chore: import upstream snapshot with attribution
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wehub-resource-sync
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/*
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* 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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*/
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using Accord.Math;
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using Python.Runtime;
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using QuantConnect.Algorithm.Framework.Alphas;
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using QuantConnect.Data;
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using QuantConnect.Data.UniverseSelection;
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using QuantConnect.Scheduling;
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using QuantConnect.Util;
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namespace QuantConnect.Algorithm.Framework.Portfolio
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{
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/// <summary>
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/// Provides an implementation of Mean-Variance portfolio optimization based on modern portfolio theory.
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/// The interval of weights in optimization method can be changed based on the long-short algorithm.
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/// The default model uses the last three months daily price to calculate the optimal weight
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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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public class MeanVarianceOptimizationPortfolioConstructionModel : PortfolioConstructionModel
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{
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private readonly int _lookback;
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private readonly int _period;
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private readonly Resolution _resolution;
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private readonly PortfolioBias _portfolioBias;
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private readonly IPortfolioOptimizer _optimizer;
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private readonly Dictionary<Symbol, ReturnsSymbolData> _symbolDataDict;
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/// <summary>
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/// Initialize the model
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/// </summary>
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/// <param name="rebalancingDateRules">The date rules used to define the next expected rebalance time
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/// in UTC</param>
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/// <param name="portfolioBias">Specifies the bias of the portfolio (Short, Long/Short, Long)</param>
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/// <param name="lookback">Historical return lookback period</param>
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/// <param name="period">The time interval of history price to calculate the weight</param>
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/// <param name="resolution">The resolution of the history price</param>
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/// <param name="targetReturn">The target portfolio return</param>
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/// <param name="optimizer">The portfolio optimization algorithm. If the algorithm is not provided then the default will be mean-variance optimization.</param>
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public MeanVarianceOptimizationPortfolioConstructionModel(IDateRule rebalancingDateRules,
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PortfolioBias portfolioBias = PortfolioBias.LongShort,
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int lookback = 1,
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int period = 63,
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Resolution resolution = Resolution.Daily,
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double targetReturn = 0.02,
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IPortfolioOptimizer optimizer = null)
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: this(rebalancingDateRules.ToFunc(), portfolioBias, lookback, period, resolution, targetReturn, optimizer)
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{
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}
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/// <summary>
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/// Initialize the model
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/// </summary>
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/// <param name="rebalanceResolution">Rebalancing frequency</param>
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/// <param name="portfolioBias">Specifies the bias of the portfolio (Short, Long/Short, Long)</param>
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/// <param name="lookback">Historical return lookback period</param>
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/// <param name="period">The time interval of history price to calculate the weight</param>
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/// <param name="resolution">The resolution of the history price</param>
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/// <param name="targetReturn">The target portfolio return</param>
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/// <param name="optimizer">The portfolio optimization algorithm. If the algorithm is not provided then the default will be mean-variance optimization.</param>
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public MeanVarianceOptimizationPortfolioConstructionModel(Resolution rebalanceResolution = Resolution.Daily,
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PortfolioBias portfolioBias = PortfolioBias.LongShort,
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int lookback = 1,
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int period = 63,
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Resolution resolution = Resolution.Daily,
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double targetReturn = 0.02,
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IPortfolioOptimizer optimizer = null)
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: this(rebalanceResolution.ToTimeSpan(), portfolioBias, lookback, period, resolution, targetReturn, optimizer)
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{
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}
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/// <summary>
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/// Initialize the model
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/// </summary>
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/// <param name="timeSpan">Rebalancing frequency</param>
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/// <param name="portfolioBias">Specifies the bias of the portfolio (Short, Long/Short, Long)</param>
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/// <param name="lookback">Historical return lookback period</param>
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/// <param name="period">The time interval of history price to calculate the weight</param>
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/// <param name="resolution">The resolution of the history price</param>
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/// <param name="targetReturn">The target portfolio return</param>
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/// <param name="optimizer">The portfolio optimization algorithm. If the algorithm is not provided then the default will be mean-variance optimization.</param>
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public MeanVarianceOptimizationPortfolioConstructionModel(TimeSpan timeSpan,
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PortfolioBias portfolioBias = PortfolioBias.LongShort,
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int lookback = 1,
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int period = 63,
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Resolution resolution = Resolution.Daily,
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double targetReturn = 0.02,
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IPortfolioOptimizer optimizer = null)
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: this(dt => dt.Add(timeSpan), portfolioBias, lookback, period, resolution, targetReturn, optimizer)
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{
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}
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/// <summary>
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/// Initialize the model
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/// </summary>
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/// <param name="rebalance">Rebalancing func or if a date rule, timedelta will be converted into func.
