private byte[] ProcessBallJoint(RigidBodyJoint ballJoint)
{
try
{
List <byte> ballJointBytes = new List <byte>();
//Adds ID to signify that it's a rotational joint
ushort ID = 4;
byte[] ballJointID = BitConverter.GetBytes(ID);
ballJointBytes.AddRange(ballJointID);
foreach (AssemblyJoint joint in ballJoint.Joints)
{
//Adds the ID of the parent STL to the output array
STLData parentSTL = new STLData();
STLDictionary.TryGetValue(NameFilter(joint.OccurrenceOne.Name), out parentSTL);
byte[] parentIDBytes = BitConverter.GetBytes((uint)(parentSTL.getID()));
ballJointBytes.AddRange(parentIDBytes);
//Adds the ID of the child STL to the output array
STLData childSTL = new STLData();
STLDictionary.TryGetValue(NameFilter(joint.OccurrenceTwo.Name), out childSTL);
byte[] childIDBytes = BitConverter.GetBytes((uint)(childSTL.getID()));
ballJointBytes.AddRange(childIDBytes);
//Adds the vector parallel to the movement onto the array of bytes
ballJoint.GetJointData(out object GeometryOne, out object GeometryTwo, out NameValueMap nameMap);
Circle vectorCircle = (Circle)GeometryTwo;
byte[] vectorJointData = new byte[12];
BitConverter.GetBytes((float)vectorCircle.Center.X).CopyTo(vectorJointData, 0);
BitConverter.GetBytes((float)vectorCircle.Center.Y).CopyTo(vectorJointData, 4);
BitConverter.GetBytes((float)vectorCircle.Center.Z).CopyTo(vectorJointData, 8);
ballJointBytes.AddRange(vectorJointData);
//Adds the point of connection relative to the parent part
byte[] transJointData = new byte[12];
AssemblyJointDefinition jointData = joint.Definition;
BitConverter.GetBytes((float)jointData.OriginTwo.Point.X).CopyTo(transJointData, 0);
BitConverter.GetBytes((float)jointData.OriginTwo.Point.Y).CopyTo(transJointData, 4);
BitConverter.GetBytes((float)jointData.OriginTwo.Point.Z).CopyTo(transJointData, 8);
ballJointBytes.AddRange(transJointData);
//Adds the degrees of freedom
byte[] freedomData = new byte[8];
ModelParameter positionData = (ModelParameter)jointData.AngularPosition;
double relataivePosition = positionData._Value;
if (jointData.HasLinearPositionEndLimit)
{
positionData = (ModelParameter)jointData.AngularPositionEndLimit;
BitConverter.GetBytes((float)(Math.Abs(relataivePosition) - Math.Abs(positionData._Value))).CopyTo(freedomData, 0);
}
positionData = (ModelParameter)jointData.AngularPositionStartLimit;
BitConverter.GetBytes((float)(Math.Abs(relataivePosition) - Math.Abs(positionData._Value))).CopyTo(freedomData, 4);
ballJointBytes.AddRange(freedomData);
}
return(ballJointBytes.ToArray());
}
catch (Exception e)
{
//catches problems
MessageBox.Show(e.Message + "\n\n\n" + e.StackTrace);
return(null);
}
}
public void Initialize()
{
IForecastingModel model = null;
Models = new List <IForecastingModel>();
AnnModelParameter annPara = new AnnModelParameter();
model = new NeuralNetworkModel(annPara);
Models.Add(model);
Heiflow.AI.SVM.Parameter p = new Heiflow.AI.SVM.Parameter();
model = new SVMModel(p);
Models.Add(model);
ModelParameter mp = new ModelParameter();
model = new MLRModel(mp);
Models.Add(model);
Recognizers = new List <IRecognizer>();
foreach (var mm in Models)
{
var recognizer = new ImageRecognizer(mm, _IImageSetsBuilder, _IColorClassification);
Recognizers.Add(recognizer);
}
}
/// <summary>
/// Ctor for linear regression model
/// </summary>
public RegressionModelEquation(string modelName, double[] coefficients, double rmse, double minX, double maxX, double minY, double maxY, string nameX, string nameY, bool normalizeValues)
{
IsLinearRegression = true;
Name = modelName;
CreationDate = DateTime.Now;
NormalizeValues = normalizeValues;
_outputParameter = new ModelParameter
{
Id = XMLWork.FindIDWithName(nameY, Properties.Settings.Default.Languages),
Coefficient = 1,
LowerBound = minY,
UpperBound = maxY
};
_RMSE = rmse;
_inputParameters = new List <ModelParameter>
{
new ModelParameter
{
Id = null,
Coefficient = coefficients[0],
LowerBound = null,
UpperBound = null
},
new ModelParameter
{
Id = XMLWork.FindIDWithName(nameX, Properties.Settings.Default.Languages),
Coefficient = coefficients[1],
LowerBound = minX,
UpperBound = maxX
}
};
}
/// <summary>
/// 获取模型的参数信息
/// </summary>
/// <param name="fileData">模型文件数据</param>
/// <returns>模型参数</returns>
public static Ai.Hong.FileFormat.QuantModelFormat.QuantModelParameter GetModelParameter(byte[] fileData)
{
ModelParameter modelInfo = QuantGetModelParameter(fileData);
if (modelInfo.allFileCount == int.MaxValue)
{
return(null);
}
Ai.Hong.FileFormat.QuantModelFormat.QuantModelParameter retModelInfo = new QuantModelFormat.QuantModelParameter();
retModelInfo.allFileCount = modelInfo.allFileCount;
retModelInfo.calFileCount = modelInfo.calFileCount;
retModelInfo.innerTestFileCount = modelInfo.innerTestFileCount;
retModelInfo.outerTestFileCount = modelInfo.outerTestFileCount;
retModelInfo.fileFirstX = modelInfo.fileFirstX;
retModelInfo.fileLastX = modelInfo.fileLastX;
retModelInfo.fileXStep = modelInfo.fileXStep;
retModelInfo.fileSpecCols = modelInfo.fileSpecCols;
retModelInfo.modelFirstX = modelInfo.modelFirstX;
retModelInfo.modelLastX = modelInfo.modelLastX;
retModelInfo.modelSpecCols = modelInfo.modelSpecCols;
retModelInfo.modelStepX = modelInfo.modelStepX;
return(retModelInfo);
}
public async Task <IActionResult> CreateActivity([FromBody] JObject activitySpecs)
{
// basic input validation
string zipFileName = activitySpecs["zipFileName"].Value <string>();
string engineName = activitySpecs["engine"].Value <string>();
// define Activities API
dynamic oauth = await OAuthController.GetInternalAsync();
ActivitiesApi activitiesApi = new ActivitiesApi();
activitiesApi.Configuration.AccessToken = oauth.access_token;
// standard name for this sample
string appBundleName = zipFileName + "AppBundle";
string activityName = zipFileName + "Activity";
//
PageString activities = await activitiesApi.ActivitiesGetItemsAsync();
string qualifiedActivityId = string.Format("{0}.{1}+{2}", NickName, activityName, Alias);
if (!activities.Data.Contains(qualifiedActivityId))
{
// define the activity
// ToDo: parametrize for different engines...
string commandLine = string.Format(@"$(engine.path)\\{0} /i $(args[inputFile].path) /al $(appbundles[{1}].path)", Executable(engineName), appBundleName);
ModelParameter inputFile = new ModelParameter(false, false, ModelParameter.VerbEnum.Get, "input file", true, "$(inputFile)");
ModelParameter inputJson = new ModelParameter(false, false, ModelParameter.VerbEnum.Get, "input json", false, "params.json");
ModelParameter outputFile = new ModelParameter(false, false, ModelParameter.VerbEnum.Put, "output file", true, "outputFile.rvt");
Activity activitySpec = new Activity(
new List <string>()
{
commandLine
},
new Dictionary <string, ModelParameter>()
{
{ "inputFile", inputFile },
{ "inputJson", inputJson },
{ "outputFile", outputFile }
},
engineName, new List <string>()
{
string.Format("{0}.{1}+{2}", NickName, appBundleName, Alias)
}, null,
string.Format("Description for {0}", activityName), null, activityName);
Activity newActivity = await activitiesApi.ActivitiesCreateItemAsync(activitySpec);
// specify the alias for this Activity
Alias aliasSpec = new Alias(1, null, Alias);
Alias newAlias = await activitiesApi.ActivitiesCreateAliasAsync(activityName, aliasSpec);
return(Ok(new { Activity = qualifiedActivityId }));
}
// as this activity points to a AppBundle "dev" alias (which points to the last version of the bundle),
// there is no need to update it (for this sample), but this may be extended for different contexts
return(Ok(new { Activity = "Activity already defined" }));
}
public Model(ModelParameter instance, int batch, int inputChannels, int inputWidth, int inputHeight)
{
Instance = instance;
BatchCount = batch;
InputChannels = inputChannels;
InputWidth = inputWidth;
InputHeight = inputHeight;
}
internal GenerationLocationValues()
{
EffectsRotation = new Value.Boolean(false);
Type = new Value.Enum<ParameterType>();
Point = new PointParameter();
Sphere = new SphereParameter();
Model = new ModelParameter();
Circle = new CircleParameter();
}
internal GenerationLocationValues()
{
EffectsRotation = new Value.Boolean(false);
Type = new Value.Enum <ParameterType>();
Point = new PointParameter();
Sphere = new SphereParameter();
Model = new ModelParameter();
Circle = new CircleParameter();
}
/// <summary>
/// Initializes the object.
/// </summary>
/// <param name="startDate">The start date.</param>
/// <param name="endDate">The end date.</param>
/// <param name="frequency">The frequency of the dates generated.</param>
public ModelParameterDateSequence(DateTime startDate, DateTime endDate, DateFrequency frequency)
{
StartDate = startDate;
EndDate = endDate;
Frequency = frequency;
GenerateSequenceFromStartDate = true;
FollowFrequency = true;
SkipPeriods = new ModelParameter((double)0);
SkipPeriodsArray = new ModelParameter((double)0);
}
/// <summary>
/// Initializes the object.
