public void Evaluate(bool addIntercept_)
{
NMathCore.DoubleMatrix matrix = new CenterSpace.NMath.Core.DoubleMatrix(m_regressionMatrix);
NMathCore.DoubleVector obs = new CenterSpace.NMath.Core.DoubleVector(m_observations);
m_lin = new NMathStats.LinearRegression(matrix, obs, addIntercept_);
m_anova = new CenterSpace.NMath.Stats.LinearRegressionAnova(m_lin);
m_params = null;
NotifyAll();
}
public static NMathStats.LinearRegression DoRegression(SwapsSummaryRow summaryRow_, Instrument regressAgainstThis_, int numDays_ = 252, bool withIntercept_=true)
{
var ys = summaryRow_.GetRawStatSeries(StatCols.Values);
if (ys.Length < numDays_)
return null;
ys = ys.GetEndValues(numDays_);
var xs = regressAgainstThis_.A_Prices.Data.Slice(regressAgainstThis_.A_Prices.Length - numDays_, numDays_);
NMathCore.DoubleVector yV = new NMathCore.DoubleVector(ys.Data);
NMathCore.DoubleMatrix matrix = new NMathCore.DoubleMatrix(xs.ToColumn());
var lin = new NMathStats.LinearRegression(obs: yV, A: matrix, addIntercept: withIntercept_);
return lin;
}
internal static RegressionResult DoRegression(this ILevels levels_, RegressionArguments args_)
{
try
{
var myLevels = levels_.Levels;
var independentLevels = args_.Independent.Levels;
if (args_.RegressOn == RegressOn.Differences)
{
myLevels = myLevels.ToDifferences();
independentLevels = independentLevels.ToDifferences();
}
var m_regressionData =
myLevels.GetEndValues(args_.RegressionWindowLength).IntersectDates(independentLevels);
NMathCore.DoubleVector yV = new NMathCore.DoubleVector(m_regressionData.Data.Select(x => x[0]).ToArray());
NMathCore.DoubleMatrix matrix = new NMathCore.DoubleMatrix(m_regressionData.Data.Select(x => x[1]).ToArray().ToColumn());
var lin = new NMathStats.LinearRegression(obs: yV, A: matrix, addIntercept: true);
var anova = new NMathStats.LinearRegressionAnova(lin);
return new RegressionResult(args_.Independent, m_regressionData, lin, anova);
}
catch (Exception ex_)
{
Logger.Error(string.Format("Error recalculating regression for {0}", levels_.Name), typeof(RegressionExtensionMethods), ex_);
return null;
}
}
internal static DatedDataCollectionGen<double> GetRollingRegressionField(this ILevels levels_, RegressionArguments args_, RegressionField field_)
{
DatedDataCollectionGen<double> ret = null;
try
{
var myLevels = levels_.Levels;
var independentLevels = args_.Independent.Levels;
if (args_.RegressOn == RegressOn.Differences)
{
myLevels = myLevels.ToDifferences();
independentLevels = independentLevels.ToDifferences();
}
var m_regressionData =
myLevels.IntersectDates(independentLevels);
if (m_regressionData.Length < args_.RegressionWindowLength)
return null;
var myBucket = new List<double>();
var indepBucket = new List<double>();
var betas = new List<double>();
var dates = new List<DateTime>();
for (int i = 0; i < m_regressionData.Length; ++i)
{
myBucket.Add(m_regressionData.Data[i][0]);
indepBucket.Add(m_regressionData.Data[i][1]);
if (myBucket.Count < args_.RegressionWindowLength)
continue;
while (myBucket.Count > args_.RegressionWindowLength)
{
myBucket.RemoveAt(0);
indepBucket.RemoveAt(0);
}
var yV = new NMathCore.DoubleVector(myBucket.ToArray());
var matrix = new NMathCore.DoubleMatrix(indepBucket.ToArray().ToColumn());
try
{
var lin = new NMathStats.LinearRegression(obs: yV, A: matrix, addIntercept: true);
var anova = new NMathStats.LinearRegressionAnova(lin);
var result = new RegressionResult(args_.Independent, m_regressionData, lin, anova);
betas.Add(result[field_]);
dates.Add(m_regressionData.Dates[i]);
}
catch { }
}
ret = new DatedDataCollectionGen<double>(dates.ToArray(), betas.ToArray());
}
catch (Exception ex_)
{
Logger.Error("Error generated doing rolling beta", typeof(RegressionExtensionMethods), ex_);
return null;
}
return ret;
}