wehub-resource-sync
67 lines
2.9 KiB
Python
67 lines
2.9 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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from QuantConnect.Data.Auxiliary import *
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from QuantConnect.Lean.Engine.DataFeeds import DefaultDataProvider
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_ticker = "GOOGL"
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_expected_raw_prices = [ 1158.72,
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1131.97, 1114.28, 1120.15, 1114.51, 1134.89, 1135.1, 571.50, 545.25, 540.63 ]
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# <summary>
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# In this algorithm we demonstrate how to use the raw data for our securities
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# and verify that the behavior is correct.
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# </summary>
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# <meta name="tag" content="using data" />
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# <meta name="tag" content="regression test" />
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class RawDataRegressionAlgorithm(QCAlgorithm):
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def initialize(self):
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self.set_start_date(2014, 3, 25)
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self.set_end_date(2014, 4, 7)
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self.set_cash(100000)
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# Set our DataNormalizationMode to raw
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self.universe_settings.data_normalization_mode = DataNormalizationMode.RAW
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self._googl = self.add_equity(_ticker, Resolution.DAILY).symbol
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# Get our factor file for this regression
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data_provider = DefaultDataProvider()
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map_file_provider = LocalDiskMapFileProvider()
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map_file_provider.initialize(data_provider)
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factor_file_provider = LocalDiskFactorFileProvider()
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factor_file_provider.initialize(map_file_provider, data_provider)
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# Get our factor file for this regression
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self._factor_file = factor_file_provider.get(self._googl)
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def on_data(self, data):
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if not self.portfolio.invested:
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self.set_holdings(self._googl, 1)
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if data.bars.contains_key(self._googl):
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googl_data = data.bars[self._googl]
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# Assert our volume matches what we expected
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expected_raw_price = _expected_raw_prices.pop(0)
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if expected_raw_price != googl_data.close:
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# Our values don't match lets try and give a reason why
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day_factor = self._factor_file.get_price_factor(googl_data.time, DataNormalizationMode.ADJUSTED)
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probable_raw_price = googl_data.close / day_factor # Undo adjustment
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raise AssertionError("Close price was incorrect; it appears to be the adjusted value"
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if expected_raw_price == probable_raw_price else
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"Close price was incorrect; Data may have changed.")
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