v1.3: 精简过滤器 + 结构突破确认优化
This commit is contained in:
560
strategy.py
560
strategy.py
@ -1,148 +1,102 @@
|
||||
# ============================================================================
|
||||
# Structure Flow Strategy v1.2
|
||||
# 纯价格结构策略 — 零技术指标，价格行为学驱动
|
||||
#
|
||||
# 版本变化 v1.1 → v1.2:
|
||||
# - 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）
|
||||
# - 新增时间止损：入场后 N 根K线内无盈利则主动出场
|
||||
# - 保留 trailing_stop（结构跟踪止损），盈利后切换
|
||||
# - 策略类重命名为 StructureFlowStrategyV12
|
||||
#
|
||||
# 设计哲学:
|
||||
# 趋势由 HH/HL 定义，支撑阻力由 Swing Point 定义，
|
||||
# 止损由 Entry Candle 失效点定义，出场由结构反转定义。
|
||||
#
|
||||
# 多时间框架:
|
||||
# D1 → 宏观结构方向
|
||||
# 4H → 中期结构位 + 入场区域判定
|
||||
# 1H → K线形态确认入场时机
|
||||
# ============================================================================
|
||||
"""
|
||||
Structure Flow Strategy v1.3
|
||||
=======================
|
||||
变更记录：
|
||||
v1.0 (2026-06-07): 纯价格结构策略，D1定方向→4H定位→1H入场，支撑/阻力来自Swing Point
|
||||
v1.1 (2026-06-07): 修复freqtrade 2026.2 Binance futures bug(use_order_book:true)，
|
||||
硬止损改为结构失效点，首次futures回测成功(+61.52%)
|
||||
v1.2 (2026-06-07): 尝试Entry Candle止损（入场K线低点/高点），增加时间止损
|
||||
结果：50笔硬止损全亏，Entry Candle查找有bug，return None退回到25%宽止损
|
||||
v1.3 (2026-06-07): ===== 重写 custom_stoploss =====
|
||||
弃用脆弱的Entry Candle查找，改用ATR动态止损：
|
||||
- 初始止损：入场价 ± 1.0 ATR（紧，快速认输）
|
||||
- 盈利>1%：移动止损至保本线
|
||||
- 盈利>2%：ATR追踪止损，锁定利润
|
||||
- 硬止损安全网：-5%（stoploss属性）
|
||||
核心哲学：预估错误的交易，早早认输止损离场，不硬扛单
|
||||
"""
|
||||
|
||||
from datetime import datetime, timedelta
|
||||
from datetime import datetime
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from pandas import DataFrame
|
||||
from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter, informative
|
||||
from freqtrade.strategy import IStrategy, IntParameter, informative
|
||||
from freqtrade.persistence import Trade
|
||||
|
||||
|
||||
class StructureFlowStrategyV12(IStrategy):
|
||||
class StructureFlowStrategyV13(IStrategy):
|
||||
"""
|
||||
Structure Flow Strategy v1.2 — 纯价格结构策略
|
||||
Structure Flow Strategy v1.3
|
||||
|
||||
不使用任何技术指标（无 EMA、ATR、RSI、MACD、布林带等）。
|
||||
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。
|
||||
核心逻辑：
|
||||
D1 定宏观方向（HH/HL 上升，LH/LL 下降）
|
||||
↓
|
||||
4H 定位结构位（Swing Point → 支撑/阻力区域）
|
||||
↓
|
||||
1H 找入场时机（K线形态 + 在结构区域内）
|
||||
|
||||
趋势判断:
|
||||
HH + HL → 上升趋势（Bullish Structure）
|
||||
LH + LL → 下降趋势（Bearish Structure）
|
||||
|
||||
入场逻辑:
|
||||
做多: D1上升结构 + 价格在4H Swing区间下半区 + 1H看涨K线形态
|
||||
做空: D1下降结构 + 价格在4H Swing区间上半区 + 1H看跌K线形态
|
||||
|
||||
止损逻辑（v1.2 核心改进）:
|
||||
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）
|
||||
动态止损: 盈利后切换为结构跟踪止损（custom_stoploss）
|
||||
时间止损: 入场后 N 根K线内无盈利则主动出场
|
||||
止损逻辑（v1.3重写）：
|
||||
- 初始止损：入场价 ± 1.0 ATR（紧，快速认输）
|
||||
- 盈利 > 1%：移动止损至保本（open_rate ± 0.1%）
|
||||
- 盈利 > 2%：ATR追踪止损（current_rate ∓ 1.0 ATR）
|
||||
- 硬止损安全网：-5%（防止极端行情）
|
||||
"""
|
||||
|
||||
# ── 基础配置 ──────────────────────────────────────────
|
||||
# =====================
|
||||
# 基础属性
|
||||
# =====================
|
||||
|
||||
timeframe = "1h"
|
||||
can_short = True
|
||||
stoploss = -0.05 # 硬止损 5%，实际由 custom_stoploss 动态管理
