Michael Handschuh
fdc866fda0
We didn't experience the expected performance improvements. Locally under unit test there was aboout an order of magnitude throughput increase, but when run against the history benchmark, this new approach was 60% slower. We're reverting this for now to perform further analysis and better understand the performance profiling of the python history stack.
72 lines
3.2 KiB
Python
72 lines
3.2 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")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Algorithm.Framework")
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AddReference("QuantConnect.Common")
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from System import *
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from QuantConnect import *
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from QuantConnect.Orders import *
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from QuantConnect.Algorithm import *
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from QuantConnect.Algorithm.Framework import QCAlgorithmFramework
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from QuantConnect.Algorithm.Framework.Selection import *
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from Alphas.HistoricalReturnsAlphaModel import HistoricalReturnsAlphaModel
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from Execution.ImmediateExecutionModel import ImmediateExecutionModel
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from Risk.NullRiskManagementModel import NullRiskManagementModel
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from Portfolio.BlackLittermanOptimizationPortfolioConstructionModel import *
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from Portfolio.UnconstrainedMeanVariancePortfolioOptimizer import UnconstrainedMeanVariancePortfolioOptimizer
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### <summary>
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### Black-Litterman framework algorithm
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### Uses the HistoricalReturnsAlphaModel and the BlackLittermanPortfolioConstructionModel
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### to create an algorithm that rebalances the portfolio according to Black-Litterman portfolio optimization
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="using quantconnect" />
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### <meta name="tag" content="trading and orders" />
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class BlackLittermanPortfolioOptimizationFrameworkAlgorithm(QCAlgorithmFramework):
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'''Black-Litterman Optimization algorithm.'''
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def Initialize(self):
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# Set requested data resolution
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self.UniverseSettings.Resolution = Resolution.Minute
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self.SetStartDate(2013,10,7) #Set Start Date
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self.SetEndDate(2013,10,11) #Set End Date
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self.SetCash(100000) #Set Strategy Cash
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self.symbols = [ Symbol.Create(x, SecurityType.Equity, Market.USA) for x in [ 'AIG', 'BAC', 'IBM', 'SPY' ] ]
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optimizer = UnconstrainedMeanVariancePortfolioOptimizer()
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# set algorithm framework models
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self.SetUniverseSelection(CoarseFundamentalUniverseSelectionModel(self.coarseSelector))
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self.SetAlpha(HistoricalReturnsAlphaModel(resolution = Resolution.Daily))
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self.SetPortfolioConstruction(BlackLittermanOptimizationPortfolioConstructionModel(optimizer = optimizer))
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self.SetExecution(ImmediateExecutionModel())
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self.SetRiskManagement(NullRiskManagementModel())
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def coarseSelector(self, coarse):
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# Drops SPY after the 8th
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last = 3 if self.Time.day > 8 else len(self.symbols)
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return self.symbols[0:last]
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def OnOrderEvent(self, orderEvent):
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if orderEvent.Status == OrderStatus.Filled:
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self.Debug(orderEvent) |