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

namespace QuantConnect.Algorithm.CSharp
{
    /// <summary>
    /// Test algorithm using a <see cref="ConstituentsUniverse"/> with test data
    /// </summary>
    public class ConstituentsUniverseRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
    {
        private readonly Symbol _appl = QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA);
        private readonly Symbol _spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
        private readonly Symbol _qqq = QuantConnect.Symbol.Create("QQQ", SecurityType.Equity, Market.USA);
        private readonly Symbol _fb = QuantConnect.Symbol.Create("FB", SecurityType.Equity, Market.USA);
        private int _step;

    /// <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, 07);  //Set Start Date
            SetEndDate(2013, 10, 11);    //Set End Date
            SetCash(100000);             //Set Strategy Cash

            UniverseSettings.Resolution = Resolution.Daily;

            var customUniverseSymbol = new Symbol(SecurityIdentifier.GenerateConstituentIdentifier(
                    "constituents-universe-qctest",
                    SecurityType.Equity,
                    Market.USA),
                "constituents-universe-qctest");

            AddUniverse(new ConstituentsUniverse(customUniverseSymbol, UniverseSettings));
        }

        /// <summary>
        /// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
        /// </summary>
        /// <param name="slice">Slice object keyed by symbol containing the stock data</param>
        public override void OnData(Slice slice)
        {
            _step++;
            if (_step == 1)
            {
                if (!slice.ContainsKey(_qqq)
                    || !slice.ContainsKey(_appl))
                {
                    throw new RegressionTestException($"Unexpected symbols found, step: {_step}");
                }
                if (slice.Count != 2)
                {
                    throw new RegressionTestException($"Unexpected data count, step: {_step}");
                }
                // AAPL will be deselected by the ConstituentsUniverse
                // but it shouldn't be removed since we hold it
                SetHoldings(_appl, 0.5);
            }
            else if (_step == 2)
            {
                if (!slice.ContainsKey(_appl))
                {
                    throw new RegressionTestException($"Unexpected symbols found, step: {_step}");
                }
                if (slice.Count != 1)
                {
                    throw new RegressionTestException($"Unexpected data count, step: {_step}");
                }
                // AAPL should now be released
                // note: takes one extra loop because the order is executed on market open
                Liquidate();
            }
            else if (_step == 3)
            {
                if (!slice.ContainsKey(_fb)
                    || !slice.ContainsKey(_spy)
                    || !slice.ContainsKey(_appl))
                {
                    throw new RegressionTestException($"Unexpected symbols found, step: {_step}");
                }
                if (slice.Count != 3)
                {
                    throw new RegressionTestException($"Unexpected data count, step: {_step}");
                }
            }
            else if (_step == 4)
            {
                if (!slice.ContainsKey(_fb)
                    || !slice.ContainsKey(_spy))
                {
                    throw new RegressionTestException($"Unexpected symbols found, step: {_step}");
                }
                if (slice.Count != 2)
                {
                    throw new RegressionTestException($"Unexpected data count, step: {_step}");
                }
            }
        }

        public override void OnEndOfAlgorithm()
        {
            // First selection is on the midnight of the 8th, start getting data from the 8th to the 11th
            if (_step != 4)
            {
                throw new RegressionTestException($"Unexpected step count: {_step}");
            }
        }

        public override void OnSecuritiesChanged(SecurityChanges changes)
        {
            foreach (var added in changes.AddedSecurities)
            {
                Log($"{Time} AddedSecurities {added}");
            }

            foreach (var removed in changes.RemovedSecurities)
            {
                Log($"{Time} RemovedSecurities {removed} {_step}");
                // we are currently notifying the removal of AAPl twice,
                // when deselected and when finally removed (since it stayed pending)
                if (removed.Symbol == _appl && _step != 1 && _step != 2
                    || removed.Symbol == _qqq && _step != 1)
                {
                    throw new RegressionTestException($"Unexpected removal step count: {_step}");
                }
            }
        }

        /// <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 List<Language> Languages { get; } = new() { Language.CSharp, Language.Python };

        /// <summary>
        /// Data Points count of all timeslices of algorithm
        /// </summary>
        public long DataPoints => 50;

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

        /// <summary>
        /// Final status of the algorithm
        /// </summary>
        public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;

        /// <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 Orders", "2"},
            {"Average Win", "0.68%"},
            {"Average Loss", "0%"},
            {"Compounding Annual Return", "70.501%"},
            {"Drawdown", "0%"},
            {"Expectancy", "0"},
            {"Start Equity", "100000"},
            {"End Equity", "100684.53"},
            {"Net Profit", "0.685%"},
            {"Sharpe Ratio", "13.41"},
            {"Sortino Ratio", "0"},
            {"Probabilistic Sharpe Ratio", "99.985%"},
            {"Loss Rate", "0%"},
            {"Win Rate", "100%"},
            {"Profit-Loss Ratio", "0"},
            {"Alpha", "0.235"},
            {"Beta", "0.15"},
            {"Annual Standard Deviation", "0.04"},
            {"Annual Variance", "0.002"},
            {"Information Ratio", "-7.587"},
            {"Tracking Error", "0.19"},
            {"Treynor Ratio", "3.546"},
            {"Total Fees", "$32.77"},
            {"Estimated Strategy Capacity", "$230000000.00"},
            {"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
            {"Portfolio Turnover", "20.15%"},
            {"Drawdown Recovery", "0"},
            {"OrderListHash", "d269ebced0796dde34f9eb775772e027"}
        };
    }
}