"""威科夫阶段与事件（启发式）。"""
from __future__ import annotations

from typing import Any, Dict, List, Tuple

import numpy as np
import pandas as pd


def _bar_time(df: pd.DataFrame, i: int):
	row = df.iloc[i]
	if "date" in df.columns and pd.notna(row["date"]):
		return row["date"]
	if "timestamp" in df.columns:
		return row["timestamp"]
	return i


def _avg_vol(df: pd.DataFrame, i: int, win: int = 20) -> float:
	a = max(0, i - win + 1)
	v = df["volume"].astype(float).iloc[a : i + 1]
	m = float(v.mean()) if len(v) else 0.0
	return m if m > 0 else 1.0


def detect_bias_and_events(
	df: pd.DataFrame,
	tr: Dict[str, Any],
) -> Tuple[str, List[Dict[str, Any]], Dict[str, Any]]:
	"""
	返回 bias、events、volume_confirm。
	"""
	hi = float(tr["high"])
	lo = float(tr["low"])
	mid = float(tr["mid"])
	tol = float(tr.get("tol") or (hi - lo) * 0.05)
	s = int(tr["abs_start_idx"])
	e = int(tr["abs_end_idx"])
	events: List[Dict[str, Any]] = []

	# 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))
	spring = None
	utad = None
	sos = None
	sod = None  # sign of weakness / distribution breakdown
	lps = None
	lpsy = None

	for i in range(s + 2, scan_end + 1):
		row = df.iloc[i]
		low = float(row["low"])
		high = float(row["high"])
		close = float(row["close"])
		vol = float(row["volume"]) if "volume" in df.columns else 0.0
		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:
			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",
				"time": _bar_time(df, i),
				"price": low,
				"note": "假破下沿后收回",
				"volume_ratio": round(ratio, 3),
				"volume_ok": bool(vol_ok),
				"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:
			vol_ok = ratio >= 0.8
			utad = {
				"type": "UTAD",
				"time": _bar_time(df, i),
				"price": high,
				"note": "假破上沿后跌回",
				"volume_ratio": round(ratio, 3),
				"volume_ok": bool(vol_ok),
				"idx": i,
			}

		# SOS: close above high with volume
		if sos is None and close > hi + tol * 0.15:
			vol_ok = ratio >= 1.15
			sos = {
				"type": "SOS",
				"time": _bar_time(df, i),
				"price": close,
				"note": "放量上破交易区间",
				"volume_ratio": round(ratio, 3),
				"volume_ok": bool(vol_ok),
				"idx": i,
			}

		# SOW / breakdown
		if sod is None and close < lo - tol * 0.15:
			vol_ok = ratio >= 1.15
			sod = {
				"type": "SOW",
				"time": _bar_time(df, i),
				"price": close,
				"note": "放量下破交易区间",
				"volume_ratio": round(ratio, 3),
				"volume_ok": bool(vol_ok),
				"idx": i,
			}

	# LPS after SOS: pullback that holds above mid/high-band with lighter volume
	if sos is not None:
		si = int(sos["idx"])
		for i in range(si + 1, min(len(df), si + 25)):
			row = df.iloc[i]
			low = float(row["low"])
			close = float(row["close"])
			vol = float(row["volume"]) if "volume" in df.columns else 0.0
			avg_v = _avg_vol(df, i)
			ratio = vol / avg_v if avg_v else 0.0
			if low >= mid - tol and close >= hi - tol * 2:
				vol_ok = ratio <= 1.05
				lps = {
					"type": "LPS",
					"time": _bar_time(df, i),
					"price": low,
					"note": "突破后缩量回踩不破",
					"volume_ratio": round(ratio, 3),
					"volume_ok": bool(vol_ok),
					"idx": i,
				}
				break

	if sod is not None:
		si = int(sod["idx"])
		for i in range(si + 1, min(len(df), si + 25)):
			row = df.iloc[i]
			high = float(row["high"])
			close = float(row["close"])
			vol = float(row["volume"]) if "volume" in df.columns else 0.0
			avg_v = _avg_vol(df, i)
			ratio = vol / avg_v if avg_v else 0.0
			if high <= mid + tol and close <= lo + tol * 2:
				vol_ok = ratio <= 1.05
				lpsy = {
					"type": "LPSY",
					"time": _bar_time(df, i),
					"price": high,
					"note": "下跌突破后缩量反抽不过",
					"volume_ratio": round(ratio, 3),
					"volume_ok": bool(vol_ok),
					"idx": i,
				}
				break

	for ev in (spring, sos, lps, utad, sod, lpsy):
		if ev:
			events.append({k: v for k, v in ev.items() if k != "idx"})

	# bias
	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))):
		bias = "accumulation"
	elif sod and (not sos or int(sod.get("idx", 0)) > int(sos.get("idx", 0))):
		bias = "distribution"
	elif spring and not utad:
		bias = "accumulation"
	elif utad and not spring:
		bias = "distribution"
	elif last_c >= mid:
		bias = "accumulation"
	else:
		bias = "distribution"

	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,
		"event_checks": {ev["type"]: {"volume_ok": ev.get("volume_ok"), "volume_ratio": ev.get("volume_ratio")} for ev in events},
	}
	return bias, events, volume_confirm


def build_phases(
	df: pd.DataFrame,
	tr: Dict[str, Any],
	bias: str,
	events: List[Dict[str, Any]],
) -> List[Dict[str, Any]]:
	"""按时间切分 A–E 粗阶段。"""
	s = int(tr["abs_start_idx"])
	e = int(tr["abs_end_idx"])
	hi = float(tr["high"])
	lo = float(tr["low"])
	mid = float(tr["mid"])
	tol = float(tr.get("tol") or (hi - lo) * 0.05)

	event_idx = {}
	for ev in events:
		# 找回 idx 近似：按时间匹配
		t = ev.get("time")
		for i in range(s, min(len(df), e + 20)):
			if _bar_time(df, i) == t:
				event_idx[ev["type"]] = i
				break

	# 分段点
	a_end = s + max(3, (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
	e_start = d_anchor

	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:
			m = {"A": "A停止下跌", "B": "B筑底", "C": "C测试", "D": "D拉升", "E": "E离开"}
		return m.get(phase, phase)

	cuts = [
		("A", s, a_end),
		("B", a_end, max(a_end + 1, c_anchor)),
		("C", max(a_end + 1, c_anchor), max(c_anchor + 1, d_anchor)),
		("D", max(c_anchor + 1, d_anchor), max(d_anchor + 1, min(len(df) - 1, e_start + max(3, (e - s) // 6)))),
		("E", max(d_anchor, e_start), min(len(df) - 1, max(e, e_start + 5))),
	]
	phases = []
	for phase, a, b in cuts:
		a = int(np.clip(a, 0, len(df) - 1))
		b = int(np.clip(b, a, len(df) - 1))
		phases.append(
			{
				"phase": phase,
				"label": _lab(phase),
				"start_time": _bar_time(df, a),
				"end_time": _bar_time(df, b),
			}
		)
	return phases