Hello,
So I am trying to code an algorithm that trades stocks that are in the best-performing sectors relative to SPY.
For this I want to rank the relative returns of the sector etfs and then go on and trade stocks that are in those sectors.
To rank the sector etfs I created a function called rank that takes the etf dictionary with log returns.
My problem is when I try to populate this etf dictionary. The key for the etf is a “None” object because I am doing something wrong with the Log Return Indicator.
I've had a very hard creating a Custome Indicator and I still get the same error when I use the Automatic Indicator so I wonder what is the best way to approach this problem.
# region imports
from AlgorithmImports import *
from sectorcodes import sectorcodes
# endregion
#sector codes is just a dictionary ex. sectorcodes = {
"MOO":"Agriculture",
..."ITB":"BuildingMateria#ls"}
class FatYellowGreenGoat(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2021, 7, 26)
self.SetEndDate(2021, 10, 26)
self.SetCash(100000)
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
self.Data = {}
barPeriod = 15
for etf in sectorcodes.keys():
symbol = self.AddEquity(etf,Resolution.Daily).Symbol
self.rollingwindow = RollingWindow[TradeBar](barPeriod)
self.EnableAutomaticIndicatorWarmUp = True
self.logr = self.LOGR(etf,barPeriod)
self.logr.Updated += self.LogrUpdated
self.logr_window = RollingWindow[IndicatorDataPoint](barPeriod)
self.Data[etf] = self.logr_window
def LogrUpdated(self,sender,updated):
self.logr_window.Add(updated)
def OnData(self, data: Slice):
for etf in sectorcodes.keys():
self.rollingwindow.Add(data[etf])
def rank(self):
if not self.logr_window.IsReady: return
temp = {}
codeswospy = sectorcodes.copy()
codeswospy.pop('SPY')
for etf in codeswospy.keys():
temp[etf] = sum(self.Data[etf]) / sum(self.Data['SPY'])
sorted_rank = (sorted(temp.items(), key=lambda item: item[1], reverse =True))
sorted_etfs = list(sorted_rank.keys())
return sorted_etfs[:3]
def CoarseSelectionFunction(self,coarse):
sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
byprice = [x for x in sortedByDollarVolume if x.DollarVolume > 1000000 and x.Price > 5 and x.HasFundamentalData]
byprice = [x.Symbol for x in byprice][:1000]
return byprice
def FineSelectionFunction(self,fine):
sorted_etfs = self.rank()
sector1 = sorted_etfs[0]
sector2 = sorted_etfs[1]
sector3 = sorted_etfs[2]
fineUniverse = [x for x in fine if x.AssetClassification.MorningstarIndustryGroupCode == MorningstarIndustryGroupCode.sector1
or x.AssetClassification.MorningstarIndustryGroupCode == MorningstarIndustryGroupCode.sector2
or x.AssetClassification.MorningstarIndustryGroupCode == MorningstarIndustryGroupCode.sector3 and x.MarketCap > 5e7]
tickerSymbolValuesOnly = [c.Symbol for c in fineUniverse]
def OnSecuritiesChanged(self, changes):
for security in changes.AddedSecurities:
self.MarketOrder(security,1)
Louis Szeto
Hi Stelios
There were quite a number of issues in the code snippets:
Please check carefully on the syntax. Good luck!
Best
Louis
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Stelios Sbilis
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.
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