| Overall Statistics |
|
Total Orders 724 Average Win 0.88% Average Loss -0.92% Compounding Annual Return 10.358% Drawdown 50.400% Expectancy 0.104 Start Equity 100000 End Equity 121822.70 Net Profit 21.823% Sharpe Ratio 0.394 Sortino Ratio 0.46 Probabilistic Sharpe Ratio 14.364% Loss Rate 44% Win Rate 56% Profit-Loss Ratio 0.96 Alpha -0.054 Beta 1.238 Annual Standard Deviation 0.422 Annual Variance 0.178 Information Ratio -0.036 Tracking Error 0.336 Treynor Ratio 0.134 Total Fees $875.52 Estimated Strategy Capacity $1200000.00 Lowest Capacity Asset HOL XI5N30EK4UP1 Portfolio Turnover 4.00% |
# region imports
# setting up the universe of stocks
# keep mind of the selection bias
# Trading Strategy: stock cap stocks have outperformed large cap stocks; 200 most liquid stocks and then take bottom 10
# Rebalance every month
from AlgorithmImports import *
# endregion
class SwimmingAsparagusTermite(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2020, 1, 1) # Set Start Date
self.SetEndDate(2022, 1, 1)
self.SetCash(100000)
# rebalance time = minimum of set date
self.rebalanceTime = datetime.min
self.activeStocks = set()
# add to universe of stock with coarse filter and fine filter
self.AddUniverse(self.CoarseFilter, self.FineFilter)
self.UniverseSettings.Resolution = Resolution.Hour
self.portfolioTargets = []
def CoarseFilter(self, coarse):
# no need to rebalance within 1 month
if self.Time <= self.rebalanceTime:
return self.Universe.Unchanged
self.rebalanceTime = self.Time + timedelta(30) # add one month ahead
# filter stocks by dollarvolume
sortedByDollarVolume = sorted(coarse, key = lambda x: x.DollarVolume, reverse = True)
return [x.Symbol for x in sortedByDollarVolume if x.Price > 10 and x.HasFundamentalData][:200]
def FineFilter(self, fine):
sortedByPE = sorted(fine, key = lambda x: x.MarketCap)
return [x.Symbol for x in sortedByPE if x.MarketCap > 0][:20]
def OnSecuritiesChanged(self, changes):
for x in changes.RemovedSecurities:
self.Liquidate(x.Symbol)
self.activeStocks.remove(x.Symbol)
for x in changes.AddedSecurities:
self.activeStocks.add(x.Symbol)
self.portfolioTargets = [PortfolioTarget(symbol, 1/len(self.activeStocks)) for symbol in self.activeStocks]
def OnData(self, data: Slice):
if self.portfolioTargets == []:
return
for symbol in self.activeStocks:
if symbol not in data:
return
self.SetHoldings(self.portfolioTargets)
self.portfolioTargets = []