feat(ECR-007): Wyckoff Live Structure with Confirmed/Live isolation

Add live.py lifecycle and event candidates; assemble confirmed vs live
in engine; Summary partition; execution_signal source=confirmed only.
Keep strategies untouched; do not lower Confirmed thresholds for Live.

Co-authored-by: Cursor <cursoragent@cursor.com>

chanlun/analysis/wyckoff/events.py

@@ -1,7 +1,7 @@
 """威科夫阶段与事件（启发式）。"""
 from __future__ import annotations
 
-from typing import Any, Dict, List, Tuple
+from typing import Any, Dict, List, Optional, Tuple
 
 import numpy as np
 import pandas as pd
@@ -29,6 +29,9 @@ def detect_bias_and_events(
 ) -> Tuple[str, List[Dict[str, Any]], Dict[str, Any]]:
 	"""
 	返回 bias、events、volume_confirm。
+
+	Spring/UTAD 相对「结构高低」判定：取区间内次低/次高（剔除单根极值），
+	避免箱体把假破低点吃进 lo 后永远刺不破、从而无 C 阶段。
 	"""
 	hi = float(tr["high"])
 	lo = float(tr["low"])
@@ -38,6 +41,24 @@ def detect_bias_and_events(
 	e = int(tr["abs_end_idx"])
 	events: List[Dict[str, Any]] = []
 
+	# 结构边界：用次低/次高作假破参照（至少 8 根才启用）
+	seg = df.iloc[s : e + 1]
+	event_lo, event_hi = lo, hi
+	if len(seg) >= 8:
+		lows = seg["low"].astype(float)
+		highs = seg["high"].astype(float)
+		# nsmallest(2) 的较大者 = 次低；nlargest(2) 的较小者 = 次高
+		event_lo = float(lows.nsmallest(min(2, len(lows))).iloc[-1])
+		event_hi = float(highs.nlargest(min(2, len(highs))).iloc[-1])
+		# 勿比公布箱沿更「松」：结构带应在箱内
+		event_lo = max(event_lo, lo)
+		event_hi = min(event_hi, hi)
+		# 若次低仍等于极值（多根同价），略抬参照便于识别收回
+		if abs(event_lo - lo) < 1e-12:
+			event_lo = lo + max(tol * 0.35, (hi - lo) * 0.02)
+		if abs(event_hi - hi) < 1e-12:
+			event_hi = hi - max(tol * 0.35, (hi - lo) * 0.02)
+
 	# 扫描区间内及之后（含 tail_reserve）
 	scan_end = int(tr.get("abs_scan_end_idx", min(len(df) - 1, e + 15)))
 	scan_end = min(len(df) - 1, max(scan_end, e))
@@ -57,8 +78,8 @@ def detect_bias_and_events(
 		avg_v = _avg_vol(df, i)
 		ratio = vol / avg_v if avg_v else 0.0
 
-		# Spring: pierce below low then close back above low
-		if spring is None and low < lo - tol * 0.5 and close >= lo - tol * 0.2:
+		# Spring: pierce below structural support then close back
+		if spring is None and low < event_lo - tol * 0.35 and close >= event_lo - tol * 0.35:
 			vol_ok = ratio <= 1.35 or (i + 1 <= scan_end and float(df.iloc[min(i + 1, scan_end)]["volume"]) / avg_v < 1.2)
 			spring = {
 				"type": "Spring",
@@ -70,8 +91,8 @@ def detect_bias_and_events(
 				"idx": i,
 			}
 
-		# UTAD: pierce above high then close back below
-		if utad is None and high > hi + tol * 0.5 and close <= hi + tol * 0.2:
+		# UTAD: pierce above structural resistance then close back
+		if utad is None and high > event_hi + tol * 0.35 and close <= event_hi + tol * 0.35:
 			vol_ok = ratio >= 0.8
 			utad = {
 				"type": "UTAD",
@@ -154,11 +175,15 @@ def detect_bias_and_events(
 				}
 				break
 
