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.Linq;
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using QuantConnect.Data;
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using QuantConnect.Interfaces;
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using System.Collections.Generic;
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using QuantConnect.Data.UniverseSelection;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Custom data universe selection regression algorithm asserting it's behavior. See GH issue #6396
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/// </summary>
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public class CustomDataUniverseRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private HashSet<Symbol> _currentUnderlyingSymbols = new();
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private readonly Queue<DateTime> _selectionTime = new (new[] {
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new DateTime(2014, 03, 24, 0, 0, 0),
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new DateTime(2014, 03, 25, 0, 0, 0),
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new DateTime(2014, 03, 26, 0, 0, 0),
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new DateTime(2014, 03, 27, 0, 0, 0),
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new DateTime(2014, 03, 28, 0, 0, 0),
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new DateTime(2014, 03, 29, 0, 0, 0)
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});
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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(2014, 03, 24);
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SetEndDate(2014, 03, 31);
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UniverseSettings.Resolution = Resolution.Daily;
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AddUniverse<CoarseFundamental>("custom-data-universe", (coarse) =>
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{
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Debug($"Universe selection called: {Time} Count: {coarse.Count()}");
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var expectedTime = _selectionTime.Dequeue();
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if (expectedTime != Time)
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{
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throw new RegressionTestException($"Unexpected selection time {Time} expected {expectedTime}");
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}
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return coarse.OfType<CoarseFundamental>().OrderByDescending(x => x.DollarVolume)
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.SelectMany(x => new[] {
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x.Symbol,
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QuantConnect.Symbol.CreateBase(typeof(CustomData), x.Symbol)})
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.Take(20);
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});
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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 slice)
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{
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if (!Portfolio.Invested)
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{
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var customData = slice.Get<CustomData>();
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if (customData.Count > 0)
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{
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foreach (var symbol in _currentUnderlyingSymbols.OrderBy(x => x.ID.Symbol))
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{
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if (!Securities[symbol].HasData)
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{
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continue;
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}
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SetHoldings(symbol, 1m / _currentUnderlyingSymbols.Count);
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if (!customData.Any(custom => custom.Key.Underlying == symbol))
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{
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throw new RegressionTestException($"Custom data was not found for underlying symbol {symbol}");
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}
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}
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}
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}
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}
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public override void OnEndOfAlgorithm()
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{
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if (_selectionTime.Count != 0)
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{
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throw new RegressionTestException($"Unexpected selection times, missing {_selectionTime.Count}");
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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 security in changes.AddedSecurities.Where(sec => sec.Symbol.SecurityType != SecurityType.Base))
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{
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_currentUnderlyingSymbols.Add(security.Symbol);
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}
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foreach (var security in changes.RemovedSecurities.Where(sec => sec.Symbol.SecurityType != SecurityType.Base))
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{
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_currentUnderlyingSymbols.Remove(security.Symbol);
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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 => 42622;
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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", "6"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "-50.796%"},
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{"Drawdown", "1.900%"},
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{"Expectancy", "0"},
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{"Start Equity", "100000"},
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{"End Equity", "98457.63"},
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{"Net Profit", "-1.542%"},
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{"Sharpe Ratio", "-4.343"},
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{"Sortino Ratio", "-3.19"},
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{"Probabilistic Sharpe Ratio", "3.833%"},
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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", "-0.804"},
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{"Beta", "1.002"},
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{"Annual Standard Deviation", "0.1"},
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{"Annual Variance", "0.01"},
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{"Information Ratio", "-14.419"},
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{"Tracking Error", "0.056"},
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{"Treynor Ratio", "-0.433"},
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{"Total Fees", "$7.86"},
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{"Estimated Strategy Capacity", "$1200000000.00"},
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{"Lowest Capacity Asset", "GOOG T1AZ164W5VTX"},
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{"Portfolio Turnover", "7.58%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "fd3e6e9f401bc140d6b7cc8f1df8e46a"}
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};
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}
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}
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