/*
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
using System.Collections.Generic;
using System.Linq;
using QuantConnect.Data.Fundamental;
using QuantConnect.Data.Market;
using QuantConnect.Data.UniverseSelection;
namespace QuantConnect.Algorithm.CSharp
{
/// <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 = 10;
private const int NumberOfSymbolsFine = 2;
// initialize our changes to nothing
private SecurityChanges _changes = SecurityChanges.None;
public override void Initialize()
{
UniverseSettings.Resolution = Resolution.Daily;
SetStartDate(2016, 01, 01);
SetCash(100000);// Take the top 50 by dollar volume using coarse
// 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);
}
// filter the data by price > 10 and sort by daily dollar volume and take the top 50
public IEnumerable<Symbol> CoarseSelectionFunction(IEnumerable<CoarseFundamental> coarse)
{
return coarse
.Where(x => x.Price > 10)
.OrderByDescending(x => x.DollarVolume)
.Take(50)
.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)));
}
}
}
}