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/engine.py

@@ -1,12 +1,18 @@
-"""威科夫分析入口。"""
+"""威科夫分析入口：Cycle → Phase → Event → VP + Live（MULTI-CYCLE / LIVE-STRUCTURE）。
+
+range.py 只产 TradingRange；Confirmed 走 events.py；Live 走 live.py。
+cycles[0]=ACTIVE；禁止 cycles[-1] 取 active。
+Execution 只消费 Confirmed（见 live.execution_signal_from_wyckoff）。
+"""
 from __future__ import annotations
 
-from typing import Any, Dict, Optional
+from typing import Any, Dict, List, Optional
 
 import pandas as pd
 
 from .events import build_phases, detect_bias_and_events
-from .range import detect_trading_range
+from .live import analyze_live_structure
+from .range import detect_trading_ranges
 from .volume_profile import compute_volume_profile
 
 
@@ -21,31 +27,55 @@ def _fmt_time(v) -> Optional[str]:
 	return str(v)
 
 
-def analyze_wyckoff(df: pd.DataFrame, lookback: int = 120, vp_bins: int = 50) -> Dict[str, Any]:
-	"""
-	对主周期 OHLCV DataFrame 做威科夫启发式分析。
-	需要列: open, high, low, close, volume；建议有 date 或 timestamp。
-	"""
-	empty = {
+def _empty(vp_bins: int) -> Dict[str, Any]:
+	return {
+		"cycles": [],
 		"trading_range": None,
 		"bias": "unknown",
 		"phases": [],
 		"events": [],
 		"volume_profile": {"bins": [], "poc": None, "vah": None, "val": None, "bin_count": vp_bins},
 		"volume_confirm": {"avg_volume": 0.0, "event_checks": {}},
+		"live": None,
 	}
-	if df is None or len(df) < 30:
-		return empty
-	if not all(c in df.columns for c in ("open", "high", "low", "close")):
-		return empty
-	work = df.copy()
-	if "volume" not in work.columns:
-		work["volume"] = 1.0
 
-	tr = detect_trading_range(work, lookback=lookback)
-	if tr is None:
-		return empty
 
+def _confidence_for_confirmed(
+	tr: Dict[str, Any],
+	phases: List[Dict[str, Any]],
+	events: List[Dict[str, Any]],
+) -> Dict[str, float]:
+	range_c = float(tr.get("range_confidence") or 0.5)
+	labels = {p.get("phase") for p in phases}
+	phase_c = 0.35
+	if "A" in labels and "B" in labels:
+		phase_c += 0.15
+	if "C" in labels:
+		phase_c += 0.2
+	if "D" in labels or "E" in labels:
+		phase_c += 0.15
+	phase_c = min(0.95, phase_c)
+	types = {e.get("type") for e in events}
+	event_c = 0.25
+	for t in ("Spring", "UTAD", "SOS", "SOW", "LPS", "LPSY"):
+		if t in types:
+			event_c += 0.12
+	event_c = min(0.95, event_c)
+	overall = 0.4 * range_c + 0.3 * phase_c + 0.3 * event_c
+	return {
+		"range": round(range_c, 3),
+		"phase": round(phase_c, 3),
+		"event": round(event_c, 3),
+		"overall": round(overall, 3),
+	}
+
+
+def _build_cycle(
+	work: pd.DataFrame,
+	tr: Dict[str, Any],
+	cycle_id: int,
+	vp_bins: int,
+) -> Dict[str, Any]:
 	bias, events, volume_confirm = detect_bias_and_events(work, tr)
 	phases = build_phases(work, tr, bias, events)
 	vp = compute_volume_profile(
@@ -54,27 +84,113 @@ def analyze_wyckoff(df: pd.DataFrame, lookback: int = 120, vp_bins: int = 50) ->
 		int(tr["abs_end_idx"]),
 		bin_count=vp_bins,
 	)
-
-	trading_range = {
-		"start_time": _fmt_time(tr.get("start_time")),
-		"end_time": _fmt_time(tr.get("end_time")),
-		"high": float(tr["high"]),
-		"low": float(tr["low"]),
-		"mid": float(tr["mid"]),
-		"active": bool(tr.get("active", True)),
-		"bars": int(tr.get("bars", 0)),
-	}
 	for ev in events:
 		ev["time"] = _fmt_time(ev.get("time"))
 	for ph in phases:
 		ph["start_time"] = _fmt_time(ph.get("start_time"))
 		ph["end_time"] = _fmt_time(ph.get("end_time"))
 