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/// For a given algorithm UTC DateTime the func returns the next expected rebalance time
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/// or null if unknown, in which case the function will be called again in the next loop. Returning current time
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/// will trigger rebalance. If null will be ignored</param>
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/// <param name="portfolioBias">Specifies the bias of the portfolio (Short, Long/Short, Long)</param>
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/// <param name="lookback">Historical return lookback period</param>
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/// <param name="period">The time interval of history price to calculate the weight</param>
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/// <param name="resolution">The resolution of the history price</param>
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/// <param name="targetReturn">The target portfolio return</param>
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/// <param name="optimizer">The portfolio optimization algorithm. If the algorithm is not provided then the default will be mean-variance optimization.</param>
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/// <remarks>This is required since python net can not convert python methods into func nor resolve the correct
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/// constructor for the date rules parameter.
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/// For performance we prefer python algorithms using the C# implementation</remarks>
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public MeanVarianceOptimizationPortfolioConstructionModel(PyObject rebalance,
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PortfolioBias portfolioBias = PortfolioBias.LongShort,
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int lookback = 1,
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int period = 63,
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Resolution resolution = Resolution.Daily,
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double targetReturn = 0.02,
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PyObject optimizer = null)
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: this((Func<DateTime, DateTime?>)null, portfolioBias, lookback, period, resolution, targetReturn, null)
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{
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SetRebalancingFunc(rebalance);
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if (optimizer != null)
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{
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if (optimizer.TryConvert<IPortfolioOptimizer>(out var csharpOptimizer))
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{
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_optimizer = csharpOptimizer;
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}
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else
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{
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_optimizer = new PortfolioOptimizerPythonWrapper(optimizer);
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}
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}
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}
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/// <summary>
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/// Initialize the model
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/// </summary>
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/// <param name="rebalancingFunc">For a given algorithm UTC DateTime returns the next expected rebalance UTC time.
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/// Returning current time will trigger rebalance. If null will be ignored</param>
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/// <param name="portfolioBias">Specifies the bias of the portfolio (Short, Long/Short, Long)</param>
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/// <param name="lookback">Historical return lookback period</param>
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/// <param name="period">The time interval of history price to calculate the weight</param>
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/// <param name="resolution">The resolution of the history price</param>
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/// <param name="targetReturn">The target portfolio return</param>
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/// <param name="optimizer">The portfolio optimization algorithm. If the algorithm is not provided then the default will be mean-variance optimization.</param>
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public MeanVarianceOptimizationPortfolioConstructionModel(Func<DateTime, DateTime> rebalancingFunc,
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PortfolioBias portfolioBias = PortfolioBias.LongShort,
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int lookback = 1,
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int period = 63,
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Resolution resolution = Resolution.Daily,
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double targetReturn = 0.02,
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IPortfolioOptimizer optimizer = null)
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: this(rebalancingFunc != null ? (Func<DateTime, DateTime?>)(timeUtc => rebalancingFunc(timeUtc)) : null,
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portfolioBias,
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lookback,
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period,
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resolution,
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targetReturn,
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optimizer)
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{
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}
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/// <summary>
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/// Initialize the model
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/// </summary>
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/// <param name="rebalancingFunc">For a given algorithm UTC DateTime returns the next expected rebalance time
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/// or null if unknown, in which case the function will be called again in the next loop. Returning current time
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/// will trigger rebalance.</param>
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/// <param name="portfolioBias">Specifies the bias of the portfolio (Short, Long/Short, Long)</param>
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/// <param name="lookback">Historical return lookback period</param>
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/// <param name="period">The time interval of history price to calculate the weight</param>
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/// <param name="resolution">The resolution of the history price</param>
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/// <param name="targetReturn">The target portfolio return</param>
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/// <param name="optimizer">The portfolio optimization algorithm. If the algorithm is not provided then the default will be mean-variance optimization.</param>
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public MeanVarianceOptimizationPortfolioConstructionModel(Func<DateTime, DateTime?> rebalancingFunc,
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PortfolioBias portfolioBias = PortfolioBias.LongShort,
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int lookback = 1,
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int period = 63,
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Resolution resolution = Resolution.Daily,
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double targetReturn = 0.02,
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IPortfolioOptimizer optimizer = null)
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: base(rebalancingFunc)
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{
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_lookback = lookback;
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_period = period;
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_resolution = resolution;
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_portfolioBias = portfolioBias;
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var lower = portfolioBias == PortfolioBias.Long ? 0 : -1;
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var upper = portfolioBias == PortfolioBias.Short ? 0 : 1;
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_optimizer = optimizer ?? new MinimumVariancePortfolioOptimizer(lower, upper, targetReturn);
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_symbolDataDict = new Dictionary<Symbol, ReturnsSymbolData>();
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}
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/// <summary>
