452 lines
17 KiB
Python
452 lines
17 KiB
Python
"""
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Structure Flow Strategy v2.2c — 冷却期修复版
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==============================================
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变更记录：
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v2.2c (2026-06-11): 1H S/R 替代 4H S/R
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v2.2c-coolfix (2026-06-11): 修复冷却期无限阻止下单 bug
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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, informative
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from freqtrade.persistence import Trade
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class StructureFlowStrategyV22d(IStrategy):
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can_short = True
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stoploss = -0.15
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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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# =====================
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# 可优化参数
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# =====================
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swing_lookback_d1 = IntParameter(8, 14, default=10, space="buy")
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swing_lookback_h4 = IntParameter(5, 10, default=8, space="buy")
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swing_lookback_1h = IntParameter(3, 7, default=5, space="buy") # 新增：1H swing参数
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pin_bar_wick_ratio = IntParameter(50, 70, default=60, space="buy")
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max_stop_dist = IntParameter(20, 50, default=50, space="buy")
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cooldown_bars = IntParameter(3, 12, default=6, space="buy")
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trend_strength_min = IntParameter(-50, 20, default=-20, space="buy")
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# =====================
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# 工具：Swing Point 检测
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# =====================
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@staticmethod
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def _detect_swing_points(
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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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# 工具：结构分析
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# =====================
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def _build_structure(
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self,
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high: pd.Series,
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low: pd.Series,
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close: pd.Series,
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swing_high: pd.Series,
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swing_low: pd.Series,
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) -> DataFrame:
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n = len(high)
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trend_up_arr = np.full(n, False)
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trend_down_arr = np.full(n, False)
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nearest_support = np.full(n, np.nan)
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nearest_resistance = np.full(n, np.nan)
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in_demand_zone = np.full(n, False)
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in_supply_zone = np.full(n, False)
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sh_prices = []
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sl_prices = []
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for i in range(n):
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if pd.notna(swing_high.iloc[i]):
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sh_prices.append(swing_high.iloc[i])
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if len(sh_prices) > 4:
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sh_prices.pop(0)
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if pd.notna(swing_low.iloc[i]):
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sl_prices.append(swing_low.iloc[i])
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if len(sl_prices) > 4:
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sl_prices.pop(0)
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if len(sh_prices) >= 2 and len(sl_prices) >= 2:
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if sh_prices[-1] > sh_prices[-2] and sl_prices[-1] > sl_prices[-2]:
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trend_up_arr[i] = True
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elif sh_prices[-1] < sh_prices[-2] and sl_prices[-1] < sl_prices[-2]:
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trend_down_arr[i] = True
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elif i > 0:
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trend_up_arr[i] = trend_up_arr[i - 1]
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trend_down_arr[i] = trend_down_arr[i - 1]
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elif i > 0:
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trend_up_arr[i] = trend_up_arr[i - 1]
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trend_down_arr[i] = trend_down_arr[i - 1]
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if sl_prices:
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nearest_support[i] = sl_prices[-1]
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if sh_prices:
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nearest_resistance[i] = sh_prices[-1]
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c = close.iloc[i]
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if not np.isnan(nearest_support[i]) and not np.isnan(nearest_resistance[i]):
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zone_range = nearest_resistance[i] - nearest_support[i]
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if zone_range > 0:
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pos_pct = (c - nearest_support[i]) / zone_range
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in_demand_zone[i] = pos_pct < 0.35
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in_supply_zone[i] = pos_pct > 0.65
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return DataFrame({
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"trend_up": trend_up_arr,
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"trend_down": trend_down_arr,
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"support": nearest_support,
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"resistance": nearest_resistance,
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"in_demand": in_demand_zone,
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"in_supply": in_supply_zone,
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}, index=high.index)
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# =====================
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# 工具：K线形态检测
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# =====================
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@staticmethod
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def _detect_candle_patterns(
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open_: pd.Series,
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high: pd.Series,
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low: pd.Series,
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close: pd.Series,
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pin_bar_wick_ratio: float = 0.6,
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) -> tuple[pd.Series, pd.Series, pd.Series, pd.Series]:
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body = (close - open_).abs()
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total_range = (high - low).replace(0, 0.0001)
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upper_wick = high - close.where(close > open_, open_)
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lower_wick = open_.where(close > open_, close) - low
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is_pin = (upper_wick + lower_wick) / total_range > pin_bar_wick_ratio
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bullish_pin = is_pin & (close > open_) & (lower_wick > upper_wick)
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bearish_pin = is_pin & (close < open_) & (upper_wick > lower_wick)
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prev_open = open_.shift(1)
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prev_close = close.shift(1)
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bullish_engulf = (close > prev_open) & (open_ < prev_close) & (close > open_)
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bearish_engulf = (close < prev_open) & (open_ > prev_close) & (close < open_)
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return bullish_pin, bearish_pin, bullish_engulf, bearish_engulf
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# =====================
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# 工具：冷却期正确实现（修复 bug）
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# =====================
