public async Task CorrelationIdStaysTheSameAfterBloodyAsyncCalls()
{
var id = Correlation.GetId();
await Task.Delay(100);
var id2 = Correlation.GetId(setIfNotThere: false);
Assert.Equal(id, id2);
}
public void TestCleanup()
{
ICallContext context = CallContextManagerInstance.CallContextOverride;
ICallContext existingContext = context.ExistingCallContext();
Correlation.CorrelationEnd();
context.EndCallContext();
}
public void Correlation_constructor_test()
{
Correlation correlation = new Correlation(strategyHeader, date, value);
Assert.AreEqual(strategyHeader, correlation.Strategy);
Assert.AreEqual(strategyHeader.Id, correlation.StrategyId);
Assert.AreEqual(date, correlation.DateTime);
Assert.AreEqual(value, correlation.Value);
}
public void SpearmanCorrelationTest()
{
var dataA = _data["lottery"].Data.Take(200);
var dataB = _data["lew"].Data.Take(200);
var corr = Correlation.Spearman(dataA, dataB);
AssertHelpers.AlmostEqual(-0.0382856977898528, corr, 14);
}
public void CorrelationStart_WithNullData_ShouldStartNewCorrelation()
{
Correlation = new Correlation(new MemoryCorrelationHandler(), CallContextManagerInstance, MachineInformation);
Correlation.CorrelationStart(null);
Assert.NotNull(Correlation.CurrentCorrelation);
CallContextManagerInstance.CallContextOverride.EndCallContext();
}
/// <summary>
/// End a request
/// </summary>
private void EndRequest()
{
ICallContext context = CallContextManagerInstance.CallContextOverride;
ICallContext existingContext = context.ExistingCallContext();
Correlation.CorrelationEnd();
context.EndCallContext();
}
public void PearsonCorrelationTest()
{
var dataA = _data["lottery"].Data.Take(200);
var dataB = _data["lew"].Data.Take(200);
var corr = Correlation.Pearson(dataA, dataB);
AssertHelpers.AlmostEqual(-0.029470861580726, corr, 14);
}
// Executed when a new order has been created
public PendingOrder RiskManagement(PendingOrder pendingOrder, AgentState state)
{
//Check if we have enough data
if (!Agent.Bars[Agent.Security, (int)BarInterval.Day, -15].IsValid)
{
return(null);
}
//Check if we can calculate the correlation
else if (!corr.ContainsKey(Agent.Security))
{
return(null);
}
//Calculate correlation for each symbol
var basevalues = new List <double>();
for (int i = 0; i < 15; i++)
{
basevalues.Add((double)Agent.Bars[Agent.Security, (int)BarInterval.Day, i].Close);
}
//Get all symbol data
foreach (var symbol in corr.Keys.ToArray())
{
var values = new List <double>();
for (int i = 0; i < 15; i++)
{
values.Add((double)Agent.Bars[symbol, (int)BarInterval.Day, i].Close);
}
//calculate correlation coeff
corr[symbol] = Correlation.Pearson(basevalues.ToArray(), values.ToArray());
}
//Get item with the highest corr
var max = corr
.Where(x => x.Key != pendingOrder.Order.Security)
.Max(n => n.Value);
var security = corr
.Where(x => x.Value == max).FirstOrDefault().Key;
//Remove old position
if (lastsecurity != security && !Agent.Positions[security].IsFlat)
{
SubmitOrder(CreateOrder(lastsecurity.Name, Direction.Flat, Agent.Positions[security].FlatQuantity));
}
//Return our current order
lastsecurity = security;
decimal quantity = pendingOrder.Order.Quantity * (arbitrage / 100M);
quantity = quantity > 0.01M ? quantity : 0.01M;
quantity += Math.Abs(Agent.Positions[security].FlatQuantity);
return(CreateOrder(security.Name, pendingOrder.Order.Direction == Direction.Long ? Direction.Short : Direction.Long, quantity, 0, 0));
}
public void checkCorrelation(Evidence e2)
{
isCorrelating = false;
if (e2 == null)
{
Debug.Log("Check Correlation WARNING: argument 'Evidence e2' is null!");
return;
}
Debug.Log("Concluding a correlation with " + e2.id);
Evidence e1 = correlationPiece1;
