public decimal? linearCoeff(DateTime updateTime)
{
List<KeyValuePair<DateTime, Price>> values = _mktData[0].TimeSeries.Values(updateTime, _interval, false);
if (values == null)
return null;
if (values.Count < 2)
return null;
var V = new NRealMatrix(values.Count, 2);
var Vt = new NRealMatrix(values.Count, 2);
var Y = new NRealMatrix(values.Count, 1);
int idxRow = 0;
DateTime startTime = updateTime - _interval;
foreach (var keyVal in values)
{
V[idxRow, 0] = 1;
V[idxRow, 1] = (double)(keyVal.Key - startTime).TotalMilliseconds / 1000.0;
Vt[idxRow, 0] = V[idxRow, 0];
Vt[idxRow, 1] = V[idxRow, 1];
Y[idxRow, 0] = Convert.ToDouble(keyVal.Value.Mid() - values[0].Value.Mid()) * _interval.TotalMinutes;
idxRow += 1;
}
Vt.Transpose();
var VtV = Vt * V;
var VtY = Vt * Y;
var X = new NRealMatrix(2, 1);
//solve VtV * X = VtY
LapackLib.Instance.SolveSle(VtV, VtY, X);
if (X[1, 0] == double.NaN)
{
Log.Instance.WriteEntry("Invalid linear regression " + _mktData[0].Name, EventLogEntryType.Error);
return null;
}
return Convert.ToDecimal(X[1, 0]);
}
void nextStep()
{
// compute jacobian
if (_retry == 0)
{
_jac = _jac_func(_inputs, _weights);
}
// compute hessian approximation with tykhonov damping coefficient
var jacT = new NRealMatrix(_jac.Rows, _jac.Columns);
jacT.SetArray(_jac.ToArray());
jacT.Transpose();
var dampedHessian = new NRealMatrix(_jac.Columns, _jac.Columns);
dampedHessian = jacT * _jac;
for (int idxRow = 0; idxRow < dampedHessian.Rows; idxRow++)
{
dampedHessian.SetAt(idxRow, idxRow, new NDouble(dampedHessian[idxRow, idxRow] * (1.0 + _lambda) + 1e-10));
}
var adj = new NRealMatrix(dampedHessian.Rows, 1);
var y = new NRealMatrix(dampedHessian.Rows, 1);
y = jacT * _error;
// solve dampedHessian * adj = y
LapackLib.Instance.SolveSle(dampedHessian, y, adj);
var nextWeights = new NRealMatrix(1, _weights.Columns);
for (int idxWeight = 0; idxWeight < nextWeights.Columns; idxWeight++)
{
nextWeights.SetAt(0, idxWeight, new NDouble(_weights[0, idxWeight] - adj[idxWeight, 0]));
}
// compute errors
var error = calcError(nextWeights);
var totalError = calcTotalError(error);
if (totalError > _totalError)
{
// revert step and increase damping factor
if (_retry < 100)
{
_lambda *= 11.0;
_retry++;
}
else
{
updateWeights();
throw new StallException();
}
}
else
{
// accept step and decrease damping factor
_lambda /= 9.0;
_weights.SetArray(nextWeights.ToArray());
_error = error;
_totalError = totalError;
_retry = 0;
}
}
decimal calcDirCoeff(IEnumerable <KeyValuePair <decimal, DateTime> > values, DateTime updateTime)
{
if (values.Count() < 2)
{
return(0m);
}
if (values.Count() > _nbPeaks)
{
values = values.Take(_nbPeaks);
}
/*
* if (values.Count() > 2)
* {
* if (values.ElementAt(0).Value > values.ElementAt(2).Value && values.ElementAt(1).Value > values.ElementAt(2).Value && Math.Abs((values.ElementAt(0).Value - values.ElementAt(1).Value).TotalSeconds) > 120)
* values = values.Take(2);
* }*/
var V = new NRealMatrix(values.Count(), 2);
var Vt = new NRealMatrix(values.Count(), 2);
var Y = new NRealMatrix(values.Count(), 1);
int idxRow = 0;
var startTime = DateTime.MaxValue;
var startValue = 0m;
foreach (var kvp in values)
{
if (kvp.Value < startTime)
