| 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);
}
}
}
}