public Solution(IModel model, IProblemData problemData)
: base(model, problemData) {
Add(new Result(TrainingSharpeRatioResultName, "Share ratio of the signals of the model on the training partition", new DoubleValue()));
Add(new Result(TestSharpeRatioResultName, "Sharpe ratio of the signals of the model on the test partition", new DoubleValue()));
Add(new Result(TrainingProfitResultName, "Profit of the model on the training partition", new DoubleValue()));
Add(new Result(TestProfitResultName, "Profit of the model on the test partition", new DoubleValue()));
}
public SymbolicSolution(IModel model, IProblemData problemData)
: base(model, problemData)
{
Add(new Result(ModelLengthResultName, "Length of the symbolic trading model.", new IntValue()));
Add(new Result(ModelDepthResultName, "Depth of the symbolic trading model.", new IntValue()));
RecalculateResults();
}
public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, IProblemData problemData, IEnumerable<int> rows) {
IEnumerable<double> signals = GetSignals(interpreter, solution, problemData.Dataset, rows);
IEnumerable<double> returns = problemData.Dataset.GetDoubleValues(problemData.PriceChangeVariable, rows);
OnlineCalculatorError errorState;
double sharpRatio = OnlineSharpeRatioCalculator.Calculate(returns, signals, problemData.TransactionCosts, out errorState);
if (errorState != OnlineCalculatorError.None) return 0.0;
else return sharpRatio;
}
public Solution(IModel model, IProblemData problemData)
: base(model, problemData)
{
Add(new Result(TrainingSharpeRatioResultName, "Share ratio of the signals of the model on the training partition", new DoubleValue()));
Add(new Result(TestSharpeRatioResultName, "Sharpe ratio of the signals of the model on the test partition", new DoubleValue()));
Add(new Result(TrainingProfitResultName, "Profit of the model on the training partition", new DoubleValue()));
Add(new Result(TestProfitResultName, "Profit of the model on the test partition", new DoubleValue()));
}
/// <summary>
/// Ants are implemented predominantly as per "Ant Colony Optimisation" Dorigo and Stutzle (2004), Ch3.8, p103.
/// </summary>
/// <param name="id">The ant's unique integer Id.</param>
/// <param name="problemData">Provides access to the problem-specific parameters and data matrices.</param>
/// <param name="nodeSelector">Used to select the next node to move to.</param>
public Ant(int id, IProblemData problemData, INodeSelector nodeSelector)
{
Id = id;
_problemData = problemData;
_nodeSelector = nodeSelector;
// Ant ultimately returns to starting node.
_tour = new int[_problemData.NodeCount + 1];
_visited = new bool[_problemData.NodeCount];
}
public SymbolicSolution(IModel model, IProblemData problemData)
: base(model, problemData) {
Add(new Result(ModelLengthResultName, "Length of the symbolic trading model.", new IntValue()));
Add(new Result(ModelDepthResultName, "Depth of the symbolic trading model.", new IntValue()));
RecalculateResults();
}
public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IProblemData problemData, IEnumerable<int> rows) {
SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
double sharpRatio = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, problemData, rows);
SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
return sharpRatio;
}
public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IProblemData problemData, IEnumerable <int> rows)
{
SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
double sharpRatio = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, problemData, rows);
SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
return(sharpRatio);
}
public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, IProblemData problemData, IEnumerable <int> rows)
{
IEnumerable <double> signals = GetSignals(interpreter, solution, problemData.Dataset, rows);
IEnumerable <double> returns = problemData.Dataset.GetDoubleValues(problemData.PriceChangeVariable, rows);
OnlineCalculatorError errorState;
double sharpRatio = OnlineSharpeRatioCalculator.Calculate(returns, signals, problemData.TransactionCosts, out errorState);
if (errorState != OnlineCalculatorError.None)
{
return(0.0);
}
else
{
return(sharpRatio);
}
}
/// <summary>
/// Constructor.
/// </summary>
/// <param name="problemData">The problem specific <seealso cref="StandardProblemData"/> object containing distance,
/// pheromone, heuristic and choice info information.</param>
/// <param name="random">The global random number generator object.</param>
public RouletteWheelSelector(IProblemData problemData, Random random)
{
_problemData = problemData;
_random = random;
_probabilities = new double[problemData.NodeCount];
}
public NearestNeighbourSelector(IProblemData problemData)
{
_problemData = problemData;
}