/// </summary>
/// <param name="startDateExpression">The expression for the start date.</param>
/// <param name="endDateExpression">The expression for the end date.</param>
/// <param name="frequencyExpression">The expression for the frequency.</param>
public ModelParameterDateSequence(string startDateExpression, string endDateExpression, string frequencyExpression)
{
StartDateExpression = startDateExpression;
EndDateExpression = endDateExpression;
FrequencyExpression = frequencyExpression;
GenerateSequenceFromStartDate = true;
FollowFrequency = true;
SkipPeriods = new ModelParameter((double)0);
SkipPeriodsArray = new ModelParameter((double)0);
}
public ModelLocal(DBandMES dbandMES, DataBaseType type, Logger log)
{
_dbandMES = dbandMES;
ModelParameter dbParam = new ModelParameter {
DataBaseType = type,
UserName = _dbandMES.UserName,
PassWord = _dbandMES.PassWord,
Host = _dbandMES.IP,
Port = _dbandMES.Port,
DBorService = _dbandMES.DBName
};
InitDataBase(dbParam, log);
}
public ModelLocal(SQLSetting setting, DataBaseType type, Logger log)
{
_setting = setting;
ModelParameter dbParam = new ModelParameter {
DataBaseType = type,
UserName = _setting.UserName,
PassWord = _setting.PassWord,
Host = _setting.IP,
Port = _setting.Port,
DBorService = _setting.DBName
};
InitDataBase(dbParam, log);
}
public ModelOracle(OracleSetting oracleMESSetting, Logger log)
{
_setting = oracleMESSetting;
ModelParameter dbParam = new ModelParameter {
DataBaseType = DataBaseType.Oracle,
UserName = _setting.UserID,
PassWord = _setting.PassWord,
Host = _setting.Host,
Port = _setting.Port,
DBorService = _setting.ServiceName
};
InitDataBase(dbParam, log);
IDValue = "SEQ_EM_WQPF_ID.NEXTVAL";
}
/// <summary>
/// Main constructor for computable regression model
/// </summary>
/// <param name="outputParameterId">Id of the output parameter of the model</param>
/// <param name="inputParametersIds">List of Ids of input parameters of the model</param>
public ComputableRegressionModel(int outputParameterId, List <int> inputParametersIds)
{
_outputParameter = new ModelParameter {
Id = outputParameterId
};
_inputParameters = new List <ModelParameter>();
foreach (var parameterId in inputParametersIds)
{
_inputParameters.Add(new ModelParameter {
Id = parameterId
});
}
_modelTrained = false;
}
/// <summary>
/// Ctor for loading from database
/// </summary>
public RegressionModelEquation(int id, string name, DateTime creationDate, double rmse, int outputParamId, List <ModelParameter> inputParameters, bool normalizeValues)
{
IsLinearRegression = inputParameters.Count == 2;
Id = id;
Name = name;
CreationDate = creationDate;
NormalizeValues = normalizeValues;
_RMSE = rmse;
_outputParameter = new ModelParameter
{
Id = outputParamId,
Coefficient = 1,
LowerBound = null,
UpperBound = null
};
_inputParameters = inputParameters;
}
/// <summary>
/// 获取模型参数信息
/// </summary>
/// <param name="fileData"></param>
/// <returns></returns>
private static ModelParameter QuantGetModelParameter(byte[] fileData)
{
try
{
IntPtr retptr = IntPtr.Zero;
if (fileData == null || fileData.Length == 0)
{
throw new Exception("Invalid Parameter");
}
if (CommonMethod.Is64BitVersion())
{
retptr = SPAGetModelParameter64(fileData, fileData.Length);
}
else
{
retptr = SPAGetModelParameter32(fileData, fileData.Length);
}
if (retptr == IntPtr.Zero)
{
throw new Exception(FileFormat.GetDLLErrorMessage());
}
bool retOK = true;
var retData = CommonMethod.CopyStructureFromIntptrAndFree <ModelParameter>(ref retptr, out retOK);
if (!retOK)
{
ErrorString = "Invalid data reading";
retData.allFileCount = int.MaxValue; //错误
}
return(retData);
}
catch (Exception ex)
{
ErrorString = ex.Message;
var retData = new ModelParameter();
retData.allFileCount = int.MaxValue;
return(retData);
}
}
// Use this for initialization
void Start()
{
if (!cell)
{
throw new System.Exception("Cell not found!!!");
}
GameController gc = GameObject.Find("GameController").GetComponent <GameController>();
mParameter = gc.Parameter;
// Overwrite parameter from unity to match screen
mParameter.EpithelialCellsPerRow = cellHorizontalCount;
mParameter.EpithelialCellsPerColumn = cellVerticalCount;
// Initializing chemokine concentration to 0
mCurrentCheConcentration = Vector <double> .Build.Dense(mParameter.EpithelialCellsPerRow *mParameter.EpithelialCellsPerColumn, 0);
//Initialize our 2D Transform array with the width and height
cellMap = new GameObject[cellHorizontalCount, cellVerticalCount];
//Iterate over each future tile positions for x and y
for (int z = 0; z < cellVerticalCount; z++)
{
for (int x = 0; x < cellHorizontalCount; x++)
{
//Instantiate tile prefab at the desired position as a Transform object
//var offset = z % 2 == 0 ? cellTileWidth / 2 : 0;
//var xCoord = x * cellTileWidth - gc.Width - offset;
//var zCoord = z * cellTileHeight - gc.Height;
var xCoord = x * gc.Width / cellHorizontalCount - gc.Width / 2;
var zCoord = z * gc.Height / cellVerticalCount - gc.Height / 2;
Transform tile = Instantiate(cell.transform, new Vector3(xCoord, 0, zCoord), Quaternion.identity, transform) as Transform;
//Set the tiles parent to the GameObject this script is attached to
//tile.parent = transform;
//Set the 2D map array element to the current tile that we just created.
cellMap[x, z] = tile.gameObject;
}
}
StartCoroutine(CalculateChemokine());
}
/// <summary>
/// Assigns new trial values for x in order to use it
/// for the evaluation of the constraints and the objective function.
/// </summary>
/// <param name="project">The project where to evaluate.</param>
/// <param name="x">The vector of x values.</param>
/// <returns>A <see cref="SquaredGaussianModel"/> set with the values.</returns>
internal static SquaredGaussianModel Assign(ProjectROV project, Vector x)
{
Engine.Parser.NewContext();
SquaredGaussianModel model = project.Processes[0].Plugin as SquaredGaussianModel;
// Assign the new values.
ModelParameter a1 = model.a1 as ModelParameter;
ModelParameter s1 = model.sigma1 as ModelParameter;
a1.m_Value = (RightValue)x[0];
s1.m_Value = (RightValue)x[1];
bool errors = model.Parse(null);
if (errors)
{
throw new Exception("Cannot Assing model");
}
return(model);
}
/// <summary>
/// Ctor for multiple regression model
/// </summary>
public RegressionModelEquation(string modelName, string outputParamName, List <string> inputParamNames, double rmse, double[] equationCoefficients,
string[] equationParametersNames, double[] equationParametersLowerBounds, double[] equationParametersUpperBounds, bool normalizeValues)
{
IsLinearRegression = false;
Name = modelName;
CreationDate = DateTime.Now;
NormalizeValues = normalizeValues;
_outputParameter = new ModelParameter
{
Id = XMLWork.FindIDWithName(outputParamName, Properties.Settings.Default.Languages),
Coefficient = 1,
LowerBound = null,
UpperBound = null
};
_RMSE = rmse;
_inputParameters = new List <ModelParameter>
{
new ModelParameter
{
Id = null,
Coefficient = equationCoefficients[0],
LowerBound = null,
UpperBound = null
}
};
for (int i = 1; i < equationCoefficients.Length; i++)
{
_inputParameters.Add(new ModelParameter
{
Id = XMLWork.FindIDWithName(equationParametersNames[i - 1], Properties.Settings.Default.Languages),
Coefficient = equationCoefficients[i],
LowerBound = equationParametersLowerBounds[i - 1],
UpperBound = equationParametersUpperBounds[i - 1]
});
}
}
/// <summary>
/// Initializes the object.
/// </summary>
/// <param name="startDateExpression">The expression for the start date.</param>
/// <param name="endDateExpression">The expression for the end date.</param>
/// <param name="frequencyExpression">The expression for the frequency.</param>
public ModelParameterDateSequence(string startDateExpression, string endDateExpression, string frequencyExpression)
{
StartDateExpression = startDateExpression;
EndDateExpression = endDateExpression;
FrequencyExpression = frequencyExpression;
GenerateSequenceFromStartDate = true;
FollowFrequency = true;
SkipPeriods = new ModelParameter((double)0);
}
/// <summary>
/// Initializes the object.
/// </summary>
/// <param name="startDate">The start date.</param>
/// <param name="endDate">The end date.</param>
/// <param name="frequency">The frequency of the dates generated.</param>
public ModelParameterDateSequence(DateTime startDate, DateTime endDate, DateFrequency frequency)
{
StartDate = startDate;
EndDate = endDate;
Frequency = frequency;
GenerateSequenceFromStartDate = true;
FollowFrequency = true;
SkipPeriods = new ModelParameter((double)0);
}
/// <summary>
/// Initializes the object based on the serialized data.
/// </summary>
/// <param name="info">The SerializationInfo that holds the serialized object data.</param>
/// <param name="context">The StreamingContext that contains contextual
/// information about the source.</param>
public ModelParameterDateSequence(SerializationInfo info, StreamingContext context)
: base(info, context)
{
// Set the default for GenerateSequenceFromStartDate and FollowFrequency
GenerateSequenceFromStartDate = true;
FollowFrequency = true;
int serialializedVersion;
try
{
serialializedVersion = info.GetInt32("_VersionDateSequence");
}
catch
{
serialializedVersion = 0;
}
if (serialializedVersion < 2)
{
#region No expressions
try
{
this.StartDate = new DateTime(info.GetInt64("_StartDate"));
this.EndDate = new DateTime(info.GetInt64("_EndDate"));
}
catch (Exception)
{
// In case the calibration time was serialized in a different way.
this.StartDate = info.GetDateTime("_StartDate");
this.EndDate = info.GetDateTime("_EndDate");
}
int frequency = info.GetInt32("_Frequency");
Array enumValues = Enum.GetValues(typeof(DateFrequency));
foreach (DateFrequency value in enumValues)
{
if ((int)value == frequency)
{
Frequency = value;
break;
}
}
if (serialializedVersion >= 1)
{
// Introduction of ExcludeStartDate
bool exclude = info.GetBoolean("_ExcludeStartDate");
SkipPeriods = exclude ? new ModelParameter(1) : new ModelParameter((double)0);
}
else
{
SkipPeriods = new ModelParameter((double)0);
}
#endregion // No expressions
}
else
{
if (serialializedVersion < 4)
{
string tmpS = info.GetString("_StartDateExpression");
DateTime tmpD;
if (DateTime.TryParseExact(tmpS, "yyyy-MM-dd", CultureInfo.CurrentCulture, DateTimeStyles.None, out tmpD))
StartDate = tmpD;
else
StartDateExpression = new ModelParameter(tmpS);
tmpS = info.GetString("_EndDateExpression");
if (DateTime.TryParseExact(tmpS, "yyyy-MM-dd", CultureInfo.CurrentCulture, DateTimeStyles.None, out tmpD))
EndDate = tmpD;
else
EndDateExpression = new ModelParameter(tmpS);
}
else
{
StartDateExpression = (ModelParameter)info.GetValue("_StartDateExpression", typeof(ModelParameter));
EndDateExpression = (ModelParameter)info.GetValue("_EndDateExpression", typeof(ModelParameter));
SetupExportedIDs();
}
FrequencyExpression = info.GetString("_FrequencyExpression");
if (serialializedVersion >= 3)
{
// FollowFrequency and GenerateSequenceFromStartDate in version 3
FollowFrequency = info.GetBoolean("_FollowFrequency");
GenerateSequenceFromStartDate = info.GetBoolean("_GenerateSequenceFromStartDate");
}
// Skip Periods introduced in version 5
if (serialializedVersion < 5)
{
bool exclude = info.GetBoolean("_ExcludeStartDate");
SkipPeriods = exclude ? new ModelParameter(1) : new ModelParameter((double)0);
}
else
{
SkipPeriods = (ModelParameter)info.GetValue("_SkipPeriods", typeof(ModelParameter));
}
}
}
public void Test()
{
Engine.MultiThread = true;
Document doc = new Document();
ProjectROV rov = new ProjectROV(doc);
doc.Part.Add(rov);
doc.DefaultProject.NMethods.m_UseAntiteticPaths = true;
int n_sim = 20000;
int n_steps = 1024;
double a = 0.2;
double DR = 0.02;
double r0 = 0.015;
double a1 = 0.02;
double sigma1 = 0.01;
double maturityOpt = 5.0;
double strike = 0.005;
double tau = 0.5;
double strike2 = 1.0 / (1.0 + strike * tau);
ModelParameter PT = new ModelParameter(maturityOpt, "TT");
PT.VarName = "TT";
rov.Symbols.Add(PT);
ModelParameter Ptau = new ModelParameter(tau, "tau");
Ptau.VarName = "tau";
rov.Symbols.Add(Ptau);
ModelParameter Pa = new ModelParameter(a, "a");
Pa.VarName = "a";
rov.Symbols.Add(Pa);
ModelParameter PDR = new ModelParameter(DR, "PDR");
PDR.VarName = "DR";
rov.Symbols.Add(PDR);
ModelParameter Pr0 = new ModelParameter(r0, "r0");
Pr0.VarName = "r0";
rov.Symbols.Add(Pr0);
ModelParameter Pstrike = new ModelParameter(strike, "strike");
Pstrike.VarName = "strike";
rov.Symbols.Add(Pstrike);
AFunction zerorate = new AFunction(rov);
zerorate.VarName = "zr";
zerorate.m_IndependentVariables = 1;
zerorate.m_Value = (RightValue)("(1-exp(-a*x1))*DR + r0");
rov.Symbols.Add(zerorate);
HW1 process = new HW1(a1, sigma1, "@zr");
StochasticProcessExtendible s = new StochasticProcessExtendible(rov, process);
rov.Processes.AddProcess(s);
// Set the discounting.
