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v3.2 (Swing): 多TF震荡确认 + 供需区动态评分

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Beast Trader
2026-06-10 21:49:00 +08:00
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strategy.py
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@ -1,21 +1,28 @@
"""
Structure Flow Swing Strategy v3.1
Structure Flow Swing Strategy v3.2
==================================
波段交易策略 — 基于4H震荡区间，保守参数 v2
波段交易策略 — 基于4H震荡区间，v3.1优化版
v3.1 改动（基于v3.0诊断结果）：
1. 双边测试 AND→OR：在10根K线内测试过支撑 OR 阻力即可（不需两者都测过）
2. 区间稳定性 15%→25%：放宽波动容忍度
3. 入场范围 2%→3%：增加候选信号密度
4. 冷却期 3根→1根：减少过渡过滤
v3.2 改动（基于v3.1诊断结果 — 三大市场感知不足）：
1. D1趋势强度过滤：D1处于强趋势时拒绝入场，防假区间陷阱
- 计算 D1 EMA20/EMA50 间距作为趋势强度指标
- 趋势强度超过阈值 → 不交易（即使4H出现区间形态）
2. 区间质量评分：从二分法升级为多维度评分
- 边界测试次数（测试越多越可靠）
- 区间持续时长（越长越成熟）
- 区间宽度适配度（3-8%最优）
- 总分>=阈值才入场
3. 主动退出机制：确认转趋势后提前离场
- 3根连续K线收盘在入场时区间外 → 结构破坏
- 不等止损，主动离场（仅在损失<2%时）
- 避免浮盈变亏损
保留：纯震荡定位、ATR×1.5止损、区间70%止盈、D1趋势过滤
预期：年交易量从9笔 → 50-80笔（约1-2单/周）
保留：纯震荡定位、ATR×1.5止损、区间70%止盈、OR双边测试、冷却期1根
版本历史：
v3.0 (2026-06-10): 初版，基于冯总波段交易新思路
v3.1 (2026-06-10): 降低条件门槛，提升交易频率
v3.1 (2026-06-10): 降低条件门槛，AND→OR等4项
v3.2 (2026-06-10): 三大市场感知改进
"""
from datetime import datetime
@ -26,10 +33,10 @@ from freqtrade.strategy import IStrategy, IntParameter, informative
from freqtrade.persistence import Trade
class StructureFlowSwingV31(IStrategy):
class StructureFlowSwingV32(IStrategy):
"""
Structure Flow Swing Strategy v3.1
4H震荡区间波段交易 — 放宽震荡判定
Structure Flow Swing Strategy v3.2
4H震荡区间波段交易 — 市场感知增强版
"""
can_short = True
@ -40,17 +47,22 @@ class StructureFlowSwingV31(IStrategy):
timeframe = "4h"
# =====================
# 可优化参数（放宽后默认值）
# 核心参数（沿用v3.1默认值）
# =====================
swing_lookback = IntParameter(4, 8, default=5, space="buy")
zone_stability_threshold = IntParameter(15, 40, default=25, space="buy") # v3.1: 15→25↑
entry_zone_pct = IntParameter(1, 5, default=3, space="buy") # v3.1: 2→3↑
zone_stability_threshold = IntParameter(15, 40, default=25, space="buy")
entry_zone_pct = IntParameter(1, 5, default=3, space="buy")
atr_stop_mult = IntParameter(10, 25, default=15, space="buy")
take_profit_pct = IntParameter(50, 80, default=70, space="sell")
# v3.2 新增参数
d1_trend_strength_max = IntParameter(6, 15, default=10, space="buy") # D1趋势强度上限%，默认10%（极端趋势才触发）
zone_quality_min = IntParameter(20, 60, default=30, space="buy") # 区间质量最低分，默认30
# 固定参数
zone_touch_lookback = 10
breakout_bars = 2
early_exit_bars = 3 # v3.2新增：连续N根在区间外触发主动退出
# =====================
# 工具：Swing Point 检测
@ -73,7 +85,7 @@ class StructureFlowSwingV31(IStrategy):
return sh, sl
# =====================
# 工具：区间震荡检测
# 工具：区间震荡检测（增强版：加入质量评分数据）
# =====================
def _detect_range(
@ -89,10 +101,14 @@ class StructureFlowSwingV31(IStrategy):
support_arr = np.full(n, np.nan)
resistance_arr = np.full(n, np.nan)
zone_width_arr = np.full(n, np.nan)
touch_count_arr = np.full(n, 0) # v3.2新增
sh_prices = []
sl_prices = []
in_range = False
touch_count = 0
for i in range(n):
if pd.notna(sh.iloc[i]):
sh_prices.append(sh.iloc[i])
@ -104,12 +120,19 @@ class StructureFlowSwingV31(IStrategy):
sl_prices.pop(0)
if len(sh_prices) < 3 or len(sl_prices) < 3:
