Overall Statistics
Total Trades
261
Average Win
3.22%
Average Loss
-0.79%
Compounding Annual Return
9.568%
Drawdown
25.200%
Expectancy
0.111
Net Profit
26.084%
Sharpe Ratio
0.501
Loss Rate
78%
Win Rate
22%
Profit-Loss Ratio
4.07
Alpha
0.175
Beta
-0.309
Annual Standard Deviation
0.239
Annual Variance
0.057
Information Ratio
-0.215
Tracking Error
0.279
Treynor Ratio
-0.387
Total Fees
$1006.84
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 -----------
    // 1) Intraday - Hourly
    // 2) 1/50 period SMA cross
    // 
    // -------VATS CHANGES -----------
    
    /// </summary>
    public class QCUMovingAverageCross : QCAlgorithm
    {
        private const string Symbol = "USO";

        private SimpleMovingAverage fast;
        private SimpleMovingAverage slow;

        
         
         //Initialize the data and resolution you require for your strategy:
   

        public override void Initialize()
        {

            SetStartDate(2013, 01, 01);
            SetEndDate(2015, 07, 15);
			SetCash(10000);

            // request SPY data with minute resolution
            AddSecurity(SecurityType.Equity, Symbol, Resolution.Minute);

            // create a 15 day exponential moving average
            fast = SMA(Symbol, 1, Resolution.Hour);

            // create a 30 day exponential moving average
            slow = SMA(Symbol, 50, Resolution.Hour);
            //SetRunMode(RunMode.Series);

           
        }

        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 
            // Commented the following line to simulate intraday - Vats
            //if (previous.Date == data.Time.Date) return;

            // define a small tolerance on our checks to avoid bouncing
            const decimal tolerance = 0.00010m;
            var holdings = Portfolio[Symbol].Quantity;

            // we only want to go long if we're currently short or flat

            // if the fast is greater than the slow, we'll go long
            if (fast > slow * (1 + tolerance))
            {
                if (holdings <= 0)
                {
                    Log("L  >> " + holdings + "@ price " + Securities[Symbol].Price);
                    SetHoldings(Symbol, 0.95);
                    Log("NET POSITION BEFORE NEXT TRANSACTION >> " + holdings);
                }
            }

            // we only want to liquidate if we're currently long
            // if the fast is less than the slow we'll liquidate our long
            if (fast < slow)
            {
                if (holdings > 0)
                {
                   Log("SELL >> " + holdings + "@ price " + Securities[Symbol].Price);
                   SetHoldings(Symbol, -0.95);
                }
            }

            Plot(Symbol, "Price", data[Symbol].Price);
            
            // easily plot indicators, the series name will be the name of the indicator
            Plot(Symbol, fast, slow);


            previous = data.Time;
        }
    }
}