543 lines
20 KiB
Python
543 lines
20 KiB
Python
# ============================================================================
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# Structure Flow Strategy v1.0
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# 纯价格结构策略 — 零技术指标，价格行为学驱动
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#
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# 设计哲学:
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# 趋势不由 EMA 定义，而由 HH/HL（Higher High / Higher Low）定义
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# 支撑阻力不由百分比定义，而由历史 Swing Point 定义
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# 止损不由 ATR 定义，而由结构失效点定义
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# 出场不由固定盈亏比定义，而由结构反转定义
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#
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# 多时间框架:
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# D1 → 宏观结构方向
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# 1H → 中期结构位 + 入场区域判定
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# 5M → K线形态确认入场时机
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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, DecimalParameter, informative
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from freqtrade.persistence import Trade
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class StructureFlowStrategy(IStrategy):
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"""
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Structure Flow Strategy v1.0 — 纯价格结构策略
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不使用任何技术指标（无 EMA、ATR、RSI、MACD、布林带等）。
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一切信号来源于价格本身的 OHLC 数据和由此推导的结构信息。
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趋势判断:
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HH + HL → 上升趋势（Bullish Structure）
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LH + LL → 下降趋势（Bearish Structure）
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其他 → 震荡（Chop / Range）
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入场逻辑:
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做多: D1上升结构 + 价格在1H Swing区间的下半区 + 5M看涨K线形态
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做空: D1下降结构 + 价格在1H Swing区间的上半区 + 5M看跌K线形态
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结构位入场区间:
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不使用固定百分比。入场区域由最近 Swing High 和 Swing Low
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的中点定义——价格在下半区为做多区域，在上半区为做空区域。
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止损逻辑:
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初始止损: 1H 最近 Swing Low（做多）/ Swing High（做空）
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跟踪止损: 随新 Swing Point 形成而上移（做多）或下移（做空）
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这是"结构失效止损"——如果止损被触发，意味着结构被破坏，
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交易逻辑不再成立。
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出场逻辑:
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D1 结构反转（上升→非上升 或 下降→非下降）
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或 1H 结构失效（做多时 Swing Low 被跌破）
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"""
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# ── 基础配置 ──────────────────────────────────────────
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timeframe = "5m"
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can_short = False # spot 回测临时关闭，实盘 futures 改回 True
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stoploss = -0.25 # 硬止损安全网（25%），实际由 custom_stoploss 动态管理
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use_custom_stoploss = True
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minimal_roi = {"0": 100} # 不设时间止盈，出场由结构决定
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max_open_trades = 1
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# 回测参数
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startup_candle_count = 20 # 需要足够的历史数据来建立 Swing Point
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# ── 可调参数 ──────────────────────────────────────────
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# 这些参数是策略唯一的"旋钮"，且都有结构含义
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# Swing Point 检测窗口（寻找局部极值需要左右各 N 根K线确认）
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swing_lookback_d1 = IntParameter(
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2, 10, default=5, space="buy",
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)
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swing_lookback_h1 = IntParameter(
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2, 10, default=5, space="buy",
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)
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# Pin Bar 确认强度：影线至少是实体的 N 倍
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pin_bar_wick_ratio = DecimalParameter(
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1.5, 4.0, default=2.0, space="buy",
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)
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# ================================================================
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# 工具函数 — 纯价格计算，不依赖任何技术指标
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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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lookback: int,
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) -> tuple[pd.Series, pd.Series]:
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"""
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检测 Swing High 和 Swing Low。
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纯价格比较：
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- Swing High: 当前高点 > 左右各 lookback 根K线的所有高点
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- Swing Low: 当前低点 < 左右各 lookback 根K线的所有低点
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这是价格行为学最基础的构件——不需要任何指标。
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"""
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n = len(high)
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is_swing_high = np.full(n, False)
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is_swing_low = np.full(n, False)
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for i in range(lookback, n - lookback):
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window_high = high.iloc[i - lookback : i + lookback + 1]
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window_low = low.iloc[i - lookback : i + lookback + 1]
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if high.iloc[i] == window_high.max():
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is_swing_high[i] = True
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if low.iloc[i] == window_low.min():
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is_swing_low[i] = True
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return (
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pd.Series(is_swing_high, index=high.index),
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pd.Series(is_swing_low, index=low.index),