RiskFreeInfo rfi = rov.GetDiscountingModel() as RiskFreeInfo;
rfi.ActualizationType = EActualizationType.Stochastic;
rfi.m_deterministicRF = (ModelParameter)"@V1";
OptionTree op = new OptionTree(rov);
// 1) RATE FUNCTION, with this the price is higher than the theoretical one
// op.PayoffInfo.PayoffExpression = "tau*max(rate(TT;tau;@v1) - strike; 0)";
// 2) OBTAIN RATE FROM bond = exp(-rate*t),
// with this the price is higher than the theoretical one but it's more near than 1)
// op.PayoffInfo.PayoffExpression = "tau*max(-ln(bond(TT;TT+tau;@v1))/tau - strike; 0)";
// 3) CONVERT RATE from discrete to continuous through (1+r_d) = exp(r_c)
// In this way the price is the same as the theoretical one.
op.PayoffInfo.PayoffExpression = "tau*max(ln(1+rate(TT;tau;@v1)) - strike; 0)";
op.PayoffInfo.Timing.EndingTime.m_Value = (RightValue)maturityOpt;
op.PayoffInfo.European = true;
rov.Map.Root = op;
rov.NMethods.Technology = ETechType.T_SIMULATION;
rov.NMethods.PathsNumber = n_sim;
rov.NMethods.SimulationSteps = n_steps;
ROVSolver solver = new ROVSolver();
solver.BindToProject(rov);
solver.DoValuation(-1);
if (rov.HasErrors)
{
Console.WriteLine(rov.m_RuntimeErrorList[0]);
}
Assert.IsFalse(rov.HasErrors);
ResultItem price = rov.m_ResultList[0] as ResultItem;
double samplePrice = price.value;
double sampleDevSt = price.stdDev / Math.Sqrt(2.0 * (double)n_sim);
// Calculation of the theoretical value of the caplet.
CapHW1 cap = new CapHW1(zerorate);
double theoreticalPrice = cap.HWCaplet(a1, sigma1, maturityOpt,
maturityOpt + tau, strike2);
Console.WriteLine("Theoretical Price = " + theoreticalPrice.ToString());
Console.WriteLine("Monte Carlo Price = " + samplePrice);
Console.WriteLine("Standard Deviation = " + sampleDevSt.ToString());
double tol = 4.0 * sampleDevSt;
Assert.Less(Math.Abs(theoreticalPrice - samplePrice), tol);
}
public void TestSimulation()
{
Engine.MultiThread = true;
Document doc = new Document();
ProjectROV rov = new ProjectROV(doc);
doc.Part.Add(rov);
doc.DefaultProject.NMethods.m_UseAntiteticPaths = true;
int n_sim = 10000;
int n_steps = 512;
double strike = 90.0;
double maturity = 2.0;
double rate = 0.1;
double dy = 0.05;
double volatility = 0.2;
double S0 = 100;
ModelParameter Pstrike = new ModelParameter(strike, string.Empty, "strike");
rov.Symbols.Add(Pstrike);
ModelParameter PS0 = new ModelParameter(S0, string.Empty, "S0");
rov.Symbols.Add(PS0);
AFunction payoff = new AFunction(rov);
payoff.VarName = "payoff";
payoff.m_IndependentVariables = 1;
payoff.m_Value = (RightValue)("max(x1 - strike ; 0)");
rov.Symbols.Add(payoff);
AFunction rfunc = new AFunction(rov);
rfunc.VarName = "r";
rfunc.m_IndependentVariables = 1;
rfunc.m_Value = (RightValue)rate;
rov.Symbols.Add(rfunc);
AFunction qfunc = new AFunction(rov);
qfunc.VarName = "q";
qfunc.m_IndependentVariables = 1;
qfunc.m_Value = (RightValue)dy;
rov.Symbols.Add(qfunc);
AFunction volfunc = new AFunction(rov);
volfunc.VarName = "localvol";
volfunc.m_IndependentVariables = 2;
volfunc.m_Value = (RightValue)volatility;
rov.Symbols.Add(volfunc);
DupireProcess process = new DupireProcess();
process.s0 = (ModelParameter)"S0";
process.r = (ModelParameter)"@r";
process.q = (ModelParameter)"@q";
process.localVol = (ModelParameter)"@localvol";
StochasticProcessExtendible s = new StochasticProcessExtendible(rov, process);
rov.Processes.AddProcess(s);
// Set the discounting.
RiskFreeInfo rfi = rov.GetDiscountingModel() as RiskFreeInfo;
rfi.ActualizationType = EActualizationType.RiskFree;
rfi.m_deterministicRF = rate;
OptionTree op = new OptionTree(rov);
op.PayoffInfo.PayoffExpression = "payoff(v1)";
op.PayoffInfo.Timing.EndingTime.m_Value = (RightValue)maturity;
op.PayoffInfo.European = true;
rov.Map.Root = op;
rov.NMethods.Technology = ETechType.T_SIMULATION;
rov.NMethods.PathsNumber = n_sim;
rov.NMethods.SimulationSteps = n_steps;
ROVSolver solver = new ROVSolver();
solver.BindToProject(rov);
solver.DoValuation(-1);
if (rov.HasErrors)
{
rov.DisplayErrors();
}
Assert.IsFalse(rov.HasErrors);
ResultItem price = rov.m_ResultList[0] as ResultItem;
double samplePrice = price.value;
double sampleDevSt = price.stdDev / Math.Sqrt((double)n_sim);
// Calculation of the theoretical value of the call.
double theoreticalPrice = BlackScholes.Call(rate, S0, strike, volatility, maturity, dy);
Console.WriteLine("Theoretical Price = " + theoreticalPrice.ToString());
Console.WriteLine("Monte Carlo Price = " + samplePrice);
Console.WriteLine("Standard Deviation = " + sampleDevSt.ToString());
double tol = 4.0 * sampleDevSt;
Assert.LessOrEqual(Math.Abs(theoreticalPrice - samplePrice), tol);
}
public void Test()
{
Engine.MultiThread = true;
Document doc = new Document();
ProjectROV rov = new ProjectROV(doc);
doc.Part.Add(rov);
doc.DefaultProject.NMethods.m_UseAntiteticPaths = true;
int n_sim = 100000;
int n_steps = 256;
double strike = 90.0;
double tau = 2.0;
double rate = 0.1;
double dy = 0.07;
ModelParameter pStrike = new ModelParameter(strike, "strike");
pStrike.VarName = "strike";
rov.Symbols.Add(pStrike);
AFunction payoff = new AFunction(rov);
payoff.VarName = "payoff";
payoff.m_IndependentVariables = 1;
payoff.m_Value = (RightValue)("max(x1 - strike ; 0)");
rov.Symbols.Add(payoff);
AFunction zrfunc = new AFunction(rov);
zrfunc.VarName = "zr";
zrfunc.m_IndependentVariables = 1;
zrfunc.m_Value = (RightValue)rate;
rov.Symbols.Add(zrfunc);
AFunction dyfunc = new AFunction(rov);
dyfunc.VarName = "dy";
dyfunc.m_IndependentVariables = 1;
dyfunc.m_Value = (RightValue)dy;
rov.Symbols.Add(dyfunc);
HestonExtendedProcess process = new HestonExtendedProcess();
process.k = (ModelParameter)2.5;
process.theta = (ModelParameter)0.4;
process.sigma = (ModelParameter)0.2;
process.S0 = (ModelParameter)100.0;
process.V0 = (ModelParameter)0.3;
process.zrReference = (ModelParameter)"@zr";
process.dyReference = (ModelParameter)"@dy";
double discount = Math.Exp(-rate * tau);
StochasticProcessExtendible s = new StochasticProcessExtendible(rov, process);
rov.Processes.AddProcess(s);
// Set the discounting.
RiskFreeInfo rfi = rov.GetDiscountingModel() as RiskFreeInfo;
rfi.ActualizationType = EActualizationType.RiskFree;
rfi.m_deterministicRF = 0.0;
OptionTree op = new OptionTree(rov);
op.PayoffInfo.PayoffExpression = "payoff(v1a)";
op.PayoffInfo.Timing.EndingTime.m_Value = (RightValue)tau;
op.PayoffInfo.European = true;
rov.Map.Root = op;
rov.NMethods.Technology = ETechType.T_SIMULATION;
rov.NMethods.PathsNumber = n_sim;
rov.NMethods.SimulationSteps = n_steps;
ROVSolver solver = new ROVSolver();
solver.BindToProject(rov);
solver.DoValuation(-1);
if (rov.HasErrors)
{
Console.WriteLine(rov.m_RuntimeErrorList[0]);
}
Assert.IsFalse(rov.HasErrors);
ResultItem price = rov.m_ResultList[0] as ResultItem;
double samplePrice = discount * price.value;
double sampleDevSt = price.stdDev / Math.Sqrt((double)n_sim);
// Calculates the theoretical value of the call.
