wehub-resource-sync
114 lines
4.5 KiB
C#
114 lines
4.5 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 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.Portfolio;
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using QuantConnect.Algorithm.Framework.Selection;
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using QuantConnect.Data.UniverseSelection;
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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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/// Example algorithm of using ETFConstituentsUniverseSelectionModel
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/// </summary>
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public class ETFConstituentsFrameworkAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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public override void Initialize()
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{
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SetStartDate(2020, 12, 1);
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SetEndDate(2020, 12, 7);
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SetCash(100000);
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UniverseSettings.Resolution = Resolution.Daily;
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var symbol = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
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AddUniverseSelection(new ETFConstituentsUniverseSelectionModel(symbol, UniverseSettings, ETFConstituentsFilter));
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AddAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromDays(1)));
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SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
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}
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private protected IEnumerable<Symbol> ETFConstituentsFilter(IEnumerable<ETFConstituentUniverse> constituents)
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{
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// Get the 10 securities with the largest weight in the index
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return constituents.OrderByDescending(c => c.Weight).Take(8).Select(c => c.Symbol);
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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 => 1068;
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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", "8"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "64.993%"},
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{"Drawdown", "0.900%"},
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{"Expectancy", "0"},
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{"Start Equity", "100000"},
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{"End Equity", "100918.77"},
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{"Net Profit", "0.919%"},
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{"Sharpe Ratio", "4.7"},
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{"Sortino Ratio", "14.706"},
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{"Probabilistic Sharpe Ratio", "67.293%"},
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{"Loss Rate", "0%"},
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{"Win Rate", "0%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "0.618"},
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{"Beta", "-0.348"},
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{"Annual Standard Deviation", "0.1"},
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{"Annual Variance", "0.01"},
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{"Information Ratio", "0.41"},
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{"Tracking Error", "0.127"},
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{"Treynor Ratio", "-1.358"},
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{"Total Fees", "$7.02"},
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{"Estimated Strategy Capacity", "$440000000.00"},
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{"Lowest Capacity Asset", "GOOCV VP83T1ZUHROL"},
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{"Portfolio Turnover", "13.71%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "21aeef113b8d043e018967d7c1916e5f"}
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};
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}
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}
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