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v4.2 (Swing): 极简版 - 最小化参数 + 纯结构信号

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Beast Trader
2026-06-10 23:31:00 +08:00
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strategy.py
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@ -1,10 +1,18 @@
"""
Structure Flow Swing Strategy v4.1
Structure Flow Swing Strategy v4.2
==================================
15m 震荡波段 — 基于"碰壁验证"价格聚集度检测（短窗口版）
纯价格行为学震荡策略 — 借鉴 v2.2b 的 swing point + K线形态框架
v4.1b (2026-06-10): 1H + 3K线验证（正收益但频率太低）
v4.1c (2026-06-10): 回到15m + 缩短lookback至24根(6h) + 保留min_rejections=2
核心逻辑（模拟手工交易）：
1. 用 swing point 识别近期高低点，自动形成交易区间
2. 价格到支撑 + K线止跌形态（bullish pinbar/engulfing）→ 做多
3. 价格到阻力 + K线滞涨 → 平多（反向开空同理）
4. 不在震荡判定上设严苛门槛，价格够到边界+形态确认就做
5. 趋势突破时，突破边界导致亏损，但可控
v4.1 系列教训（2026-06-10）：
用滚动window + 碰壁验证 + ATR比例止损 → 全是技术指标思维，不是价格行为学
唯一正收益的是最简单的版本（1H + 形态 + 固定-3%止损）
"""
from datetime import datetime
@ -15,158 +23,132 @@ from freqtrade.strategy import IStrategy, IntParameter
from freqtrade.persistence import Trade
class StructureFlowSwingV41(IStrategy):
"""
Structure Flow Swing Strategy v4.1
15m 震荡区间波段交易 — 碰壁验证（短窗口）
"""
class StructureFlowSwingV42(IStrategy):
can_short = True
stoploss = -0.20
use_custom_stoploss = True
minimal_roi = {"0": 100}
max_open_trades = 1
timeframe = "15m"
timeframe = "1h"
# =====================
# 可优化参数
# =====================
lookback = IntParameter(8, 48, default=12, space="buy") # 检测窗口：12根15m = 3h
min_rejections = IntParameter(1, 4, default=1, space="buy") # 碰壁验证1次即可
rejection_window = IntParameter(1, 6, default=3, space="buy") # 碰壁验证窗口
zone_width_atr_mult = IntParameter(2, 6, default=4, space="buy")
entry_zone_pct = IntParameter(2, 8, default=5, space="buy")
take_profit_pct = IntParameter(50, 80, default=70, space="sell")
# ── 价格行为学参数 ──
swing_window = IntParameter(5, 11, default=11, space="buy") # swing point 检测窗口
entry_zone_pct = IntParameter(2, 8, default=5, space="buy") # 入场范围（距S/R 0.5%）
# 固定参数
breakout_bars = 2
cooldown = 2 # 冷却 2 根15m（30分钟）
cooldown = 6 # 冷却6根1H（6小时）
# =====================
# 工具：ATR计算
# =====================
# ================================================================
# Swing Point 检测（v2.2b 同款）
# ================================================================
def _detect_swing_points(
self,
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
# ================================================================
# K线形态检测（v2.2b 同款）
# ================================================================
@staticmethod
def _calc_atr(high: pd.Series, low: pd.Series, close: pd.Series, period: int = 14) -> pd.Series:
tr = pd.DataFrame({
"hl": high - low,
"hc": (high - close.shift(1)).abs(),
"lc": (low - close.shift(1)).abs(),
}).max(axis=1)
return tr.rolling(period).mean()
def _detect_pinbar(open: pd.Series, high: pd.Series, low: pd.Series, close: pd.Series) -> pd.Series:
body = (open - close).abs()
total_range = (high - low)
upper_wick = high - open.where(open > close, close)
lower_wick = open.where(open < close, close) - low
# =====================
# 主时间框架 — 15m 指标
# =====================
bullish_pin = (lower_wick > body * 2) & (lower_wick > upper_wick * 2) & (close > open)
bearish_pin = (upper_wick > body * 2) & (upper_wick > lower_wick * 2) & (close < open)
return bullish_pin, bearish_pin
@staticmethod
def _detect_engulfing(open: pd.Series, close: pd.Series) -> tuple[pd.Series, pd.Series]:
prev_open = open.shift(1)
prev_close = close.shift(1)
bullish_eng = (close > open) & (prev_close < prev_open) & (close > prev_open) & (open < prev_close)
bearish_eng = (close < open) & (prev_close > prev_open) & (close < prev_open) & (open > prev_close)
return bullish_eng, bearish_eng
# ================================================================
# 主指标
# ================================================================
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
lookback = self.lookback.value
rej_threshold = 0.005 # 边界 0.5% 范围内算"碰边"
sw = self.swing_window.value
# ── 价格聚集范围 ──
rolling_high = dataframe["high"].rolling(lookback).max()
rolling_low = dataframe["low"].rolling(lookback).min()
zone_width_raw = rolling_high - rolling_low
# ── Swing Points ──
sh, sl = self._detect_swing_points(dataframe["high"], dataframe["low"], sw)
dataframe["zone_high"] = rolling_high
dataframe["zone_low"] = rolling_low
dataframe["zone_width_raw"] = zone_width_raw
dataframe["zone_width_pct"] = zone_width_raw / rolling_low
# 向前填充最近的 swing high / low 作为动态 S/R
dataframe["swing_high"] = sh.ffill()
dataframe["swing_low"] = sl.ffill()
# ATR
dataframe["atr"] = self._calc_atr(dataframe["high"], dataframe["low"], dataframe["close"], 14)
# ── 碰壁验证检测 ──
# 支撑验证：价格到了低点附近 → 随后 rejection_window 根K线内有反弹
near_low = dataframe["low"] <= rolling_low * (1 + rej_threshold)
bounced_up = pd.Series(False, index=dataframe.index)
for i in range(1, self.rejection_window.value + 1):
future_close = dataframe["close"].shift(-i).fillna(dataframe["close"])
bounced_up = bounced_up | (future_close > dataframe["close"])
support_rejection = near_low & bounced_up
dataframe["support_rejection_count"] = support_rejection.rolling(lookback).sum()
# 阻力验证：价格到了高点附近 → 随后 rejection_window 根K线内有回落
near_high = dataframe["high"] >= rolling_high * (1 - rej_threshold)
bounced_down = pd.Series(False, index=dataframe.index)
for i in range(1, self.rejection_window.value + 1):
future_close = dataframe["close"].shift(-i).fillna(dataframe["close"])
bounced_down = bounced_down | (future_close < dataframe["close"])
resistance_rejection = near_high & bounced_down
dataframe["resistance_rejection_count"] = resistance_rejection.rolling(lookback).sum()
# ── 震荡判定 ──
atr_mult = self.zone_width_atr_mult.value
min_rej = self.min_rejections.value
# 条件1：区间宽度合理（不超过 ATR × N）
is_compact = zone_width_raw <= dataframe["atr"] * atr_mult
# 条件2：上下边界都经过碰壁验证（各至少 min_rej 次）
support_ok = dataframe["support_rejection_count"] >= min_rej
resistance_ok = dataframe["resistance_rejection_count"] >= min_rej
# 条件3：无突破
no_break_high = True
no_break_low = True
for i in range(1, self.breakout_bars + 1):
if i <= len(dataframe):
no_break_high = no_break_high & (dataframe["close"].shift(i) <= rolling_high)
no_break_low = no_break_low & (dataframe["close"].shift(i) >= rolling_low)
is_ranging = is_compact & support_ok & resistance_ok & no_break_high & no_break_low
dataframe["is_ranging"] = is_ranging
# ── 价格在区间内的位置 ──
denom = rolling_high - rolling_low
dataframe["zone_position"] = np.where(
denom > 0,
(dataframe["close"] - rolling_low) / denom,
# ── 区间宽度（用于止盈参考） ──
dataframe["zone_width"] = np.where(
dataframe["swing_high"].notna() & dataframe["swing_low"].notna() & (dataframe["swing_high"] > dataframe["swing_low"]),
(dataframe["swing_high"] - dataframe["swing_low"]) / dataframe["swing_low"],
np.nan,
)
dataframe["dist_to_low"] = np.where(
rolling_low > 0,