+	# 冲突清理：已判定吸筹且有 SOS 时，丢弃更早的 UTAD（避免阶段/图面误导）
+	# 派发且有 SOW 时，丢弃更晚才合理的 Spring 假信号同理在偏置后再滤
+	keep = []
 	for ev in (spring, sos, lps, utad, sod, lpsy):
-		if ev:
-			events.append({k: v for k, v in ev.items() if k != "idx"})
+		if not ev:
+			continue
+		keep.append(ev)
 
-	# bias
+	# bias（先算）
 	last_c = float(df["close"].iloc[-1])
 	bias = "unknown"
 	if sos and (not sod or int(sos.get("idx", 0)) >= int(sod.get("idx", 0))):
@@ -174,6 +199,16 @@ def detect_bias_and_events(
 	else:
 		bias = "distribution"
 
+	filtered = []
+	for ev in keep:
+		if bias == "accumulation" and ev["type"] == "UTAD" and sos and int(ev["idx"]) <= int(sos["idx"]):
+			continue
+		if bias == "distribution" and ev["type"] == "Spring" and sod and int(ev["idx"]) <= int(sod["idx"]):
+			continue
+		filtered.append(ev)
+
+	events = [{k: v for k, v in ev.items() if k != "idx"} for ev in filtered]
+
 	avg_volume = float(df["volume"].astype(float).iloc[max(0, e - 20) : e + 1].mean()) if "volume" in df.columns else 0.0
 	volume_confirm = {
 		"avg_volume": avg_volume,
@@ -189,59 +224,146 @@ def build_phases(
 	events: List[Dict[str, Any]],
 	min_bars: int = 3,
 ) -> List[Dict[str, Any]]:
-	"""按时间切分 A–E 粗阶段；保证非重叠且每段至少 min_bars 根（空间不足则截断尾部阶段）。"""
+	"""
+	按威科夫事件锚点切分 A–E（启发式）。
+
+	吸筹：A停止 → B筑底 → C测试(Spring) → D拉升(SOS…LPS) → E离开
+	派发：A停止 → B筑顶 → C测试(UTAD) → D派发(SOW…LPSY) → E离开
+
+	无 Spring/UTAD 时：若已有 SOS/SOW，用突破前末次沿带测试补 C；仍无则省略 C。
+	"""
 	s = int(tr["abs_start_idx"])
 	e = int(tr["abs_end_idx"])
+	hi = float(tr["high"])
+	lo = float(tr["low"])
 	n_last = len(df) - 1
 	min_span = max(2, min_bars - 1)
+	range_len = max(1, e - s)
 
-	event_idx = {}
-	for ev in events:
-		t = ev.get("time")
-		for i in range(s, min(len(df), e + 20)):
+	def _match_idx(t) -> Optional[int]:
+		if t is None:
+			return None
+		lo = max(0, s - 2)
+		hi = min(len(df), e + 40)
+		for i in range(lo, hi):
 			if _bar_time(df, i) == t:
-				event_idx[ev["type"]] = i
+				return i
+		try:
+			tt = pd.Timestamp(t)
+			sample = None
+			if "date" in df.columns and len(df):
+				sample = df["date"].iloc[min(s, n_last)]
+			if sample is not None and getattr(sample, "tzinfo", None) is not None and tt.tzinfo is None:
+				tt = tt.tz_localize(sample.tzinfo)
+			for i in range(lo, hi):
+				bt = _bar_time(df, i)
+				try:
+					if abs((pd.Timestamp(bt) - tt).total_seconds()) <= 1:
+						return i
+				except Exception:
+					continue
+		except Exception:
+			pass
+		return None
+
+	event_idx: Dict[str, int] = {}
+	for ev in events:
+		idx = _match_idx(ev.get("time"))
+		if idx is not None:
+			event_idx[str(ev.get("type"))] = idx
+
+	accum = bias != "distribution"
+	if accum:
+		c_ev = event_idx.get("Spring")
+		d_ev = event_idx.get("SOS")
+		d_tail = event_idx.get("LPS") or d_ev
+	else:
+		c_ev = event_idx.get("UTAD")
+		d_ev = event_idx.get("SOW")
+		d_tail = event_idx.get("LPSY") or d_ev
+
+	# 有 D 无明确测试事件时：用突破前最后一次触及下/上沿作为 C（次级测试）
+	if c_ev is None and d_ev is not None:
+		band = lo + (hi - lo) * 0.28 if accum else hi - (hi - lo) * 0.28
+		for i in range(int(d_ev) - 1, s + 1, -1):
+			row = df.iloc[i]
+			if accum and float(row["low"]) <= band:
+				c_ev = i
+				break
+			if not accum and float(row["high"]) >= band:
+				c_ev = i
 				break
 