|
||||
stoploss = -0.05 # 硬止损安全网 5%，实际由 custom_stoploss 动态管理
|
||||
use_custom_stoploss = True
|
||||
minimal_roi = {"0": 100} # 不设时间止盈，出场由结构决定
|
||||
minimal_roi = {"0": 100} # 不设时间止盈，靠移动止损出场
|
||||
max_open_trades = 1
|
||||
timeframe = "1h"
|
||||
|
||||
# 回测参数
|
||||
startup_candle_count = 40
|
||||
# =====================
|
||||
# 可优化参数
|
||||
# =====================
|
||||
|
||||
# ── 可调参数 ──────────────────────────────────────────
|
||||
swing_lookback_d1 = IntParameter(8, 14, default=10, space="buy")
|
||||
swing_lookback_h4 = IntParameter(5, 10, default=8, space="buy")
|
||||
pin_bar_wick_ratio = IntParameter(50, 70, default=60, space="buy")
|
||||
|
||||
swing_lookback_d1 = IntParameter(
|
||||
2, 10, default=5, space="buy",
|
||||
)
|
||||
swing_lookback_h4 = IntParameter(
|
||||
2, 10, default=5, space="buy",
|
||||
)
|
||||
|
||||
# Pin Bar 确认强度：影线至少是实体的 N 倍
|
||||
pin_bar_wick_ratio = DecimalParameter(
|
||||
1.5, 4.0, default=2.0, space="buy",
|
||||
)
|
||||
|
||||
# Entry Candle 止损缓冲（%）：略低于/高于 Entry Candle 低点/高点
|
||||
entry_sl_buffer = DecimalParameter(
|
||||
0.001, 0.01, default=0.005, space="sell",
|
||||
optimize=True,
|
||||
)
|
||||
|
||||
# 时间止损：入场后 N 根K线内无盈利则出场
|
||||
time_stop_bars = IntParameter(
|
||||
6, 48, default=12, space="sell",
|
||||
)
|
||||
|
||||
# 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）
|
||||
profit_to_structure_sl_pct = DecimalParameter(
|
||||
0.01, 0.05, default=0.02, space="sell",
|
||||
optimize=True,
|
||||
)
|
||||
|
||||
# ================================================================
|
||||
# 工具函数 — 纯价格计算，不依赖任何技术指标
|
||||
# ================================================================
|
||||
# =====================
|
||||
# 工具：Swing Point 检测
|
||||
# =====================
|
||||
|
||||
@staticmethod
|
||||
def _detect_swing_points(
|
||||
high: pd.Series,
|
||||
low: pd.Series,
|
||||
lookback: int,
|
||||
window: int = 5,
|
||||
) -> tuple[pd.Series, pd.Series]:
|
||||
"""
|
||||
检测 Swing High 和 Swing Low。
|
||||
|
||||
纯价格比较：
|
||||
- Swing High: 当前高点 > 左右各 lookback 根K线的所有高点
|
||||
- Swing Low: 当前低点 < 左右各 lookback 根K线的所有低点
|
||||
"""
|
||||
"""检测 Swing High / Swing Low。"""
|
||||
n = len(high)
|
||||
is_swing_high = np.full(n, False)
|
||||
is_swing_low = np.full(n, False)
|
||||
sh = pd.Series(np.nan, index=high.index, dtype=float)
|
||||
sl = pd.Series(np.nan, index=low.index, dtype=float)
|
||||
|
||||
for i in range(lookback, n - lookback):
|
||||
window_high = high.iloc[i - lookback : i + lookback + 1]
|
||||
window_low = low.iloc[i - lookback : i + lookback + 1]
|
||||
for i in range(window, n - window):
|
||||
if high.iloc[i] > high.iloc[i - window : i].max() and high.iloc[i] > high.iloc[i + 1 : i + window + 1].max():
|
||||
sh.iloc[i] = high.iloc[i]
|
||||
if low.iloc[i] < low.iloc[i - window : i].min() and low.iloc[i] < low.iloc[i + 1 : i + window + 1].min():
|
||||
sl.iloc[i] = low.iloc[i]
|
||||
|
||||
if high.iloc[i] == window_high.max():
|
||||
is_swing_high[i] = True
|
||||
if low.iloc[i] == window_low.min():
|
||||
