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 QuantConnect.Interfaces;
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using System.Collections.Generic;
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using System.Linq;
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using QuantConnect.Data.Market;
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using QuantConnect.Data.UniverseSelection;
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using QuantConnect.Orders;
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using QuantConnect.Data;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// In this algorithm we demonstrate how to use the coarse fundamental data to
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/// define a universe as the top dollar volume
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/// </summary>
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/// <meta name="tag" content="using data" />
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/// <meta name="tag" content="universes" />
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/// <meta name="tag" content="coarse universes" />
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/// <meta name="tag" content="regression test" />
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public class CoarseFundamentalTop3Algorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private const int NumberOfSymbols = 3;
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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, 24);
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SetEndDate(2014, 04, 07);
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SetCash(50000);
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// this add universe method accepts a single parameter that is a function that
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// accepts an IEnumerable<CoarseFundamental> and returns IEnumerable<Symbol>
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AddUniverse(CoarseSelectionFunction);
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}
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// sort the data by daily dollar volume and take the top 'NumberOfSymbols'
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public static IEnumerable<Symbol> CoarseSelectionFunction(IEnumerable<CoarseFundamental> coarse)
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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="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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Log($"OnData({UtcTime:o}): Keys: {string.Join(", ", slice.Keys.OrderBy(x => x))}");
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// if we have no changes, do nothing
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if (_changes == SecurityChanges.None) return;
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// liquidate removed securities
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foreach (var security in _changes.RemovedSecurities)
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{
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if (security.Invested)
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{
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Liquidate(security.Symbol);
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}
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}
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// we want 1/N allocation in each security in our universe
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foreach (var security in _changes.AddedSecurities)
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{
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SetHoldings(security.Symbol, 1m / NumberOfSymbols);
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}
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_changes = SecurityChanges.None;
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}
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// this event fires whenever we have changes to our universe
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public override void OnSecuritiesChanged(SecurityChanges changes)
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{
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_changes = changes;
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Log($"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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Log($"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, 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 => 78088;
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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", "12"},
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{"Average Win", "0.63%"},
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{"Average Loss", "-0.49%"},
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{"Compounding Annual Return", "-35.851%"},
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{"Drawdown", "2.700%"},
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{"Expectancy", "-0.542"},
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{"Start Equity", "50000"},
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{"End Equity", "49096.01"},
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{"Net Profit", "-1.808%"},
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{"Sharpe Ratio", "-1.989"},
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{"Sortino Ratio", "-3.359"},
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{"Probabilistic Sharpe Ratio", "23.563%"},
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{"Loss Rate", "80%"},
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{"Win Rate", "20%"},
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{"Profit-Loss Ratio", "1.29"},
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{"Alpha", "-0.172"},
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{"Beta", "1.068"},
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{"Annual Standard Deviation", "0.141"},
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{"Annual Variance", "0.02"},
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{"Information Ratio", "-1.865"},
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{"Tracking Error", "0.096"},
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{"Treynor Ratio", "-0.263"},
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{"Total Fees", "$26.72"},
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{"Estimated Strategy Capacity", "$630000000.00"},
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{"Lowest Capacity Asset", "FB V6OIPNZEM8V9"},
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{"Portfolio Turnover", "24.59%"},
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{"Drawdown Recovery", "6"},
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{"OrderListHash", "90b57d40d047eedbff7111d2a73a1290"}
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};
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}
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}
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