/*
 * 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.Data;
using QuantConnect.Interfaces;
using QuantConnect.Algorithm.Framework.Risk;

namespace QuantConnect.Algorithm.CSharp
{
    /// <summary>
    /// Regression algorithm which tests that a trailing stop liquidates and restarts correctly
    /// </summary>
    public class TrailingStopRiskFrameworkRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
    {
        public override void Initialize()
        {
            SetStartDate(2014, 6, 5);
            SetEndDate(2014, 6, 9);
            SetCash(100000);
            AddEquity("AAPL");
            AddRiskManagement(new TrailingStopRiskManagementModel(0.01m));
        }

        public override void OnData(Slice slice)
        {
            if (!Portfolio.Invested)
            {
                SetHoldings("AAPL", 1);
            }
        }

        /// <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 Language[] Languages { get; } = { Language.CSharp, Language.Python };

        /// <summary>
        /// Data Points count of all timeslices of algorithm
        /// </summary>
        public long DataPoints => 2371;

        /// <summary>
        /// Data Points count of the algorithm history
        /// </summary>
        public int AlgorithmHistoryDataPoints => 0;

        /// <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 Trades", "3"},
            {"Average Win", "0%"},
            {"Average Loss", "-0.31%"},
            {"Compounding Annual Return", "202.556%"},
            {"Drawdown", "1.400%"},
            {"Expectancy", "-1"},
            {"Net Profit", "1.426%"},
            {"Sharpe Ratio", "9.374"},
            {"Probabilistic Sharpe Ratio", "81.575%"},
            {"Loss Rate", "100%"},
            {"Win Rate", "0%"},
            {"Profit-Loss Ratio", "0"},
            {"Alpha", "1.741"},
            {"Beta", "-1.155"},
            {"Annual Standard Deviation", "0.133"},
            {"Annual Variance", "0.018"},
            {"Information Ratio", "5.447"},
            {"Tracking Error", "0.149"},
            {"Treynor Ratio", "-1.075"},
            {"Total Fees", "$71.90"},
            {"Estimated Strategy Capacity", "$20000000.00"},
            {"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
            {"Portfolio Turnover", "59.13%"},
            {"OrderListHash", "87da67837d4a2c4c4a419f01b467d9c6"}
        };
    }
}