if (e1.id == e2.id)
{
GameObject.Find("DialogueName").GetComponent <Text>().text = "Correlation cancelled";
GameObject.Find("DialogueContent").GetComponent <Text>().text = "";
Debug.Log("Cancelling correlation");
return;
}
bool isValidCorrelation = false;
// check if the static variable correlationPiece1 correlates with e2
// then repsond appropriately
Correlation foundC = null;
foreach (Correlation c in XMLRead.staticXMLR.validCorrelations)
{
if ((c.evidence0 == e1.id && c.evidence1 == e2.id) || (c.evidence0 == e2.id && c.evidence1 == e1.id))
{
isValidCorrelation = true;
foundC = c;
break;
}
}
Debug.Log("Valid correlation?" + (isValidCorrelation ? "Yes" : "No"));
GameObject.Find("DialogueName").GetComponent <Text>().text = isValidCorrelation ? "Correlation successful" : "Incorrect correlation";
GameObject.Find("DialogueContent").GetComponent <Text>().text = "";
if (isValidCorrelation)
{
// uIManager.changeFactionValue("police", 10);
// uIManager.changeFactionValue("mob", 10);
uIManager.changeFactionValue("stress", 10);
GameData.AddID(foundC.id);
}
else
{
// uIManager.changeFactionValue("police", -10);
// uIManager.changeFactionValue("mob", -10)
uIManager.changeFactionValue("stress", -25);
}
}
public void Correlate()
{
if (SelectedSignalCorrelate1 != null && SelectedSignalCorrelate1.HasData() && SelectedSignalCorrelate2 != null && SelectedSignalCorrelate2.HasData())
{
SampledSignal signal = new SampledSignal();
signal.PointsY = Correlation.ComputeSignal(SelectedSignalCorrelate1.PointsY, SelectedSignalCorrelate2.PointsY);
signal.Name = $"({SelectedSignalCorrelate1.Name})C({SelectedSignalCorrelate2.Name})";
SignalCreator.AddSignal(signal);
}
}
public void PearsonCorrelationConsistentWithCovariance()
{
var dataA = _data["lottery"].Data.Take(200).ToArray();
var dataB = _data["lew"].Data.Take(200).ToArray();
var direct = Correlation.Pearson(dataA, dataB);
var covariance = dataA.Covariance(dataB) / (dataA.StandardDeviation() * dataB.StandardDeviation());
AssertHelpers.AlmostEqual(covariance, direct, 14);
}
public static double GetCorrelationBetween(TimeSeries ts1, TimeSeries ts2)
{
DateTime[] commonDomain = ts1.GetCommonDates(ts2)
.OrderBy(date => date)
.ToArray();
IEnumerable <double> x = commonDomain.Select(date => ts1[date]);
IEnumerable <double> y = commonDomain.Select(date => ts2[date]);
return(Correlation.Pearson(x, y));
}
private static double LogReturnsCorrelation(ReadOnlySpan <double> priceSims1Start, ReadOnlySpan <double> priceSims1End,
ReadOnlySpan <double> priceSims2Start, ReadOnlySpan <double> priceSims2End)
{
Vector <double> logChanges1 = LogChange(priceSims1Start, priceSims1End);
Vector <double> logChanges2 = LogChange(priceSims2Start, priceSims2End);
double correlation = Correlation.Pearson(logChanges1, logChanges2);
return(correlation);
}
/// <summary>
/// Adds correlation header *unless* the passed value is null (allows for easier use when chaining methods calls)
/// </summary>
/// <param name="correlation"></param>
/// <returns></returns>
public WebRequestBuilder WithCorrelation(Correlation correlation, bool setIfNull = true)
{
if (correlation != null || setIfNull)
{
_webRequest.Headers[CorrelationKey] = JsonConvert.SerializeObject(correlation, new JsonSerializerSettings {
ContractResolver = new CamelCasePropertyNamesContractResolver()
});
}
return(this);
}
public async Task CorrelationIdStaysTheSameAfterBloodyAsyncCallsAndPublishEtw()
{
var id = Correlation.GetId();
await Task.Delay(100);
InstrumentationEventSource.Instance.WriteInstrumentationEvent("blah", "ff", 12, "gfg", id.ToString());
var id2 = Correlation.GetId(setIfNotThere: false);
Assert.Equal(id, id2);
}
public void ConstantWeightedPearsonCorrelationTest()
