516 lines
19 KiB
Python
516 lines
19 KiB
Python
# 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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from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter, informative
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from pandas import DataFrame
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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 StructureFlowMomentumScalp(IStrategy):
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"""
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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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# ── 时间框架 ──
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timeframe = "5m"
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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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# ── 合约参数 ──
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margin_mode = "cross"
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trading_mode = "futures"
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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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# ── 常数 ──
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time_stop_minutes = 60 # 最大持仓时间
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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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# 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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"""15m级别：EMA趋势方向 + swing point S/R。"""
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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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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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# ── 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 + EMA趋势整合。"""
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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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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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atr_mean = dataframe["atr"].mean()
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dataframe["atr"] = dataframe["atr"].fillna(atr_mean)
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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"], dataframe["high"], dataframe["low"],
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dataframe["close"], self.pin_bar_wick_ratio.value / 100.0,
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)
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)
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dataframe["bullish_signal"] = bullish_pin | bullish_engulf
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dataframe["bearish_signal"] = bearish_pin | bearish_engulf
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# ── 5m EMA（用于短期拉回确认） ──
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dataframe["ema5"] = close.ewm(span=5, adjust=False).mean()
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dataframe["ema8"] = close.ewm(span=8, adjust=False).mean()
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# ── 布尔列NaN填充 ──
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for col in ["bullish_signal", "bearish_signal"]:
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dataframe[col] = dataframe[col].fillna(False)
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return dataframe
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# ================================================================
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# 入场逻辑
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# ================================================================
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def populate_entry_trend(
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self, dataframe: DataFrame, metadata: dict
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) -> DataFrame:
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"""
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入场逻辑。
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只做顺趋势回调入场，不做S/R反向交易：
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做多条件：
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1. 15m 上升趋势（EMA_fast > EMA_slow）
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2. 5m 价格回调到15m EMA_fast 或 支撑位附近
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3. 5m K线止跌信号（pinbar/engulfing）
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做空条件（对称）：
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1. 15m 下降趋势
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2. 5m 价格反弹到15m EMA_fast 或 阻力位附近
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3. 5m K线止涨信号
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"""
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cooldown = self.cooldown_bars.value
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dev = self.pullback_deviation.value / 100.0 # 0.5% → 0.005
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# ── 必要列检查 ──
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required = [
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"ema_fast_15m", "trend_up_15m", "trend_down_15m",
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"support_15m", "resistance_15m",
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]
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for col in required:
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if col not in dataframe.columns:
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return dataframe
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# ── 布尔列填充 ──
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for col in [
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"bullish_signal", "bearish_signal",
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"trend_up_15m", "trend_down_15m",
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]:
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if col in dataframe.columns:
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dataframe[col] = dataframe[col].fillna(False)
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# ═══════════════════════════════════════════════════════════
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# 做多：上升趋势 + 回调到EMA/支撑 + 止跌信号
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# ═══════════════════════════════════════════════════════════
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# 条件1：15m 上升趋势
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trend_up = dataframe["trend_up_15m"]
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# 条件2：价格在EMA20或支撑位附近（回调到顺趋势的支撑区）
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near_ema = (
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(dataframe["low"] <= dataframe["ema_fast_15m"] * (1.0 + dev * 0.5)) &
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(dataframe["low"] >= dataframe["ema_fast_15m"] * (1.0 - dev * 2.0))
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)
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near_support = (
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(dataframe["low"] <= dataframe["support_15m"] * (1.0 + dev)) &
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(dataframe["low"] >= dataframe["support_15m"] * (1.0 - dev))
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)
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pullback_long = near_ema | near_support
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# 条件3：K线止跌信号
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signal_long = dataframe["bullish_signal"]
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# 综合入场
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enter_long = trend_up & pullback_long & signal_long
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long_recent = enter_long.rolling(cooldown, min_periods=1).max().shift(1) == 0
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dataframe.loc[enter_long & long_recent, "enter_long"] = 1
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# ═══════════════════════════════════════════════════════════
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# 做空：下降趋势 + 反弹到EMA/阻力 + 止涨信号
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# ═══════════════════════════════════════════════════════════
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# 条件1：15m 下降趋势
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trend_down = dataframe["trend_down_15m"]
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# 条件2：价格在EMA20或阻力位附近（反弹到顺趋势的阻力区）
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near_ema_short = (
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(dataframe["high"] >= dataframe["ema_fast_15m"] * (1.0 - dev * 0.5)) &
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(dataframe["high"] <= dataframe["ema_fast_15m"] * (1.0 + dev * 2.0))
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)
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near_resistance = (
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(dataframe["high"] >= dataframe["resistance_15m"] * (1.0 - dev)) &
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(dataframe["high"] <= dataframe["resistance_15m"] * (1.0 + dev))
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)
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pullback_short = near_ema_short | near_resistance
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# 条件3：K线止涨信号
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signal_short = dataframe["bearish_signal"]
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# 综合入场
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enter_short = trend_down & pullback_short & signal_short
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short_recent = enter_short.rolling(cooldown, min_periods=1).max().shift(1) == 0
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dataframe.loc[enter_short & short_recent, "enter_short"] = 1
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return dataframe
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# ================================================================
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# exit_trend（freqtrade 2025.11 强制要求，即使 use_exit_signal=False）
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# ================================================================
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""出场完全由 custom_stoploss + custom_exit 管理。"""
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return dataframe
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# ================================================================
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# 出场 — 止损（ATR动态）
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# ================================================================
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def custom_stoploss(
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self,
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pair: str,
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trade: Trade,
