Overall Statistics |
Total Trades 4 Average Win 4.5% Average Loss 0% Compounding Annual Return 40.358% Drawdown 17.300% Expectancy 0 Net Profit 128.357% Sharpe Ratio 1.415 Loss Rate 0% Win Rate 100% Profit-Loss Ratio 0 Alpha -0.038 Beta 2.279 Annual Standard Deviation 0.265 Annual Variance 0.07 Information Ratio 1.237 Tracking Error 0.157 Treynor Ratio 0.164 Total Fees $76.00 |
namespace QuantConnect { /* * QuantConnect University: Full Basic Template: * * The underlying QCAlgorithm class is full of helper methods which enable you to use QuantConnect. * We have explained some of these here, but the full algorithm can be found at: * https://github.com/QuantConnect/QCAlgorithm/blob/master/QuantConnect.Algorithm/QCAlgorithm.cs */ public class BasicTemplateAlgorithm : QCAlgorithm { RollingWindow<TradeBar> history = new RollingWindow<TradeBar>(5); DateTime lastTrade = new DateTime(); public override void Initialize() { //Start and End Date range for the backtest: SetStartDate(2013, 1, 1); SetEndDate(DateTime.Now.Date.AddDays(-1)); //Cash allocation SetCash(25000); //Add as many securities as you like. All the data will be passed into the event handler: AddSecurity(SecurityType.Equity, "SPY", Resolution.Daily); } //Data Event Handler: New data arrives here. "TradeBars" type is a dictionary of strings so you can access it by symbol. public void OnData(TradeBars data) { history.Add(data["SPY"]); if (history.IsReady && Time > lastTrade.AddDays(5)) { var delta = history[0].Close - history.MostRecentlyRemoved.Close; var relative = delta / data["SPY"].Close; SetHoldings("SPY", 0.5m + relative); lastTrade = Time; } } } }