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

namespace QuantConnect.Algorithm.CSharp
{
    /// <summary>
    /// This algorithm is a test case for a history request including symbol changes during the requested period.
    /// </summary>
    public class HistoryWithSymbolChangesRegressionAlgorithm : 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);
            SetCash(100000);

            var symbol = AddEquity("WM", Resolution.Daily).Symbol;

            var history = History(new [] {symbol}, TimeSpan.FromDays(5700), Resolution.Daily).ToList();
            Debug($"{Time} - history.Count: {history.Count}");

            const int expectedSliceCount = 3926;
            if (history.Count != expectedSliceCount)
            {
                throw new Exception($"History slices - expected: {expectedSliceCount}, actual: {history.Count}");
            }

            var totalBars = history.Count(slice => slice.Bars.Count > 0 && slice.Bars.ContainsKey(symbol));

            if (totalBars != expectedSliceCount)
            {
                throw new Exception($"History bars - expected: {expectedSliceCount}, actual: {totalBars}");
            }

            var firstBar = history.First().Bars.GetValue(symbol);
            if (firstBar.EndTime != new DateTime(1998, 3, 3) || firstBar.Close != 26.3607004m)
            {
                throw new Exception("First History bar - unexpected data received");
            }
        }

        /// <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", "0"},
            {"Average Win", "0%"},
            {"Average Loss", "0%"},
            {"Compounding Annual Return", "0%"},
            {"Drawdown", "0%"},
            {"Expectancy", "0"},
            {"Net Profit", "0%"},
            {"Sharpe Ratio", "0"},
            {"Loss Rate", "0%"},
            {"Win Rate", "0%"},
            {"Profit-Loss Ratio", "0"},
            {"Alpha", "0"},
            {"Beta", "0"},
            {"Annual Standard Deviation", "0"},
            {"Annual Variance", "0"},
            {"Information Ratio", "0"},
            {"Tracking Error", "0"},
            {"Treynor Ratio", "0"},
            {"Total Fees", "$0.00"}
        };
    }
}