Hi,
I'm trying to get a pandas dataframe of historical prices for the last year from my universe.
Right now I'm getting the error:
During the algorithm initialization, the following exception has occurred: Framework algorithms must specify a portfolio selection model using the 'UniverseSelection' property.
How would I fix this?
Some other questions I have are:
How would I get historical prices from the Universe?
Could I filter based on historical prices in the fine selection function?
Here is what I have so far.
from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Common")
from System import *
from QuantConnect import *
from QuantConnect.Orders import *
from QuantConnect.Algorithm import *
from QuantConnect.Algorithm.Framework import *
from QuantConnect.Algorithm.Framework.Alphas import *
from QuantConnect.Algorithm.Framework.Portfolio import *
from QuantConnect.Algorithm.Framework.Selection import *
from datetime import datetime, timedelta
class HistoricalPriceUniverse(QCAlgorithmFramework):
def Initialize(self):
self.UniverseSettings.Resolution = Resolution.Daily
self.SetStartDate(2017, 1, 1)
self.SetEndDate(2017, 2, 1)
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
self._changes = None
def CoarseSelectionFunction(self, coarse):
filtered = [ x.Symbol for x in coarse if x.HasFundamentalData ]
return filtered[:]
def FineSelectionFunction(self, fine):
# for possible future implimentation
return fine
def OnData(self, data):
# liquidate securities that were removed from universe
for security in self._changes.RemovedSecurities:
self.Liquidate(security.symbol)
# This is where I would like to get dataframe of historical prices for securities
self.Debug(Portfolio.keys)
self._changes = none
# this event fires whenever we have changes to our universe
def OnSecuritiesChanged(self, changes):
self._changes = changes
self.Log(f"OnSecuritiesChanged({self.UtcTime}):: {changes}")
Jack Simonson
Hi,
When using a Framework Algorithm, you need to specify specific models for each aspect -- Universe Selection, Alpha Model, Portfolio Construction, Execution Model, and Risk Management, and so for these algorithms, using self.AddUniverse() won't work. If you want to use Coarse/Fine Universe selection, then you'd want to employ the FineFundamentalSelectionModel. You can see the basic implementation of this in the code I've attached below.
class BasicTemplateFrameworkAlgorithm(QCAlgorithmFramework): def Initialize(self): # Set requested data resolution self.UniverseSettings.Resolution = Resolution.Minute self.SetStartDate(2018, 9, 27) #Set Start Date self.SetEndDate(2019, 3, 27) #Set End Date self.SetCash(100000) #Set Strategy Cash self.__numberOfSymbols = 100 self.__numberOfSymbolsFine = 5 self.SetUniverseSelection(FineFundamentalUniverseSelectionModel(self.CoarseSelectionFunction, self.FineSelectionFunction, None, None)) self.SetAlpha(NullAlphaModel()) self.SetPortfolioConstruction(NullPortfolioConstructionModel()) self.SetExecution(NullExecutionModel()) self.SetRiskManagement(NullRiskManagementModel()) # sort the data by daily dollar volume and take the top 'NumberOfSymbols' def CoarseSelectionFunction(self, coarse): # sort descending by daily dollar volume sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True) # return the symbol objects of the top entries from our sorted collection return [ x.Symbol for x in sortedByDollarVolume[:self.__numberOfSymbols] ] # sort the data by P/E ratio and take the top 'NumberOfSymbolsFine' def FineSelectionFunction(self, fine): # sort descending by P/E ratio sortedByPeRatio = sorted(fine, key=lambda x: x.ValuationRatios.PERatio, reverse=True) # take the top entries from our sorted collection return [ x.Symbol for x in sortedByPeRatio[:self.__numberOfSymbolsFine] ] def OnOrderEvent(self, orderEvent): if orderEvent.Status == OrderStatus.Filled: # self.Debug("Purchased Stock: {0}".format(orderEvent.Symbol)) pass
N8
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