Overall Statistics |
Total Trades 119 Average Win 3.21% Average Loss -2.2% Compounding Annual Return 25.757% Drawdown 33.400% Expectancy 0.588 Net Profit 295.704% Sharpe Ratio 1.206 Loss Rate 35% Win Rate 65% Profit-Loss Ratio 1.45 Alpha 0.131 Beta 0.688 Annual Standard Deviation 0.208 Annual Variance 0.043 Information Ratio 0.436 Tracking Error 0.176 Treynor Ratio 0.365 Total Fees $3888.87 |
-no value-
namespace QuantConnect.Algorithm.Examples { /// <summary> /// /// 20/50/100 SMA Cross /// /// SMA 20 bull crossover SMA 100 = buy /// SMA 20 bear crossover SMA 50 = sell if 50 SMA > 100 SMA /// </summary> public class QCUMovingAverageCross : QCAlgorithm { private const string Symbol = "SPY"; private SimpleMovingAverage fast; private SimpleMovingAverage medium; private SimpleMovingAverage slow; private SimpleMovingAverage[] ribbon; public override void Initialize() { // set up our analysis span SetStartDate(2009, 01, 01); SetEndDate(2015, 01, 01); SetCash(100000); // request SPY data with minute resolution AddSecurity(SecurityType.Equity, Symbol, Resolution.Minute); // create a 10 day simple moving average fast = SMA(Symbol, 10, Resolution.Daily); // create a 30 day simple moving average medium = SMA(Symbol, 30, Resolution.Daily); // create a 100 day simple moving average slow = SMA(Symbol, 100, 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 medium or slow liquidate if (holdings > 0 && (fast < medium | 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; } } }