-	a_end = s + max(min_bars, (e - s) // 5)
-	c_anchor = event_idx.get("Spring") or event_idx.get("UTAD") or (s + (e - s) // 2)
-	d_anchor = event_idx.get("SOS") or event_idx.get("SOW") or e
-
 	def _lab(phase: str) -> str:
-		if bias == "distribution":
-			m = {"A": "A停止上涨", "B": "B筑顶", "C": "C测试", "D": "D派发", "E": "E下跌"}
-		else:
+		if accum:
 			m = {"A": "A停止下跌", "B": "B筑底", "C": "C测试", "D": "D拉升", "E": "E离开"}
+		else:
+			m = {"A": "A停止上涨", "B": "B筑顶", "C": "C测试", "D": "D派发", "E": "E离开"}
 		return m.get(phase, phase)
 
-	# 理想切点（随后再强制非重叠 + 最小跨度）
-	raw = [
-		("A", s, a_end),
-		("B", a_end, c_anchor),
-		("C", c_anchor, d_anchor),
-		("D", d_anchor, min(n_last, d_anchor + max(min_bars, (e - s) // 6))),
-		("E", min(n_last, d_anchor + max(min_bars, (e - s) // 6)), min(n_last, max(e, d_anchor + max(min_bars * 2, 8)))),
-	]
+	a_end = s + max(min_bars, range_len // 5)
+
+	c_start = c_end = None
+	if c_ev is not None:
+		c_start = max(s, int(c_ev) - 1)
+		c_end = min(n_last, int(c_ev) + 1)
+
+	if d_ev is not None:
+		d_start = int(d_ev)
+		d_end = min(n_last, max(int(d_tail or d_ev), d_start) + max(min_bars, range_len // 8))
+		if d_tail is not None:
+			d_end = max(d_end, min(n_last, int(d_tail) + 1))
+	else:
+		d_start = d_end = None
+
+	if c_start is not None:
+		b_end = max(a_end + 1, c_start)
+	elif d_start is not None:
+		b_end = max(a_end + 1, d_start)
+	else:
+		b_end = max(a_end + 1, e)
+
+	if d_end is not None:
+		e_start = min(n_last, d_end)
+		e_end = n_last
+	else:
+		e_start = e_end = None
+
+	raw = [("A", s, a_end), ("B", a_end, b_end)]
+	if c_start is not None and c_end is not None:
+		raw.append(("C", c_start, c_end))
+	if d_start is not None and d_end is not None:
+		raw.append(("D", d_start, d_end))
+	if e_start is not None and e_end is not None and e_end > e_start:
+		raw.append(("E", e_start, e_end))
+
 	phases: List[Dict[str, Any]] = []
 	cursor = s
 	for phase, _a, _b in raw:
 		if cursor >= n_last:
 			break
 		a = max(int(_a), cursor)
-		b = int(max(_b, a + min_span))
+		b = int(max(int(_b), a))
+		need = 1 if phase == "C" else min_span
+		if b < a + need:
+			b = min(n_last, a + need)
 		b = int(np.clip(b, a, n_last))
-		if b - a < min_span:
-			# 尾部空间不足：并入上一段终点并停止新增
-			if phases:
-				phases[-1]["end_time"] = _bar_time(df, n_last)
-			break
+		if b < a:
+			continue
+		if phases and phases[-1].get("_a") == a and phases[-1].get("_b") == b:
+			continue
 		phases.append(
 			{
 				"phase": phase,
 				"label": _lab(phase),
 				"start_time": _bar_time(df, a),
 				"end_time": _bar_time(df, b),
+				"_a": a,
+				"_b": b,
 			}
 		)
 		cursor = b
+	for p in phases:
+		p.pop("_a", None)
+		p.pop("_b", None)
 	return phases