quantconnect--lean/Algorithm.CSharp/AddRemoveSecurityRegressionAlgorithm.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.Market;
using QuantConnect.Orders;
using QuantConnect.Interfaces;
using QuantConnect.Data;

namespace QuantConnect.Algorithm.CSharp
{
    /// <summary>
    /// This algorithm demonstrates the runtime addition and removal of securities from your algorithm.
    /// With LEAN it is possible to add and remove securities after the initialization.
    /// </summary>
    /// <meta name="tag" content="using data" />
    /// <meta name="tag" content="assets" />
    /// <meta name="tag" content="regression test" />
    public class AddRemoveSecurityRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
    {
        private DateTime lastAction;

        private Symbol _spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
        private Symbol _aig = QuantConnect.Symbol.Create("AIG", SecurityType.Equity, Market.USA);
        private Symbol _bac = QuantConnect.Symbol.Create("BAC", SecurityType.Equity, Market.USA);

        /// <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
            AddSecurity(SecurityType.Equity, "SPY");
        }

        /// <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 slice)
        {
            if (lastAction.Date == Time.Date) return;

            if (!Portfolio.Invested)
            {
                SetHoldings(_spy, 0.5);
                lastAction = Time;
            }
            if (Time.DayOfWeek == DayOfWeek.Tuesday)
            {
                AddSecurity(SecurityType.Equity, "AIG");
                AddSecurity(SecurityType.Equity, "BAC");
                lastAction = Time;
            }
            else if (Time.DayOfWeek == DayOfWeek.Wednesday)
            {
                SetHoldings(_aig, .25);
                SetHoldings(_bac, .25);
                lastAction = Time;
            }
            else if (Time.DayOfWeek == DayOfWeek.Thursday)
            {
                RemoveSecurity(_aig);
                RemoveSecurity(_bac);
                lastAction = Time;
            }
        }

        /// <summary>
        /// Order events are triggered on order status changes. There are many order events including non-fill messages.
        /// </summary>
        /// <param name="orderEvent">OrderEvent object with details about the order status</param>
        public override void OnOrderEvent(OrderEvent orderEvent)
        {
            if (orderEvent.Status == OrderStatus.Submitted)
            {
                Debug(Time + ": Submitted: " + Transactions.GetOrderById(orderEvent.OrderId));
            }
            if (orderEvent.Status.IsFill())
            {
                Debug(Time + ": Filled: " + Transactions.GetOrderById(orderEvent.OrderId));
            }
        }

        /// <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 => 7065;

        /// <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", "5"},
            {"Average Win", "0.46%"},
            {"Average Loss", "0%"},
            {"Compounding Annual Return", "296.356%"},
            {"Drawdown", "1.400%"},
            {"Expectancy", "0"},
            {"Start Equity", "100000"},
            {"End Equity", "101776.32"},
            {"Net Profit", "1.776%"},
            {"Sharpe Ratio", "12.966"},
            {"Sortino Ratio", "0"},
            {"Probabilistic Sharpe Ratio", "80.179%"},
            {"Loss Rate", "0%"},
            {"Win Rate", "100%"},
            {"Profit-Loss Ratio", "0"},
            {"Alpha", "0.678"},
            {"Beta", "0.707"},
            {"Annual Standard Deviation", "0.16"},
            {"Annual Variance", "0.026"},
            {"Information Ratio", "1.378"},
            {"Tracking Error", "0.072"},
            {"Treynor Ratio", "2.935"},
            {"Total Fees", "$28.30"},
            {"Estimated Strategy Capacity", "$4700000.00"},
            {"Lowest Capacity Asset", "AIG R735QTJ8XC9X"},
            {"Portfolio Turnover", "29.88%"},
            {"Drawdown Recovery", "2"},
            {"OrderListHash", "f04b3521256c7d6740966bc3df34e7b1"}
        };
    }
}