/* * 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.Data; using QuantConnect.Data.Market; using QuantConnect.Interfaces; namespace QuantConnect.Algorithm.CSharp { /// /// Regression test algorithm simply fetch history on boarder of Daylight Saving Time shift /// public class DaylightSavingTimeHistoryRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition { private Symbol[] _symbols = new[] { QuantConnect.Symbol.Create("EURUSD", SecurityType.Forex, Market.FXCM), QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA) }; /// /// 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(2011, 11, 10); //Set Start Date SetEndDate(2011, 11, 11); //Set End Date SetCash(100000); //Set Strategy Cash for (int i = 0; i < _symbols.Length; i++) { var symbol = _symbols[i]; IEnumerable history; if (symbol.SecurityType == SecurityType.Equity) { try { history = History(symbol, 10, Resolution.Daily).Select(bar => bar as BaseData); throw new RegressionTestException("We were expecting an argument exception to be thrown. Equity does not have daily QuoteBars!"); } catch (ArgumentException) { // expected } history = History(symbol, 10, Resolution.Daily).Select(bar => bar as BaseData); } else { history = History(symbol, 10, Resolution.Daily) .Select(bar => bar as BaseData); } var duplications = history .GroupBy(k => k.Time) .Where(g => g.Count() > 1); if (duplications.Any()) { var time = duplications.First().Key; throw new RegressionTestException($"Duplicated bars were issued for time {time}"); } } } /// /// 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 }; /// /// Data Points count of all timeslices of algorithm /// public long DataPoints => 21; /// /// Data Points count of the algorithm history /// public int AlgorithmHistoryDataPoints => 20; /// /// 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", "0"}, {"Average Win", "0%"}, {"Average Loss", "0%"}, {"Compounding Annual Return", "0%"}, {"Drawdown", "0%"}, {"Expectancy", "0"}, {"Start Equity", "100000"}, {"End Equity", "100000"}, {"Net Profit", "0%"}, {"Sharpe Ratio", "0"}, {"Sortino Ratio", "0"}, {"Probabilistic Sharpe Ratio", "0%"}, {"Loss Rate", "0%"}, {"Win Rate", "0%"}, {"Profit-Loss Ratio", "0"}, {"Alpha", "0"}, {"Beta", "0"}, {"Annual Standard Deviation", "0"}, {"Annual Variance", "0"}, {"Information Ratio", "0"}, {"Tracking Error", "0"}, {"Treynor Ratio", "0"}, {"Total Fees", "$0.00"}, {"Estimated Strategy Capacity", "$0"}, {"Lowest Capacity Asset", ""}, {"Portfolio Turnover", "0%"}, {"Drawdown Recovery", "0"}, {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"} }; } }