quantconnect--lean/Algorithm.CSharp/CustomUniverseSelectionRegressionAlgorithm.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.Interfaces;

namespace QuantConnect.Algorithm.CSharp
{
    /// <summary>
    /// Test algorithm that verifies that securities added through
    /// <see cref="QCAlgorithm.AddEquity"/> API and universe selection
    /// both start sending data at the same time
    /// </summary>
    public class CustomUniverseSelectionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
    {
        /// <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);
            SetEndDate(2013, 10, 11);

            AddEquity("AAPL", Resolution.Daily);

            UniverseSettings.Resolution = Resolution.Daily;
            AddUniverse(SecurityType.Equity,
                "SecondUniverse",
                Resolution.Daily,
                Market.USA,
                UniverseSettings,
                time => new[] { "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 data)
        {
            if (data.Count != 2)
            {
                throw new Exception($"Unexpected data count: {data.Count}");
            }
            if (ActiveSecurities.Count != 2)
            {
                throw new Exception($"Unexpected ActiveSecurities count: {ActiveSecurities.Count}");
            }
            if (!Portfolio.Invested)
            {
                SetHoldings(Securities.Keys.First(symbol => symbol.Value == "SPY"), 1);
                Debug("Purchased Stock");
            }
        }

        /// <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", "240.939%"},
            {"Drawdown", "1.200%"},
            {"Expectancy", "0"},
            {"Net Profit", "1.694%"},
            {"Sharpe Ratio", "6.988"},
            {"Probabilistic Sharpe Ratio", "68.188%"},
            {"Loss Rate", "0%"},
            {"Win Rate", "0%"},
            {"Profit-Loss Ratio", "0"},
            {"Alpha", "1.172"},
            {"Beta", "0.14"},
            {"Annual Standard Deviation", "0.196"},
            {"Annual Variance", "0.038"},
            {"Information Ratio", "-0.118"},
            {"Tracking Error", "0.256"},
            {"Treynor Ratio", "9.783"},
            {"Total Fees", "$3.45"},
            {"Estimated Strategy Capacity", "$840000000.00"},
            {"Fitness Score", "0.201"},
            {"Kelly Criterion Estimate", "0"},
            {"Kelly Criterion Probability Value", "0"},
            {"Sortino Ratio", "79228162514264337593543950335"},
            {"Return Over Maximum Drawdown", "204.701"},
            {"Portfolio Turnover", "0.201"},
            {"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", "33d01821923c397f999cfb2e5b5928ad"}
        };
    }
}