/// <summary>
/// Generate the input to the neural network to predict. It will look at the current
/// date as well as the number of days leading up to it specified by EvalWindow.
/// This method is used both internally and externally.
/// </summary>
/// <param name="marketData">The market data to use.</param>
/// <param name="marketDataIndex">The point that we want to predict from.</param>
/// <returns></returns>
public INeuralData CreateData(
List <LoadedMarketData> marketData,
int marketDataIndex)
{
INeuralData neuralData = new BasicNeuralData(14);
int totalPatterns = 0;
var patternCount = new int[14];
for (var i = 0; i < EvalWindow; i++)
{
var data = marketData[(marketDataIndex - EvalWindow) + i];
var candle = new IdentifyCandleStick();
candle.SetStats(data);
var pattern = candle.DeterminePattern();
if (pattern == IdentifyCandleStick.UNKNOWN)
{
continue;
}
totalPatterns++;
patternCount[pattern]++;
}
if (totalPatterns == 0)
{
return(null);
}
for (var i = 0; i < 14; i++)
{
neuralData[i] = patternCount[i] / ((double)totalPatterns);
}
return(neuralData);
}
/// <summary>
/// Generate the input to the neural network to predict. It will look at the current
/// date as well as the number of days leading up to it specified by EvalWindow.
/// This method is used both internally and externally.
/// </summary>
/// <param name="marketData">The market data to use.</param>
/// <param name="marketDataIndex">The point that we want to predict from.</param>
/// <returns></returns>
public INeuralData CreateData(
List<LoadedMarketData> marketData,
int marketDataIndex)
{
INeuralData neuralData = new BasicNeuralData(14);
int totalPatterns = 0;
int[] patternCount = new int[14];
for (int i = 0; i < EvalWindow; i++)
{
LoadedMarketData data = marketData[(marketDataIndex-EvalWindow) + i];
IdentifyCandleStick candle = new IdentifyCandleStick();
candle.SetStats(data);
int pattern = candle.DeterminePattern();
if (pattern != IdentifyCandleStick.UNKNOWN)
{
totalPatterns++;
patternCount[pattern]++;
}
}
if (totalPatterns == 0)
return null;
for (int i = 0; i < 14; i++)
{
neuralData[i] = ((double)patternCount[i]) / ((double)totalPatterns);
}
return neuralData;
}