v3.0 (Swing): 纯震荡波段策略 - 4H框架供需区波段交易
This commit is contained in:
610
strategy.py
610
strategy.py
@ -1,9 +1,24 @@
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"""
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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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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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@ -14,25 +29,31 @@ 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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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 = -0.15
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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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timeframe = "4h"
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# =====================
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# 可优化参数
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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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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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# 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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@ -47,352 +68,250 @@ class StructureFlowStrategyV22d(IStrategy):
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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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# 工具：区间震荡检测
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# =====================
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def _build_structure(
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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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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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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(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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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) > 4:
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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) >= 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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if len(sh_prices) < 3 or len(sl_prices) < 3:
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continue
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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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current_sh = sh_prices[-1]
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current_sl = sl_prices[-1]
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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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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 — 1H 指标（含 1H S/R + 活支撑/阻力）
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# 主时间框架 — 4H 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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# ── 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"],
|
||||
dataframe["close"],
|
||||
self.pin_bar_wick_ratio.value / 100.0,
|
||||
)
|
||||
)
|
||||
dataframe["bullish_pinbar"] = bullish_pin
|
||||
dataframe["bearish_pinbar"] = bearish_pin
|
||||
dataframe["bullish_engulfing"] = bullish_engulf
|
||||
dataframe["bearish_engulfing"] = bearish_engulf
|
||||
dataframe["bullish_signal"] = bullish_pin | bullish_engulf
|
||||
dataframe["bearish_signal"] = bearish_pin | bearish_engulf
|
||||
|
||||
# ── 1H级别 Swing Point + 结构（替代原4H S/R） ──
|
||||
sh_1h, sl_1h = self._detect_swing_points(
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
sh, sl = self._detect_swing_points(
|
||||
dataframe["high"], dataframe["low"],
|
||||
self.swing_lookback_1h.value,
|
||||
self.swing_lookback.value,
|
||||
)
|
||||
structure_1h = self._build_structure(
|
||||
dataframe["high"], dataframe["low"], dataframe["close"],
|
||||
sh_1h, sl_1h,
|
||||
)
|
||||
dataframe["trend_up_1h"] = structure_1h["trend_up"]
|
||||
dataframe["trend_down_1h"] = structure_1h["trend_down"]
|
||||
dataframe["support"] = structure_1h["support"]
|
||||
dataframe["resistance"] = structure_1h["resistance"]
|
||||
dataframe["in_demand"] = structure_1h["in_demand"]
|
||||
dataframe["in_supply"] = structure_1h["in_supply"]
|
||||
|
||||
# ── 1H 活支撑/阻力检查 ──
|
||||
touched_support = (
|
||||
(dataframe["low"] <= dataframe["support"] * 1.005) &
|
||||
(dataframe["low"] >= dataframe["support"] * 0.995)
|
||||
)
|
||||
held_support = dataframe["close"] > dataframe["support"]
|
||||
support_tested_and_held = touched_support & held_support
|
||||
dataframe["support_alive"] = support_tested_and_held.rolling(3, min_periods=1).max() > 0
|
||||
range_info = self._detect_range(sh, sl, dataframe["high"], dataframe["low"], dataframe["close"])