Vector param = new Vector(5);
param[0] = process.k.V();
param[1] = process.theta.V();
param[2] = process.sigma.V();
param[3] = 0.0;
param[4] = process.V0.V();
HestonCall hestonCall = new HestonCall();
double theoreticalPrice = hestonCall.HestonCallPrice(param, process.S0.V(),
tau, strike, rate, dy);
Console.WriteLine("Theoretical Price = " + theoreticalPrice.ToString());
Console.WriteLine("Monte Carlo Price = " + samplePrice);
Console.WriteLine("Standard Deviation = " + sampleDevSt.ToString());
double tol = 4.0 * sampleDevSt;
Assert.Less(Math.Abs(theoreticalPrice - samplePrice), tol);
}
private byte[] ProcessLinearJoint(RigidBodyJoint linearJoint, JointData jointDataObject)
{
try {
ArrayList linearJointBytes = new ArrayList();
//Adds ID to signify that it's a linear joint
ushort ID = 1;
byte[] linearJointID = BitConverter.GetBytes(ID);
linearJointBytes.Add(linearJointID);
foreach (AssemblyJoint joint in linearJoint.Joints)
{
//Adds the ID of the parent STL to the output array
STLData parentSTL = new STLData();
STLDictionary.TryGetValue(NameFilter(joint.OccurrenceOne.Name), out parentSTL);
byte[] parentIDBytes = BitConverter.GetBytes((uint)(parentSTL.getID()));
linearJointBytes.Add(parentIDBytes);
//Adds the ID of the child STL to the output array
STLData childSTL = new STLData();
STLDictionary.TryGetValue(NameFilter(joint.OccurrenceTwo.Name), out childSTL);
byte[] childIDBytes = BitConverter.GetBytes((uint)(childSTL.getID()));
linearJointBytes.Add(childIDBytes);
//Adds the vector parallel to the movement onto the array of bytes
object GeometryOne, GeometryTwo;
NameValueMap nameMap;
linearJoint.GetJointData(out GeometryOne, out GeometryTwo, out nameMap);
Line vectorLine = (Line)GeometryTwo;
byte[] vectorJointData = new byte[12];
BitConverter.GetBytes(vectorLine.RootPoint.X).CopyTo(vectorJointData, 0);
BitConverter.GetBytes(vectorLine.RootPoint.Y).CopyTo(vectorJointData, 4);
BitConverter.GetBytes(vectorLine.RootPoint.Z).CopyTo(vectorJointData, 8);
linearJointBytes.Add(vectorJointData);
//Adds the point of connection relative to the parent part
byte[] transJointData = new byte[12];
AssemblyJointDefinition jointData = joint.Definition;
BitConverter.GetBytes(jointData.OriginTwo.Point.X).CopyTo(transJointData, 0);
BitConverter.GetBytes(jointData.OriginTwo.Point.Y).CopyTo(transJointData, 4);
BitConverter.GetBytes(jointData.OriginTwo.Point.Z).CopyTo(transJointData, 8);
linearJointBytes.Add(transJointData);
//Adds the degrees of freedom
byte[] freedomData = new byte[8];
ModelParameter positionData = (ModelParameter)jointData.LinearPosition;
double relataivePosition = positionData._Value;
positionData = (ModelParameter)jointData.LinearPositionEndLimit;
BitConverter.GetBytes((float)(Math.Abs(relataivePosition) - Math.Abs(positionData._Value))).CopyTo(freedomData, 0);
positionData = (ModelParameter)jointData.LinearPositionStartLimit;
BitConverter.GetBytes((float)(Math.Abs(relataivePosition) - Math.Abs(positionData._Value))).CopyTo(freedomData, 4);
linearJointBytes.Add(freedomData);
linearJointBytes.Add(processJointAttributes(jointDataObject));
}
object[] tempArray = linearJointBytes.ToArray();
byte[] returnedArray = new byte[tempArray.Length];
tempArray.CopyTo(returnedArray, 0);
return(returnedArray);
}
catch (Exception e) {
//catches problems
MessageBox.Show(e.Message + "\n\n\n" + e.StackTrace);
return(null);
}
}
private byte[] ProcessCylindricalJoint(RigidBodyJoint cylindricalJoint)
{
try
{
List <byte> cylindricalJointBytes = new List <byte>();
//Adds ID to signify that it's a cylindrical joint
cylindricalJointBytes.AddRange(BitConverter.GetBytes((ushort)2));
foreach (AssemblyJoint joint in cylindricalJoint.Joints)
{
//Adds the ID of the parent STL to the output array
STLData parentSTL = new STLData();
STLDictionary.TryGetValue(NameFilter(joint.OccurrenceOne.Name), out parentSTL);
byte[] parentIDBytes = BitConverter.GetBytes((uint)(parentSTL.getID()));
cylindricalJointBytes.AddRange(parentIDBytes);
//Adds the ID of the child STL to the output array
STLData childSTL = new STLData();
STLDictionary.TryGetValue(NameFilter(joint.OccurrenceTwo.Name), out childSTL);
byte[] childIDBytes = BitConverter.GetBytes((uint)(childSTL.getID()));
cylindricalJointBytes.AddRange(childIDBytes);
//Adds the vector normal to the plane of rotation
cylindricalJoint.GetJointData(out object GeometryOne, out object GeometryTwo, out NameValueMap nameMap);
/* Circle vectorCircle = (Circle)GeometryTwo;
* byte[] vectorJointData = new byte[12];
* BitConverter.GetBytes((float)vectorCircle.Center.X).CopyTo(vectorJointData, 0);
* BitConverter.GetBytes((float)vectorCircle.Center.Y).CopyTo(vectorJointData, 4);
* BitConverter.GetBytes((float)vectorCircle.Center.Z).CopyTo(vectorJointData, 8);
* cylindricalJointBytes.AddRange(vectorJointData);
*///Adds the vector parallel to the movement
MessageBox.Show(GeometryTwo.GetType().ToString());
//Adds the vector parallel to movement
Line vectorLine = (Line)GeometryTwo;
byte[] vectorJointData = new byte[12];
BitConverter.GetBytes((float)vectorLine.RootPoint.X).CopyTo(vectorJointData, 0);
BitConverter.GetBytes((float)vectorLine.RootPoint.Y).CopyTo(vectorJointData, 4);
BitConverter.GetBytes((float)vectorLine.RootPoint.Z).CopyTo(vectorJointData, 8);
cylindricalJointBytes.AddRange(vectorJointData);
//Adds the point of connection relative to the parent part
byte[] transJointData = new byte[12];
AssemblyJointDefinition jointData = joint.Definition;
BitConverter.GetBytes((float)jointData.OriginTwo.Point.X).CopyTo(transJointData, 0);
BitConverter.GetBytes((float)jointData.OriginTwo.Point.Y).CopyTo(transJointData, 4);
BitConverter.GetBytes((float)jointData.OriginTwo.Point.Z).CopyTo(transJointData, 8);
cylindricalJointBytes.AddRange(transJointData);
//Adds the degrees of freedom
List <byte> freedomData = new List <byte>();
ModelParameter positionData = (ModelParameter)jointData.AngularPosition;
double relataivePosition = positionData._Value;
if (jointData.HasLinearPositionEndLimit)
{
positionData = (ModelParameter)jointData.LinearPositionEndLimit;
freedomData.AddRange(BitConverter.GetBytes((float)(Math.Abs(relataivePosition) - Math.Abs(positionData._Value))));
}
if (jointData.HasLinearPositionStartLimit)
{
positionData = (ModelParameter)jointData.LinearPositionStartLimit;
freedomData.AddRange(BitConverter.GetBytes((float)(Math.Abs(relataivePosition) - Math.Abs(positionData._Value))));
}
if (jointData.HasAngularPositionLimits)
{
positionData = (ModelParameter)jointData.AngularPositionEndLimit;
freedomData.AddRange(BitConverter.GetBytes((float)(Math.Abs(relataivePosition) - Math.Abs(positionData._Value))));
positionData = (ModelParameter)jointData.AngularPositionStartLimit;
freedomData.AddRange(BitConverter.GetBytes((float)(Math.Abs(relataivePosition) - Math.Abs(positionData._Value))));
}
if (freedomData != null)
{
cylindricalJointBytes.AddRange(freedomData);
}
}
return(cylindricalJointBytes.ToArray());
}
catch (Exception e)
{
//catches problems
MessageBox.Show(e.Message + "\n\n\n" + e.StackTrace);
return(null);
}
}
public void Test()
{
double nu = 0.6;
double theta = -0.2;
double sigma = 0.2;
double rate = 0.02;
double dy = 0.01;
double s0 = 1;
double maturity = 2.0;
double strike = 1.2;
Vector mat = new Vector(1) + maturity;
Vector k = new Vector(1) + strike;
// Calculates the theoretical value of the call.
double theoreticalPrice = VarianceGammaOptionsCalibration.VGCall(theta, sigma, nu,
maturity, strike,
dy, s0, rate);
Engine.MultiThread = true;
Document doc = new Document();
ProjectROV rov = new ProjectROV(doc);
doc.Part.Add(rov);
doc.DefaultProject.NMethods.m_UseAntiteticPaths = true;
int n_sim = 50000;
int n_steps = 512;
ModelParameter paramStrike = new ModelParameter(strike, "strike");
paramStrike.VarName = "strike";
rov.Symbols.Add(paramStrike);
ModelParameter paramRate = new ModelParameter(rate, "rfrate");
paramRate.VarName = "rfrate";
rov.Symbols.Add(paramRate);
AFunction payoff = new AFunction(rov);
payoff.VarName = "payoff";
payoff.m_IndependentVariables = 1;
payoff.m_Value = (RightValue)("max(x1 - strike ; 0)");
rov.Symbols.Add(payoff);
VarianceGamma process = new VarianceGamma(s0, theta, sigma, nu, rate, dy);
StochasticProcessExtendible s = new StochasticProcessExtendible(rov, process);
rov.Processes.AddProcess(s);
// Set the discounting.
RiskFreeInfo rfi = rov.GetDiscountingModel() as RiskFreeInfo;
rfi.ActualizationType = EActualizationType.RiskFree;
rfi.m_deterministicRF = rate;
OptionTree op = new OptionTree(rov);
op.PayoffInfo.PayoffExpression = "payoff(v1)";
op.PayoffInfo.Timing.EndingTime.m_Value = (RightValue)maturity;
op.PayoffInfo.European = true;
rov.Map.Root = op;
rov.NMethods.Technology = ETechType.T_SIMULATION;
rov.NMethods.PathsNumber = n_sim;
rov.NMethods.SimulationSteps = n_steps;
ROVSolver solver = new ROVSolver();
solver.BindToProject(rov);
solver.DoValuation(-1);
if (rov.HasErrors)
{
rov.DisplayErrors();
}
Assert.IsFalse(rov.HasErrors);
ResultItem price = rov.m_ResultList[0] as ResultItem;
double samplePrice = price.value;
double sampleDevSt = price.stdDev / Math.Sqrt((double)n_sim);
Console.WriteLine("Theoretical Price = " + theoreticalPrice);
Console.WriteLine("Monte Carlo Price = " + samplePrice);
Console.WriteLine("Standard Deviation = " + sampleDevSt.ToString());
double tol = 4.0 * sampleDevSt;
Assert.Less(Math.Abs(theoreticalPrice - samplePrice), tol);
}
public void Test()
{
Engine.MultiThread = true;
Document doc = new Document();
ProjectROV rov = new ProjectROV(doc);
doc.Part.Add(rov);
doc.DefaultProject.NMethods.m_UseAntiteticPaths = true;
int n_sim = 10000;
int n_steps = 512;
double a = 0.2;
double DR = 0.02;
double r0 = 0.015;
double a1 = 1.0;
double sigma1 = 0.01;
double a2 = 0.1;
double sigma2 = 0.0165;
double correlation = 0.6;
double maturityOpt = 5.0;
double strike = 0.927;
double tau = 2.0;
ModelParameter PT = new ModelParameter(maturityOpt, "TT");
PT.VarName = "TT";
rov.Symbols.Add(PT);
ModelParameter Ptau = new ModelParameter(tau, "tau");
Ptau.VarName = "tau";
rov.Symbols.Add(Ptau);
ModelParameter Pa = new ModelParameter(a, "a");
Pa.VarName = "a";
rov.Symbols.Add(Pa);
ModelParameter PDR = new ModelParameter(DR, "PDR");
PDR.VarName = "DR";
rov.Symbols.Add(PDR);
ModelParameter Pr0 = new ModelParameter(r0, "r0");
Pr0.VarName = "r0";
rov.Symbols.Add(Pr0);
ModelParameter Pstrike = new ModelParameter(strike, "strike");
Pstrike.VarName = "strike";
rov.Symbols.Add(Pstrike);
AFunction zerorate = new AFunction(rov);
zerorate.VarName = "zr";
zerorate.m_IndependentVariables = 1;
zerorate.m_Value = (RightValue)("(1-exp(-a*x1))*DR + r0");
rov.Symbols.Add(zerorate);
HW2ProcessType pt = new HW2ProcessType();
// Set the short rate process.
pt = HW2ProcessType.ShortRate;
StocasticProcessHW2 process1 = new StocasticProcessHW2(rov, pt);
process1.zero_rate_curve = "@zr";
process1._a = (ModelParameter)a1;
process1._b = (ModelParameter)sigma1;
rov.Processes.AddProcess(process1);
// Set the mean reversion process.
pt = HW2ProcessType.Unobservable;
StocasticProcessHW2 process2 = new StocasticProcessHW2(rov, pt);
process2._a = (ModelParameter)a2;
process2._b = (ModelParameter)sigma2;
rov.Processes.AddProcess(process2);
// Set the correlation.
rov.Processes.r[0, 1] = (RightValue)correlation;
// Set the discounting.
