Martin Molinero
90 lines
4.7 KiB
Python
90 lines
4.7 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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import numpy as np
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import pandas as pd
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from scipy.optimize import minimize
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### <summary>
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### Provides an implementation of a portfolio optimizer that calculate the optimal weights
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### with the weight range from -1 to 1 and minimize the portfolio variance with a target return of 2%
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### </summary>
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class MinimumVariancePortfolioOptimizer:
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'''Provides an implementation of a portfolio optimizer that calculate the optimal weights
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with the weight range from -1 to 1 and minimize the portfolio variance with a target return of 2%'''
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def __init__(self,
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minimum_weight = -1,
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maximum_weight = 1,
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target_return = 0.02):
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'''Initialize the MinimumVariancePortfolioOptimizer
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Args:
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minimum_weight(float): The lower bounds on portfolio weights
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maximum_weight(float): The upper bounds on portfolio weights
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target_return(float): The target portfolio return'''
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self.minimum_weight = minimum_weight
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self.maximum_weight = maximum_weight
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self.target_return = target_return
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def Optimize(self, historicalReturns, expectedReturns = None, covariance = None):
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'''
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Perform portfolio optimization for a provided matrix of historical returns and an array of expected returns
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args:
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historicalReturns: Matrix of annualized historical returns where each column represents a security and each row returns for the given date/time (size: K x N).
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expectedReturns: Array of double with the portfolio annualized expected returns (size: K x 1).
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covariance: Multi-dimensional array of double with the portfolio covariance of annualized returns (size: K x K).
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Returns:
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Array of double with the portfolio weights (size: K x 1)
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'''
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if covariance is None:
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covariance = historicalReturns.cov()
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if expectedReturns is None:
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expectedReturns = historicalReturns.mean()
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size = historicalReturns.columns.size # K x 1
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x0 = np.array(size * [1. / size])
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constraints = [
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{'type': 'eq', 'fun': lambda weights: self.get_budget_constraint(weights)},
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{'type': 'eq', 'fun': lambda weights: self.get_target_constraint(weights, expectedReturns)}]
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opt = minimize(lambda weights: self.portfolio_variance(weights, covariance), # Objective function
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x0, # Initial guess
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bounds = self.get_boundary_conditions(size), # Bounds for variables
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constraints = constraints, # Constraints definition
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method='SLSQP', # Optimization method: Sequential Least SQuares Programming
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options={'ftol': 1e-04}) # Precision goal for the value of f in the stopping criterion.
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return opt['x'] if opt['success'] else x0
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def portfolio_variance(self, weights, covariance):
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'''Computes the portfolio variance
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Args:
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weighs: Portfolio weights
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covariance: Covariance matrix of historical returns'''
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variance = np.dot(weights.T, np.dot(covariance, weights))
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if variance == 0 and np.any(weights):
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# variance can't be zero, with non zero weights
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raise ValueError(f'MinimumVariancePortfolioOptimizer.portfolio_variance: Volatility cannot be zero. Weights: {weights}')
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return variance
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def get_boundary_conditions(self, size):
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'''Creates the boundary condition for the portfolio weights'''
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return tuple((self.minimum_weight, self.maximum_weight) for x in range(size))
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def get_budget_constraint(self, weights):
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'''Defines a budget constraint: the sum of the weights equals unity'''
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return np.sum(weights) - 1
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def get_target_constraint(self, weights, expectedReturns):
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'''Ensure that the portfolio return target a given return'''
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return np.dot(np.matrix(expectedReturns), np.matrix(weights).T).item() - self.target_return |