428 lines
16 KiB
Python
428 lines
16 KiB
Python
"""
|
||
Structure Flow Strategy v2.2b — 1H S/R 实验版
|
||
==============================================
|
||
变更记录：
|
||
v2.2b (2026-06-09): 原版 — 4H级别S/R + 趋势强度
|
||
v2.2b-1h-sr (2026-06-10): 实验版 — 将S/R从4H改为1H级别，趋势强度仍用4H
|
||
|
||
改动：
|
||
support_alive/resistance_alive 从4H级别 → 1H级别
|
||
support/resistance 引用 从4H → 1H
|
||
in_demand/in_supply 从4H → 1H
|
||
趋势强度（strong_uptrend/downtrend）保持在4H
|
||
"""
|
||
|
||
from datetime import datetime
|
||
import numpy as np
|
||
import pandas as pd
|
||
from pandas import DataFrame
|
||
from freqtrade.strategy import IStrategy, IntParameter, informative
|
||
from freqtrade.persistence import Trade
|
||
|
||
|
||
class StructureFlowStrategyV22c(IStrategy):
|
||
can_short = True
|
||
stoploss = -0.15
|
||
use_custom_stoploss = True
|
||
minimal_roi = {"0": 100}
|
||
max_open_trades = 1
|
||
timeframe = "1h"
|
||
|
||
# =====================
|
||
# 可优化参数
|
||
# =====================
|
||
|
||
swing_lookback_d1 = IntParameter(8, 14, default=10, space="buy")
|
||
swing_lookback_h4 = IntParameter(5, 10, default=8, space="buy")
|
||
swing_lookback_1h = IntParameter(3, 7, default=5, space="buy") # 新增：1H swing参数
|
||
pin_bar_wick_ratio = IntParameter(50, 70, default=60, space="buy")
|
||
max_stop_dist = IntParameter(20, 50, default=50, space="buy")
|
||
cooldown_bars = IntParameter(3, 12, default=6, space="buy")
|
||
trend_strength_min = IntParameter(-50, 20, default=-20, space="buy")
|
||
|
||
# =====================
|
||
# 工具：Swing Point 检测
|
||
# =====================
|
||
|
||
@staticmethod
|
||
def _detect_swing_points(
|
||
high: pd.Series,
|
||
low: pd.Series,
|
||
window: int = 5,
|
||
) -> tuple[pd.Series, pd.Series]:
|
||
n = len(high)
|
||
sh = pd.Series(np.nan, index=high.index, dtype=float)
|
||
sl = pd.Series(np.nan, index=low.index, dtype=float)
|
||
|
||
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]
|
||
|
||
return sh, sl
|
||
|
||
# =====================
|
||
# 工具：结构分析
|
||
# =====================
|
||
|
||
def _build_structure(
|
||
self,
|
||
high: pd.Series,
|
||
low: pd.Series,
|
||
close: pd.Series,
|
||
swing_high: pd.Series,
|
||
swing_low: pd.Series,
|
||
) -> DataFrame:
|
||
n = len(high)
|
||
|
||
trend_up_arr = np.full(n, False)
|
||
trend_down_arr = np.full(n, False)
|
||
nearest_support = np.full(n, np.nan)
|
||
nearest_resistance = np.full(n, np.nan)
|
||
in_demand_zone = np.full(n, False)
|
||
in_supply_zone = np.full(n, False)
|
||
|
||
sh_prices = []
|
||
sl_prices = []
|
||
|
||
for i in range(n):
|
||
if pd.notna(swing_high.iloc[i]):
|
||
sh_prices.append(swing_high.iloc[i])
|
||
if len(sh_prices) > 4:
|
||
sh_prices.pop(0)
|
||
|
||
if pd.notna(swing_low.iloc[i]):
|
||
sl_prices.append(swing_low.iloc[i])
|
||
if len(sl_prices) > 4:
|
||
sl_prices.pop(0)
|
||
|
||
if len(sh_prices) >= 2 and len(sl_prices) >= 2:
|
||
if sh_prices[-1] > sh_prices[-2] and sl_prices[-1] > sl_prices[-2]:
|
||
trend_up_arr[i] = True
|
||
elif sh_prices[-1] < sh_prices[-2] and sl_prices[-1] < sl_prices[-2]:
|
||