RiskFreeInfo rfi = rov.GetDiscountingModel() as RiskFreeInfo;
rfi.ActualizationType = EActualizationType.Stochastic;
rfi.m_deterministicRF = (ModelParameter)"@v1";
OptionTree op = new OptionTree(rov);
op.PayoffInfo.PayoffExpression = "max(bond(TT;TT+tau;@v1)-strike;0)";
op.PayoffInfo.Timing.EndingTime.m_Value = (RightValue)maturityOpt;
op.PayoffInfo.European = true;
rov.Map.Root = op;
rov.NMethods.Technology = ETechType.T_SIMULATION;
rov.NMethods.PathsNumber = n_sim;
rov.NMethods.SimulationSteps = n_steps;
ROVSolver solver = new ROVSolver();
solver.BindToProject(rov);
solver.DoValuation(-1);
if (rov.HasErrors)
{
Console.WriteLine(rov.m_RuntimeErrorList[0]);
}
Assert.IsFalse(rov.HasErrors);
ResultItem price = rov.m_ResultList[0] as ResultItem;
double sampleMean = price.value;
double sampleDevSt = price.stdDev / Math.Sqrt((double)n_sim);
double theoreticalPrice = HW2BondCall(zerorate, maturityOpt, maturityOpt + tau, strike,
a1, sigma1, a2, sigma2, correlation);
Console.WriteLine("\nTheoretical Price = " + theoreticalPrice.ToString());
Console.WriteLine("Monte Carlo Price = " + sampleMean.ToString());
Console.WriteLine("Standard Deviation = " + sampleDevSt.ToString());
bool result;
double fact = 4.0;
result = (Math.Abs(sampleMean - theoreticalPrice) < fact * sampleDevSt);
Assert.IsTrue(result);
}
/// <summary>
/// Initializes the object based on the serialized data.
/// </summary>
/// <param name="info">The SerializationInfo that holds the serialized object data.</param>
/// <param name="context">The StreamingContext that contains contextual
/// information about the source.</param>
public ModelParameterDateSequence(SerializationInfo info, StreamingContext context)
: base(info, context)
{
// Set the default for GenerateSequenceFromStartDate and FollowFrequency
GenerateSequenceFromStartDate = true;
FollowFrequency = true;
int serialializedVersion;
try
{
serialializedVersion = info.GetInt32("_VersionDateSequence");
}
catch
{
serialializedVersion = 0;
}
if (serialializedVersion < 2)
{
#region No expressions
try
{
this.StartDate = new DateTime(info.GetInt64("_StartDate"));
this.EndDate = new DateTime(info.GetInt64("_EndDate"));
}
catch (Exception)
{
// In case the calibration time was serialized in a different way.
this.StartDate = info.GetDateTime("_StartDate");
this.EndDate = info.GetDateTime("_EndDate");
}
int frequency = info.GetInt32("_Frequency");
Array enumValues = Enum.GetValues(typeof(DateFrequency));
foreach (DateFrequency value in enumValues)
{
if ((int)value == frequency)
{
Frequency = value;
break;
}
}
if (serialializedVersion >= 1)
{
// Introduction of ExcludeStartDate
bool exclude = info.GetBoolean("_ExcludeStartDate");
SkipPeriods = exclude ? new ModelParameter(1) : new ModelParameter((double)0);
}
else
{
SkipPeriods = new ModelParameter((double)0);
}
#endregion // No expressions
}
else
{
if (serialializedVersion < 4)
{
string tmpS = info.GetString("_StartDateExpression");
DateTime tmpD;
if (DateTime.TryParseExact(tmpS, "yyyy-MM-dd", CultureInfo.CurrentCulture, DateTimeStyles.None, out tmpD))
{
StartDate = tmpD;
}
else
{
StartDateExpression = new ModelParameter(tmpS);
}
tmpS = info.GetString("_EndDateExpression");
if (DateTime.TryParseExact(tmpS, "yyyy-MM-dd", CultureInfo.CurrentCulture, DateTimeStyles.None, out tmpD))
{
EndDate = tmpD;
}
else
{
EndDateExpression = new ModelParameter(tmpS);
}
}
else
{
StartDateExpression = (ModelParameter)info.GetValue("_StartDateExpression", typeof(ModelParameter));
EndDateExpression = (ModelParameter)info.GetValue("_EndDateExpression", typeof(ModelParameter));
SetupExportedIDs();
}
FrequencyExpression = info.GetString("_FrequencyExpression");
if (serialializedVersion >= 3)
{
// FollowFrequency and GenerateSequenceFromStartDate in version 3
FollowFrequency = info.GetBoolean("_FollowFrequency");
GenerateSequenceFromStartDate = info.GetBoolean("_GenerateSequenceFromStartDate");
}
// Skip Periods introduced in version 5
if (serialializedVersion < 5)
{
bool exclude = info.GetBoolean("_ExcludeStartDate");
SkipPeriods = exclude ? new ModelParameter(1) : new ModelParameter((double)0);
}
else
{
SkipPeriods = (ModelParameter)info.GetValue("_SkipPeriods", typeof(ModelParameter));
}
if (serialializedVersion < 6)
{
VectorReferenceExpr = string.Empty;
}
else
{
VectorReferenceExpr = info.GetString("_VectorReferenceExpr");
SkipPeriodsArray = ObjectSerialization.GetValue <ModelParameter>(info, "_SkipPeriodsArray", new ModelParameter(0.0));
}
}
}
public void Test()
{
Engine.MultiThread = true;
Document doc = new Document();
ProjectROV rov = new ProjectROV(doc);
doc.Part.Add(rov);
AFunction zerorate = new AFunction(rov);
zerorate.VarName = "zr";
zerorate.m_IndependentVariables = 1;
zerorate.m_Value = (RightValue)0.05;
rov.Symbols.Add(zerorate);
int n_sim = 4000;
double maturityOpt = 6.5;
// Simulation steps for a year. With stepPerYear = 150 the test will be passed.
// But notice that the price calculated through Monte Carlo is unstable when
// changing this value, even till 1000 steps per year.
int stepsPerYear = 150;
int n_steps = stepsPerYear * ((int)maturityOpt);
double strike = 0.01;
double tau = 0.5;
SquaredGaussianModel process = new SquaredGaussianModel();
process.a1 = (ModelParameter)0.1;
process.sigma1 = (ModelParameter)0.01;
process.zr = (ModelParameter)"@zr";
StochasticProcessExtendible s = new StochasticProcessExtendible(rov, process);
rov.Processes.AddProcess(s);
ModelParameter PT = new ModelParameter(maturityOpt, "TT");
PT.VarName = "TT";
rov.Symbols.Add(PT);
ModelParameter Ptau = new ModelParameter(tau, "tau");
Ptau.VarName = "tau";
rov.Symbols.Add(Ptau);
ModelParameter Pstrike = new ModelParameter(strike, "strike");
Pstrike.VarName = "strike";
rov.Symbols.Add(Pstrike);
// Set the discounting.
RiskFreeInfo rfi = rov.GetDiscountingModel() as RiskFreeInfo;
rfi.ActualizationType = EActualizationType.Stochastic;
rfi.m_deterministicRF = (ModelParameter)"@V1";
// Set the payoff.
OptionTree op = new OptionTree(rov);
op.PayoffInfo.PayoffExpression = "tau*max(rate(TT;tau;@v1) - strike; 0)";
op.PayoffInfo.Timing.EndingTime.m_Value = (RightValue)(maturityOpt + tau);
op.PayoffInfo.European = true;
rov.Map.Root = op;
rov.NMethods.Technology = ETechType.T_SIMULATION;
rov.NMethods.PathsNumber = n_sim;
rov.NMethods.SimulationSteps = n_steps;
ROVSolver solver = new ROVSolver();
solver.BindToProject(rov);
solver.DoValuation(-1);
if (rov.HasErrors)
{
rov.DisplayErrors();
}
Assert.IsFalse(rov.HasErrors);
ResultItem price = rov.m_ResultList[0] as ResultItem;
double mcPrice = price.value;
double mcDevST = price.stdDev / Math.Sqrt((double)n_sim);
Caplet cplt = new Caplet();
Vector Mat, fwd, Rk;
Vector capMatV;
double delta_k;
double capMat;
delta_k = 0.5;
capMat = maturityOpt + tau;
int nmat = 2 * ((int)capMat) + 1;
Mat = new Vector(nmat);
fwd = new Vector(nmat);
Mat[0] = 0;
fwd[0] = zerorate.Evaluate(0);
for (int k = 1; k < nmat; k++)
{
Mat[k] = tau * ((double)k);
fwd[k] = zerorate.Evaluate(Mat[k]) * Mat[k] - zerorate.Evaluate(Mat[k - 1]) * Mat[k - 1];
}
fwd = fwd / tau;
Rk = new Vector(1);
Rk[0] = strike;
capMatV = new Vector(2);
capMatV[0] = maturityOpt;
capMatV[1] = maturityOpt + tau;
Matrix caplet = cplt.PGSMCaplets(process, Mat, fwd, Rk, delta_k, capMatV);
double theoreticalPrice = caplet[1, 0] - caplet[0, 0];
Console.WriteLine("\nTheoretical Price = " + theoreticalPrice.ToString());
Console.WriteLine("Monte Carlo Price = " + mcPrice);
Console.WriteLine("Standard Deviation = " + mcDevST);
double tol = 4.0 * mcDevST;
Assert.Less(Math.Abs(theoreticalPrice - mcPrice), tol);
}
/// <summary>
/// Creates Activity
/// </summary>
/// <returns>True if successful</returns>
public static async Task <dynamic> CreateActivity()
{
Bearer bearer = (await Get2LeggedTokenAsync(new Scope[] { Scope.CodeAll })).ToObject <Bearer>();
string nickName = ConsumerKey;
AppBundlesApi appBundlesApi = new AppBundlesApi();
appBundlesApi.Configuration.AccessToken = bearer.AccessToken;
PageString appBundles = await appBundlesApi.AppBundlesGetItemsAsync();
string appBundleID = string.Format("{0}.{1}+{2}", nickName, APPNAME, ALIAS);
if (!appBundles.Data.Contains(appBundleID))
{
if (!System.IO.File.Exists(LocalAppPackageZip))
{
return(new Output(Output.StatusEnum.Error, "Bundle not found at " + LocalAppPackageZip));
}
// create new bundle
AppBundle appBundleSpec = new AppBundle(APPNAME, null, EngineName, null, null, APPNAME, null, APPNAME);
AppBundle newApp = await appBundlesApi.AppBundlesCreateItemAsync(appBundleSpec);
if (newApp == null)
{
return(new Output(Output.StatusEnum.Error, "Cannot create new app"));
}
// create alias
Alias aliasSpec = new Alias(1, null, ALIAS);
Alias newAlias = await appBundlesApi.AppBundlesCreateAliasAsync(APPNAME, aliasSpec);
// upload the zip bundle
RestClient uploadClient = new RestClient(newApp.UploadParameters.EndpointURL);
RestRequest request = new RestRequest(string.Empty, Method.POST);
request.AlwaysMultipartFormData = true;
foreach (KeyValuePair <string, object> x in newApp.UploadParameters.FormData)
{
request.AddParameter(x.Key, x.Value);
}
request.AddFile("file", LocalAppPackageZip);
request.AddHeader("Cache-Control", "no-cache");
var res = await uploadClient.ExecuteTaskAsync(request);
}
ActivitiesApi activitiesApi = new ActivitiesApi();
activitiesApi.Configuration.AccessToken = bearer.AccessToken;
PageString activities = await activitiesApi.ActivitiesGetItemsAsync();
string activityID = string.Format("{0}.{1}+{2}", nickName, ACTIVITY_NAME, ALIAS);
if (!activities.Data.Contains(activityID))
{
// create activity
string commandLine = string.Format(@"$(engine.path)\\inventorcoreconsole.exe /i $(args[InputIPT].path) /al $(appbundles[{0}].path) $(args[InputParams].path)", APPNAME);
ModelParameter iptFile = new ModelParameter(false, false, ModelParameter.VerbEnum.Get, "Input Ipt File", true, inputFileName);
ModelParameter result = new ModelParameter(false, false, ModelParameter.VerbEnum.Put, "Resulting Ipt File", true, outputFileName);
ModelParameter inputParams = new ModelParameter(false, false, ModelParameter.VerbEnum.Get, "Input params", false, "params.json");
Activity activitySpec = new Activity(
new List <string> {
commandLine
},
new Dictionary <string, ModelParameter>()
{
{ "InputIPT", iptFile },
{ "InputParams", inputParams },
{ "ResultIPT", result },
},
EngineName,
new List <string>()
{
string.Format("{0}.{1}+{2}", nickName, APPNAME, ALIAS)
},
null,
ACTIVITY_NAME,
null,
ACTIVITY_NAME
);
Activity newActivity = await activitiesApi.ActivitiesCreateItemAsync(activitySpec);
Alias aliasSpec = new Alias(1, null, ALIAS);
Alias newAlias = await activitiesApi.ActivitiesCreateAliasAsync(ACTIVITY_NAME, aliasSpec);
}
return(new Output(Output.StatusEnum.Sucess, "Activity created"));
}
public Bacteria(ModelParameter parameter) : base(parameter)
{
}
public void Test()
{
double nu = 0.6;
double theta = -0.2;
double sigma = 0.2;
double rate = 0.02;
double dy = 0.01;
double s0 = 1;
double maturity = 2.0;
double strike = 1.2;
Vector mat = new Vector(1) + maturity;
Vector k = new Vector(1) + strike;
// Calculates the theoretical value of the call.
