Overall Statistics |
Total Trades 1 Average Win 0% Average Loss 0% Compounding Annual Return 11.682% Drawdown 13.700% Expectancy 0 Net Profit 0% Sharpe Ratio 0.814 Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0.096 Beta -0.007 Annual Standard Deviation 0.12 Annual Variance 0.014 Information Ratio 0.591 Tracking Error 0.419 Treynor Ratio -14.084 Total Fees $1.00 |
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 { //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(DateTime.Now.Date.AddDays(-1)); SetBenchmark ("GDX"); //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.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); //Debug sends messages to the user console: "Time" is the algorithm time keeper object Debug("Purchased SPY on " + Time.ToShortDateString()); //You can also use log to send longer messages to a file. You are capped to 10kb //Log("This is a longer message send to log."); } } } }