wehub-resource-sync
130 lines
5.5 KiB
C#
130 lines
5.5 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 QuantConnect.Algorithm.Framework.Alphas;
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using QuantConnect.Algorithm.Framework.Execution;
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using QuantConnect.Algorithm.Framework.Portfolio;
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using QuantConnect.Algorithm.Framework.Risk;
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using QuantConnect.Algorithm.Framework.Selection;
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using QuantConnect.Interfaces;
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using System.Collections.Generic;
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using System.Linq;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Framework algorithm that uses the <see cref="PearsonCorrelationPairsTradingAlphaModel"/>.
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/// This model extendes <see cref="BasePairsTradingAlphaModel"/> and uses Pearson correlation
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/// to rank the pairs trading candidates and use the best candidate to trade.
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/// </summary>
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public class PearsonCorrelationPairsTradingAlphaModelFrameworkAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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public override void Initialize()
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{
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SetStartDate(2013, 10, 07);
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SetEndDate(2013, 10, 11);
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var symbols = new[] { "SPY", "AIG", "BAC", "IBM" }
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.Select(ticker => QuantConnect.Symbol.Create(ticker, SecurityType.Equity, Market.USA))
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.ToList();
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// Manually add SPY and AIG when the algorithm starts
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SetUniverseSelection(new ManualUniverseSelectionModel(symbols.Take(2)));
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// At midnight, add all securities every day except on the last data
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// With this procedure, the Alpha Model will experience multiple universe changes
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AddUniverseSelection(new ScheduledUniverseSelectionModel(
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DateRules.EveryDay(), TimeRules.Midnight,
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dt => dt < EndDate.AddDays(-1) ? symbols : Enumerable.Empty<Symbol>()));
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SetAlpha(new PearsonCorrelationPairsTradingAlphaModel(252, Resolution.Daily));
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SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
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SetExecution(new ImmediateExecutionModel());
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SetRiskManagement(new NullRiskManagementModel());
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}
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public override void OnEndOfAlgorithm()
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{
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// We have removed all securities from the universe. The Alpha Model should remove the consolidator
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var consolidatorCount = SubscriptionManager.Subscriptions.Sum(s => s.Consolidators.Count);
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if (consolidatorCount > 0)
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{
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throw new RegressionTestException($"The number of consolidator is should be zero. Actual: {consolidatorCount}");
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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 => 14088;
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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 => 1008;
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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", "6"},
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{"Average Win", "0.99%"},
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{"Average Loss", "-0.84%"},
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{"Compounding Annual Return", "25.943%"},
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{"Drawdown", "0.800%"},
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{"Expectancy", "0.089"},
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{"Start Equity", "100000"},
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{"End Equity", "100295.35"},
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{"Net Profit", "0.295%"},
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{"Sharpe Ratio", "4.205"},
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{"Sortino Ratio", "0"},
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{"Probabilistic Sharpe Ratio", "61.122%"},
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{"Loss Rate", "50%"},
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{"Win Rate", "50%"},
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{"Profit-Loss Ratio", "1.18"},
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{"Alpha", "0.08"},
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{"Beta", "0.06"},
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{"Annual Standard Deviation", "0.047"},
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{"Annual Variance", "0.002"},
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{"Information Ratio", "-8.305"},
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{"Tracking Error", "0.214"},
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{"Treynor Ratio", "3.313"},
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{"Total Fees", "$31.60"},
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{"Estimated Strategy Capacity", "$3200000.00"},
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{"Lowest Capacity Asset", "AIG R735QTJ8XC9X"},
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{"Portfolio Turnover", "80.47%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "476d54ac7295563a79add3a80310a0a8"}
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};
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}
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}
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