trend_down_arr[i] = True
|
||
elif i > 0:
|
||
trend_up_arr[i] = trend_up_arr[i - 1]
|
||
trend_down_arr[i] = trend_down_arr[i - 1]
|
||
elif i > 0:
|
||
trend_up_arr[i] = trend_up_arr[i - 1]
|
||
trend_down_arr[i] = trend_down_arr[i - 1]
|
||
|
||
if sl_prices:
|
||
nearest_support[i] = sl_prices[-1]
|
||
if sh_prices:
|
||
nearest_resistance[i] = sh_prices[-1]
|
||
|
||
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
|
||
|
||
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(
|
||
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]:
|
||
body = (close - open_).abs()
|
||
total_range = (high - low).replace(0, 0.0001)
|
||
|
||
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 = is_pin & (close > open_) & (lower_wick > upper_wick)
|
||
bearish_pin = is_pin & (close < open_) & (upper_wick > lower_wick)
|
||
|
||
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_)
|
||
|
||
return bullish_pin, bearish_pin, bullish_engulf, bearish_engulf
|
||
|
||
# ================================================================
|
||
# 信息时间框架 — D1 宏观结构
|
||
# ================================================================
|
||
|
||
@informative("1d")
|
||
def populate_indicators_1d(
|
||
self, dataframe: DataFrame, metadata: dict
|
||
) -> DataFrame:
|
||
sh, sl = self._detect_swing_points(
|
||
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
|
||
|
||
# ================================================================
|
||
# 信息时间框架 — 4H 趋势强度（原版保留）
|
||
# ================================================================
|
||
|
||
@informative("4h")
|
||
def populate_indicators_4h(
|
||
self, dataframe: DataFrame, metadata: dict
|
||
) -> DataFrame:
|
||
sh, sl = self._detect_swing_points(
|
||
dataframe["high"], dataframe["low"],
|
||
self.swing_lookback_h4.value,
|
||
)
|
||
structure = self._build_structure(
|
||
dataframe["high"], dataframe["low"], dataframe["close"],
|
||
sh, sl,
|
||
)
|
||
|
||
# 趋势强度计算（原版逻辑）
|
||
sh_prices = []
|
||
sl_prices = []
|
||
trend_strength_up = np.full(len(dataframe), np.nan)
|
||
trend_strength_down = np.full(len(dataframe), np.nan)
|
||
|
||
for i in range(len(dataframe)):
|
||
if pd.notna(sh.iloc[i]):
|
||
sh_prices.append(sh.iloc[i])
|
||
if len(sh_prices) > 4:
|
||
sh_prices.pop(0)
|
||
if pd.notna(sl.iloc[i]):
|
||
sl_prices.append(sl.iloc[i])
|
||
if len(sl_prices) > 4:
|
||
sl_prices.pop(0)
|
||
|
||
if len(sh_prices) >= 2 and len(sl_prices) >= 2:
|
||
hh_dist = (sh_prices[-1] - sh_prices[-2]) / sh_prices[-2] if sh_prices[-2] > 0 else 0
|
||
hl_dist = (sl_prices[-1] - sl_prices[-2]) / sl_prices[-2] if sl_prices[-2] > 0 else 0
|
||
trend_strength_up[i] = hh_dist + hl_dist
|
||
trend_strength_down[i] = -(hh_dist + hl_dist)
|
||
|
||
dataframe["trend_strength_up"] = pd.Series(trend_strength_up, index=dataframe.index)
|
||
dataframe["trend_strength_down"] = pd.Series(trend_strength_down, index=dataframe.index)
|
||
|
||
min_strength = self.trend_strength_min.value / 100.0
|
||
dataframe["strong_uptrend"] = dataframe["trend_strength_up"] > min_strength
|
||
