/*
 * 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>
    /// In this algorithm we show how you can easily use the universe selection feature to fetch symbols
    /// to be traded using the BaseData custom data system in combination with the AddUniverse{T} method.
    /// AddUniverse{T} requires a function that will return the symbols to be traded.
    /// </summary>
    /// <meta name="tag" content="using data" />
    /// <meta name="tag" content="universes" />
    /// <meta name="tag" content="custom universes" />
    public class DropboxBaseDataUniverseSelectionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
    {
        // the changes from the previous universe selection
        private SecurityChanges _changes = SecurityChanges.None;

        /// <summary>
        /// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
        /// </summary>
        /// <seealso cref="QCAlgorithm.SetStartDate(System.DateTime)"/>
        /// <seealso cref="QCAlgorithm.SetEndDate(System.DateTime)"/>
        /// <seealso cref="QCAlgorithm.SetCash(decimal)"/>
        public override void Initialize()
        {
            UniverseSettings.Resolution = Resolution.Daily;

            // Order margin value has to have a minimum of 0.5% of Portfolio value, allows filtering out small trades and reduce fees.
            // Commented so regression algorithm is more sensitive
            //Settings.MinimumOrderMarginPortfolioPercentage = 0.005m;

            SetStartDate(2017, 07, 06);
            SetEndDate(2018, 07, 04);

            var universe = AddUniverse<StockDataSource>(stockDataSource =>
            {
                return stockDataSource.OfType<StockDataSource>().SelectMany(x => x.Symbols);
            });

            var historicalSelectionData = History(universe, 3).ToList();
            if (historicalSelectionData.Count != 3)
            {
                throw new RegressionTestException($"Unexpected universe data count {historicalSelectionData.Count}");
            }

            foreach (var universeData in historicalSelectionData)
            {
                var stockDataSource = (StockDataSource)universeData.Single();
                if (stockDataSource.Symbols.Count != 5)
                {
                    throw new RegressionTestException($"Unexpected universe data receieved");
                }
            }
        }

        /// <summary>
        /// Event - v3.0 DATA EVENT HANDLER: (Pattern) Basic template for user to override for receiving all subscription data in a single event
        /// </summary>
        /// <code>
        /// TradeBars bars = slice.Bars;
        /// Ticks ticks = slice.Ticks;
        /// TradeBar spy = slice["SPY"];
        /// List{Tick} aaplTicks = slice["AAPL"]
        /// Quandl oil = slice["OIL"]
        /// dynamic anySymbol = slice[symbol];
        /// DataDictionary{Quandl} allQuandlData = slice.Get{Quand}
        /// Quandl oil = slice.Get{Quandl}("OIL")
        /// </code>
        /// <param name="slice">The current slice of data keyed by symbol string</param>
        public override void OnData(Slice slice)
        {
            if (slice.Bars.Count == 0) return;
            if (_changes == SecurityChanges.None) return;

            // start fresh

            Liquidate();

            var percentage = 1m / slice.Bars.Count;
            foreach (var tradeBar in slice.Bars.Values)
            {
                SetHoldings(tradeBar.Symbol, percentage);
            }

            // reset changes
            _changes = SecurityChanges.None;
        }

        /// <summary>
        /// Event fired each time the we add/remove securities from the data feed
        /// </summary>
        /// <param name="changes"></param>
        public override void OnSecuritiesChanged(SecurityChanges changes)
        {
            // each time our securities change we'll be notified here
            _changes = changes;
        }

        /// <summary>
        /// Our custom data type that defines where to get and how to read our backtest and live data.
        /// </summary>
        class StockDataSource : BaseDataCollection
        {
            private const string LiveUrl = @"https://www.dropbox.com/s/2l73mu97gcehmh7/daily-stock-picker-live.csv?dl=1";
            private const string BacktestUrl = @"https://www.dropbox.com/s/ae1couew5ir3z9y/daily-stock-picker-backtest.csv?dl=1";

