Overall Statistics
Total Trades
11
Average Win
31.31%
Average Loss
0%
Compounding Annual Return
7.257%
Drawdown
17.300%
Expectancy
0
Net Profit
253.161%
Sharpe Ratio
0.632
Loss Rate
0%
Win Rate
100%
Profit-Loss Ratio
0
Alpha
0.078
Beta
-0.018
Annual Standard Deviation
0.122
Annual Variance
0.015
Information Ratio
-0.005
Tracking Error
0.235
Treynor Ratio
-4.303
Total Fees
$97.05
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";

        private SimpleMovingAverage fast;
        private SimpleMovingAverage slow;

        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);

            // create a 15 day exponential moving average
            fast = SMA(Symbol, 50, Resolution.Daily);

            // create a 30 day exponential moving average
            slow = SMA(Symbol, 250, Resolution.Daily);
        }

        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
            if (!slow.IsReady) return;

            // 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))
                {
                    Log("BUY  >> " + Securities[Symbol].Price);
                    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);
                Order(Symbol, -holdings);
                // Liquidate(Symbol);    
            }

            Plot(Symbol, "SPY ", 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;
        }
    }
}