dataframe["strong_downtrend"] = dataframe["trend_strength_down"] > min_strength
|
||
|
||
return dataframe
|
||
|
||
# ================================================================
|
||
# 主时间框架 — 1H 指标（含 1H S/R + 活支撑/阻力）
|
||
# ================================================================
|
||
|
||
def populate_indicators(
|
||
self, dataframe: DataFrame, metadata: dict
|
||
) -> DataFrame:
|
||
# ── K线形态 ──
|
||
bullish_pin, bearish_pin, bullish_engulf, bearish_engulf = (
|
||
self._detect_candle_patterns(
|
||
dataframe["open"],
|
||
dataframe["high"],
|
||
dataframe["low"],
|
||
dataframe["close"],
|
||
self.pin_bar_wick_ratio.value / 100.0,
|
||
)
|
||
)
|
||
dataframe["bullish_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
|
||
|
||
# ── 1H级别 Swing Point + 结构（替代原4H S/R） ──
|
||
sh_1h, sl_1h = self._detect_swing_points(
|
||
dataframe["high"], dataframe["low"],
|
||
self.swing_lookback_1h.value,
|
||
)
|
||
structure_1h = self._build_structure(
|
||
dataframe["high"], dataframe["low"], dataframe["close"],
|
||
sh_1h, sl_1h,
|
||
)
|
||
dataframe["trend_up_1h"] = structure_1h["trend_up"]
|
||
dataframe["trend_down_1h"] = structure_1h["trend_down"]
|
||
dataframe["support"] = structure_1h["support"]
|
||
dataframe["resistance"] = structure_1h["resistance"]
|
||
dataframe["in_demand"] = structure_1h["in_demand"]
|
||
dataframe["in_supply"] = structure_1h["in_supply"]
|
||
|
||
# ── 1H 活支撑/阻力检查 ──
|
||
touched_support = (
|
||
(dataframe["low"] <= dataframe["support"] * 1.005) &
|
||
(dataframe["low"] >= dataframe["support"] * 0.995)
|
||
)
|
||
held_support = dataframe["close"] > dataframe["support"]
|
||
support_tested_and_held = touched_support & held_support
|
||
dataframe["support_alive"] = support_tested_and_held.rolling(3, min_periods=1).max() > 0
|
||
|
||
touched_resistance = (
|
||
(dataframe["high"] >= dataframe["resistance"] * 0.995) &
|
||
(dataframe["high"] <= dataframe["resistance"] * 1.005)
|
||
)
|
||
held_resistance = dataframe["close"] < dataframe["resistance"]
|
||
resistance_tested_and_held = touched_resistance & held_resistance
|
||
dataframe["resistance_alive"] = resistance_tested_and_held.rolling(3, min_periods=1).max() > 0
|
||
|
||
# ── NaN 安全处理 ──
|
||
bool_cols = [
|
||
"trend_up_1d", "trend_down_1d",
|
||
"trend_up_4h", "trend_down_4h",
|
||
"in_demand", "in_supply",
|
||
"support_alive", "resistance_alive",
|
||
"strong_uptrend_4h", "strong_downtrend_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
|
||
# =====================
|
||
|
||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
max_dist = self.max_stop_dist.value / 100.0
|
||
cooldown = self.cooldown_bars.value
|
||
|
||
bool_cols = [
|
||
"trend_up_1d", "trend_down_1d",
|
||
"trend_up_4h", "trend_down_4h",
|
||
"in_demand", "in_supply",
|
||
"support_alive", "resistance_alive",
|
||
"strong_uptrend_4h", "strong_downtrend_4h",
|
||
"bullish_signal", "bearish_signal",
|
||
]
|
||
for col in bool_cols:
|
||
if col in dataframe.columns:
|
||
dataframe[col] = dataframe[col].fillna(False)
|
||
|
||
# ── 做多（使用1H S/R） ──
|
||