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def _apply_cooldown(self, signal: pd.Series, cooldown_bars: int) -> pd.Series:
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"""
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正确应用冷却期：入场后才冷却，而非条件满足就冷却。
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原逻辑 bug：long_base.rolling(cooldown).max().shift(1) == 0
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- 当市场持续满足入场条件时，rolling window 里永远有 True
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- 导致冷却期无限阻止下单
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修复逻辑：遍历 K 线，模拟"入场 -> 冷却"过程。
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- 满足条件 + 距离上次入场 > cooldown -> 允许入场
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- 入场后 cooldown 根 K 线内不再入场
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"""
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n = len(signal)
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result = [False] * n
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last_entry = -99999 # 上次入场的 bar 索引
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# 遍历（对 numpy array 操作，O(n) 约几毫秒）
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values = signal.values # numpy array，快速访问
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for i in range(n):
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if values[i] and (i - last_entry) > cooldown_bars:
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result[i] = True
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last_entry = i
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return pd.Series(result, index=signal.index)
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# ================================================================
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# 信息时间框架 — D1 宏观结构
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# ================================================================
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@informative("1d")
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def populate_indicators_1d(
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self, dataframe: DataFrame, metadata: dict
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) -> DataFrame:
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sh, sl = self._detect_swing_points(
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dataframe["high"], dataframe["low"],
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self.swing_lookback_d1.value,
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)
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structure = self._build_structure(
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dataframe["high"], dataframe["low"], dataframe["close"],
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sh, sl,
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)
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dataframe["trend_up"] = structure["trend_up"]
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dataframe["trend_down"] = structure["trend_down"]
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return dataframe
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# ================================================================
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# 信息时间框架 — 4H 趋势强度（原版保留）
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# ================================================================
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@informative("4h")
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def populate_indicators_4h(
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self, dataframe: DataFrame, metadata: dict
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) -> DataFrame:
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sh, sl = self._detect_swing_points(
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dataframe["high"], dataframe["low"],
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self.swing_lookback_h4.value,
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)
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structure = self._build_structure(
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dataframe["high"], dataframe["low"], dataframe["close"],
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sh, sl,
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)
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# 趋势强度计算（原版逻辑）
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sh_prices = []
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sl_prices = []
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trend_strength_up = np.full(len(dataframe), np.nan)
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trend_strength_down = np.full(len(dataframe), np.nan)
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for i in range(len(dataframe)):
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if pd.notna(sh.iloc[i]):
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sh_prices.append(sh.iloc[i])
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if len(sh_prices) > 4:
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sh_prices.pop(0)
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if pd.notna(sl.iloc[i]):
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sl_prices.append(sl.iloc[i])
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if len(sl_prices) > 4:
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sl_prices.pop(0)
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if len(sh_prices) >= 2 and len(sl_prices) >= 2:
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hh_dist = (sh_prices[-1] - sh_prices[-2]) / sh_prices[-2] if sh_prices[-2] > 0 else 0
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hl_dist = (sl_prices[-1] - sl_prices[-2]) / sl_prices[-2] if sl_prices[-2] > 0 else 0
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trend_strength_up[i] = hh_dist + hl_dist
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trend_strength_down[i] = -(hh_dist + hl_dist)
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dataframe["trend_strength_up"] = pd.Series(trend_strength_up, index=dataframe.index)
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dataframe["trend_strength_down"] = pd.Series(trend_strength_down, index=dataframe.index)
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min_strength = self.trend_strength_min.value / 100.0
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dataframe["strong_uptrend"] = dataframe["trend_strength_up"] > min_strength
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dataframe["strong_downtrend"] = dataframe["trend_strength_down"] > min_strength
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return dataframe
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# ================================================================
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# 主时间框架 — 1H 指标（含 1H S/R + 活支撑/阻力）
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# ================================================================
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def populate_indicators(
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self, dataframe: DataFrame, metadata: dict
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) -> DataFrame:
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# ── K线形态 ──
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bullish_pin, bearish_pin, bullish_engulf, bearish_engulf = (
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self._detect_candle_patterns(
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dataframe["open"],
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dataframe["high"],
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dataframe["low"],
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dataframe["close"],
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self.pin_bar_wick_ratio.value / 100.0,
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)
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)
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dataframe["bullish_pinbar"] = bullish_pin
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dataframe["bearish_pinbar"] = bearish_pin
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dataframe["bullish_engulfing"] = bullish_engulf
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dataframe["bearish_engulfing"] = bearish_engulf
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dataframe["bullish_signal"] = bullish_pin | bullish_engulf
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dataframe["bearish_signal"] = bearish_pin | bearish_engulf
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# ── 1H级别 Swing Point + 结构（替代原4H S/R） ──
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sh_1h, sl_1h = self._detect_swing_points(
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dataframe["high"], dataframe["low"],
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self.swing_lookback_1h.value,
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)
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structure_1h = self._build_structure(
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dataframe["high"], dataframe["low"], dataframe["close"],
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sh_1h, sl_1h,
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)
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dataframe["trend_up_1h"] = structure_1h["trend_up"]
