至少是前一根的 N 倍 def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ 纯价格行为 — 只从 OHLCV 提取结构信息，不计算任何技术指标。 """ df = dataframe # ========== K线基础属性 ========== df["body"] = abs(df["close"] - df["open"]) df["candle_range"] = df["high"] - df["low"] df["body_ratio"] = df["body"] / (df["candle_range"] + 1e-10) df["upper_shadow"] = df["high"] - df[["close", "open"]].max(axis=1) df["lower_shadow"] = df[["close", "open"]].min(axis=1) - df["low"] df["is_bull"] = (df["close"] > df["open"]).astype(int) df["is_bear"] = (df["close"] < df["open"]).astype(int) # ========== Swing High / Swing Low ========== # Swing High: 当前 high 是前后 N 根K线中最高的 # Swing Low: 当前 low 是前后 N 根K线中最低的 n = self.swing_lookback df["swing_high"] = df["high"].rolling(window=2 * n + 1, center=True).apply( lambda x: 1 if x.iloc[n] == x.max() else 0, raw=False ) df["swing_low"] = df["low"].rolling(window=2 * n + 1, center=True).apply( lambda x: 1 if x.iloc[n] == x.min() else 0, raw=False ) # 记录最近的 Swing High/Low 价格 df["last_swing_high"] = np.nan df["last_swing_low"] = np.nan df["prev_swing_high"] = np.nan df["prev_swing_low"] = np.nan df.loc[df["swing_high"] == 1, "last_swing_high"] = df["high"] df["last_swing_high"] = df["last_swing_high"].ffill() df.loc[df["swing_low"] == 1, "last_swing_low"] = df["low"] df["last_swing_low"] = df["last_swing_low"].ffill() # 前一个 Swing High/Low（用于判断 HH/HL/LH/LL） swing_high_prices = df.loc[df["swing_high"] == 1, "high"] swing_low_prices = df.loc[df["swing_low"] == 1, "low"] # 构建 prev_swing_high: 每个 swing high 点对应的上一个 swing high sh_idx = swing_high_prices.index.tolist() for i in range(1, len(sh_idx)): df.loc[sh_idx[i], "prev_swing_high"] = swing_high_prices.loc[sh_idx[i - 1]] df["prev_swing_high"] = df["prev_swing_high"].ffill() sl_idx = swing_low_prices.index.tolist() for i in range(1, len(sl_idx)): df.loc[sl_idx[i], "prev_swing_low"] = swing_low_prices.loc[sl_idx[i - 1]] df["prev_swing_low"] = df["prev_swing_low"].ffill() # ========== 市场结构（趋势）========== # Higher High + Higher Low = 上升趋势 # Lower High + Lower Low = 下降趋势 df["higher_high"] = (df["last_swing_high"] > df["prev_swing_high"]).astype(int) df["higher_low"] = (df["last_swing_low"] > df["prev_swing_low"]).astype(int) df["lower_high"] = (df["last_swing_high"] < df["prev_swing_high"]).astype(int) df["lower_low"] = (df["last_swing_low"] < df["prev_swing_low"]).astype(int) df["uptrend"] = ((df["higher_high"] == 1) & (df["higher_low"] == 1)).astype(int) df["downtrend"] = ((df["lower_high"] == 1) & (df["lower_low"] == 1)).astype(int) # ========== 价格行为形态 ========== # --- Pin Bar（锤子线 / 射击之星）--- # 看涨 Pin Bar: 长下影线，短上影线，实体在上半部分 df["bullish_pin"] = ( (df["lower_shadow"] > df["body"] * self.pin_shadow_ratio) & (df["lower_shadow"] > df["upper_shadow"] * 2) & (df["body_ratio"] > 0.15) # 不是十字星 & (df["is_bull"] == 1) ).astype(int) # 看跌 Pin Bar: 长上影线，短下影线，实体在下半部分 df["bearish_pin"] = ( (df["upper_shadow"] > df["body"] * self.pin_shadow_ratio) & (df["upper_shadow"] > df["lower_shadow"] * 2) & (df["body_ratio"] > 0.15) & (df["is_bear"] == 1) ).astype(int) # --- 吞没形态（Engulfing）--- prev_body = df["body"].shift(1) prev_open = df["open"].shift(1) prev_close = df["close"].shift(1) # 看涨吞没: 前一根阴线，当前阳线完全包住前一根 df["bullish_engulf"] = ( (df["is_bull"] == 1) & (prev_close < prev_open) # 前一根是阴线 & (df["open"] <= prev_close) # 开盘 <= 前收盘（低开或平开） & (df["close"] >= prev_open) # 收盘 >= 前开盘（完全吞没） & (df["body"] > prev_body * self.engulf_body_ratio) # 实体更大 ).astype(int) # 看跌吞没 df["bearish_engulf"] = ( (df["is_bear"] == 1) & (prev_close > prev_open) # 前一根是阳线 & (df["open"] >= prev_close) # 开盘 >= 前收盘 & (df["close"] <= prev_open) # 收盘 <= 前开盘 & (df["body"] > prev_body * self.engulf_body_ratio) ).astype(int) # --- Inside Bar 突破 --- # Inside Bar: 当前K线的 high/low 完全在前一根范围内 prev_high = df["high"].shift(1) prev_low = df["low"].shift(1) df["inside_bar"] = ( (df["high"] <= prev_high) & (df["low"] >= prev_low) ).astype(int) # Inside Bar 之后的突破 # 向上突破: 前一根是 inside bar，当前收盘 > 母线（前两根）的 high mother_high = df["high"].shift(2) mother_low = df["low"].shift(2) df["inside_break_up"] = ( (df["inside_bar"].shift(1) == 