is_swing_low[i] = True
|
||||
return sh, sl
|
||||
|
||||
return (
|
||||
pd.Series(is_swing_high, index=high.index),
|
||||
pd.Series(is_swing_low, index=low.index),
|
||||
)
|
||||
# =====================
|
||||
# 工具：结构分析
|
||||
# =====================
|
||||
|
||||
@staticmethod
|
||||
def _build_structure(
|
||||
self,
|
||||
high: pd.Series,
|
||||
low: pd.Series,
|
||||
close: pd.Series,
|
||||
swing_high: pd.Series,
|
||||
swing_low: pd.Series,
|
||||
) -> DataFrame:
|
||||
"""
|
||||
从 Swing Points 构建市场结构信息。
|
||||
|
||||
返回值包含:
|
||||
trend_up / trend_down：当前处于上升/下降结构
|
||||
support：最近 Swing Low 价格
|
||||
resistance：最近 Swing High 价格
|
||||
in_demand：价格在下半区（做多区域）
|
||||
in_supply：价格在上半区（做空区域）
|
||||
"""
|
||||
"""从 Swing Points 构建市场结构信息。"""
|
||||
n = len(high)
|
||||
|
||||
trend_up_arr = np.full(n, False)
|
||||
@ -152,11 +106,10 @@ class StructureFlowStrategyV12(IStrategy):
|
||||
in_demand_zone = np.full(n, False)
|
||||
in_supply_zone = np.full(n, False)
|
||||
|
||||
sh_prices: list[float] = []
|
||||
sl_prices: list[float] = []
|
||||
sh_prices = []
|
||||
sl_prices = []
|
||||
|
||||
for i in range(n):
|
||||
# ── 更新 Swing Point 队列 ──
|
||||
if swing_high.iloc[i] and not np.isnan(high.iloc[i]):
|
||||
sh_prices.append(high.iloc[i])
|
||||
if len(sh_prices) > 4:
|
||||
@ -167,117 +120,95 @@ class StructureFlowStrategyV12(IStrategy):
|
||||
if len(sl_prices) > 4:
|
||||
sl_prices.pop(0)
|
||||
|
||||
# ── 趋势判断 ──
|
||||
# 趋势判断
|
||||
if len(sh_prices) >= 2 and len(sl_prices) >= 2:
|
||||
latest_sh, prev_sh = sh_prices[-1], sh_prices[-2]
|
||||
latest_sl, prev_sl = sl_prices[-1], sl_prices[-2]
|
||||
|
||||
if latest_sh > prev_sh and latest_sl > prev_sl:
|
||||
trend_up_arr[i] = True
|
||||
trend_down_arr[i] = False
|
||||
elif latest_sh < prev_sh and latest_sl < prev_sl:
|
||||
trend_up_arr[i] = False
|
||||
trend_down_arr[i] = True
|
||||
else:
|
||||
if i > 0:
|
||||
trend_up_arr[i] = trend_up_arr[i - 1]
|
||||
trend_down_arr[i] = trend_down_arr[i - 1]
|
||||
# 沿用上一根K线的状态
|
||||
trend_up_arr[i] = trend_up_arr[i - 1] if i > 0 else False
|
||||
trend_down_arr[i] = trend_down_arr[i - 1] if i > 0 else False
|
||||
elif i > 0:
|
||||
trend_up_arr[i] = trend_up_arr[i - 1]
|
||||
trend_down_arr[i] = trend_down_arr[i - 1]
|
||||
|
||||
# ── 最近支撑/阻力 ──
|
||||
# 支撑/阻力
|
||||
if sl_prices:
|
||||
nearest_support[i] = sl_prices[-1]
|
||||
elif i > 0:
|
||||
nearest_support[i] = nearest_support[i - 1]
|
||||
|
||||
if sh_prices:
|
||||
nearest_resistance[i] = sh_prices[-1]
|
||||
elif i > 0:
|
||||
nearest_resistance[i] = nearest_resistance[i - 1]
|
||||
|
||||
# ── 入场区域：用 Swing 区间中点划分 ──
|
||||
if (
|
||||
not np.isnan(nearest_support[i])
|
||||
and not np.isnan(nearest_resistance[i])
|
||||
and nearest_resistance[i] > nearest_support[i]
|
||||
):
|
||||
mid = (nearest_support[i] + nearest_resistance[i]) / 2.0
|
||||
in_demand_zone[i] = low.iloc[i] <= mid
|
||||
in_supply_zone[i] = high.iloc[i] >= mid
|
||||
elif i > 0:
|
||||
in_demand_zone[i] = in_demand_zone[i - 1]
|
||||
in_supply_zone[i] = in_supply_zone[i - 1]
|
||||
# 需求/供给区域
|
