chore: import upstream snapshot with attribution
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wehub-resource-sync
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/*
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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.Data.UniverseSelection;
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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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/// Test algorithm using a <see cref="ConstituentsUniverse"/> with test data
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/// </summary>
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public class ConstituentsUniverseRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private readonly Symbol _appl = QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA);
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private readonly Symbol _spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
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private readonly Symbol _qqq = QuantConnect.Symbol.Create("QQQ", SecurityType.Equity, Market.USA);
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private readonly Symbol _fb = QuantConnect.Symbol.Create("FB", SecurityType.Equity, Market.USA);
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private int _step;
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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); //Set Start Date
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SetEndDate(2013, 10, 11); //Set End Date
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SetCash(100000); //Set Strategy Cash
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UniverseSettings.Resolution = Resolution.Daily;
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var customUniverseSymbol = new Symbol(SecurityIdentifier.GenerateConstituentIdentifier(
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"constituents-universe-qctest",
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SecurityType.Equity,
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Market.USA),
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"constituents-universe-qctest");
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AddUniverse(new ConstituentsUniverse(customUniverseSymbol, UniverseSettings));
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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="slice">Slice object keyed by symbol containing the stock data</param>
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public override void OnData(Slice slice)
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{
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_step++;
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if (_step == 1)
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{
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if (!slice.ContainsKey(_qqq)
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|| !slice.ContainsKey(_appl))
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{
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throw new RegressionTestException($"Unexpected symbols found, step: {_step}");
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}
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if (slice.Count != 2)
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{
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throw new RegressionTestException($"Unexpected data count, step: {_step}");
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}
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// AAPL will be deselected by the ConstituentsUniverse
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// but it shouldn't be removed since we hold it
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SetHoldings(_appl, 0.5);
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}
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else if (_step == 2)
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{
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if (!slice.ContainsKey(_appl))
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{
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throw new RegressionTestException($"Unexpected symbols found, step: {_step}");
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}
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if (slice.Count != 1)
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{
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throw new RegressionTestException($"Unexpected data count, step: {_step}");
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}
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// AAPL should now be released
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// note: takes one extra loop because the order is executed on market open
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Liquidate();
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}
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else if (_step == 3)
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{
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if (!slice.ContainsKey(_fb)
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|| !slice.ContainsKey(_spy)
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|| !slice.ContainsKey(_appl))
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{
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throw new RegressionTestException($"Unexpected symbols found, step: {_step}");
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}
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if (slice.Count != 3)
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{
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throw new RegressionTestException($"Unexpected data count, step: {_step}");
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}
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}
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else if (_step == 4)
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{
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if (!slice.ContainsKey(_fb)
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|| !slice.ContainsKey(_spy))
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{
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throw new RegressionTestException($"Unexpected symbols found, step: {_step}");
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}
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if (slice.Count != 2)
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{
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throw new RegressionTestException($"Unexpected data count, step: {_step}");
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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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// First selection is on the midnight of the 8th, start getting data from the 8th to the 11th
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if (_step != 4)
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{
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throw new RegressionTestException($"Unexpected step count: {_step}");
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}
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}
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public override void OnSecuritiesChanged(SecurityChanges changes)
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{
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foreach (var added in changes.AddedSecurities)
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{
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Log($"{Time} AddedSecurities {added}");
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}
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foreach (var removed in changes.RemovedSecurities)
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{
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Log($"{Time} RemovedSecurities {removed} {_step}");
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// we are currently notifying the removal of AAPl twice,
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// when deselected and when finally removed (since it stayed pending)
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if (removed.Symbol == _appl && _step != 1 && _step != 2
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|| removed.Symbol == _qqq && _step != 1)
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{
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throw new RegressionTestException($"Unexpected removal step count: {_step}");
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}
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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, 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 => 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", "2"},
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{"Average Win", "0.68%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "70.501%"},
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{"Drawdown", "0%"},
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{"Expectancy", "0"},
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{"Start Equity", "100000"},
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{"End Equity", "100684.53"},
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{"Net Profit", "0.685%"},
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{"Sharpe Ratio", "13.41"},
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{"Sortino Ratio", "0"},
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{"Probabilistic Sharpe Ratio", "99.985%"},
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{"Loss Rate", "0%"},
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{"Win Rate", "100%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "0.235"},
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{"Beta", "0.15"},
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{"Annual Standard Deviation", "0.04"},
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{"Annual Variance", "0.002"},
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{"Information Ratio", "-7.587"},
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{"Tracking Error", "0.19"},
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{"Treynor Ratio", "3.546"},
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{"Total Fees", "$32.77"},
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{"Estimated Strategy Capacity", "$230000000.00"},
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{"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
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{"Portfolio Turnover", "20.15%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "d269ebced0796dde34f9eb775772e027"}
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};
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}
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}
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