{
var dataA = _data["lottery"].Data.Take(200);
var dataB = _data["lew"].Data.Take(200);
var weights = Generate.Repeat(200, 2.0);
var corr = Correlation.Pearson(dataA, dataB);
var corr2 = Correlation.WeightedPearson(dataA, dataB, weights);
AssertHelpers.AlmostEqual(corr, corr2, 14);
}
/// <summary>Create a best-fit monic for the given points</summary>
public static Monic FromLinearRegression(IList <v2> points)
{
var corr = new Correlation();
foreach (var pt in points)
{
corr.Add(pt.x, pt.y);
}
return(corr.LinearRegression);
}
public void validateObviousData()
{
var axisX = new double[] { 1, 2, 3, 4, 5 };
var axisY = new double[] { 2, 4, 9, 16, 25 };
var correlationByPearsman = Correlation.Pearson(axisX, axisY);
var correlationBySpearsman = Correlation.Spearman(axisX, axisY);
Assert.IsTrue(correlationByPearsman > 0.9, $"Shitty library: pearsman validation failed");
Assert.IsTrue(correlationBySpearsman > 0.9, $"Shitty library: spearsman validation failed");
Warn.If(correlationByPearsman <0.9 && correlationByPearsman> 0.8, $"Better, but shitty, pearsman correlation");
Warn.If(correlationBySpearsman <0.9 && correlationBySpearsman> 0.8, $"Better, but shitty, spearsman correlation");
}
/// <summary>
/// Called on each bar update event (incoming tick)
/// </summary>
protected override void OnBarUpdate()
{
if (CurrentBarArray[0] > 0 && CurrentBarArray[1] > 0 && CurrentBarArray[2] > 0)
{
if (BarsInProgress == 0)
{
CloseOfOne.Set(Closes[1][0]);
CloseOfTwo.Set(Closes[2][0]);
double SumOfOne = 0;
int x = 0;
while (x < CorrelPeriod)
{
SumOfOne = SumOfOne + CloseOfOne[x];
x = x + 1;
}
double SumOfTwo = 0;
int y = 0;
while (y < CorrelPeriod)
{
SumOfTwo = SumOfTwo + CloseOfTwo[y];
y = y + 1;
}
double SumOfOneSq = 0;
int a = 0;
while (a < CorrelPeriod)
{
SumOfOneSq = SumOfOneSq + (CloseOfOne[a] * CloseOfOne[a]);
a = a + 1;
}
double SumOfTwoSq = 0;
int b = 0;
while (b < CorrelPeriod)
{
SumOfTwoSq = SumOfTwoSq + (CloseOfTwo[b] * CloseOfTwo[b]);
b = b + 1;
}
double Product = 0;
int z = 0;
while (z < CorrelPeriod)
{
Product = Product + (CloseOfOne[z] * CloseOfTwo[z]);
z = z + 1;
}
Correlation.Set(((CorrelPeriod * Product) - (SumOfOne * SumOfTwo)) / Math.Sqrt((((CorrelPeriod * SumOfOneSq) - (SumOfOne * SumOfOne)) * ((CorrelPeriod * SumOfTwoSq) - (SumOfTwo * SumOfTwo)))));
}
}
}
public void Correlation_ToString_test()
{
Correlation correlation = new Correlation(strategyHeader, date, value);
CultureInfo ci = CultureInfo.InvariantCulture;
string expect = String.Format("Correlation: {0}, {1}, {2}", correlation.StrategyId,
correlation.DateTime.ToString(ci),
correlation.Value.ToString("0.0000", ci));
Assert.AreEqual(expect, correlation.ToString());
}
private Correlation <Vector2>[] getFilteredCorrespondances(Heap assoc)
{
int cnew = ((int)(assoc.Count * 100 * F)) / 100;
Correlation <Vector2>[] ret = new Correlation <Vector2> [cnew];
for (int i = 0; i < cnew; i++)
{
ret[i] = (Correlation <Vector2>)assoc[assoc.Count - i - 1];
}
return(ret);
}
private void CountCorreletionInSeries()
{
for (int i = 0; i < AmountOfElements / 2; i++)
{
var coef = CountCorreletion(i);
Correlation.Add(coef);
if (CheckCoefOfCorr(i, coef) == 0)
{
IsCoefCorrSignificant = false;
}
}
}
public void SampleTest()
{
//Variances and Means
double[] Sdv = new double[2];
double[] Mean = new double[2];
for (int i = 0; i < 2; i++)
{
Sdv[i] = RandomVar();
Mean[i] = 5 * rnd.NextDouble();
}
//Cross Variance
double rho = 1.0 - RandomVar();
DensityLn <double[]> pdfLn = new DensityLn <double[]>(x => LogDen(x, Sdv, Mean, rho));
HybridMC Hybrid = new HybridMC(new double[2] {
0, 0
}, pdfLn, 10, 0.1, 1000);
Hybrid.BurnInterval = 0;
int SampleSize = 10000;
