/*
 * 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.Collections.Generic;
using QuantConnect.Algorithm.Framework.Selection;
using QuantConnect.Interfaces;
using System;
using QuantConnect.Securities;
using QuantConnect.Data.UniverseSelection;

namespace QuantConnect.Algorithm.CSharp
{
    /// <summary>
    /// This example demonstrates how to use the FutureUniverseSelectionModel to select futures contracts for a given underlying asset.
    /// The model is set to update daily, and the algorithm ensures that the selected contracts meet specific criteria.
    /// This also includes a check to ensure that only future contracts are added to the algorithm's universe.
    /// </summary>
    public class AddFutureUniverseSelectionModelRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
    {
        public override void Initialize()
        {
            SetStartDate(2013, 10, 08);
            SetEndDate(2013, 10, 10);

            SetUniverseSelection(new FutureUniverseSelectionModel(
                TimeSpan.FromDays(1),
                time => new List<Symbol> {
                    QuantConnect.Symbol.Create(Futures.Indices.SP500EMini, SecurityType.Future, Market.CME)
                }
            ));
        }

        public override void OnSecuritiesChanged(SecurityChanges changes)
        {
            if (changes.AddedSecurities.Count > 0)
            {
                foreach (var security in changes.AddedSecurities)
                {
                    if (security.Symbol.SecurityType != SecurityType.Future)
                    {
                        throw new RegressionTestException($"Expected future security, but found '{security.Symbol.SecurityType}'");
                    }
                    if (security.Symbol.ID.Symbol != "ES")
                    {
                        throw new RegressionTestException($"Expected future symbol 'ES', but found '{security.Symbol.ID.Symbol}");
                    }
                }
            }
        }

        public override void OnEndOfAlgorithm()
        {
            if (ActiveSecurities.Count == 0)
            {
                throw new RegressionTestException("No active securities found. Expected at least one active security");
            }
        }

        /// <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 => 26094;

        /// <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", "-66.775"},
            {"Tracking Error", "0.243"},
            {"Treynor Ratio", "0"},
            {"Total Fees", "$0.00"},
            {"Estimated Strategy Capacity", "$0"},
            {"Lowest Capacity Asset", ""},
            {"Portfolio Turnover", "0%"},
            {"Drawdown Recovery", "0"},
            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
        };
    }
}