Overall Statistics
Total Trades
104
Average Win
0.68%
Average Loss
-0.28%
Compounding Annual Return
167.022%
Drawdown
5.500%
Expectancy
1.009
Net Profit
18.169%
Sharpe Ratio
4.423
Loss Rate
41%
Win Rate
59%
Profit-Loss Ratio
2.40
Alpha
0.437
Beta
22.668
Annual Standard Deviation
0.18
Annual Variance
0.032
Information Ratio
4.336
Tracking Error
0.18
Treynor Ratio
0.035
Total Fees
$137.36
using System;
using System.Collections.Generic;
using System.Linq;
using QuantConnect.Data.Market;
using QuantConnect.Data.UniverseSelection;

namespace QuantConnect.Algorithm.CSharp
{
	// In this algorithm we show how you can easily define a
	// universe using our coarse selection data. This data includes
	// a few properties, including the daily DollarVolume, the daily Volume
	// and also the daily closing price via the Value property.
    public class CoarseFundamentalTop5Algorithm : QCAlgorithm
    {
        // initialize our security changes to nothing
        SecurityChanges _changes = SecurityChanges.None;
        private List<Securities.Security> My_Universe = new List<Securities.Security>{};

        public override void Initialize()
        {
        	// this sets the resolution for securities added via universe selection
            UniverseSettings.Resolution = Resolution.Minute;
            AddEquity("SPY",Resolution.Minute);
            Schedule.On(DateRules.EveryDay("SPY"), TimeRules.At(09, 0), CountUniv);

            SetStartDate(2015, 10, 1);
            SetEndDate(2015, 12, 1);
            SetCash(50000);

            // this add universe method accepts a single parameter that is a function that
            // accepts an IEnumerable<CoarseFundamental> and returns IEnumerable<Symbol>
            
            AddUniverse(CoarseFunc);

        }

        // sort the data by daily dollar volume and take the top 5 symbols
        public static IEnumerable<Symbol> CoarseFunc(IEnumerable<CoarseFundamental> coarse)
        {
            // sort descending by daily dollar volume
            var sortedByDollarVolume = coarse.OrderByDescending(x => x.DollarVolume);
            // take the top 5 entries from our sorted collection
            var top5 = sortedByDollarVolume.Take(5);
            // we need to return only the symbols
            return top5.Select(x => x.Symbol);
        }

        //Data Event Handler: New data arrives here. "TradeBars" type is a dictionary of strings so you can access it by symbol.
        public void OnData(TradeBars data)
        {
            // if we have no changes, do nothing
            if (_changes == SecurityChanges.None) return;

            // liquidate removed securities
            foreach (var security in _changes.RemovedSecurities)
            {
            	My_Universe.Remove(security);
                if (security.Invested)
                {
                    Liquidate(security.Symbol);
                }
            }

            // we want 25% allocation in each security in our universe (total of 150% invested)
            foreach (var security in _changes.AddedSecurities)
            {
				My_Universe.Add(security);            	
                SetHoldings(security.Symbol, 0.25m);
            }
            
            // reset our changes
            _changes = SecurityChanges.None;
        }

        // this event fires whenever we have changes to our universe
        public override void OnSecuritiesChanged(SecurityChanges changes)
        {
            _changes = changes;
        }
        
            public void CountUniv()
    	{
    		string[] rpt = {"Count: " ,My_Universe.Count.ToString(), " ",
                            "Time: ", Time.ToString(), " ",
				            "Value: ", Portfolio.TotalPortfolioValue.ToString(), " "
                            };  
			Debug(String.Join(" ",rpt));

    	}
    
    }
    

}