590 lines
23 KiB
Python
590 lines
23 KiB
Python
"""
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Structure Flow Scalp — 震荡市剥头皮策略
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==========================================
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基于Al Brooks价格行为学：
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- 在已识别的震荡区间内，支撑位做多、阻力位做空
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- 15m级别支撑/阻力决定交易区间，5m级别入场
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- 100x全仓杠杆，每次10%仓位
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- 区间高度40%止盈，15m支撑/阻力外侧0.3%止损
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变更记录：
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v1 (2026-06-10): 初版，基于v2.2b核心逻辑重构
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v1.1 (2026-06-10): 支撑阻力从4H改为15m
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v1.2 (2026-06-10): 去掉4H趋势强度判断（冗余）；启用100x全仓杠杆，10%仓位
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v1.3 (2026-06-10): 代码审查修复——移除populate_exit_trend死循环，NaN安全，杠杆上限
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v1.4 (2026-06-10): EMA动态S/R + 入场锁定S/R——止损止盈使用入场时的锁定值，不追最新
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v1.5 (2026-06-10): 扩展入场信号 + 追踪止损保护 + 延长活S/R窗口
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v1.6 (2026-06-10): 止损改为ATR动态计算——绑入场价，不绑支撑位；追踪改为ATR×0.5自适应
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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 StructureFlowScalp(IStrategy):
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"""
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震荡市剥头皮策略 — 5m框架，100x全仓杠杆。
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去掉4H趋势强度判断——15m支撑阻力本身就是最好的过滤器。
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"""
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can_short = True
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stoploss = -0.15
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use_custom_stoploss = True
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use_custom_exit = True
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minimal_roi = {"0": 100}
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max_open_trades = 1
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timeframe = "5m"
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# =====================
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# 杠杆设置 - 全仓 100x
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# =====================
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def leverage(self, pair: str, current_time: datetime, current_rate: float,
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proposed_leverage: float, max_leverage: float, side: str,
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**kwargs) -> float:
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"""返回固定 100x 杠杆，不超过交易所允许的最大值"""
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return min(100.0, max_leverage)
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# =====================
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# 工具：查找入场K线（锁定S/R用）
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# =====================
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def _get_entry_row(self, dataframe: DataFrame, trade: Trade) -> pd.Series | None:
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"""
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从 dataframe 中找到入场 trade 对应的 K 线行。
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兼容 live/dry_run（DatetimeIndex）和 backtesting（RangeIndex + date 列）两种模式。
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"""
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if 'date' in dataframe.columns:
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# Backtesting 模式：dataframe 有 date 列，index 是 int
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entry_mask = pd.to_datetime(dataframe['date']) <= trade.open_date
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if not entry_mask.any():
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return None
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return dataframe[entry_mask].iloc[-1]
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else:
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# Live/Dry-run 模式：index 是 DatetimeIndex
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try:
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entry_idx = dataframe.index.get_indexer([trade.open_date], method="pad")
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if entry_idx[0] < 0 or entry_idx[0] >= len(dataframe):
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return None
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return dataframe.iloc[entry_idx[0]]
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except (TypeError, ValueError):
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return None
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# =====================
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# 可优化参数
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# =====================
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# 15m支撑阻力计算窗口
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swing_lookback_15m = IntParameter(5, 15, default=10, space="buy")
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pin_bar_wick_ratio = IntParameter(50, 70, default=60, space="buy")
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cooldown_bars = IntParameter(2, 8, default=3, space="buy")
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# 区间高度止盈比例（%）
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profit_zone_pct = IntParameter(20, 60, default=40, 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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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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# EMA平滑：不取最后一个，而是对最近swing lows做指数加权
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# alpha=0.3，每个新swing point向它移动30%，有"惯性"不跳变
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ema_s = sl_prices[0]
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for p in sl_prices[1:]:
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ema_s = 0.3 * p + 0.7 * ema_s
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nearest_support[i] = ema_s
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if sh_prices:
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ema_r = sh_prices[0]
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for p in sh_prices[1:]:
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ema_r = 0.3 * p + 0.7 * ema_r
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nearest_resistance[i] = ema_r
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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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}, 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 — 15m 短期支撑阻力（核心过滤器）
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# ================================================================
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@informative("15m")
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def populate_indicators_15m(
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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_15m.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["support"] = structure["support"]
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dataframe["resistance"] = structure["resistance"]
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# ── 活支撑检查（15根15m ≈ 3.75小时，震荡市中支撑可长期有效）──
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touched_support = (
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(dataframe["low"] <= dataframe["support"] * 1.005) &
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(dataframe["low"] >= dataframe["support"] * 0.995)
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)
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held_support = dataframe["close"] > dataframe["support"]
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support_tested_and_held = touched_support & held_support
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dataframe["support_alive"] = support_tested_and_held.rolling(15, min_periods=1).max() > 0
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# ── 活阻力检查（15根窗口）──
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touched_resistance = (
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(dataframe["high"] >= dataframe["resistance"] * 0.995) &
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(dataframe["high"] <= dataframe["resistance"] * 1.005)
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)
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held_resistance = dataframe["close"] < dataframe["resistance"]
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resistance_tested_and_held = touched_resistance & held_resistance
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dataframe["resistance_alive"] = resistance_tested_and_held.rolling(15, min_periods=1).max() > 0
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# 区间高度（用于止盈计算）
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dataframe["zone_height"] = (dataframe["resistance"] - dataframe["support"]).fillna(0)
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return dataframe
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# ================================================================
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# 主时间框架 — 5m 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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"""5m级别：ATR + K线形态 + 信号整合。"""
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# ── ATR(14) — 用于动态止损，根据市场波动自适应 ──
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high = dataframe["high"]
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low = dataframe["low"]
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close = dataframe["close"]
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prev_close = close.shift(1)
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tr = pd.concat([
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high - low,
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(high - prev_close).abs(),
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(low - prev_close).abs(),
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], axis=1).max(axis=1)
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dataframe["atr"] = tr.rolling(14).mean()
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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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# ── 扩展信号：长下影线（比pinbar更宽松，只要下影线>总范围50%） ──
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total_range = (dataframe["high"] - dataframe["low"]).replace(0, 0.0001)
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body = (dataframe["close"] - dataframe["open"]).abs()
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# 下影线 = min(open, close) - low
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lower_wick = (
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dataframe[["open", "close"]].min(axis=1) - dataframe["low"]
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)
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# 上影线 = high - max(open, close)
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upper_wick = (
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dataframe["high"] - dataframe[["open", "close"]].max(axis=1)
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)
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# 长下影线：下影线>总范围50% 且 下影线>上影线
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long_lower_wick = (
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(lower_wick / total_range > 0.5) &
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(lower_wick > upper_wick)
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)
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dataframe["long_lower_wick"] = long_lower_wick
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# ── 扩展信号：支撑位附近的强力反弹阳线 ──
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# 条件：价格在支撑0.5%范围内 + 阳线 + 实体>0.2%
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if "support_15m" in dataframe.columns:
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near_support = (
