public override double GetSchemeProfit(StockHistorySet testData, AbstractPredictor Predictor, int today, int daysToPredict)
{
double profit = 0D;
// If prediction is higher, buy 1 unit. Otherwise, short 1 unit
foreach (StockHistory stockHistory in testData.AllStockHistories)
{
double startV = stockHistory.Closes[today];
double predictedEndV = Predictor.PredictValue(stockHistory, today, daysToPredict);
double actualEndV = stockHistory.Closes[today + daysToPredict];
if (Predictor is RandomPredictor)
{
;;
}
if (predictedEndV > startV)
{
profit += (actualEndV - startV);
}
else
{
profit += (startV - actualEndV);
}
}
return(profit / testData.AllStockHistories.Count);
}
public MainWindow()
{
InitializeComponent();
m_Data = new Sp500History();
m_ShareNum = 200;
AddFunctors();
AddSchemes();
UpdateImage();
}
public override double GetSchemeProfit(StockHistorySet testData, AbstractPredictor Predictor, int today, int daysToPredict)
{
double profit = 0D;
foreach (StockHistory stockHistory in testData.AllStockHistories)
{
double startV = stockHistory.Closes[today];
double actualEndV = stockHistory.Closes[today + daysToPredict];
profit += (actualEndV - startV);
}
return(profit / testData.AllStockHistories.Count);
}
public override double GetSchemeProfit(StockHistorySet testData, AbstractPredictor Predictor, int today, int daysToPredict)
{
var stocksInOrder = testData.AllStockHistories.OrderByDescending(x => random.NextDouble()).ToArray();
var tenRandom = stocksInOrder.Take(10).ToArray();
double profit = 0D;
foreach (StockHistory stockHistory in tenRandom)
{
double startV = stockHistory.Closes[today];
double actualEndV = stockHistory.Closes[today + daysToPredict];
profit += (actualEndV - startV);
}
return(profit / 10);
}
public void Setup(IEnumerable <IFunctor> functors, StockHistorySet stockHistorySet)
{
Fns = functors.ToArray();
m_StockHistorySet = stockHistorySet;
if (this.Fns == null)
{
throw new ApplicationException();
}
if (!Fns.Any())
{
throw new ApplicationException();
}
int numInput = Fns.Length;
int numHidden = 8;
int numOutput = 1;
int numWeights = (numInput * numHidden) + (numHidden * numOutput) + (numHidden + numOutput);
DebugWrite("Creating a " + numInput + "-input, " + numHidden + "-hidden, " + numOutput + "-output neural network");
DebugWrite("Using hard-coded tanh function for hidden layer activation");
DebugWrite("Using hard-coded log-sigmoid function for output layer activation");
m_Bnn = new BackPropNeuralNet(numInput, numHidden, numOutput);
DebugWrite("\nGenerating random initial weights and bias values");
double[] initWeights = new double[numWeights];
for (int i = 0; i < initWeights.Length; ++i)
{
initWeights[i] = (rnd.NextDouble() - 0.5d) * 1.0d;
}
DebugWrite("Loading neural network initial weights and biases into neural network");
m_Bnn.SetWeights(initWeights);
double learnRate = 0.2; // learning rate - controls the maginitude of the increase in the change in weights.
double momentum = 0.1; // momentum - to discourage oscillation.
DebugWrite("Setting learning rate = " + learnRate.ToString("F2") + " and momentum = " + momentum.ToString("F2"));
int maxEpochs = 8000000;
double errorThresh = 0.01;
DebugWrite("\nSetting max epochs = " + maxEpochs + " and error threshold = " + errorThresh.ToString("F6"));
// Train
int epoch = 0;
double error = double.MaxValue;
DebugWrite("\nBeginning training using back-propagation\n");
int stocksCount = stockHistorySet.AllStockHistories.Count();;
while (epoch < maxEpochs) // train
{
int stockNum = rnd.Next(0, stocksCount);
var stock = stockHistorySet.AllStockHistories[stockNum];
double realValue = stock.Closes[Today + DaysInFuture] / stock.Closes[Today];
double updateValue = Math.Tanh(realValue);
double predictedOutput = m_Bnn.ComputeOutputs(ComputeInputs(stock))[0];
double predictedValue = ATanh(predictedOutput) * stock.Closes[Today];
m_Bnn.UpdateWeights(new[] { updateValue }, learnRate, momentum);
++epoch;
if (epoch % 20000 == 0)
{
error = GetAverageError(true);
if (error < errorThresh)
{
DebugWrite("Found weights and bias values that meet the error criterion at epoch " + epoch);
break;
}
DebugWrite("epoch = " + epoch);
DebugWrite(" error = " + error + "\n");
}
} // train loop
double[] finalWeights = m_Bnn.GetWeights();
DebugWrite("");
DebugWrite("Final neural network weights and bias values are:");
Helpers.ShowVector(finalWeights, 5, 8, true);
}
/// <summary> /// How good the Predictor is with this purchasing scheme. Specifically, if you invested $1 in it, how much would you get back. /// </summary> /// <returns>The profit of this scheme</returns> public abstract double GetSchemeProfit(StockHistorySet testData, AbstractPredictor Predictor, int today, int daysToPredict);