v2.0 (Scalp): 顺趋势动量剥头皮 - 方向正确但20x杠杆过重
This commit is contained in:
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strategy.py
860
strategy.py
@ -1,259 +1,130 @@
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"""
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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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# structure_flow_momentum_scalp.py
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# 顺趋势剥头皮策略 v2.0
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#
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# 核心思路：不再在S/R处做反向交易接飞刀，而是顺趋势方向，等回调后入场。
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#
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# ┌─────────────────────────────────────────────────────────────┐
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# │ 15m趋势方向判断（EMA20 vs EMA50） │
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# │ ↓ │
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# │ 上升趋势 → 只等5m回调到EMA20/支撑附近 → 止跌信号 → 做多 │
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# │ 下降趋势 → 只等5m反弹到EMA20/阻力附近 → 止涨信号 → 做空 │
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# │ ↓ │
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# │ 止损：ATR×1.0 | 止盈：ATR×1.5 | 时间止损：60分钟 │
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# └─────────────────────────────────────────────────────────────┘
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#
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# v2.0 (2026-06-10): 初始版本，完全重写
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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 freqtrade.strategy import IStrategy, IntParameter, DecimalParameter, informative
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from pandas import DataFrame
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from freqtrade.strategy import IStrategy, IntParameter, informative
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import pandas as pd
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import numpy as np
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from datetime import datetime
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from freqtrade.persistence import Trade
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class StructureFlowScalp(IStrategy):
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class StructureFlowMomentumScalp(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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顺趋势剥头皮策略 v2.0
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核心逻辑：
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- 15m EMA趋势方向过滤，只做顺趋势方向的单
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- 5m 回调到EMA20或S/R支撑/阻力区域时，等待K线信号确认后入场
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- 止损 ATR×1.0，止盈 ATR×1.5，时间止损 60 分钟
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- 不做方向猜测，不吃鱼头鱼尾，只吃回调结束那一小段
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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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# ── 时间框架 ──
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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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# ── 交易参数 ──
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can_short = True
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max_open_trades = 1
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stake_amount = "unlimited"
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use_custom_stoploss = True
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use_exit_signal = False # 出场完全由 custom_stoploss + custom_exit 管理
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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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# ── 合约参数 ──
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margin_mode = "cross"
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trading_mode = "futures"
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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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# ── 可优化参数 ──
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# 趋势检测
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trend_ema_period = IntParameter(10, 30, default=20, space="buy")