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)
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@staticmethod
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def _build_structure(
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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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"""
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从 Swing Points 构建市场结构信息。
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对每一个 K 线时刻，计算：
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1. trend_up / trend_down：当前处于上升/下降结构？
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- 最近两个 SH 和两个 SL 同时上移 → 上升
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- 最近两个 SH 和两个 SL 同时下移 → 下降
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- 其他 → 保持上一个状态（结构延续）
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2. nearest_support：最近 Swing Low 的价格
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3. nearest_resistance：最近 Swing High 的价格
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4. in_demand_zone：价格在下半区（做多区域）
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- 用区间中点划分：price_low < midpoint = 在下半区
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- 这比固定百分比更合理，因为区间大小由波动自然决定
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5. in_supply_zone：价格在上半区（做空区域）
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返回值是一个 DataFrame，包含上述所有列。
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"""
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n = len(high)
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# 输出数组
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trend_up_arr = np.full(n, False)
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trend_down_arr = np.full(n, False)
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nearest_support = np.full(n, np.nan)
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nearest_resistance = np.full(n, np.nan)
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in_demand_zone = np.full(n, False)
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in_supply_zone = np.full(n, False)
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# 用于追踪 Swing Point 序列的队列
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sh_prices: list[float] = [] # 最近几个 Swing High 价格
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sl_prices: list[float] = [] # 最近几个 Swing Low 价格
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for i in range(n):
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# ── 更新 Swing Point 队列 ──
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if swing_high.iloc[i] and not np.isnan(high.iloc[i]):
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sh_prices.append(high.iloc[i])
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# 只保留最近 4 个（用于判断结构）
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if len(sh_prices) > 4:
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sh_prices.pop(0)
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if swing_low.iloc[i] and not np.isnan(low.iloc[i]):
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sl_prices.append(low.iloc[i])
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if len(sl_prices) > 4:
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sl_prices.pop(0)
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# ── 趋势判断：至少需要 2 个 SH 和 2 个 SL ──
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if len(sh_prices) >= 2 and len(sl_prices) >= 2:
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latest_sh, prev_sh = sh_prices[-1], sh_prices[-2]
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latest_sl, prev_sl = sl_prices[-1], sl_prices[-2]
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if latest_sh > prev_sh and latest_sl > prev_sl:
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trend_up_arr[i] = True
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trend_down_arr[i] = False
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elif latest_sh < prev_sh and latest_sl < prev_sl:
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trend_up_arr[i] = False
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trend_down_arr[i] = True
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else:
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# 结构不明确，延续前一个状态
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if 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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# 数据不足，延续前一个状态
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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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# ── 最近支撑/阻力 ──
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if sl_prices:
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nearest_support[i] = sl_prices[-1]
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elif i > 0:
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nearest_support[i] = nearest_support[i - 1]
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if sh_prices:
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nearest_resistance[i] = sh_prices[-1]
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elif i > 0:
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nearest_resistance[i] = nearest_resistance[i - 1]
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# ── 入场区域：用 Swing 区间中点划分 ──
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# 有有效的支撑和阻力时才能判断
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if (
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not np.isnan(nearest_support[i])
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and not np.isnan(nearest_resistance[i])
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and nearest_resistance[i] > nearest_support[i]
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):
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mid = (nearest_support[i] + nearest_resistance[i]) / 2.0
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# 做多区域：价格低点触及下半区（有回落需求）
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in_demand_zone[i] = low.iloc[i] <= mid
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# 做空区域：价格高点触及上半区（有反弹供给）
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in_supply_zone[i] = high.iloc[i] >= mid
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elif i > 0:
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in_demand_zone[i] = in_demand_zone[i - 1]
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in_supply_zone[i] = in_supply_zone[i - 1]
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result = DataFrame(
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{
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"trend_up": trend_up_arr,
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"trend_down": trend_down_arr,
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"support": nearest_support,
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"resistance": nearest_resistance,
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"in_demand": in_demand_zone,
