254 lines
9.8 KiB
C#
254 lines
9.8 KiB
C#
/*
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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.Globalization;
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using System.Linq;
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using QuantConnect.Optimizer.Objectives;
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using QuantConnect.Optimizer.Parameters;
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namespace QuantConnect.Optimizer.Strategies
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{
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/// <summary>
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/// Base class for any optimization built on top of brute force optimization method
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/// </summary>
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public abstract class StepBaseOptimizationStrategy : IOptimizationStrategy
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{
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private int _i;
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/// <summary>
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/// Indicates was strategy initialized or no
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/// </summary>
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protected bool Initialized { get; set; }
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/// <summary>
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/// Optimization parameters
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/// </summary>
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protected HashSet<OptimizationParameter> OptimizationParameters { get; set; }
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/// <summary>
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/// Optimization target, i.e. maximize or minimize
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/// </summary>
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protected Target Target { get; set; }
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/// <summary>
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/// Optimization constraints; if it doesn't comply just drop the backtest
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/// </summary>
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protected IEnumerable<Constraint> Constraints { get; set; }
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/// <summary>
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/// Keep the best found solution - lean computed job result and corresponding parameter set
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/// </summary>
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public OptimizationResult Solution { get; protected set; }
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/// <summary>
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/// Advanced strategy settings
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/// </summary>
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public OptimizationStrategySettings Settings { get; protected set; }
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/// <summary>
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/// Fires when new parameter set is generated
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/// </summary>
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public event EventHandler<ParameterSet> NewParameterSet;
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/// <summary>
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/// Initializes the strategy using generator, extremum settings and optimization parameters
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/// </summary>
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/// <param name="target">The optimization target</param>
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/// <param name="constraints">The optimization constraints to apply on backtest results</param>
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/// <param name="parameters">Optimization parameters</param>
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/// <param name="settings">Optimization strategy settings</param>
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public virtual void Initialize(Target target, IReadOnlyList<Constraint> constraints, HashSet<OptimizationParameter> parameters, OptimizationStrategySettings settings)
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{
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if (Initialized)
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{
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throw new InvalidOperationException($"GridSearchOptimizationStrategy.Initialize: can not be re-initialized.");
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}
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Target = target;
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Constraints = constraints;
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OptimizationParameters = parameters;
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Settings = settings;
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foreach (var optimizationParameter in OptimizationParameters.OfType<OptimizationStepParameter>())
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{
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// if the Step optimization parameter does not provide a step to use, we calculate one based on settings
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if (!optimizationParameter.Step.HasValue)
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{
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var stepSettings = Settings as StepBaseOptimizationStrategySettings;
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if (stepSettings == null)
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{
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throw new ArgumentException($"OptimizationStrategySettings is not of {nameof(StepBaseOptimizationStrategySettings)} type", nameof(settings));
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}
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CalculateStep(optimizationParameter, stepSettings.DefaultSegmentAmount);
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}
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}
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Initialized = true;
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}
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/// <summary>
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/// Checks whether new lean compute job better than previous and run new iteration if necessary.
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/// </summary>
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/// <param name="result">Lean compute job result and corresponding parameter set</param>
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public abstract void PushNewResults(OptimizationResult result);
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/// <summary>
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/// Calculate number of parameter sets within grid
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/// </summary>
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/// <returns>Number of parameter sets for given optimization parameters</returns>
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public int GetTotalBacktestEstimate()
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{
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var total = 1;
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foreach (var arg in OptimizationParameters)
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{
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total *= Estimate(arg);
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}
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return total;
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}
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/// <summary>
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/// Calculates number od data points for step based optimization parameter based on min/max and step values
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/// </summary>
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private int Estimate(OptimizationParameter parameter)
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{
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if (parameter is StaticOptimizationParameter)
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{
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return 1;
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}
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var stepParameter = parameter as OptimizationStepParameter;
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if (stepParameter == null)
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{
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throw new InvalidOperationException($"Cannot estimate parameter of type {parameter.GetType().FullName}");
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}
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if (!stepParameter.Step.HasValue)
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{
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throw new InvalidOperationException("Optimization parameter cannot be estimated due to step value is not initialized");
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}
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return (int)Math.Floor((stepParameter.MaxValue - stepParameter.MinValue) / stepParameter.Step.Value) + 1;
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}
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/// <summary>
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/// Handles new parameter set
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/// </summary>
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/// <param name="parameterSet">New parameter set</param>
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protected virtual void OnNewParameterSet(ParameterSet parameterSet)
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{
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NewParameterSet?.Invoke(this, parameterSet);
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}
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protected virtual void ProcessNewResult(OptimizationResult result)
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{
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// check if the incoming result is not the initial seed
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if (result.Id > 0)
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{
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if (Constraints?.All(constraint => constraint.IsMet(result.JsonBacktestResult)) != false)
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{
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if (Target.MoveAhead(result.JsonBacktestResult))
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{
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Solution = result;
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Target.CheckCompliance();
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}
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}
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}
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}
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/// <summary>
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/// Enumerate all possible arrangements
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/// </summary>
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/// <param name="args"></param>
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/// <returns>Collection of possible combinations for given optimization parameters settings</returns>
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protected IEnumerable<ParameterSet> Step(HashSet<OptimizationParameter> args)
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{
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foreach (var step in Recursive(new Queue<OptimizationParameter>(args)))
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{
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yield return new ParameterSet(
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++_i,
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step.ToDictionary(kvp => kvp.Key, kvp => kvp.Value));
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}
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}
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/// <summary>
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/// Calculate step and min step values based on default number of fragments
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/// </summary>
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private void CalculateStep(OptimizationStepParameter parameter, int defaultSegmentAmount)
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{
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if (defaultSegmentAmount < 1)
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{
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throw new ArgumentException($"Number of segments should be positive number, but specified '{defaultSegmentAmount}'", nameof(defaultSegmentAmount));
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}
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parameter.Step = Math.Abs(parameter.MaxValue - parameter.MinValue) / defaultSegmentAmount;
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parameter.MinStep = parameter.Step / 10;
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}
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private IEnumerable<Dictionary<string, string>> Recursive(Queue<OptimizationParameter> args)
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{
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if (args.Count == 1)
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{
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var optimizationParameterLast = args.Dequeue();
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using (var optimizationParameterLastEnumerator = GetEnumerator(optimizationParameterLast))
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{
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while (optimizationParameterLastEnumerator.MoveNext())
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{
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yield return new Dictionary<string, string>()
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{
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{optimizationParameterLast.Name, optimizationParameterLastEnumerator.Current}
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};
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}
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}
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yield break;
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}
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var optimizationParameter = args.Dequeue();
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using (var optimizationParameterEnumerator = GetEnumerator(optimizationParameter))
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{
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while (optimizationParameterEnumerator.MoveNext())
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{
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foreach (var inner in Recursive(new Queue<OptimizationParameter>(args)))
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{
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inner.Add(optimizationParameter.Name, optimizationParameterEnumerator.Current);
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yield return inner;
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}
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}
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}
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}
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private IEnumerator<string> GetEnumerator(OptimizationParameter parameter)
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{
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var staticOptimizationParameter = parameter as StaticOptimizationParameter;
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if (staticOptimizationParameter != null)
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{
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return new List<string> { staticOptimizationParameter.Value }.GetEnumerator();
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}
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var stepParameter = parameter as OptimizationStepParameter;
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if (stepParameter == null)
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{
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throw new InvalidOperationException("");
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}
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return new OptimizationStepParameterEnumerator(stepParameter);
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}
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}
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}
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