Martin-Molinero
7540af454c
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* Respect warmup resolution given - The data feed will respect the warmup resolution given and override the resolution used by the algorithm when adding a subscription. Updating regression algorithm to keep previous statistics. Adding new regression algorithm asserting the desired behavior * Testing improvements - Add more unit tests and regresion test - Add missing data for crypto - Fix bug with FFed data crossing after the end time of the warmup request * Add more Warmup resolution regression algorithms - Adding more warmup resolution regression algorithms, using Settings.WarmupResolution and an option selection case * Add more warmup regression tests - Adding more warmup regression tests. - Will no longer skip universe selection subscriptions from warmup resolution enforcement. Updating regression algorithms data points * Fix bug with data rounding - Fix data rounding bug when warmup resolution is set to a different value than the original configuration. Updating regression algorithms to assert the expected behavior * Address reviews - Revert regression algorithms changes to use Resolution during warmup. Updating their stats. - Adding new regression algorithms asserting the behavior warming up using a timespan and no warmup resolution - Fix bug where data used to warmup the 'normal' enumerator will make it through into the warmup time span. Updating tests * Address reviews - Add missing comments, explaning warmup algorithms time span calculations. - Revert changes in existing `WarmupOptionTimeSpanRegressionAlgorithm` to reduce diff to minimum - Adding new warmup unit tests asseting algorithm warmup start time, for different combinations of bar count, timespan, resolution
29 lines
1.8 KiB
Python
29 lines
1.8 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 OptionPriceModelForSupportedEuropeanOptionRegressionAlgorithm import OptionPriceModelForSupportedEuropeanOptionRegressionAlgorithm
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### <summary>
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### Regression algorithm excersizing an equity covered European style option, using an option price model
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### that supports European style options and asserting that the option price model is used.
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### </summary>
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class OptionPriceModelForSupportedEuropeanOptionTimeSpanWarmupRegressionAlgorithm(OptionPriceModelForSupportedEuropeanOptionRegressionAlgorithm):
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def Initialize(self):
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OptionPriceModelForSupportedEuropeanOptionRegressionAlgorithm.Initialize(self)
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# We want to match the start time of the base algorithm. SPX index options data time zone is chicago, algorithm time zone is new york (default).
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# Base algorithm warmup is 7 bar of daily resolution starts at 23 PM new york time of T-1. So to match the same start time
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# we go back a 9 day + 23 hours, we need to account for a single weekend. This is calculated by 'Time.GetStartTimeForTradeBars'
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self.SetWarmup(TimeSpan.FromHours(24 * 9 + 23))
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