Martin Molinero
10fd48417b
- `SubscriptionSynchronizer` will emit a `TimeSlice.TimePulse` before performing any universe selection on each time loop. This will advance `Algorithm.Time` which will allow universe selection data time and `Algorithm.Time` to be aligned. - Updating Regression algorithms that were using `algorithm.Time` in the selection method. - Coarse selection will start from the algorithms start date (not in the next day) - Adding regression algorithm
70 lines
3.1 KiB
Python
70 lines
3.1 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 clr import AddReference
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AddReference("System.Core")
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AddReference("QuantConnect.Common")
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AddReference("QuantConnect.Algorithm")
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from System import *
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from QuantConnect import *
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from QuantConnect.Algorithm import QCAlgorithm
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from QuantConnect.Data.UniverseSelection import Universe
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from QuantConnect.Algorithm.Framework.Alphas import *
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from QuantConnect.Algorithm.Framework.Portfolio import *
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from datetime import date, timedelta
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### <summary>
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### Regression algorithm used to test a fine and coarse selection methods returning Universe.Unchanged
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### </summary>
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class UniverseUnchangedRegressionAlgorithm(QCAlgorithm):
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def Initialize(self):
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self.UniverseSettings.Resolution = Resolution.Daily
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self.SetStartDate(2014,3,25)
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self.SetEndDate(2014,4,7)
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self.SetAlpha(ConstantAlphaModel(InsightType.Price, InsightDirection.Up, timedelta(days = 1), 0.025, None))
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self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
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self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
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self.numberOfSymbolsFine = 2
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def CoarseSelectionFunction(self, coarse):
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# the first and second selection
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if self.Time.date() <= date(2014, 3, 26):
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tickers = [ "AAPL", "AIG", "IBM" ]
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return [ Symbol.Create(x, SecurityType.Equity, Market.USA) for x in tickers ]
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# will skip fine selection
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return Universe.Unchanged
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def FineSelectionFunction(self, fine):
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if self.Time.date() == date(2014, 3, 25):
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sortedByPeRatio = sorted(fine, key=lambda x: x.ValuationRatios.PERatio, reverse=True)
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return [ x.Symbol for x in sortedByPeRatio[:self.numberOfSymbolsFine] ]
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# the second selection will return unchanged, in the following fine selection will be skipped
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return Universe.Unchanged
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# assert security changes, throw if called more than once
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def OnSecuritiesChanged(self, changes):
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addedSymbols = [ x.Symbol for x in changes.AddedSecurities ]
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if (len(changes.AddedSecurities) != 2
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or self.Time.date() != date(2014, 3, 25)
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or Symbol.Create("AAPL", SecurityType.Equity, Market.USA) not in addedSymbols
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or Symbol.Create("IBM", SecurityType.Equity, Market.USA) not in addedSymbols):
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raise ValueError("Unexpected security changes")
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self.Log(f"OnSecuritiesChanged({self.Time}):: {changes}")
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