AlexCatarino
92238a02fc
This framework algorithm alpha model is HistoricalReturnsAlphaModel and the portfolio construction model is MeanVarianceOptimizationPortfolioConstructionModel. This examples implements an algorithm that rebalances the portfolio according to modern portfolio theory.
98 lines
4.4 KiB
Python
98 lines
4.4 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.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 *
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from QuantConnect.Algorithm.Framework.Execution import *
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from QuantConnect.Algorithm.Framework.Risk import *
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from QuantConnect.Algorithm.Framework.Selection import *
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from Alphas.HistoricalReturnsAlphaModel import *
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from Portfolio.MeanVarianceOptimizationPortfolioConstructionModel import *
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from datetime import timedelta
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import numpy as np
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### <summary>
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### Mean Variance Optimization algorithm
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### Uses the HistoricalReturnsAlphaModel and the MeanVarianceOptimizationPortfolioConstructionModel to create an
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### algorithm that rebalances the portfolio according to modern portfolio theory
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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 MeanVarianceOptimizationAlgorithm(QCAlgorithmFramework):
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'''BMean Variance Optimization algorithm.'''
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def Initialize(self):
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''' Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
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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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# In this example, we are using an universe composed by only four assets
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symbols = [ Symbol.Create(x, SecurityType.Equity, Market.USA) for x in ['AIG', 'BAC', 'IBM', 'SPY'] ]
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self.minimum_weight = -1
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self.maximum_weight = 1
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# set algorithm framework models
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self.UniverseSelection = ManualUniverseSelectionModel(symbols)
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self.SetAlpha(HistoricalReturnsAlphaModel(period = 63, resolution = Resolution.Daily))
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self.SetPortfolioConstruction(MeanVarianceOptimizationPortfolioConstructionModel(optimization_method = self.maximum_sharpe_ratio))
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self.Execution = ImmediateExecutionModel()
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self.RiskManagement = NullRiskManagementModel()
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def OnOrderEvent(self, orderEvent):
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if orderEvent.Status == OrderStatus.Filled:
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self.Debug("Purchased Stock: {0}".format(orderEvent.Symbol))
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def maximum_sharpe_ratio(self, returns):
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'''Maximum Sharpe Ratio optimization method'''
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# Objective function
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fun = lambda weights: self.sharpe_ratio(returns, weights)
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# Constraint #1: The weights can be negative, which means investors can short a security.
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constraints = [{'type': 'eq', 'fun': lambda w: np.sum(w) - 1}]
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size = returns.columns.size
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x0 = np.array(size * [1. / size])
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bounds = tuple((self.minimum_weight, self.maximum_weight) for x in range(size))
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opt = minimize(fun, # Objective function
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x0, # Initial guess
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method='SLSQP', # Optimization method: Sequential Least SQuares Programming
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bounds = bounds, # Bounds for variables
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constraints = constraints) # Constraints definition
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weights = pd.Series(opt['x'], index = returns.columns)
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self.Log('{}:\n\r{}'.format(self.Time, weights))
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return opt, weights
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def sharpe_ratio(self, returns, weights):
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annual_return = np.dot(np.matrix(returns.mean()), np.matrix(weights).T).item()
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annual_volatility = np.sqrt(np.dot(weights.T, np.dot(returns.cov(), weights)))
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return -annual_return/annual_volatility |