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# 回调确认幅度
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pullback_deviation = DecimalParameter(0.2, 1.0, default=0.5, decimals=1, space="buy")
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# 入场冷却期
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cooldown_bars = IntParameter(2, 8, default=3, space="buy")
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# K线形态灵敏度
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pin_bar_wick_ratio = IntParameter(50, 80, default=60, space="buy")
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# 止损ATR倍数
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atr_mult_stop = DecimalParameter(0.8, 2.0, default=1.0, decimals=1, space="sell")
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# 止盈ATR倍数
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atr_mult_tp = DecimalParameter(1.0, 3.0, default=1.5, decimals=1, space="sell")
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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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# ── 常数 ──
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time_stop_minutes = 60 # 最大持仓时间
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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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# ── 保护性止损 ──
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stoploss = -0.10 # 硬止损 10%
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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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# 杠杆
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# ================================================================
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def leverage(
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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,
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) -> float:
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"""20x 杠杆起步，验证胜率后再上量"""
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return min(20.0, max_leverage)
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# ================================================================
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# 信息时间框架 — 15m 趋势判断 + S/R
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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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"""15m级别：EMA趋势方向 + swing point S/R。"""
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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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# ── EMA 趋势方向 ──
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ema_period = self.trend_ema_period.value
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dataframe["ema_fast"] = dataframe["close"].ewm(span=ema_period, adjust=False).mean()
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dataframe["ema_slow"] = dataframe["close"].ewm(span=ema_period * 2.5, adjust=False).mean()
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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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dataframe["trend_up"] = dataframe["ema_fast"] > dataframe["ema_slow"]
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dataframe["trend_down"] = dataframe["ema_fast"] < dataframe["ema_slow"]
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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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# ── Swing Point 支撑/阻力 ──
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high = dataframe["high"].tolist()
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low = dataframe["low"].tolist()
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close = dataframe["close"].tolist()
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sh, sl = self._detect_swing_points(high, low, window=5)
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trend_up_arr, trend_down_arr, support_arr, resistance_arr = self._build_structure(
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high, low, close, sh, sl,
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)
