public void EfficientFrontierPerformanceTest()
{
// Create new portfolio
var portf = new Portfolio("TestPortfolio");
// Create instruments from data
var instruments = from k in cov.Keys
select new Instrument(k, mean[k], cov[k]);
portf.AddRange(instruments);
// Add portfolio constraints
portf.AddAllInvestedConstraint();
portf.AddLongOnlyConstraint();
double rf = 0.05;
int runs = 100;
for (int c = 0; c < runs; c++)
{
PerformanceLogger.Start("EfficientFrontierTests", "EfficientFrontierPerformanceTest", "Optimization.CalcEfficientFrontier");
var res = PortfolioOptimizer.CalcEfficientFrontier(portf, rf, 50);
Console.WriteLine(c);
PerformanceLogger.Stop("EfficientFrontierTests", "EfficientFrontierPerformanceTest", "Optimization.CalcEfficientFrontier");
}
PerformanceLogger.WriteToCSV("performancedata.csv");
}
public void MaxSharpeRatioPortfolioTest()
{
// Create new portfolio
var portf = new Portfolio("TestPortfolio");
// Create instruments from data
var instruments = from k in cov.Keys
select new Instrument(k, mean[k], cov[k]);
portf.AddRange(instruments);
// Add portfolio constraints
portf.AddAllInvestedConstraint();
portf.AddLongOnlyConstraint();
//
double rf = 0.05 / 12;
var res = PortfolioOptimizer.CalcEfficientFrontier(portf, rf, 50);
var metrics = (from p in res
let sharpe = PortfolioAnalytics.SharpeRatio(p, rf)
select new { p.StdDev, p.Mean, sharpe });
foreach (var m in metrics)
{
Console.WriteLine("Risk {0}, Return {1}, Sharpe {2}, Sharpe Ratio {3} ", m.StdDev, m.Mean, (m.Mean - rf) / m.StdDev, m.sharpe);
}
var maxsharpe = (from p in res
let sharpe = PortfolioAnalytics.SharpeRatio(p, rf)
select sharpe).Max();
Console.WriteLine("Max Sharpe Ratio Portfolio {0}", maxsharpe);
}
public void ItRuns()
{
// Create new portfolio
var portf = new Portfolio("TestPortfolio");
// Create instruments from data
var instruments = from k in cov.Keys
select new Instrument(k, mean[k], cov[k]);
portf.AddRange(instruments);
// Add portfolio constraints
portf.AddAllInvestedConstraint();
portf.AddLongOnlyConstraint();
//
double rf = 0.05;
var res = PortfolioOptimizer.CalcEfficientFrontier(portf, rf, 50);
var riskreturn = from p in res
select new { p.StdDev, p.Mean };
foreach (var rr in riskreturn)
{
Console.WriteLine("Risk {0}, Return {1} ", rr.StdDev, rr.Mean);
}
}
static void Main(string[] args)
{
Dictionary <string, double> mean;
CovarianceMatrix cov;
// Load data
cov = CovarianceMatrix.Create("D:/repos/windows/portfolio_optimization/datasetcov.csv");
var norm = new Normal(0.008, 0.02);
norm.RandomSource = new Random(11);
var data = norm.Samples();
var iter = data.GetEnumerator();
var meankv = from k in cov.Keys
let d = iter.MoveNext()
select new KeyValuePair <string, double>(k, Math.Round(iter.Current, 4));
mean = meankv.ToDictionary(a => a.Key, b => b.Value);
//PortfolioOptimizer.Initialize();
// Create new portfolio
var portf = new Portfolio("TestPortfolio");
// Create instruments from data
var instruments = from k in cov.Keys
select new Instrument(k, mean[k], cov[k]);
portf.AddRange(instruments);
// Add portfolio constraints
portf.AddAllInvestedConstraint();
portf.AddLongOnlyConstraint();
double rf = 0.05;
int runs = 100;
for (int c = 0; c < runs; c++)
{
var res = PortfolioOptimizer.CalcEfficientFrontier(portf, rf, 50);
Console.WriteLine(c);
}
}