public static NeuralStockSettings GetDefault()
{
var obj = new NeuralStockSettings
{
InitialCash = 20000,
StartDate = DateTime.Today.AddYears(-1),
PercentageTraining = 0.6,
NumberANNs = 100,
NumberHiddenLayers = 1,
NumberNeuronsHiddenLayer = 15
};
return(obj);
}
public TrainingSession(StockPortfolio portfolio, Stock stock, BestNetworkDTO dto, NeuralStockSettings settings)
{
this._statisticsService = ApplicationHelper.CurrentCompositionContainer.GetExportedValue <IStatisticsService>();
this.Portfolio = portfolio;
this.Stock = stock;
this.TrainSamplePercentage = settings.PercentageTraining;
this.NumberAnns = settings.NumberANNs;
this.NumberHiddenLayers = settings.NumberHiddenLayers;
this.NumberNeuronsPerHiddenLayer = settings.NumberNeuronsHiddenLayer;
this.BuyLevel = dto.BuyLevel;
this.SellLevel = dto.SellLevel;
var strategy = new StrategyI(StrategySettings.FromJson(dto.StrategySettings))
{
TrainingMeansInput = dto.TrainingMeansInput?.ToArray(),
TrainingStdDevsInput = dto.TrainingStdDevsInput?.ToArray(),
TrainingMeansOutput = dto.TrainingMeansOutput?.ToArray(),
TrainingStdDevsOutput = dto.TrainingStdDevsOutput?.ToArray()
};
var tmpFileName = Path.GetTempFileName();
File.WriteAllBytes(tmpFileName, dto.BestNeuralNet);
var net = new NeuralNet(tmpFileName);
this._cachedPredictions.Clear();
this.SplitTrainTestData();
var trainingTestingData = this.PrepareAnnData(strategy, false);
var prediction = this.Predict(trainingTestingData, net, false);
var profitLossCalculator = new ProfitLossCalculator(this.Portfolio.Reset(), this, prediction.Item1);
this._cachedPredictions.Add(profitLossCalculator.PL, new Prediction(profitLossCalculator, strategy, net, prediction.Item2, prediction.Item3));
}
