Overall Statistics |
Total Trades 85 Average Win 7.51% Average Loss -4.31% Compounding Annual Return 7.814% Drawdown 38.600% Expectancy 0.436 Net Profit 106.379% Sharpe Ratio 0.405 Loss Rate 48% Win Rate 52% Profit-Loss Ratio 1.74 Alpha 0.081 Beta 0.017 Annual Standard Deviation 0.203 Annual Variance 0.041 Information Ratio 0.101 Tracking Error 0.274 Treynor Ratio 4.872 Total Fees $202.45 |
using System; using System.Collections.Generic; using System.Globalization; using System.Linq; using QuantConnect.Data; namespace QuantConnect.Algorithm.Examples { public class ETFGlobalRotationAlgorithm : QCAlgorithm { // we'll use this to tell us when the month has ended DateTime LastRotationTime = DateTime.MinValue; TimeSpan RotationInterval = TimeSpan.FromDays(30); private bool first = true; // these are the growth symbols we'll rotate through List<string> GrowthSymbols = new List<string> { "MDY", // US S&P mid cap 400 "IEV", // iShares S&P europe 350 "EEM", // iShared MSCI emerging markets "ILF", // iShares S&P latin america "EPP" // iShared MSCI Pacific ex-Japan }; // these are the safety symbols we go to when things are looking bad for growth List<string> SafetySymbols = new List<string> { "EDV", // Vangaurd TSY 25yr+ "SHY" // Barclays Low Duration TSY }; // we'll hold some computed data in these guys List<SymbolData> SymbolData = new List<SymbolData>(); /// <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() { SetCash(25000); SetStartDate(2007, 1, 1); foreach (var symbol in GrowthSymbols.Union(SafetySymbols)) { // ideally we would use daily data AddSecurity(SecurityType.Equity, symbol, Resolution.Minute); var oneMonthPerformance = MOM(symbol, 30, Resolution.Daily); var threeMonthPerformance = MOM(symbol, 90, Resolution.Daily); SymbolData.Add(new SymbolData { Symbol = symbol, OneMonthPerformance = oneMonthPerformance, ThreeMonthPerformance = threeMonthPerformance }); } } /// <summary> /// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here. /// </summary> /// <param name="data">TradeBars IDictionary object with your stock data</param> public void OnData(TradeBars data) { try { // the first time we come through here we'll need to do some things such as allocation // and initializing our symbol data if (first) { first = false; LastRotationTime = Time; return; } var delta = Time.Subtract(LastRotationTime); if (delta > RotationInterval) { LastRotationTime = Time; // pick which one is best from growth and safety symbols var orderedObjScores = SymbolData.OrderByDescending(x => x.ObjectiveScore).ToList(); foreach (var orderedObjScore in orderedObjScores) { Log(">>SCORE>>" + orderedObjScore.Symbol + ">>" + orderedObjScore.ObjectiveScore); } var bestGrowth = orderedObjScores.First(); if (bestGrowth.ObjectiveScore > 0) { if (Portfolio[bestGrowth.Symbol].Quantity == 0) { Log("PREBUY>>LIQUIDATE>>"); Liquidate(); } Log(">>BUY>>" + bestGrowth.Symbol + "@" + (100 * bestGrowth.OneMonthPerformance).ToString("00.00")); decimal qty = Portfolio.Cash / Securities[bestGrowth.Symbol].Close; MarketOrder(bestGrowth.Symbol, (int) qty); } else { // if no one has a good objective score then let's hold cash this month to be safe Log(">>LIQUIDATE>>CASH"); Liquidate(); } } } catch (Exception ex) { Error("OnTradeBar: " + ex.Message + "\r\n\r\n" + ex.StackTrace); } } } class SymbolData { public string Symbol; public Momentum OneMonthPerformance { get; set; } public Momentum ThreeMonthPerformance { get; set; } public decimal ObjectiveScore { get { // we weight the one month performance higher decimal weight1 = 100; decimal weight2 = 75; return (weight1 * OneMonthPerformance + weight2 * ThreeMonthPerformance) / (weight1 + weight2); } } } }