wehub-resource-sync
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.universe_settings.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.minimum_order_margin_portfolio_percentage = 0.005
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self.set_start_date(2013,10,7) #Set Start Date
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self.set_end_date(2013,10,11) #Set End Date
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self.set_cash(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.set_universe_selection(CoarseFundamentalUniverseSelectionModel(self.coarse_selector))
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self.set_alpha(HistoricalReturnsAlphaModel(resolution = Resolution.DAILY))
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self.set_portfolio_construction(BlackLittermanOptimizationPortfolioConstructionModel(optimizer = optimizer))
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self.set_execution(ImmediateExecutionModel())
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self.set_risk_management(NullRiskManagementModel())
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def coarse_selector(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 on_order_event(self, order_event):
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if order_event.status == OrderStatus.FILLED:
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self.debug(order_event)
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