quantconnect--lean/Algorithm.CSharp/UniverseSharingSubscriptionTradableRegressionAlgorithm.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;
using QuantConnect.Data.UniverseSelection;
using QuantConnect.Interfaces;

namespace QuantConnect.Algorithm.CSharp
{
    /// <summary>
    /// This regression algorithm has two different Universe using the same SubscriptionDataConfig.
    /// Reproduces GH issue 3877: 1- universe 'TestUniverse' selects and deselects SPY. 2- UserDefinedUniverse
    /// reselects SPY, which should be marked as tradable.
    /// </summary>
    /// <meta name="tag" content="regression test" />
    public class UniverseSharingSubscriptionTradableRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
    {
        private Symbol _spy;
        private int _reselectedSpy = -1;

        /// <summary>
        /// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
        /// </summary>
        public override void Initialize()
        {
            SetStartDate(2013, 10, 01);
            SetEndDate(2013, 10, 30);
            AddEquity("AAPL", Resolution.Daily);

            UniverseSettings.Resolution = Resolution.Daily;
            AddUniverse(SecurityType.Equity,
                "TestUniverse",
                Resolution.Daily,
                Market.USA,
                UniverseSettings,
                time => time.Day == 1 ? new[] {"SPY"} : Enumerable.Empty<string>());
        }

        /// <summary>
        /// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
        /// </summary>
        /// <param name="data">Slice object keyed by symbol containing the stock data</param>
        public override void OnData(Slice data)
        {
            if (_reselectedSpy == 0)
            {
                if (!Securities[_spy].IsTradable)
                {
                    throw new Exception($"{_spy} should be tradable");
                }

                if (!Portfolio.Invested)
                {
                    SetHoldings(_spy, 1);
                }
            }

            if (_reselectedSpy == 1)
            {
                // SPY should be re added in the next loop
                _reselectedSpy = 0;
            }
        }

        public override void OnSecuritiesChanged(SecurityChanges changes)
        {
            if (changes.RemovedSecurities.Any())
            {
                // OnSecuritiesChanged is called before OnData, so SPY will still not be
                // present
                _reselectedSpy = 1;
                _spy = AddEquity("SPY", Resolution.Daily).Symbol;

                if (Securities[_spy].IsTradable)
                {
                    throw new Exception($"{_spy} should not be tradable");
                }
            }
        }

        /// <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 };

        /// <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", "1"},
            {"Average Win", "0%"},
            {"Average Loss", "0%"},
            {"Compounding Annual Return", "75.079%"},
            {"Drawdown", "2.200%"},
            {"Expectancy", "0"},
            {"Net Profit", "4.711%"},
            {"Sharpe Ratio", "5.067"},
            {"Probabilistic Sharpe Ratio", "84.391%"},
            {"Loss Rate", "0%"},
            {"Win Rate", "0%"},
            {"Profit-Loss Ratio", "0"},
            {"Alpha", "0.562"},
            {"Beta", "0.02"},
            {"Annual Standard Deviation", "0.113"},
            {"Annual Variance", "0.013"},
            {"Information Ratio", "0.511"},
            {"Tracking Error", "0.159"},
            {"Treynor Ratio", "28.945"},
            {"Total Fees", "$3.22"},
            {"Fitness Score", "0.037"},
            {"Kelly Criterion Estimate", "0"},
            {"Kelly Criterion Probability Value", "0"},
            {"Sortino Ratio", "17.868"},
            {"Return Over Maximum Drawdown", "34.832"},
            {"Portfolio Turnover", "0.037"},
            {"Total Insights Generated", "0"},
            {"Total Insights Closed", "0"},
            {"Total Insights Analysis Completed", "0"},
            {"Long Insight Count", "0"},
            {"Short Insight Count", "0"},
            {"Long/Short Ratio", "100%"},
            {"Estimated Monthly Alpha Value", "$0"},
            {"Total Accumulated Estimated Alpha Value", "$0"},
            {"Mean Population Estimated Insight Value", "$0"},
            {"Mean Population Direction", "0%"},
            {"Mean Population Magnitude", "0%"},
            {"Rolling Averaged Population Direction", "0%"},
            {"Rolling Averaged Population Magnitude", "0%"},
            {"OrderListHash", "836605283"}
        };
    }
}