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dataframe["trend_up_sp"] = trend_up_arr
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dataframe["trend_down_sp"] = trend_down_arr
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# EMA平滑S/R（避免跳变）
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dataframe["support"] = self._ema_smooth(support_arr, alpha=0.3)
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dataframe["resistance"] = self._ema_smooth(resistance_arr, alpha=0.3)
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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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# 主框架 — 5m 级别指标
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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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"""5m级别：ATR + K线形态 + EMA趋势整合。"""
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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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# ── ATR(14) ──
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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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@ -264,170 +135,136 @@ class StructureFlowScalp(IStrategy):
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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 = (
|
||||
dataframe[["open", "close"]].min(axis=1) - dataframe["low"]
|
||||
)
|
||||
# 上影线 = high - max(open, close)
|
||||
upper_wick = (
|
||||
dataframe["high"] - dataframe[["open", "close"]].max(axis=1)
|
||||
)
|
||||
# 长下影线：下影线>总范围50% 且 下影线>上影线
|
||||
long_lower_wick = (
|
||||
(lower_wick / total_range > 0.5) &
|
||||
(lower_wick > upper_wick)
|
||||
)
|
||||
dataframe["long_lower_wick"] = long_lower_wick
|
||||
|
||||
# ── 扩展信号：支撑位附近的强力反弹阳线 ──
|
||||
# 条件：价格在支撑0.5%范围内 + 阳线 + 实体>0.2%
|
||||
if "support_15m" in dataframe.columns:
|
||||
near_support = (
|
||||
(dataframe["low"] <= dataframe["support_15m"] * 1.005) &
|
||||
(dataframe["low"] >= dataframe["support_15m"] * 0.995)
|
||||
)
|
||||
is_bullish = dataframe["close"] > dataframe["open"]
|
||||
body_pct = body / dataframe["open"]
|
||||
strong_recovery = near_support & is_bullish & (body_pct > 0.002)
|
||||
else:
|
||||
strong_recovery = pd.Series(False, index=dataframe.index)
|
||||
dataframe["strong_recovery"] = strong_recovery
|
||||
|
||||
# ── 综合止跌/止涨信号（扩展后） ──
|
||||
dataframe["bullish_signal"] = (
|
||||
bullish_pin | bullish_engulf | long_lower_wick | strong_recovery
|
||||
)
|
||||
dataframe["bearish_signal"] = (
|
||||
bearish_pin | bearish_engulf
|
||||
)
|
||||
# 做空对称：阻力位附近的强力下跌阴线
|
||||
if "resistance_15m" in dataframe.columns:
|
||||
near_resistance = (
|
||||
(dataframe["high"] >= dataframe["resistance_15m"] * 0.995) &
|
||||
(dataframe["high"] <= dataframe["resistance_15m"] * 1.005)
|
||||
)
|
||||
is_bearish = dataframe["close"] < dataframe["open"]
|
||||
body_pct = body / dataframe["open"]
|
||||
strong_rejection = near_resistance & is_bearish & (body_pct > 0.002)
|
||||
else:
|
||||
strong_rejection = pd.Series(False, index=dataframe.index)
|
||||
dataframe["strong_rejection"] = strong_rejection
|
||||
dataframe["bearish_signal"] = (
|
||||
bearish_pin | bearish_engulf | strong_rejection
|
||||
)
|
||||
|
||||
# NaN 安全处理
|
||||
bool_cols = [
|
||||
"support_alive_15m", "resistance_alive_15m",
|
||||
"bullish_signal", "bearish_signal",
|
||||
]
|
||||
for col in bool_cols:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].fillna(False)
|
||||
|
||||
# ATR fillna（前14根无ATR值用均值填补）
|
||||
if "atr" in dataframe.columns:
|
||||
atr_mean = dataframe["atr"].mean()
|
||||
dataframe["atr"] = dataframe["atr"].fillna(atr_mean)
|
||||
|
||||
return dataframe
|
||||
# ── K线形态 ──
|
||||
bullish_pin, bearish_pin, bullish_engulf, bearish_engulf = (
|
||||
self._detect_candle_patterns(
|
||||
dataframe["open"], dataframe["high"], dataframe["low"],
|
||||
dataframe["close"], self.pin_bar_wick_ratio.value / 100.0,
|
||||
)
|
||||
)
|
||||
dataframe["bullish_signal"] = bullish_pin | bullish_engulf
|
||||
dataframe["bearish_signal"] = bearish_pin | bearish_engulf
|
||||
|
||||
# =====================
|
||||
# 入场信号
|
||||
# =====================
|
||||
# ── 5m EMA（用于短期拉回确认） ──
|
||||
dataframe["ema5"] = close.ewm(span=5, adjust=False).mean()
|
||||
dataframe["ema8"] = close.ewm(span=8, adjust=False).mean()
