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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*/
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using NUnit.Framework;
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using QuantConnect.Optimizer;
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using QuantConnect.Optimizer.Objectives;
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using QuantConnect.Optimizer.Parameters;
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using QuantConnect.Optimizer.Strategies;
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using System;
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using System.Collections.Generic;
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namespace QuantConnect.Tests.Optimizer.Strategies
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{
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[TestFixture, Parallelizable(ParallelScope.Fixtures)]
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public class EulerSearchOptimizationStrategyTests : OptimizationStrategyTests
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{
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[TestFixture]
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public class EulerSearchTests
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{
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private EulerSearchOptimizationStrategy _strategy;
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[SetUp]
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public void Init()
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{
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this._strategy = new EulerSearchOptimizationStrategy();
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}
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[TestCase(10)]
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[TestCase(5)]
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[TestCase(2)]
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public void Depth(int _defaultSegmentAmount)
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{
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var param = new OptimizationStepParameter("ema-fast", 10, 100, 10, 0.1m);
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var set = new HashSet<OptimizationParameter> { param, new StaticOptimizationParameter("pepe", "pipi") };
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_strategy.Initialize(
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new Target("Profit", new Maximization(), null),
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new List<Constraint>(),
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set,
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new StepBaseOptimizationStrategySettings { DefaultSegmentAmount = _defaultSegmentAmount });
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Queue<OptimizationResult> _pendingOptimizationResults = new Queue<OptimizationResult>();
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int depth = -1;
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_strategy.NewParameterSet += (s, parameterSet) =>
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{
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if (_pendingOptimizationResults.Count == 0)
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{
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depth++;
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}
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_pendingOptimizationResults.Enqueue(new OptimizationResult(_stringify(_profit(parameterSet), _drawdown(parameterSet)), parameterSet, ""));
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};
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_strategy.PushNewResults(OptimizationResult.Initial);
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while (_pendingOptimizationResults.TryDequeue(out var item))
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{
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_strategy.PushNewResults(item);
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}
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Assert.AreEqual(Math.Ceiling(Math.Log((double)(param.Step / param.MinStep), _defaultSegmentAmount)), depth);
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}
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[Test]
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public void ThrowIfNoSettingsPassed()
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{
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Assert.Throws<ArgumentNullException>(() =>
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{
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_strategy.Initialize(
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new Target("Profit", new Maximization(), null),
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new List<Constraint>(),
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new HashSet<OptimizationParameter>
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{
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new OptimizationStepParameter("ema-fast", 10, 100, 10, 0.1m),
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new StaticOptimizationParameter("pepe", "pipi")
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},
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null);
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});
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}
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[TestCase(5)]
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[TestCase(10)]
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public void Reduce(int amountOfSegments)
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{
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var param = new OptimizationStepParameter("ema-fast", 10, 100, 10, 0.1m);
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var set = new HashSet<OptimizationParameter> { param, new StaticOptimizationParameter("pepe", "pipi") };
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_strategy.Initialize(
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new Target("Profit", new Maximization(), null),
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new List<Constraint>(),
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set,
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new StepBaseOptimizationStrategySettings { DefaultSegmentAmount = amountOfSegments });
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Queue<OptimizationResult> pendingOptimizationResults = new Queue<OptimizationResult>();
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int depth = -1;
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_strategy.NewParameterSet += (s, parameterSet) =>
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{
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if (pendingOptimizationResults.Count == 0)
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{
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depth++;
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}
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pendingOptimizationResults.Enqueue(new OptimizationResult(_stringify(_profit(parameterSet), _drawdown(parameterSet)), parameterSet, ""));
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};
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_strategy.PushNewResults(OptimizationResult.Initial);
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var step = param.Step ?? 1;
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var datapoint = param.MinValue;
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while (pendingOptimizationResults.Count > 0)
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{
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var count = pendingOptimizationResults.Count;
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for (int i = 0; i < count; i++)
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{
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var optimizationResult = pendingOptimizationResults.Dequeue();
