Backtest

Overall Statistics
Total Orders 62 Average Win 1.46% Average Loss -1.67% Compounding Annual Return -1.613% Drawdown 7.000% Expectancy 0.004 Start Equity 100000 End Equity 98786.38 Net Profit -1.214% Sharpe Ratio -0.535 Sortino Ratio -0.657 Probabilistic Sharpe Ratio 11.741% Loss Rate 46% Win Rate 54% Profit-Loss Ratio 0.87 Alpha -0.039 Beta 0.033 Annual Standard Deviation 0.07 Annual Variance 0.005 Information Ratio -0.483 Tracking Error 0.149 Treynor Ratio -1.127 Total Fees $0.00 Estimated Strategy Capacity $0 Lowest Capacity Asset XAGUSD 8I Portfolio Turnover 19.83%

#region imports
    using Newtonsoft.Json;
    using System;
    using System.Collections;
    using System.Collections.Generic;
    using System.Linq;
    using System.Globalization;
    using System.Drawing;
    using QuantConnect;
    using QuantConnect.Algorithm.Framework;
    using QuantConnect.Algorithm.Framework.Selection;
    using QuantConnect.Algorithm.Framework.Alphas;
    using QuantConnect.Algorithm.Framework.Portfolio;
    using QuantConnect.Algorithm.Framework.Portfolio.SignalExports;
    using QuantConnect.Algorithm.Framework.Execution;
    using QuantConnect.Algorithm.Framework.Risk;
    using QuantConnect.Algorithm.Selection;
    using QuantConnect.Api;
    using QuantConnect.Parameters;
    using QuantConnect.Benchmarks;
    using QuantConnect.Brokerages;
    using QuantConnect.Configuration;
    using QuantConnect.Util;
    using QuantConnect.Interfaces;
    using QuantConnect.Algorithm;
    using QuantConnect.Indicators;
    using QuantConnect.Data;
    using QuantConnect.Data.Auxiliary;
    using QuantConnect.Data.Consolidators;
    using QuantConnect.Data.Custom;
    using QuantConnect.Data.Custom.IconicTypes;
    using QuantConnect.DataSource;
    using QuantConnect.Data.Fundamental;
    using QuantConnect.Data.Market;
    using QuantConnect.Data.Shortable;
    using QuantConnect.Data.UniverseSelection;
    using QuantConnect.Notifications;
    using QuantConnect.Orders;
    using QuantConnect.Orders.Fees;
    using QuantConnect.Orders.Fills;
    using QuantConnect.Orders.OptionExercise;
    using QuantConnect.Orders.Slippage;
    using QuantConnect.Orders.TimeInForces;
    using QuantConnect.Python;
    using QuantConnect.Scheduling;
    using QuantConnect.Securities;
    using QuantConnect.Securities.Equity;
    using QuantConnect.Securities.Future;
    using QuantConnect.Securities.Option;
    using QuantConnect.Securities.Positions;
    using QuantConnect.Securities.Forex;
    using QuantConnect.Securities.Crypto;
    using QuantConnect.Securities.CryptoFuture;
    using QuantConnect.Securities.Interfaces;
    using QuantConnect.Securities.Volatility;
    using QuantConnect.Storage;
    using QuantConnect.Statistics;
    using QCAlgorithmFramework = QuantConnect.Algorithm.QCAlgorithm;
    using QCAlgorithmFrameworkBridge = QuantConnect.Algorithm.QCAlgorithm;
#endregion

namespace QuantConnect
{
    public class GoldSilverPairsTradingAlgorithm : QCAlgorithm
    {
        // Use 500-step mean and SD indicator on determine the spread relative difference for trading signal generation
        private SimpleMovingAverage _spreadMean = new SimpleMovingAverage(500);
        private StandardDeviation _spreadStd = new StandardDeviation(500);
        private Security[] _pair = new Security[2];

        public override void Initialize()
        {
            SetStartDate(2018, 7, 1);  
            SetEndDate(2019, 3, 31);  
            SetCash(100000);  

            // Request gold and sliver spot CFDs for trading their spread difference, assuming their spread series is cointegrated
            AddCfd("XAUUSD", Resolution.Hour);
            AddCfd("XAGUSD", Resolution.Hour);
        }

        public override void OnData(Slice slice) 
        {
            // Update the indicator with updated spread difference, such that the an updated cointegration threshold is calculated for trade inception
            var spread = _pair[1].Price - _pair[0].Price;
            _spreadMean.Update(Time, spread);
            _spreadStd.Update(Time, spread);
            
            var upperthreshold = _spreadMean + _spreadStd;
            var lowerthreshold = _spreadMean - _spreadStd;
            
            // If the spread is higher than upper threshold, bet theie spread series will revert to mean
            if (spread > upperthreshold)
            {
                SetHoldings(_pair[0].Symbol, 1);
                SetHoldings(_pair[1].Symbol, -1);
            }
            else if (spread < lowerthreshold)
            {
                SetHoldings(_pair[0].Symbol, -1);
                SetHoldings(_pair[1].Symbol, 1);
            }
            // Close positions if mean reverted
            else if ((Portfolio[_pair[0].Symbol].Quantity > 0m && spread < _spreadMean)
            || (Portfolio[_pair[0].Symbol].Quantity < 0m && spread > _spreadMean))
            {
                Liquidate();
            }
        }
        
        public override void OnSecuritiesChanged(SecurityChanges changes)
        {    
            _pair = changes.AddedSecurities.ToArray();
            
            //1. Call for 500 days of history data for each symbol in the pair and save to the variable history
            var history = History(_pair.Select(x => x.Symbol), 500);
            
            //2. Iterate through the history tuple and update the mean and standard deviation with historical data 
            foreach(var slice in history)
            {
                var spread = slice[_pair[1].Symbol].Close - slice[_pair[0].Symbol].Close;
                _spreadMean.Update(slice.Time, spread);
                _spreadStd.Update(slice.Time, spread);
            }
        }
    }
}