Martin Molinero
109 lines
4.8 KiB
C#
109 lines
4.8 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.Collections.Generic;
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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.Linq;
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using QuantConnect.Data.UniverseSelection;
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namespace QuantConnect.Algorithm.CSharp
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{
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public class MeanVarianceOptimizationFrameworkAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private IEnumerable<Symbol> _symbols = (new string[] { "AIG", "BAC", "IBM", "SPY" }).Select(s => QuantConnect.Symbol.Create(s, SecurityType.Equity, Market.USA));
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/// <summary>
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/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
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/// </summary>
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public override void Initialize()
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{
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// Set requested data resolution
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UniverseSettings.Resolution = Resolution.Minute;
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SetStartDate(2013, 10, 07); //Set Start Date
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SetEndDate(2013, 10, 11); //Set End Date
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SetCash(100000); //Set Strategy Cash
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// Find more symbols here: http://quantconnect.com/data
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// Forex, CFD, Equities Resolutions: Tick, Second, Minute, Hour, Daily.
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// Futures Resolution: Tick, Second, Minute
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// Options Resolution: Minute Only.
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// set algorithm framework models
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SetUniverseSelection(new CoarseFundamentalUniverseSelectionModel(CoarseSelector));
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SetAlpha(new HistoricalReturnsAlphaModel(resolution: Resolution.Daily));
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SetPortfolioConstruction(new MeanVarianceOptimizationPortfolioConstructionModel());
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SetExecution(new ImmediateExecutionModel());
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SetRiskManagement(new NullRiskManagementModel());
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}
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public IEnumerable<Symbol> CoarseSelector(IEnumerable<CoarseFundamental> coarse)
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{
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int last = Time.Day > 8 ? 3 : _symbols.Count();
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return _symbols.Take(last);
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}
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public bool CanRunLocally => 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 Language[] Languages { get; } = { Language.CSharp, Language.Python };
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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 Trades", "14"},
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{"Average Win", "0.09%"},
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{"Average Loss", "-0.69%"},
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{"Compounding Annual Return", "573.640%"},
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{"Drawdown", "1.200%"},
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{"Expectancy", "-0.322"},
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{"Net Profit", "2.647%"},
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{"Sharpe Ratio", "6.567"},
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{"Loss Rate", "40%"},
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{"Win Rate", "60%"},
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{"Profit-Loss Ratio", "0.13"},
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{"Alpha", "1.075"},
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{"Beta", "0.102"},
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{"Annual Standard Deviation", "0.169"},
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{"Annual Variance", "0.029"},
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{"Information Ratio", "3.164"},
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{"Tracking Error", "0.239"},
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{"Treynor Ratio", "10.917"},
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{"Total Fees", "$26.45"},
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{"Total Insights Generated", "17"},
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{"Total Insights Closed", "14"},
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{"Total Insights Analysis Completed", "14"},
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{"Long Insight Count", "6"},
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{"Short Insight Count", "7"},
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{"Long/Short Ratio", "85.71%"},
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{"Estimated Monthly Alpha Value", "$46431.9340"},
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{"Total Accumulated Estimated Alpha Value", "$7996.6108"},
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{"Mean Population Estimated Insight Value", "$571.1865"},
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{"Mean Population Direction", "50%"},
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{"Mean Population Magnitude", "50%"},
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{"Rolling Averaged Population Direction", "12.6429%"},
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{"Rolling Averaged Population Magnitude", "12.6429%"}
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};
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}
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} |