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Accessing IBaseDataBar from CoarseSelectionFunction

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 = False

I 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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did you try to use history to get some bars? maybe that works.

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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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