/*
 * 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 System.Linq;
using QuantConnect.Data;
using QuantConnect.Interfaces;
using QuantConnect.Securities;
using QuantConnect.Securities.Future;

namespace QuantConnect.Algorithm.CSharp
{
    /// <summary>
    /// Regression algorithm that validates that when using a continuous future (without a filter)
    /// the option chains are correctly populated using the mapped symbol.
    /// </summary>
    public class FutureOptionContinuousFutureRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
    {
        protected Future Future { get; private set; }
        private bool _hasAnyOptionChainForMappedSymbol;
        public override void Initialize()
        {
            SetStartDate(2020, 1, 4);
            SetEndDate(2020, 1, 8);

            Future = AddFuture(Futures.Indices.SP500EMini, Resolution.Minute, Market.CME);
            SetFilter();

            AddFutureOption(Future.Symbol, universe => universe.Strikes(-1, 1));
        }

        public virtual void SetFilter()
        {
        }

        public override void OnData(Slice slice)
        {
            if (slice.OptionChains.Count == 0)
            {
                return;
            }

            ValidateOptionChains(slice);

            // OptionChain for the mapped symbol must exist with or without a future filter
            if (!slice.OptionChains.TryGetValue(Future.Mapped, out var chain) || chain == null || !chain.Any())
            {
                throw new RegressionTestException("No option chain found for mapped symbol during algorithm execution");
            }

            // Mark that we successfully received a non-empty OptionChain for mapped symbol
            _hasAnyOptionChainForMappedSymbol = true;
        }

        public virtual void ValidateOptionChains(Slice slice)
        {
            if (slice.OptionChains.Count != 1)
            {
                throw new RegressionTestException("Expected only one option chain for the mapped symbol");
            }
        }

        public override void OnEndOfAlgorithm()
        {
            if (!_hasAnyOptionChainForMappedSymbol)
            {
                throw new RegressionTestException("No non-empty option chain found for mapped symbol during algorithm execution");
            }
        }

        /// <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 virtual long DataPoints => 15767;

        /// <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", "-8.363"},
            {"Tracking Error", "0.059"},
            {"Treynor Ratio", "0"},
            {"Total Fees", "$0.00"},
            {"Estimated Strategy Capacity", "$0"},
            {"Lowest Capacity Asset", ""},
            {"Portfolio Turnover", "0%"},
            {"Drawdown Recovery", "0"},
            {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
        };
    }
}