||||
c = close.iloc[i]
|
||||
if not np.isnan(nearest_support[i]) and not np.isnan(nearest_resistance[i]):
|
||||
zone_range = nearest_resistance[i] - nearest_support[i]
|
||||
if zone_range > 0:
|
||||
pos_pct = (c - nearest_support[i]) / zone_range
|
||||
in_demand_zone[i] = pos_pct < 0.35
|
||||
in_supply_zone[i] = pos_pct > 0.65
|
||||
|
||||
result = DataFrame(
|
||||
{
|
||||
"trend_up": trend_up_arr,
|
||||
"trend_down": trend_down_arr,
|
||||
"support": nearest_support,
|
||||
"resistance": nearest_resistance,
|
||||
"in_demand": in_demand_zone,
|
||||
"in_supply": in_supply_zone,
|
||||
},
|
||||
index=high.index,
|
||||
)
|
||||
return result
|
||||
return DataFrame({
|
||||
"trend_up": trend_up_arr,
|
||||
"trend_down": trend_down_arr,
|
||||
"support": nearest_support,
|
||||
"resistance": nearest_resistance,
|
||||
"in_demand": in_demand_zone,
|
||||
"in_supply": in_supply_zone,
|
||||
}, index=high.index)
|
||||
|
||||
# =====================
|
||||
# 工具：K线形态检测
|
||||
# =====================
|
||||
|
||||
@staticmethod
|
||||
def _detect_candle_patterns(
|
||||
o: pd.Series,
|
||||
h: pd.Series,
|
||||
l: pd.Series,
|
||||
c: pd.Series,
|
||||
pin_ratio: float,
|
||||
open_: pd.Series,
|
||||
high: pd.Series,
|
||||
low: pd.Series,
|
||||
close: pd.Series,
|
||||
pin_bar_wick_ratio: float = 0.6,
|
||||
) -> tuple[pd.Series, pd.Series, pd.Series, pd.Series]:
|
||||
"""
|
||||
检测 K 线形态 — 纯 OHLC 计算。
|
||||
"""
|
||||
body = abs(c - o)
|
||||
upper_wick = h - np.maximum(o, c)
|
||||
lower_wick = np.minimum(o, c) - l
|
||||
total_range = h - l
|
||||
"""检测 Pin Bar 和 Engulfing 形态。"""
|
||||
body = (close - open_).abs()
|
||||
total_range = (high - low).replace(0, 0.0001)
|
||||
|
||||
valid_range = total_range > 0
|
||||
valid_body = body > 0
|
||||
upper_wick = high - close.where(close > open_, open_)
|
||||
lower_wick = open_.where(close > open_, close) - low
|
||||
is_pin = (upper_wick + lower_wick) / total_range > pin_bar_wick_ratio
|
||||
|
||||
bullish_pin = (
|
||||
valid_range
|
||||
& valid_body
|
||||
& (lower_wick >= pin_ratio * body)
|
||||
& (upper_wick <= 0.5 * body)
|
||||
)
|
||||
bullish_pin = is_pin & (close > open_) & (lower_wick > upper_wick)
|
||||
bearish_pin = is_pin & (close < open_) & (upper_wick > lower_wick)
|
||||
|
||||
bearish_pin = (
|
||||
valid_range
|
||||
& valid_body
|
||||
& (upper_wick >= pin_ratio * body)
|
||||
& (lower_wick <= 0.5 * body)
|
||||
)
|
||||
prev_open = open_.shift(1)
|
||||
prev_close = close.shift(1)
|
||||
bullish_engulf = (close > prev_open) & (open_ < prev_close) & (close > open_)
|
||||
bearish_engulf = (close < prev_open) & (open_ > prev_close) & (close < open_)
|
||||
|
||||
prev_body = body.shift(1)
|
||||
prev_o = o.shift(1)
|
||||
prev_c = c.shift(1)
|
||||
return bullish_pin, bearish_pin, bullish_engulf, bearish_engulf
|
||||
|
||||
bullish_engulf = (
|
||||
(c > o)
|
||||
& (prev_c < prev_o)
|
||||
& (body > prev_body)
|
||||
)
|
||||
# =====================
|
||||
# 工具：ATR 计算
|
||||
# =====================
|
||||
|
||||
bearish_engulf = (
|
||||
(c < o)
|
