305 lines
11 KiB
Python
305 lines
11 KiB
Python
"""
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Structure Flow Swing Strategy v4.0
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==================================
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15m 震荡区间波段策略 — 基于价格聚集度检测
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核心变革（相对于 v3.x）：
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1. 时间框架从 4H → 15m：直接在小周期检测和执行
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2. 震荡判定从 "swing points 宽度稳定性" → "价格聚集度 + 边界测试次数"
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3. 检测周期 4-8 小时即可识别震荡，覆盖 1-3 天的 mini-震荡
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v3.1 诊断回顾（2026-06-10 全周期回测）：
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- 122笔全部做空，+76%，CAGR 10.97%
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- is_ranging 仅 13.7%，用 4H 判定只抓到大周期震荡
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- 1-3 天的小震荡完全被漏掉，这才是手工交易的利润来源
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版本历史：
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v3.0 (2026-06-10): 初版，4H swing points + 双边测试
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v3.1 (2026-06-10): AND→OR，降门槛
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v4.0 (2026-06-10): 全面重写，15m 价格聚集度检测
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"""
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from datetime import datetime
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import numpy as np
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import pandas as pd
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from pandas import DataFrame
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from freqtrade.strategy import IStrategy, IntParameter
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from freqtrade.persistence import Trade
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class StructureFlowSwingV40(IStrategy):
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"""
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Structure Flow Swing Strategy v4.0
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15m 震荡区间波段交易 — 价格聚集度检测
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"""
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can_short = True
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stoploss = -0.20
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use_custom_stoploss = True
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minimal_roi = {"0": 100}
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max_open_trades = 1
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timeframe = "15m"
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# =====================
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# 可优化参数
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# =====================
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lookback = IntParameter(24, 96, default=48, space="buy") # 检测窗口：24~96根15m（6h~24h）
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min_touches = IntParameter(1, 4, default=2, space="buy") # 边界至少测试次数
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zone_width_atr_mult = IntParameter(2, 6, default=4, space="buy") # 区间宽度上限 = ATR × N
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entry_zone_pct = IntParameter(2, 8, default=5, space="buy") # 入场范围：距边界千分比（0.5%）
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atr_stop_mult = IntParameter(10, 25, default=15, space="buy") # ATR止损倍数
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take_profit_pct = IntParameter(50, 80, default=70, space="sell") # 区间高度止盈比例
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# 固定参数
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breakout_bars = 2 # 连续几根K线突破才算真突破
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cooldown = 4 # 入场后冷却 4 根15m（1小时）
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# =====================
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# 工具：ATR计算
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# =====================
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@staticmethod
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def _calc_atr(high: pd.Series, low: pd.Series, close: pd.Series, period: int = 14) -> pd.Series:
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tr = pd.DataFrame({
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"hl": high - low,
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"hc": (high - close.shift(1)).abs(),
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"lc": (low - close.shift(1)).abs(),
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}).max(axis=1)
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return tr.rolling(period).mean()
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# =====================
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# 主时间框架 — 15m 指标
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# =====================
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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lookback = self.lookback.value
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# ── 价格聚集度检测 ──
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rolling_high = dataframe["high"].rolling(lookback).max()
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rolling_low = dataframe["low"].rolling(lookback).min()
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# 区间宽度（绝对值和百分比）
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zone_width = rolling_high - rolling_low
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zone_width_pct = zone_width / rolling_low
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dataframe["zone_high"] = rolling_high
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dataframe["zone_low"] = rolling_low
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dataframe["zone_width_raw"] = zone_width
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dataframe["zone_width_pct"] = zone_width_pct
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# ATR
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dataframe["atr"] = self._calc_atr(dataframe["high"], dataframe["low"], dataframe["close"], 14)
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# ── 边界测试计数 ──
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# 价格在区间上边界 0.5% 范围内 → 算一次测试
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touch_upper = dataframe["high"] >= rolling_high * 0.995
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touch_lower = dataframe["low"] <= rolling_low * 1.005
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# 滚动窗口内测试次数
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dataframe["upper_touches"] = touch_upper.rolling(lookback).sum()
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dataframe["lower_touches"] = touch_lower.rolling(lookback).sum()
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# ── 震荡判定条件 ──
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atr_mult = self.zone_width_atr_mult.value
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min_touches = self.min_touches.value
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# 条件1：区间宽度合理（不超过 ATR × N）
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is_compact = zone_width <= dataframe["atr"] * atr_mult
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# 条件2：上下边界都被测试过至少 min_touches 次
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is_tested = (dataframe["upper_touches"] >= min_touches) & (dataframe["lower_touches"] >= min_touches)
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# 条件3：无突破（最近 breakout_bars 根收盘价在边界内）
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no_break_high = True
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no_break_low = True
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for i in range(1, self.breakout_bars + 1):
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if i <= len(dataframe):
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no_break_high = no_break_high & (dataframe["close"].shift(i) <= rolling_high)
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no_break_low = no_break_low & (dataframe["close"].shift(i) >= rolling_low)
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is_ranging = is_compact & is_tested & no_break_high & no_break_low
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dataframe["is_ranging"] = is_ranging
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# ── 价格在区间内的位置 ──
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denom = rolling_high - rolling_low
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dataframe["zone_position"] = np.where(
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denom > 0,
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(dataframe["close"] - rolling_low) / denom,
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np.nan,
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)
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# 距边界百分比
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dataframe["dist_to_low"] = np.where(
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rolling_low > 0,
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(dataframe["close"] - rolling_low) / dataframe["close"],
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np.nan,
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)
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dataframe["dist_to_high"] = np.where(
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rolling_high > 0,
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(rolling_high - dataframe["close"]) / dataframe["close"],
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np.nan,
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)
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# ── 填充 ──
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for col in ["is_ranging", "zone_position", "dist_to_low", "dist_to_high"]:
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if col in dataframe.columns:
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dataframe[col] = dataframe[col].fillna(False if col == "is_ranging" else 999)
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return dataframe
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# ================================================================
