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GeezerVison is showing github.com — Lean/Algorithm.CSharp/AddAlphaModelAlgorithm.cs at master · QuantConnect/Lean Expand file tree/ Copy pathAddAlphaModelAlgorithm.csMore file actions 158 lines (141 loc) · 6.14 KB / Copy pathAddAlphaModelAlgorithm.csFile metadata and controls158 lines (141 loc) · 6.14 KB 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 /* * 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.Selection; using QuantConnect.Data; using QuantConnect.Interfaces; namespace QuantConnect.Algorithm.CSharp { /// <summary> /// Test algorithm using <see cref="QCAlgorithm.AddAlphaModel(IAlphaModel)"/> /// </summary> public class AddAlphaModelAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition { private Symbol _spy; private Symbol _fb; private Symbol _ibm; /// <summary> /// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized. /// </summary> public override void Initialize() { SetStartDate(2013, 10, 07); //Set Start Date SetEndDate(2013, 10, 11); //Set End Date SetCash(100000); //Set Strategy Cash UniverseSettings.Resolution = Resolution.Daily; _spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA); _fb = QuantConnect.Symbol.Create("FB", SecurityType.Equity, Market.USA); _ibm = QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA); SetUniverseSelection(new ManualUniverseSelectionModel(_spy, _fb, _ibm)); SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel()); SetExecution(new ImmediateExecutionModel()); AddAlpha(new OneTimeAlphaModel(_spy)); AddAlpha(new OneTimeAlphaModel(_fb)); AddAlpha(new OneTimeAlphaModel(_ibm)); InsightsGenerated += OnInsightsGeneratedVerifier; } private void OnInsightsGeneratedVerifier(IAlgorithm algorithm, GeneratedInsightsCollection insightsCollection) { if (insightsCollection.Insights.Count(insight => insight.Symbol == _fb) != 1 || insightsCollection.Insights.Count(insight => insight.Symbol == _spy) != 1 || insightsCollection.Insights.Count(insight => insight.Symbol == _ibm) != 1) { throw new RegressionTestException("Unexpected insights were emitted"); } } private class OneTimeAlphaModel : AlphaModel { private readonly Symbol _symbol; private bool _triggered; public OneTimeAlphaModel(Symbol symbol) { _symbol = symbol; } public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice data) { if (!_triggered) { _triggered = true; yield return Insight.Price( _symbol, Resolution.Daily, 1, InsightDirection.Down ); } } } /// <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 List<Language> Languages { get; } = new() { Language.CSharp, Language.Python }; /// <summary> /// Data Points count of all timeslices of algorithm /// </summary> public long DataPoints => 58; /// <summary> /// Data Points count of the algorithm history /// </summary> public 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 Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string> { {"Total Orders", "9"}, {"Average Win", "0.86%"}, {"Average Loss", "-0.27%"}, {"Compounding Annual Return", "206.404%"}, {"Drawdown", "1.700%"}, {"Expectancy", "1.781"}, {"Start Equity", "100000"}, {"End Equity", "101441.92"}, {"Net Profit", "1.442%"}, {"Sharpe Ratio", "4.836"}, {"Sortino Ratio", "10.481"}, {"Probabilistic Sharpe Ratio", "59.374%"}, {"Loss Rate", "33%"}, {"Win Rate", "67%"}, {"Profit-Loss Ratio", "3.17"}, {"Alpha", "4.164"}, {"Beta", "-1.322"}, {"Annual Standard Deviation", "0.321"}, {"Annual Variance", "0.103"}, {"Information Ratio", "-0.795"}, {"Tracking Error", "0.532"}, {"Treynor Ratio", "-1.174"}, {"Total Fees", "$14.78"}, {"Estimated Strategy Capacity", "$120000000.00"}, {"Lowest Capacity Asset", "IBM R735QTJ8XC9X"}, {"Portfolio Turnover", "41.18%"}, {"Drawdown Recovery", "0"}, {"OrderListHash", "713c956deb193bed2290e9f379c0f9f9"} }; } } |
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