Overall Statistics
Total Trades
0
Average Win
0%
Average Loss
0%
Compounding Annual Return
0%
Drawdown
0%
Expectancy
0
Net Profit
0%
Sharpe Ratio
NaN
Loss Rate
0%
Win Rate
0%
Profit-Loss Ratio
0
Alpha
0
Beta
0
Annual Standard Deviation
0
Annual Variance
0
Information Ratio
NaN
Tracking Error
NaN
Treynor Ratio
NaN
Total Fees
$0.00
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
    {
    	private string symbol = "AGN";
    	
        //Initialize the data and resolution you require for your strategy:
        public override void Initialize() 
        {
            //Start and End Date range for the backtest:
            SetStartDate(2015, 8, 4);
            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, symbol, Resolution.Minute, true, 1, true);
        }

        //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 (data[symbol].Low < 325m) {
            	Log("Below 325!");
            }
            
            Plot("AGN", data[symbol].Low);
       }
    }
}