/* * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals. * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ using System; using System.Collections.Generic; using System.Linq; using QuantConnect.Data; using QuantConnect.Data.UniverseSelection; using QuantConnect.Interfaces; namespace QuantConnect.Algorithm.CSharp { /// /// Assert that custom data universe selection happens right away after algorithm starts /// public class CustomDataUniverseImmediateSelectionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition { private bool _selected; private bool _securitiesChanged; private bool _firstOnData = true; public override void Initialize() { SetStartDate(2017, 07, 04); SetEndDate(2018, 07, 04); UniverseSettings.Resolution = Resolution.Daily; AddUniverse("my-stock-data-source", stockDataSource => { _selected = true; return stockDataSource.OfType().SelectMany(x => x.Symbols); }); } public override void OnData(Slice slice) { if (_firstOnData) { if (!_selected) { throw new RegressionTestException("Universe selection should have been triggered right away. " + "The first OnData call should have had happened after the universe selection"); } _firstOnData = false; } } public override void OnSecuritiesChanged(SecurityChanges changes) { if (!_selected) { throw new RegressionTestException("Universe selection should have been triggered right away"); } if (!_securitiesChanged) { // Selection should be happening right on algorithm start if (Time != StartDate) { throw new RegressionTestException("Universe selection should have been triggered right away"); } if (changes.AddedSecurities.Count == 0) { throw new RegressionTestException($"Expected multiple stocks to be added to the algorithm, " + $"but found {changes.AddedSecurities.Count}"); } _securitiesChanged = true; } } public override void OnEndOfAlgorithm() { if (_firstOnData || !_selected || !_securitiesChanged) { throw new RegressionTestException("Expected events didn't happen"); } } /// /// Our custom data type that defines where to get and how to read our backtest and live data. /// class StockDataSource : BaseData { private const string Url = @"https://www.dropbox.com/s/ae1couew5ir3z9y/daily-stock-picker-backtest.csv?dl=1"; public List Symbols { get; set; } public StockDataSource() { Symbols = new List(); } public override SubscriptionDataSource GetSource(SubscriptionDataConfig config, DateTime date, bool isLiveMode) { return new SubscriptionDataSource(Url, SubscriptionTransportMedium.RemoteFile); } public override BaseData Reader(SubscriptionDataConfig config, string line, DateTime date, bool isLiveMode) { try { // create a new StockDataSource and set the symbol using config.Symbol var stocks = new StockDataSource { Symbol = config.Symbol }; // break our line into csv pieces var csv = line.ToCsv(); if (isLiveMode) { // our live mode format does not have a date in the first column, so use date parameter stocks.Time = date; stocks.Symbols.AddRange(csv); } else { // our backtest mode format has the first column as date, parse it stocks.Time = DateTime.ParseExact(csv[0], "yyyyMMdd", null); // any following comma separated values are symbols, save them off stocks.Symbols.AddRange(csv.Skip(1)); } return stocks; } // return null if we encounter any errors catch { return null; } } } /// /// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm. /// public bool CanRunLocally { get; } = true; /// /// This is used by the regression test system to indicate which languages this algorithm is written in. /// public List Languages { get; } = new() { Language.CSharp }; /// /// Data Points count of all timeslices of algorithm /// public long DataPoints => 3287; /// /// Data Points count of the algorithm history /// public int AlgorithmHistoryDataPoints => 0; /// /// Final status of the algorithm /// public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed; /// /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm /// public Dictionary ExpectedStatistics => new Dictionary { {"Total Orders", "0"}, {"Average Win", "0%"}, {"Average Loss", "0%"}, {"Compounding Annual Return", "0%"}, {"Drawdown", "0%"}, {"Expectancy", "0"}, {"Start Equity", "100000"}, {"End Equity", "100000"}, {"Net Profit", "0%"}, {"Sharpe Ratio", "0"}, {"Sortino Ratio", "0"}, {"Probabilistic Sharpe Ratio", "0%"}, {"Loss Rate", "0%"}, {"Win Rate", "0%"}, {"Profit-Loss Ratio", "0"}, {"Alpha", "0"}, {"Beta", "0"}, {"Annual Standard Deviation", "0"}, {"Annual Variance", "0"}, {"Information Ratio", "-0.97"}, {"Tracking Error", "0.104"}, {"Treynor Ratio", "0"}, {"Total Fees", "$0.00"}, {"Estimated Strategy Capacity", "$0"}, {"Lowest Capacity Asset", ""}, {"Portfolio Turnover", "0%"}, {"Drawdown Recovery", "0"}, {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"} }; } }