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 -1.158 Tracking Error 0.663 Treynor Ratio 0 Total Fees $0.00 Estimated Strategy Capacity $0 Lowest Capacity Asset |
import numpy as np class WellDressedOrangeGoshawk(QCAlgorithm): def Initialize(self): self.SetStartDate(2021, 1, 1) self.SetEndDate(2021, 9, 27) self.SetCash(100000) self.SetBrokerageModel(BrokerageName.Bitfinex, AccountType.Cash) self.UniverseSettings.Resolution = Resolution.Daily self.AddUniverse(self.Universe.DollarVolume.Top(100)) self.selected = None def CoarseSelectionFunction(self, coarse): #1. Sort coarse by dollar volume sortedByMarketCap = sorted(coarse, key=lambda c: c.MarketCapitalization, reverse=True) #2. Filter out the stocks less than $10 and return selected self.selected = self.Universe.MarketCap.Top(10) return self.selected def OnSecuritiesChanged(self, changes): # liquidate removed securities for security in changes.RemovedSecurities: if security.Invested: self.Liquidate(security.Symbol) # we want 10% allocation in each security in our universe for security in changes.AddedSecurities: self.SetHoldings(security.Symbol, 0.1) self.Debug(security.Symbol)