||||
& (prev_c > prev_o)
|
||||
& (body > prev_body)
|
||||
)
|
||||
|
||||
return (
|
||||
pd.Series(bullish_pin, index=c.index),
|
||||
pd.Series(bearish_pin, index=c.index),
|
||||
pd.Series(bullish_engulf, index=c.index),
|
||||
pd.Series(bearish_engulf, index=c.index),
|
||||
)
|
||||
@staticmethod
|
||||
def _calc_atr(
|
||||
high: pd.Series,
|
||||
low: pd.Series,
|
||||
close: pd.Series,
|
||||
period: int = 14,
|
||||
) -> pd.Series:
|
||||
"""计算 ATR。"""
|
||||
prev_close = close.shift(1)
|
||||
tr = pd.concat(
|
||||
[high - low, (high - prev_close).abs(), (low - prev_close).abs()],
|
||||
axis=1,
|
||||
).max(axis=1)
|
||||
return tr.rolling(period).mean()
|
||||
|
||||
# ================================================================
|
||||
# 信息时间框架 — D1 宏观结构
|
||||
@ -291,15 +222,12 @@ class StructureFlowStrategyV12(IStrategy):
|
||||
dataframe["high"], dataframe["low"],
|
||||
self.swing_lookback_d1.value,
|
||||
)
|
||||
|
||||
structure = self._build_structure(
|
||||
dataframe["high"], dataframe["low"], dataframe["close"],
|
||||
sh, sl,
|
||||
)
|
||||
|
||||
dataframe["trend_up"] = structure["trend_up"]
|
||||
dataframe["trend_down"] = structure["trend_down"]
|
||||
|
||||
return dataframe
|
||||
|
||||
# ================================================================
|
||||
@ -314,12 +242,10 @@ class StructureFlowStrategyV12(IStrategy):
|
||||
dataframe["high"], dataframe["low"],
|
||||
self.swing_lookback_h4.value,
|
||||
)
|
||||
|
||||
structure = self._build_structure(
|
||||
dataframe["high"], dataframe["low"], dataframe["close"],
|
||||
sh, sl,
|
||||
)
|
||||
|
||||
dataframe["trend_up"] = structure["trend_up"]
|
||||
dataframe["trend_down"] = structure["trend_down"]
|
||||
dataframe["support"] = structure["support"]
|
||||
@ -327,63 +253,59 @@ class StructureFlowStrategyV12(IStrategy):
|
||||
dataframe["in_demand"] = structure["in_demand"]
|
||||
dataframe["in_supply"] = structure["in_supply"]
|
||||
|
||||
# 4H ATR（保留，可能用于未来优化）
|
||||
dataframe["atr"] = self._calc_atr(
|
||||
dataframe["high"], dataframe["low"], dataframe["close"], period=14
|
||||
)
|
||||
return dataframe
|
||||
|
||||
# ================================================================
|
||||
# 主时间框架 — 1H K线形态 + Entry Candle 记录
|
||||
# 主时间框架 — 1H 指标
|
||||
# ================================================================
|
||||
|
||||
# 类级别缓存：记录每笔交易的 Entry Candle 信息
|
||||
# {trade_id: {"entry_low": float, "entry_high": float, "entry_idx": int}}
|
||||
_entry_candle_cache = {}
|
||||
|
||||
def populate_indicators(
|
||||
self, dataframe: DataFrame, metadata: dict
|
||||
) -> DataFrame:
|
||||
"""
|
||||
1H 一小时线：检测 K 线形态。
|
||||
同时预标记可能的入场 K 线（供 custom_stoploss 使用）。
|
||||
"""
|
||||
"""1H 级别：K线形态 + ATR。"""
|
||||
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,
|
||||
self.pin_bar_wick_ratio.value / 100.0,
|
||||
)
|
||||
)
|
||||
|
||||
dataframe["bullish_pinbar"] = bullish_pin
|
||||
dataframe["bearish_pinbar"] = bearish_pin
|
||||
dataframe["bullish_engulfing"] = bullish_engulf
|
||||
dataframe["bearish_engulfing"] = bearish_engulf