double[][] Sample = Hybrid.Sample(SampleSize);
double[][] NewSamples = ArrangeSamples(SampleSize, Sample);
double[] Convergence = new double[2];
double[] SampleMean = new double[2];
double[] SampleSdv = new double[2];
for (int i = 0; i < 2; i++)
{
Convergence[i] = 1 / Math.Sqrt(MCMCDiagnostics.EffectiveSize(Sample, x => x[i]));
DescriptiveStatistics Stats = new DescriptiveStatistics(NewSamples[i]);
SampleMean[i] = Stats.Mean;
SampleSdv[i] = Stats.StandardDeviation;
}
double SampleRho = Correlation.Pearson(NewSamples[0], NewSamples[1]);
for (int i = 0; i < 2; i++)
{
string index = i.ToString();
Assert.AreEqual(Mean[i], SampleMean[i], 10 * Convergence[i], index + "Mean");
Assert.AreEqual(SampleSdv[i] * SampleSdv[i], Sdv[i] * Sdv[i], 10 * Convergence[i], index + "Standard Deviation");
}
double ConvergenceRho = 1 / Math.Sqrt(MCMCDiagnostics.EffectiveSize(Sample, x => (x[0] - SampleMean[0]) * (x[1] - SampleMean[1])));
Assert.AreEqual(SampleRho * SampleSdv[0] * SampleSdv[1], rho * Sdv[0] * Sdv[1], 10 * ConvergenceRho, "Rho");
}
static void Main()
{
double [] a = new double [5] {
1, 2, 3, 4, 5
};
double [] b = new double [5] {
10, 9, 7, 5, 2
};
double rho = Correlation.Spearman(a, b);
Console.WriteLine("Spearman Rho is {0}", rho);
}
public void CurrentCorrelation_WithNoActiveCorrelation_ShouldReturnNull()
{
Correlation = new Correlation(new MemoryCorrelationHandler(), CallContextManagerInstance, MachineInformation);
Correlation.CorrelationStart(new CorrelationData());
while (CallContextManagerInstance.CallContextHandler(MachineInformation).ExistingCallContext() != null)
{
CallContextManagerInstance.CallContextHandler(MachineInformation).EndCallContext();
}
Assert.Null(Correlation.CurrentCorrelation);
}
private static WorkflowRun CreateWorkflowRun(WorkflowRunTrigger trigger)
{
var correlation = new Correlation(BogusGenerator.Random.String().OrNull(BogusGenerator)).OrNull(BogusGenerator);
var workflowRun = new WorkflowRun(
name: BogusGenerator.Internet.DomainWord(),
startTime: BogusGenerator.Date.Recent(),
status: GenerateStatus(),
error: BogusGenerator.Random.Bytes(10).OrNull(BogusGenerator),
correlation: correlation,
trigger: trigger);
return(workflowRun);
}
private static double PearsonR(SparseVector Vector_a, SparseVector Vector_b)
{
double correlation = Correlation.Pearson(Vector_a, Vector_b);
if (double.IsNaN(correlation))
{
// This means one of the row has 0 standard divation,
// it does not correlate to anyone
// so I assign the correlatino to be 0. however, strictly speaking, it should be left NaN
correlation = 0;
}
return(correlation);
}
public static double[] Test2()
{
var frame3 = Generators.InpulseSignal(1024, 128, 10);
var frame2 = Generators.Sin(1024, 4, 1);
var frame = Generators.Sin(1024, 64, 1);
//for (var i = 0; i < frame.Length; i++)
//{
// frame[i] += frame2[i];// + frame3[i];
//}
var func = Correlation.AutoCorrelation(TableFunction.TableToFunction(frame, 1, 0, 1024), 1, 0, 1024);
return(func);
}
internal static double MeasureCorrelation(double[] totals, double[] goods)
{
if (totals.Length <= 1 || goods.Length <= 1)
{
return(0);
}
var result = Math.Round(Correlation.Pearson(totals, goods), 3);
if (double.IsNaN(result))
{
return(0);
}
return(result);
}
private string GetCorrelation(Session currentSession)
{
var frametimes = currentSession.FrameTimes.Where((x, i) => !_session.AppMissed[i]);
var displayChangedTimes = currentSession.DisplayTimes.Where((x, i) => !_session.AppMissed[i]);
if (frametimes.Count() != displayChangedTimes.Count())
{
return("NaN");
}
var correlation = Correlation.Pearson(frametimes, displayChangedTimes);
return(Math.Round(correlation * 100, 0).ToString(CultureInfo.InvariantCulture) + "%");
}
