| 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)