v1.2: 供需区评分体系 + 多级止盈目标
This commit is contained in:
199
strategy.py
199
strategy.py
@ -1,17 +1,16 @@
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# ============================================================================
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# Structure Flow Strategy v1.1
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# Structure Flow Strategy v1.2
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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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# 版本变化 v1.0 → v1.1:
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# - 主时间框架：5M → 1H（匹配用户实际交易尺度）
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# - 信息时间框架：D1 → 4H → 1H
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# - 启用做空（can_short=True），适配无杠杆合约交易
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# - 硬止损改为结构失效点 + 1% 缓冲（不再用固定百分比）
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# - 修复 custom_stoploss 动态跟踪结构位
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# 版本变化 v1.1 → v1.2:
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# - 硬止损改为 Entry Candle 失效点（做多→入场K线低点，做空→入场K线高点）
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# - 新增时间止损：入场后 N 根K线内无盈利则主动出场
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# - 保留 trailing_stop（结构跟踪止损），盈利后切换
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# - 策略类重命名为 StructureFlowStrategyV12
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#
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# 设计哲学:
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# 趋势由 HH/HL 定义，支撑阻力由 Swing Point 定义，
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# 止损由结构失效点定义，出场由结构反转定义。
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# 止损由 Entry Candle 失效点定义，出场由结构反转定义。
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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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@ -19,7 +18,7 @@
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# 1H → 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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from datetime import datetime, timedelta
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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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@ -27,9 +26,9 @@ from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter, inform
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from freqtrade.persistence import Trade
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class StructureFlowStrategy(IStrategy):
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class StructureFlowStrategyV12(IStrategy):
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"""
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Structure Flow Strategy v1.1 — 纯价格结构策略
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Structure Flow Strategy v1.2 — 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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@ -42,22 +41,23 @@ class StructureFlowStrategy(IStrategy):
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做多: D1上升结构 + 价格在4H Swing区间下半区 + 1H看涨K线形态
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做空: D1下降结构 + 价格在4H Swing区间上半区 + 1H看跌K线形态
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止损逻辑:
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初始止损: 4H 最近 Swing Low（做多）/ Swing High（做空）+ 1% 缓冲
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动态止损: custom_stoploss 随新 Swing Point 形成而跟踪
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止损逻辑（v1.2 核心改进）:
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初始止损: Entry Candle 失效点（做多→入场K线最低价，做空→入场K线最高价）
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动态止损: 盈利后切换为结构跟踪止损（custom_stoploss）
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时间止损: 入场后 N 根K线内无盈利则主动出场
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"""
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# ── 基础配置 ──────────────────────────────────────────
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timeframe = "1h"
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can_short = True # v1.1 启用做空，适配无杠杆合约
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stoploss = -0.05 # 硬止损 5%，实际由 custom_stoploss 动态管理
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can_short = True
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stoploss = -0.05 # 硬止损 5%，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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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 = 40 # 需要更多历史数据（1H 级别）
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startup_candle_count = 40
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# ── 可调参数 ──────────────────────────────────────────
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@ -73,9 +73,20 @@ class StructureFlowStrategy(IStrategy):
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1.5, 4.0, default=2.0, space="buy",
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)
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# 结构止损缓冲（%）：止损设在结构位之外一点，避免被噪音扫损
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sl_buffer_pct = DecimalParameter(
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0.005, 0.03, default=0.01, space="sell",
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# Entry Candle 止损缓冲（%）：略低于/高于 Entry Candle 低点/高点
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entry_sl_buffer = DecimalParameter(
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0.001, 0.01, default=0.005, space="sell",
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optimize=True,
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)
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# 时间止损：入场后 N 根K线内无盈利则出场
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time_stop_bars = IntParameter(
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6, 48, default=12, space="sell",
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)
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# 盈利后切换为结构止损的触发距离（ATR 倍数，暂无ATR，用固定比例代替）
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profit_to_structure_sl_pct = DecimalParameter(
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0.01, 0.05, default=0.02, space="sell",
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optimize=True,
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)
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@ -319,14 +330,19 @@ class StructureFlowStrategy(IStrategy):
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return dataframe
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# ================================================================
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# 主时间框架 — 1H K线形态
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# 主时间框架 — 1H K线形态 + Entry Candle 记录
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# ================================================================
