393 lines
14 KiB
Python
393 lines
14 KiB
Python
"""
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Structure Flow Strategy v1.5
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=======================
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变更记录：
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v1.0 (2026-06-07): 纯价格结构策略，D1定方向→4H定位→1H入场
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v1.1 (2026-06-07): 1H futures，结构止损，首次回测成功(+61.52%)
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v1.2 (2026-06-07): Entry Candle止损，bug导致50笔硬止损全亏
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v1.3 (2026-06-07): ATR动态止损，结果-63.72%，胜率20.2%
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v1.4 (2026-06-07): 回归纯价格结构止损，+140.71%，胜率38.7%
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v1.5 (2026-06-07): ===== 参数调优 =====
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- stoploss -5% → -15%，释放结构止损空间
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- max_stop_dist 3% → 5%，增加交易频率
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其他逻辑与v1.4完全相同
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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 StructureFlowStrategyV15(IStrategy):
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"""
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Structure Flow Strategy v1.5 — 纯价格结构，零指标
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v1.5改动（相对于v1.4）：
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- stoploss -5% → -15%（硬止损放宽，让结构止损真正生效）
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- max_stop_dist 3% → 5%（增加交易频率）
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"""
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can_short = True
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stoploss = -0.15 # v1.5: -5% → -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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pin_bar_wick_ratio = IntParameter(50, 70, default=60, space="buy")
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# v1.5: 默认值从30→50（3%→5%）
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max_stop_dist = IntParameter(20, 50, default=50, 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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# 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 中期结构
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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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dataframe["trend_up"] = structure["trend_up"]
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dataframe["trend_down"] = structure["trend_down"]
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dataframe["support"] = structure["support"]
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dataframe["resistance"] = structure["resistance"]
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dataframe["in_demand"] = structure["in_demand"]
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dataframe["in_supply"] = structure["in_supply"]
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return dataframe
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# ================================================================
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# 主时间框架 — 1H 指标
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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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"""1H 级别：K线形态（零指标）。"""
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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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# 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_4h", "in_supply_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
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# =====================
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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入场逻辑（1H 时间框架）。
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做多条件:
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1. D1 上升结构（trend_up_1d）— 宏观方向
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2. 4H 需求区域（in_demand_4h）— 在支撑附近
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3. 1H 看涨 K 线形态（bullish_signal）
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4. 止损距离 ≤ max_stop_dist% — 赔率过滤（v1.5: 默认5%）
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做空条件:
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1. D1 下降结构（trend_down_1d）
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2. 4H 供给区域（in_supply_4h）
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3. 1H 看跌 K 线形态（bearish_signal）
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4. 止损距离 ≤ max_stop_dist%
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"""
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max_dist = self.max_stop_dist.value / 100.0
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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_4h", "in_supply_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 ──
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long_stop_dist = (dataframe["open"] - dataframe["support_4h"]) / dataframe["open"]
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long_conditions = (
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dataframe["trend_up_1d"]
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& dataframe["in_demand_4h"]
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& dataframe["bullish_signal"]
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& (long_stop_dist <= max_dist)
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& (long_stop_dist > 0.003) # 至少0.3%距离（避免support就在眼前）
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)
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dataframe.loc[long_conditions, "enter_long"] = 1
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# ── 做空 ──
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short_stop_dist = (dataframe["resistance_4h"] - dataframe["open"]) / dataframe["open"]
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short_conditions = (
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dataframe["trend_down_1d"]
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& dataframe["in_supply_4h"]
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& dataframe["bearish_signal"]
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& (short_stop_dist <= max_dist)
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& (short_stop_dist > 0.003)
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)
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dataframe.loc[short_conditions, "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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"""出场逻辑 — 由结构反转触发。"""
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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 — 纯价格结构（基于Swing Point）
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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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"""
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止损逻辑：完全基于价格结构，零指标。
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止损位：
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做多 → support_4h - 0.1%缓冲（最近4H Swing Low下方）
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做空 → resistance_4h + 0.1%缓冲（最近4H Swing High上方）
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support_4h / resistance_4h 随新Swing Point自动更新，
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天然形成追踪止损效果。
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v1.5: 硬止损从-5%放宽到-15%，让结构止损真正生效。
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"""
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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_4h", np.nan)
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if pd.isna(support) or support <= 0:
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return -0.02 # fallback
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# 止损 = support_4h 下方 0.1%
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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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# v1.5: 硬止损从-5% → -15%
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return max(sl_ratio, -0.15)
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else:
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resistance = last.get("resistance_4h", np.nan)
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if pd.isna(resistance) or resistance <= 0:
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return 0.02 # fallback
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# 止损 = resistance_4h 上方 0.1%
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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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# v1.5: 硬止损从+5% → +15%
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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_4h": {"color": "green", "type": "line"},
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"resistance_4h": {"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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},
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}
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