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dataframe["trend_down_1h"] = structure_1h["trend_down"]
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dataframe["support"] = structure_1h["support"]
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dataframe["resistance"] = structure_1h["resistance"]
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dataframe["in_demand"] = structure_1h["in_demand"]
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dataframe["in_supply"] = structure_1h["in_supply"]
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# ── 1H 活支撑/阻力检查 ──
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touched_support = (
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(dataframe["low"] <= dataframe["support"] * 1.005) &
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(dataframe["low"] >= dataframe["support"] * 0.995)
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)
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held_support = dataframe["close"] > dataframe["support"]
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support_tested_and_held = touched_support & held_support
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dataframe["support_alive"] = support_tested_and_held.rolling(3, min_periods=1).max() > 0
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touched_resistance = (
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(dataframe["high"] >= dataframe["resistance"] * 0.995) &
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(dataframe["high"] <= dataframe["resistance"] * 1.005)
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)
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held_resistance = dataframe["close"] < dataframe["resistance"]
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resistance_tested_and_held = touched_resistance & held_resistance
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dataframe["resistance_alive"] = resistance_tested_and_held.rolling(3, min_periods=1).max() > 0
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# ── NaN 安全处理 ──
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bool_cols = [
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"trend_up_1d", "trend_down_1d",
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"trend_up_4h", "trend_down_4h",
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"in_demand", "in_supply",
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"support_alive", "resistance_alive",
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"strong_uptrend_4h", "strong_downtrend_4h",
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"bullish_signal", "bearish_signal",
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]
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for col in bool_cols:
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if col in dataframe.columns:
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dataframe[col] = dataframe[col].fillna(False)
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return dataframe
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# =====================
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# 入场信号（修复冷却期逻辑）
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# =====================
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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max_dist = self.max_stop_dist.value / 100.0
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cooldown = self.cooldown_bars.value
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bool_cols = [
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"trend_up_1d", "trend_down_1d",
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"trend_up_4h", "trend_down_4h",
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"in_demand", "in_supply",
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"support_alive", "resistance_alive",
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"strong_uptrend_4h", "strong_downtrend_4h",
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"bullish_signal", "bearish_signal",
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]
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for col in bool_cols:
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if col in dataframe.columns:
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dataframe[col] = dataframe[col].fillna(False)
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# ── 做多（使用1H S/R） ──
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long_stop_dist = (dataframe["open"] - dataframe["support"]) / dataframe["open"]
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long_base = (
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dataframe["trend_up_1d"]
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& dataframe["in_demand"]
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& (long_stop_dist <= max_dist)
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& (long_stop_dist > 0.003)
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& dataframe["support_alive"]
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& dataframe["strong_uptrend_4h"]
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)
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# ✅ 修复：正确应用冷却期（基于实际入场，而非条件满足）
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long_entries = self._apply_cooldown(long_base, cooldown)
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dataframe.loc[long_entries, "enter_long"] = 1
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# ── 做空（使用1H S/R） ──
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short_stop_dist = (dataframe["resistance"] - dataframe["open"]) / dataframe["open"]
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short_base = (
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dataframe["trend_down_1d"]
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& dataframe["in_supply"]
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& (short_stop_dist <= max_dist)
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& (short_stop_dist > 0.003)
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& dataframe["resistance_alive"]
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& dataframe["strong_downtrend_4h"]
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)
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# ✅ 修复：正确应用冷却期（基于实际入场，而非条件满足）
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short_entries = self._apply_cooldown(short_base, cooldown)
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dataframe.loc[short_entries, "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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exit_long = ~dataframe["trend_up_1d"].fillna(True)
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dataframe.loc[exit_long, "exit_long"] = 1
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exit_short = dataframe["trend_up_1d"].fillna(False)
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dataframe.loc[exit_short, "exit_short"] = 1
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return dataframe
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# =====================
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# 动态止损（基于1H S/R）
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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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if not trade.is_short:
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support = last.get("support", np.nan)
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if pd.isna(support) or support <= 0:
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return -0.02
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sl_price = support * 0.999
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sl_ratio = (sl_price / current_rate) - 1.0
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return max(sl_ratio, -0.15)
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else:
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resistance = last.get("resistance", np.nan)
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if pd.isna(resistance) or resistance <= 0:
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return 0.02
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sl_price = resistance * 1.001
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sl_ratio = 1.0 - (sl_price / current_rate)
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return min(sl_ratio, 0.15)
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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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"support": {"color": "green", "type": "line"},
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"resistance": {"color": "red", "type": "line"},
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},
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"subplots": {
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"signals": {
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"bullish_pinbar": {"color": "green", "type": "scatter"},
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"bearish_pinbar": {"color": "red", "type": "scatter"},
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},
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"filters": {
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"support_alive": {"color": "green", "type": "line"},
|
||
"resistance_alive": {"color": "red", "type": "line"},
|
||
"strong_uptrend_4h": {"color": "blue", "type": "line"},
|
||
"strong_downtrend_4h": {"color": "orange", "type": "line"},
|
||
},
|
||
},
|
||
}
|