"""威科夫分析入口。"""
from __future__ import annotations

from typing import Any, Dict, Optional

import pandas as pd

from .events import build_phases, detect_bias_and_events
from .range import detect_trading_range
from .volume_profile import compute_volume_profile


def _fmt_time(v) -> Optional[str]:
	if v is None:
		return None
	if hasattr(v, "isoformat"):
		try:
			return v.isoformat()
		except Exception:
			pass
	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 = {
		"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": {}},
	}
	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

	bias, events, volume_confirm = detect_bias_and_events(work, tr)
	phases = build_phases(work, tr, bias, events)
	vp = compute_volume_profile(
		work,
		int(tr["abs_start_idx"]),
		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"))

	return {
		"trading_range": trading_range,
		"bias": bias,
		"phases": phases,
		"events": events,
		"volume_profile": vp,
		"volume_confirm": volume_confirm,
	}