public static double[] PriceDataFE_pred(Series <DateTime, double> input)
{
int shiftn = 0;
var data = input.Shift(shiftn);
var MA3 = data.Window(3).Select(x => x.Value.Mean());
var MA5 = data.Window(5).Select(x => x.Value.Mean());
var MA10 = data.Window(10).Select(x => x.Value.Mean());
var dataMA3 = (data / MA3).Select(x => Math.Log(x.Value));
var dataMA5 = (data / MA5).Select(x => Math.Log(x.Value));
var dataMA10 = (data / MA10).Select(x => Math.Log(x.Value));
var MA3MA5 = (MA3 / MA5).Select(x => Math.Log(x.Value));
var MA3MA10 = (MA3 / MA10).Select(x => Math.Log(x.Value));
var MA5MA10 = (MA5 / MA10).Select(x => Math.Log(x.Value));
var MMP3 = data.Window(3).Select(x => Math.Log(x.Value.Max() / x.Value.Min()));
var MMP5 = data.Window(5).Select(x => Math.Log(x.Value.Max() / x.Value.Min()));
var MMP10 = data.Window(10).Select(x => Math.Log(x.Value.Max() / x.Value.Min()));
string dataMA3column = "PriceMA3" + Convert.ToString(shiftn);
string dataMA5column = "PriceMA5" + Convert.ToString(shiftn);
string dataMA10column = "PriceMA10" + Convert.ToString(shiftn);
string MA3MA5column = "MA3MA5PRICE" + Convert.ToString(shiftn);
string MA3MA10column = "MA3MA10PRICE" + Convert.ToString(shiftn);
string MA5MA10column = "MA5MA10PRICE" + Convert.ToString(shiftn);
string MMP3column = "MMP3" + Convert.ToString(shiftn);
string MMP5column = "MMP5" + Convert.ToString(shiftn);
string MMP10column = "MMP10" + Convert.ToString(shiftn);
var Features = new FrameBuilder.Columns <DateTime, string> {
{ dataMA3column, dataMA3 },
{ dataMA5column, dataMA5 },
{ dataMA10column, dataMA10 },
{ MA3MA5column, MA3MA5 },
{ MA3MA10column, MA3MA10 },
{ MA5MA10column, MA5MA10 },
{ MMP3column, MMP3 },
{ MMP5column, MMP5 },
{ MMP10column, MMP10 }
}.Frame;
var row_length = Features.RowCount;
var col_length = Features.ColumnCount;
double[] delay_1 = Features.GetRowAt <double>(row_length - 1).Values.ToArray <double>();
double[] features_pred = new double[col_length];
for (int i = 0; i < col_length; i++)
{
features_pred[i] = delay_1[i];
}
return(features_pred);
}
public static double[] Features_engineering_pred(Series <DateTime, double> input)
{
// Features engineering function for generating prediction input
int shiftn = 0;
var data = input.Shift(shiftn);
var data2 = data.Select(x => Math.Pow(x.Value, 2));
var MA3 = data.Window(3).Select(x => x.Value.Mean());
var MA5 = data.Window(5).Select(x => x.Value.Mean());
var MA10 = data.Window(10).Select(x => x.Value.Mean());
var diffdataMA3 = (data / MA3).Select(x => x.Value - 1);
var diffdataMA5 = (data / MA5).Select(x => x.Value - 1);
var diffdataMA10 = (data / MA10).Select(x => x.Value - 1);
var diffMA3MA5 = (MA3 / MA5).Select(x => x.Value - 1);
var diffMA3MA10 = (MA3 / MA10).Select(x => x.Value - 1);
var diffMA5MA10 = (MA5 / MA10).Select(x => x.Value - 1);
var SD3 = data.Window(3).Select(x => x.Value.StdDev());
var SD5 = data.Window(5).Select(x => x.Value.StdDev());
var SD10 = data.Window(10).Select(x => x.Value.StdDev());
var RSI3 = data.Window(3).Select(x => RSI_func(x, 3));
var RSI5 = data.Window(5).Select(x => RSI_func(x, 5));
var RSI10 = data.Window(10).Select(x => RSI_func(x, 10));
var K3 = data.Window(3).Select(x => K_func(x, 3));
var K5 = data.Window(5).Select(x => K_func(x, 5));
var K10 = data.Window(10).Select(x => K_func(x, 10));
string Returncolumn = "Return" + Convert.ToString(shiftn);
string Powercolumn = "Power" + Convert.ToString(shiftn);
string MA3column = "MA3" + Convert.ToString(shiftn);
string MA5column = "MA5" + Convert.ToString(shiftn);
string MA10column = "MA10" + Convert.ToString(shiftn);
string SD3column = "SD3" + Convert.ToString(shiftn);
string SD5column = "SD5" + Convert.ToString(shiftn);
string SD10column = "SD10" + Convert.ToString(shiftn);
string CompareDataColumn1 = "dataMA3" + Convert.ToString(shiftn);
string CompareDataColumn2 = "dataMA5" + Convert.ToString(shiftn);
string CompareDataColumn3 = "dataMA10" + Convert.ToString(shiftn);
string Compare1column = "MA3MA5" + Convert.ToString(shiftn);
string Compare2column = "MA3MA10" + Convert.ToString(shiftn);
string Compare3column = "MA5MA10" + Convert.ToString(shiftn);
string RSI3column = "RSI3" + Convert.ToString(shiftn);
string RSI5column = "RSI5" + Convert.ToString(shiftn);
string RSI10column = "RSI10" + Convert.ToString(shiftn);
string K3column = "K3" + Convert.ToString(shiftn);
string K5column = "K5" + Convert.ToString(shiftn);
string K10column = "K10" + Convert.ToString(shiftn);
var Features = new FrameBuilder.Columns <DateTime, string> {
{ Returncolumn, data },
{ Powercolumn, data2 },
{ MA3column, MA3 },
{ MA5column, MA5 },
{ MA10column, MA10 },
{ Compare1column, diffMA3MA5 },
{ Compare2column, diffMA3MA10 },
{ Compare3column, diffMA5MA10 },
{ CompareDataColumn1, diffdataMA3 },
{ CompareDataColumn2, diffdataMA5 },
{ CompareDataColumn3, diffdataMA10 },
{ SD3column, SD3 },
{ SD5column, SD5 },
{ SD10column, SD10 },
{ RSI3column, RSI3 },
{ RSI5column, RSI5 },
{ RSI10column, RSI10 },
{ K3column, K3 },
{ K5column, K5 },
{ K10column, K10 }
}.Frame;
var row_length = Features.RowCount;
var col_length = Features.ColumnCount;
double[] delay_1 = Features.GetRowAt <double>(row_length - 1).Values.ToArray <double>();
double[] delay_2 = Features.GetRowAt <double>(row_length - 2).Values.ToArray <double>();
double[] delay_3 = Features.GetRowAt <double>(row_length - 3).Values.ToArray <double>();
double[] features_pred = new double[col_length * 3];
for (int i = 0; i < col_length; i++)
{
features_pred[i] = delay_1[i];
}
for (int i = 0; i < col_length; i++)
{
features_pred[i + col_length] = delay_2[i];
}
for (int i = 0; i < col_length; i++)
{
features_pred[i + col_length * 2] = delay_3[i];
}
return(features_pred);
}