Michael Handschuh
4fd16f6daf
Fixes a bug where we were using the security's data resolution to compute the insight's close time. This led a case such as insight.Period == 20days to step 20days worth of tradable minutes (assuming minute data resolution), yielding a close time that was very far in the future. We also add different means of specifying an insight's period/close time: 1. Specify insight period as a TimeSpan and we compute close time 2. Specify insight period and a resolution and bar count and we compute close time 3. Specify insight close time local directly and we compute the insight period The key here is maintaining consistency between the three different approaches which is heavily validated with the corresponding unit tests. Edits also made to trust the insight's close time as the analysis end time in the case where the analysis period == insight period (extra analysis period = 0). Given the current setup (extra analysis period == 0), this guarantees that close and analysis end times are equivalent. Regression statistics were updated and expectedly we get many more insights that have completed analysis, and as such, average scores have also changed.
111 lines
4.8 KiB
C#
111 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;
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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 : QCAlgorithmFramework, 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", "10"},
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{"Average Win", "0.50%"},
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{"Average Loss", "-0.14%"},
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{"Compounding Annual Return", "573.292%"},
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{"Drawdown", "0.600%"},
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{"Expectancy", "1.248"},
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{"Net Profit", "2.647%"},
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{"Sharpe Ratio", "8.544"},
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{"Loss Rate", "50%"},
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{"Win Rate", "50%"},
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{"Profit-Loss Ratio", "3.50"},
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{"Alpha", "0"},
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{"Beta", "96.224"},
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{"Annual Standard Deviation", "0.129"},
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{"Annual Variance", "0.017"},
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{"Information Ratio", "8.462"},
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{"Tracking Error", "0.129"},
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{"Treynor Ratio", "0.011"},
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{"Total Fees", "$23.04"},
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{"Total Insights Generated", "14"},
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{"Total Insights Closed", "11"},
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{"Total Insights Analysis Completed", "11"},
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{"Long Insight Count", "6"},
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{"Short Insight Count", "4"},
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{"Long/Short Ratio", "150.0%"},
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{"Estimated Monthly Alpha Value", "$-85612.32"},
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{"Total Accumulated Estimated Alpha Value", "$-14744.34"},
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{"Mean Population Estimated Insight Value", "$-1340.395"},
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{"Mean Population Direction", "27.2727%"},
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{"Mean Population Magnitude", "27.2727%"},
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{"Rolling Averaged Population Direction", "5.8237%"},
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{"Rolling Averaged Population Magnitude", "5.8237%"}
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};
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}
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}
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