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105 lines
5.1 KiB
Python
105 lines
5.1 KiB
Python
from catalyst.exchange.utils.exchange_utils import transform_candles_to_df, forward_fill_df_if_needed, get_candles_df
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from catalyst.testing.fixtures import WithLogger, ZiplineTestCase
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from pandas import Timestamp, Series, DataFrame
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import numpy as np
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class TestExchangeUtils(WithLogger, ZiplineTestCase):
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@classmethod
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def get_specific_field_from_df(cls, df, field, asset):
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new_df = DataFrame(df[field])
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new_df.columns = [asset]
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new_df.index.name = None
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return new_df
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def test_transform_candles_to_series(self):
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asset = 'btc_usdt'
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candles = [{'high': 595, 'volume': 10, 'low': 594,
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'close': 595, 'open': 594,
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'last_traded': Timestamp('2018-03-01 09:45:00+0000', tz='UTC')},
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{'high': 594, 'volume': 108, 'low': 592,
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'close': 593, 'open': 592,
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'last_traded': Timestamp('2018-03-01 09:50:00+0000', tz='UTC')}]
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expected = [{'high': 595.0, 'volume': 10.0, 'low': 594.0,
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'close': 595.0, 'open': 594.0,
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'last_traded': Timestamp('2018-03-01 09:45:00+0000', tz='UTC')},
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{'high': 594.0, 'volume': 108.0, 'low': 592.0,
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'close': 593.0, 'open': 592.0,
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'last_traded': Timestamp('2018-03-01 09:50:00+0000', tz='UTC')},
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{'high': 593.0, 'volume': 0.0, 'low': 593.0,
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'close': 593.0, 'open': 593.0,
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'last_traded': Timestamp('2018-03-01 09:55:00+0000', tz='UTC')}
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]
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periods = [Timestamp('2018-03-01 09:45:00+0000', tz='UTC'),
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Timestamp('2018-03-01 09:50:00+0000', tz='UTC'),
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Timestamp('2018-03-01 09:55:00+0000', tz='UTC')]
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observed_df = forward_fill_df_if_needed(transform_candles_to_df(candles), periods)
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expected_df = transform_candles_to_df(expected)
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assert (expected_df.equals(observed_df))
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for field in ['volume', 'open', 'close', 'high', 'low']:
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assert(self.get_specific_field_from_df(observed_df, field, asset).equals(
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get_candles_df({asset:candles}, field, '5T', 3, end_dt=periods[2])))
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candles = [{'high': 595, 'volume': 10, 'low': 594,
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'close': 595, 'open': 594,
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'last_traded': Timestamp('2018-03-01 09:45:00+0000', tz='UTC')},
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{'high': 594, 'volume': 108, 'low': 592,
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'close': 593, 'open': 592,
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'last_traded': Timestamp('2018-03-01 09:55:00+0000', tz='UTC')}]
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expected = [{'high': 595.0, 'volume': 10.0, 'low': 594.0,
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'close': 595.0, 'open': 594.0,
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'last_traded': Timestamp('2018-03-01 09:45:00+0000', tz='UTC')},
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{'high': 595.0, 'volume': 0.0, 'low': 595.0,
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'close': 595.0, 'open': 595.0,
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'last_traded': Timestamp('2018-03-01 09:50:00+0000', tz='UTC')},
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{'high': 594.0, 'volume': 108.0, 'low': 592.0,
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'close': 593.0, 'open': 592.0,
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'last_traded': Timestamp('2018-03-01 09:55:00+0000', tz='UTC')}
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]
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df = transform_candles_to_df(candles)
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observed_df = forward_fill_df_if_needed(df, periods)
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assert (transform_candles_to_df(expected).equals(observed_df))
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for field in ['volume', 'open', 'close', 'high', 'low']:
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assert(self.get_specific_field_from_df(observed_df, field, asset).equals(
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get_candles_df({asset:candles}, field, '5T', 3, end_dt=periods[2])))
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candles = [{'high': 595, 'volume': 10, 'low': 594,
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'close': 595, 'open': 594,
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'last_traded': Timestamp('2018-03-01 09:50:00+0000', tz='UTC')},
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{'high': 594, 'volume': 108, 'low': 592,
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'close': 593, 'open': 592,
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'last_traded': Timestamp('2018-03-01 09:55:00+0000', tz='UTC')}]
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expected = [{'high': np.NaN, 'volume': 0.0, 'low': np.NaN,
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'close': np.NaN, 'open': np.NaN,
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'last_traded': Timestamp('2018-03-01 09:45:00+0000', tz='UTC')},
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{'high': 595, 'volume': 10, 'low': 594,
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'close': 595, 'open': 594,
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'last_traded': Timestamp('2018-03-01 09:50:00+0000', tz='UTC')},
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{'high': 594, 'volume': 108, 'low': 592,
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'close': 593, 'open': 592,
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'last_traded': Timestamp('2018-03-01 09:55:00+0000', tz='UTC')}
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]
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df = transform_candles_to_df(candles)
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observed_df = forward_fill_df_if_needed(df, periods)
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assert (transform_candles_to_df(expected).equals(observed_df))
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# Not the same due to dropna - commenting out for now
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"""
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for field in ['volume', 'open', 'close', 'high', 'low']:
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assert(self.get_specific_field_from_df(observed_df, field, asset).equals(
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get_candles_df({asset:candles}, field, '5T', 3, end_dt=periods[2])))
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""" |