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"in_supply": in_supply_zone,
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},
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index=high.index,
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)
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return result
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@staticmethod
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def _detect_candle_patterns(
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o: pd.Series,
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h: pd.Series,
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l: pd.Series,
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c: pd.Series,
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pin_ratio: float,
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) -> tuple[pd.Series, pd.Series, pd.Series, pd.Series]:
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"""
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检测 K 线形态 — 纯 OHLC 计算。
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Pin Bar (锤子线/流星线):
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影线远大于实体，实体在K线的一端。
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看涨 Pin Bar: 长下影线 + 小实体在上方 = 买方在低位介入
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看跌 Pin Bar: 长上影线 + 小实体在下方 = 卖方在高位施压
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Engulfing (吞没形态):
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当前实体完全包裹前一实体，表示力量转换。
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"""
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body = abs(c - o)
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upper_wick = h - np.maximum(o, c)
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lower_wick = np.minimum(o, c) - l
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total_range = h - l
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# 避免除零
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valid_range = total_range > 0
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valid_body = body > 0
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# ── Pin Bar ──
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# 看涨：下影线 ≥ pin_ratio × 实体，上影线 ≤ 0.5 × 实体，实体在K线上方
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bullish_pin = (
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valid_range
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& valid_body
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& (lower_wick >= pin_ratio * body)
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& (upper_wick <= 0.5 * body)
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)
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# 看跌：上影线 ≥ pin_ratio × 实体，下影线 ≤ 0.5 × 实体
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bearish_pin = (
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valid_range
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& valid_body
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& (upper_wick >= pin_ratio * body)
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& (lower_wick <= 0.5 * body)
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)
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# ── Engulfing ──
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prev_body = body.shift(1)
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prev_o = o.shift(1)
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prev_c = c.shift(1)
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bullish_engulf = (
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(c > o) # 当前阳线
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& (prev_c < prev_o) # 前一根阴线
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& (body > prev_body) # 当前实体更大
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)
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bearish_engulf = (
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(c < o) # 当前阴线
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& (prev_c > prev_o) # 前一根阳线
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& (body > prev_body) # 当前实体更大
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)
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return (
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pd.Series(bullish_pin, index=c.index),
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pd.Series(bearish_pin, index=c.index),
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pd.Series(bullish_engulf, index=c.index),
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pd.Series(bearish_engulf, index=c.index),
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)
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# ================================================================
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# 信息时间框架 — D1 宏观结构
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# ================================================================
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@informative("1d")
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def populate_indicators_1d(
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self, dataframe: DataFrame, metadata: dict
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) -> DataFrame:
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"""
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D1 日线分析：宏观结构方向。
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计算 Swing Point → 结构趋势 → 支撑/阻力。
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"""
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sh, sl = self._detect_swing_points(
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dataframe["high"], dataframe["low"],
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self.swing_lookback_d1.value,
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)
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structure = self._build_structure(
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dataframe["high"], dataframe["low"], dataframe["close"],
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sh, sl,
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)
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dataframe["trend_up"] = structure["trend_up"]
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dataframe["trend_down"] = structure["trend_down"]
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return dataframe
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# ================================================================
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# 信息时间框架 — 1H 中期结构
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# ================================================================
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@informative("1h")
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def populate_indicators_1h(
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self, dataframe: DataFrame, metadata: dict
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) -> DataFrame:
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"""
