wehub-resource-sync
95 lines
4.3 KiB
Python
95 lines
4.3 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from AlgorithmImports import *
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### <summary>
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### Regression algorithm demonstrating use of map files with custom data
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="custom data" />
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### <meta name="tag" content="regression test" />
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### <meta name="tag" content="rename event" />
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### <meta name="tag" content="map" />
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### <meta name="tag" content="mapping" />
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### <meta name="tag" content="map files" />
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class CustomDataUsingMapFileRegressionAlgorithm(QCAlgorithm):
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def initialize(self):
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# Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
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self.set_start_date(2013, 6, 27)
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self.set_end_date(2013, 7, 2)
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self.initial_mapping = False
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self.execution_mapping = False
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self.foxa = Symbol.create("FOXA", SecurityType.EQUITY, Market.USA)
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self._symbol = self.add_data(CustomDataUsingMapping, self.foxa).symbol
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for config in self.subscription_manager.subscription_data_config_service.get_subscription_data_configs(self._symbol):
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if config.resolution != Resolution.MINUTE:
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raise ValueError("Expected resolution to be set to Minute")
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def on_data(self, slice):
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date = self.time.date()
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if slice.symbol_changed_events.contains_key(self._symbol):
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mapping_event = slice.symbol_changed_events[self._symbol]
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self.log("{0} - Ticker changed from: {1} to {2}".format(str(self.time), mapping_event.old_symbol, mapping_event.new_symbol))
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if date == datetime(2013, 6, 27).date():
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# we should Not receive the initial mapping event
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if mapping_event.new_symbol != "NWSA" or mapping_event.old_symbol != "FOXA":
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raise AssertionError("Unexpected mapping event mapping_event")
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self.initial_mapping = True
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if date == datetime(2013, 6, 29).date():
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if mapping_event.new_symbol != "FOXA" or mapping_event.old_symbol != "NWSA":
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raise AssertionError("Unexpected mapping event mapping_event")
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self.set_holdings(self._symbol, 1)
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self.execution_mapping = True
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def on_end_of_algorithm(self):
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if self.initial_mapping:
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raise AssertionError("The ticker generated the initial rename event")
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if not self.execution_mapping:
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raise AssertionError("The ticker did not rename throughout the course of its life even though it should have")
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class CustomDataUsingMapping(PythonData):
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'''Test example custom data showing how to enable the use of mapping.
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Implemented as a wrapper of existing NWSA->FOXA equity'''
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def get_source(self, config, date, is_live_mode):
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return TradeBar().get_source(SubscriptionDataConfig(config, CustomDataUsingMapping,
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# create a new symbol as equity so we find the existing data files
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Symbol.create(config.mapped_symbol, SecurityType.EQUITY, config.market)),
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date,
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is_live_mode)
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def reader(self, config, line, date, is_live_mode):
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return TradeBar.parse_equity(config, line, date)
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def requires_mapping(self):
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'''True indicates mapping should be done'''
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return True
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def is_sparse_data(self):
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'''Indicates that the data set is expected to be sparse'''
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return True
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def default_resolution(self):
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'''Gets the default resolution for this data and security type'''
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return Resolution.MINUTE
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def supported_resolutions(self):
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'''Gets the supported resolution for this data and security type'''
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return [ Resolution.MINUTE ]
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