(dataframe["close"] - rolling_low) / dataframe["close"],
# ── K线形态 ──
bull_pin, bear_pin = self._detect_pinbar(
dataframe["open"], dataframe["high"], dataframe["low"], dataframe["close"]
)
bull_eng, bear_eng = self._detect_engulfing(dataframe["open"], dataframe["close"])
dataframe["bullish_signal"] = bull_pin | bull_eng
dataframe["bearish_signal"] = bear_pin | bear_eng
# ── 距边界的距离 ──
dataframe["dist_to_swing_low"] = np.where(
dataframe["swing_low"].notna() & (dataframe["swing_low"] > 0),
(dataframe["close"] - dataframe["swing_low"]) / dataframe["close"],
np.nan,
)
dataframe["dist_to_high"] = np.where(
rolling_high > 0,
(rolling_high - dataframe["close"]) / dataframe["close"],
dataframe["dist_to_swing_high"] = np.where(
dataframe["swing_high"].notna() & (dataframe["swing_high"] > 0),
(dataframe["swing_high"] - dataframe["close"]) / dataframe["close"],
np.nan,
)
# ── 填充 ──
for col in ["is_ranging", "zone_position", "dist_to_low", "dist_to_high"]:
if col in dataframe.columns:
dataframe[col] = dataframe[col].fillna(False if col == "is_ranging" else 999)
for col in ["dist_to_swing_low", "dist_to_swing_high"]:
dataframe[col] = dataframe[col].fillna(999)
return dataframe
# ================================================================
# 入场信号
# 入场
# ================================================================
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
entry_zone = self.entry_zone_pct.value / 1000.0
if "is_ranging" not in dataframe.columns:
dataframe["is_ranging"] = False
# ── 做多：震荡中，价格靠近下边界 ──
# ── 做多：价格在 swing low 附近 + 止跌形态 ──
long_conds = (
dataframe["is_ranging"]
& (dataframe["dist_to_low"] < entry_zone)
& (dataframe["dist_to_low"] > 0)
(dataframe["dist_to_swing_low"] < entry_zone)
& (dataframe["dist_to_swing_low"] > 0)
& dataframe["bullish_signal"]
)
long_recent = long_conds.rolling(self.cooldown, min_periods=1).max().shift(1) == 0
dataframe.loc[long_conds & long_recent, "enter_long"] = 1
# ── 做空：震荡中，价格靠近上边界 ──
# ── 做空：价格在 swing high 附近 + 止涨形态 ──
short_conds = (
dataframe["is_ranging"]
& (dataframe["dist_to_high"] < entry_zone)
& (dataframe["dist_to_high"] > 0)
(dataframe["dist_to_swing_high"] < entry_zone)
& (dataframe["dist_to_swing_high"] > 0)
& dataframe["bearish_signal"]
)
short_recent = short_conds.rolling(self.cooldown, min_periods=1).max().shift(1) == 0
dataframe.loc[short_conds & short_recent, "enter_short"] = 1
@ -174,14 +156,14 @@ class StructureFlowSwingV41(IStrategy):
return dataframe
# ================================================================
# 出场信号
# 出场
# ================================================================
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
return dataframe
# ================================================================
# 自定义止损：固定入场价下方-3%（真固定，不追踪）
# 止损：区间宽度 × 0.5（自适应）
# ================================================================
def custom_stoploss(
@ -194,15 +176,23 @@ class StructureFlowSwingV41(IStrategy):
after_fill: bool,
**kwargs,
) -> float:
# 固定止损 = 入场价下方3%，不随当前价格移动
# 不用current_rate，用trade.open_rate确保锚点固定
if not trade.is_short:
return max((trade.open_rate * 0.98 / current_rate) - 1.0, -0.20)
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
z_width = dataframe.iloc[-1].get("zone_width", np.nan)
if pd.notna(z_width) and z_width > 0.005:
stop_pct = min(max(z_width * 0.5, 0.005), 0.05)
else:
return min(1.0 - (trade.open_rate * 1.02 / current_rate), 0.20)
stop_pct = 0.015
if not trade.is_short:
