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;
}
}
}                        ```