feat: ECR-003 主站威科夫分析与图表叠层（已审）

独立 wyckoff 引擎 + 按需 include_wyckoff；主站 Lightweight 绘制区间/阶段/事件/VP。

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

chanlun/analysis/wyckoff/range.py

@@ -0,0 +1,108 @@
+"""交易区间检测：ATR 容差下的近期震荡箱。"""
+from __future__ import annotations
+
+from typing import Any, Dict, Optional
+
+import numpy as np
+import pandas as pd
+
+
+def _atr(df: pd.DataFrame, period: int = 14) -> pd.Series:
+	high = df["high"].astype(float)
+	low = df["low"].astype(float)
+	close = df["close"].astype(float)
+	prev_close = close.shift(1)
+	tr = pd.concat(
+		[
+			(high - low).abs(),
+			(high - prev_close).abs(),
+			(low - prev_close).abs(),
+		],
+		axis=1,
+	).max(axis=1)
+	return tr.rolling(period, min_periods=max(3, period // 2)).mean()
+
+
+def detect_trading_range(
+	df: pd.DataFrame,
+	lookback: int = 120,
+	min_bars: int = 24,
+	atr_mult: float = 1.2,
+	tail_reserve: int = 12,
+) -> Optional[Dict[str, Any]]:
+	"""
+	在最近 lookback 根内寻找高低点波动受控的连续段作为交易区间。
+	尾部预留 tail_reserve 根用于事件（Spring/SOS），不参与箱体边界计算。
+	"""
+	if df is None or len(df) < min_bars + 5:
+		return None
+	work = df.tail(lookback).reset_index(drop=True)
+	n = len(work)
+	reserve = min(tail_reserve, max(0, n - min_bars - 2))
+	core_end = n - reserve if reserve > 0 else n
+	core = work.iloc[:core_end]
+	if len(core) < min_bars:
+		core = work
+		core_end = n
+		reserve = 0
+
+	atr = _atr(work)
+	last_atr = float(atr.iloc[core_end - 1]) if atr.notna().iloc[:core_end].any() else float(
+		(core["high"] - core["low"]).mean()
+	)
+	if not np.isfinite(last_atr) or last_atr <= 0:
+		last_atr = float(core["close"].iloc[-1]) * 0.01
+
+	best = None
+	cn = len(core)
+	for length in range(min(cn, lookback), min_bars - 1, -4):
+		seg = core.iloc[-length:]
+		hi = float(seg["high"].max())
+		lo = float(seg["low"].min())
+		width = hi - lo
+		if width <= 0 or width > last_atr * atr_mult * 3.5:
+			continue
+		tol = last_atr * atr_mult * 0.35
+		near_hi = int((seg["high"] >= hi - tol).sum())
+		near_lo = int((seg["low"] <= lo + tol).sum())
+		if near_hi < 2 or near_lo < 2:
+			continue
+		inside = ((seg["close"] >= lo - tol) & (seg["close"] <= hi + tol)).mean()
+		if inside < 0.75:
+			continue
+		start_i = cn - length
+		end_i = cn - 1
+		mid = (hi + lo) / 2.0
+		last_c = float(work["close"].iloc[-1])
+		active = (lo - tol * 1.5) <= last_c <= (hi + tol * 1.5)
+		best = {
+			"start_idx": int(start_i),
+			"end_idx": int(end_i),
+			"high": hi,
+			"low": lo,
+			"mid": mid,
+			"active": bool(active),
+			"atr": last_atr,
+			"tol": tol,
+			"bars": int(length),
+		}
+		break
+
+	if best is None:
+		return None
+
+	def _ts(row) -> Any:
+		if "date" in work.columns and pd.notna(row["date"]):
+			return row["date"]
+		if "timestamp" in work.columns:
+			return row["timestamp"]
+		return None
+
+	best["start_time"] = _ts(work.iloc[best["start_idx"]])
+	# 区间时间结束取 core 末，事件可落在其后
+	best["end_time"] = _ts(work.iloc[best["end_idx"]])
+	offset = len(df) - len(work)
+	best["abs_start_idx"] = offset + best["start_idx"]
+	best["abs_end_idx"] = offset + best["end_idx"]
+	best["abs_scan_end_idx"] = offset + n - 1
+	return best