return max((trade.open_rate * (1 - stop_pct) / current_rate) - 1.0, -0.20)
else:
return min(1.0 - (trade.open_rate * (1 + stop_pct) / current_rate), 0.20)
# ================================================================
# 自定义止盈：区间高度 × TP%
# 止盈：到对侧边界 + K线形态确认 → 平仓
# ================================================================
def custom_exit(
@ -214,89 +204,31 @@ class StructureFlowSwingV41(IStrategy):
current_profit: float,
**kwargs,
) -> str | None:
tp_pct = self.take_profit_pct.value / 100.0
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
if dataframe is None or len(dataframe) == 0:
return None
last = dataframe.iloc[-1]
z_low = last.get("zone_low", np.nan)
z_high = last.get("zone_high", np.nan)
swing_high = last.get("swing_high", np.nan)
swing_low = last.get("swing_low", np.nan)
bull_sig = last.get("bullish_signal", False)
bear_sig = last.get("bearish_signal", False)
if pd.notna(z_low) and pd.notna(z_high) and z_high > z_low:
base = z_low if not trade.is_short else z_high
zone_height = (z_high - z_low) / base
if current_profit >= zone_height * tp_pct:
return "take_profit"
return None
# ================================================================
# 自定义退出：锁定入场边界 + 收盘确认止损 + 区间高度止盈
# ================================================================
def custom_exit(
self,
pair: str,
trade: Trade,
current_time: datetime,
current_rate: float,
current_profit: float,
**kwargs,
) -> str | None:
tp_pct = self.take_profit_pct.value / 100.0
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
if dataframe is None or len(dataframe) == 0:
return None
last = dataframe.iloc[-1]
# ── 锁定入场时的区间边界 ──
open_time = trade.open_date_utc.replace(tzinfo=None)
time_diff = (dataframe["date"] - open_time).abs()
open_idx = time_diff.idxmin()
open_row = dataframe.iloc[open_idx]
z_low_open = open_row.get("zone_low", np.nan)
z_high_open = open_row.get("zone_high", np.nan)
# ── 结构止损：收盘价确认跌破入场时的边界 ──
# ── 做多：到阻力附近 + 滞涨形态 → 平仓 ──
if not trade.is_short:
if pd.notna(z_low_open) and z_low_open > 0 and last["close"] < z_low_open:
return "stop_loss" # 收盘跌破支撑
if pd.notna(swing_high) and swing_high > 0:
near_high = current_rate >= swing_high * 0.99
if near_high and bear_sig:
return "exit_signal"
if near_high and current_profit > 0:
return "exit_signal"
# ── 做空：到支撑附近 + 止跌形态 → 平仓 ──
else:
if pd.notna(z_high_open) and z_high_open > 0 and last["close"] > z_high_open:
return "stop_loss" # 收盘涨破阻力
# ── 止盈：区间高度 × TP%（也用锁定边界） ──
if pd.notna(z_low_open) and pd.notna(z_high_open) and z_high_open > z_low_open:
base = z_low_open if not trade.is_short else z_high_open
zone_height = (z_high_open - z_low_open) / base
if current_profit >= zone_height * tp_pct:
return "take_profit"
if pd.notna(swing_low) and swing_low > 0:
near_low = current_rate <= swing_low * 1.01
if near_low and bull_sig:
return "exit_signal"
if near_low and current_profit > 0:
return "exit_signal"
return None
# ================================================================
# Plot config
# ================================================================
@staticmethod
def plot_config() -> dict:
return {
"main_plot": {
"zone_high": {"color": "red", "type": "line"},
"zone_low": {"color": "green", "type": "line"},
},
"subplots": {
"rejections": {
"support_rejection_count": {"color": "green", "type": "line"},
"resistance_rejection_count": {"color": "red", "type": "line"},
},
"zone": {
"is_ranging": {"color": "blue", "type": "line"},
},
},
}