426 lines
15 KiB
Python
426 lines
15 KiB
Python
"""
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Structure Flow Swing Strategy v3.0
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==================================
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波段交易策略 — 基于4H震荡区间，保守参数
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核心思路（冯总指示）：
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1. 在4H级别识别震荡区间
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2. 只在确认震荡时交易（区间宽度稳定、价格测试过边界、无突破）
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3. 止损设在支撑/阻力外侧，确保几乎不被噪音触发
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4. 止损被触发 = 结构已坏，离场正确
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5. 止盈：区间高度的70%
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保守参数：
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- 杠杆：1x（无杠杆）
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- 止损安全边际：ATR(4H, 14) * 1.5
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- 区间宽度稳定阈值：15%
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- 止盈：区间70%
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- 入场范围：支撑/阻力2%以内
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版本历史：
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v3.0 (2026-06-10): 初版，基于冯总波段交易新思路
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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 StructureFlowSwingV30(IStrategy):
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"""
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Structure Flow Swing Strategy v3.0
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4H震荡区间波段交易
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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 = "4h"
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# =====================
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# 可优化参数（保守默认值）
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# =====================
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swing_lookback = IntParameter(4, 8, default=5, space="buy")
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zone_stability_threshold = IntParameter(10, 25, default=15, space="buy")
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entry_zone_pct = IntParameter(1, 3, default=2, space="buy")
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atr_stop_mult = IntParameter(10, 25, default=15, space="buy") # /10, e.g. 15 = 1.5x
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take_profit_pct = IntParameter(50, 80, default=70, space="sell")
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# 固定参数
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zone_touch_lookback = 10
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breakout_bars = 2
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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：区间震荡检测
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# =====================
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def _detect_range(
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self,
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sh: pd.Series,
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sl: 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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) -> DataFrame:
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n = len(high)
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is_ranging = np.full(n, False)
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support_arr = np.full(n, np.nan)
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resistance_arr = np.full(n, np.nan)
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zone_width_arr = np.full(n, np.nan)
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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(sh.iloc[i]):
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sh_prices.append(sh.iloc[i])
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if len(sh_prices) > 5:
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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) > 5:
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sl_prices.pop(0)
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if len(sh_prices) < 3 or len(sl_prices) < 3:
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continue
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current_sh = sh_prices[-1]
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current_sl = sl_prices[-1]
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if current_sh <= current_sl:
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continue
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zone_width = (current_sh - current_sl) / current_sl
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support_arr[i] = current_sl
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resistance_arr[i] = current_sh
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zone_width_arr[i] = zone_width
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# 条件1：区间宽度稳定性
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widths = []
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for j in range(min(len(sh_prices), len(sl_prices)) - 1, -1, -1):
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w = (sh_prices[j] - sl_prices[j]) / sl_prices[j]
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widths.append(w)
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if len(widths) >= 3:
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break
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if len(widths) >= 3:
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mean_width = np.mean(widths)
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if mean_width > 0:
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max_dev = max(abs(w - mean_width) / mean_width for w in widths)
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stability_threshold = self.zone_stability_threshold.value / 100.0
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is_stable = max_dev <= stability_threshold
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else:
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is_stable = False
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else:
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is_stable = False
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if not is_stable:
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continue
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# 条件2：价格测试过边界
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start_idx = max(0, i - self.zone_touch_lookback)
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support_zone_upper = current_sl * 1.01
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touched_support = any(
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low.iloc[j] <= support_zone_upper
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for j in range(start_idx, i + 1)
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)
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resistance_zone_lower = current_sh * 0.99
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touched_resistance = any(
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high.iloc[j] >= resistance_zone_lower
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for j in range(start_idx, i + 1)
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)
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if not (touched_support and touched_resistance):
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continue
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# 条件3：无突破
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consecutive_outside = 0
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for j in range(i, max(0, i - self.breakout_bars) - 1, -1):
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if close.iloc[j] > current_sh or close.iloc[j] < current_sl:
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consecutive_outside += 1
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else:
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break
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if consecutive_outside >= self.breakout_bars:
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continue
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is_ranging[i] = True
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return DataFrame({
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"is_ranging": is_ranging,
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"support": support_arr,
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"resistance": resistance_arr,
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"zone_width": zone_width_arr,
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}, index=high.index)
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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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# D1 信息时间框架 — 宏观趋势参考
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# ================================================================
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@informative("1d")
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def populate_indicators_1d(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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sh, sl = self._detect_swing_points(
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dataframe["high"], dataframe["low"], window=5