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(dataframe["low"] <= dataframe["support_15m"] * 1.005) &
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(dataframe["low"] >= dataframe["support_15m"] * 0.995)
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)
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is_bullish = dataframe["close"] > dataframe["open"]
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body_pct = body / dataframe["open"]
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strong_recovery = near_support & is_bullish & (body_pct > 0.002)
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else:
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strong_recovery = pd.Series(False, index=dataframe.index)
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dataframe["strong_recovery"] = strong_recovery
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# ── 综合止跌/止涨信号（扩展后） ──
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dataframe["bullish_signal"] = (
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bullish_pin | bullish_engulf | long_lower_wick | strong_recovery
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)
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dataframe["bearish_signal"] = (
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bearish_pin | bearish_engulf
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)
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# 做空对称：阻力位附近的强力下跌阴线
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if "resistance_15m" in dataframe.columns:
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near_resistance = (
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(dataframe["high"] >= dataframe["resistance_15m"] * 0.995) &
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(dataframe["high"] <= dataframe["resistance_15m"] * 1.005)
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)
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is_bearish = dataframe["close"] < dataframe["open"]
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body_pct = body / dataframe["open"]
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strong_rejection = near_resistance & is_bearish & (body_pct > 0.002)
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else:
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strong_rejection = pd.Series(False, index=dataframe.index)
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dataframe["strong_rejection"] = strong_rejection
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dataframe["bearish_signal"] = (
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bearish_pin | bearish_engulf | strong_rejection
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)
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# NaN 安全处理
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bool_cols = [
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"support_alive_15m", "resistance_alive_15m",
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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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# ATR fillna（前14根无ATR值用均值填补）
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if "atr" in dataframe.columns:
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atr_mean = dataframe["atr"].mean()
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dataframe["atr"] = dataframe["atr"].fillna(atr_mean)
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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（5m 时间框架）。
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不做4H趋势判断——15m支撑阻力本身就是过滤器：
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- 趋势强时价格直接突破15m S/R，不会在支撑/阻力附近停留
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- 在支撑/阻力附近停留 = 震荡市
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入场条件（3个，去掉了冗余的4H趋势判断）：
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- 做多：价格贴近15m支撑 + 支撑有效 + K线止跌信号
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- 做空：价格贴近15m阻力 + 阻力有效 + K线止涨信号
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出场只依赖 custom_stoploss 和 custom_exit，不需要 D1 结构反转退出。
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（去掉 populate_exit_trend：震荡市入场 → D1 非上升趋势 → 立即出场 的死循环）
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"""
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cooldown = self.cooldown_bars.value
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# NaN 安全处理 — 如果 15m informative 列还没对齐，直接跳过本根 K 线
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required_cols = ["support_15m", "resistance_15m",
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"support_alive_15m", "resistance_alive_15m"]
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for col in required_cols:
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if col not in dataframe.columns:
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return dataframe # 数据尚未就绪，跳过
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for col in ["bullish_signal", "bearish_signal",
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"support_alive_15m", "resistance_alive_15m"]:
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dataframe[col] = dataframe[col].fillna(False)
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# ── 做多 ──
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# 条件：价格贴近15m支撑（0.5%范围内）- 使用 low 而非 open
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# 因为支撑测试看的是价格是否到达支撑位，不是开盘在哪
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near_support = (
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(dataframe["low"] <= dataframe["support_15m"] * 1.005) &
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(dataframe["low"] >= dataframe["support_15m"] * 0.995)
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)
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long_conditions = (
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near_support
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& dataframe["support_alive_15m"]
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& dataframe["bullish_signal"]
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)
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long_recent = long_conditions.rolling(cooldown, min_periods=1).max().shift(1) == 0
