public void OrderByMarginImpactDoesNotReturnTargetsForWhichUnorderedQuantityIsZeroBecauseOpenOrder()
{
var algorithm = new FakeAlgorithm();
var orderProcessor = new FakeOrderProcessor();
algorithm.Transactions.SetOrderProcessor(orderProcessor);
var symbol = new Symbol(SecurityIdentifier.GenerateEquity(_symbol, Market.USA), _symbol);
var equity = algorithm.AddEquity(symbol);
equity.Cache.AddData(new TradeBar(DateTime.UtcNow, symbol, 1, 1, 1, 1, 1));
var collection = new PortfolioTargetCollection();
var target = new PortfolioTarget(symbol, 1);
collection.Add(target);
var openOrderRequest = new SubmitOrderRequest(OrderType.Market, symbol.SecurityType, symbol, 1, 0, 0, DateTime.UtcNow, "");
openOrderRequest.SetOrderId(1);
var openOrderTicket = new OrderTicket(algorithm.Transactions, openOrderRequest);
orderProcessor.AddOrder(new MarketOrder(symbol, 1, DateTime.UtcNow));
orderProcessor.AddTicket(openOrderTicket);
var targets = collection.OrderByMarginImpact(algorithm);
Assert.AreEqual(collection.Count, 1);
Assert.IsTrue(targets.IsNullOrEmpty());
}
private QCAlgorithm GetAlgorithm(IOrderProcessor orderProcessor)
{
var algorithm = new FakeAlgorithm();
algorithm.SetFinishedWarmingUp();
algorithm.Transactions.SetOrderProcessor(orderProcessor);
return(algorithm);
}
public void DefaultInputSplit(List<object> samples, List<double> labels)
{
var people = samples.ConvertAll(x => (Person)x);
var trainingSetSize = (int)Math.Ceiling(((double)2 / 3) * people.Count);
var crossValidationSetSize = (int)Math.Ceiling(((double)2 / 3) * people.Count);
var algorithm = new FakeAlgorithm(people, labels, new Settings {ScaleAndNormalize = false});
AssertInput(algorithm.MyTrainingSetX, algorithm.MyTrainingSetY, people, labels, 0, trainingSetSize);
AssertInput(algorithm.MyCrossValidationSetX, algorithm.MyCrossValidationSetY, people, labels, trainingSetSize, crossValidationSetSize);
Assert.That(algorithm.MyTestSetX, Is.Null);
Assert.That(algorithm.MyTestSetY, Is.Null);
}
public void ScaledInput(List <object> samples, List <double> labels)
{
var people = samples.ConvertAll(x => (Person)x);
var age = new { Mean = people.Select(x => x.Age).Average(), Deviation = Math.Sqrt(((double)1 / (people.Count - 1)) * people.Select(x => Math.Pow(x.Age - people.Select(xx => xx.Age).Average(), 2)).Sum()) };
var height = new { Mean = people.Select(x => x.Height).Average(), Deviation = Math.Sqrt(((double)1 / (people.Count - 1)) * people.Select(x => Math.Pow(x.Height - people.Select(xx => xx.Height).Average(), 2)).Sum()) };
var algorithm = new FakeAlgorithm(people, labels, new Settings {
InputSplitRatio = InputSplitRatio.No
});
Assert.That(algorithm.MyTrainingSetX[0, 0], Is.EqualTo(1), "A column of ones should be added to the input matrix.");
Assert.That(algorithm.MyTrainingSetX[0, 1], Is.EqualTo((people[0].Age - age.Mean) / age.Deviation));
Assert.That(algorithm.MyTrainingSetX[0, 2], Is.EqualTo((people[0].Height - height.Mean) / height.Deviation));
Assert.That(algorithm.MyTrainingSetY[0], Is.EqualTo(labels[0]));
}
public void DefaultInputSplit(List <object> samples, List <double> labels)
{
var people = samples.ConvertAll(x => (Person)x);
var trainingSetSize = (int)Math.Ceiling(((double)2 / 3) * people.Count);
var crossValidationSetSize = (int)Math.Ceiling(((double)2 / 3) * people.Count);
var algorithm = new FakeAlgorithm(people, labels, new Settings {
ScaleAndNormalize = false
});
AssertInput(algorithm.MyTrainingSetX, algorithm.MyTrainingSetY, people, labels, 0, trainingSetSize);
AssertInput(algorithm.MyCrossValidationSetX, algorithm.MyCrossValidationSetY, people, labels, trainingSetSize, crossValidationSetSize);
Assert.That(algorithm.MyTestSetX, Is.Null);
Assert.That(algorithm.MyTestSetY, Is.Null);
}
public void ClearRemovesUnreachedTarget()
{
var algorithm = new FakeAlgorithm();
