Overall Statistics |
Total Trades 1 Average Win 0% Average Loss 0% Compounding Annual Return 6.028% Drawdown 55.300% Expectancy 0 Net Profit 0% Sharpe Ratio 0.394 Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0.083 Beta -0.063 Annual Standard Deviation 0.197 Annual Variance 0.039 Information Ratio -0.002 Tracking Error 0.288 Treynor Ratio -1.231 Total Fees $7.04 |
using System; using System.Linq; using QuantConnect.Indicators; using QuantConnect.Models; namespace QuantConnect.Algorithm.Examples { /// <summary> /// /// QuantConnect University: SMA + 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 = "SPY"; public override void Initialize() { // set up our analysis span SetStartDate(1998, 01, 01); SetEndDate(2016, 01, 01); // request SPY data with minute resolution AddSecurity(SecurityType.Equity, Symbol, Resolution.Minute); } 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 // only once per day if (previous.Date == data.Time.Date) return; var holdings = Portfolio[Symbol].Quantity; // we only want to go long if we're currently short or flat if (holdings <= 0) { Log("BUY >> " + Securities[Symbol].Price); SetHoldings(Symbol, 1.0); } Plot(Symbol, "SPY ", data[Symbol].Price); // Plot("Ribbon", "Price", data[Symbol].Price); previous = data.Time; } } }