|
||||
|
||||
dataframe["bullish_signal"] = bullish_pin | bullish_engulf
|
||||
dataframe["bearish_signal"] = bearish_pin | bearish_engulf
|
||||
|
||||
# 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）
|
||||
# 注意：这里只是标记，实际入场由 populate_entry_trend 决定
|
||||
dataframe["potential_entry_low"] = np.where(
|
||||
dataframe["bullish_signal"] | dataframe["bearish_signal"],
|
||||
dataframe["low"],
|
||||
np.nan,
|
||||
)
|
||||
dataframe["potential_entry_high"] = np.where(
|
||||
dataframe["bullish_signal"] | dataframe["bearish_signal"],
|
||||
dataframe["high"],
|
||||
np.nan,
|
||||
# 1H ATR（用于动态止损）
|
||||
dataframe["atr_1h"] = self._calc_atr(
|
||||
dataframe["high"], dataframe["low"], dataframe["close"], period=14
|
||||
)
|
||||
|
||||
# NaN 安全处理
|
||||
bool_cols = [
|
||||
"trend_up_1d", "trend_down_1d",
|
||||
"trend_up_4h", "trend_down_4h",
|
||||
"in_demand_4h", "in_supply_4h",
|
||||
"bullish_signal", "bearish_signal",
|
||||
]
|
||||
for col in bool_cols:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].fillna(False)
|
||||
|
||||
return dataframe
|
||||
|
||||
# ================================================================
|
||||
# 入场信号
|
||||
# ================================================================
|
||||
# =====================
|
||||
# 入场信号
|
||||
# =====================
|
||||
|
||||
def populate_entry_trend(
|
||||
self, dataframe: DataFrame, metadata: dict
|
||||
) -> DataFrame:
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
入场逻辑（1H 时间框架）。
|
||||
|
||||
@ -397,8 +319,7 @@ class StructureFlowStrategyV12(IStrategy):
|
||||
2. 4H 上半区 / 供给区域（in_supply_4h）
|
||||
3. 1H 看跌 K 线形态（bearish_signal）
|
||||
"""
|
||||
|
||||
# ── NaN 安全处理 ──
|
||||
# NaN 安全处理
|
||||
bool_cols = [
|
||||
"trend_up_1d", "trend_down_1d",
|
||||
"trend_up_4h", "trend_down_4h",
|
||||
@ -407,9 +328,9 @@ class StructureFlowStrategyV12(IStrategy):
|
||||
]
|
||||
for col in bool_cols:
|
||||
if col in dataframe.columns:
|
||||
dataframe[col] = dataframe[col].fillna(False).infer_objects(copy=False)
|
||||
dataframe[col] = dataframe[col].fillna(False)
|
||||
|
||||
# ── 做多 ──
|
||||
# 做多
|
||||
long_conditions = (
|
||||
dataframe["trend_up_1d"]
|
||||
& dataframe["in_demand_4h"]
|
||||
@ -417,7 +338,7 @@ class StructureFlowStrategyV12(IStrategy):
|
||||
)
|
||||
dataframe.loc[long_conditions, "enter_long"] = 1
|
||||
|
||||
# ── 做空 ──
|
||||
# 做空
|
||||
short_conditions = (
|
||||
dataframe["trend_down_1d"]
|
||||
& dataframe["in_supply_4h"]
|
||||
@ -427,34 +348,23 @@ class StructureFlowStrategyV12(IStrategy):
|
||||
|
||||
return dataframe
|
||||
|
||||
# ================================================================
|
||||
# 出场信号
|
||||
# ================================================================
|
||||
# =====================
|
||||
# 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 不再上升
|
||||
exit_long = (
|
||||
~dataframe["trend_up_1d"].fillna(True)
|
||||
)
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""出场逻辑 — 由结构反转触发。"""
|
||||
exit_long = ~dataframe["trend_up_1d"].fillna(True)
|
||||
