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https://github.com/wassname/catalyst.git
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lenak25
176 lines
8.1 KiB
Python
176 lines
8.1 KiB
Python
from catalyst.exchange.utils.exchange_utils import transform_candles_to_df, \
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forward_fill_df_if_needed, get_candles_df
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from catalyst.testing.fixtures import WithLogger, ZiplineTestCase
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from datetime import timedelta
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from pandas import Timestamp, DataFrame, concat
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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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@classmethod
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def verify_forward_fill_df_if_needed(cls, candles, periods, expected_df):
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observed_df = forward_fill_df_if_needed(
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transform_candles_to_df(candles),
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periods)
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assert (expected_df.equals(observed_df))
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@classmethod
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def verify_get_candles_df(cls, assets, candles, end_fixed_dt,
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expected_df, check_next_candle=False):
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# run on all the fields
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for field in ['volume', 'open', 'close', 'high', 'low']:
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field_dt = cls.get_specific_field_from_df(expected_df,
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field,
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assets[0])
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# run on several timestamps
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for delta in range(5):
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end_dt = end_fixed_dt + timedelta(minutes=delta)
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assert (field_dt.equals(get_candles_df({assets[0]: candles},
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field, '5T', 3,
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end_dt=end_dt)))
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field_dt_a1 = cls.get_specific_field_from_df(expected_df,
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field,
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assets[0])
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field_dt_a2 = cls.get_specific_field_from_df(expected_df,
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field,
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assets[1])
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observed_df = get_candles_df({assets[0]: candles,
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assets[1]: candles},
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field, '5T', 3,
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end_dt=end_dt)
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assert (observed_df.equals(concat([field_dt_a1, field_dt_a2],
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axis=1)))
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if check_next_candle:
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# one candle forward
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end_dt = end_fixed_dt + timedelta(minutes=6)
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observed_df = get_candles_df({assets[0]: candles,
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assets[1]: candles},
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field, '5T', 3,
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end_dt=end_dt)
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assert (not observed_df.equals(concat([field_dt_a1,
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field_dt_a2],
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axis=1)))
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assert (concat([field_dt_a1, field_dt_a2],
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axis=1)[1:].equals(observed_df[:-1]))
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def test_get_candles_df(self):
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assets = ['btc_usdt', 'eth_usdt']
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# test forward fill in the end
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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',
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tz='UTC')
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},
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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',
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tz='UTC')
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}]
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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',
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tz='UTC')
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},
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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',
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tz='UTC')
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},
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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',
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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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expected_df = transform_candles_to_df(expected)
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self.verify_forward_fill_df_if_needed(candles, periods,
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expected_df)
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self.verify_get_candles_df(assets, candles, periods[2],
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expected_df, True)
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# test forward fill in the middle
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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',
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tz='UTC')
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},
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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',
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tz='UTC')
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}]
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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',
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tz='UTC')
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},
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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',
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tz='UTC')
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},
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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',
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tz='UTC')
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}]
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expected_df = transform_candles_to_df(expected)
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self.verify_forward_fill_df_if_needed(candles, periods, expected_df)
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self.verify_get_candles_df(assets, candles, periods[2], expected_df)
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# test "forward fill" at the beginning
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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',
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tz='UTC')
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},
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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',
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tz='UTC')
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}]
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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',
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tz='UTC')
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},
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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',
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tz='UTC')
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},
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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',
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tz='UTC')
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}]
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expected_df = transform_candles_to_df(expected)
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self.verify_forward_fill_df_if_needed(candles, periods, expected_df)
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# Not the same due to dropna - commenting out for now
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# self.verify_get_candles_df(assets, candles, periods[2], expected_df)
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