Overall Statistics |
Total Trades 0 Average Win 0% Average Loss 0% Compounding Annual Return 0% Drawdown 0% Expectancy 0 Net Profit 0% Sharpe Ratio 0 Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio 0 Tracking Error 0 Treynor Ratio 0 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 */ class MyBrokerage: InteractiveBrokersBrokerageModel{ } public class BasicTemplateAlgorithm : QCAlgorithm { public string[] Symbols = {"EURUSD", "EURJPY", "USDJPY"}; public Resolution _dataresolution = Resolution.Tick; //Initialize the data and resolution you require for your strategy: public override void Initialize() { //Start and End Date range for the backtest: SetStartDate(DateTime.Now.Date.AddDays(-2)); SetEndDate(DateTime.Now.Date.AddDays(-1)); //Cash allocation SetCash(10000); //Add as many securities as you like. All the data will be passed into the event handler: foreach( var symbol in Symbols) { AddForex(symbol,_dataresolution, Market.FXCM); } ///AddForex(string ticker, Resolution resolution = Resolution.Minute, string market = Market.FXCM, bool fillDataForward = true, decimal leverage = 0m) } //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"] var eurusdclose = data["EURUSD"].Close; var eurjpyclose = data["USDJPY"].Close; var usdjpyclose = data["USDJPY"].Close; var conversion = (eurusdclose * (1/eurjpyclose) * usdjpyclose) - 1.002m; if( conversion > 0){ Console.WriteLine("now"); } } } }