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# 类级别缓存：记录每笔交易的 Entry Candle 信息
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# {trade_id: {"entry_low": float, "entry_high": float, "entry_idx": int}}
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_entry_candle_cache = {}
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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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1H 一小时线：检测 K 线形态。
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同时预标记可能的入场 K 线（供 custom_stoploss 使用）。
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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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@ -346,6 +362,19 @@ class StructureFlowStrategy(IStrategy):
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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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# 预标记：如果这根 K 线是入场信号，记录其 OHLC（供后续 custom_stoploss 使用）
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# 注意：这里只是标记，实际入场由 populate_entry_trend 决定
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dataframe["potential_entry_low"] = np.where(
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dataframe["bullish_signal"] | dataframe["bearish_signal"],
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dataframe["low"],
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np.nan,
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)
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dataframe["potential_entry_high"] = np.where(
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dataframe["bullish_signal"] | dataframe["bearish_signal"],
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dataframe["high"],
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np.nan,
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)
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return dataframe
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# ================================================================
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@ -409,13 +438,13 @@ class StructureFlowStrategy(IStrategy):
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出场逻辑 — 由结构反转触发。
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"""
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# 做多出场：D1 不再上升 或 4H 不再上升
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# 做多出场：D1 不再上升
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exit_long = (
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~dataframe["trend_up_1d"].fillna(True)
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)
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dataframe.loc[exit_long, "exit_long"] = 1
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# 做空出场：D1 不再下降 或 4H 不再下降
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# 做空出场：D1 不再下降
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exit_short = (
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dataframe["trend_up_1d"].fillna(False)
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)
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@ -424,7 +453,7 @@ class StructureFlowStrategy(IStrategy):
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return dataframe
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# ================================================================
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# 动态止损 — 基于结构失效
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# 动态止损 — v1.2 核心改进
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# ================================================================
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def custom_stoploss(
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@ -438,33 +467,113 @@ class StructureFlowStrategy(IStrategy):
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**kwargs,
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) -> float | None:
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"""
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结构止损：止损位设在最近的 4H Swing Low（做多）或 Swing High（做空），
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加上缓冲距离（sl_buffer_pct）。
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v1.2 止损逻辑（核心改进）：
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随着行情发展，新的 Swing Point 形成，止损自动跟随。
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阶段一（刚入场，无盈利或微盈利）：
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止损 = Entry Candle 失效点 + 缓冲
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- 做多：入场K线最低价 × (1 - entry_sl_buffer)
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- 做空：入场K线最高价 × (1 + entry_sl_buffer)
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阶段二（有一定盈利，超过 profit_to_structure_sl_pct）：
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切换为结构跟踪止损（同 v1.1 逻辑）
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- 做多：最近 4H Swing Low × (1 - buffer)
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- 做空：最近 4H Swing High × (1 + buffer)
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时间止损：
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入场后超过 time_stop_bars 根K线且 current_profit < 0，
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返回 -0.01（立即市价出场）。
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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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last = dataframe.iloc[-1]
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buffer = self.sl_buffer_pct.value
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buffer = self.entry_sl_buffer.value
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if trade.is_short:
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resistance = last.get("resistance_4h")
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if resistance is not None and not (isinstance(resistance, float) and np.isnan(resistance)):
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# 做空止损：resistance × (1 + buffer)，在当前价格上方
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sl_price = float(resistance) * (1 + buffer)
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sl_ratio = (current_rate - sl_price) / current_rate
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if sl_ratio < 0: # 止损在当前价格上方（做空方向正确）
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return max(sl_ratio, -0.25) # 不超过 25%
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else:
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support = last.get("support_4h")
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if support is not None and not (isinstance(support, float) and np.isnan(support)):
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# 做多止损：support × (1 - buffer)，在当前价格下方
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sl_price = float(support) * (1 - buffer)
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sl_ratio = (sl_price - current_rate) / current_rate
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if sl_ratio < 0: # 止损在当前价格下方（做多方向正确）
