"""威科夫引擎单测：合成震荡箱 + Spring/SOS + VP POC（ECR-004 收紧）。"""
from __future__ import annotations

import sys
from pathlib import Path

import numpy as np
import pandas as pd

ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))

from chanlun.analysis.wyckoff import analyze_wyckoff  # noqa: E402
from chanlun.analysis.wyckoff.range import detect_trading_range  # noqa: E402


def _box_df(n_box: int = 60, spring: bool = True, sos: bool = True) -> pd.DataFrame:
	"""构造明显箱体：40~60，前 20 根下跌趋势，可选假破与上破。"""
	rng = np.random.default_rng(7)
	rows = []
	t0 = pd.Timestamp("2024-06-01", tz="UTC")
	price = 50.0
	# 进入箱体前下跌
	for i in range(20):
		price -= 0.3 + rng.random() * 0.1
		o, c = price + 0.2, price
		h, l = max(o, c) + 0.15, min(o, c) - 0.15
		rows.append((t0 + pd.Timedelta(minutes=5 * i), o, h, l, c, 100 + rng.random() * 20))
	# 箱体 40-60
	lo, hi = 40.0, 60.0
	for i in range(n_box):
		c = lo + (hi - lo) * (0.3 + 0.4 * rng.random())
		o = c + rng.normal(0, 0.5)
		h = min(hi + 0.5, max(o, c) + abs(rng.normal(0.5, 0.2)))
		l = max(lo - 0.5, min(o, c) - abs(rng.normal(0.5, 0.2)))
		if i % 7 == 0:
			h = hi - 0.1
		if i % 7 == 3:
			l = lo + 0.1
		rows.append(
			(
				t0 + pd.Timedelta(minutes=5 * (20 + i)),
				o,
				h,
				l,
				c,
				80 + rng.random() * 40,
			)
		)
	base = 20 + n_box
	if spring:
		rows.append(
			(
				t0 + pd.Timedelta(minutes=5 * base),
				42.0,
				43.0,
				37.0,
				41.5,
				90.0,
			)
		)
		base += 1
	if sos:
		rows.append(
			(
				t0 + pd.Timedelta(minutes=5 * base),
				58.0,
				66.0,
				57.0,
				64.0,
				220.0,
			)
		)
		base += 1
		rows.append(
			(
				t0 + pd.Timedelta(minutes=5 * base),
				62.0,
				63.0,
				59.5,
				61.0,
				70.0,
			)
		)

	return pd.DataFrame(rows, columns=["date", "open", "high", "low", "close", "volume"])


def test_wyckoff_detects_range_and_events():
	df = _box_df()
	out = analyze_wyckoff(df, lookback=200)
	assert out["trading_range"] is not None
	tr = out["trading_range"]
	assert 38.0 <= tr["low"] <= 42.0
	assert 58.0 <= tr["high"] <= 62.0
	# 起点不应落入前 20 根下跌段（允许少量 overlap）
	box_start = df["date"].iloc[20]
	assert tr["start_time"] is not None
	start_ts = pd.Timestamp(tr["start_time"])
	assert start_ts >= box_start - pd.Timedelta(minutes=5 * 8)
	types = {e["type"] for e in out["events"]}
	assert "Spring" in types
	assert "SOS" in types
	assert out["bias"] in ("accumulation", "distribution", "unknown")
	assert len(out["phases"]) >= 3
	keys = [(p["start_time"], p["end_time"]) for p in out["phases"]]
	assert len(keys) == len(set(keys)), "phases must not share identical start/end"

def test_range_scoring_skips_pretrend():
	df = _box_df(spring=False, sos=False)
	tr = detect_trading_range(df, lookback=200)
	assert tr is not None
	assert tr["abs_start_idx"] >= 12  # 不应从 bar 0 吞掉整段下跌


def test_volume_profile_poc_on_heavy_bin():
	dates = pd.date_range("2024-01-01", periods=40, freq="5min", tz="UTC")
	rows = []
	for i, d in enumerate(dates):
		c = 50.0 + (i % 5) * 0.1
		vol = 1000.0 if 49.8 <= c <= 50.2 else 10.0
		rows.append((d, c, c + 0.2, c - 0.2, c, vol))
	df = pd.DataFrame(rows, columns=["date", "open", "high", "low", "close", "volume"])
	out = analyze_wyckoff(df, lookback=80, vp_bins=20)
	vp = out["volume_profile"]
	assert vp["poc"] is not None
	assert vp["vah"] is not None and vp["val"] is not None
	assert abs(vp["poc"] - 50.0) < 1.0