{
startTime = kvp.Value;
startValue = kvp.Key;
}
}
var interval = (updateTime - startTime);
foreach (var keyVal in values)
{
V[idxRow, 0] = 1;
V[idxRow, 1] = (double)(keyVal.Value - startTime).TotalMilliseconds / 1000.0;
Vt[idxRow, 0] = V[idxRow, 0];
Vt[idxRow, 1] = V[idxRow, 1];
Y[idxRow, 0] = Convert.ToDouble(keyVal.Key - startValue) * interval.TotalSeconds;
idxRow += 1;
}
Vt.Transpose();
var VtV = Vt * V;
var VtY = Vt * Y;
var X = new NRealMatrix(2, 1);
//solve VtV * X = VtY
LapackLib.Instance.SolveSle(VtV, VtY, X);
if (X[1, 0] == double.NaN)
{
Log.Instance.WriteEntry("IndicatorTrend: Invalid linear regression " + _mktData[0].Name, EventLogEntryType.Error);
return(0m);
}
return(Convert.ToDecimal(X[1, 0]));
}
public decimal?linearCoeff(DateTime updateTime)
{
List <KeyValuePair <DateTime, Price> > values = _mktData[0].TimeSeries.Values(updateTime, _interval, false);
if (values == null)
{
return(null);
}
if (values.Count < 2)
{
return(null);
}
var V = new NRealMatrix(values.Count, 2);
var Vt = new NRealMatrix(values.Count, 2);
var Y = new NRealMatrix(values.Count, 1);
int idxRow = 0;
DateTime startTime = updateTime - _interval;
foreach (var keyVal in values)
{
V[idxRow, 0] = 1;
V[idxRow, 1] = (double)(keyVal.Key - startTime).TotalMilliseconds / 1000.0;
Vt[idxRow, 0] = V[idxRow, 0];
Vt[idxRow, 1] = V[idxRow, 1];
Y[idxRow, 0] = Convert.ToDouble(keyVal.Value.Mid() - values[0].Value.Mid()) * _interval.TotalMinutes;
idxRow += 1;
}
Vt.Transpose();
var VtV = Vt * V;
var VtY = Vt * Y;
var X = new NRealMatrix(2, 1);
//solve VtV * X = VtY
LapackLib.Instance.SolveSle(VtV, VtY, X);
if (X[1, 0] == double.NaN)
{
Log.Instance.WriteEntry("Invalid linear regression " + _mktData[0].Name, EventLogEntryType.Error);
return(null);
}
return(Convert.ToDecimal(X[1, 0]));
}
void nextStep()
{
// compute jacobian
if (_retry == 0)
_jac = _jac_func(_inputs, _weights);
// compute hessian approximation with tykhonov damping coefficient
var jacT = new NRealMatrix(_jac.Rows, _jac.Columns);
jacT.SetArray(_jac.ToArray());
jacT.Transpose();
var dampedHessian = new NRealMatrix(_jac.Columns, _jac.Columns);
dampedHessian = jacT * _jac;
for (int idxRow = 0; idxRow < dampedHessian.Rows; idxRow++)
dampedHessian.SetAt(idxRow, idxRow, new NDouble(dampedHessian[idxRow, idxRow] * (1.0 + _lambda) + 1e-10));
var adj = new NRealMatrix(dampedHessian.Rows, 1);
var y = new NRealMatrix(dampedHessian.Rows, 1);
y = jacT * _error;
// solve dampedHessian * adj = y
LapackLib.Instance.SolveSle(dampedHessian, y, adj);
var nextWeights = new NRealMatrix(1, _weights.Columns);
for (int idxWeight = 0; idxWeight < nextWeights.Columns; idxWeight++)
nextWeights.SetAt(0, idxWeight, new NDouble(_weights[0, idxWeight] - adj[idxWeight, 0]));
// compute errors
var error = calcError(nextWeights);
var totalError = calcTotalError(error);
if (totalError > _totalError)
{
// revert step and increase damping factor
if (_retry < 100)
{
_lambda *= 11.0;
_retry++;
}
else
{
updateWeights();
throw new StallException();
}
}
else
{
// accept step and decrease damping factor
_lambda /= 9.0;
_weights.SetArray(nextWeights.ToArray());
_error = error;
_totalError = totalError;
_retry = 0;
}
}