+	is_active = cycle_id == 0
+	trading_range = {
+		"start_time": _fmt_time(tr.get("start_time")),
+		"end_time": _fmt_time(tr.get("end_time")),
+		"high": float(tr["high"]),
+		"low": float(tr["low"]),
+		"mid": float(tr["mid"]),
+		"active": bool(is_active),
+		"bars": int(tr.get("bars", 0)),
+	}
+	conf = _confidence_for_confirmed(tr, phases, events)
+
+	# Live 层：仅 ACTIVE 周期做推演；历史周期归档为 COMPLETED
+	if is_active:
+		live = analyze_live_structure(
+			work, tr, confirmed_events=events, confirmed_phases=phases, bias=bias,
+		)
+		lifecycle = live.get("lifecycle") or "FORMING"
+	else:
+		live = None
+		lifecycle = "COMPLETED"
+
 	return {
+		"id": int(cycle_id),
+		"role": "latest" if is_active else "historical",
+		# MULTI-CYCLE：时间线角色
+		"status": "ACTIVE" if is_active else "HISTORICAL",
+		# LIVE-STRUCTURE：生命周期
+		"lifecycle": lifecycle,
+		"direction": "latest" if is_active else "historical",
+		"period": {
+			"start_time": _fmt_time(tr.get("start_time")),
+			"end_time": _fmt_time(tr.get("end_time")),
+			"bars": int(tr.get("bars", 0)),
+		},
+		"confidence": conf,
 		"trading_range": trading_range,
 		"bias": bias,
+		# 兼容旧读法：顶层 phases/events = confirmed
 		"phases": phases,
 		"events": events,
+		"confirmed": {
+			"phases": phases,
+			"events": events,
+			"volume_confirm": volume_confirm,
+		},
+		"live": live,
 		"volume_profile": vp,
 		"volume_confirm": volume_confirm,
 	}
+
+
+def analyze_wyckoff(
+	df: pd.DataFrame,
+	lookback: int = 120,
+	vp_bins: int = 50,
+	min_bars: int = 24,
+	atr_mult: float = 1.2,
+	range_start_time=None,
+	prefer_start_time=None,
+	max_cycles: int = 8,
+) -> Dict[str, Any]:
+	"""
+	多周期威科夫分析。
+	cycles[0] = ACTIVE；顶层 phases/events 只镜像 Confirmed。
+	顶层 live 镜像 cycles[0].live。
+	"""
+	empty = _empty(vp_bins)
+	if df is None or len(df) < 30:
+		return empty
+	if not all(c in df.columns for c in ("open", "high", "low", "close")):
+		return empty
+	work = df.copy()
+	if "volume" not in work.columns:
+		work["volume"] = 1.0
+
+	trs = detect_trading_ranges(
+		work,
+		lookback=lookback,
+		min_bars=max(8, int(min_bars)),
+		atr_mult=atr_mult,
+		max_cycles=max(1, min(8, int(max_cycles))),
+		prefer_start_time=prefer_start_time,
+		range_start_time=range_start_time,
+	)
+	if not trs:
+		return empty
+
+	cycles: List[Dict[str, Any]] = []
+	for i, tr in enumerate(trs):
+		cycles.append(_build_cycle(work, tr, cycle_id=i, vp_bins=vp_bins))
+
+	active = cycles[0]
+	return {
+		"cycles": cycles,
+		"trading_range": active["trading_range"],
+		"bias": active["bias"],
+		"phases": active["confirmed"]["phases"],
+		"events": active["confirmed"]["events"],
+		"volume_profile": active["volume_profile"],
+		"volume_confirm": active["volume_confirm"],
+		"live": active.get("live"),
+		"lifecycle": active.get("lifecycle"),
+	}