wehub-resource-sync
127 lines
5.2 KiB
C#
127 lines
5.2 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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/// Regression algorithm testing GH feature 3790, using SetHoldings with a collection of targets
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/// which will be ordered by margin impact before being executed, with the objective of avoiding any
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/// margin errors
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/// </summary>
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public class SetHoldingsMultipleTargetsRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private Symbol _spy;
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private Symbol _ibm;
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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(2013, 10, 07);
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SetEndDate(2013, 10, 11);
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// use leverage 1 so we test the margin impact ordering
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_spy = AddEquity("SPY", Resolution.Minute, Market.USA, false, 1).Symbol;
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_ibm = AddEquity("IBM", Resolution.Minute, Market.USA, false, 1).Symbol;
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// Order margin value has to have a minimum of 0.5% of Portfolio value, allows filtering out small trades and reduce fees.
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// Commented so regression algorithm is more sensitive
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//Settings.MinimumOrderMarginPortfolioPercentage = 0.005m;
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}
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/// <summary>
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/// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
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/// </summary>
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/// <param name="data">Slice object keyed by symbol containing the stock data</param>
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public override void OnData(Slice data)
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{
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if (!Portfolio.Invested)
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{
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SetHoldings(new List<PortfolioTarget> { new PortfolioTarget(_spy, 0.8m), new PortfolioTarget(_ibm, 0.2m) });
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}
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else
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{
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SetHoldings(new List<PortfolioTarget> { new PortfolioTarget(_ibm, 0.8m), new PortfolioTarget(_spy, 0.2m) });
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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 virtual 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 virtual 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 virtual long DataPoints => 7842;
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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 virtual 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 virtual Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
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{
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{"Total Orders", "11"},
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{"Average Win", "0.00%"},
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{"Average Loss", "-0.01%"},
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{"Compounding Annual Return", "353.938%"},
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{"Drawdown", "2.300%"},
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{"Expectancy", "-0.749"},
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{"Start Equity", "100000"},
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{"End Equity", "101952.99"},
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{"Net Profit", "1.953%"},
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{"Sharpe Ratio", "11.757"},
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{"Sortino Ratio", "0"},
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{"Probabilistic Sharpe Ratio", "65.498%"},
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{"Loss Rate", "75%"},
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{"Win Rate", "25%"},
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{"Profit-Loss Ratio", "0.00"},
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{"Alpha", "0.96"},
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{"Beta", "0.993"},
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{"Annual Standard Deviation", "0.248"},
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{"Annual Variance", "0.062"},
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{"Information Ratio", "8.324"},
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{"Tracking Error", "0.114"},
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{"Treynor Ratio", "2.942"},
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{"Total Fees", "$15.02"},
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{"Estimated Strategy Capacity", "$2600000.00"},
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{"Lowest Capacity Asset", "IBM R735QTJ8XC9X"},
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{"Portfolio Turnover", "44.15%"},
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{"Drawdown Recovery", "2"},
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{"OrderListHash", "14d509658aa542a210a3d6d41c05cd22"}
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};
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}
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}
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