public static ITimeSeries <double> RandomWalk(double drift, double stepsigma, int numsteps = 100, DataFrequency freq = DataFrequency.Daily)
{
var norm = new Normal(drift, stepsigma);
norm.RandomSource = new Random(RandomSeed);
var data = norm.Samples();
var walk = new List <double>();
var last = 1.0;
walk.Add(last);
int count = 0;
// Use TakeWhile ??
foreach (var d in data)
{
last = last + d;
walk.Add(last);
count++;
if (count >= numsteps)
{
break;
}
}
return(TimeSeriesFactory <double> .Create(walk, integrationOrder : 1, freq : freq));
}
public static ITimeSeries <double> Gaussian(double mu, double sigma, int numperiods = 100, DataFrequency freq = DataFrequency.Daily)
{
var norm = new Normal(mu, sigma);
norm.RandomSource = new Random(RandomSeed);
var data = norm.Samples();
return(TimeSeriesFactory <double> .Create(data, 0, numperiods, freq));
}
public static ITimeSeries <double> FirstDifference(this ITimeSeries <double> timeseries, ReturnMethod method = ReturnMethod.Geometric)
{
if (method == ReturnMethod.Arithmetic)
{
var res = from cur in timeseries.Skip(1)
select new TSDataPoint <double>(cur.Date, cur.Value / timeseries[(from prev in timeseries where prev.Date < cur.Date select prev.Date).Max()] - 1);
return(TimeSeriesFactory <double> .Create(res, timeseries.Name, timeseries.IntegrationOrder - 1, timeseries.Frequency));
}
else
{
var res = from cur in timeseries.Skip(1)
select new TSDataPoint <double>(cur.Date, Math.Log(cur.Value / timeseries[(from prev in timeseries where prev.Date < cur.Date select prev.Date).Max()]));
return(TimeSeriesFactory <double> .Create(res, timeseries.Name, timeseries.IntegrationOrder - 1, timeseries.Frequency));
}
}
