62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
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import pandas as pd
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import numpy as np
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from freqtrade.strategy import merge_informative_pairs, timeframe_to_minutes
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def generate_test_data(timeframe: str, size: int):
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np.random.seed(42)
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tf_mins = timeframe_to_minutes(timeframe)
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base = np.random.normal(20, 2, size=size)
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date = pd.period_range('2020-07-05', periods=size, freq=f'{tf_mins}min').to_timestamp()
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df = pd.DataFrame({
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'date': date,
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'open': base,
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'high': base + np.random.normal(2, 1, size=size),
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'low': base - np.random.normal(2, 1, size=size),
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'close': base + np.random.normal(0, 1, size=size),
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'volume': np.random.normal(200, size=size)
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}
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)
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df = df.dropna()
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return df
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def test_merge_informative_pairs():
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data = generate_test_data('15m', 40)
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informative = generate_test_data('1h', 40)
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result = merge_informative_pairs(data, informative, '1h', ffill=True)
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assert isinstance(result, pd.DataFrame)
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assert len(result) == len(data)
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assert 'date' in result.columns
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assert result['date'].equals(data['date'])
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assert 'date_1h' in result.columns
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assert 'open' in result.columns
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assert 'open_1h' in result.columns
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assert result['open'].equals(data['open'])
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assert 'close' in result.columns
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assert 'close_1h' in result.columns
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assert result['close'].equals(data['close'])
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assert 'volume' in result.columns
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assert 'volume_1h' in result.columns
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assert result['volume'].equals(data['volume'])
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# First 4 rows are empty
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assert result.iloc[0]['date_1h'] is pd.NaT
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assert result.iloc[1]['date_1h'] is pd.NaT
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assert result.iloc[2]['date_1h'] is pd.NaT
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assert result.iloc[3]['date_1h'] is pd.NaT
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# Next 4 rows contain the starting date (0:00)
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assert result.iloc[4]['date_1h'] == result.iloc[0]['date']
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assert result.iloc[5]['date_1h'] == result.iloc[0]['date']
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assert result.iloc[6]['date_1h'] == result.iloc[0]['date']
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assert result.iloc[7]['date_1h'] == result.iloc[0]['date']
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# Next 4 rows contain the next Hourly date original date row 4
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assert result.iloc[8]['date_1h'] == result.iloc[4]['date']
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