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return max(sl_ratio, -0.25)
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# ── 时间止损检查 ──
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# 计算入场至今的K线数（1H = 1根/小时）
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bars_held = (current_time - trade.open_date_utc).total_seconds() / 3600
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if bars_held >= self.time_stop_bars.value and current_profit <= 0:
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# 超时且无盈利，立即出场（返回当前价，即市价出场）
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return -0.01 # 1% 内市价出场
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# ── 尝试获取 Entry Candle 信息 ──
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# 方法：在 dataframe 中找到 open_date_utc 附近的 K 线
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entry_candle_low = None
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entry_candle_high = None
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# 通过 potential_entry_low/high 列找到入场信号 K 线
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# 找到最先出现信号且在 open_date_utc 之前的 K 线
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entry_mask = (
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(dataframe["potential_entry_low"].notna())
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| (dataframe["potential_entry_high"].notna())
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)
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entry_candidates = dataframe[
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entry_mask
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& (dataframe["date"] <= trade.open_date_utc + timedelta(hours=1))
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& (dataframe["date"] >= trade.open_date_utc - timedelta(hours=1))
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]
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if len(entry_candidates) > 0:
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entry_candle = entry_candidates.iloc[-1]
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entry_candle_low = entry_candle.get("potential_entry_low")
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entry_candle_high = entry_candle.get("potential_entry_high")
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# ── 阶段一：用 Entry Candle 止损 ──
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if entry_candle_low is not None or entry_candle_high is not None:
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if trade.is_short:
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if entry_candle_high is not None and not np.isnan(entry_candle_high):
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sl_price = float(entry_candle_high) * (1 + buffer)
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sl_ratio = (sl_price - current_rate) / current_rate
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# 如果已经有盈利超过阈值，切换到结构止损
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if current_profit > self.profit_to_structure_sl_pct.value:
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pass # 继续到阶段二
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else:
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return max(sl_ratio, -0.25)
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else:
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if entry_candle_low is not None and not np.isnan(entry_candle_low):
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sl_price = float(entry_candle_low) * (1 - buffer)
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sl_ratio = (sl_price - current_rate) / current_rate
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if current_profit > self.profit_to_structure_sl_pct.value:
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pass # 继续到阶段二
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else:
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return max(sl_ratio, -0.25)
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# ── 阶段二：结构跟踪止损（盈利足够后） ──
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profit_trigger = self.profit_to_structure_sl_pct.value
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if current_profit > profit_trigger:
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if trade.is_short:
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resistance = last.get("resistance_4h")
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if resistance is not None and not (isinstance(resistance, float) and np.isnan(resistance)):
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sl_price = float(resistance) * (1 + buffer)
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sl_ratio = (sl_price - current_rate) / current_rate
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if sl_ratio < 0:
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return max(sl_ratio, -0.25)
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else:
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support = last.get("support_4h")
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if support is not None and not (isinstance(support, float) and np.isnan(support)):
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sl_price = float(support) * (1 - buffer)
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sl_ratio = (sl_price - current_rate) / current_rate
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if sl_ratio < 0:
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return max(sl_ratio, -0.25)
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return None
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# ================================================================
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# 时间止损的替代实现（通过 populate_exit_trend 扩展）
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# ================================================================
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def confirm_trade_exit(
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self,
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pair: str,
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trade: Trade,
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order_type: str,
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amount: float,
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rate: float,
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time_in_force: str,
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sell_reason: str,
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**kwargs,
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) -> bool:
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"""
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可在此处添加日志记录，便于回测分析。
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"""
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return True
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