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Add test for behavior in #9490.
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@@ -61,6 +61,32 @@ def test_merge_informative_pair():
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assert result.iloc[8]['date_1h'] is pd.NaT
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def test_merge_informative_pair_high_tf():
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# Covers roughly 2 months - until 2023-01-10
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data = generate_test_data('1h', 1040, '2022-11-28')
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informative = generate_test_data('1M', 40, '2022-01-01')
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result = merge_informative_pair(data, informative, '1h', '1M', ffill=True)
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assert isinstance(result, pd.DataFrame)
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candle1 = result.loc[(result['date'] == '2022-12-31T22:00:00.000Z')]
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assert candle1.iloc[0]['date'] == pd.Timestamp('2022-12-31T22:00:00.000Z')
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assert candle1.iloc[0]['date_1M'] == pd.Timestamp('2022-11-01T00:00:00.000Z')
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candle2 = result.loc[(result['date'] == '2022-12-31T23:00:00.000Z')]
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assert candle2.iloc[0]['date'] == pd.Timestamp('2022-12-31T23:00:00.000Z')
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assert candle2.iloc[0]['date_1M'] == pd.Timestamp('2022-12-01T00:00:00.000Z')
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# Candle is empty, as the start-date did fail.
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candle3 = result.loc[(result['date'] == '2022-11-30T22:00:00.000Z')]
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assert candle3.iloc[0]['date'] == pd.Timestamp('2022-11-30T22:00:00.000Z')
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assert candle3.iloc[0]['date_1M'] is pd.NaT
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# First candle with 1M data merged.
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candle4 = result.loc[(result['date'] == '2022-11-30T23:00:00.000Z')]
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assert candle4.iloc[0]['date'] == pd.Timestamp('2022-11-30T23:00:00.000Z')
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assert candle4.iloc[0]['date_1M'] == pd.Timestamp('2022-11-01T00:00:00.000Z')
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def test_merge_informative_pair_same():
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data = generate_test_data('15m', 40)
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informative = generate_test_data('15m', 40)
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