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Assert.AreEqual(datapoint + step * i,
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optimizationResult.ParameterSet.Value["ema-fast"].ToDecimal());
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_strategy.PushNewResults(optimizationResult);
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}
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step = Math.Max(param.MinStep.Value, step / amountOfSegments);
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datapoint = param.MaxValue - step * ((decimal)amountOfSegments / 2);
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}
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}
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}
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protected override IOptimizationStrategy CreateStrategy()
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{
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return new EulerSearchOptimizationStrategy();
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}
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protected override OptimizationStrategySettings CreateSettings()
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{
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return new StepBaseOptimizationStrategySettings(){DefaultSegmentAmount = 10};
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}
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private static TestCaseData[] StrategySettings => new[]
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{
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new TestCaseData(new Maximization(), OptimizationStepParameters, new ParameterSet(-1, new Dictionary<string, string>{{"ema-slow", "5.0"}, { "ema-fast" , "6.0"} })),
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new TestCaseData(new Minimization(), OptimizationStepParameters, new ParameterSet(-1, new Dictionary<string, string>{{"ema-slow", "1"}, { "ema-fast" , "3"} })),
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new TestCaseData(new Maximization(), OptimizationMixedParameters, new ParameterSet(-1, new Dictionary<string, string>{{"ema-slow", "5"}, { "ema-fast" , "6.0" }, { "skipFromResultSum", "SPY" } })),
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new TestCaseData(new Minimization(), OptimizationMixedParameters, new ParameterSet(-1, new Dictionary<string, string>{{"ema-slow", "1"}, { "ema-fast" , "3"}, { "skipFromResultSum", "SPY" } }))
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};
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[Test, TestCaseSource(nameof(StrategySettings))]
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public override void StepInsideNoTargetNoConstraints(Extremum extremum, HashSet<OptimizationParameter> optimizationParameters, ParameterSet solution)
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{
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base.StepInsideNoTargetNoConstraints(extremum, optimizationParameters, solution);
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}
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private static TestCaseData[] OptimizeWithConstraint => new[]
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{
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new TestCaseData(0.05m, OptimizationStepParameters,new ParameterSet(-1, new Dictionary<string, string>{{"ema-slow", "1.1"}, { "ema-fast" , "3.8"} })),
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new TestCaseData(0.06m, OptimizationStepParameters,new ParameterSet(-1, new Dictionary<string, string>{{"ema-slow", "1.9"}, { "ema-fast" , "4.0"} })),
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new TestCaseData(0.05m, OptimizationMixedParameters, new ParameterSet(-1, new Dictionary<string, string>{{"ema-slow", "1"}, { "ema-fast" , "3.9" }, { "skipFromResultSum", "SPY" } })),
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new TestCaseData(0.06m, OptimizationMixedParameters, new ParameterSet(-1, new Dictionary<string, string>{{"ema-slow", "2"}, { "ema-fast" , "3.9" }, { "skipFromResultSum", "SPY" } }))
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};
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[Test, TestCaseSource(nameof(OptimizeWithConstraint))]
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public override void StepInsideWithConstraints(decimal drawdown, HashSet<OptimizationParameter> optimizationParameters, ParameterSet solution)
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{
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base.StepInsideWithConstraints(drawdown, optimizationParameters, solution);
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}
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private static TestCaseData[] OptimizeWithTarget => new[]
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{
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new TestCaseData(0m, OptimizationStepParameters, new ParameterSet(-1, new Dictionary<string, string>{{"ema-slow", "1"}, { "ema-fast" , "3"} })),
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new TestCaseData(4m, OptimizationStepParameters, new ParameterSet(-1, new Dictionary<string, string>{{"ema-slow", "1"}, { "ema-fast" , "3"} })),
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new TestCaseData(5m, OptimizationStepParameters, new ParameterSet(-1, new Dictionary<string, string>{{"ema-slow", "1"}, { "ema-fast" , "5"} })),
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new TestCaseData(8m, OptimizationStepParameters, new ParameterSet(-1, new Dictionary<string, string>{{"ema-slow", "3"}, { "ema-fast" , "5"} })),
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new TestCaseData(0m, OptimizationMixedParameters, new ParameterSet(-1, new Dictionary<string, string>{{"ema-slow", "1"}, { "ema-fast" , "3"}, { "skipFromResultSum", "SPY" } })),
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new TestCaseData(5m, OptimizationMixedParameters, new ParameterSet(-1, new Dictionary<string, string>{{"ema-slow", "1"}, { "ema-fast" , "5"}, { "skipFromResultSum", "SPY" } })),
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new TestCaseData(8m, OptimizationMixedParameters, new ParameterSet(-1, new Dictionary<string, string>{{"ema-slow", "3"}, { "ema-fast" , "5"}, { "skipFromResultSum", "SPY" } }))
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};
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[Test, TestCaseSource(nameof(OptimizeWithTarget))]
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public override void StepInsideWithTarget(decimal targetValue, HashSet<OptimizationParameter> optimizationParameters, ParameterSet solution)
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{
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base.StepInsideWithTarget(targetValue, optimizationParameters, solution);
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}
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private static TestCaseData[] OptimizeWithTargetNotReached => new[]
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{
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new TestCaseData(15m, OptimizationStepParameters, new ParameterSet(-1, new Dictionary<string, string>{{"ema-slow", "5.0"}, { "ema-fast" , "6.0" } })),
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new TestCaseData(155m, OptimizationMixedParameters, new ParameterSet(-1, new Dictionary<string, string>{{"ema-slow", "5"}, { "ema-fast" , "6.0"}, { "skipFromResultSum", "SPY" } }))
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};
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[Test, TestCaseSource(nameof(OptimizeWithTargetNotReached))]
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public override void TargetNotReached(decimal targetValue, HashSet<OptimizationParameter> optimizationParameters, ParameterSet solution)
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{
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base.TargetNotReached(targetValue, optimizationParameters, solution);
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}
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}
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}
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