| Overall Statistics |
|
Total Trades 64 Average Win 13.28% Average Loss -3.21% Compounding Annual Return 31.607% Drawdown 35.300% Expectancy 1.474 Net Profit 161.592% Sharpe Ratio 0.969 Loss Rate 52% Win Rate 48% Profit-Loss Ratio 4.14 Alpha 0.255 Beta 0.576 Annual Standard Deviation 0.341 Annual Variance 0.116 Information Ratio 0.59 Tracking Error 0.337 Treynor Ratio 0.574 Total Fees $772.00 |
# https://quantpedia.com/Screener/Details/25
class SmallCapInvestmentAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2016, 1, 1)
self.SetEndDate(2019, 7, 1)
self.SetCash(100000)
self.UniverseSettings.Resolution = Resolution.Daily
self.count = 10
self.year = -1
self.symbols = []
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
def CoarseSelectionFunction(self, coarse):
''' Drop stocks which have no fundamental data or have low price '''
if self.year == self.Time.year:
return self.symbols
return [x.Symbol for x in coarse if x.HasFundamentalData and x.Price > 5]
def FineSelectionFunction(self, fine):
''' Selects the stocks by lowest market cap '''
if self.year == self.Time.year:
return self.symbols
market_cap = {}
# Calculate the market cap and add the "MarketCap" property to fine universe object
for i in fine:
market_cap[i] = (i.EarningReports.BasicAverageShares.ThreeMonths *
i.EarningReports.BasicEPS.TwelveMonths *
i.ValuationRatios.PERatio)
sorted_market_cap = sorted([x for x in fine if market_cap[x] > 0], key=lambda x: market_cap[x])
self.symbols = [i.Symbol for i in sorted_market_cap[:self.count]]
return self.symbols
def OnData(self, data):
if self.year == self.Time.year:
return
self.year = self.Time.year
for symbol in self.symbols:
self.SetHoldings(symbol, 1.0/self.count)
def OnSecuritiesChanged(self, changes):
''' Liquidate the securities that were removed from the universe '''
for security in changes.RemovedSecurities:
symbol = security.Symbol
if self.Portfolio[symbol].Invested:
self.Liquidate(symbol)