wehub-resource-sync
136 lines
5.0 KiB
C#
136 lines
5.0 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.Linq;
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using QuantConnect.Data;
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using QuantConnect.Interfaces;
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using System.Collections.Generic;
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using QuantConnect.Data.UniverseSelection;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Custom data universe selection regression algorithm asserting it's behavior. See GH issue #6396
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/// </summary>
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public class NoUniverseSelectorRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private SecurityChanges _changes = SecurityChanges.None;
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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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SetStartDate(2014, 03, 24);
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SetEndDate(2014, 03, 31);
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UniverseSettings.Resolution = Resolution.Daily;
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AddUniverse<CoarseFundamental>();
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}
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public void OnData(Slice slice)
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{
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// if we have no changes, do nothing
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if (_changes == SecurityChanges.None) return;
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// liquidate removed securities
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foreach (var security in _changes.RemovedSecurities)
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{
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if (security.Invested)
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{
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Liquidate(security.Symbol);
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}
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}
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var activeAndWithDataSecurities = ActiveSecurities.Count(x => x.Value.HasData);
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// we want 1/N allocation in each security in our universe
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foreach (var security in _changes.AddedSecurities)
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{
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if (security.HasData)
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{
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SetHoldings(security.Symbol, 1m / activeAndWithDataSecurities);
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}
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}
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_changes = SecurityChanges.None;
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}
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public override void OnSecuritiesChanged(SecurityChanges changes)
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{
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_changes = changes;
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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 => 42596;
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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", "15"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "-50.972%"},
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{"Drawdown", "1.700%"},
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{"Expectancy", "0"},
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{"Start Equity", "100000"},
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{"End Equity", "98449.86"},
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{"Net Profit", "-1.550%"},
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{"Sharpe Ratio", "-4.375"},
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{"Sortino Ratio", "-3.048"},
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{"Probabilistic Sharpe Ratio", "2.531%"},
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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", "-0.766"},
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{"Beta", "0.896"},
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{"Annual Standard Deviation", "0.099"},
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{"Annual Variance", "0.01"},
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{"Information Ratio", "-12.019"},
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{"Tracking Error", "0.067"},
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{"Treynor Ratio", "-0.486"},
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{"Total Fees", "$17.93"},
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{"Estimated Strategy Capacity", "$220000.00"},
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{"Lowest Capacity Asset", "BNO UN3IMQ2JU1YD"},
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{"Portfolio Turnover", "14.29%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "f751fd0ba1203f81e6b40f0cb74d959f"}
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};
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}
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}
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