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# 入场信号
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# ================================================================
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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entry_zone = self.entry_zone_pct.value / 1000.0 # 千分比
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if "is_ranging" not in dataframe.columns:
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dataframe["is_ranging"] = False
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# ── 做多：震荡中，价格靠近下边界 ──
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long_conds = (
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dataframe["is_ranging"]
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& (dataframe["dist_to_low"] < entry_zone)
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& (dataframe["dist_to_low"] > 0)
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)
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# 冷却
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long_recent = long_conds.rolling(self.cooldown, min_periods=1).max().shift(1) == 0
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dataframe.loc[long_conds & long_recent, "enter_long"] = 1
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# ── 做空：震荡中，价格靠近上边界 ──
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short_conds = (
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dataframe["is_ranging"]
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& (dataframe["dist_to_high"] < entry_zone)
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& (dataframe["dist_to_high"] > 0)
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)
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short_recent = short_conds.rolling(self.cooldown, min_periods=1).max().shift(1) == 0
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dataframe.loc[short_conds & short_recent, "enter_short"] = 1
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return dataframe
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# ================================================================
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# 出场信号
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# ================================================================
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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return dataframe
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# ================================================================
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# 自定义止损：区间边界外侧 + ATR 缓冲
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# ================================================================
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def custom_stoploss(
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self,
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pair: str,
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trade: Trade,
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current_time: datetime,
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current_rate: float,
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current_profit: float,
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after_fill: bool,
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**kwargs,
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) -> float:
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dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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if dataframe is None or len(dataframe) == 0:
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return -0.02 if not trade.is_short else 0.02
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last = dataframe.iloc[-1]
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atr_mult = self.atr_stop_mult.value / 10.0
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if not trade.is_short:
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zone_low = last.get("zone_low", np.nan)
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atr = last.get("atr", np.nan)
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if pd.isna(zone_low) or zone_low <= 0:
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return -0.02
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if pd.notna(atr) and atr > 0:
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sl_price = zone_low - atr * atr_mult
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else:
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sl_price = zone_low * 0.985
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sl_ratio = (sl_price / current_rate) - 1.0
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return max(sl_ratio, -0.20)
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else:
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zone_high = last.get("zone_high", np.nan)
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atr = last.get("atr", np.nan)
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if pd.isna(zone_high) or zone_high <= 0:
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return 0.02
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if pd.notna(atr) and atr > 0:
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sl_price = zone_high + atr * atr_mult
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else:
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sl_price = zone_high * 1.015
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sl_ratio = 1.0 - (sl_price / current_rate)
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return min(sl_ratio, 0.20)
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# ================================================================
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# 自定义止盈：区间高度 × TP%
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# ================================================================
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def custom_exit(
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self,
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pair: str,
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trade: Trade,
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current_time: datetime,
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current_rate: float,
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current_profit: float,
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**kwargs,
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) -> str | None:
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tp_pct = self.take_profit_pct.value / 100.0
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dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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if dataframe is None or len(dataframe) == 0:
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return None
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last = dataframe.iloc[-1]
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if not trade.is_short:
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zone_low = last.get("zone_low", np.nan)
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zone_high = last.get("zone_high", np.nan)
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if pd.notna(zone_low) and pd.notna(zone_high) and zone_high > zone_low:
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zone_height = (zone_high - zone_low) / zone_low
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tp_target = zone_height * tp_pct
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if current_profit >= tp_target:
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return "take_profit"
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else:
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zone_low = last.get("zone_low", np.nan)
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zone_high = last.get("zone_high", np.nan)
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if pd.notna(zone_low) and pd.notna(zone_high) and zone_high > zone_low:
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zone_height = (zone_high - zone_low) / zone_high
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tp_target = zone_height * tp_pct
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if current_profit >= tp_target:
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return "take_profit"
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return None
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# ================================================================
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# Plot config
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# ================================================================
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@staticmethod
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def plot_config() -> dict:
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return {
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"main_plot": {
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"zone_high": {"color": "red", "type": "line"},
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"zone_low": {"color": "green", "type": "line"},
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},
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"subplots": {
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"zone": {
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"is_ranging": {"color": "blue", "type": "line"},
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"zone_width_pct": {"color": "purple", "type": "line"},
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},
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"touches": {
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"upper_touches": {"color": "red", "type": "line"},
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"lower_touches": {"color": "green", "type": "line"},
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},
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},
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}
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