152 lines
6.1 KiB
C#
152 lines
6.1 KiB
C#
/*
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* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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using System;
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using System.Linq;
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using System.Collections.Generic;
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using QuantConnect.Interfaces;
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using QuantConnect.Securities;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Regression algorithm illustrating how to request history data for different data mapping modes.
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/// </summary>
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public class HistoryWithDifferentDataMappingModeRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private Symbol _continuousContractSymbol;
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public override void Initialize()
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{
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SetStartDate(2013, 10, 6);
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SetEndDate(2014, 1, 1);
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_continuousContractSymbol = AddFuture(Futures.Indices.SP500EMini, Resolution.Daily).Symbol;
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}
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public override void OnEndOfAlgorithm()
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{
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var dataMappingModes = ((DataMappingMode[])Enum.GetValues(typeof(DataMappingMode))).ToList();
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var historyResults = dataMappingModes.Select(dataMappingMode =>
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{
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return History(new [] { _continuousContractSymbol }, StartDate, EndDate, Resolution.Daily, dataMappingMode: dataMappingMode).ToList();
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}).ToList();
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if (historyResults.Any(x => x.Count != historyResults[0].Count))
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{
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throw new RegressionTestException("History results bar count did not match");
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}
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// Check that all history results have a mapping date at some point in the history
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HashSet<DateTime> mappingDates = new HashSet<DateTime>();
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for (int i = 0; i < historyResults.Count; i++)
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{
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var underlying = historyResults[i].First().Bars.Keys.First().Underlying;
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int mappingsCount = 0;
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foreach (var slice in historyResults[i])
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{
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var dataUnderlying = slice.Bars.Keys.First().Underlying;
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if (dataUnderlying != underlying)
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{
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underlying = dataUnderlying;
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mappingsCount++;
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mappingDates.Add(slice.Time.Date);
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}
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}
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if (mappingsCount == 0)
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{
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throw new RegressionTestException($"History results for {dataMappingModes[i]} data mapping mode did not contain any mappings");
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}
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}
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if (mappingDates.Count < dataMappingModes.Count)
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{
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throw new RegressionTestException($"History results should have had different mapping dates for each data mapping mode");
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}
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// Check that close prices at each time are different for different data mapping modes
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for (int j = 0; j < historyResults[0].Count; j++)
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{
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var closePrices = historyResults.Select(hr => hr[j].Bars.First().Value.Close).ToHashSet();
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if (closePrices.Count != dataMappingModes.Count)
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{
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throw new RegressionTestException($"History results close prices should have been different for each data mapping mode at each time");
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}
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}
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}
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/// <summary>
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/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
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/// </summary>
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public bool CanRunLocally { get; } = true;
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/// <summary>
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/// This is used by the regression test system to indicate which languages this algorithm is written in.
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/// </summary>
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public List<Language> Languages { get; } = new() { Language.CSharp, Language.Python };
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/// <summary>
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/// Data Points count of all timeslices of algorithm
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/// </summary>
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public long DataPoints => 970;
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/// <summary>
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/// Data Points count of the algorithm history
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/// </summary>
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public int AlgorithmHistoryDataPoints => 488;
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/// <summary>
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/// Final status of the algorithm
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/// </summary>
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public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
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/// <summary>
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/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
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/// </summary>
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public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
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{
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{"Total Orders", "0"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "0%"},
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{"Drawdown", "0%"},
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{"Expectancy", "0"},
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{"Start Equity", "100000"},
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{"End Equity", "100000"},
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{"Net Profit", "0%"},
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{"Sharpe Ratio", "0"},
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{"Sortino Ratio", "0"},
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{"Probabilistic Sharpe Ratio", "0%"},
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{"Loss Rate", "0%"},
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{"Win Rate", "0%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "0"},
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{"Beta", "0"},
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{"Annual Standard Deviation", "0"},
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{"Annual Variance", "0"},
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{"Information Ratio", "-3.738"},
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{"Tracking Error", "0.087"},
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{"Treynor Ratio", "0"},
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{"Total Fees", "$0.00"},
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{"Estimated Strategy Capacity", "$0"},
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{"Lowest Capacity Asset", ""},
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{"Portfolio Turnover", "0%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
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};
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}
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}
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