import pandas as pd
import numpy as np

from freqtrade.strategy import merge_informative_pair, timeframe_to_minutes


def generate_test_data(timeframe: str, size: int):
    np.random.seed(42)
    tf_mins = timeframe_to_minutes(timeframe)

    base = np.random.normal(20, 2, size=size)

    date = pd.period_range('2020-07-05', periods=size, freq=f'{tf_mins}min').to_timestamp()
    df = pd.DataFrame({
        'date': date,
        'open': base,
        'high': base + np.random.normal(2, 1, size=size),
        'low': base - np.random.normal(2, 1, size=size),
        'close': base + np.random.normal(0, 1, size=size),
        'volume': np.random.normal(200, size=size)
    }
    )
    df = df.dropna()
    return df


def test_merge_informative_pair():
    data = generate_test_data('15m', 40)
    informative = generate_test_data('1h', 40)

    result = merge_informative_pair(data, informative, '15m', '1h', ffill=True)
    assert isinstance(result, pd.DataFrame)
    assert len(result) == len(data)
    assert 'date' in result.columns
    assert result['date'].equals(data['date'])
    assert 'date_1h' in result.columns

    assert 'open' in result.columns
    assert 'open_1h' in result.columns
    assert result['open'].equals(data['open'])

    assert 'close' in result.columns
    assert 'close_1h' in result.columns
    assert result['close'].equals(data['close'])

    assert 'volume' in result.columns
    assert 'volume_1h' in result.columns
    assert result['volume'].equals(data['volume'])

    # First 4 rows are empty
    assert result.iloc[0]['date_1h'] is pd.NaT
    assert result.iloc[1]['date_1h'] is pd.NaT
    assert result.iloc[2]['date_1h'] is pd.NaT
    assert result.iloc[3]['date_1h'] is pd.NaT
    # Next 4 rows contain the starting date (0:00)
    assert result.iloc[4]['date_1h'] == result.iloc[0]['date']
    assert result.iloc[5]['date_1h'] == result.iloc[0]['date']
    assert result.iloc[6]['date_1h'] == result.iloc[0]['date']
    assert result.iloc[7]['date_1h'] == result.iloc[0]['date']
    # Next 4 rows contain the next Hourly date original date row 4
    assert result.iloc[8]['date_1h'] == result.iloc[4]['date']


def test_merge_informative_pair_same():
    data = generate_test_data('15m', 40)
    informative = generate_test_data('15m', 40)

    result = merge_informative_pair(data, informative, '15m', '15m', ffill=True)
    assert isinstance(result, pd.DataFrame)
    assert len(result) == len(data)
    assert 'date' in result.columns
    assert result['date'].equals(data['date'])
    assert 'date_15m' in result.columns

    assert 'open' in result.columns
    assert 'open_15m' in result.columns
    assert result['open'].equals(data['open'])

    assert 'close' in result.columns
    assert 'close_15m' in result.columns
    assert result['close'].equals(data['close'])

    assert 'volume' in result.columns
    assert 'volume_15m' in result.columns
    assert result['volume'].equals(data['volume'])

    # Dates match 1:1
    assert result['date_15m'].equals(result['date'])