# 不在区间中
if in_range:
in_range = False
touch_count = 0
continue
current_sh = sh_prices[-1]
current_sl = sl_prices[-1]
if current_sh <= current_sl:
if in_range:
in_range = False
touch_count = 0
continue
zone_width = (current_sh - current_sl) / current_sl
@ -137,10 +160,12 @@ class StructureFlowSwingV31(IStrategy):
is_stable = False
if not is_stable:
if in_range:
in_range = False
touch_count = 0
continue
# 条件2：价格测试过边界 — v3.1: AND→OR
# 只需要测试过支撑或阻力之一，不需要两者都测过
start_idx = max(0, i - self.zone_touch_lookback)
support_zone_upper = current_sl * 1.01
touched_support = any(
@ -153,8 +178,10 @@ class StructureFlowSwingV31(IStrategy):
for j in range(start_idx, i + 1)
)
# v3.1: AND → OR
if not (touched_support or touched_resistance):
if in_range:
in_range = False
touch_count = 0
continue
# 条件3：无突破
@ -166,15 +193,30 @@ class StructureFlowSwingV31(IStrategy):
break
if consecutive_outside >= self.breakout_bars:
if in_range:
in_range = False
touch_count = 0
continue
# === 通过所有条件 → 在区间中 ===
is_ranging[i] = True
# v3.2: 跟踪区间内的边界触碰次数（质量评分数据）
if not in_range:
in_range = True
touch_count = 0
c = close.iloc[i]
if (c <= current_sl * 1.015) or (c >= current_sh * 0.985):
touch_count += 1
touch_count_arr[i] = touch_count
return DataFrame({
"is_ranging": is_ranging,
"support": support_arr,
"resistance": resistance_arr,
"zone_width": zone_width_arr,
"touch_count": touch_count_arr, # v3.2新增
}, index=high.index)
# =====================
@ -191,11 +233,12 @@ class StructureFlowSwingV31(IStrategy):
return tr.rolling(period).mean()
# ================================================================
# D1 信息时间框架 — 宏观趋势参考
# D1 信息时间框架 — v3.2: 新增趋势强度计算
# ================================================================
@informative("1d")
def populate_indicators_1d(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# 原有：D1趋势方向（swing point比较）
sh, sl = self._detect_swing_points(
dataframe["high"], dataframe["low"], window=5
)
@ -213,10 +256,16 @@ class StructureFlowSwingV31(IStrategy):
dataframe["d1_uptrend"] = is_uptrend
dataframe["d1_downtrend"] = is_downtrend
# v3.2新增：D1趋势强度 = EMA20与EMA50的偏离程度
ema_20 = dataframe["close"].ewm(span=20, adjust=False).mean()
ema_50 = dataframe["close"].ewm(span=50, adjust=False).mean()
dataframe["trend_strength"] = abs(ema_20 - ema_50) / ema_50
return dataframe
# ================================================================
# 主时间框架 — 4H 指标
# 主时间框架 — 4H 指标（v3.2: 新增区间质量评分）
# ================================================================
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@ -230,6 +279,7 @@ class StructureFlowSwingV31(IStrategy):
dataframe["range_support"] = range_info["support"]
dataframe["range_resistance"] = range_info["resistance"]
dataframe["zone_width_pct"] = range_info["zone_width"]
dataframe["range_touch_count"] = range_info["touch_count"]
dataframe["atr"] = self._calc_atr(dataframe["high"], dataframe["low"], dataframe["close"], 14)
@ -253,14 +303,66 @@ class StructureFlowSwingV31(IStrategy):
np.nan,
)
# ── v3.2新增：区间质量评分 ──
self._compute_zone_quality(dataframe)
# ── v3.2新增：区间连续计数 ──
is_ranging_int = dataframe["is_ranging"].astype(int)
consecutive = np.zeros(len(dataframe), dtype=int)
