Overall Statistics |
Total Trades
154
Average Win
7.71%
Average Loss
-4.04%
Compounding Annual Return
2980.571%
Drawdown
33.900%
Expectancy
0.399
Net Profit
180.510%
Sharpe Ratio
3.362
Loss Rate
52%
Win Rate
48%
Profit-Loss Ratio
1.91
Alpha
3.914
Beta
0.27
Annual Standard Deviation
1.175
Annual Variance
1.382
Information Ratio
3.23
Tracking Error
1.181
Treynor Ratio
14.62
Total Fees
$2764.13
|
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 { public string ticker = "NUGT"; //Initialize the data and resolution you require for your strategy: public decimal pricePaid = 0; public override void Initialize() { //Start and End Date range for the backtest: SetStartDate(2016, 1, 4); 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); AddSecurity(SecurityType.Equity, ticker, 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) { var currentClose = data[ticker].Close; if ( data[ticker].EndTime.Hour==9 && data[ticker].EndTime.Minute == 31) { int quantity = (int)Math.Floor(Portfolio.Cash / data[ticker].Close); pricePaid = data[ticker].Close; //Order function places trades: enter the string symbol and the quantity you want: Order(ticker, quantity); } if ( data[ticker].EndTime.Hour==15 && data[ticker].EndTime.Minute == 45) { Liquidate(); } if(currentClose <= pricePaid * 0.95m) { //big drawdown...stop loss. Liquidate(); } } } }