|
||||
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
入场逻辑（5m 时间框架）。
|
||||
|
||||
不做4H趋势判断——15m支撑阻力本身就是过滤器：
|
||||
- 趋势强时价格直接突破15m S/R，不会在支撑/阻力附近停留
|
||||
- 在支撑/阻力附近停留 = 震荡市
|
||||
|
||||
入场条件（3个，去掉了冗余的4H趋势判断）：
|
||||
- 做多：价格贴近15m支撑 + 支撑有效 + K线止跌信号
|
||||
- 做空：价格贴近15m阻力 + 阻力有效 + K线止涨信号
|
||||
|
||||
出场只依赖 custom_stoploss 和 custom_exit，不需要 D1 结构反转退出。
|
||||
（去掉 populate_exit_trend：震荡市入场 → D1 非上升趋势 → 立即出场 的死循环）
|
||||
"""
|
||||
cooldown = self.cooldown_bars.value
|
||||
|
||||
# NaN 安全处理 — 如果 15m informative 列还没对齐，直接跳过本根 K 线
|
||||
required_cols = ["support_15m", "resistance_15m",
|
||||
"support_alive_15m", "resistance_alive_15m"]
|
||||
for col in required_cols:
|
||||
if col not in dataframe.columns:
|
||||
return dataframe # 数据尚未就绪，跳过
|
||||
|
||||
for col in ["bullish_signal", "bearish_signal",
|
||||
"support_alive_15m", "resistance_alive_15m"]:
|
||||
# ── 布尔列NaN填充 ──
|
||||
for col in ["bullish_signal", "bearish_signal"]:
|
||||
dataframe[col] = dataframe[col].fillna(False)
|
||||
|
||||
# ── 做多 ──
|
||||
# 条件：价格贴近15m支撑（0.5%范围内）- 使用 low 而非 open
|
||||
# 因为支撑测试看的是价格是否到达支撑位，不是开盘在哪
|
||||
return dataframe
|
||||
|
||||
# ================================================================
|
||||
# 入场逻辑
|
||||
# ================================================================
|
||||
|
||||
def populate_entry_trend(
|
||||
self, dataframe: DataFrame, metadata: dict
|
||||
) -> DataFrame:
|
||||
"""
|
||||
入场逻辑。
|
||||
|
||||
只做顺趋势回调入场，不做S/R反向交易：
|
||||
|
||||
做多条件：
|
||||
1. 15m 上升趋势（EMA_fast > EMA_slow）
|
||||
2. 5m 价格回调到15m EMA_fast 或 支撑位附近
|
||||
3. 5m K线止跌信号（pinbar/engulfing）
|
||||
|
||||
做空条件（对称）：
|
||||
1. 15m 下降趋势
|
||||
2. 5m 价格反弹到15m EMA_fast 或 阻力位附近
|
||||
3. 5m K线止涨信号
|
||||
"""
|
||||
cooldown = self.cooldown_bars.value
|
||||
dev = self.pullback_deviation.value / 100.0 # 0.5% → 0.005
|
||||
|
||||
# ── 必要列检查 ──
|
||||
required = [
|
||||
"ema_fast_15m", "trend_up_15m", "trend_down_15m",
|
||||
"support_15m", "resistance_15m",
|
||||
]
|
||||
for col in required:
|
||||
if col not in dataframe.columns:
|
||||
return dataframe
|
||||
|
||||
# ── 布尔列填充 ──
|
||||
for col in [
|
||||
"bullish_signal", "bearish_signal",
|
||||
"trend_up_15m", "trend_down_15m",
|
||||
]:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].fillna(False)
|
||||
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
# 做多：上升趋势 + 回调到EMA/支撑 + 止跌信号
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
|
||||
# 条件1：15m 上升趋势
|
||||
trend_up = dataframe["trend_up_15m"]
|
||||
|
||||
# 条件2：价格在EMA20或支撑位附近（回调到顺趋势的支撑区）
|
||||
near_ema = (
|
||||
(dataframe["low"] <= dataframe["ema_fast_15m"] * (1.0 + dev * 0.5)) &
|
||||
(dataframe["low"] >= dataframe["ema_fast_15m"] * (1.0 - dev * 2.0))
|
||||
)
|
||||
near_support = (
|
||||
(dataframe["low"] <= dataframe["support_15m"] * 1.005) &
|
||||
(dataframe["low"] >= dataframe["support_15m"] * 0.995)
|
||||
(dataframe["low"] <= dataframe["support_15m"] * (1.0 + dev)) &
|
||||
(dataframe["low"] >= dataframe["support_15m"] * (1.0 - dev))
|
||||
)
|
||||
pullback_long = near_ema | near_support
|
||||
|
||||
long_conditions = (
|
||||
near_support
|
||||
& dataframe["support_alive_15m"]
|
||||
& dataframe["bullish_signal"]
|
||||
# 条件3：K线止跌信号
|
||||
signal_long = dataframe["bullish_signal"]
|
||||
|
||||
# 综合入场
|
||||
enter_long = trend_up & pullback_long & signal_long
|
||||
long_recent = enter_long.rolling(cooldown, min_periods=1).max().shift(1) == 0
|
||||
dataframe.loc[enter_long & long_recent, "enter_long"] = 1
|
||||
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
# 做空：下降趋势 + 反弹到EMA/阻力 + 止涨信号
|
||||
# ═══════════════════════════════════════════════════════════
|
||||
|
||||
# 条件1：15m 下降趋势
|
||||
trend_down = dataframe["trend_down_15m"]
|
||||
|
||||
# 条件2：价格在EMA20或阻力位附近（反弹到顺趋势的阻力区）
|
||||
near_ema_short = (
|
||||
(dataframe["high"] >= dataframe["ema_fast_15m"] * (1.0 - dev * 0.5)) &
|
||||
(dataframe["high"] <= dataframe["ema_fast_15m"] * (1.0 + dev * 2.0))
|
||||
)
|
||||
|
||||
long_recent = long_conditions.rolling(cooldown, min_periods=1).max().shift(1) == 0
|
||||
dataframe.loc[long_conditions & long_recent, "enter_long"] = 1
|
||||
|
||||
# ── 做空 ──
|
||||
# 条件：价格贴近15m阻力（0.5%范围内）- 使用 high 而非 open
|
||||
near_resistance = (
|
||||
(dataframe["high"] >= dataframe["resistance_15m"] * 0.995) &
|
||||
(dataframe["high"] <= dataframe["resistance_15m"] * 1.005)