for i in range(1, len(dataframe)):
if is_ranging_int.iloc[i] and is_ranging_int.iloc[i-1]:
consecutive[i] = consecutive[i-1] + 1
elif is_ranging_int.iloc[i]:
consecutive[i] = 1
dataframe["range_consecutive"] = consecutive
for col in ["is_ranging", "zone_position", "dist_to_support", "dist_to_resistance"]:
if col in dataframe.columns:
dataframe[col] = dataframe[col].fillna(False if col == "is_ranging" else 999)
return dataframe
def _compute_zone_quality(self, dataframe: DataFrame) -> None:
"""
v3.2新增：区间质量三因子评分
- 边界测试次数（0-45分）：0→15, 1→20, 2→32, 3+→45
- 区间持续时长（0-30分）：<5→0, 5-9→12, 10-19→22, 20+→30
- 区间宽度适配（0-25分）：3-8%→25, 2-3%→15, 8-12%→15, 其他→0
满分100，合格线默认30
"""
touch_count = dataframe["range_touch_count"].fillna(0).values
zone_width = dataframe["zone_width_pct"].fillna(0).values
is_ranging = dataframe["is_ranging"].values
quality = np.zeros(len(dataframe))
# 因子1：边界测试次数（放宽：0次触碰也有基础分）
quality += np.where(
touch_count >= 3, 45,
np.where(touch_count >= 2, 32,
np.where(touch_count >= 1, 20, 15))
)
# 因子2：区间持续时长（用连续计数表示暂存，后续由 populate_indicators 补充）
# 这里先按最少给分，populate_indicators 中会基于 range_consecutive 二次修正
# 实际上 touche_count > 0 就意味着至少有一些持续性
# 因子3：区间宽度适配度
quality += np.where(
(zone_width >= 0.03) & (zone_width <= 0.08), 25,
np.where(
((zone_width >= 0.02) & (zone_width < 0.03)) |
((zone_width > 0.08) & (zone_width <= 0.12)), 15, 0
)
)
# 只在区间内有效
quality = np.where(is_ranging, quality, 0)
dataframe["zone_quality_base"] = quality
# ================================================================
# 入场信号 — v3.1: 冷却期 3→1
# 入场信号 — v3.2: D1趋势强度 + 区间质量过滤 + 持续时间因子
# ================================================================
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@ -268,22 +370,54 @@ class StructureFlowSwingV31(IStrategy):
d1_downtrend_col = "d1_downtrend_1d"
d1_uptrend_col = "d1_uptrend_1d"
d1_strength_col = "trend_strength_1d" # v3.2新增
for col in ["is_ranging", d1_uptrend_col, d1_downtrend_col]:
for col in ["is_ranging", d1_uptrend_col, d1_downtrend_col, d1_strength_col]:
if col in dataframe.columns:
dataframe[col] = dataframe[col].fillna(False)
else:
dataframe[col] = False
# ── v3.2: 计算完整区间质量评分（加入持续性因子） ──
range_consec = dataframe.get("range_consecutive", pd.Series(0, index=dataframe.index))
quality_base = dataframe.get("zone_quality_base", pd.Series(0, index=dataframe.index))
# 持续性因子：<5→0, 5-9→12, 10-19→22, 20+→30
duration_score = np.where(
range_consec >= 20, 30,
np.where(range_consec >= 10, 22,
np.where(range_consec >= 5, 12, 0))
)
# 完整质量分 = 基础分（测试+宽度，max=70）+ 持续性分（max=30）
dataframe["zone_quality"] = quality_base + duration_score
dataframe["zone_quality"] = np.where(dataframe["is_ranging"], dataframe["zone_quality"], 0)
# ── v3.2: D1趋势强度过滤（方向感知） ──
# 逻辑：只有在极端趋势中，同向的4H区间才有"假区间"风险
# - 做多：D1处于极端上升趋势 → 回调可能很深 → 不进场
# - 做空：D1处于极端下降趋势 → 反弹可能很高 → 不进场
threshold = self.d1_trend_strength_max.value / 100.0
d1_strength_strong = dataframe[d1_strength_col] > threshold
long_d1_ok = ~(dataframe[d1_uptrend_col] & d1_strength_strong) # 极端上升趋势不做多
short_d1_ok = ~(dataframe[d1_downtrend_col] & d1_strength_strong) # 极端下降趋势不做空
# ── v3.2: 区间质量过滤 ──
quality_min = self.zone_quality_min.value
