Overall Statistics Total Trades17Average Win14.23%Average Loss-6.33%Compounding Annual Return22.567%Drawdown31.800%Expectancy1.292Net Profit235.402%Sharpe Ratio0.977Loss Rate29%Win Rate71%Profit-Loss Ratio2.25Alpha0.076Beta0.89Annual Standard Deviation0.235Annual Variance0.055Information Ratio0.331Tracking Error0.173Treynor Ratio0.258
```/*
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
*
* you may not use this file except in compliance with the License.
*
* Unless required by applicable law or agreed to in writing, software
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
*/

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

namespace QuantConnect.Algorithm.Examples
{
/// <summary>
/// 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 MovingAverageCross : QCAlgorithm
{
private const string Symbol = "SPY";

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

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, 15, Resolution.Daily);

// create a 30 day exponential moving average
slow = EMA(Symbol, 30, 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 = Enumerable.Range(0, ribbonCount).Select(x => SMA(Symbol, (x + 1)*ribbonInterval, Resolution.Hour)).ToArray();
}

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;
}
}
}```