/*
 * 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.Data.UniverseSelection;
using QuantConnect.Interfaces;

namespace QuantConnect.Algorithm.CSharp
{
    /// <summary>
    /// This regression algorithm has two different Universe using the same SubscriptionDataConfig.
    /// Reproduces GH issue 3877: 1- universe 'TestUniverse' selects and deselects SPY. 2- UserDefinedUniverse
    /// reselects SPY, which should be marked as tradable.
    /// </summary>
    /// <meta name="tag" content="regression test" />
    public class UniverseSharingSubscriptionTradableRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
    {
        private Symbol _spy;
        private int _reselectedSpy = -1;
        private DateTime lastDataTime = DateTime.MinValue;

        /// <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, 01);
            SetEndDate(2013, 10, 30);
            AddEquity("AAPL", Resolution.Daily);

            UniverseSettings.Resolution = Resolution.Daily;
            AddUniverse(SecurityType.Equity,
                "TestUniverse",
                Resolution.Daily,
                Market.USA,
                UniverseSettings,
                time => time.Day == 1 ? new[] {"SPY"} : Enumerable.Empty<string>());
        }

        /// <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 (lastDataTime == slice.Time)
            {
                throw new RegressionTestException("Duplicate time for current data and last data slice");
            }

            lastDataTime = slice.Time;

            if (_reselectedSpy == 0)
            {
                if (!Securities[_spy].IsTradable)
                {
                    throw new RegressionTestException($"{_spy} should be tradable");
                }

                if (!Portfolio.Invested)
                {
                    SetHoldings(_spy, 1);
                }
            }

            if (_reselectedSpy == 1)
            {
                // SPY should be re added in the next loop
                _reselectedSpy = 0;
            }
        }

        public override void OnSecuritiesChanged(SecurityChanges changes)
        {
            if (changes.RemovedSecurities.Any())
            {
                // OnSecuritiesChanged is called before OnData, so SPY will still not be
                // present
                _reselectedSpy = 1;
                _spy = AddEquity("SPY", Resolution.Daily).Symbol;

                if (!Securities[_spy].IsTradable)
                {
                    throw new RegressionTestException($"{_spy} should be tradable");
                }
            }
        }

        /// <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 => 228;

        /// <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", "1"},
            {"Average Win", "0%"},
            {"Average Loss", "0%"},
            {"Compounding Annual Return", "84.550%"},
            {"Drawdown", "2.000%"},
            {"Expectancy", "0"},
            {"Start Equity", "100000"},
            {"End Equity", "105106.43"},
            {"Net Profit", "5.106%"},
            {"Sharpe Ratio", "5.253"},
            {"Sortino Ratio", "11.491"},
            {"Probabilistic Sharpe Ratio", "88.500%"},
            {"Loss Rate", "0%"},
            {"Win Rate", "0%"},
            {"Profit-Loss Ratio", "0"},
            {"Alpha", "0.157"},
            {"Beta", "0.922"},
            {"Annual Standard Deviation", "0.103"},
            {"Annual Variance", "0.011"},
            {"Information Ratio", "4.703"},
            {"Tracking Error", "0.026"},
            {"Treynor Ratio", "0.588"},
            {"Total Fees", "$3.44"},
            {"Estimated Strategy Capacity", "$700000000.00"},
            {"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
            {"Portfolio Turnover", "3.30%"},
            {"Drawdown Recovery", "3"},
            {"OrderListHash", "032561818d8c8c17b30d3c9b0d52fa17"}
        };
    }
}