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 System.Linq;
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using QuantConnect.Data;
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using QuantConnect.Data.Fundamental;
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using QuantConnect.Data.Market;
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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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/// Demonstration of how to define a universe
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/// as a combination of use the coarse fundamental data and fine fundamental data
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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 CoarseFineFundamentalRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private const int NumberOfSymbolsFine = 2;
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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 two parameters:
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// - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>
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// - fine selection function: accepts an IEnumerable<FineFundamental> and returns an IEnumerable<Symbol>
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AddUniverse(CoarseSelectionFunction, FineSelectionFunction);
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}
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// return a list of three fixed symbol objects
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public IEnumerable<Symbol> CoarseSelectionFunction(IEnumerable<CoarseFundamental> coarse)
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{
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if (Time.Date < new DateTime(2014, 4, 1))
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{
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return new List<Symbol>
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{
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QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA),
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QuantConnect.Symbol.Create("AIG", SecurityType.Equity, Market.USA),
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QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA)
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};
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}
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return new List<Symbol>
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{
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QuantConnect.Symbol.Create("BAC", SecurityType.Equity, Market.USA),
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QuantConnect.Symbol.Create("GOOG", SecurityType.Equity, Market.USA),
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QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA)
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};
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}
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// sort the data by market capitalization and take the top 'NumberOfSymbolsFine'
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public IEnumerable<Symbol> FineSelectionFunction(IEnumerable<FineFundamental> fine)
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{
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// sort descending by market capitalization
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var sortedByMarketCap = fine.OrderByDescending(x => x.MarketCap);
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// take the top entries from our sorted collection
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var topFine = sortedByMarketCap.Take(NumberOfSymbolsFine);
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// we need to return only the symbol objects
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return topFine.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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// verify we don't receive data for inactive securities
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var inactiveSymbols = slice.Keys
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.Where(sym => !UniverseManager.ActiveSecurities.ContainsKey(sym))
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// on daily data we'll get the last data point and the delisting at the same time
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.Where(sym => !slice.Delistings.ContainsKey(sym) || slice.Delistings[sym].Type != DelistingType.Delisted)
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.ToList();
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if (inactiveSymbols.Any())
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{
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var symbols = string.Join(", ", inactiveSymbols);
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throw new RegressionTestException($"Received data for non-active security: {symbols}.");
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}
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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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Debug("Liquidated Stock: " + security.Symbol.Value);
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}
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}
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// we want 50% 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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if (security.Fundamentals.EarningRatios.EquityPerShareGrowth.OneYear > 0.25)
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{
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SetHoldings(security.Symbol, 0.5m);
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Debug("Purchased Stock: " + security.Symbol.Value);
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}
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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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if (changes.AddedSecurities.Count > 0)
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{
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Debug("Securities added: " + string.Join(",", changes.AddedSecurities.Select(x => x.Symbol.Value)));
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}
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if (changes.RemovedSecurities.Count > 0)
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{
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Debug("Securities removed: " + string.Join(",", changes.RemovedSecurities.Select(x => x.Symbol.Value)));
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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 => 7244;
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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", "1.39%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "40.025%"},
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{"Drawdown", "1.400%"},
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{"Expectancy", "0"},
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{"Start Equity", "50000"},
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{"End Equity", "50696.56"},
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{"Net Profit", "1.393%"},
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{"Sharpe Ratio", "3.192"},
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{"Sortino Ratio", "4.952"},
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{"Probabilistic Sharpe Ratio", "67.812%"},
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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", "0.328"},
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{"Beta", "0.474"},
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{"Annual Standard Deviation", "0.088"},
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{"Annual Variance", "0.008"},
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{"Information Ratio", "4.219"},
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{"Tracking Error", "0.09"},
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{"Treynor Ratio", "0.59"},
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{"Total Fees", "$2.00"},
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{"Estimated Strategy Capacity", "$81000000.00"},
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{"Lowest Capacity Asset", "IBM R735QTJ8XC9X"},
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{"Portfolio Turnover", "6.65%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "4eaacdd341a5be0d04cb32647d931471"}
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};
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}
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}
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