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```using System;
using System.Linq;
using QuantConnect.Indicators;
using QuantConnect.Models;
using QuantConnect.Orders;

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 = "SPY";

private ExponentialMovingAverage fast;
private ExponentialMovingAverage fast_prev;
private ExponentialMovingAverage slow_prev;
private ExponentialMovingAverage slow;
private AverageTrueRange atr;
private SimpleMovingAverage[] ribbon;

private int Length1;
private int Length2;
private ExponentialMovingAverage Avg1, Avg2;
private RollingWindow<decimal> window;
private RollingWindow<decimal> window2;
private RollingWindow<decimal> atr_win;

public override void Initialize()
{
// set up our analysis span
SetStartDate(2009, 01, 01);
SetEndDate(2015, 01, 01);

// request SPY data with minute resolution

// create a 15 day exponential moving average
fast = EMA(Symbol, 8, Resolution.Daily);
fast_prev = EMA(Symbol, 7, Resolution.Daily);
// create a 30 day exponential moving average
slow = EMA(Symbol, 40, Resolution.Daily);
slow_prev = EMA(Symbol, 39, Resolution.Daily);
atr = new AverageTrueRange("ATR",10);
// 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);
}
Length1 = 40;
Length2 = 8;
Avg1 = EMA(Symbol,Length1,Resolution.Daily);
Avg2 = EMA(Symbol,Length2,Resolution.Daily);
window = new RollingWindow<decimal>(Length1);
window2 = new RollingWindow<decimal>(Length2);
}

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;

// define a small tolerance on our checks to avoid bouncing
const decimal tolerance = 0.00015m;
var holdings = Portfolio[Symbol].Quantity;

// Do our calculations.
// this next line says each time we get a new ema value, add it to our window
Avg1.Updated += (sender, args) => window.Add(args);
// this next line says each time we get a new ema value, add it to our window
Avg2.Updated += (sender, args) => window2.Add(args);
// this next line says each time we get a new atr value, add it to our window
atr.Updated += (sender, args) => atr_win.Add(args);

// window => Avg1, window2 => Avg2
bool Condition1 = window > window && window2 < window2;
bool Condition2 = window < window && window2 > window2;

// 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
// EMA Cross:
//if (fast > slow * (1 + tolerance))
if(Condition1)
{
// ? How to setup stop loss based on ATR ?
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
// EMA Cross:
//if (holdings > 0 && fast < slow)
if (holdings > 0 && Condition2)
{
Log("SELL >> " + Securities[Symbol].Price);
// ? How to setup stop loss based on ATR ?
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;
}
}
}```