/*
 * 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.Linq;
using QuantConnect.Interfaces;
using System.Collections.Generic;
using QuantConnect.Data.UniverseSelection;
using QuantConnect.Data;

namespace QuantConnect.Algorithm.CSharp
{
    /// <summary>
    /// Assert that CoarseFundamentals universe selection happens right away after algorithm starts
    /// </summary>
    public class CoarseFundamentalImmediateSelectionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
    {
        private const int NumberOfSymbols = 3;

        private bool _initialSelectionDone;

        public override void Initialize()
        {
            UniverseSettings.Resolution = Resolution.Daily;

            SetStartDate(2014, 03, 25);
            SetEndDate(2014, 03, 30);
            SetCash(100000);

            AddUniverse(CoarseSelectionFunction);
        }

        // sort the data by daily dollar volume and take the top 'NumberOfSymbols'
        public IEnumerable<Symbol> CoarseSelectionFunction(IEnumerable<CoarseFundamental> coarse)
        {
            if (!_initialSelectionDone)
            {
                if (Time != StartDate)
                {
                    throw new RegressionTestException($"CoarseSelectionFunction called at unexpected time. " +
                        $"Expected it to be called on {StartDate} but was called on {Time}");
                }
            }

            // sort descending by daily dollar volume
            var sortedByDollarVolume = coarse.OrderByDescending(x => x.DollarVolume);

            // take the top entries from our sorted collection
            var top = sortedByDollarVolume.Take(NumberOfSymbols);

            // we need to return only the symbol objects
            return top.Select(x => x.Symbol);
        }

        public void OnData(Slice data)
        {
            Log($"OnData({UtcTime:o}): Keys: {string.Join(", ", data.Keys.OrderBy(x => x))}");
        }

        public override void OnSecuritiesChanged(SecurityChanges changes)
        {
            Log($"OnSecuritiesChanged({UtcTime:o}):: {changes}");

            // This should also happen right away
            if (!_initialSelectionDone)
            {
                _initialSelectionDone = true;

                if (Time != StartDate)
                {
                    throw new RegressionTestException($"OnSecuritiesChanged called at unexpected time. " +
                        $"Expected it to be called on {StartDate} but was called on {Time}");
                }

                if (changes.AddedSecurities.Count != NumberOfSymbols)
                {
                    throw new RegressionTestException($"Unexpected number of added securities. " +
                        $"Expected {NumberOfSymbols} but was {changes.AddedSecurities.Count}");
                }

                if (changes.RemovedSecurities.Count != 0)
                {
                    throw new RegressionTestException($"Unexpected number of removed securities. " +
                        $"Expected 0 but was {changes.RemovedSecurities.Count}");
                }
            }
        }

        /// <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 => 35402;

        /// <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", "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", "3.134"},
            {"Tracking Error", "0.097"},
            {"Treynor Ratio", "0"},
            {"Total Fees", "$0.00"},
            {"Estimated Strategy Capacity", "$0"},
            {"Lowest Capacity Asset", ""},
            {"Portfolio Turnover", "0%"},
            {"Drawdown Recovery", "0"},
            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
        };
    }
}