Overall Statistics |
Total Trades 0 Average Win 0% Average Loss 0% Compounding Annual Return 0% Drawdown 0% Expectancy 0 Net Profit 0% Sharpe Ratio 0 Probabilistic Sharpe Ratio 0% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio -5.809 Tracking Error 0.114 Treynor Ratio 0 Total Fees $0.00 |
class TestAlgo(QCAlgorithm): def Initialize(self): self.SetStartDate(2018, 5, 28) self.SetEndDate(2018, 6, 9) self.SetWarmUp(10) self.SetCash(10000) # Universe selection settings self.UniverseSettings.Resolution = Resolution.Daily self.UniverseSettings.DataNormalizationMode = DataNormalizationMode.Adjusted self.UniverseSettings.ExtendedMarketHours = False self.AddUniverseSelection( FineFundamentalUniverseSelectionModel(self.SelectCoarse, self.SelectFine) ) # Other initialization code def OnData(self, data): '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here. Arguments: data: Slice object keyed by symbol containing the stock data ''' def SelectCoarse(self, coarse): return Universe.Unchanged def SelectFine(self, fine): return self.symbols
from clr import AddReference AddReference("System") AddReference("QuantConnect.Common") AddReference("QuantConnect.Algorithm.Framework") from QuantConnect.Data.UniverseSelection import * from QuantConnect.Algorithm.Framework import * from QuantConnect.Algorithm.Framework.Selection import FineFundamentalUniverseSelectionModel class MyUniverseModel(FineFundamentalUniverseSelectionModel): def __init__(self): super().__init__(coarseSelector=self.SelectCoarse, fineSelector=self.SelectFine) def SelectCoarse(self, coarse): tickers = ["AAPL", "AIG", "IBM"] return [Symbol.Create(x, SecurityType.Equity, Market.USA) for x in tickers] def SelectFine(self, fine): return [f.Symbol for f in fine]