Overall Statistics |

Total Trades 1 Average Win 0% Average Loss 0% Compounding Annual Return 59.640% Drawdown 1.000% Expectancy 0 Net Profit 0% Sharpe Ratio 7.595 Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0.5 Beta -0.183 Annual Standard Deviation 0.059 Annual Variance 0.004 Information Ratio 2.067 Tracking Error 0.084 Treynor Ratio -2.457 Total Fees $3.64 |

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 { //Initialize the data and resolution you require for your strategy: public override void Initialize() { //Start and End Date range for the backtest: SetStartDate(2013, 1, 1); SetEndDate(2013,2,2); //SetEndDate(DateTime.Now.Date.AddDays(-1)); //Cash allocation SetCash(100000); //Add as many securities as you like. All the data will be passed into the event handler: AddSecurity(SecurityType.Equity, "SPY", Resolution.Minute); } //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) { // "TradeBars" object holds many "TradeBar" objects: it is a dictionary indexed by the symbol: // // e.g. data["MSFT"] data["GOOG"] if (!Portfolio.HoldStock) { int quantity = (int)Math.Floor(Portfolio.Cash / data["SPY"].Close); //Order function places trades: enter the string symbol and the quantity you want: Order("SPY", quantity); } } } }