public object DeepClone()
{
Model clone = new Model(old_price, H, output, output2, mt_MAPrice, Count, old_volatilities);
clone.old_volatilities = this.old_volatilities;
clone.mean_reversion_rate = this.mean_reversion_rate;
clone.mean_reversion_level = this.mean_reversion_level;
clone.dt = this.dt;
clone.variance_variance = this.variance_variance;
clone.risk_free_rate = this.risk_free_rate;
return clone;
}
private void Hoskin(int n, double L, double H, bool cum, double startPoint, double volatiles)
{
int m = n;// (int)Math.Pow(n, 2);
double[] phi=new double[m];
double[] cov = new double[m];
double[] psi = new double[m];
double[] output = new double[m];
double[] output2 = new double[m];
while (true)
{
double v = 1;
output[0]=snorm();
phi[0]=0;
for (int i = 0; i < m; i++)
cov[i]=covariance(i, H);
/* simulation */
for (int i = 1; i < m; i++)
{
phi[i - 1] = cov[i];
for (int j = 0; j < i - 1; j++)
{
psi[j] = phi[j];
phi[i - 1] -= psi[j] * cov[i - j - 1];
}
phi[i - 1] /= v;
for (int j = 0; j < i - 1; j++)
{
phi[j] = psi[j] - phi[i - 1] * psi[i - j - 2];
}
v *= (1 - phi[i - 1] * phi[i - 1]);
output[i] = 0;
for (int j = 0; j < i; j++)
{
output[i] += phi[j] * output[i - j - 1];
}
output[i] += Math.Sqrt(v) * snorm();
}
/* rescale to obtain a sample of size 2^(*n) on [0,L] */
double scaling = Math.Pow(L / m, H);
for (int i = 0; i < m; i++)
{
output2[i] = scaling * (output[i]);
if (cum && i > 0)
{
output2[i] += output[i - 1];
}
}
//Start the stopwatch
Stopwatch s = new Stopwatch();
s.Start();
//Set up the Model
List<double>outputv=new List<double>();
List<double>outputv2=new List<double>();
for (int i = 0; i < m; i++)
{
outputv.Add(output[i]);
outputv2.Add(output2[i]);
}
Model model = new Model(startPoint, H, outputv, outputv2, startPoint + 0.01000, m, volatiles);
//Set up the GA and run it
Int32 threads = 10;
GA ga = new GA(model,threads, 0.8, 0.0005, 1000);
ga.Go();
//===============================================================================================
//In this example we have set the model, progress reporter delegate (which is optional), number
//of threads, crossover rate, mutation rate, population size, number of generations
//===============================================================================================
//Get the results
s.Stop();
Console.WriteLine();
Console.WriteLine("Optimisation run finished in " + s.ElapsedMilliseconds.ToString() + " ms.");
double[] bestGenes; double bestFitness;
ga.GetBest(out bestGenes, out bestFitness);
if (bestFitness > 0.1)
{
double old_price = startPoint;//clo->stock_price;
double old_volatilities = volatiles; //bestGenes[0];//clo->variance;
double mean_reversion_rate = bestGenes[0] / 10000000;
double mean_reversion_level = bestGenes[1] / 1000000;
double dt = bestGenes[2] / 10000000;
double variance_variance = bestGenes[3] / 10000000;
double risk_free_rate = bestGenes[4] / 10000000;
for (int i = 0; i < n; i++)
{
double new_volatilities = old_volatilities +
mean_reversion_rate * (mean_reversion_level - old_volatilities) * dt +
variance_variance * output[i];
double new_price = old_price +
risk_free_rate * old_price * dt +
new_volatilities * old_price * output2[i]; //These are already scaled via hoskin */
Ticks.Add(new_price);
old_price = new_price;
old_volatilities = new_volatilities;
}
break;
}
}
/**/
/* for (int i = 0; i < n; i++)
{
/* double new_volatilities = old_volatilities +
mean_reversion_rate * (mean_reversion_level - old_volatilities) * dt +
variance_variance * output[i];
double new_price = old_price +
risk_free_rate * old_price * dt +
new_volatilities * old_price * output2[i]; //These are already scaled via hoskin */
/*double new_volatilities = old_volatilities + variance_variance*output[i];
double new_price = old_price + new_volatilities * output2[i];
Ticks.Add(new_price);
old_price = new_price;
old_volatilities = new_volatilities;
}*/
}