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1H 小时线分析：中期结构位 + 入场区域。
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计算 Swing Point → 结构趋势 → 支撑/阻力 → 供需区域。
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"""
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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_h1.value,
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)
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structure = self._build_structure(
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dataframe["high"], dataframe["low"], dataframe["close"],
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sh, sl,
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)
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dataframe["trend_up"] = structure["trend_up"]
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dataframe["trend_down"] = structure["trend_down"]
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dataframe["support"] = structure["support"]
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dataframe["resistance"] = structure["resistance"]
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dataframe["in_demand"] = structure["in_demand"]
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dataframe["in_supply"] = structure["in_supply"]
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return dataframe
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# ================================================================
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# 主时间框架 — 5M K线形态
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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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"""
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5M 五分钟线：仅检测 K 线形态。
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不需要任何指标——形态来自 OHLC 的几何关系。
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"""
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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,
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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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# 综合看涨/看跌信号（任一形态触发即可）
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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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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(
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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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做多条件（全部满足）:
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1. D1 处于上升结构（trend_up_1d）
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2. 价格在 1H 下半区 / 需求区域（in_demand_1h）
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——这意味着价格已回调到支撑位附近
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3. 5M 出现看涨 K 线形态（bullish_signal）
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——Pin Bar 或 Engulfing 在结构位确认入场
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做空条件（全部满足）:
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1. D1 处于下降结构（trend_down_1d）
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2. 价格在 1H 上半区 / 供给区域（in_supply_1h）
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3. 5M 出现看跌 K 线形态（bearish_signal）
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"""
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# ── NaN 安全处理 ──
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# 多时间框架合并后，前部可能有 NaN
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bool_cols = [
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"trend_up_1d", "trend_down_1d",
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"trend_up_1h", "trend_down_1h",
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"in_demand_1h", "in_supply_1h",
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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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# ── 做多 ──
|
||
long_conditions = (
|
||
dataframe["trend_up_1d"] # D1 上升结构
|
||
& dataframe["in_demand_1h"] # 1H 下半区（需求区域）
|
||
& dataframe["bullish_signal"] # 5M 看涨形态
|
||
)
|
||
dataframe.loc[long_conditions, "enter_long"] = 1
|
||
|
||
# ── 做空 ──
|
||
if self.can_short:
|
||
short_conditions = (
|
||
dataframe["trend_down_1d"] # D1 下降结构
|
||
& dataframe["in_supply_1h"] # 1H 上半区（供给区域）
|
||
& dataframe["bearish_signal"] # 5M 看跌形态
|
||
)
|
||
dataframe.loc[short_conditions, "enter_short"] = 1
|
||
|
||
return dataframe
|
||
|
||
# ================================================================
|
||
# 出场信号
|
||
# ================================================================
|
||
|
||
def populate_exit_trend(
|
||
self, dataframe: DataFrame, metadata: dict
|
||
) -> DataFrame:
|
||
"""
|
||
出场逻辑 — 由结构反转触发。
|
||
|
||
做多出场:
|
||
D1 不再处于上升结构 → 宏观环境改变
|
||
或 1H 不再处于上升结构 → 中期结构失效
|
||
|
||
做空出场:
|
||
D1 不再处于下降结构 → 宏观环境改变
|
||
或 1H 不再处于下降结构 → 中期结构失效
|
||
"""
|
||
|
||
# 做多出场
|
||
exit_long = (
|
||
~dataframe["trend_up_1d"].fillna(True) # D1 结构反转（NaN = 初始区，不出场）
|
||
)
|
||
dataframe.loc[exit_long, "exit_long"] = 1
|
||
|
||
# 做空出场
|
||
if self.can_short:
|
||
exit_short = (
|
||
dataframe["trend_up_1d"].fillna(False) # D1 转为上升
|
||
)
|
||
dataframe.loc[exit_short, "exit_short"] = 1
|
||
|
||
return dataframe
|
||
|
||
# ================================================================
|
||
# 动态止损 — 基于结构失效
|
||
# ================================================================
|
||
|
||
def custom_stoploss(
|
||
self,
|
||
pair: str,
|
||
trade: Trade,
|
||
current_time: datetime,
|
||
current_rate: float,
|
||
current_profit: float,
|
||
after_fill: bool,
|
||
**kwargs,
|
||
) -> float | None:
|
||
"""
|
||
结构止损：止损位设在最近的 1H Swing Low（做多）或 Swing High（做空）。
|
||
|
||
如果价格突破这个结构位，说明结构失效，交易逻辑不再成立。
|
||
这与传统的百分比止损或 ATR 止损不同——它不是"跌了N%就走"，
|
||
而是"结构破了就走"。
|
||
|
||
随着行情发展，新的 Swing Point 形成，止损自动跟随，
|
||
实现自然的移动止损——不依赖任何参数。
|
||
"""
|
||
|
||
# 获取已分析的 5M 数据（包含合并后的 1H 信息）
|
||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||
if dataframe is None or len(dataframe) == 0:
|
||
return None # 使用默认 stoploss
|
||
|
||
last = dataframe.iloc[-1]
|
||
|
||
if trade.is_short:
|
||
# 做空止损：放在最近的 1H Swing High 上方
|
||
resistance = last.get("resistance_1h")
|
||
if resistance is not None and not (isinstance(resistance, float) and np.isnan(resistance)):
|
||
# stoploss = (current - stop_price) / current
|
||
# 做空时 stop 在 current 上方，所以 (current - resistance) 为负
|
||
# 转为负的比例
|
||
sl_ratio = (current_rate - float(resistance)) / current_rate
|
||
# 只使用比默认止损更紧的止损
|
||
if sl_ratio > self.stoploss and sl_ratio < 0:
|
||
return sl_ratio
|
||
else:
|
||
# 做多止损：放在最近的 1H Swing Low 下方
|
||
support = last.get("support_1h")
|
||
if support is not None and not (isinstance(support, float) and np.isnan(support)):
|
||
# stoploss = (stop_price - current) / current
|
||
# 做多时 stop 在 current 下方，结果为负
|
||
sl_ratio = (float(support) - current_rate) / current_rate
|
||
# 只使用比默认止损更紧的止损
|
||
if sl_ratio > self.stoploss and sl_ratio < 0:
|
||
return sl_ratio
|
||
|
||
# 无法获取有效的结构位，使用默认硬止损
|
||
return None
|