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https://github.com/wassname/catalyst.git
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Frederic Fortier
145 lines
4.4 KiB
Python
145 lines
4.4 KiB
Python
import random
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import pandas as pd
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from logbook import Logger
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from pandas.util.testing import assert_frame_equal
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from catalyst import get_calendar
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from catalyst.exchange.exchange_asset_finder import ExchangeAssetFinder
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from catalyst.exchange.exchange_data_portal import DataPortalExchangeBacktest
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from catalyst.exchange.exchange_utils import get_candles_df
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from catalyst.exchange.factory import get_exchange
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from catalyst.exchange.test_utils import output_df, \
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select_random_assets
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log = Logger('TestSuiteExchange')
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pd.set_option('display.expand_frame_repr', False)
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pd.set_option('precision', 8)
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pd.set_option('display.width', 1000)
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pd.set_option('display.max_colwidth', 1000)
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class TestSuiteBundle:
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@staticmethod
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def get_data_portal(exchange_names):
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open_calendar = get_calendar('OPEN')
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asset_finder = ExchangeAssetFinder()
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data_portal = DataPortalExchangeBacktest(
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exchange_names=exchange_names,
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asset_finder=asset_finder,
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trading_calendar=open_calendar,
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first_trading_day=None # will set dynamically based on assets
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)
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return data_portal
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def compare_bundle_with_exchange(self, exchange, assets, end_dt, bar_count,
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freq, data_frequency, data_portal):
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"""
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Creates DataFrames from the bundle and exchange for the specified
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data set.
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Parameters
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----------
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exchange: Exchange
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assets
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end_dt
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bar_count
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sample_minutes
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Returns
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-------
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"""
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data = dict()
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log.info('creating data sample from bundle')
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data['bundle'] = data_portal.get_history_window(
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assets=assets,
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end_dt=end_dt,
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bar_count=bar_count,
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frequency=freq,
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field='close',
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data_frequency=data_frequency,
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)
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log.info('bundle data:\n{}'.format(
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data['bundle'].tail(10))
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)
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log.info('creating data sample from exchange api')
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candles = exchange.get_candles(
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end_dt=end_dt,
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freq=freq,
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assets=assets,
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bar_count=bar_count,
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)
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data['exchange'] = get_candles_df(
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candles=candles,
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field='close',
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freq=freq,
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bar_count=bar_count,
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end_dt=end_dt,
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)
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log.info('exchange data:\n{}'.format(
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data['exchange'].tail(10))
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)
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for source in data:
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df = data[source]
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path = output_df(df, assets, '{}_{}'.format(freq, source))
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log.info('saved {}:\n{}'.format(source, path))
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assert_frame_equal(
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right=data['bundle'],
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left=data['exchange'],
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check_less_precise=True,
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)
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def test_validate_bundles(self):
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exchange_population = 3
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asset_population = 3
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data_frequency = random.choice(['minute', 'daily'])
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bundle = 'dailyBundle' if data_frequency == 'daily' else 'minuteBundle'
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# exchanges = select_random_exchanges(
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# population=exchange_population,
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# features=[bundle],
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# ) # Type: list[Exchange]
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exchanges = [get_exchange('bitfinex', skip_init=True)]
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data_portal = TestSuiteBundle.get_data_portal(
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[exchange.name for exchange in exchanges]
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)
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for exchange in exchanges:
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exchange.init()
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frequencies = exchange.get_candle_frequencies(data_frequency)
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freq = random.sample(frequencies, 1)[0]
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bar_count = random.randint(1, 10)
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assets = select_random_assets(
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exchange.assets, asset_population
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)
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end_dt = None
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for asset in assets:
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attribute = 'end_{}'.format(data_frequency)
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asset_end_dt = getattr(asset, attribute)
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if end_dt is None or asset_end_dt < end_dt:
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end_dt = asset_end_dt
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dt_range = pd.date_range(
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end=end_dt, periods=bar_count, freq=freq
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)
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self.compare_bundle_with_exchange(
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exchange=exchange,
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assets=assets,
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end_dt=dt_range[-1],
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bar_count=bar_count,
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freq=freq,
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data_frequency=data_frequency,
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data_portal=data_portal,
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)
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pass
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