| Overall Statistics |
|
Total Trades 39440 Average Win 0.01% Average Loss -0.01% Compounding Annual Return -3.207% Drawdown 24.300% Expectancy -0.064 Net Profit -16.645% Sharpe Ratio -0.302 Probabilistic Sharpe Ratio 0.007% Loss Rate 45% Win Rate 55% Profit-Loss Ratio 0.69 Alpha -0.018 Beta -0.022 Annual Standard Deviation 0.066 Annual Variance 0.004 Information Ratio -0.624 Tracking Error 0.17 Treynor Ratio 0.897 Total Fees $125321.13 Estimated Strategy Capacity $5000.00 Lowest Capacity Asset HHS R735QTJ8XC9X |
#region imports
from AlgorithmImports import *
#endregion
# https://quantpedia.com/Screener/Details/199
class ROAEffectWithinStocks(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2015, 1, 1)
self.SetEndDate(2020, 8, 1)
self.SetCash(10000000)
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
self.AddEquity("SPY", Resolution.Daily)
self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY"), self.Rebalance)
self.monthly_rebalance = False
self.coarse = False
def CoarseSelectionFunction(self, coarse):
if self.monthly_rebalance:
self.coarse = True
filteredCoarse = [x.Symbol for x in coarse if x.HasFundamentalData]
return filteredCoarse
else:
return Universe.Unchanged
def FineSelectionFunction(self, fine):
if self.monthly_rebalance:
fine =[i for i in fine if i.EarningReports.BasicAverageShares.ThreeMonths != 0
and i.EarningReports.BasicEPS.TwelveMonths != 0
and i.ValuationRatios.PERatio != 0
# sales is greater than 10 million
and i.ValuationRatios.SalesPerShare*i.EarningReports.DilutedAverageShares.Value > 10000000
and i.OperationRatios.ROA.Value != 0]
# sort into 2 halfs based on market capitalization
sorted_market_cap = sorted(fine, key = lambda x:x.MarketCap, reverse=True)
top = sorted_market_cap[:int(len(sorted_market_cap)*0.5)]
bottom = sorted_market_cap[-int(len(sorted_market_cap)*0.5):]
# each half is then divided into deciles based on Return on Assets (ROA)
sortedTopByROA = sorted(top, key = lambda x: x.OperationRatios.ROA.Value, reverse = True)
sortedBottomByROA = sorted(bottom, key = lambda x: x.OperationRatios.ROA.Value, reverse = True)
# long top decile from each market capitalization group
long_ = sortedTopByROA[:int(len(sortedTopByROA)*0.1)] + sortedBottomByROA[:int(len(sortedTopByROA)*0.1)]
self.longStocks = [i.Symbol for i in long_]
# short bottom decile from each market capitalization group
short = sortedTopByROA[-int(len(sortedTopByROA)*0.1):] + sortedBottomByROA[-int(len(sortedTopByROA)*0.1):]
self.shortStocks = [i.Symbol for i in short]
return self.longStocks + self.shortStocks
else:
return Universe.Unchanged
def Rebalance(self):
self.monthly_rebalance = True
def OnData(self, data):
if not (self.monthly_rebalance and self.coarse): return
self.coarse = False
self.monthly_rebalance = False
stocks_invested = [x.Key for x in self.Portfolio if x.Value.Invested]
for i in stocks_invested:
if i not in self.longStocks+self.shortStocks:
self.Liquidate(i)
long_weight = 0.5/len(self.longStocks)
for i in self.longStocks:
self.SetHoldings(i, long_weight)
short_weight = 0.5/len(self.shortStocks)
for i in self.shortStocks:
self.SetHoldings(i, -short_weight)