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.Orders;
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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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/// Regression algorithm asserting universe selection happens during warmup
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/// </summary>
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public class WarmupSelectionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private const int NumberOfSymbols = 3;
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private Queue<DateTime> _selection = new Queue<DateTime>(new[]
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{
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new DateTime(2014, 03, 24),
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new DateTime(2014, 03, 25),
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new DateTime(2014, 03, 26),
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new DateTime(2014, 03, 27),
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new DateTime(2014, 03, 28),
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new DateTime(2014, 03, 29),
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new DateTime(2014, 04, 01),
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new DateTime(2014, 04, 02),
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new DateTime(2014, 04, 03),
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new DateTime(2014, 04, 04),
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new DateTime(2014, 04, 05),
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});
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// initialize our changes to nothing
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private SecurityChanges _changes = SecurityChanges.None;
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public override void Initialize()
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{
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UniverseSettings.Resolution = Resolution.Daily;
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SetStartDate(2014, 03, 26);
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SetEndDate(2014, 04, 07);
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AddUniverse(CoarseSelectionFunction);
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SetWarmup(2, Resolution.Daily);
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}
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// sort the data by daily dollar volume and take the top 'NumberOfSymbols'
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private IEnumerable<Symbol> CoarseSelectionFunction(IEnumerable<CoarseFundamental> coarse)
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{
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Debug($"Coarse selection happening at {Time} {IsWarmingUp}");
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var expected = _selection.Dequeue();
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if (expected != Time && !LiveMode)
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{
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throw new RegressionTestException($"Unexpected selection time: {Time}. Expected {expected}");
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}
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// sort descending by daily dollar volume
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var sortedByDollarVolume = coarse.OrderByDescending(x => x.DollarVolume);
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// take the top entries from our sorted collection
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var top = sortedByDollarVolume.Take(NumberOfSymbols);
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// we need to return only the symbol objects
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return top.Select(x => x.Symbol);
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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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Debug($"OnData({UtcTime:o}): {IsWarmingUp}. {string.Join(", ", slice.Values.OrderBy(x => x.Symbol))}");
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// if we have no changes, do nothing
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if (_changes == SecurityChanges.None || IsWarmingUp)
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{
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return;
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}
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// liquidate removed securities
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foreach (var security in _changes.RemovedSecurities)
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{
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if (security.Invested)
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{
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Liquidate(security.Symbol);
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}
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}
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// we want 1/N allocation in each security in our universe
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foreach (var security in _changes.AddedSecurities)
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{
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SetHoldings(security.Symbol, 1m / NumberOfSymbols);
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}
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_changes = SecurityChanges.None;
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}
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// this event fires whenever we have changes to our universe
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public override void OnSecuritiesChanged(SecurityChanges changes)
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{
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_changes = changes;
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Debug($"OnSecuritiesChanged({UtcTime:o}):: {changes}");
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}
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public override void OnOrderEvent(OrderEvent orderEvent)
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{
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Debug($"OnOrderEvent({UtcTime:o}):: {orderEvent}");
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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 virtual long DataPoints => 78067;
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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 virtual Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
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{
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{"Total Orders", "8"},
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{"Average Win", "0.64%"},
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{"Average Loss", "-0.13%"},
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{"Compounding Annual Return", "11.057%"},
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{"Drawdown", "0.900%"},
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{"Expectancy", "0.938"},
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{"Start Equity", "100000"},
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{"End Equity", "100374.24"},
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{"Net Profit", "0.374%"},
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{"Sharpe Ratio", "1.048"},
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{"Sortino Ratio", "1.627"},
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{"Probabilistic Sharpe Ratio", "50.021%"},
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{"Loss Rate", "67%"},
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{"Win Rate", "33%"},
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{"Profit-Loss Ratio", "4.81"},
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{"Alpha", "0.088"},
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{"Beta", "0.152"},
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{"Annual Standard Deviation", "0.073"},
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{"Annual Variance", "0.005"},
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{"Information Ratio", "1.369"},
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{"Tracking Error", "0.109"},
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{"Treynor Ratio", "0.504"},
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{"Total Fees", "$43.94"},
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{"Estimated Strategy Capacity", "$620000000.00"},
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{"Lowest Capacity Asset", "FB V6OIPNZEM8V9"},
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{"Portfolio Turnover", "15.44%"},
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{"Drawdown Recovery", "5"},
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{"OrderListHash", "e401e24ad8c273d99611d79d59e804d7"}
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};
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}
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}
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