Overall Statistics |
Total Trades 991 Average Win 1.25% Average Loss -0.86% Compounding Annual Return 8.887% Drawdown 38.300% Expectancy 0.178 Net Profit 53.104% Sharpe Ratio 0.335 Loss Rate 52% Win Rate 48% Profit-Loss Ratio 1.45 Alpha 0.071 Beta 0.68 Annual Standard Deviation 0.435 Annual Variance 0.19 Information Ratio 0.083 Tracking Error 0.427 Treynor Ratio 0.215 Total Fees $4525.23 |
using System.Collections.Concurrent; namespace QuantConnect { 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); SetCash(100*1000); 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.Price) // 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) { Liquidate(security.Symbol); } } // 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; } } }