Overall Statistics |

Total Trades 2 Average Win 0% Average Loss -1.42% Compounding Annual Return -96.567% Drawdown 16.800% Expectancy -1 Net Profit -4.514% Sharpe Ratio -1.124 Loss Rate 100% Win Rate 0% Profit-Loss Ratio 0 Alpha -1.503 Beta -4.14 Annual Standard Deviation 1.201 Annual Variance 1.443 Information Ratio -0.978 Tracking Error 1.343 Treynor Ratio 0.326 Total Fees $4.38 |

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 MarginCallTest : QCAlgorithm { //Initialize the data and resolution you require for your strategy: public override void Initialize() { //Start and End Date range for the backtest: SetStartDate(2011, 12, 5); SetEndDate(2011, 12, 10); //Cash allocation SetCash(2000); //Add as many securities as you like. All the data will be passed into the event handler: AddSecurity(SecurityType.Equity, "XIV", Resolution.Hour); } //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"] Log(String.Format("Current Portfolio Value: {0}\tMargin Remaining: {1}", Portfolio.TotalPortfolioValue.ToString("0.00"), Portfolio.MarginRemaining.ToString("0.00"))); if (Time.Hour == 15 && Portfolio.Invested == false) { SetHoldings("XIV", 2.0, true); } } } }