235 lines
9.4 KiB
Python
235 lines
9.4 KiB
Python
"""
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Structure Flow Swing Strategy v4.2
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==================================
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纯价格行为学震荡策略 — 借鉴 v2.2b 的 swing point + K线形态框架
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核心逻辑（模拟手工交易）：
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1. 用 swing point 识别近期高低点，自动形成交易区间
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2. 价格到支撑 + K线止跌形态（bullish pinbar/engulfing）→ 做多
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3. 价格到阻力 + K线滞涨 → 平多（反向开空同理）
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4. 不在震荡判定上设严苛门槛，价格够到边界+形态确认就做
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5. 趋势突破时，突破边界导致亏损，但可控
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v4.1 系列教训（2026-06-10）：
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用滚动window + 碰壁验证 + ATR比例止损 → 全是技术指标思维，不是价格行为学
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唯一正收益的是最简单的版本（1H + 形态 + 固定-3%止损）
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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 StructureFlowSwingV42(IStrategy):
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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 = "1h"
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# ── 价格行为学参数 ──
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swing_window = IntParameter(5, 11, default=11, space="buy") # swing point 检测窗口
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entry_zone_pct = IntParameter(2, 8, default=5, space="buy") # 入场范围（距S/R 0.5%）
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# 固定参数
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cooldown = 6 # 冷却6根1H（6小时）
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# ================================================================
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# Swing Point 检测（v2.2b 同款）
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# ================================================================
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def _detect_swing_points(
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self,
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high: pd.Series,
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low: pd.Series,
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window: int = 5,
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) -> tuple[pd.Series, pd.Series]:
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n = len(high)
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sh = pd.Series(np.nan, index=high.index, dtype=float)
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sl = pd.Series(np.nan, index=low.index, dtype=float)
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for i in range(window, n - window):
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if high.iloc[i] > high.iloc[i - window:i].max() and high.iloc[i] > high.iloc[i + 1:i + window + 1].max():
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sh.iloc[i] = high.iloc[i]
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if low.iloc[i] < low.iloc[i - window:i].min() and low.iloc[i] < low.iloc[i + 1:i + window + 1].min():
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sl.iloc[i] = low.iloc[i]
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return sh, sl
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# ================================================================
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# K线形态检测（v2.2b 同款）
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# ================================================================
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@staticmethod
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def _detect_pinbar(open: pd.Series, high: pd.Series, low: pd.Series, close: pd.Series) -> pd.Series:
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body = (open - close).abs()
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total_range = (high - low)
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upper_wick = high - open.where(open > close, close)
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lower_wick = open.where(open < close, close) - low
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bullish_pin = (lower_wick > body * 2) & (lower_wick > upper_wick * 2) & (close > open)
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bearish_pin = (upper_wick > body * 2) & (upper_wick > lower_wick * 2) & (close < open)
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return bullish_pin, bearish_pin
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@staticmethod
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def _detect_engulfing(open: pd.Series, close: pd.Series) -> tuple[pd.Series, pd.Series]:
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prev_open = open.shift(1)
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prev_close = close.shift(1)
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bullish_eng = (close > open) & (prev_close < prev_open) & (close > prev_open) & (open < prev_close)
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bearish_eng = (close < open) & (prev_close > prev_open) & (close < prev_open) & (open > prev_close)
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return bullish_eng, bearish_eng
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# ================================================================
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# 主指标
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# ================================================================
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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sw = self.swing_window.value
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# ── Swing Points ──
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sh, sl = self._detect_swing_points(dataframe["high"], dataframe["low"], sw)
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# 向前填充最近的 swing high / low 作为动态 S/R
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dataframe["swing_high"] = sh.ffill()
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dataframe["swing_low"] = sl.ffill()
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# ── 区间宽度（用于止盈参考） ──
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dataframe["zone_width"] = np.where(
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dataframe["swing_high"].notna() & dataframe["swing_low"].notna() & (dataframe["swing_high"] > dataframe["swing_low"]),
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(dataframe["swing_high"] - dataframe["swing_low"]) / dataframe["swing_low"],
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np.nan,
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)
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# ── K线形态 ──
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bull_pin, bear_pin = self._detect_pinbar(
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dataframe["open"], dataframe["high"], dataframe["low"], dataframe["close"]
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)
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bull_eng, bear_eng = self._detect_engulfing(dataframe["open"], dataframe["close"])
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dataframe["bullish_signal"] = bull_pin | bull_eng
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dataframe["bearish_signal"] = bear_pin | bear_eng
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# ── 距边界的距离 ──
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dataframe["dist_to_swing_low"] = np.where(
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dataframe["swing_low"].notna() & (dataframe["swing_low"] > 0),
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(dataframe["close"] - dataframe["swing_low"]) / dataframe["close"],
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np.nan,
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)
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dataframe["dist_to_swing_high"] = np.where(
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dataframe["swing_high"].notna() & (dataframe["swing_high"] > 0),
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(dataframe["swing_high"] - dataframe["close"]) / dataframe["close"],
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np.nan,
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)
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for col in ["dist_to_swing_low", "dist_to_swing_high"]:
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dataframe[col] = dataframe[col].fillna(999)
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return dataframe
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# ================================================================
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# 入场
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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
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# ── 做多：价格在 swing low 附近 + 止跌形态 ──
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long_conds = (
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(dataframe["dist_to_swing_low"] < entry_zone)
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& (dataframe["dist_to_swing_low"] > 0)
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& dataframe["bullish_signal"]
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)
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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：价格在 swing high 附近 + 止涨形态 ──
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short_conds = (
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(dataframe["dist_to_swing_high"] < entry_zone)
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& (dataframe["dist_to_swing_high"] > 0)
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& dataframe["bearish_signal"]
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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
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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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# 止损：区间宽度 × 0.5（自适应）
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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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z_width = dataframe.iloc[-1].get("zone_width", np.nan)
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if pd.notna(z_width) and z_width > 0.005:
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stop_pct = min(max(z_width * 0.5, 0.005), 0.05)
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else:
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stop_pct = 0.015
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if not trade.is_short:
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return max((trade.open_rate * (1 - stop_pct) / current_rate) - 1.0, -0.20)
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else:
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return min(1.0 - (trade.open_rate * (1 + stop_pct) / current_rate), 0.20)
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# ================================================================
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# 止盈：到对侧边界 + K线形态确认 → 平仓
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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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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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swing_high = last.get("swing_high", np.nan)
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swing_low = last.get("swing_low", np.nan)
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bull_sig = last.get("bullish_signal", False)
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bear_sig = last.get("bearish_signal", False)
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# ── 做多：到阻力附近 + 滞涨形态 → 平仓 ──
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if not trade.is_short:
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if pd.notna(swing_high) and swing_high > 0:
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near_high = current_rate >= swing_high * 0.99
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if near_high and bear_sig:
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return "exit_signal"
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if near_high and current_profit > 0:
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return "exit_signal"
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# ── 做空：到支撑附近 + 止跌形态 → 平仓 ──
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else:
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if pd.notna(swing_low) and swing_low > 0:
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near_low = current_rate <= swing_low * 1.01
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if near_low and bull_sig:
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return "exit_signal"
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if near_low and current_profit > 0:
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return "exit_signal"
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return None
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