dataframe.loc[exit_long, "exit_long"] = 1
|
||||
|
||||
# 做空出场：D1 不再下降
|
||||
exit_short = (
|
||||
dataframe["trend_up_1d"].fillna(False)
|
||||
)
|
||||
exit_short = dataframe["trend_up_1d"].fillna(False)
|
||||
dataframe.loc[exit_short, "exit_short"] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
# ================================================================
|
||||
# 动态止损 — v1.2 核心改进
|
||||
# ================================================================
|
||||
# =====================
|
||||
# 动态止损 — v1.3 重写
|
||||
# =====================
|
||||
|
||||
def custom_stoploss(
|
||||
self,
|
||||
@ -465,115 +375,65 @@ class StructureFlowStrategyV12(IStrategy):
|
||||
current_profit: float,
|
||||
after_fill: bool,
|
||||
**kwargs,
|
||||
) -> float | None:
|
||||
) -> float:
|
||||
"""
|
||||
v1.2 止损逻辑（核心改进）：
|
||||
v1.3 止损逻辑（完全重写）：
|
||||
|
||||
阶段一（刚入场，无盈利或微盈利）：
|
||||
止损 = Entry Candle 失效点 + 缓冲
|
||||
- 做多：入场K线最低价 × (1 - entry_sl_buffer)
|
||||
- 做空：入场K线最高价 × (1 + entry_sl_buffer)
|
||||
核心哲学：「预估错误的交易，早早认输止损离场，而不要硬扛单」
|
||||
|
||||
阶段二（有一定盈利，超过 profit_to_structure_sl_pct）：
|
||||
切换为结构跟踪止损（同 v1.1 逻辑）
|
||||
- 做多：最近 4H Swing Low × (1 - buffer)
|
||||
- 做空：最近 4H Swing High × (1 + buffer)
|
||||
三阶段止损：
|
||||
|
||||
时间止损：
|
||||
入场后超过 time_stop_bars 根K线且 current_profit < 0，
|
||||
返回 -0.01（立即市价出场）。
|
||||
阶段一（无盈利或微盈利 < 1%）：
|
||||
止损 = 入场价 ± 1.0 ATR
|
||||
→ 距离近，价格稍有不利变动就止损，快速认输
|
||||
|
||||
阶段二（盈利 1% ~ 2%）：
|
||||
止损移动至保本线（open_rate ± 0.1%）
|
||||
→ 这笔交易已经不亏了，卸下心理负担
|
||||
|
||||
阶段三（盈利 > 2%）：
|
||||
追踪止损 = current_rate ∓ 1.0 ATR
|
||||
→ 价格回调超过1ATR才出场，给趋势足够的呼吸空间
|
||||
|
||||
参数说明：
|
||||
- ATR 来自当前1H K线的 atr_1h 值
|
||||
- 如果 ATR 为 NaN，fallback 到 2% 固定止损
|
||||
- 最终返回的止损比率不会超过 -5%（硬止损安全网）
|
||||
"""
|
||||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
if dataframe is None or len(dataframe) == 0:
|
||||
return None
|
||||
return -0.02 if not trade.is_short else 0.02
|
||||
|
||||
last = dataframe.iloc[-1]
|
||||
buffer = self.entry_sl_buffer.value
|
||||
last_candle = dataframe.iloc[-1]
|
||||
atr = last_candle.get("atr_1h", np.nan)
|
||||
|
||||
# ── 时间止损检查 ──
|
||||
# 计算入场至今的K线数（1H = 1根/小时）
|
||||
bars_held = (current_time - trade.open_date_utc).total_seconds() / 3600
|
||||
if bars_held >= self.time_stop_bars.value and current_profit <= 0:
|
||||
# 超时且无盈利，立即出场（返回当前价，即市价出场）
|
||||
return -0.01 # 1% 内市价出场
|
||||
if pd.isna(atr) or atr <= 0:
|
||||
atr = current_rate * 0.02
|
||||
else:
|
||||
atr = float(atr)
|
||||
|
||||
# ── 尝试获取 Entry Candle 信息 ──
|
||||
# 方法：在 dataframe 中找到 open_date_utc 附近的 K 线
|
||||
entry_candle_low = None
|
||||
entry_candle_high = None
|
||||
open_rate = trade.open_rate
|
||||
|
||||
# 通过 potential_entry_low/high 列找到入场信号 K 线
|
||||
# 找到最先出现信号且在 open_date_utc 之前的 K 线
|
||||
entry_mask = (
|
||||
(dataframe["potential_entry_low"].notna())
|
||||
| (dataframe["potential_entry_high"].notna())
|
||||
)
|
||||
entry_candidates = dataframe[
|
||||
entry_mask
|
||||
& (dataframe["date"] <= trade.open_date_utc + timedelta(hours=1))
|
||||
& (dataframe["date"] >= trade.open_date_utc - timedelta(hours=1))
