import os
import sys

sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")))

import pandas as pd

from .test_helpers import check_and_notify


def test_compute_macd_hist_monkeypatch(monkeypatch):
    # Arrange: monkeypatch pandas_ta.macd to return a DataFrame with MACDh column
    dummy_macd = pd.DataFrame({"MACDh_12_26_9": [None, 0.5, 1.2, 2.3]})

    def fake_macd(series, fast, slow, signal):
        return dummy_macd

    # 올바른 모듈 경로로 monkey patch
    monkeypatch.setattr("src.indicators.ta.macd", fake_macd)

    close = pd.Series([1, 2, 3, 4])

    # Act: import directly from indicators
    from src.indicators import compute_macd_hist

    hist = compute_macd_hist(close)

    # Assert
    assert isinstance(hist, pd.Series)
    assert list(hist.dropna()) == [0.5, 1.2, 2.3]


def test_check_and_notify_positive_sends(monkeypatch):
    # Prepare a fake OHLCV DataFrame with required OHLCV columns
    idx = pd.date_range(end=pd.Timestamp.now(), periods=250, freq="h")
    df = pd.DataFrame(
        {
            "open": list(range(100, 350)),
            "high": list(range(105, 355)),
            "low": list(range(95, 345)),
            "close": list(range(100, 350)),
            "volume": [1000] * 250,
        },
        index=idx,
    )

    # Monkeypatch at the point of use: src.signals imports from indicators
    from src import signals

    # Patch fetch_ohlcv to return complete OHLCV data
    monkeypatch.setattr(signals, "fetch_ohlcv", lambda symbol, timeframe, limit=200, log_buffer=None: df)

    # Fake pandas_ta.macd to return MACD crossover (signal cross)
    def fake_macd(close, fast=12, slow=26, signal=9):
        macd_df = pd.DataFrame(index=close.index)
        # Create crossover: prev < signal, curr > signal
        macd_values = [-0.5] * (len(close) - 1) + [1.5]  # Last value crosses above
        signal_values = [0.5] * len(close)  # Constant signal line
        macd_df["MACD_12_26_9"] = pd.Series(macd_values, index=close.index)
        macd_df["MACDs_12_26_9"] = pd.Series(signal_values, index=close.index)
        macd_df["MACDh_12_26_9"] = pd.Series(
            [v - s for v, s in zip(macd_values, signal_values, strict=True)], index=close.index
        )
        return macd_df

    monkeypatch.setattr(signals.ta, "macd", fake_macd)

    # Fake pandas_ta.adx to return valid ADX data
    def fake_adx(high, low, close, length=14):
        adx_df = pd.DataFrame(index=close.index)
        adx_df[f"ADX_{length}"] = pd.Series([30.0] * len(close), index=close.index)
        return adx_df

    monkeypatch.setattr(signals.ta, "adx", fake_adx)

    # Capture calls to safe_send_telegram
    called = {"count": 0}

    def fake_safe_send(token, chat_id, text, **kwargs):
        called["count"] += 1
        return True

    # Monkeypatch test_helpers module
    from . import test_helpers

    monkeypatch.setattr(test_helpers, "safe_send_telegram", fake_safe_send)

    # Act: call check_and_notify (not dry-run)
    check_and_notify("upbit", "KRW-BTC", "1h", "token", "chat", limit=10, dry_run=False)

    # Assert: safe_send_telegram was called
    assert called["count"] == 1