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cleanup: remove 23 duplicate meta.jsons, restructure strategies/ by version

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2026-06-12 00:08:30 +08:00
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"""
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"},
},
},
}