|
||||
dataframe["is_ranging"] = range_info["is_ranging"]
|
||||
dataframe["range_support"] = range_info["support"]
|
||||
dataframe["range_resistance"] = range_info["resistance"]
|
||||
dataframe["zone_width_pct"] = range_info["zone_width"]
|
||||
|
||||
touched_resistance = (
|
||||
(dataframe["high"] >= dataframe["resistance"] * 0.995) &
|
||||
(dataframe["high"] <= dataframe["resistance"] * 1.005)
|
||||
)
|
||||
held_resistance = dataframe["close"] < dataframe["resistance"]
|
||||
resistance_tested_and_held = touched_resistance & held_resistance
|
||||
dataframe["resistance_alive"] = resistance_tested_and_held.rolling(3, min_periods=1).max() > 0
|
||||
dataframe["atr"] = self._calc_atr(dataframe["high"], dataframe["low"], dataframe["close"], 14)
|
||||
|
||||
# ── NaN 安全处理 ──
|
||||
bool_cols = [
|
||||
"trend_up_1d", "trend_down_1d",
|
||||
"trend_up_4h", "trend_down_4h",
|
||||
"in_demand", "in_supply",
|
||||
"support_alive", "resistance_alive",
|
||||
"strong_uptrend_4h", "strong_downtrend_4h",
|
||||
"bullish_signal", "bearish_signal",
|
||||
]
|
||||
for col in bool_cols:
|
||||
# 价格在区间内的位置
|
||||
denom = dataframe["range_resistance"] - dataframe["range_support"]
|
||||
dataframe["zone_position"] = np.where(
|
||||
denom > 0,
|
||||
(dataframe["close"] - dataframe["range_support"]) / denom,
|
||||
np.nan,
|
||||
)
|
||||
|
||||
# 距离边界百分比
|
||||
dataframe["dist_to_support"] = np.where(
|
||||
dataframe["range_support"] > 0,
|
||||
(dataframe["close"] - dataframe["range_support"]) / dataframe["close"],
|
||||
np.nan,
|
||||
)
|
||||
dataframe["dist_to_resistance"] = np.where(
|
||||
dataframe["range_resistance"] > 0,
|
||||
(dataframe["range_resistance"] - dataframe["close"]) / dataframe["close"],
|
||||
np.nan,
|
||||
)
|
||||
|
||||
for col in ["is_ranging", "zone_position", "dist_to_support", "dist_to_resistance"]:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].fillna(False)
|
||||
dataframe[col] = dataframe[col].fillna(False if col == "is_ranging" else 999)
|
||||
|
||||
return dataframe
|
||||
|
||||
# =====================
|
||||
# 入场信号（修复冷却期逻辑）
|
||||
# =====================
|
||||
# ================================================================
|
||||
# 入场信号
|
||||
# ================================================================
|
||||
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
max_dist = self.max_stop_dist.value / 100.0
|
||||
cooldown = self.cooldown_bars.value
|
||||
entry_zone = self.entry_zone_pct.value / 100.0
|
||||
|
||||
bool_cols = [
|
||||
"trend_up_1d", "trend_down_1d",
|
||||
"trend_up_4h", "trend_down_4h",
|
||||
"in_demand", "in_supply",
|
||||
"support_alive", "resistance_alive",
|
||||
"strong_uptrend_4h", "strong_downtrend_4h",
|
||||
"bullish_signal", "bearish_signal",
|
||||
]
|
||||
for col in bool_cols:
|
||||
# freqtrade adds _1d suffix to informative columns
|
||||
d1_downtrend_col = "d1_downtrend_1d"
|
||||
d1_uptrend_col = "d1_uptrend_1d"
|
||||
|
||||
for col in ["is_ranging", d1_uptrend_col, d1_downtrend_col]:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].fillna(False)
|
||||
else:
|
||||
dataframe[col] = False
|
||||
|
||||
# ── 做多（使用1H S/R） ──
|
||||
long_stop_dist = (dataframe["open"] - dataframe["support"]) / dataframe["open"]
|
||||
|
||||
long_base = (
|
||||
dataframe["trend_up_1d"]
|
||||
& dataframe["in_demand"]
|
||||
& (long_stop_dist <= max_dist)
|
||||
& (long_stop_dist > 0.003)
|
||||
& dataframe["support_alive"]
|
||||
& dataframe["strong_uptrend_4h"]
|
||||
# ── 做多：震荡市中，价格靠近支撑位 ──
|
||||
long_conds = (
|
||||
dataframe["is_ranging"]
|
||||
& (dataframe["dist_to_support"] <= entry_zone)
|
||||
& (dataframe["dist_to_support"] > 0)
|
||||
& (~dataframe[d1_downtrend_col])
|
||||
)
|
||||
|
||||
# ✅ 修复：正确应用冷却期（基于实际入场，而非条件满足）
|
||||
long_entries = self._apply_cooldown(long_base, cooldown)
|
||||
dataframe.loc[long_entries, "enter_long"] = 1
|
||||
cooldown = 3
|
||||
long_recent = long_conds.rolling(cooldown, min_periods=1).max().shift(1) == 0
|
||||
dataframe.loc[long_conds & long_recent, "enter_long"] = 1
|
||||
|
||||
# ── 做空（使用1H S/R） ──
|
||||
short_stop_dist = (dataframe["resistance"] - dataframe["open"]) / dataframe["open"]
|
||||
|
||||
short_base = (
|
||||
dataframe["trend_down_1d"]
|
||||
& dataframe["in_supply"]
|
||||
& (short_stop_dist <= max_dist)
|
||||
& (short_stop_dist > 0.003)
|
||||
& dataframe["resistance_alive"]
|
||||
& dataframe["strong_downtrend_4h"]
|
||||
# ── 做空：震荡市中，价格靠近阻力位 ──
|
||||
short_conds = (
|
||||
dataframe["is_ranging"]
|
||||
& (dataframe["dist_to_resistance"] <= entry_zone)
|
||||
& (dataframe["dist_to_resistance"] > 0)
|
||||
& (~dataframe[d1_uptrend_col])
|
||||
)
|
||||
|
||||
# ✅ 修复：正确应用冷却期（基于实际入场，而非条件满足）
|
||||
short_entries = self._apply_cooldown(short_base, cooldown)
|
||||
dataframe.loc[short_entries, "enter_short"] = 1
|
||||
short_recent = short_conds.rolling(cooldown, min_periods=1).max().shift(1) == 0
