public static void Go()
{
var encog = new EncogPersistedCollection("market-network.dat", FileMode.Open);
Console.WriteLine(@"Loading network");
var network = (BasicNetwork)encog.Find("market-network");
Console.WriteLine(@"Reading current data from db");
var market = new StockMarket();
market.Init(false);
var data = market.GetCurrentData();
Console.WriteLine(@"Running network on data");
var madness = new ModelMadness();
foreach (StockMarket.WorkableStockInfo info in data)
{
var input = InputOutputMadness.CreateInputs(info);
var neuralInput = new BasicMLData(input);
var output = network.Compute(neuralInput);
Console.WriteLine(@"Stock " + info.ViewToday.stock + @" will change " + output[0] + @"% in the next 20 trading days");
var future = new prediction
{
day = DateTime.Now.Date,
C20_Days_Close = 100 * (decimal)output[0],
stock = info.ViewToday.stock
};
madness.AddTopredictions(future);
}
madness.SaveChanges();
Console.WriteLine(@"Done - begin making $millions");
}
public static void Create()
{
var market = new StockMarket();
market.Init(true);
Console.WriteLine(@"Fetching training data from db");
var trainingData = market.GetTrainingData();
Console.WriteLine(@"Creating training set");
var inputs = new ArrayList();
var outputs = new ArrayList();
var cur = 1;
foreach (var info in trainingData)
{
Console.WriteLine(@"Adding record " + (cur++) + @" of " + trainingData.Count);
var input = InputOutputMadness.CreateInputs(info);
var output = new[] { InputOutputMadness.CreateOutput(info) };
inputs.Add(input);
outputs.Add(output);
}
Console.WriteLine(@"Created training set - saving");
var trainingSet = new BasicMLDataSet((double[][])inputs.ToArray(typeof(double[])), (double[][])outputs.ToArray(typeof(double[])));
var encog = new EncogPersistedCollection("market-training.dat", FileMode.Create);
encog.Add("market-training", trainingSet);
Console.WriteLine(@"saved");
}
