/*
 * 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.Globalization;
using System.Linq;

using QuantConnect.Data;
using QuantConnect.Interfaces;

namespace QuantConnect.Algorithm.CSharp
{
    /// <summary>
    /// Algorithm asserting that
    /// </summary>
    public class CustomDataTypeHistoryAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
    {
        private Symbol _symbol;

        public override void Initialize()
        {
            SetStartDate(2017, 8, 20);
            SetEndDate(2017, 8, 20);

            _symbol = AddData<CustomDataType>("CustomDataType", Resolution.Hour).Symbol;

            var history = History<CustomDataType>(_symbol, 48, Resolution.Hour).ToList();

            Log($"History count: {history.Count}");

            if (history.Count == 0)
            {
                throw new RegressionTestException("History request returned no data");
            }

            var history2 = History<CustomDataType>(new[] { _symbol }, 48, Resolution.Hour).ToList();

            if (history2.Count != history.Count)
            {
                throw new RegressionTestException("History requests returned different data");
            }

        }

        public class CustomDataType : DynamicData
        {
            public decimal Open { get; set; }
            public decimal High { get; set; }
            public decimal Low { get; set; }
            public decimal Close { get; set; }

            public override SubscriptionDataSource GetSource(SubscriptionDataConfig config, DateTime date, bool isLiveMode)
            {
                var source = "https://www.dl.dropboxusercontent.com/s/d83xvd7mm9fzpk0/path_to_my_csv_data.csv?dl=0";
                return new SubscriptionDataSource(source, SubscriptionTransportMedium.RemoteFile);
            }

            public override BaseData Reader(SubscriptionDataConfig config, string line, DateTime date, bool isLiveMode)
            {
                if (string.IsNullOrWhiteSpace(line.Trim()))
                {
                    return null;
                }

                try
                {
                    var csv = line.Split(",");
                    var data = new CustomDataType()
                    {
                        Symbol = config.Symbol,
                        Time = DateTime.ParseExact(csv[0], DateFormat.DB, CultureInfo.InvariantCulture).AddHours(20),
                        Value = csv[4].ToDecimal(),
                        Open = csv[1].ToDecimal(),
                        High = csv[2].ToDecimal(),
                        Low = csv[3].ToDecimal(),
                        Close = csv[4].ToDecimal()
                    };

                    return data;
                }
                catch
                {
                    return null;
                }
            }
        }

        /// <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 => 28;

        /// <summary>
        /// Data Points count of the algorithm history
        /// </summary>
        public int AlgorithmHistoryDataPoints => 54;

        /// <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", "0"},
            {"Average Win", "0%"},
            {"Average Loss", "0%"},
            {"Compounding Annual Return", "0%"},
            {"Drawdown", "0%"},
            {"Expectancy", "0"},
            {"Start Equity", "100000"},
            {"End Equity", "100000"},
            {"Net Profit", "0%"},
            {"Sharpe Ratio", "0"},
            {"Sortino Ratio", "0"},
            {"Probabilistic 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"},
            {"Estimated Strategy Capacity", "$0"},
            {"Lowest Capacity Asset", ""},
            {"Portfolio Turnover", "0%"},
            {"Drawdown Recovery", "0"},
            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
        };
    }
}