double theoreticalPrice = VarianceGammaOptionsCalibration.VGCall(theta, sigma, nu,
maturity, strike,
dy, s0, rate);
Engine.MultiThread = true;
Document doc = new Document();
ProjectROV rov = new ProjectROV(doc);
doc.Part.Add(rov);
doc.DefaultProject.NMethods.m_UseAntiteticPaths = true;
int n_sim = 50000;
int n_steps = 512;
ModelParameter paramStrike = new ModelParameter(strike, "strike");
paramStrike.VarName = "strike";
rov.Symbols.Add(paramStrike);
ModelParameter paramRate = new ModelParameter(rate, "rfrate");
paramRate.VarName = "rfrate";
rov.Symbols.Add(paramRate);
AFunction payoff = new AFunction(rov);
payoff.VarName = "payoff";
payoff.m_IndependentVariables = 1;
payoff.m_Value = (RightValue)("max(x1 - strike ; 0)");
rov.Symbols.Add(payoff);
VarianceGamma process = new VarianceGamma(s0, theta, sigma, nu, rate, dy);
StochasticProcessExtendible s = new StochasticProcessExtendible(rov, process);
rov.Processes.AddProcess(s);
// Set the discounting.
RiskFreeInfo rfi = rov.GetDiscountingModel() as RiskFreeInfo;
rfi.ActualizationType = EActualizationType.RiskFree;
rfi.m_deterministicRF = rate;
OptionTree op = new OptionTree(rov);
op.PayoffInfo.PayoffExpression = "payoff(v1)";
op.PayoffInfo.Timing.EndingTime.m_Value = (RightValue)maturity;
op.PayoffInfo.European = true;
rov.Map.Root = op;
rov.NMethods.Technology = ETechType.T_SIMULATION;
rov.NMethods.PathsNumber = n_sim;
rov.NMethods.SimulationSteps = n_steps;
ROVSolver solver = new ROVSolver();
solver.BindToProject(rov);
solver.DoValuation(-1);
if (rov.HasErrors)
{
rov.DisplayErrors();
}
Assert.IsFalse(rov.HasErrors);
ResultItem price = rov.m_ResultList[0] as ResultItem;
double samplePrice = price.value;
double sampleDevSt = price.stdDev / Math.Sqrt((double)n_sim);
Console.WriteLine("Theoretical Price = " + theoreticalPrice);
Console.WriteLine("Monte Carlo Price = " + samplePrice);
Console.WriteLine("Standard Deviation = " + sampleDevSt.ToString());
double tol = 4.0 * sampleDevSt;
Assert.Less(Math.Abs(theoreticalPrice - samplePrice), tol);
}
public void TestSimulation()
{
Engine.MultiThread = true;
Document doc = new Document();
ProjectROV rov = new ProjectROV(doc);
doc.Part.Add(rov);
doc.DefaultProject.NMethods.m_UseAntiteticPaths = true;
int n_sim = 10000;
int n_steps = 512;
double strike = 90.0;
double maturity = 2.0;
double rate = 0.1;
double dy = 0.05;
double volatility = 0.2;
double S0 = 100;
ModelParameter Pstrike = new ModelParameter(strike, string.Empty, "strike");
rov.Symbols.Add(Pstrike);
ModelParameter PS0 = new ModelParameter(S0, string.Empty, "S0");
rov.Symbols.Add(PS0);
AFunction payoff = new AFunction(rov);
payoff.VarName = "payoff";
payoff.m_IndependentVariables = 1;
payoff.m_Value = (RightValue)("max(x1 - strike ; 0)");
rov.Symbols.Add(payoff);
AFunction rfunc = new AFunction(rov);
rfunc.VarName = "r";
rfunc.m_IndependentVariables = 1;
rfunc.m_Value = (RightValue)rate;
rov.Symbols.Add(rfunc);
AFunction qfunc = new AFunction(rov);
qfunc.VarName = "q";
qfunc.m_IndependentVariables = 1;
qfunc.m_Value = (RightValue)dy;
rov.Symbols.Add(qfunc);
AFunction volfunc = new AFunction(rov);
volfunc.VarName = "localvol";
volfunc.m_IndependentVariables = 2;
volfunc.m_Value = (RightValue)volatility;
rov.Symbols.Add(volfunc);
DupireProcess process = new DupireProcess();
process.s0 = (ModelParameter)"S0";
process.r = (ModelParameter)"@r";
process.q = (ModelParameter)"@q";
process.localVol = (ModelParameter)"@localvol";
StochasticProcessExtendible s = new StochasticProcessExtendible(rov, process);
rov.Processes.AddProcess(s);
// Set the discounting.
RiskFreeInfo rfi = rov.GetDiscountingModel() as RiskFreeInfo;
rfi.ActualizationType = EActualizationType.RiskFree;
rfi.m_deterministicRF = rate;
OptionTree op = new OptionTree(rov);
op.PayoffInfo.PayoffExpression = "payoff(v1)";
op.PayoffInfo.Timing.EndingTime.m_Value = (RightValue)maturity;
op.PayoffInfo.European = true;
rov.Map.Root = op;
rov.NMethods.Technology = ETechType.T_SIMULATION;
rov.NMethods.PathsNumber = n_sim;
rov.NMethods.SimulationSteps = n_steps;
ROVSolver solver = new ROVSolver();
solver.BindToProject(rov);
solver.DoValuation(-1);
if (rov.HasErrors)
{
rov.DisplayErrors();
}
Assert.IsFalse(rov.HasErrors);
ResultItem price = rov.m_ResultList[0] as ResultItem;
double samplePrice = price.value;
double sampleDevSt = price.stdDev / Math.Sqrt((double)n_sim);
// Calculation of the theoretical value of the call.
double theoreticalPrice = BlackScholes.Call(rate, S0, strike, volatility, maturity, dy);
Console.WriteLine("Theoretical Price = " + theoreticalPrice.ToString());
Console.WriteLine("Monte Carlo Price = " + samplePrice);
Console.WriteLine("Standard Deviation = " + sampleDevSt.ToString());
double tol = 4.0 * sampleDevSt;
Assert.LessOrEqual(Math.Abs(theoreticalPrice - samplePrice), tol);
}
public void Test()
{
Engine.MultiThread = true;
Document doc = new Document();
ProjectROV rov = new ProjectROV(doc);
doc.Part.Add(rov);
AFunction zerorate = new AFunction(rov);
zerorate.VarName = "zr";
zerorate.m_IndependentVariables = 1;
zerorate.m_Value = (RightValue)0.05;
rov.Symbols.Add(zerorate);
int n_sim = 10000;
double maturityOpt = 6.5;
// Simulation steps for a year. With stepPerYear = 150 the test will be passed.
// But notice that the price calculated through Monte Carlo is unstable when
// changing this value, even till 1000 steps per year.
int stepsPerYear = 500;
int n_steps = stepsPerYear * ((int)maturityOpt);
double strike = 100.0;
double tau = 0.5;
SquaredGaussianModel process = new SquaredGaussianModel();
process.a1 = (ModelParameter)0.1;
process.sigma1 = (ModelParameter)0.01;
process.zr = (ModelParameter)"@zr";
StochasticProcessExtendible s = new StochasticProcessExtendible(rov, process);
rov.Processes.AddProcess(s);
ModelParameter PT = new ModelParameter(maturityOpt, "TT");
PT.VarName = "TT";
rov.Symbols.Add(PT);
ModelParameter Ptau = new ModelParameter(tau, "tau");
Ptau.VarName = "tau";
rov.Symbols.Add(Ptau);
ModelParameter Pstrike = new ModelParameter(strike, "strike");
Pstrike.VarName = "strike";
rov.Symbols.Add(Pstrike);
// Set the discounting.
RiskFreeInfo rfi = rov.GetDiscountingModel() as RiskFreeInfo;
rfi.ActualizationType = EActualizationType.Stochastic;
rfi.m_deterministicRF = (ModelParameter)"@V1";
// Set the payoff.
