/*
 * 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 QuantConnect.Data;
using QuantConnect.Interfaces;
using QuantConnect.Securities;
using QuantConnect.Securities.Interfaces;
using System;
using System.Collections.Generic;

namespace QuantConnect.Algorithm.CSharp
{
    /// <summary>
    /// Regression algorithm asserting we can specify a custom security data filter
    /// </summary>
    public class CustomSecurityDataFilterRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
    {
        private int _dataPoints;

        public override void Initialize()
        {
            SetCash(2500000);
            SetStartDate(2013, 10, 7);
            SetEndDate(2013, 10, 7);

            var security = AddSecurity(SecurityType.Equity, "SPY");
            security.SetDataFilter(new CustomDataFilter());
            _dataPoints = 0;
        }

        public override void OnData(Slice slice)
        {
            _dataPoints++;
            SetHoldings("SPY", 0.2);
            if (_dataPoints > 5)
            {
                throw new RegressionTestException($"There should not be more than 5 data points, but there were {_dataPoints}");
            }
        }

        private class CustomDataFilter : ISecurityDataFilter
        {
            public bool Filter(Security vehicle, BaseData data)
            {
                // Skip data after 9:35am
                if (data.Time >= new DateTime(2013, 10, 7, 9, 35, 0, 0))
                {
                    return false;
                }
                else
                {
                    return true;
                }
            }
        }

        /// <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 virtual List<Language> Languages { get; } = new() { Language.CSharp, Language.Python };

        /// <summary>
        /// Data Points count of all timeslices of algorithm
        /// </summary>
        public long DataPoints => 25;

        /// <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 virtual Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
        {
            {"Total Orders", "1"},
            {"Average Win", "0%"},
            {"Average Loss", "0%"},
            {"Compounding Annual Return", "0%"},
            {"Drawdown", "0%"},
            {"Expectancy", "0"},
            {"Start Equity", "2500000"},
            {"End Equity", "2500131.71"},
            {"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", "$17.23"},
            {"Estimated Strategy Capacity", "$130000000.00"},
            {"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
            {"Portfolio Turnover", "19.95%"},
            {"Drawdown Recovery", "0"},
            {"OrderListHash", "3bf23b02f24b7eb6177929ec31fdb63b"}
        };
    }
}