|
||||
]
|
||||
if len(entry_candidates) > 0:
|
||||
entry_candle = entry_candidates.iloc[-1]
|
||||
entry_candle_low = entry_candle.get("potential_entry_low")
|
||||
entry_candle_high = entry_candle.get("potential_entry_high")
|
||||
|
||||
# ── 阶段一：用 Entry Candle 止损 ──
|
||||
if entry_candle_low is not None or entry_candle_high is not None:
|
||||
if trade.is_short:
|
||||
if entry_candle_high is not None and not np.isnan(entry_candle_high):
|
||||
sl_price = float(entry_candle_high) * (1 + buffer)
|
||||
sl_ratio = (sl_price - current_rate) / current_rate
|
||||
# 如果已经有盈利超过阈值，切换到结构止损
|
||||
if current_profit > self.profit_to_structure_sl_pct.value:
|
||||
pass # 继续到阶段二
|
||||
else:
|
||||
return max(sl_ratio, -0.25)
|
||||
if not trade.is_short:
|
||||
# ── 做多 ──
|
||||
if current_profit <= 0.01:
|
||||
sl_price = open_rate - atr * 1.0
|
||||
elif current_profit <= 0.02:
|
||||
sl_price = open_rate * 0.999
|
||||
else:
|
||||
if entry_candle_low is not None and not np.isnan(entry_candle_low):
|
||||
sl_price = float(entry_candle_low) * (1 - buffer)
|
||||
sl_ratio = (sl_price - current_rate) / current_rate
|
||||
if current_profit > self.profit_to_structure_sl_pct.value:
|
||||
pass # 继续到阶段二
|
||||
else:
|
||||
return max(sl_ratio, -0.25)
|
||||
sl_price = current_rate - atr * 1.0
|
||||
|
||||
# ── 阶段二：结构跟踪止损（盈利足够后） ──
|
||||
profit_trigger = self.profit_to_structure_sl_pct.value
|
||||
if current_profit > profit_trigger:
|
||||
if trade.is_short:
|
||||
resistance = last.get("resistance_4h")
|
||||
if resistance is not None and not (isinstance(resistance, float) and np.isnan(resistance)):
|
||||
sl_price = float(resistance) * (1 + buffer)
|
||||
sl_ratio = (sl_price - current_rate) / current_rate
|
||||
if sl_ratio < 0:
|
||||
return max(sl_ratio, -0.25)
|
||||
sl_ratio = (sl_price / current_rate) - 1.0
|
||||
return max(sl_ratio, -0.05)
|
||||
|
||||
else:
|
||||
# ── 做空 ──
|
||||
if current_profit <= 0.01:
|
||||
sl_price = open_rate + atr * 1.0
|
||||
elif current_profit <= 0.02:
|
||||
sl_price = open_rate * 1.001
|
||||
else:
|
||||
support = last.get("support_4h")
|
||||
if support is not None and not (isinstance(support, float) and np.isnan(support)):
|
||||
sl_price = float(support) * (1 - buffer)
|
||||
sl_ratio = (sl_price - current_rate) / current_rate
|
||||
if sl_ratio < 0:
|
||||
return max(sl_ratio, -0.25)
|
||||
sl_price = current_rate + atr * 1.0
|
||||
|
||||
return None
|
||||
|
||||
# ================================================================
|
||||
# 时间止损的替代实现（通过 populate_exit_trend 扩展）
|
||||
# ================================================================
|
||||
|
||||
def confirm_trade_exit(
|
||||
self,
|
||||
pair: str,
|
||||
trade: Trade,
|
||||
order_type: str,
|
||||
amount: float,
|
||||
rate: float,
|
||||
time_in_force: str,
|
||||
sell_reason: str,
|
||||
**kwargs,
|
||||
) -> bool:
|
||||
"""
|
||||
可在此处添加日志记录，便于回测分析。
|
||||
"""
|
||||
return True
|
||||
sl_ratio = 1.0 - (sl_price / current_rate)
|
||||
return min(sl_ratio, 0.05)
|
||||
|
||||
Reference in New Issue
Block a user