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* 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
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* See the License for the specific language governing permissions and
* limitations under the License.
using System;
using System.Collections.Concurrent;
using System.Linq;
using QuantConnect.Data.Market;
using QuantConnect.Data.UniverseSelection;
using QuantConnect.Indicators;
namespace QuantConnect.Algorithm.CSharp
/// <summary>
/// In this algorithm we demonstrate how to perform some technical analysis as
/// part of your coarse fundamental universe selection
/// </summary>
/// <meta name="tag" content="using data" />
/// <meta name="tag" content="indicators" />
/// <meta name="tag" content="universes" />
/// <meta name="tag" content="coarse universes" />
public class EmaCrossUniverseSelectionAlgorithm : QCAlgorithm
// tolerance to prevent bouncing
const decimal Tolerance = 0.01m;
private const int Count = 10;
// use Buffer+Count to leave a little in cash
private const decimal TargetPercent = 0.1m;
private SecurityChanges _changes = SecurityChanges.None;
// holds our coarse fundamental indicators by symbol
private readonly ConcurrentDictionary<Symbol, SelectionData> _averages = new ConcurrentDictionary<Symbol, SelectionData>();
// class used to improve readability of the coarse selection function
private class SelectionData
public readonly ExponentialMovingAverage Fast;
public readonly ExponentialMovingAverage Slow;
public SelectionData()
Fast = new ExponentialMovingAverage(100);
Slow = new ExponentialMovingAverage(300);
// computes an object score of how much large the fast is than the slow
public decimal ScaledDelta
get { return (Fast - Slow)/((Fast + Slow)/2m); }
// updates the EMA50 and EMA100 indicators, returning true when they're both ready
public bool Update(DateTime time, decimal value)
return Fast.Update(time, value) && Slow.Update(time, value);
/// <summary>
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
/// </summary>
public override void Initialize()
UniverseSettings.Leverage = 2.0m;
UniverseSettings.Resolution = Resolution.Daily;
SetStartDate(2010, 01, 01);
SetEndDate(2015, 01, 01);
AddUniverse(coarse =>
return (from cf in coarse
// grab th SelectionData instance for this symbol
let avg = _averages.GetOrAdd(cf.Symbol, sym => new SelectionData())
// Update returns true when the indicators are ready, so don't accept until they are
where avg.Update(cf.EndTime, cf.AdjustedPrice)
// only pick symbols who have their 50 day ema over their 100 day ema
where avg.Fast > avg.Slow*(1 + Tolerance)
// prefer symbols with a larger delta by percentage between the two averages
orderby avg.ScaledDelta descending
// we only need to return the symbol and return 'Count' symbols
select cf.Symbol).Take(Count);
/// <summary>
/// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
/// </summary>
/// <param name="data">TradeBars dictionary object keyed by symbol containing the stock data</param>
public void OnData(TradeBars data)
if (_changes == SecurityChanges.None) return;
// liquidate securities removed from our universe
foreach (var security in _changes.RemovedSecurities)
if (security.Invested)
// we'll simply go long each security we added to the universe
foreach (var security in _changes.AddedSecurities)
SetHoldings(security.Symbol, TargetPercent);
/// <summary>
/// Event fired each time the we add/remove securities from the data feed
/// </summary>
/// <param name="changes">Object containing AddedSecurities and RemovedSecurities</param>
public override void OnSecuritiesChanged(SecurityChanges changes)
_changes = changes;