wehub-resource-sync
132 lines
5.1 KiB
C#
132 lines
5.1 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 QuantConnect.Interfaces;
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using System.Collections.Generic;
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using QuantConnect.Orders;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Basic algorithm demonstrating the use of a MarketOnClose order
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/// </summary>
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public class MarketOnCloseOrderRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private Symbol _symbol;
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private OrderTicket _ticket;
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protected virtual bool AsynchronousOrders => false;
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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(2021, 03, 01);
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SetEndDate(2021, 03, 03);
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SetCash(100000);
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_symbol = AddEquity("SPY", Resolution.Hour).Symbol;
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Schedule.On(DateRules.Tomorrow, TimeRules.Noon, () =>
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{
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_ticket = MarketOnCloseOrder(_symbol, 1, asynchronous: AsynchronousOrders);
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if (_ticket.Status != OrderStatus.New && _ticket.Status != OrderStatus.Submitted)
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{
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throw new RegressionTestException($"Expected the MarketOnClose order to be New or Submitted, instead found {_ticket.Status}");
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}
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});
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}
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public override void OnEndOfAlgorithm()
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{
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if (_ticket == null)
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{
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throw new RegressionTestException("Expected to have placed a MarketOnClose order");
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}
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if (_ticket.Status != OrderStatus.Filled)
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{
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throw new RegressionTestException($"Expected the MarketOnClose order to be filled, instead found {_ticket.Status}");
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}
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if (_ticket.SubmitRequest.Asynchronous != AsynchronousOrders)
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{
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throw new RegressionTestException("Expected all orders to have the same asynchronous flag as the algorithm.");
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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 => 50;
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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", "1"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "-0.832%"},
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{"Drawdown", "0.000%"},
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{"Expectancy", "0"},
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{"Start Equity", "100000"},
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{"End Equity", "99993.90"},
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{"Net Profit", "-0.006%"},
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{"Sharpe Ratio", "-22.06"},
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{"Sortino Ratio", "-22.06"},
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{"Probabilistic Sharpe Ratio", "0%"},
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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", "-0.003"},
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{"Beta", "0.008"},
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{"Annual Standard Deviation", "0"},
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{"Annual Variance", "0"},
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{"Information Ratio", "15.221"},
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{"Tracking Error", "0.061"},
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{"Treynor Ratio", "-1.348"},
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{"Total Fees", "$1.00"},
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{"Estimated Strategy Capacity", "$53000000000.00"},
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{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
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{"Portfolio Turnover", "0.13%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "1add16936335a9c85b72eed80dcacb39"}
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};
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}
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}
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