|
||||
(dataframe["high"] >= dataframe["resistance_15m"] * (1.0 - dev)) &
|
||||
(dataframe["high"] <= dataframe["resistance_15m"] * (1.0 + dev))
|
||||
)
|
||||
pullback_short = near_ema_short | near_resistance
|
||||
|
||||
short_conditions = (
|
||||
near_resistance
|
||||
& dataframe["resistance_alive_15m"]
|
||||
& dataframe["bearish_signal"]
|
||||
)
|
||||
# 条件3：K线止涨信号
|
||||
signal_short = 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
|
||||
# 综合入场
|
||||
enter_short = trend_down & pullback_short & signal_short
|
||||
short_recent = enter_short.rolling(cooldown, min_periods=1).max().shift(1) == 0
|
||||
dataframe.loc[enter_short & short_recent, "enter_short"] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
# =====================
|
||||
# exit_trend（freqtrade 2025.11 要求必须实现，即使 use_custom_exit=True）
|
||||
# =====================
|
||||
# ================================================================
|
||||
# exit_trend（freqtrade 2025.11 强制要求，即使 use_exit_signal=False）
|
||||
# ================================================================
|
||||
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""退出逻辑完全由 custom_stoploss + custom_exit 管理。"""
|
||||
"""出场完全由 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）
|
||||
# =====================
|
||||
# ================================================================
|
||||
# 出场 — 止损（ATR动态）
|
||||
# ================================================================
|
||||
|
||||
def custom_stoploss(
|
||||
self,
|
||||
@ -440,87 +277,39 @@ class StructureFlowScalp(IStrategy):
|
||||
**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。
|
||||
止损 = 入场价 ± ATR × atr_mult_stop
|
||||
|
||||
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
|
||||
- ATR值从入场K线锁定，持仓期间不变
|
||||
- 做多：entry_price - (locked_atr × mult)
|
||||
- 做空：entry_price + (locked_atr × mult)
|
||||
- 配20x杠杆，ATR×1.0 ≈ 对应约 $3.7 止损（当前5m ATR~$3.74）
|
||||
"""
|
||||
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:
|
||||
atr = entry_row.get("atr", np.nan)
|
||||
if pd.isna(atr) or atr <= 0:
|
||||
return -0.02 if not trade.is_short else 0.02
|
||||
|
||||
mult = self.atr_mult_stop.value
|
||||
|
||||
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
|
||||
sl_price = trade.open_rate - (atr * mult)
|
||||
sl_ratio = (sl_price / trade.open_rate) - 1.0
|
||||
return max(sl_ratio, -self.stoploss)
|
||||
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)
|
||||
sl_price = trade.open_rate + (atr * mult)
|
||||
sl_ratio = 1.0 - (sl_price / trade.open_rate)
|
||||
return min(sl_ratio, self.stoploss)
|
||||
|
||||
# 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
|
||||
# =====================
|
||||
# ================================================================
|
||||
# 出场 — 止盈（ATR动态）+ 时间止损
|
||||
# ================================================================
|
||||
|
||||
def custom_exit(
|
||||
self,
|
||||
@ -532,58 +321,195 @@ class StructureFlowScalp(IStrategy):
|
||||
**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%
|
||||
出场逻辑：
|
||||
1. ATR止盈：利润达到入场时锁定的 ATR × atr_mult_tp → 止盈
|
||||
2. 时间止损：持仓超过 time_stop_minutes → 强制出场
|
||||
"""
|
||||
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:
|
||||
atr = entry_row.get("atr", np.nan)
|
||||
if pd.isna(atr) or atr <= 0:
|
||||
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
|
||||
# 1. ATR 止盈
|
||||
tp_mult = self.atr_mult_tp.value
|
||||
tp_ratio = (atr * tp_mult) / trade.open_rate
|
||||
|
||||
if current_profit >= target_pct:
|
||||
return "zone_tp"
|
||||
if current_profit >= tp_ratio:
|
||||
return "atr_tp"
|
||||
|
||||
# 2. 时间止损
|
||||
elapsed = (current_time - trade.open_date).total_seconds() / 60.0
|
||||
if elapsed >= self.time_stop_minutes:
|
||||
return "time_stop"
|
||||
|
||||
return None
|
||||
|
||||
# =====================
|
||||
# Plot config
|
||||
# =====================
|
||||
# ================================================================
|
||||
# 工具函数
|
||||
# ================================================================
|
||||
|
||||
@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"},
|
||||
},
|
||||
},
|
||||
}
|
||||
def _detect_swing_points(
|
||||
self, highs: list, lows: list, window: int = 5
|
||||
):
|
||||
"""
|
||||
Swing High / Swing Low 检测。
|
||||
|
||||
当一根K线的最高价高于其两侧window根K线的最高价时，标记为Swing High。
