chore: import upstream snapshot with attribution
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/*
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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.Collections.Generic;
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using System.Linq;
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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.Selection;
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using QuantConnect.Interfaces;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Test algorithm using <see cref="QCAlgorithm.AddUniverseSelection(IUniverseSelectionModel)"/>
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/// </summary>
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public class AddUniverseSelectionModelCoarseAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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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.Daily;
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// Order margin value has to have a minimum of 0.5% of Portfolio value, allows filtering out small trades and reduce fees.
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// Commented so regression algorithm is more sensitive
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//Settings.MinimumOrderMarginPortfolioPercentage = 0.005m;
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SetStartDate(2014, 03, 24);
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SetEndDate(2014, 04, 07);
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SetCash(100000);
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// set algorithm framework models
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SetAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromMinutes(20), 0.025, null));
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SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
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SetExecution(new ImmediateExecutionModel());
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SetUniverseSelection(new CoarseFundamentalUniverseSelectionModel(
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enumerable => enumerable
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.Select(fundamental => fundamental.Symbol)
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.Where(symbol => symbol.Value == "AAPL")));
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AddUniverseSelection(new CoarseFundamentalUniverseSelectionModel(
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enumerable => enumerable
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.Select(fundamental => fundamental.Symbol)
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.Where(symbol => symbol.Value == "SPY")));
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AddUniverseSelection(new CoarseFundamentalUniverseSelectionModel(
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enumerable => enumerable
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.Select(fundamental => fundamental.Symbol)
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.Where(symbol => symbol.Value == "FB")));
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}
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public override void OnEndOfAlgorithm()
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{
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if (UniverseManager.Count != 3)
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{
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throw new RegressionTestException("Unexpected universe count");
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}
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if (UniverseManager.ActiveSecurities.Count != 3
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|| UniverseManager.ActiveSecurities.Keys.All(symbol => symbol.Value != "SPY")
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|| UniverseManager.ActiveSecurities.Keys.All(symbol => symbol.Value != "AAPL")
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|| UniverseManager.ActiveSecurities.Keys.All(symbol => symbol.Value != "FB"))
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{
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throw new RegressionTestException("Unexpected active securities");
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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 };
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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 => 234015;
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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 => 0;
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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", "21"},
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{"Average Win", "0.01%"},
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{"Average Loss", "-0.01%"},
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{"Compounding Annual Return", "-77.566%"},
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{"Drawdown", "6.000%"},
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{"Expectancy", "-0.811"},
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{"Start Equity", "100000"},
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{"End Equity", "94042.73"},
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{"Net Profit", "-5.957%"},
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{"Sharpe Ratio", "-3.345"},
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{"Sortino Ratio", "-3.766"},
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{"Probabilistic Sharpe Ratio", "4.444%"},
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{"Loss Rate", "89%"},
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{"Win Rate", "11%"},
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{"Profit-Loss Ratio", "0.70"},
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{"Alpha", "-0.519"},
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{"Beta", "1.491"},
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{"Annual Standard Deviation", "0.2"},
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{"Annual Variance", "0.04"},
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{"Information Ratio", "-3.878"},
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{"Tracking Error", "0.147"},
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{"Treynor Ratio", "-0.449"},
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{"Total Fees", "$29.11"},
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{"Estimated Strategy Capacity", "$680000000.00"},
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{"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
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{"Portfolio Turnover", "7.48%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "2c814c55e7d7c56482411c065b861b33"}
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};
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}
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}
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