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import numpy as np
### <summary>
### Basic template algorithm simply initializes the date range and cash. This is a skeleton
### framework you can use for designing an algorithm.
### </summary>
class BasicTemplateAlgorithm(QCAlgorithm):
def Initialize(self):
'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
self.SetStartDate(2014,1,1) #Set Start Date
self.SetEndDate(2015,1,1) #Set End Date
self.SetCash(50000) #Set Strategy Cash
self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage)
# what resolution should the data *added* to the universe be?
self.UniverseSettings.Resolution = Resolution.Daily
# this add universe method accepts two parameters:
# - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>
# - fine selection function: accepts an IEnumerable<FineFundamental> and returns an IEnumerable<Symbol>
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
self.__numberOfSymbols = 5
self.__numberOfSymbolsFine = 2
self._changes = None
self.stateData = {}
# 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)
for cf in sortedByDollarVolume:
if cf.Symbol not in self.stateData:
self.stateData[cf.Symbol] = SelectionData(cf.Symbol, 20, 4)
# update the SelectionData object with current EOD price
breaches = self.stateData[cf.Symbol]
breaches.update(cf.EndTime, cf.AdjustedPrice)
# Filter the values of the dict: we only want positive breaches
values = list(filter(lambda x: x.pos_breach, self.stateData.Values()))
# look for upper breach of the keltner channel
# return the symbol objects of the top entries from our sorted collection
return [ x.Symbol for x in values[: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 OnData(self, data):
self.Log(f"OnData({self.UtcTime}): Keys: {', '.join([key.Value for key in self.Securities.Keys if self.Securities[key].Invested])}")
# if we have no changes, do nothing
if self._changes is None: return
# liquidate removed securities
for security in self._changes.RemovedSecurities:
if security.Invested:
self.Liquidate(security.Symbol)
self.Log(f"Liquidated: {security.Symbol}")
# we want 20% allocation in each security in our universe
for security in self._changes.AddedSecurities:
self.SetHoldings(security.Symbol, 0.2)
self.Log(f"SetHoldings: {security.Symbol}")
self._changes = None
# this event fires whenever we have changes to our universe
def OnSecuritiesChanged(self, changes):
self._changes = changes
class SelectionData(object):
def __init__(self, symbol, period, k):
self.symbol = symbol
self.keltner = KeltnerChannels(period, k)
self.pos_breach = False
self.neg_breach = False
def update(self, time, price):
if self.keltner.Update(time, price):
if price > self.keltner:
self.pos_breach = True
self.neg_breach = False
elif price < self.keltner:
self.pos_breach = False
self.neg_breach = True
else:
self.pos_breach = False
self.neg_breach = FalseI am trying to learn how to use the AddUniverse method. I am using a combination of coarse and fine selection functions. I have created SelectionData class which includes the KeltnerChannel indicator. When I run the algorithm, the compiler reports an error stating the KelternChannel should be updated using IBaseDataBar. I am calling the SymbolData update method from a CoarseSelectionFunction. The coarse parameter does not seem to contain a Bar property. How can I access IBaseDataBar from within the CoarseSelectionFunction?
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