|
||||
Swing Low同理。
|
||||
"""
|
||||
n = len(highs)
|
||||
swing_high = [np.nan] * n
|
||||
swing_low = [np.nan] * n
|
||||
|
||||
for i in range(window, n - window):
|
||||
# Swing High
|
||||
is_high = True
|
||||
for j in range(i - window, i + window + 1):
|
||||
if j == i:
|
||||
continue
|
||||
if highs[j] >= highs[i]:
|
||||
is_high = False
|
||||
break
|
||||
if is_high:
|
||||
swing_high[i] = highs[i]
|
||||
|
||||
# Swing Low
|
||||
is_low = True
|
||||
for j in range(i - window, i + window + 1):
|
||||
if j == i:
|
||||
continue
|
||||
if lows[j] <= lows[i]:
|
||||
is_low = False
|
||||
break
|
||||
if is_low:
|
||||
swing_low[i] = lows[i]
|
||||
|
||||
return swing_high, swing_low
|
||||
|
||||
def _build_structure(
|
||||
self, highs: list, lows: list, closes: list,
|
||||
swing_high: list, swing_low: list,
|
||||
):
|
||||
"""构建趋势结构和支撑/阻力位。"""
|
||||
n = len(highs)
|
||||
trend_up = [False] * n
|
||||
trend_down = [False] * n
|
||||
support = [np.nan] * n
|
||||
resistance = [np.nan] * n
|
||||
|
||||
# 用最近4个swing point的位置判断
|
||||
last_sh_idx = -1
|
||||
last_sl_idx = -1
|
||||
prev_sh = []
|
||||
prev_sl = []
|
||||
|
||||
for i in range(n):
|
||||
if not np.isnan(swing_high[i]):
|
||||
prev_sh.append(swing_high[i])
|
||||
last_sh_idx = i
|
||||
if len(prev_sh) > 4:
|
||||
prev_sh.pop(0)
|
||||
|
||||
if not np.isnan(swing_low[i]):
|
||||
prev_sl.append(swing_low[i])
|
||||
last_sl_idx = i
|
||||
if len(prev_sl) > 4:
|
||||
prev_sl.pop(0)
|
||||
|
||||
# 趋势判断：最新的HH > 次新的HH = 上升趋势中的higher high
|
||||
if len(prev_sh) >= 2 and prev_sh[-1] > prev_sh[-2]:
|
||||
trend_up[i] = True
|
||||
|
||||
# 趋势判断：最新的LL < 次新的LL = 下降趋势中的lower low
|
||||
if len(prev_sl) >= 2 and prev_sl[-1] < prev_sl[-2]:
|
||||
trend_down[i] = True
|
||||
|
||||
# 支撑 = 最近的有效Swing Low（EMA平滑后在调用侧处理）
|
||||
if prev_sl:
|
||||
support[i] = prev_sl[-1]
|
||||
if prev_sh:
|
||||
resistance[i] = prev_sh[-1]
|
||||
|
||||
return trend_up, trend_down, support, resistance
|
||||
|
||||
def _ema_smooth(self, values: list, alpha: float = 0.3):
|
||||
"""对数组做EMA平滑，避免跳变。"""
|
||||
result = [np.nan] * len(values)
|
||||
ema = None
|
||||
for i, v in enumerate(values):
|
||||
if pd.isna(v) or v is None:
|
||||
if ema is not None:
|
||||
result[i] = ema
|
||||
continue
|
||||
if ema is None:
|
||||
ema = v
|
||||
else:
|
||||
ema = alpha * v + (1 - alpha) * ema
|
||||
result[i] = ema
|
||||
return np.array(result)
|
||||
|
||||
def _detect_candle_patterns(
|
||||
self, opens, highs, lows, closes, wick_ratio=0.6,
|
||||
):
|
||||
"""检测K线形态：pinbar（锤子线/射击星）和吞没形态。"""
|
||||
n = len(opens)
|
||||
bullish_pin = [False] * n
|
||||
bearish_pin = [False] * n
|
||||
bullish_engulf = [False] * n
|
||||
bearish_engulf = [False] * n
|
||||
|
||||
for i in range(n):
|
||||
o, h, l, c = opens[i], highs[i], lows[i], closes[i]
|
||||
total_range = h - l if h > l else 0.001
|
||||
|
||||
is_bullish = c > o
|
||||
is_bearish = c < o
|
||||
|
||||
body = abs(c - o)
|
||||
upper_wick = h - max(c, o)
|
||||
lower_wick = min(c, o) - l
|
||||
|
||||
# Pinbar：影线 > total_range × wick_ratio
|
||||
if is_bullish and lower_wick / total_range > wick_ratio:
|
||||
bullish_pin[i] = True
|
||||
if is_bearish and upper_wick / total_range > wick_ratio:
|
||||
bearish_pin[i] = True
|
||||
|
||||
# 吞没形态
|
||||
if i > 0:
|
||||
prev_o = opens[i - 1]
|
||||
prev_c = closes[i - 1]
|
||||
if is_bullish and c > prev_o and o < prev_c:
|
||||
bullish_engulf[i] = True
|
||||
if is_bearish and c < prev_o and o > prev_c:
|
||||
bearish_engulf[i] = True
|
||||
|
||||
return (
|
||||
pd.Series(bullish_pin),
|
||||
pd.Series(bearish_pin),
|
||||
pd.Series(bullish_engulf),
|
||||
pd.Series(bearish_engulf),
|
||||
)
|
||||
|
||||
def _get_entry_row(self, dataframe: DataFrame, trade: Trade):
|
||||
"""查找入场K线行，兼容live/backtesting两种模式。"""
|
||||
if "date" in dataframe.columns:
|
||||
entry_mask = pd.to_datetime(dataframe["date"]) <= trade.open_date
|
||||
if not entry_mask.any():
|
||||
return None
|
||||
return dataframe[entry_mask].iloc[-1]
|
||||
else:
|
||||
try:
|
||||
idx = dataframe.index.get_indexer([trade.open_date], method="pad")
|
||||
if idx[0] < 0 or idx[0] >= len(dataframe):
|
||||
return None
|
||||
return dataframe.iloc[idx[0]]
|
||||
except (TypeError, ValueError):
|
||||
return None
|
||||
|
||||
Reference in New Issue
Block a user