from datetime import datetime, timedelta

import pandas as pd

from app.core.signal_taxonomy import signal_code
from app.services.altcoin_confirm import detect_box_breakout_pullback_1h, detect_box_breakout_pullback_4h


def _bar(ts, open_, high, low, close, volume):
    return {
        "timestamp": ts,
        "open": open_,
        "high": high,
        "low": low,
        "close": close,
        "volume": volume,
    }


def _box_breakout_rows(fail=False):
    start = datetime(2026, 5, 20)
    rows = []
    for i in range(32):
        base = 1.03 + (i % 5) * 0.006
        rows.append(_bar(start + timedelta(hours=4 * i), base, 1.095 + (i % 3) * 0.002, 0.985 - (i % 2) * 0.002, base + 0.004, 1000 + (i % 4) * 30))

    rows.append(_bar(start + timedelta(hours=4 * 32), 1.085, 1.16, 1.08, 1.135, 2600))
    if fail:
        rows.append(_bar(start + timedelta(hours=4 * 33), 1.13, 1.135, 1.00, 1.055, 1800))
    else:
        rows.append(_bar(start + timedelta(hours=4 * 33), 1.13, 1.145, 1.092, 1.118, 1600))

    for i in range(12):
        close = (1.04 + i * 0.002) if fail else (1.12 + i * 0.008)
        rows.append(_bar(start + timedelta(hours=4 * (34 + i)), close - 0.01, close + 0.018, close - 0.025, close, 1300 + i * 40))
    return rows


def test_detect_box_breakout_pullback_4h_finds_box_top_retest():
    df = pd.DataFrame(_box_breakout_rows())

    result = detect_box_breakout_pullback_4h(df)

    assert result["detected"] is True
    assert result["score"] >= 7
    assert result["quality"] in {"良好", "优质"}
    assert result["entry_zone"] > 1.08
    assert result["stop_level"] < result["entry_zone"]
    assert "箱体" in result["signals"][0]
    assert signal_code(result["signals"][0]) == "box_breakout_pullback_4h"


def test_detect_box_breakout_pullback_4h_rejects_failed_retest():
    df = pd.DataFrame(_box_breakout_rows(fail=True))

    result = detect_box_breakout_pullback_4h(df)

    assert result["detected"] is False


def test_detect_box_breakout_pullback_1h_finds_early_retest():
    start = datetime(2026, 5, 20)
    rows = []
    for i in range(44):
        base = 1.0 + (i % 4) * 0.003
        rows.append(_bar(start + timedelta(hours=i), base, 1.035 + (i % 3) * 0.001, 0.985 - (i % 2) * 0.001, base + 0.002, 1000 + (i % 5) * 20))

    rows.append(_bar(start + timedelta(hours=44), 1.032, 1.085, 1.03, 1.068, 2300))
    rows.append(_bar(start + timedelta(hours=45), 1.064, 1.073, 1.034, 1.055, 1700))
    for i in range(8):
        close = 1.058 + i * 0.004
        rows.append(_bar(start + timedelta(hours=46 + i), close - 0.006, close + 0.012, close - 0.018, close, 1250 + i * 25))

    result = detect_box_breakout_pullback_1h(pd.DataFrame(rows))

    assert result["detected"] is True
    assert result["score"] >= 7
    assert result["quality"] in {"良好", "优质"}
    assert signal_code(result["signals"][0]) == "box_breakout_pullback_1h"