/* * 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 QuantConnect.Data; using QuantConnect.Data.UniverseSelection; using QuantConnect.Interfaces; namespace QuantConnect.Algorithm.CSharp { /// /// Regression algorithm asserting the behavior of a ScheduledUniverse /// public class ScheduledUniverseRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition { private readonly Symbol _spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA); private readonly Queue _selectionTime = new(new[] { new DateTime(2013, 10, 7, 1, 0, 0), new DateTime(2013, 10, 8, 1, 0, 0) }); /// /// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized. /// public override void Initialize() { SetStartDate(2013, 10, 07); SetEndDate(2013, 10, 08); AddUniverse(new ScheduledUniverse(DateRules.EveryDay(), TimeRules.At(1, 0), SelectAssets)); } private IEnumerable SelectAssets(DateTime time) { Debug($"Universe selection called: {Time}"); var expectedTime = _selectionTime.Dequeue(); if (expectedTime != Time) { throw new RegressionTestException($"Unexpected selection time {Time} expected {expectedTime}"); } return new[] { _spy }; } /// /// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here. /// /// Slice object keyed by symbol containing the stock data public override void OnData(Slice slice) { if (!Portfolio.Invested) { SetHoldings(_spy, 1); } } public override void OnEndOfAlgorithm() { if (_selectionTime.Count > 0) { throw new RegressionTestException("Unexpected selection times"); } } /// /// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm. /// public bool CanRunLocally { get; } = true; /// /// This is used by the regression test system to indicate which languages this algorithm is written in. /// public List Languages { get; } = new() { Language.CSharp, Language.Python }; /// /// Data Points count of all timeslices of algorithm /// public long DataPoints => 1584; /// /// Data Points count of the algorithm history /// public int AlgorithmHistoryDataPoints => 0; /// /// Final status of the algorithm /// public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed; /// /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm /// public Dictionary ExpectedStatistics => new Dictionary { {"Total Orders", "1"}, {"Average Win", "0%"}, {"Average Loss", "0%"}, {"Compounding Annual Return", "-87.920%"}, {"Drawdown", "1.700%"}, {"Expectancy", "0"}, {"Start Equity", "100000"}, {"End Equity", "98824.68"}, {"Net Profit", "-1.175%"}, {"Sharpe Ratio", "-5.981"}, {"Sortino Ratio", "0"}, {"Probabilistic Sharpe Ratio", "0%"}, {"Loss Rate", "0%"}, {"Win Rate", "0%"}, {"Profit-Loss Ratio", "0"}, {"Alpha", "-0.002"}, {"Beta", "0.996"}, {"Annual Standard Deviation", "0.13"}, {"Annual Variance", "0.017"}, {"Information Ratio", "2.618"}, {"Tracking Error", "0.001"}, {"Treynor Ratio", "-0.778"}, {"Total Fees", "$3.44"}, {"Estimated Strategy Capacity", "$56000000.00"}, {"Lowest Capacity Asset", "SPY R735QTJ8XC9X"}, {"Portfolio Turnover", "33.21%"}, {"Drawdown Recovery", "0"}, {"OrderListHash", "3da9fa60bf95b9ed148b95e02e0cfc9e"} }; } }