quantconnect--lean/Algorithm.CSharp/UniverseOnlyRegressionAlgorithm.cs

/*
 * 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.UniverseSelection;
using QuantConnect.Interfaces;

namespace QuantConnect.Algorithm.CSharp
{
    /// <summary>
    /// Asserts that algorithms can be universe-only, that is, universe selection is performed even if the ETF security is not explicitly added.
    /// Reproduces https://github.com/QuantConnect/Lean/issues/7473
    /// </summary>
    public class UniverseOnlyRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
    {
        private bool _selectionDone;

        public override void Initialize()
        {
            SetStartDate(2020, 12, 1);
            SetEndDate(2020, 12, 12);
            SetCash(100000);

            UniverseSettings.Resolution = Resolution.Daily;

            // Add universe without a security added
            AddUniverse(Universe.ETF("GDVD", UniverseSettings, FilterUniverse));
        }

        public override void OnEndOfAlgorithm()
        {
            if (!_selectionDone)
            {
                throw new Exception("Universe selection was not performed");
            }
        }

        private IEnumerable<Symbol> FilterUniverse(IEnumerable<ETFConstituentData> constituents)
        {
            _selectionDone = true;
            return constituents.Select(x => x.Symbol);
        }

        /// <summary>
        /// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
        /// </summary>
        public bool CanRunLocally { get; } = true;

        /// <summary>
        /// This is used by the regression test system to indicate which languages this algorithm is written in.
        /// </summary>
        public Language[] Languages { get; } = { Language.CSharp, Language.Python };

        /// <summary>
        /// Data Points count of all timeslices of algorithm
        /// </summary>
        public long DataPoints => 118;

        /// <summary>
        /// Data Points count of the algorithm history
        /// </summary>
        public int AlgorithmHistoryDataPoints => 0;

        /// <summary>
        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
        /// </summary>
        public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
        {
            {"Total Trades", "0"},
            {"Average Win", "0%"},
            {"Average Loss", "0%"},
            {"Compounding Annual Return", "0%"},
            {"Drawdown", "0%"},
            {"Expectancy", "0"},
            {"Net Profit", "0%"},
            {"Sharpe 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", "-0.311"},
            {"Tracking Error", "0.07"},
            {"Treynor Ratio", "0"},
            {"Total Fees", "$0.00"},
            {"Estimated Strategy Capacity", "$0"},
            {"Lowest Capacity Asset", ""},
            {"Portfolio Turnover", "0%"},
            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
        };
    }
}