Overall Statistics Total Trades1Average Win0%Average Loss0%Compounding Annual Return6.028%Drawdown55.300%Expectancy0Net Profit0%Sharpe Ratio0.394Loss Rate0%Win Rate0%Profit-Loss Ratio0Alpha0.083Beta-0.063Annual Standard Deviation0.197Annual Variance0.039Information Ratio-0.002Tracking Error0.288Treynor Ratio-1.231Total 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
}

private DateTime previous;
{
// 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)
{
SetHoldings(Symbol, 1.0);
}

Plot(Symbol, "SPY ", data[Symbol].Price);
// Plot("Ribbon", "Price", data[Symbol].Price);

previous = data.Time;
}
}
}```