Overall Statistics
using System.Collections.Generic;
using System.Linq;
using QuantConnect.Data.Fundamental;
using QuantConnect.Data.Market;
using QuantConnect.Data.UniverseSelection;

namespace QuantConnect
{
    /// <summary>
    /// In this algorithm we demonstrate how to define a universe
    /// as a combination of use the coarse fundamental data and fine fundamental data
    /// </summary>
    public class CoarseFineFundamentalComboAlgorithm : QCAlgorithm
    {
        private const int NumberOfSymbolsCoarse = 5;
        private const int NumberOfSymbolsFine = 2;

        // initialize our changes to nothing
        private SecurityChanges _changes = SecurityChanges.None;

        public override void Initialize()
        {
            UniverseSettings.Resolution = Resolution.Daily;

            SetStartDate(2014, 04, 01);
            SetEndDate(2014, 04, 30);
            SetCash(50000);

            // this add universe method accepts two parameters:
            // - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>
            // - fine selection function: accepts an IEnumerable<FineFundamental> and returns an IEnumerable<Symbol>
            AddUniverse(CoarseSelectionFunction, FineSelectionFunction);
        }

        // sort the data by daily dollar volume and take the top 'NumberOfSymbolsCoarse'
        public IEnumerable<Symbol> CoarseSelectionFunction(IEnumerable<CoarseFundamental> coarse)
        {
            // select only symbols with fundamental data and sort descending by daily dollar volume
            var sortedByDollarVolume = coarse
                .Where(x => x.HasFundamentalData)
                .OrderByDescending(x => x.DollarVolume);

            // take the top entries from our sorted collection
            var top5 = sortedByDollarVolume.Take(NumberOfSymbolsCoarse);

            // we need to return only the symbol objects
            return top5.Select(x => x.Symbol);
        }

        // sort the data by P/E ratio and take the top 'NumberOfSymbolsFine'
        public IEnumerable<Symbol> FineSelectionFunction(IEnumerable<FineFundamental> fine)
        {
            // sort descending by P/E ratio
            var sortedByPeRatio = fine.OrderByDescending(x => x.ValuationRatios.PERatio);

            // take the top entries from our sorted collection
            var topFine = sortedByPeRatio.Take(NumberOfSymbolsFine);

            // we need to return only the symbol objects
            return topFine.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)
            {
                if (security.Invested)
                {
                    Liquidate(security.Symbol);
                    Debug("Liquidated Stock: " + security.Symbol.Value);
                }
            }

            // we want 50% allocation in each security in our universe
            foreach (var security in _changes.AddedSecurities)
            {
                SetHoldings(security.Symbol, 0.5m);
                Debug("Purchased Stock: " + security.Symbol.Value);
            }

            _changes = SecurityChanges.None;
        }

        // this event fires whenever we have changes to our universe
        public override void OnSecuritiesChanged(SecurityChanges changes)
        {
            _changes = changes;

            if (changes.AddedSecurities.Count > 0)
            {
                Debug("Securities added: " + string.Join(",", changes.AddedSecurities.Select(x => x.Symbol.Value)));
            }
            if (changes.RemovedSecurities.Count > 0)
            {
                Debug("Securities removed: " + string.Join(",", changes.RemovedSecurities.Select(x => x.Symbol.Value)));
            }
        }
    }
}