1) & (df["close"] > mother_high) & (df["is_bull"] == 1) ).astype(int) df["inside_break_down"] = ( (df["inside_bar"].shift(1) == 1) & (df["close"] < mother_low) & (df["is_bear"] == 1) ).astype(int) # --- 支撑/阻力突破 --- # 突破前一个 Swing High（做多） df["break_swing_high"] = ( (df["close"] > df["last_swing_high"].shift(1)) & (df["close"].shift(1) <= df["last_swing_high"].shift(1)) & (df["is_bull"] == 1) & (df["body_ratio"] > self.min_body_ratio) # 实体饱满（有力度） ).astype(int) # 跌破前一个 Swing Low（做空） df["break_swing_low"] = ( (df["close"] < df["last_swing_low"].shift(1)) & (df["close"].shift(1) >= df["last_swing_low"].shift(1)) & (df["is_bear"] == 1) & (df["body_ratio"] > self.min_body_ratio) ).astype(int) # --- 成交量确认（只用原始 volume 对比，不算均线）--- # 当前成交量 > 前3根的最大成交量 = 放量 df["vol_expand"] = ( df["volume"] > df["volume"].rolling(3).max().shift(1) ).astype(int) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ 入场条件 — 纯价格行为 做多条件（满足任一组）： A) 上升趋势 + 看涨 Pin Bar（回调到支撑后反弹信号） B) 上升趋势 + 看涨吞没（回调后强势反转） C) 上升趋势 + Inside Bar 向上突破（蓄力后爆发） D) 突破 Swing High + 放量（结构性突破） 做空条件（镜像） """ df = dataframe # ===== 做多 ===== conditions_long = [] # A) 上升趋势 + 看涨 Pin Bar conditions_long.append( (df["uptrend"] == 1) & (df["bullish_pin"] == 1) & (df["vol_expand"] == 1) ) # B) 上升趋势 + 看涨吞没 conditions_long.append( (df["uptrend"] == 1) & (df["bullish_engulf"] == 1) ) # C) 上升趋势 + Inside Bar 向上突破 conditions_long.append( (df["uptrend"] == 1) & (df["inside_break_up"] == 1) & (df["vol_expand"] == 1) ) # D) 突破 Swing High + 放量（不需要已确认趋势，突破本身建立趋势） conditions_long.append( (df["break_swing_high"] == 1) & (df["vol_expand"] == 1) ) if conditions_long: import pandas as pd combined = pd.concat(conditions_long, axis=1).any(axis=1) dataframe.loc[combined, "enter_long"] = 1 # ===== 做空 ===== conditions_short = [] # A) 下降趋势 + 看跌 Pin Bar conditions_short.append( (df["downtrend"] == 1) & (df["bearish_pin"] == 1) & (df["vol_expand"] == 1) ) # B) 下降趋势 + 看跌吞没 conditions_short.append( (df["downtrend"] == 1) & (df["bearish_engulf"] == 1) ) # C) 下降趋势 + Inside Bar 向下突破 conditions_short.append( (df["downtrend"] == 1) & (df["inside_break_down"] == 1) & (df["vol_expand"] == 1) ) # D) 跌破 Swing Low + 放量 conditions_short.append( (df["break_swing_low"] == 1) & (df["vol_expand"] == 1) ) if conditions_short: import pandas as pd combined = pd.concat(conditions_short, axis=1).any(axis=1) dataframe.loc[combined, "enter_short"] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ 出场条件 — 纯价格行为 做多出场： - 出现看跌吞没 - 出现看跌 Pin Bar - 市场结构转为下降趋势 - 跌破前一个 Swing Low 做空出场（镜像） """ df = dataframe # 做多出场 dataframe.loc[ (df["bearish_engulf"] == 1) | (df["bearish_pin"] == 1) | (df["downtrend"] == 1) | (df["break_swing_low"] == 1), "exit_long", ] = 1 # 做空出场 dataframe.loc[ (df["bullish_engulf"] == 1) | (df["bullish_pin"] == 1) | (df["uptrend"] == 1) | (df["break_swing_high"] == 1), "exit_short", ] = 1 return dataframe def custom_exit( self, pair: str, trade, current_time, current_rate: float, current_profit: float, **kwargs, ): """ 自定义出场 — 基于价格行为的动态止盈 1. 利润 > 2% 且出现反转K线 → 锁利 2. 持仓超过 2 小时且利润 < 0.3% → 超时退出（行情没走出来） """ dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if dataframe.empty or len(dataframe) < 2: return None last = dataframe.iloc[-1] trade_duration = (current_time - trade.open_date_utc).total_seconds() / 60 # 1. 有利润 + 反转K线 → 锁利 if not trade.is_short: if current_profit > 0.02: if last.get("bearish_pin", 0) == 1 or last.get("bearish_engulf", 0) == 1: return "reversal_signal_tp" if current_profit > 0.035: # 大利润时，任何阴线都考虑锁利 if last.get("is_bear", 0) == 1 and last.get("body_ratio", 0) > 0.6: return "strong_bear_candle_tp" else: if current_profit > 0.02: if last.get("bullish_pin", 0) == 1 or last.get("bullish_engulf", 0) == 1: return "reversal_signal_tp" if current_profit > 0.035: if last.get("is_bull", 0) == 1 and last.get("body_ratio", 0) > 0.6: return "strong_bull_candle_tp" # 2. 超时退出 — 行情没走出来 if trade_duration > 120 and current_profit < 0.003: return "timeout_exit" return None def leverage( self, pair: str, current_time, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: str | None, side: str, **kwargs, ) -> float: return 3.0