OptionTree op = new OptionTree(rov);
op.PayoffInfo.PayoffExpression = "max(strike - Bond(TT;TT+tau;@v1); 0)";
op.PayoffInfo.Timing.EndingTime.m_Value = (RightValue)maturityOpt;
op.PayoffInfo.European = true;
rov.Map.Root = op;
rov.NMethods.Technology = ETechType.T_SIMULATION;
rov.NMethods.PathsNumber = n_sim;
rov.NMethods.SimulationSteps = n_steps;
ROVSolver solver = new ROVSolver();
solver.BindToProject(rov);
solver.DoValuation(-1);
if (rov.HasErrors)
{
Console.WriteLine(rov.m_RuntimeErrorList[0]);
}
Assert.IsFalse(rov.HasErrors);
ResultItem price = rov.m_ResultList[0] as ResultItem;
double mcPrice = price.value;
double mcDevST = price.stdDev / Math.Sqrt(2.0 * ((double)n_sim));
Caplet cplt = new Caplet();
Vector Mat;
Vector fwd;
Vector Rk;
Vector capMatV;
double deltaK;
double capMat;
deltaK = 0.5;
capMat = maturityOpt + tau;
int nmat = 2 * ((int)capMat) + 1;
Mat = new Vector(nmat);
fwd = new Vector(nmat);
Mat[0] = 0;
fwd[0] = zerorate.Evaluate(0);
for (int k = 1; k < nmat; k++)
{
Mat[k] = tau * ((double)k);
fwd[k] = zerorate.Evaluate(Mat[k]) * Mat[k] - zerorate.Evaluate(Mat[k - 1]) * Mat[k - 1];
}
fwd = fwd / tau;
Rk = new Vector(1);
Rk[0] = (1 / strike - 1.0) / tau;
capMatV = new Vector(2);
capMatV[0] = maturityOpt;
capMatV[1] = maturityOpt + tau;
Matrix caplet = cplt.PGSMCaplets(process, Mat, fwd, Rk, deltaK, capMatV);
Console.WriteLine("rows = " + caplet.R);
Console.WriteLine("columns = " + caplet.C);
double theoreticalPrice = caplet[1, 0] - caplet[0, 0];
Console.WriteLine("\nTheoretical Price = " + theoreticalPrice.ToString());
Console.WriteLine("Monte Carlo Price = " + mcPrice);
Console.WriteLine("Standard Deviation = " + mcDevST);
double tol = 4.0 * mcDevST;
Assert.Less(Math.Abs(theoreticalPrice - mcPrice), tol);
}
public void Test()
{
Engine.MultiThread = true;
Document doc = new Document();
ProjectROV rov = new ProjectROV(doc);
doc.Part.Add(rov);
doc.DefaultProject.NMethods.m_UseAntiteticPaths = true;
int n_sim = 50000;
int n_steps = 512;
double strike = 100.0;
double tau = 5.0;
double rate = 0.1;
double dy = 0.07;
ModelParameter pStrike = new ModelParameter(strike, "strike");
pStrike.VarName = "strike";
rov.Symbols.Add(pStrike);
ModelParameter pRate = new ModelParameter(rate, "rfrate");
pRate.VarName = "rfrate";
rov.Symbols.Add(pRate);
AFunction payoff = new AFunction(rov);
payoff.VarName = "payoff";
payoff.m_IndependentVariables = 1;
payoff.m_Value = (RightValue)("max(x1 - strike ; 0)");
rov.Symbols.Add(payoff);
HestonProcess process = new HestonProcess();
process.r = (ModelParameter)rate;
process.q = (ModelParameter)dy;
process.k = (ModelParameter)2.5;
process.theta = (ModelParameter)0.4;
process.sigma = (ModelParameter)0.2;
process.S0 = (ModelParameter)100.0;
process.V0 = (ModelParameter)0.3;
StochasticProcessExtendible s = new StochasticProcessExtendible(rov, process);
rov.Processes.AddProcess(s);
// Set the discounting.
RiskFreeInfo rfi = rov.GetDiscountingModel() as RiskFreeInfo;
rfi.ActualizationType = EActualizationType.RiskFree;
rfi.m_deterministicRF = rate;
OptionTree op = new OptionTree(rov);
op.PayoffInfo.PayoffExpression = "payoff(v1a)";
op.PayoffInfo.Timing.EndingTime.m_Value = (RightValue)tau;
op.PayoffInfo.European = true;
rov.Map.Root = op;
rov.NMethods.Technology = ETechType.T_SIMULATION;
rov.NMethods.PathsNumber = n_sim;
rov.NMethods.SimulationSteps = n_steps;
ROVSolver solver = new ROVSolver();
solver.BindToProject(rov);
solver.DoValuation(-1);
if (rov.HasErrors)
{
Console.WriteLine(rov.m_RuntimeErrorList[0]);
}
Assert.IsFalse(rov.HasErrors);
ResultItem price = rov.m_ResultList[0] as ResultItem;
double samplePrice = price.value;
double sampleDevSt = price.stdDev / Math.Sqrt((double)n_sim);
// Calculates the theoretical value of the call.
Vector param = new Vector(5);
param[0] = process.k.V();
param[1] = process.theta.V();
param[2] = process.sigma.V();
param[3] = 0.0;
param[4] = process.V0.V();
HestonCall hestonCall = new HestonCall();
double thPrice = hestonCall.HestonCallPrice(param, process.S0.V(),
tau, strike, rate, dy);
Console.WriteLine("Theoretical Price = " + thPrice.ToString());
Console.WriteLine("Monte Carlo Price = " + samplePrice);
Console.WriteLine("Standard Deviation = " + sampleDevSt.ToString());
double tol = 4.0 * sampleDevSt;
Assert.Less(Math.Abs(thPrice - samplePrice), tol);
}
public void Test()
{
Engine.MultiThread = true;
Document doc = new Document();
ProjectROV rov = new ProjectROV(doc);
doc.Part.Add(rov);
doc.DefaultProject.NMethods.m_UseAntiteticPaths = true;
int n_sim = 10000;
int n_steps = 512;
double a = 0.2;
double DR = 0.02;
double r0 = 0.015;
double a1 = 1.0;
double sigma1 = 0.01;
double a2 = 0.1;
double sigma2 = 0.0165;
double correlation = 0.6;
double maturityOpt = 5.0;
double strike = 0.927;
double tau = 2.0;
ModelParameter PT = new ModelParameter(maturityOpt, "TT");
PT.VarName = "TT";
rov.Symbols.Add(PT);
ModelParameter Ptau = new ModelParameter(tau, "tau");
Ptau.VarName = "tau";
rov.Symbols.Add(Ptau);
ModelParameter Pa = new ModelParameter(a, "a");
Pa.VarName = "a";
rov.Symbols.Add(Pa);
ModelParameter PDR = new ModelParameter(DR, "PDR");
PDR.VarName = "DR";
rov.Symbols.Add(PDR);
ModelParameter Pr0 = new ModelParameter(r0, "r0");
Pr0.VarName = "r0";
rov.Symbols.Add(Pr0);
ModelParameter Pstrike = new ModelParameter(strike, "strike");
Pstrike.VarName = "strike";
rov.Symbols.Add(Pstrike);
AFunction zerorate = new AFunction(rov);
zerorate.VarName = "zr";
zerorate.m_IndependentVariables = 1;
zerorate.m_Value = (RightValue)("(1-exp(-a*x1))*DR + r0");
rov.Symbols.Add(zerorate);
HW2ProcessType pt = new HW2ProcessType();
// Set the short rate process.
pt = HW2ProcessType.ShortRate;
StocasticProcessHW2 process1 = new StocasticProcessHW2(rov, pt);
process1.zero_rate_curve = "@zr";
process1._a = (ModelParameter)a1;
process1._b = (ModelParameter)sigma1;
rov.Processes.AddProcess(process1);
// Set the mean reversion process.
pt = HW2ProcessType.Unobservable;
StocasticProcessHW2 process2 = new StocasticProcessHW2(rov, pt);
process2._a = (ModelParameter)a2;
process2._b = (ModelParameter)sigma2;
rov.Processes.AddProcess(process2);
// Set the correlation.
rov.Processes.r[0, 1] = (RightValue)correlation;
// Set the discounting.
RiskFreeInfo rfi = rov.GetDiscountingModel() as RiskFreeInfo;
rfi.ActualizationType = EActualizationType.Stochastic;
rfi.m_deterministicRF = (ModelParameter)"@v1";
OptionTree op = new OptionTree(rov);
op.PayoffInfo.PayoffExpression = "max(bond(TT;TT+tau;@v1)-strike;0)";
op.PayoffInfo.Timing.EndingTime.m_Value = (RightValue)maturityOpt;
op.PayoffInfo.European = true;
rov.Map.Root = op;
rov.NMethods.Technology = ETechType.T_SIMULATION;
rov.NMethods.PathsNumber = n_sim;
rov.NMethods.SimulationSteps = n_steps;
ROVSolver solver = new ROVSolver();
solver.BindToProject(rov);
solver.DoValuation(-1);
if (rov.HasErrors)
{
Console.WriteLine(rov.m_RuntimeErrorList[0]);
}
Assert.IsFalse(rov.HasErrors);
ResultItem price = rov.m_ResultList[0] as ResultItem;
double sampleMean = price.value;
double sampleDevSt = price.stdDev / Math.Sqrt((double)n_sim);
double theoreticalPrice = HW2BondCall(zerorate, maturityOpt, maturityOpt + tau, strike,
a1, sigma1, a2, sigma2, correlation);
Console.WriteLine("\nTheoretical Price = " + theoreticalPrice.ToString());
Console.WriteLine("Monte Carlo Price = " + sampleMean.ToString());
Console.WriteLine("Standard Deviation = " + sampleDevSt.ToString());
bool result;
double fact = 4.0;
result = (Math.Abs(sampleMean - theoreticalPrice) < fact * sampleDevSt);
Assert.IsTrue(result);
}
void ApplyParametersFilter(
Dictionary <Parameter, ListViewItem> paramDict,
ListView listView,
ParamFilterEnum filter)
{
foreach (Parameter parameter in paramDict.Keys)
{
switch (filter)
{
case ParamFilterEnum.kAll:
break;
case ParamFilterEnum.kKey:
if (!parameter.IsKey)
{
if (paramDict[parameter].ListView != null)
{
paramDict[parameter].Remove();
}
continue;
}
break;
case ParamFilterEnum.kNonKey:
if (parameter.IsKey)
{
if (paramDict[parameter].ListView != null)
{
paramDict[parameter].Remove();
}
continue;
}
break;
case ParamFilterEnum.kRenamed:
if (parameter.Type != ObjectTypeEnum.kModelParameterObject)
{
if (paramDict[parameter].ListView != null)
{
paramDict[parameter].Remove();
}
continue;
}
ModelParameter mparameter = parameter as ModelParameter;
if (!mparameter.Renamed)
{
if (paramDict[parameter].ListView != null)
{
paramDict[parameter].Remove();
}
continue;
}
break;
default:
break;
}
if (paramDict[parameter].ListView == null)
{
ListViewItem item = paramDict[parameter];
listView.Items.Add(item);
}
}
}
public void Test()
{
// Tests HW1 dynamics comparing the price of a call option on a bond
// calculated through simulation and the theoretical one.
Engine.MultiThread = true;
Document doc = new Document();
ProjectROV rov = new ProjectROV(doc);
doc.Part.Add(rov);
doc.DefaultProject.NMethods.m_UseAntiteticPaths = true;
int n_sim = 20000;
int n_steps = 1024;
double a = 0.2;
double DR = 0.02;
double r0 = 0.015;
double a1 = 0.02;
double sigma1 = 0.01;
double maturityOpt = 5.0;
double strike = 0.98192;
double tau = 1.0;
ModelParameter PT = new ModelParameter(maturityOpt, "TT");
PT.VarName = "TT";
rov.Symbols.Add(PT);
ModelParameter Ptau = new ModelParameter(tau, "tau");
Ptau.VarName = "tau";
rov.Symbols.Add(Ptau);
ModelParameter Pa = new ModelParameter(a, "a");
Pa.VarName = "a";
rov.Symbols.Add(Pa);
ModelParameter PDR = new ModelParameter(DR, "PDR");
PDR.VarName = "DR";
rov.Symbols.Add(PDR);
ModelParameter Pr0 = new ModelParameter(r0, "r0");
Pr0.VarName = "r0";
rov.Symbols.Add(Pr0);
ModelParameter Pstrike = new ModelParameter(strike, "strike");
Pstrike.VarName = "strike";
rov.Symbols.Add(Pstrike);
AFunction zerorate = new AFunction(rov);
zerorate.VarName = "zr";
zerorate.m_IndependentVariables = 1;
zerorate.m_Value = (RightValue)("(1-exp(-a*x1))*DR + r0");
rov.Symbols.Add(zerorate);
HW1 process = new HW1(a1, sigma1, "@zr");
StochasticProcessExtendible s = new StochasticProcessExtendible(rov, process);
rov.Processes.AddProcess(s);
// Set the discounting.
