| Overall Statistics |
|
Total Trades 8143 Average Win 0.88% Average Loss -0.88% Compounding Annual Return -22.421% Drawdown 96.900% Expectancy -0.063 Net Profit -93.887% Sharpe Ratio -0.432 Loss Rate 53% Win Rate 47% Profit-Loss Ratio 1.00 Alpha 0.312 Beta -24.352 Annual Standard Deviation 0.399 Annual Variance 0.159 Information Ratio -0.482 Tracking Error 0.399 Treynor Ratio 0.007 Total Fees $104976.45 |
from dateutil.relativedelta import relativedelta
class PuppiesOfTheDow(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2008, 1, 1) # Set Start Date
self.SetEndDate(2019, 1, 1) # Set Start Date
self.SetCash(100000) # Set Strategy Cash
self.UniverseSettings.Resolution = Resolution.Daily
self.coarseUniverseSize = 500
self.portfolioSize = 5
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
self.selectUniverse = True
self.monthCounter = 0
self.lastRebalancing = self.Time - relativedelta(years=1)
def CoarseSelectionFunction(self, coarse):
if self.lastRebalancing.year != self.Time.year:
selected = [x for x in coarse if (x.HasFundamentalData) and (float(x.Price) > 5)]
filtered = sorted(selected, key=lambda x: x.DollarVolume, reverse=True)
self.filteredCoarse = [ x.Symbol for x in filtered[:self.coarseUniverseSize]]
return self.filteredCoarse
else:
return self.filteredCoarse
def FineSelectionFunction(self, fine):
if self.lastRebalancing.year != self.Time.year:
dogsOfTheDow = sorted(fine, key = lambda x: x.ValuationRatios.TotalYield, reverse=True)
puppiesOfTheDow = sorted(dogsOfTheDow, key = lambda x: x.Price)[:self.portfolioSize]
self.filteredFine = [x.Symbol for x in puppiesOfTheDow]
return self.filteredFine
else:
return self.filteredFine
def OnData(self, data):
pass
def OnSecuritiesChanged(self, changes):
for shortStock in changes.RemovedSecurities: self.Liquidate(shortStock.Symbol)
for longStock in changes.AddedSecurities: self.SetHoldings(longStock.Symbol,1/self.portfolioSize)