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/// Method that will determine if the portfolio construction model should create a
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/// target for this insight
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/// </summary>
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/// <param name="insight">The insight to create a target for</param>
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/// <returns>True if the portfolio should create a target for the insight</returns>
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protected override bool ShouldCreateTargetForInsight(Insight insight)
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{
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var filteredInsight = FilterInvalidInsightMagnitude(Algorithm, new[] { insight }).FirstOrDefault();
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if (filteredInsight == null)
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{
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return false;
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}
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ReturnsSymbolData data;
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if (_symbolDataDict.TryGetValue(insight.Symbol, out data))
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{
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if (!insight.Magnitude.HasValue)
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{
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Algorithm.SetRunTimeError(
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new ArgumentNullException(
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insight.Symbol.Value,
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"MeanVarianceOptimizationPortfolioConstructionModel does not accept 'null' as Insight.Magnitude. " +
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"Please checkout the selected Alpha Model specifications: " + insight.SourceModel));
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return false;
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}
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data.Add(Algorithm.Time, insight.Magnitude.Value.SafeDecimalCast());
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}
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return true;
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}
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/// <summary>
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/// Will determine the target percent for each insight
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/// </summary>
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/// <param name="activeInsights">The active insights to generate a target for</param>
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/// <returns>A target percent for each insight</returns>
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protected override Dictionary<Insight, double> DetermineTargetPercent(List<Insight> activeInsights)
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{
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var targets = new Dictionary<Insight, double>();
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// If we have no insights just return an empty target list
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if (activeInsights.IsNullOrEmpty())
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{
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return targets;
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}
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var symbols = activeInsights.Select(x => x.Symbol).ToList();
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// Get symbols' returns, we use simple return according to
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// Meucci, Attilio, Quant Nugget 2: Linear vs. Compounded Returns – Common Pitfalls in Portfolio Management (May 1, 2010).
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// GARP Risk Professional, pp. 49-51, April 2010 , Available at SSRN: https://ssrn.com/abstract=1586656
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var returns = _symbolDataDict.FormReturnsMatrix(symbols);
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// The optimization method processes the data frame
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var w = _optimizer.Optimize(returns);
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// process results
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if (w.Length > 0)
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{
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var sidx = 0;
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foreach (var symbol in symbols)
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{
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var weight = w[sidx];
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// don't trust the optimizer
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if (_portfolioBias != PortfolioBias.LongShort
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&& Math.Sign(weight) != (int)_portfolioBias)
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{
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weight = 0;
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}
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targets[activeInsights.First(insight => insight.Symbol == symbol)] = weight;
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sidx++;
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}
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}
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return targets;
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}
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/// <summary>
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/// Event fired each time the we add/remove securities from the data feed
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/// </summary>
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/// <param name="algorithm">The algorithm instance that experienced the change in securities</param>
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/// <param name="changes">The security additions and removals from the algorithm</param>
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public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
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{
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base.OnSecuritiesChanged(algorithm, changes);
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// clean up data for removed securities
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foreach (var removed in changes.RemovedSecurities)
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{
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ReturnsSymbolData data;
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if (_symbolDataDict.TryGetValue(removed.Symbol, out data))
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{
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_symbolDataDict.Remove(removed.Symbol);
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}
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}
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if (changes.AddedSecurities.Count == 0)
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return;
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// initialize data for added securities
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foreach (var added in changes.AddedSecurities)
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{
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if (!_symbolDataDict.ContainsKey(added.Symbol))
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{
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var symbolData = new ReturnsSymbolData(added.Symbol, _lookback, _period);
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_symbolDataDict[added.Symbol] = symbolData;
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}
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}
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// warmup our indicators by pushing history through the consolidators
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algorithm.History(changes.AddedSecurities.Select(security => security.Symbol), _lookback * _period, _resolution)
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.PushThrough(bar =>
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{
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ReturnsSymbolData symbolData;
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if (_symbolDataDict.TryGetValue(bar.Symbol, out symbolData))
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{
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symbolData.Update(bar.EndTime, bar.Value);
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}
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});
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}
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}
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}
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