zone_quality_ok = dataframe["zone_quality"] >= quality_min
# ── 做多：震荡市中，价格靠近支撑位 ──
long_conds = (
dataframe["is_ranging"]
& (dataframe["dist_to_support"] <= entry_zone)
& (dataframe["dist_to_support"] > 0)
& (~dataframe[d1_downtrend_col])
& (~dataframe[d1_downtrend_col]) # 原有：D1不能是下降趋势
& long_d1_ok # v3.2新增：极端上升趋势不做多
& zone_quality_ok # v3.2新增：区间质量达标
)
cooldown = 1 # v3.1: 3→1
cooldown = 1
long_recent = long_conds.rolling(cooldown, min_periods=1).max().shift(1) == 0
dataframe.loc[long_conds & long_recent, "enter_long"] = 1
@ -292,7 +426,9 @@ class StructureFlowSwingV31(IStrategy):
dataframe["is_ranging"]
& (dataframe["dist_to_resistance"] <= entry_zone)
& (dataframe["dist_to_resistance"] > 0)
& (~dataframe[d1_uptrend_col])
& (~dataframe[d1_uptrend_col]) # 原有：D1不能是上升趋势
& short_d1_ok # v3.2新增：极端下降趋势不做空
& zone_quality_ok # v3.2新增：区间质量达标
)
short_recent = short_conds.rolling(cooldown, min_periods=1).max().shift(1) == 0
@ -308,7 +444,7 @@ class StructureFlowSwingV31(IStrategy):
return dataframe
# ================================================================
# 自定义止损：支撑/阻力外侧，ATR*1.5 缓冲
# 自定义止损：支撑/阻力外侧，ATR*1.5 缓冲（v3.1逻辑保持不变）
# ================================================================
def custom_stoploss(
@ -358,7 +494,7 @@ class StructureFlowSwingV31(IStrategy):
return min(sl_ratio, 0.20)
# ================================================================
# 自定义止盈：区间70%
# 自定义止盈：区间70% + v3.2主动退出机制
# ================================================================
def custom_exit(
@ -378,6 +514,7 @@ class StructureFlowSwingV31(IStrategy):
last = dataframe.iloc[-1]
# ── 原有：区间70%止盈 ──
if not trade.is_short:
support = last.get("range_support", np.nan)
resistance = last.get("range_resistance", np.nan)
@ -397,6 +534,34 @@ class StructureFlowSwingV31(IStrategy):
if current_profit >= tp_target:
return "take_profit"
# ── v3.2新增：主动退出机制 ──
# 区间结构破坏 → 提前离场
# 条件：连续3根K线收盘在入场时区间外，且当前亏损<2%
if current_profit > -0.02:
# 找到入场时的K线（取最后一根确认的K线，不是当前正在形成的）
entry_date = trade.open_date
entry_mask = dataframe["date"] <= entry_date
if entry_mask.any():
entry_idx = dataframe[entry_mask].index[-1]
entry_support = dataframe.loc[entry_idx, "range_support"]
entry_resistance = dataframe.loc[entry_idx, "range_resistance"]
if pd.notna(entry_support) and pd.notna(entry_resistance) and entry_resistance > entry_support:
# 取最后3根已完成的K线
check_bars = min(self.early_exit_bars, len(dataframe) - 1)
recent = dataframe.iloc[-(check_bars + 1):-1] # 排除当前正在形成的K线
if len(recent) >= self.early_exit_bars:
outside_count = 0
for _, bar in recent.iterrows():
c = bar["close"]
# 缓冲0.5%避免噪音触发
if c < entry_support * 0.995 or c > entry_resistance * 1.005:
outside_count += 1
if outside_count >= self.early_exit_bars:
return "early_exit_structure_broken"
return None
# ================================================================
@ -414,6 +579,7 @@ class StructureFlowSwingV31(IStrategy):
"range": {
"is_ranging": {"color": "blue", "type": "line"},
"zone_width_pct": {"color": "purple", "type": "line"},
"zone_quality": {"color": "orange", "type": "line"},
},
"position": {
"dist_to_support": {"color": "green", "type": "line"},