RiskFreeInfo rfi = rov.GetDiscountingModel() as RiskFreeInfo;
rfi.ActualizationType = EActualizationType.Stochastic;
rfi.m_deterministicRF = (ModelParameter)"@V1";
OptionTree op = new OptionTree(rov);
op.PayoffInfo.PayoffExpression = "Max(bond(TT;TT+tau;@v1)-strike;0)";
op.PayoffInfo.Timing.EndingTime.m_Value = (RightValue)maturityOpt;
op.PayoffInfo.European = true;
rov.Map.Root = op;
rov.NMethods.Technology = ETechType.T_SIMULATION;
rov.NMethods.PathsNumber = n_sim;
rov.NMethods.SimulationSteps = n_steps;
ROVSolver solver = new ROVSolver();
solver.BindToProject(rov);
solver.DoValuation(-1);
if (rov.HasErrors)
{
Console.WriteLine(rov.m_RuntimeErrorList[0]);
}
Assert.IsFalse(rov.HasErrors);
ResultItem price = rov.m_ResultList[0] as ResultItem;
double samplePrice = price.value;
double sampleDevSt = price.stdDev / Math.Sqrt((double)n_sim);
// Calculation of the theoretical value of the call.
CapHW1 cap = new CapHW1(zerorate);
double d1 = cap.D1(a1, sigma1, maturityOpt, maturityOpt + tau, strike);
double d2 = cap.D2(a1, sigma1, maturityOpt, maturityOpt + tau, strike);
double theoreticalPrice = ZCB(zerorate, maturityOpt + tau) * SpecialFunctions.NormCdf(d1) - strike * ZCB(zerorate, maturityOpt) * SpecialFunctions.NormCdf(d2);
Console.WriteLine("Theoretical Price = " + theoreticalPrice.ToString());
Console.WriteLine("Monte Carlo Price = " + samplePrice);
Console.WriteLine("Standard Deviation = " + sampleDevSt.ToString());
double tol = 4.0 * sampleDevSt;
Assert.Less(Math.Abs(theoreticalPrice - samplePrice), tol);
}
public void TestCalibration()
{
InterestRateMarketData IData = InterestRateMarketData.FromFile("../../TestData/IRMD-sample.xml");
CallPriceMarketData HData = CallPriceMarketData.FromFile("../../TestData/CallData-sample.xml");
//InterestRateMarketData IData = InterestRateMarketData.FromFile("../../../EquityModels.Tests/TestData/IRMD-EU-30102012-close.xml");
//CallPriceMarketData HData = CallPriceMarketData.FromFile("../../../EquityModels.Tests/TestData/30102012-SX5E_Index-HestonData.xml");
//CallPriceMarketData HData = ObjectSerialization.ReadFromXMLFile("../../../EquityModels.Tests/TestData/FTSE.xml") as CallPriceMarketData;
List <object> l = new List <object>();
l.Add(IData.DiscountingCurve);
l.Add(HData);
DupireEstimator DE = new DupireEstimator();
DupireCalibrationSettings settings = new DupireCalibrationSettings();
settings.LocalVolatilityCalculation = LocalVolatilityCalculation.Method1;
//settings.LocalVolatilityCalculation = LocalVolatilityCalculation.QuantLib;
EstimationResult res = DE.Estimate(l, settings);
//int nmat = HData.Maturity.Length;
//int nstrike = HData.Strike.Length;
int i = 5; // Maturity.
int j = 4; // Strike.
Engine.MultiThread = true;
Document doc = new Document();
ProjectROV rov = new ProjectROV(doc);
doc.Part.Add(rov);
doc.DefaultProject.NMethods.m_UseAntiteticPaths = true;
int n_sim = 10000;
int n_steps = 500;
double strike = HData.Strike[j];
//double volatility = HData.Volatility[i, j];
/*
* PFunction2D.PFunction2D impvolfunc = new PFunction2D.PFunction2D(rov);
* impvolfunc = res.Objects[3] as PFunction2D.PFunction2D;
* impvolfunc.VarName = "impvol";
* rov.Symbols.Add(impvolfunc);
* double volatility = impvolfunc.Evaluate(HData.Maturity[i], HData.Strike[j]);
*/
double volatility = 0.2;
double maturity = HData.Maturity[i];
ModelParameter Pstrike = new ModelParameter(strike, string.Empty, "strike");
rov.Symbols.Add(Pstrike);
AFunction payoff = new AFunction(rov);
payoff.VarName = "payoff";
payoff.m_IndependentVariables = 1;
payoff.m_Value = (RightValue)("max(x1 - strike ; 0)");
rov.Symbols.Add(payoff);
bool found;
double S0 = PopulateHelper.GetValue("S0", res.Names, res.Values, out found);
ModelParameter PS0 = new ModelParameter(S0, string.Empty, "S0");
rov.Symbols.Add(PS0);
PFunction rfunc = new PFunction(rov);
rfunc = res.Objects[0] as PFunction;
rfunc.VarName = "r";
rov.Symbols.Add(rfunc);
PFunction qfunc = new PFunction(rov);
qfunc = res.Objects[1] as PFunction;
qfunc.VarName = "q";
rov.Symbols.Add(qfunc);
PFunction2D.PFunction2D volfunc = new PFunction2D.PFunction2D(rov);
volfunc = res.Objects[2] as PFunction2D.PFunction2D;
volfunc.VarName = "localvol";
rov.Symbols.Add(volfunc);
DupireProcess process = new DupireProcess();
process.s0 = (ModelParameter)"S0";
process.r = (ModelParameter)"@r";
process.q = (ModelParameter)"@q";
process.localVol = (ModelParameter)"@localvol";
double rate = rfunc.Evaluate(maturity);
double dy = qfunc.Evaluate(maturity);
StochasticProcessExtendible s = new StochasticProcessExtendible(rov, process);
rov.Processes.AddProcess(s);
// Set the discounting.
RiskFreeInfo rfi = rov.GetDiscountingModel() as RiskFreeInfo;
rfi.ActualizationType = EActualizationType.RiskFree;
rfi.m_deterministicRF = rate;
OptionTree op = new OptionTree(rov);
op.PayoffInfo.PayoffExpression = "payoff(v1)";
op.PayoffInfo.Timing.EndingTime.m_Value = (RightValue)maturity;
op.PayoffInfo.European = true;
rov.Map.Root = op;
rov.NMethods.Technology = ETechType.T_SIMULATION;
rov.NMethods.PathsNumber = n_sim;
rov.NMethods.SimulationSteps = n_steps;
ROVSolver solver = new ROVSolver();
solver.BindToProject(rov);
solver.DoValuation(-1);
if (rov.HasErrors)
{
rov.DisplayErrors();
}
Assert.IsFalse(rov.HasErrors);
ResultItem price = rov.m_ResultList[0] as ResultItem;
double samplePrice = price.value;
double sampleDevSt = price.stdDev / Math.Sqrt((double)n_sim);
Console.WriteLine("Surf = " + volfunc.Expr);
// Calculation of the theoretical value of the call.
double theoreticalPrice = BlackScholes.Call(rate, S0, strike, volatility, maturity, dy);
Console.WriteLine("Theoretical Price = " + theoreticalPrice.ToString());
Console.WriteLine("Monte Carlo Price = " + samplePrice);
Console.WriteLine("Standard Deviation = " + sampleDevSt.ToString());
double tol = 4.0 * sampleDevSt;
doc.WriteToXMLFile("Dupire.fair");
Assert.LessOrEqual(Math.Abs(theoreticalPrice - samplePrice), tol);
}
protected ModelValue DetermineParameterValue(ModelParameter parameter)
{
ModelValues values = new ModelValues();
values.Add(parameter.Values);
//Parameter values, can be specified in numerous ways:
// 1. Value list - simply choose one of them
// 2. Expression - generate value that meets the criteria (ie: <, >, !=, etc)
// 3. Variable - simply obtain the value by calling a method/field
//#3. Variable - simply obtain the value by calling a method/field
if(parameter.Variable != null)
{
object current = parameter.Variable.CachedValue;
if(current is IEnumerable && !typeof(IEnumerable).IsAssignableFrom(parameter.Type))
{
foreach(object v in (IEnumerable)current)
values.Add(new ModelValue(v));
}
else
{
values.Add(new ModelValue(current));
}
}
//First ensure we have a set of values/requirements to choose from
if(values.Count <= 0)
throw new ModelException(parameter, "No values specified to choose from");
//Note: Since we allow the operator on the individual values, this is a little more complex.
//Note: We allow multiple operators, not just one, (ie: x > 5, < 10, and != 7).
//This gives you great power and flexibility in expressions, but means we have to work a
//little hard in determing a value that meets the requirements
//#1. Value list - simply choose one of them
//Note: Bitmask is already exploded into combinations
ModelValues equalvalues = values.FindOperator(ModelValueOperator.Equal);
//#2. Expression - generate value that meets the criteria (ie: <, >, !=, etc)
//Note: Since we allow the operator on the individual values, this is a little more complex.
//Note: We allow multiple operators, not just one, (ie: x > 5, < 10, and != 7).
//This gives you great power and flexibility in expressions, but means we have to work a
//little hard in determing a value that meets the requirements.
int min = Int32.MinValue;
int max = Int32.MaxValue;
//Adjust our parameter, simplier to loop over all of them
foreach(ModelValue value in values.FindOperator(ModelValueOperator.Equal, false).FindOperator(ModelValueOperator.NotEqual, false))
{
//To keep this simple, we just support integers (for now).
if(!(value.Value is int || value.Value is Nullable<int>))
throw new ModelException(parameter, "Generated value range must be specified in terms of integers, not '" + value.Type + "'");
//Simplify our life
int v = (int)value.Value;
//Adjust Max (if there is one)
switch(value.Operator)
{
case ModelValueOperator.LessThanOrEqual:
if(v < max)
max = v;
break;
case ModelValueOperator.GreaterThanOrEqual:
if(v > min)
min = v;
break;
case ModelValueOperator.LessThan:
if(v-1 < max && v > Int32.MinValue/*prevent underflow*/)
max = v-1;
break;
case ModelValueOperator.GreaterThan:
if(v+1 > min && v < Int32.MaxValue/*prevent overflow*/)
min = v+1;
break;
};
}
//Choose a new value, within the specified range
//Note: We retry in case it equals one of the existing invalid values (ie: !=)
while(true)
{
ModelValue choice = null;
if(equalvalues.Count > 0)
{
//Simple: Choose one of the specified values
choice = new ModelValue(equalvalues.Choose(this).Value);
}
else
{
//Otherwise: Choose a value within in the range
//Note: Random.Next = min <= x < max (so we have to add one)
int index = _options.Random.Next(min, max < Int32.MaxValue ? max + 1 : max); //Prevent overflow
choice = new ModelValue(index);
}
//As soon as we find a value, within the range, (and not in the invalid list), were done
bool valid = true;
foreach(ModelValue invalid in values.FindOperator(ModelValueOperator.NotEqual))
{
if(invalid.Evaluate(choice.Value, ModelValueOperator.Equal))
valid = false;
}
if(valid)
return choice;
}
}
