/// <summary>
/// Method called in the backgound to validate the chromosome on out-of-sample data
/// </summary>
protected void ValidateOutOfSample(
FitnessResult insampeResult,
FitnessScore fitScore,
DateTime validationStartDate,
DateTime validationEndDate)
{
// Create a deep copy
var copy = (Chromosome)insampeResult.Chromosome.CreateNew();
// Run a backtest.
// Do not apply fitness filtering to validation results - let it expose a true picture ->
IFitness fitness;
if (Shared.Config.TaskExecutionMode == TaskExecutionMode.Azure)
{
fitness = new AzureFitness(validationStartDate, validationEndDate, fitScore, false);
fitness.EvaluateAsync(copy).GetAwaiter().GetResult();
}
else
{
fitness = new OptimizerFitness(validationStartDate, validationEndDate, fitScore, false);
fitness.EvaluateAsync(copy).GetAwaiter().GetResult();;
}
// Raise an event
ValidationCompleted?.Invoke(this, e:
new WalkForwardValidationEventArgs(insampeResult, copy.FitnessResult));
}
/// <summary>
/// Initializes a new instance of the <see cref="AlgorithmOptimumFinder"/> class
/// </summary>
/// <param name="start">Algorithm start date</param>
/// <param name="end">Algorithm end date</param>
/// <param name="fitScore">Argument of <see cref="FitnessScore"/> type. Fintess function to rank the backtest results</param>
/// <param name="filterEnabled">Indicates whether to apply fitness filter to backtest results</param>
public AlgorithmOptimumFinder(DateTime start, DateTime end, FitnessScore fitScore, bool filterEnabled)
{
// Assign Dates and Criteria to sort the results
StartDate = start;
EndDate = end;
FitnessScore = fitScore;
// Common properties
var selection = new RouletteWheelSelection();
// Properties specific to optimization modes
IFitness fitness;
PopulationBase population;
ITaskExecutor executor;
ITermination termination;
// Task execution mode
switch (Shared.Config.TaskExecutionMode)
{
// Enable fitness filtering while searching for optimum parameters
case TaskExecutionMode.Linear:
executor = new LinearTaskExecutor();
fitness = new OptimizerFitness(StartDate, EndDate, fitScore, filterEnabled);
break;
case TaskExecutionMode.Parallel:
executor = new ParallelTaskExecutor();
fitness = new OptimizerFitness(StartDate, EndDate, fitScore, filterEnabled);
break;
case TaskExecutionMode.Azure:
executor = new ParallelTaskExecutor();
fitness = new AzureFitness(StartDate, EndDate, fitScore, filterEnabled);
break;
default:
throw new Exception("Executor initialization failed");
}
// Optimization mode
switch (Shared.Config.OptimizationMode)
{
case OptimizationMode.BruteForce:
{
// Create cartesian population
population = new PopulationCartesian(Shared.Config.GeneConfigArray);
termination = new GenerationNumberTermination(1);
break;
}
case OptimizationMode.Genetic:
{
// Create random population
population = new PopulationRandom(Shared.Config.GeneConfigArray, Shared.Config.PopulationInitialSize)
{
GenerationMaxSize = Shared.Config.GenerationMaxSize
};
// Logical terminaton
var localTerm = new LogicalOrTermination();
localTerm.AddTermination(new FruitlessGenerationsTermination(3));
if (Shared.Config.Generations.HasValue)
{
localTerm.AddTermination(new GenerationNumberTermination(Shared.Config.Generations.Value));
}
if (Shared.Config.StagnationGenerations.HasValue)
{
localTerm.AddTermination(new FitnessStagnationTermination(Shared.Config.StagnationGenerations.Value));
}
termination = localTerm;
break;
}
default:
throw new Exception("Optimization mode specific objects were not initialized");
}
// Create GA itself
GenAlgorithm = new GeneticAlgorithm(population, fitness, executor)
{
// Reference types
Selection = selection,
Termination = termination,
// Values types
CrossoverParentsNumber = Shared.Config.CrossoverParentsNumber,
CrossoverMixProbability = Shared.Config.CrossoverMixProbability,
MutationProbability = Shared.Config.MutationProbability
};
}
