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/*
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
using System;
using System.Collections.Generic;
using System.Linq;
using QuantConnect.Algorithm.Framework.Alphas;
using QuantConnect.Algorithm.Framework.Execution;
using QuantConnect.Algorithm.Framework.Portfolio;
using QuantConnect.Algorithm.Framework.Risk;
using QuantConnect.Algorithm.Framework.Selection;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Abstract regression framework algorithm for multiple framework regression tests
/// </summary>
public abstract class BaseFrameworkRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
public override void Initialize()
{
SetStartDate(2014, 6, 1);
SetEndDate(2014, 6, 30);
UniverseSettings.Resolution = Resolution.Hour;
UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw;
var symbols = new[] { "AAPL", "AIG", "BAC", "SPY" }
.Select(ticker => QuantConnect.Symbol.Create(ticker, SecurityType.Equity, Market.USA))
.ToList();
// Manually add AAPL and AIG when the algorithm starts
SetUniverseSelection(new ManualUniverseSelectionModel(symbols.Take(2)));
// At midnight, add all securities every day except on the last data
// With this procedure, the Alpha Model will experience multiple universe changes
AddUniverseSelection(new ScheduledUniverseSelectionModel(
DateRules.EveryDay(), TimeRules.Midnight,
dt => dt < EndDate.AddDays(-1) ? symbols : Enumerable.Empty<Symbol>()));
SetAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromDays(31), 0.025, null));
SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
SetExecution(new ImmediateExecutionModel());
SetRiskManagement(new NullRiskManagementModel());
}
public override void OnEndOfAlgorithm()
{
// The base implementation checks for active insights
var insightsCount = Insights.GetInsights(insight => insight.IsActive(UtcTime)).Count;
if (insightsCount != 0)
{
throw new RegressionTestException($"The number of active insights should be 0. Actual: {insightsCount}");
}
}
/// <summary>
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
/// </summary>
public bool CanRunLocally { get; } = true;
/// <summary>
/// This is used by the regression test system to indicate which languages this algorithm is written in.
/// </summary>
public virtual List<Language> Languages { get; } = new() { Language.CSharp, Language.Python };
/// <summary>
/// Data Points count of all timeslices of algorithm
/// </summary>
public virtual long DataPoints => 765;
/// <summary>
/// Data Points count of the algorithm history
/// </summary>
public virtual int AlgorithmHistoryDataPoints => 0;
/// <summary>
/// Final status of the algorithm
/// </summary>
public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
/// <summary>
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
/// </summary>
public abstract Dictionary<string, string> ExpectedStatistics { get; }
}
}