Martin-Molinero
cb326788b3
Regression Tests / build (push) Has been cancelled
Build & Test Lean / build (push) Has been cancelled
* Filter out small orders based on Setting - BuyingPowerModel will filter out small orders based on algorithm setting, a % of PTV, instead of hard coded 1 share value. Addin unit and regression tests - Updating regression algorithms to use new setting, reduce order trades * Update regression algorithms
65 lines
3.0 KiB
Python
65 lines
3.0 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 Alphas.HistoricalReturnsAlphaModel import HistoricalReturnsAlphaModel
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from Portfolio.BlackLittermanOptimizationPortfolioConstructionModel import *
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from Portfolio.UnconstrainedMeanVariancePortfolioOptimizer import UnconstrainedMeanVariancePortfolioOptimizer
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from Risk.NullRiskManagementModel import NullRiskManagementModel
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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(QCAlgorithm):
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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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# Order margin value has to have a minimum of 0.5% of Portfolio value, allows filtering out small trades and reduce fees.
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# Commented so regression algorithm is more sensitive
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#self.Settings.MinimumOrderMarginPortfolioPercentage = 0.005
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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)
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