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current_time: datetime,
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current_rate: float,
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current_profit: float,
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after_fill: bool,
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**kwargs,
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) -> float:
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"""
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止损 = 入场价 ± ATR × atr_mult_stop
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- ATR值从入场K线锁定，持仓期间不变
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- 做多：entry_price - (locked_atr × mult)
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- 做空：entry_price + (locked_atr × mult)
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- 配20x杠杆，ATR×1.0 ≈ 对应约 $3.7 止损（当前5m ATR~$3.74）
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"""
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dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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if dataframe is None or len(dataframe) == 0:
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return -0.02 if not trade.is_short else 0.02
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entry_row = self._get_entry_row(dataframe, trade)
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if entry_row is None:
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return -0.02 if not trade.is_short else 0.02
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atr = entry_row.get("atr", np.nan)
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if pd.isna(atr) or atr <= 0:
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return -0.02 if not trade.is_short else 0.02
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mult = self.atr_mult_stop.value
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if not trade.is_short:
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sl_price = trade.open_rate - (atr * mult)
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sl_ratio = (sl_price / trade.open_rate) - 1.0
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return max(sl_ratio, -self.stoploss)
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else:
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sl_price = trade.open_rate + (atr * mult)
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sl_ratio = 1.0 - (sl_price / trade.open_rate)
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return min(sl_ratio, self.stoploss)
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# ================================================================
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# 出场 — 止盈（ATR动态）+ 时间止损
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# ================================================================
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def custom_exit(
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self,
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pair: str,
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trade: Trade,
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current_time: datetime,
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current_rate: float,
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current_profit: float,
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**kwargs,
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) -> str | None:
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"""
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出场逻辑：
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1. ATR止盈：利润达到入场时锁定的 ATR × atr_mult_tp → 止盈
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2. 时间止损：持仓超过 time_stop_minutes → 强制出场
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"""
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dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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if dataframe is None or len(dataframe) == 0:
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return None
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entry_row = self._get_entry_row(dataframe, trade)
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if entry_row is None:
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return None
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atr = entry_row.get("atr", np.nan)
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if pd.isna(atr) or atr <= 0:
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return None
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# 1. ATR 止盈
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tp_mult = self.atr_mult_tp.value
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tp_ratio = (atr * tp_mult) / trade.open_rate
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if current_profit >= tp_ratio:
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return "atr_tp"
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# 2. 时间止损
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elapsed = (current_time - trade.open_date).total_seconds() / 60.0
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if elapsed >= self.time_stop_minutes:
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return "time_stop"
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return None
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# ================================================================
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# 工具函数
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# ================================================================
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def _detect_swing_points(
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self, highs: list, lows: list, window: int = 5
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):
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"""
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Swing High / Swing Low 检测。
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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。
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Swing Low同理。
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"""
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n = len(highs)
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swing_high = [np.nan] * n
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swing_low = [np.nan] * n
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for i in range(window, n - window):
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# Swing High
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is_high = True
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for j in range(i - window, i + window + 1):
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if j == i:
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continue
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if highs[j] >= highs[i]:
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is_high = False
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break
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if is_high:
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swing_high[i] = highs[i]
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# Swing Low
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is_low = True
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for j in range(i - window, i + window + 1):
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if j == i:
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continue
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if lows[j] <= lows[i]:
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is_low = False
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break
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if is_low:
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swing_low[i] = lows[i]
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return swing_high, swing_low
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def _build_structure(
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self, highs: list, lows: list, closes: list,
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swing_high: list, swing_low: list,
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):
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"""构建趋势结构和支撑/阻力位。"""
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n = len(highs)
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trend_up = [False] * n
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trend_down = [False] * n
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support = [np.nan] * n
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resistance = [np.nan] * n
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# 用最近4个swing point的位置判断
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last_sh_idx = -1
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last_sl_idx = -1
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prev_sh = []
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prev_sl = []
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for i in range(n):
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if not np.isnan(swing_high[i]):
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prev_sh.append(swing_high[i])
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last_sh_idx = i
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if len(prev_sh) > 4:
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prev_sh.pop(0)
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if not np.isnan(swing_low[i]):
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prev_sl.append(swing_low[i])
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last_sl_idx = i
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if len(prev_sl) > 4:
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prev_sl.pop(0)
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# 趋势判断：最新的HH > 次新的HH = 上升趋势中的higher high
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if len(prev_sh) >= 2 and prev_sh[-1] > prev_sh[-2]:
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trend_up[i] = True
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# 趋势判断：最新的LL < 次新的LL = 下降趋势中的lower low
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if len(prev_sl) >= 2 and prev_sl[-1] < prev_sl[-2]:
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trend_down[i] = True
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# 支撑 = 最近的有效Swing Low（EMA平滑后在调用侧处理）
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if prev_sl:
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support[i] = prev_sl[-1]
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if prev_sh:
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resistance[i] = prev_sh[-1]
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return trend_up, trend_down, support, resistance
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def _ema_smooth(self, values: list, alpha: float = 0.3):
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"""对数组做EMA平滑，避免跳变。"""
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||
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
|