wehub-resource-sync
134 lines
4.9 KiB
C#
134 lines
4.9 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 QuantConnect.Data;
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using QuantConnect.Interfaces;
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using QuantConnect.Securities;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// This regression algorithm reproduces GH issue 3763 (performing just 1 trade)
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/// </summary>
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public class MarginRemainingRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private Symbol _spy;
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private Security _appl;
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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(2007, 1, 1);
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SetEndDate(2010, 1, 1);
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_spy = AddEquity("SPY", Resolution.Daily, leverage: 1).Symbol;
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_appl = AddEquity("AAPL", Resolution.Daily, leverage: 1);
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Schedule.On(DateRules.EveryDay(), TimeRules.Noon, () =>
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{
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Plot("Info", "Portfolio.MarginRemaining", Portfolio.MarginRemaining);
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Plot("Info", "Portfolio.Cash", Portfolio.Cash);
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});
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}
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public override void OnData(Slice slice)
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{
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if (!Portfolio.Invested)
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{
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// 70% SPY
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SetHoldings(_spy, 0.7);
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Debug("Purchased Stock SPY");
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}
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if (Portfolio.MarginRemaining <= 0)
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{
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throw new RegressionTestException($"Unexpected margin remaining value {Portfolio.MarginRemaining}");
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}
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// in the 2009 dip buy AAPL
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if (Time.Year == 2009 && !_appl.Invested)
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{
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// 30% SPY
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SetHoldings(_appl.Symbol, 0.3);
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Debug("Purchased Stock AAPL");
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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 => 6800;
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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", "2"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "6.056%"},
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{"Drawdown", "42.100%"},
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{"Expectancy", "0"},
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{"Start Equity", "100000"},
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{"End Equity", "119303.75"},
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{"Net Profit", "19.304%"},
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{"Sharpe Ratio", "0.162"},
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{"Sortino Ratio", "0.183"},
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{"Probabilistic Sharpe Ratio", "3.869%"},
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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", "0.057"},
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{"Beta", "0.708"},
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{"Annual Standard Deviation", "0.177"},
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{"Annual Variance", "0.031"},
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{"Information Ratio", "0.8"},
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{"Tracking Error", "0.087"},
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{"Treynor Ratio", "0.04"},
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{"Total Fees", "$45.18"},
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{"Estimated Strategy Capacity", "$410000000.00"},
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{"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
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{"Portfolio Turnover", "0.09%"},
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{"Drawdown Recovery", "707"},
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{"OrderListHash", "39bdab2dcde5bed30c6fc3200d39e83c"}
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};
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}
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}
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