Overall Statistics |
Total Trades 6 Average Win 0% Average Loss -0.32% Compounding Annual Return -32.824% Drawdown 7.400% Expectancy -1 Net Profit -3.217% Sharpe Ratio -0.75 Loss Rate 100% Win Rate 0% Profit-Loss Ratio 0 Alpha -0.444 Beta 2.358 Annual Standard Deviation 0.346 Annual Variance 0.119 Information Ratio -1.285 Tracking Error 0.262 Treynor Ratio -0.11 Total Fees $6.99 |
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))); } } } }