quantconnect--lean/Algorithm.CSharp/BasicTemplateIndexAlgorithm.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 QuantConnect.Data;
using System.Collections.Generic;
using QuantConnect.Indicators;
using QuantConnect.Interfaces;

namespace QuantConnect.Algorithm.CSharp
{
    /// <summary>
    /// This example demonstrates how to add index asset types.
    /// </summary>
    /// <meta name="tag" content="using data" />
    /// <meta name="tag" content="benchmarks" />
    /// <meta name="tag" content="indexes" />
    public class BasicTemplateIndexAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
    {
        protected Symbol Spx { get; set; }
        protected Symbol SpxOption { get; set; }
        private ExponentialMovingAverage _emaSlow;
        private ExponentialMovingAverage _emaFast;

        protected virtual Resolution Resolution => Resolution.Minute;
        protected virtual int StartDay => 4;

        /// <summary>
        /// Initialize your algorithm and add desired assets.
        /// </summary>
        public override void Initialize()
        {
            SetStartDate(2021, 1, StartDay);
            SetEndDate(2021, 1, 18);
            SetCash(1000000);

            // Use indicator for signal; but it cannot be traded
            Spx = AddIndex("SPX", Resolution).Symbol;

            // Trade on SPX ITM calls
            SpxOption = QuantConnect.Symbol.CreateOption(
                Spx,
                Market.USA,
                OptionStyle.European,
                OptionRight.Call,
                3200m,
                new DateTime(2021, 1, 15));

            AddIndexOptionContract(SpxOption, Resolution);

            _emaSlow = EMA(Spx, Resolution > Resolution.Minute ? 6 : 80);
            _emaFast = EMA(Spx, Resolution > Resolution.Minute ? 2 : 200);

            Settings.DailyPreciseEndTime = true;
        }

        /// <summary>
        /// Index EMA Cross trading underlying.
        /// </summary>
        public override void OnData(Slice slice)
        {
            if (!slice.Bars.ContainsKey(Spx) || !slice.Bars.ContainsKey(SpxOption))
            {
                return;
            }

            // Warm up indicators
            if (!_emaSlow.IsReady)
            {
                return;
            }

            if (_emaFast > _emaSlow)
            {
                SetHoldings(SpxOption, 1);
            }
            else
            {
                Liquidate();
            }
        }

        /// <summary>
        /// Asserts indicators are ready
        /// </summary>
        /// <exception cref="RegressionTestException"></exception>
        protected void AssertIndicators()
        {
            if (!_emaSlow.IsReady || !_emaFast.IsReady)
            {
                throw new RegressionTestException("Indicators are not ready!");
            }
        }

        public override void OnEndOfAlgorithm()
        {
            if (Portfolio[Spx].TotalSaleVolume > 0)
            {
                throw new RegressionTestException("Index is not tradable.");
            }
            AssertIndicators();
        }

        /// <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 virtual bool CanRunLocally { get; } = true;

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

        /// <summary>
        /// Data Points count of all timeslices of algorithm
        /// </summary>
        public virtual long DataPoints => 16199;

        /// <summary>
        /// Data Points count of the algorithm history
        /// </summary>
        public virtual 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 virtual Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
        {
            {"Total Orders", "3"},
            {"Average Win", "7.08%"},
            {"Average Loss", "0%"},
            {"Compounding Annual Return", "603.355%"},
            {"Drawdown", "3.400%"},
            {"Expectancy", "0"},
            {"Start Equity", "1000000"},
            {"End Equity", "1064395"},
            {"Net Profit", "6.440%"},
            {"Sharpe Ratio", "-4.563"},
            {"Sortino Ratio", "0"},
            {"Probabilistic Sharpe Ratio", "0.604%"},
            {"Loss Rate", "0%"},
            {"Win Rate", "100%"},
            {"Profit-Loss Ratio", "0"},
            {"Alpha", "-0.169"},
            {"Beta", "0.073"},
            {"Annual Standard Deviation", "0.028"},
            {"Annual Variance", "0.001"},
            {"Information Ratio", "-6.684"},
            {"Tracking Error", "0.099"},
            {"Treynor Ratio", "-1.771"},
            {"Total Fees", "$0.00"},
            {"Estimated Strategy Capacity", "$3000.00"},
            {"Lowest Capacity Asset", "SPX XL80P3GHIA9A|SPX 31"},
            {"Portfolio Turnover", "23.97%"},
            {"Drawdown Recovery", "9"},
            {"OrderListHash", "4b560d2a8cfae510c3c8dc92603470fc"}
        };
    }
}