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dataframe.loc[long_conditions & long_recent, "enter_long"] = 1
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# ── 做空 ──
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# 条件：价格贴近15m阻力（0.5%范围内）- 使用 high 而非 open
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near_resistance = (
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(dataframe["high"] >= dataframe["resistance_15m"] * 0.995) &
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(dataframe["high"] <= dataframe["resistance_15m"] * 1.005)
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)
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short_conditions = (
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near_resistance
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& dataframe["resistance_alive_15m"]
|
||
& dataframe["bearish_signal"]
|
||
)
|
||
|
||
short_recent = short_conditions.rolling(cooldown, min_periods=1).max().shift(1) == 0
|
||
dataframe.loc[short_conditions & short_recent, "enter_short"] = 1
|
||
|
||
return dataframe
|
||
|
||
# =====================
|
||
# exit_trend（freqtrade 2025.11 要求必须实现，即使 use_custom_exit=True）
|
||
# =====================
|
||
|
||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
"""退出逻辑完全由 custom_stoploss + custom_exit 管理。"""
|
||
return dataframe
|
||
|
||
# =====================
|
||
# 动态止损 — 入场价 - ATR×2.0（基于市场波动，非固定比例）
|
||
# =====================
|
||
|
||
def custom_stoploss(
|
||
self,
|
||
pair: str,
|
||
trade: Trade,
|
||
current_time: datetime,
|
||
current_rate: float,
|
||
current_profit: float,
|
||
after_fill: bool,
|
||
**kwargs,
|
||
) -> float:
|
||
"""
|
||
止损锚定入场价，宽度根据市场波动（ATR）动态计算，而非固定比例。
|
||
|
||
核心逻辑：
|
||
- 做多止损 = entry_price - ATR_5m × 2.0
|
||
- 做空止损 = entry_price + ATR_5m × 2.0
|
||
- ATR值从入场时的K线锁定，持仓期间不漂移
|
||
|
||
为什么用ATR不用固定比例：
|
||
- ATR自动适应市场：波动大时止损放宽免误扫，波动小时收紧控风险
|
||
- 固定比例是拍脑袋，ATR是算出来的
|
||
|
||
追踪保护（v1.6 ATR自适应版）：
|
||
- 利润达止盈目标50%：上移到保本（入场价）
|
||
- 利润达止盈目标80%：启动ATR×0.5窄追踪
|
||
"""
|
||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||
if dataframe is None or len(dataframe) == 0:
|
||
return -0.02 if not trade.is_short else 0.02
|
||
|
||
# 查找入场时的 K 线，锁定当时的 ATR 值
|
||
entry_row = self._get_entry_row(dataframe, trade)
|
||
if entry_row is None:
|
||
return -0.02 if not trade.is_short else 0.02
|
||
|
||
# 锁定入场时的 ATR 值，用于全程止损/追踪计算（不追最新，防止漂移）
|
||
atr_value = entry_row.get("atr", np.nan)
|
||
if pd.isna(atr_value) or atr_value <= 0:
|
||
return -0.02 if not trade.is_short else 0.02
|
||
|
||
if not trade.is_short:
|
||
# 做多：止损 = 入场价 - ATR × 2.0
|
||
base_sl_price = trade.open_rate - (atr_value * 2.0)
|
||
base_sl = (base_sl_price / trade.open_rate) - 1.0
|
||
base_sl = max(base_sl, -0.15)
|
||
|
||
# 追踪保护：需要入场行计算止盈目标
|
||
support = entry_row.get("support_15m", np.nan)
|
||
resistance = entry_row.get("resistance_15m", np.nan)
|
||
if (not pd.isna(support) and not pd.isna(resistance)
|
||
and resistance > support and current_profit > 0):
|
||
zone_height = resistance - support
|
||
tp_target = (zone_height * self.profit_zone_pct.value / 100.0) / trade.open_rate
|
||
|
||
if current_profit >= tp_target * 0.8:
|
||
# 利润达止盈80%：ATR自适应窄追踪
|
||
trail_price = current_rate - (atr_value * 0.5)
|
||
trail_ratio = (trail_price / trade.open_rate) - 1.0
|
||
return max(trail_ratio, base_sl)
|
||
elif current_profit >= tp_target * 0.5:
|
||
# 利润达止盈50%：保本
|
||
return max(0.0, base_sl)
|
||
|
||
return base_sl
|
||
else:
|
||
# 做空：止损 = 入场价 + ATR × 2.0
|
||
base_sl_price = trade.open_rate + (atr_value * 2.0)
|
||
base_sl = 1.0 - (base_sl_price / trade.open_rate)
|
||
base_sl = min(base_sl, 0.15)
|
||
|
||
# 追踪保护（做空对称）
|
||
support = entry_row.get("support_15m", np.nan)
|
||
resistance = entry_row.get("resistance_15m", np.nan)
|
||
if (not pd.isna(support) and not pd.isna(resistance)
|
||
and resistance > support and current_profit > 0):
|
||
zone_height = resistance - support
|
||
tp_target = (zone_height * self.profit_zone_pct.value / 100.0) / trade.open_rate
|
||
|
||
if current_profit >= tp_target * 0.8:
|
||
# ATR自适应窄追踪（做空对称）
|
||
trail_price = current_rate + (atr_value * 0.5)
|
||
trail_ratio = (trail_price / trade.open_rate) - 1.0
|
||
return min(trail_ratio, base_sl)
|
||
elif current_profit >= tp_target * 0.5:
|
||
# 保本
|
||
return min(0.0, base_sl)
|
||
|
||
return base_sl
|
||
|
||
# =====================
|
||
# 区间高度止盈
|
||
# =====================
|
||
|
||
def custom_exit(
|
||
self,
|
||
pair: str,
|
||
trade: Trade,
|
||
current_time: datetime,
|
||
current_rate: float,
|
||
current_profit: float,
|
||
**kwargs,
|
||
) -> str | None:
|
||
"""
|
||
当利润达到入场时锁定的15m区间高度的设定比例时止盈。
|
||
|
||
使用入场时锁定的S/R值计算区间高度（zone_height），而非最新的值：
|
||
- 入场后如果区间收缩，止盈目标不会跟着变小
|
||
- 让入场时确定的止盈逻辑"钉死"
|
||
- profit_zone_pct 默认40%，即锁定区间高度的40%
|
||
"""
|
||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||
if dataframe is None or len(dataframe) == 0:
|
||
return None
|
||
|
||
# 查找入场时的 K 线，锁定当时的 S/R 值
|
||
entry_row = self._get_entry_row(dataframe, trade)
|
||
if entry_row is None:
|
||
return None
|
||
|
||
support = entry_row.get("support_15m", np.nan)
|
||
resistance = entry_row.get("resistance_15m", np.nan)
|
||
|
||
if pd.isna(support) or pd.isna(resistance) or resistance <= support:
|
||
return None
|
||
|
||
# 用锁定的区间高度计算止盈目标（不随市场漂移）
|
||
locked_zone_height = resistance - support
|
||
target_pct = (locked_zone_height * self.profit_zone_pct.value / 100.0) / trade.open_rate
|
||
|
||
if current_profit >= target_pct:
|
||
return "zone_tp"
|
||
|
||
return None
|
||
|
||
# =====================
|
||
# Plot config
|
||
# =====================
|
||
|
||
@staticmethod
|
||
def plot_config() -> dict:
|
||
return {
|
||
"main_plot": {
|
||
"support_15m": {"color": "green", "type": "line"},
|
||
"resistance_15m": {"color": "red", "type": "line"},
|
||
},
|
||
"subplots": {
|
||
"signals": {
|
||
"bullish_pinbar": {"color": "green", "type": "scatter"},
|
||
"bearish_pinbar": {"color": "red", "type": "scatter"},
|
||
"bullish_signal": {"color": "lime", "type": "scatter"},
|
||
"bearish_signal": {"color": "orange", "type": "scatter"},
|
||
},
|
||
"filters": {
|
||
"support_alive_15m": {"color": "green", "type": "line"},
|
||
"resistance_alive_15m": {"color": "red", "type": "line"},
|
||
},
|
||
},
|
||
}
|