Hi,

I'm new to QuantConnect and I have to say this is totally awesome! Quick question. Seems like a lot of things are taken care of when using the Cloud-based IDE when streaming data. Everything seems like a line of code around here so I was wondering how I can stream real-time 1 min data to my desktop. I have downloaded the source from github. For example I would like to do this but in my desk top app:

using System; using System.Linq; using QuantConnect.Indicators; using QuantConnect.Models; namespace QuantConnect.Algorithm.Examples { /// <summary> /// /// QuantConnect University: EMA + SMA Cross /// /// In this example we look at the canonical 15/30 day moving average cross. This algorithm /// will go long when the 15 crosses above the 30 and will liquidate when the 15 crosses /// back below the 30. /// </summary> public class QCUMovingAverageCross : QCAlgorithm { private const string Symbol = "NLNK"; private ExponentialMovingAverage fast; private ExponentialMovingAverage slow; private SimpleMovingAverage[] ribbon; public override void Initialize() { // set up our analysis span SetStartDate(2009, 01, 01); SetEndDate(2017, 09, 11); // request SPY data with minute resolution AddSecurity(SecurityType.Equity, Symbol, Resolution.Minute); // create a 15 day exponential moving average fast = EMA(Symbol, 15, Resolution.Daily); // create a 30 day exponential moving average slow = EMA(Symbol, 30, Resolution.Daily); // the following lines produce a simple moving average ribbon, this isn't // actually used in the algorithm's logic, but shows how easy it is to make // indicators and plot them! // note how we can easily define these indicators to receive hourly data int ribbonCount = 7; int ribbonInterval = 15*8; ribbon = new SimpleMovingAverage[ribbonCount]; for(int i = 0; i < ribbonCount; i++) { ribbon[i] = SMA(Symbol, (i + 1)*ribbonInterval, Resolution.Hour); } } private DateTime previous; public void OnData(TradeBars data) { // a couple things to notice in this method: // 1. We never need to 'update' our indicators with the data, the engine takes care of this for us // 2. We can use indicators directly in math expressions // 3. We can easily plot many indicators at the same time // wait for our slow ema to fully initialize if (!slow.IsReady) return; // only once per day if (previous.Date == data.Time.Date) return; // define a small tolerance on our checks to avoid bouncing const decimal tolerance = 0.00015m; var holdings = Portfolio[Symbol].Quantity; // we only want to go long if we're currently short or flat if (holdings <= 0) { // if the fast is greater than the slow, we'll go long if (fast > slow * (1 + tolerance)) { Log("BUY >> " + Securities[Symbol].Price); SetHoldings(Symbol, 1.0); } } // we only want to liquidate if we're currently long // if the fast is less than the slow we'll liquidate our long if (holdings > 0 && fast < slow) { Log("SELL >> " + Securities[Symbol].Price); Liquidate(Symbol); } Plot(Symbol, "Price", data[Symbol].Price); Plot("Ribbon", "Price", data[Symbol].Price); // easily plot indicators, the series name will be the name of the indicator Plot(Symbol, fast, slow); Plot("Ribbon", ribbon); previous = data.Time; } } }