|
||||
dataframe.loc[short_conds & short_recent, "enter_short"] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
# =====================
|
||||
# ================================================================
|
||||
# 出场信号
|
||||
# =====================
|
||||
# ================================================================
|
||||
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
exit_long = ~dataframe["trend_up_1d"].fillna(True)
|
||||
dataframe.loc[exit_long, "exit_long"] = 1
|
||||
|
||||
exit_short = dataframe["trend_up_1d"].fillna(False)
|
||||
dataframe.loc[exit_short, "exit_short"] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
# =====================
|
||||
# 动态止损（基于1H S/R）
|
||||
# =====================
|
||||
# ================================================================
|
||||
# 自定义止损：支撑/阻力外侧，ATR*1.5 缓冲
|
||||
# ================================================================
|
||||
|
||||
def custom_stoploss(
|
||||
self,
|
||||
@ -409,43 +328,98 @@ class StructureFlowStrategyV22d(IStrategy):
|
||||
return -0.02 if not trade.is_short else 0.02
|
||||
|
||||
last = dataframe.iloc[-1]
|
||||
atr_mult = self.atr_stop_mult.value / 10.0
|
||||
|
||||
if not trade.is_short:
|
||||
support = last.get("support", np.nan)
|
||||
support = last.get("range_support", np.nan)
|
||||
atr = last.get("atr", np.nan)
|
||||
|
||||
if pd.isna(support) or support <= 0:
|
||||
return -0.02
|
||||
sl_price = support * 0.999
|
||||
sl_ratio = (sl_price / current_rate) - 1.0
|
||||
return max(sl_ratio, -0.15)
|
||||
|
||||
if pd.notna(atr) and atr > 0:
|
||||
sl_price = support - atr * atr_mult
|
||||
else:
|
||||
resistance = last.get("resistance", np.nan)
|
||||
sl_price = support * 0.985
|
||||
|
||||
sl_ratio = (sl_price / current_rate) - 1.0
|
||||
return max(sl_ratio, -0.20)
|
||||
else:
|
||||
resistance = last.get("range_resistance", np.nan)
|
||||
atr = last.get("atr", np.nan)
|
||||
|
||||
if pd.isna(resistance) or resistance <= 0:
|
||||
return 0.02
|
||||
sl_price = resistance * 1.001
|
||||
sl_ratio = 1.0 - (sl_price / current_rate)
|
||||
return min(sl_ratio, 0.15)
|
||||
|
||||
# =====================
|
||||
if pd.notna(atr) and atr > 0:
|
||||
sl_price = resistance + atr * atr_mult
|
||||
else:
|
||||
sl_price = resistance * 1.015
|
||||
|
||||
sl_ratio = 1.0 - (sl_price / current_rate)
|
||||
return min(sl_ratio, 0.20)
|
||||
|
||||
# ================================================================
|
||||
# 自定义止盈：区间70%
|
||||
# ================================================================
|
||||
|
||||
def custom_exit(
|
||||
self,
|
||||
pair: str,
|
||||
trade: Trade,
|
||||
current_time: datetime,
|
||||
current_rate: float,
|
||||
current_profit: float,
|
||||
**kwargs,
|
||||
) -> str | None:
|
||||
tp_pct = self.take_profit_pct.value / 100.0
|
||||
|
||||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
if dataframe is None or len(dataframe) == 0:
|
||||
return None
|
||||
|
||||
last = dataframe.iloc[-1]
|
||||
|
||||
if not trade.is_short:
|
||||
support = last.get("range_support", np.nan)
|
||||
resistance = last.get("range_resistance", np.nan)
|
||||
|
||||
if pd.notna(support) and pd.notna(resistance) and resistance > support:
|
||||
zone_height = (resistance - support) / support
|
||||
tp_target = zone_height * tp_pct
|
||||
if current_profit >= tp_target:
|
||||
return "take_profit"
|
||||
else:
|
||||
support = last.get("range_support", np.nan)
|
||||
resistance = last.get("range_resistance", np.nan)
|
||||
|
||||
if pd.notna(support) and pd.notna(resistance) and resistance > support:
|
||||
zone_height = (resistance - support) / resistance
|
||||
tp_target = zone_height * tp_pct
|
||||
if current_profit >= tp_target:
|
||||
return "take_profit"
|
||||
|
||||
return None
|
||||
|
||||
# ================================================================
|
||||
# Plot config
|
||||
# =====================
|
||||
# ================================================================
|
||||
|
||||
@staticmethod
|
||||
def plot_config() -> dict:
|
||||
return {
|
||||
"main_plot": {
|
||||
"support": {"color": "green", "type": "line"},
|
||||
"resistance": {"color": "red", "type": "line"},
|
||||
"range_support": {"color": "green", "type": "line"},
|
||||
"range_resistance": {"color": "red", "type": "line"},
|
||||
},
|
||||
"subplots": {
|
||||
"signals": {
|
||||
"bullish_pinbar": {"color": "green", "type": "scatter"},
|
||||
"bearish_pinbar": {"color": "red", "type": "scatter"},
|
||||
"range": {
|
||||
"is_ranging": {"color": "blue", "type": "line"},
|
||||
"zone_width_pct": {"color": "purple", "type": "line"},
|
||||
},
|
||||
"filters": {
|
||||
"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"},
|
||||
"position": {
|
||||
"dist_to_support": {"color": "green", "type": "line"},
|
||||
"dist_to_resistance": {"color": "red", "type": "line"},
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user