AutomaticIndicatorWarmupRegressionAlgorithm.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.Indicators;
using QuantConnect.Interfaces;

namespace QuantConnect.Algorithm.CSharp
{
    /// <summary>
    /// Algorithm which reproduces GH issue 3861, where in some cases 2 consolidators were added when
    /// using the automatic indicator warmup feature
    /// </summary>
    public class AutomaticIndicatorWarmupRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
    {
        private Symbol _spy;
        public override void Initialize()
        {
            SetStartDate(2013, 10, 07);
            SetEndDate(2013, 10, 11);

            Settings.AutomaticIndicatorWarmUp = true;

            // Test case 1
            _spy = AddEquity("SPY").Symbol;
            var sma = SMA(_spy, 10);
            if (!sma.IsReady)
            {
                throw new RegressionTestException("Expected SMA to be warmed up");
            }

            // Test case 2
            var indicator = new CustomIndicator(10);
            RegisterIndicator(_spy, indicator, Resolution.Minute, (Func<IBaseData, decimal>) null);

            if (indicator.IsReady)
            {
                throw new RegressionTestException("Expected CustomIndicator Not to be warmed up");
            }
            WarmUpIndicator(_spy, indicator);
            if (!indicator.IsReady)
            {
                throw new RegressionTestException("Expected CustomIndicator to be warmed up");
            }
        }

        /// <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)
        {
            if (!Portfolio.Invested)
            {
                var subscription = SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(_spy).First(config => config.TickType == TickType.Trade);

                // we expect 1 consolidator per indicator
                if (subscription.Consolidators.Count != 2)
                {
                    throw new RegressionTestException($"Unexpected consolidator count for subscription: {subscription.Consolidators.Count}");
                }
                SetHoldings(_spy, 1);
            }
        }

        private class CustomIndicator : SimpleMovingAverage
        {
            private IndicatorDataPoint _previous;
            public CustomIndicator(int period) : base(period)
            {
            }
            protected override decimal ComputeNextValue(IReadOnlyWindow<IndicatorDataPoint> window, IndicatorDataPoint input)
            {
                if (_previous != null && input.EndTime == _previous.EndTime)
                {
                    throw new RegressionTestException($"Unexpected indicator double data point call: {_previous}");
                }
                _previous = input;
                return base.ComputeNextValue(window, input);
            }
        }

        /// <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 };

        /// <summary>
        /// Data Points count of all timeslices of algorithm
        /// </summary>
        public long DataPoints => 3943;

        /// <summary>
        /// Data Points count of the algorithm history
        /// </summary>
        public int AlgorithmHistoryDataPoints => 40;

        /// <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", "1"},
            {"Average Win", "0%"},
            {"Average Loss", "0%"},
            {"Compounding Annual Return", "271.453%"},
            {"Drawdown", "2.200%"},
            {"Expectancy", "0"},
            {"Start Equity", "100000"},
            {"End Equity", "101691.92"},
            {"Net Profit", "1.692%"},
            {"Sharpe Ratio", "8.854"},
            {"Sortino Ratio", "0"},
            {"Probabilistic Sharpe Ratio", "67.609%"},
            {"Loss Rate", "0%"},
            {"Win Rate", "0%"},
            {"Profit-Loss Ratio", "0"},
            {"Alpha", "-0.005"},
            {"Beta", "0.996"},
            {"Annual Standard Deviation", "0.222"},
            {"Annual Variance", "0.049"},
            {"Information Ratio", "-14.565"},
            {"Tracking Error", "0.001"},
            {"Treynor Ratio", "1.97"},
            {"Total Fees", "$3.44"},
            {"Estimated Strategy Capacity", "$56000000.00"},
            {"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
            {"Portfolio Turnover", "19.93%"},
            {"Drawdown Recovery", "2"},
            {"OrderListHash", "3da9fa60bf95b9ed148b95e02e0cfc9e"}
        };
    }
}