var symbol = new Symbol(SecurityIdentifier.GenerateEquity(_symbol, Market.USA), _symbol);
var equity = algorithm.AddEquity(symbol);
var dummySecurityHolding = new FakeSecurityHolding(equity);
equity.Holdings = dummySecurityHolding;
var collection = new PortfolioTargetCollection();
var target = new PortfolioTarget(symbol, -1);
collection.Add(target);
collection.Clear();
Assert.AreEqual(collection.Count, 0);
}
public void OrderByMarginImpactDoesNotReturnTargetsWithNoData()
{
var algorithm = new FakeAlgorithm();
var symbol = new Symbol(SecurityIdentifier.GenerateEquity(_symbol, Market.USA), _symbol);
algorithm.AddEquity(symbol);
var collection = new PortfolioTargetCollection();
var target = new PortfolioTarget(symbol, -1);
collection.Add(target);
var targets = collection.OrderByMarginImpact(algorithm);
Assert.AreEqual(collection.Count, 1);
Assert.IsTrue(targets.IsNullOrEmpty());
}
public void OrderByMarginImpactDoesNotReturnTargetsForWhichUnorderdQuantityIsZeroBecauseTargetIsZero()
{
var algorithm = new FakeAlgorithm();
algorithm.Transactions.SetOrderProcessor(new FakeOrderProcessor());
var symbol = new Symbol(SecurityIdentifier.GenerateEquity(_symbol, Market.USA), _symbol);
var equity = algorithm.AddEquity(symbol);
equity.Cache.AddData(new TradeBar(DateTime.UtcNow, symbol, 1, 1, 1, 1, 1));
var collection = new PortfolioTargetCollection();
var target = new PortfolioTarget(symbol, 0);
collection.Add(target);
var targets = collection.OrderByMarginImpact(algorithm);
Assert.AreEqual(collection.Count, 1);
Assert.IsTrue(targets.IsNullOrEmpty());
}
public void ClearFulfilledRemovesPositiveTarget()
{
var algorithm = new FakeAlgorithm();
algorithm.SetFinishedWarmingUp();
var symbol = new Symbol(SecurityIdentifier.GenerateEquity(_symbol, Market.USA), _symbol);
var equity = algorithm.AddEquity(symbol);
var dummySecurityHolding = new FakeSecurityHolding(equity);
equity.Holdings = dummySecurityHolding;
var collection = new PortfolioTargetCollection();
var target = new PortfolioTarget(symbol, 1);
collection.Add(target);
dummySecurityHolding.SetQuantity(1);
collection.ClearFulfilled(algorithm);
Assert.AreEqual(collection.Count, 0);
}
public void OrderByMarginImpactReturnsExpectedTargets()
{
var algorithm = new FakeAlgorithm();
algorithm.Transactions.SetOrderProcessor(new FakeOrderProcessor());
var symbol = new Symbol(SecurityIdentifier.GenerateEquity(_symbol, Market.USA), _symbol);
var equity = algorithm.AddEquity(symbol);
equity.Cache.AddData(new TradeBar(DateTime.UtcNow, symbol, 1, 1, 1, 1, 1));
var collection = new PortfolioTargetCollection();
var target = new PortfolioTarget(symbol, -1);
collection.Add(target);
var targets = collection.OrderByMarginImpact(algorithm);
Assert.AreEqual(collection.Count, 1);
Assert.AreEqual(targets.Count(), 1);
Assert.AreEqual(targets.First(), target);
}
public void ScaledInput(List<object> samples, List<double> labels)
{
var people = samples.ConvertAll(x => (Person)x);
var age = new { Mean = people.Select(x => x.Age).Average(), Deviation = Math.Sqrt( ((double)1/(people.Count - 1)) * people.Select(x => Math.Pow(x.Age - people.Select(xx => xx.Age).Average(), 2)).Sum() ) };
var height = new { Mean = people.Select(x => x.Height).Average(), Deviation = Math.Sqrt(((double)1 / (people.Count - 1)) * people.Select(x => Math.Pow(x.Height - people.Select(xx => xx.Height).Average(), 2)).Sum()) };
var algorithm = new FakeAlgorithm(people, labels, new Settings {InputSplitRatio = InputSplitRatio.No});
Assert.That(algorithm.MyTrainingSetX[0, 0], Is.EqualTo(1), "A column of ones should be added to the input matrix.");
Assert.That(algorithm.MyTrainingSetX[0, 1], Is.EqualTo( (people[0].Age - age.Mean) / age.Deviation ));
Assert.That(algorithm.MyTrainingSetX[0, 2], Is.EqualTo( (people[0].Height - height.Mean) / height.Deviation ));
Assert.That(algorithm.MyTrainingSetY[0], Is.EqualTo(labels[0]));
}
