| Overall Statistics |
|
Total Trades 785 Average Win 0.38% Average Loss -0.40% Compounding Annual Return 11.764% Drawdown 31.700% Expectancy 0.032 Net Profit 39.563% Sharpe Ratio 0.564 Loss Rate 47% Win Rate 53% Profit-Loss Ratio 0.96 Alpha 0.235 Beta -7.539 Annual Standard Deviation 0.197 Annual Variance 0.039 Information Ratio 0.481 Tracking Error 0.197 Treynor Ratio -0.015 Total Fees $796.88 |
from QuantConnect.Data.UniverseSelection import *
import operator
from math import ceil,floor
class CoarseFineFundamentalComboAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2015, 1, 1) #Set Start Date
self.SetEndDate(2018, 1, 1) #Set End Date
self.SetCash(100000) #Set Strategy Cash
self.flag1 = 1
self.flag2 = 0
self.flag3 = 0
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
self.AddEquity("SPY")
self.__numberOfSymbols = 100
self.__numberOfSymbolsFine = 50
self.num_portfolios = 5
self._changes = None
self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY"), Action(self.Rebalancing))
def CoarseSelectionFunction(self, coarse):
if self.flag1 == 2:
CoarseWithFundamental = [x for x in coarse if x.HasFundamentalData]
sortedByDollarVolume = sorted(CoarseWithFundamental, key=lambda x: x.DollarVolume, reverse=True)
top = sortedByDollarVolume[:self.__numberOfSymbols]
return [x.Symbol for x in top]
else:
return [x.Symbol for x in self.topFine]
def FineSelectionFunction(self, fine):
if self.flag1 == 2:
self.flag1 = 0
self.flag2 = 1
filtered_fine = [x for x in fine if x.OperationRatios.OperationMargin.Value
and x.ValuationRatios.PriceChange1M
and x.ValuationRatios.BookValuePerShare]
sortedByfactor1 = sorted(filtered_fine, key=lambda x: x.OperationRatios.OperationMargin.Value, reverse=True)
sortedByfactor2 = sorted(filtered_fine, key=lambda x: x.ValuationRatios.PriceChange1M, reverse=True)
sortedByfactor3 = sorted(filtered_fine, key=lambda x: x.ValuationRatios.BookValuePerShare, reverse=True)
num_stocks = floor(len(filtered_fine)/self.num_portfolios)
self.Log(str(num_stocks))
stock_dict = {}
for i,ele in enumerate(sortedByfactor1):
rank1 = i
rank2 = sortedByfactor2.index(ele)
rank3 = sortedByfactor3.index(ele)
score = sum([rank1*0.2,rank2*0.4,rank3*0.4])
stock_dict[ele] = score
self.sorted_stock = sorted(stock_dict.items(), key=lambda d:d[1],reverse=False)
sorted_symbol = [self.sorted_stock[i][0] for i in range(len(self.sorted_stock))]
self.topFine = sorted_symbol[:self.__numberOfSymbolsFine]
self.flag3 += 1
return [x.Symbol for x in self.topFine]
else:
return [x.Symbol for x in self.topFine]
def OnData(self, data):
if self.flag3 > 0:
if self.flag2 == 1:
self.flag2 = 0
# if we have no changes, do nothing
if self._changes == None: return
# liquidate removed securities
for security in self._changes.RemovedSecurities:
if security.Invested:
self.Liquidate(security.Symbol)
for security in self._changes.AddedSecurities:
self.SetHoldings(security.Symbol, 1/float(len(self._changes.AddedSecurities)))
self._changes = None
# this event fires whenever we have changes to our universe
def OnSecuritiesChanged(self, changes):
self._changes = changes
def Rebalancing(self):
self.flag1 += 1