/*
 * 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.Portfolio;
using QuantConnect.Data;
using QuantConnect.Interfaces;

namespace QuantConnect.Algorithm.CSharp
{
    /// <summary>
    /// Regression algorithm testing GH feature 3790, using SetHoldings with a collection of targets
    /// which will be ordered by margin impact before being executed, with the objective of avoiding any
    /// margin errors
    /// </summary>
    public class SetHoldingsMultipleTargetsRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
    {
        private Symbol _spy;
        private Symbol _ibm;

        /// <summary>
        /// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
        /// </summary>
        public override void Initialize()
        {
            SetStartDate(2013, 10, 07);
            SetEndDate(2013, 10, 11);

            // use leverage 1 so we test the margin impact ordering
            _spy = AddEquity("SPY", Resolution.Minute, Market.USA, false, 1).Symbol;
            _ibm = AddEquity("IBM", Resolution.Minute, Market.USA, false, 1).Symbol;

            // Order margin value has to have a minimum of 0.5% of Portfolio value, allows filtering out small trades and reduce fees.
            // Commented so regression algorithm is more sensitive
            //Settings.MinimumOrderMarginPortfolioPercentage = 0.005m;
        }

        /// <summary>
        /// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
        /// </summary>
        /// <param name="data">Slice object keyed by symbol containing the stock data</param>
        public override void OnData(Slice data)
        {
            if (!Portfolio.Invested)
            {
                SetHoldings(new List<PortfolioTarget> { new PortfolioTarget(_spy, 0.8m), new PortfolioTarget(_ibm, 0.2m) });
            }
            else
            {
                SetHoldings(new List<PortfolioTarget> { new PortfolioTarget(_ibm, 0.8m), new PortfolioTarget(_spy, 0.2m) });
            }
        }

        /// <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 virtual 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 virtual long DataPoints => 7842;

        /// <summary>
        /// Data Points count of the algorithm history
        /// </summary>
        public virtual 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", "11"},
            {"Average Win", "0.00%"},
            {"Average Loss", "-0.01%"},
            {"Compounding Annual Return", "353.938%"},
            {"Drawdown", "2.300%"},
            {"Expectancy", "-0.749"},
            {"Start Equity", "100000"},
            {"End Equity", "101952.99"},
            {"Net Profit", "1.953%"},
            {"Sharpe Ratio", "11.757"},
            {"Sortino Ratio", "0"},
            {"Probabilistic Sharpe Ratio", "65.582%"},
            {"Loss Rate", "75%"},
            {"Win Rate", "25%"},
            {"Profit-Loss Ratio", "0.00"},
            {"Alpha", "0.96"},
            {"Beta", "0.993"},
            {"Annual Standard Deviation", "0.248"},
            {"Annual Variance", "0.062"},
            {"Information Ratio", "8.324"},
            {"Tracking Error", "0.114"},
            {"Treynor Ratio", "2.942"},
            {"Total Fees", "$15.02"},
            {"Estimated Strategy Capacity", "$2600000.00"},
            {"Lowest Capacity Asset", "IBM R735QTJ8XC9X"},
            {"Portfolio Turnover", "44.15%"},
            {"Drawdown Recovery", "2"},
            {"OrderListHash", "14d509658aa542a210a3d6d41c05cd22"}
        };
    }
}