wehub-resource-sync
118 lines
4.5 KiB
C#
118 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.Collections.Generic;
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using QuantConnect.Algorithm.Framework.Portfolio;
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using QuantConnect.Data;
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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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/// Validates that SetHoldings returns the correct number of order tickets on each execution.
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/// </summary>
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public class SetHoldingReturnsOrderTicketsRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private Symbol _spy;
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private Symbol _ibm;
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public override void Initialize()
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{
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SetStartDate(2018, 1, 4);
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SetEndDate(2018, 1, 10);
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_spy = AddEquity("SPY", Resolution.Daily).Symbol;
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_ibm = AddEquity("IBM", Resolution.Daily).Symbol;
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}
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public override void OnData(Slice slice)
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{
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var tickets = SetHoldings(new List<PortfolioTarget> { new(_spy, 0.8m), new(_ibm, 0.2m) });
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if (!Portfolio.Invested)
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{
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// Ensure exactly 2 tickets are created when the portfolio is not yet invested
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if (tickets.Count != 2)
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{
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throw new RegressionTestException("Expected 2 tickets, got " + tickets.Count);
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}
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}
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else if (tickets.Count != 0)
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{
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// Ensure no tickets are created when the portfolio is already invested
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throw new RegressionTestException("Expected 0 tickets, got " + tickets.Count);
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}
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}
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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 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 => 53;
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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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/// 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", "2"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "43.490%"},
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{"Drawdown", "0.100%"},
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{"Expectancy", "0"},
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{"Start Equity", "100000"},
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{"End Equity", "100661.71"},
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{"Net Profit", "0.662%"},
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{"Sharpe Ratio", "12.329"},
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{"Sortino Ratio", "0"},
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{"Probabilistic Sharpe Ratio", "95.183%"},
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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", "0.108"},
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{"Beta", "0.424"},
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{"Annual Standard Deviation", "0.024"},
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{"Annual Variance", "0.001"},
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{"Information Ratio", "-5.097"},
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{"Tracking Error", "0.03"},
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{"Treynor Ratio", "0.707"},
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{"Total Fees", "$2.56"},
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{"Estimated Strategy Capacity", "$170000000.00"},
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{"Lowest Capacity Asset", "IBM R735QTJ8XC9X"},
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{"Portfolio Turnover", "14.24%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "587e1a69d3c83cbd9907f9f9586697e1"}
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};
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}
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}
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