            /// <summary>
            /// The symbols to be selected
            /// </summary>
            public List<string> Symbols { get; set; }

            /// <summary>
            /// Required default constructor
            /// </summary>
            public StockDataSource()
            {
                // initialize our list to empty
                Symbols = new List<string>();
            }

            /// <summary>
            /// Return the URL string source of the file. This will be converted to a stream
            /// </summary>
            /// <param name="config">Configuration object</param>
            /// <param name="date">Date of this source file</param>
            /// <param name="isLiveMode">true if we're in live mode, false for backtesting mode</param>
            /// <returns>String URL of source file.</returns>
            public override SubscriptionDataSource GetSource(SubscriptionDataConfig config, DateTime date, bool isLiveMode)
            {
                var url = isLiveMode ? LiveUrl : BacktestUrl;
                return new SubscriptionDataSource(url, SubscriptionTransportMedium.RemoteFile, FileFormat.FoldingCollection);
            }

            /// <summary>
            /// Reader converts each line of the data source into BaseData objects. Each data type creates its own factory method, and returns a new instance of the object
            /// each time it is called. The returned object is assumed to be time stamped in the config.ExchangeTimeZone.
            /// </summary>
            /// <param name="config">Subscription data config setup object</param>
            /// <param name="line">Line of the source document</param>
            /// <param name="date">Date of the requested data</param>
            /// <param name="isLiveMode">true if we're in live mode, false for backtesting mode</param>
            /// <returns>Instance of the T:BaseData object generated by this line of the CSV</returns>
            public override BaseData Reader(SubscriptionDataConfig config, string line, DateTime date, bool isLiveMode)
            {
                try
                {
                    // create a new StockDataSource and set the symbol using config.Symbol
                    var stocks = new StockDataSource {Symbol = config.Symbol};
                    // break our line into csv pieces
                    var csv = line.ToCsv();
                    if (isLiveMode)
                    {
                        // our live mode format does not have a date in the first column, so use date parameter
                        stocks.Time = date;
                        stocks.Symbols.AddRange(csv);
                    }
                    else
                    {
                        // our backtest mode format has the first column as date, parse it
                        stocks.Time = DateTime.ParseExact(csv[0], "yyyyMMdd", null);
                        // any following comma separated values are symbols, save them off
                        stocks.Symbols.AddRange(csv.Skip(1));
                    }
                    return stocks;
                }
                // return null if we encounter any errors
                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 => 5269;

        /// <summary>
        /// Data Points count of the algorithm history
        /// </summary>
        public int AlgorithmHistoryDataPoints => 3;

        /// <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", "6415"},
            {"Average Win", "0.07%"},
            {"Average Loss", "-0.07%"},
            {"Compounding Annual Return", "15.655%"},
            {"Drawdown", "10.500%"},
            {"Expectancy", "0.071"},
            {"Start Equity", "100000"},
            {"End Equity", "115562.68"},
            {"Net Profit", "15.563%"},
            {"Sharpe Ratio", "0.844"},
            {"Sortino Ratio", "0.788"},
            {"Probabilistic Sharpe Ratio", "48.632%"},
            {"Loss Rate", "46%"},
            {"Win Rate", "54%"},
            {"Profit-Loss Ratio", "0.98"},
            {"Alpha", "0.008"},
            {"Beta", "0.986"},
            {"Annual Standard Deviation", "0.11"},
            {"Annual Variance", "0.012"},
            {"Information Ratio", "0.155"},
            {"Tracking Error", "0.041"},
            {"Treynor Ratio", "0.094"},
            {"Total Fees", "$7460.54"},
            {"Estimated Strategy Capacity", "$450000.00"},
            {"Lowest Capacity Asset", "BNO UN3IMQ2JU1YD"},
            {"Portfolio Turnover", "135.63%"},
            {"Drawdown Recovery", "36"},
            {"OrderListHash", "29c715831bd675f04226f9fd8855a52e"}
        };
    }
}