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)
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sh_vals = sh.dropna()
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sl_vals = sl.dropna()
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is_uptrend = pd.Series(False, index=dataframe.index)
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is_downtrend = pd.Series(False, index=dataframe.index)
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if len(sh_vals) >= 2 and len(sl_vals) >= 2:
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if sh_vals.iloc[-1] > sh_vals.iloc[-2] and sl_vals.iloc[-1] > sl_vals.iloc[-2]:
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is_uptrend[:] = True
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elif sh_vals.iloc[-1] < sh_vals.iloc[-2] and sl_vals.iloc[-1] < sl_vals.iloc[-2]:
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is_downtrend[:] = True
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dataframe["d1_uptrend"] = is_uptrend
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dataframe["d1_downtrend"] = is_downtrend
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return dataframe
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# ================================================================
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# 主时间框架 — 4H 指标
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# ================================================================
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> 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.value,
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)
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range_info = self._detect_range(sh, sl, dataframe["high"], dataframe["low"], dataframe["close"])
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dataframe["is_ranging"] = range_info["is_ranging"]
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dataframe["range_support"] = range_info["support"]
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dataframe["range_resistance"] = range_info["resistance"]
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dataframe["zone_width_pct"] = range_info["zone_width"]
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dataframe["atr"] = self._calc_atr(dataframe["high"], dataframe["low"], dataframe["close"], 14)
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# 价格在区间内的位置
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denom = dataframe["range_resistance"] - dataframe["range_support"]
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dataframe["zone_position"] = np.where(
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denom > 0,
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(dataframe["close"] - dataframe["range_support"]) / denom,
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np.nan,
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)
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# 距离边界百分比
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dataframe["dist_to_support"] = np.where(
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dataframe["range_support"] > 0,
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(dataframe["close"] - dataframe["range_support"]) / dataframe["close"],
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np.nan,
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)
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dataframe["dist_to_resistance"] = np.where(
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dataframe["range_resistance"] > 0,
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(dataframe["range_resistance"] - dataframe["close"]) / dataframe["close"],
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np.nan,
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)
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for col in ["is_ranging", "zone_position", "dist_to_support", "dist_to_resistance"]:
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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 / 100.0
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# freqtrade adds _1d suffix to informative columns
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d1_downtrend_col = "d1_downtrend_1d"
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d1_uptrend_col = "d1_uptrend_1d"
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for col in ["is_ranging", d1_uptrend_col, d1_downtrend_col]:
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if col in dataframe.columns:
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dataframe[col] = dataframe[col].fillna(False)
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else:
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dataframe[col] = False
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# ── 做多：震荡市中，价格靠近支撑位 ──
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long_conds = (
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dataframe["is_ranging"]
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& (dataframe["dist_to_support"] <= entry_zone)
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& (dataframe["dist_to_support"] > 0)
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& (~dataframe[d1_downtrend_col])
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)
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cooldown = 3
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long_recent = long_conds.rolling(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：震荡市中，价格靠近阻力位 ──
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short_conds = (
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dataframe["is_ranging"]
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& (dataframe["dist_to_resistance"] <= entry_zone)
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& (dataframe["dist_to_resistance"] > 0)
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& (~dataframe[d1_uptrend_col])
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)
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short_recent = short_conds.rolling(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：支撑/阻力外侧，ATR*1.5 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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support = last.get("range_support", np.nan)
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atr = last.get("atr", np.nan)
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if pd.isna(support) or support <= 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 = support - atr * atr_mult
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else:
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sl_price = support * 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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resistance = last.get("range_resistance", np.nan)
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atr = last.get("atr", np.nan)
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if pd.isna(resistance) or resistance <= 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 = resistance + atr * atr_mult
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else:
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sl_price = resistance * 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70%
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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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support = last.get("range_support", np.nan)
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resistance = last.get("range_resistance", np.nan)
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if pd.notna(support) and pd.notna(resistance) and resistance > support:
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zone_height = (resistance - support) / support
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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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support = last.get("range_support", np.nan)
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resistance = last.get("range_resistance", np.nan)
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if pd.notna(support) and pd.notna(resistance) and resistance > support:
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zone_height = (resistance - support) / resistance
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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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"range_support": {"color": "green", "type": "line"},
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"range_resistance": {"color": "red", "type": "line"},
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},
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"subplots": {
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"range": {
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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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"position": {
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"dist_to_support": {"color": "green", "type": "line"},
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"dist_to_resistance": {"color": "red", "type": "line"},
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},
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},
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}
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