long_stop_dist = (dataframe["open"] - dataframe["support"]) / dataframe["open"]
|
||
|
||
long_base = (
|
||
dataframe["trend_up_1d"]
|
||
& dataframe["in_demand"]
|
||
& (long_stop_dist <= max_dist)
|
||
& (long_stop_dist > 0.003)
|
||
& dataframe["support_alive"]
|
||
& dataframe["strong_uptrend_4h"]
|
||
)
|
||
|
||
long_recent = long_base.rolling(cooldown, min_periods=1).max().shift(1) == 0
|
||
dataframe.loc[long_base & long_recent, "enter_long"] = 1
|
||
|
||
# ── 做空（使用1H S/R） ──
|
||
short_stop_dist = (dataframe["resistance"] - dataframe["open"]) / dataframe["open"]
|
||
|
||
short_base = (
|
||
dataframe["trend_down_1d"]
|
||
& dataframe["in_supply"]
|
||
& (short_stop_dist <= max_dist)
|
||
& (short_stop_dist > 0.003)
|
||
& dataframe["resistance_alive"]
|
||
& dataframe["strong_downtrend_4h"]
|
||
)
|
||
|
||
short_recent = short_base.rolling(cooldown, min_periods=1).max().shift(1) == 0
|
||
dataframe.loc[short_base & short_recent, "enter_short"] = 1
|
||
|
||
return dataframe
|
||
|
||
# =====================
|
||
# 出场信号
|
||
# =====================
|
||
|
||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
exit_long = ~dataframe["trend_up_1d"].fillna(True)
|
||
dataframe.loc[exit_long, "exit_long"] = 1
|
||
|
||
exit_short = dataframe["trend_up_1d"].fillna(False)
|
||
dataframe.loc[exit_short, "exit_short"] = 1
|
||
|
||
return dataframe
|
||
|
||
# =====================
|
||
# 动态止损（基于1H S/R）
|
||
# =====================
|
||
|
||
def custom_stoploss(
|
||
self,
|
||
pair: str,
|
||
trade: Trade,
|
||
current_time: datetime,
|
||
current_rate: float,
|
||
current_profit: float,
|
||
after_fill: bool,
|
||
**kwargs,
|
||
) -> float:
|
||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||
if dataframe is None or len(dataframe) == 0:
|
||
return -0.02 if not trade.is_short else 0.02
|
||
|
||
last = dataframe.iloc[-1]
|
||
|
||
if not trade.is_short:
|
||
support = last.get("support", np.nan)
|
||
if pd.isna(support) or support <= 0:
|
||
return -0.02
|
||
sl_price = support * 0.999
|
||
sl_ratio = (sl_price / current_rate) - 1.0
|
||
return max(sl_ratio, -0.15)
|
||
else:
|
||
resistance = last.get("resistance", np.nan)
|
||
if pd.isna(resistance) or resistance <= 0:
|
||
return 0.02
|
||
sl_price = resistance * 1.001
|
||
sl_ratio = 1.0 - (sl_price / current_rate)
|
||
return min(sl_ratio, 0.15)
|
||
|
||
# =====================
|
||
# Plot config
|
||
# =====================
|
||
|
||
@staticmethod
|
||
def plot_config() -> dict:
|
||
return {
|
||
"main_plot": {
|
||
"support": {"color": "green", "type": "line"},
|
||
"resistance": {"color": "red", "type": "line"},
|
||
},
|
||
"subplots": {
|
||
"signals": {
|
||
"bullish_pinbar": {"color": "green", "type": "scatter"},
|
||
"bearish_pinbar": {"color": "red", "type": "scatter"},
|
||
},
|
||
"filters": {
|
||
"support_alive": {"color": "green", "type": "line"},
|
||
"resistance_alive": {"color": "red", "type": "line"},
|
||
"strong_uptrend_4h": {"color": "blue", "type": "line"},
|
||
"strong_downtrend_4h": {"color": "orange", "type": "line"},
|
||
},
|
||
},
|
||
}
|
||
|