Overall Statistics Total Trades10Average Win2.16%Average Loss-3.59%Compounding Annual Return-7.118%Drawdown18.100%Expectancy-0.359Net Profit-6.542%Sharpe Ratio-0.423Loss Rate60%Win Rate40%Profit-Loss Ratio0.60Alpha-0.071Beta0.069Annual Standard Deviation0.148Annual Variance0.022Information Ratio-0.932Tracking Error0.186Treynor Ratio-0.907Total Fees\$253.74
```using System;
using System.Linq;
using QuantConnect.Indicators;
using QuantConnect.Models;

namespace QuantConnect.Algorithm.Examples
{
/// <summary>
///
/// QuantConnect University: EMA + SMA Cross
///
/// In this example we look at the canonical 20/50 day moving average cross. This algorithm
/// will go long when the 20 crosses above the 50 and will liquidate when the 20 crosses
/// back below the 50.

// Vats Changes -----------
// Simulating Price - 20 DMA cross
//-------------------------------------

/// </summary>
public class QCUMovingAverageCross : QCAlgorithm
{
private const string Symbol = "USO";

private ExponentialMovingAverage fast;
private ExponentialMovingAverage slow;
private SimpleMovingAverage[] ribbon;

public override void Initialize()
{

// set up our analysis span
SetStartDate(2014, 08, 01);
SetEndDate(2015, 07, 01);

// request SPY data with minute resolution

// create a 1 day exponential moving average to simulate price
fast = EMA(Symbol, 1, Resolution.Daily);

// create a 30 day exponential moving average
slow = EMA(Symbol, 50, 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;
{
// 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;

// 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))
{
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
if (holdings > 0 && 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;
}
}
}```