mirror of
https://github.com/wassname/catalyst.git
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lenak25
282 lines
9.0 KiB
Python
282 lines
9.0 KiB
Python
import random
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import os
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import pandas as pd
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from datetime import timedelta
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from logbook import TestHandler
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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.utils.exchange_utils import get_candles_df
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from catalyst.exchange.utils.factory import get_exchange
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from catalyst.exchange.utils.test_utils import output_df, \
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select_random_assets
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from catalyst.exchange.utils.stats_utils import set_print_settings
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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(exchanges):
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open_calendar = get_calendar('OPEN')
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asset_finder = ExchangeAssetFinder(exchanges)
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exchange_names = [exchange.name for exchange in exchanges]
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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, field):
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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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freq
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data_frequency
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data_portal
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Returns
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-------
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"""
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data = dict()
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log_catcher = TestHandler()
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with log_catcher:
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symbols = [asset.symbol for asset in assets]
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print(
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'comparing {} for {}/{} with {} timeframe until {}'.format(
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field, exchange.name, symbols, freq, end_dt
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)
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)
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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=field,
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data_frequency=data_frequency,
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)
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set_print_settings()
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print(
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'the bundle data:\n{}'.format(
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data['bundle']
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)
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)
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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=field,
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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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print(
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'the exchange data:\n{}'.format(
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data['exchange']
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)
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)
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for source in data:
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df = data[source]
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path, folder = output_df(
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df, assets, '{}_{}'.format(freq, source)
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)
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print('saved {} test results: {}'.format(end_dt, folder))
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assert_frame_equal(
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right=data['bundle'][:-1],
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left=data['exchange'][:-1],
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check_less_precise=1,
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)
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try:
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assert_frame_equal(
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right=data['bundle'][:-1],
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left=data['exchange'][:-1],
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check_less_precise=min([a.decimals for a in assets]),
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)
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except Exception as e:
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print(
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'Some differences were found within a 1 decimal point '
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'interval of confidence: {}'.format(e)
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)
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with open(os.path.join(folder, 'compare.txt'), 'w+') as handle:
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handle.write(e.args[0])
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pass
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def compare_current_with_last_candle(self, exchange, assets, end_dt,
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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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freq
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data_frequency
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data_portal
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Returns
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-------
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"""
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data = dict()
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assets = sorted(assets, key=lambda a: a.symbol)
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log_catcher = TestHandler()
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with log_catcher:
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symbols = [asset.symbol for asset in assets]
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print(
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'comparing data for {}/{} with {} timeframe on {}'.format(
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exchange.name, symbols, freq, end_dt
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)
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)
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data['candle'] = 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=1,
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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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set_print_settings()
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print(
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'the bundle first / last row:\n{}'.format(
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data['candle'].iloc[[-1]]
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)
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)
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current = data_portal.get_spot_value(
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assets=assets,
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field='close',
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dt=end_dt,
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data_frequency=data_frequency,
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)
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data['current'] = pd.Series(data=current, index=assets)
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print(
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'the current price:\n{}'.format(
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data['current']
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)
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)
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pass
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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'])
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# bundle = 'dailyBundle' if data_frequency
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# == '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('poloniex', skip_init=True)]
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data_portal = TestSuiteBundle.get_data_portal(exchanges)
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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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rnd = random.SystemRandom()
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# field = rnd.choice(['open', 'high', 'low', 'close', 'volume'])
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field = rnd.choice(['volume'])
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bar_count = random.randint(3, 6)
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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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end_dt = end_dt + timedelta(minutes=3)
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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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field=field,
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)
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pass
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def test_validate_last_candle(self):
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# exchange_population = 3
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asset_population = 3
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data_frequency = random.choice(['minute'])
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# bundle = 'dailyBundle' if data_frequency
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# == '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('poloniex', skip_init=True)]
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data_portal = TestSuiteBundle.get_data_portal(exchanges)
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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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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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end_dt = end_dt + timedelta(minutes=3)
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self.compare_current_with_last_candle(
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exchange=exchange,
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assets=assets,
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end_dt=end_dt,
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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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