public override void Update(Bar bar)
{
double scale;
if (Data.Length < WindowSize)
{
// always normalize the first open price as (0, 10), and normalize other data points as (N, 10 * price / first open price)
scale = Data.Length == 0 ? DefaultPriceScale / bar.OpenPrice : DefaultPriceScale / Data[0].OpenPrice;
_intermediateResult.Add((double)Data.Length, bar.OpenPrice * scale);
_intermediateResult.Add((double)Data.Length, bar.ClosePrice * scale);
Data.Add(bar);
}
else
{
// existing data have been normalized, but newly added data will cause original (0, 1) being spoiled out,
// so we need to normalize it again.
var bar0 = Data[0];
scale = DefaultPriceScale / bar0.OpenPrice;
_intermediateResult.Remove(0.0, bar0.OpenPrice * scale);
_intermediateResult.Remove(0.0, bar0.ClosePrice * scale);
_intermediateResult.Add((double)WindowSize, bar.OpenPrice * scale);
_intermediateResult.Add((double)WindowSize, bar.ClosePrice * scale);
_intermediateResult.ShiftX(-1.0);
// now we use the newly first point to normalize the points
scale = bar0.OpenPrice / Data[1].OpenPrice;
_intermediateResult.ScaleY(scale);
Data.Add(bar);
}
if (Data.Length < 2)
{
SetValue(0.0, 0.0, 0.0, 0.0);
}
else
{
var result = _intermediateResult.Compute();
SetValue(result.Slope, result.Intercept, result.CorrelationCoefficient, result.SquareStandardError);
}
}
public void ShiftXTest()
{
var ir = new LinearRegression.IntermediateResult();
ir.Add(1.0, 2.0);
ir.Add(2.0, 3.0);
ir.Add(3.0, 4.0);
ir.ShiftX(-1.0);
var result = ir.Compute();
Assert.AreEqual(result.Intercept, 2.0);
Assert.AreEqual(result.Slope, 1.0);
Assert.AreEqual(result.CorrelationCoefficient, 1.0);
Assert.AreEqual(result.SquareStandardError, 0.0);
}