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Universe selection - EMA + Volume

Hello,

I'm interested in adding volume to the EMA universe selection, but I am having trouble. Any help is appreciated. I basically tried to mesh the volume example on the Universe page with the EMA code. The error I'm getting is;

Runtime Error: UnboundLocalError : local variable 'x' referenced before assignment
at CoarseSelectionFunction in main.py:line 55
UnboundLocalError : local variable 'x' referenced before assignment (Open Stacktrace)

from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Indicators")
AddReference("QuantConnect.Common")

from System import *
from QuantConnect import *
from QuantConnect.Data import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
from System.Collections.Generic import List

### <summary>
### In this algorithm we demonstrate how to perform some technical analysis as
### part of your coarse fundamental universe selection
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="indicators" />
### <meta name="tag" content="universes" />
### <meta name="tag" content="coarse universes" />
class EmaCrossUniverseSelectionAlgorithm(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(2020,1,1) #Set Start Date
self.SetEndDate(2020,7,22) #Set End Date
self.SetCash(100000) #Set Strategy Cash

self.UniverseSettings.Resolution = Resolution.Daily
#self.UniverseSettings.Leverage = 2

self.coarse_count = 10
self.averages = { };

# this add universe method accepts two parameters:
# - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>
self.AddUniverse(self.CoarseSelectionFunction)


# sort the data by daily dollar volume and take the top 'NumberOfSymbols'
def CoarseSelectionFunction(self, coarse):

# We are going to use a dictionary to refer the object that will keep the moving averages
for cf in coarse:
if cf.Symbol not in self.averages:
self.averages[cf.Symbol] = SymbolData(cf.Symbol)

# Updates the SymbolData object with current EOD price
avg = self.averages[cf.Symbol]
avg.update(cf.EndTime, cf.AdjustedPrice, cf.DollarVolume)

# Filter the values of the dict: we only want up-trending securities
values = list(filter(lambda x: x.is_uptrend, x.volume>1000000, self.averages.values()))

# Sorts the values of the dict: we want those with greater difference between the moving averages
values.sort(key=lambda x: x.scale, reverse=True)

for x in values[:self.coarse_count]:
self.Log('symbol: ' + str(x.symbol.Value) + ' scale: ' + str(x.scale))

# we need to return only the symbol objects
return [ x.symbol for x in values[:self.coarse_count] ]

# this event fires whenever we have changes to our universe
def OnSecuritiesChanged(self, changes):
# liquidate removed securities
for security in changes.RemovedSecurities:
if security.Invested:
self.Liquidate(security.Symbol)

# we want 20% allocation in each security in our universe
for security in changes.AddedSecurities:
self.SetHoldings(security.Symbol, 0.1)


class SymbolData(object):
def __init__(self, symbol):
self.symbol = symbol
self.tolerance = 1.01
self.fast = ExponentialMovingAverage(100)
self.slow = ExponentialMovingAverage(300)
self.is_uptrend = False
self.scale = 0
self.volume=0

def update(self, time, value, volume):
if self.fast.Update(time, value) and self.slow.Update(time, value):
fast = self.fast.Current.Value
slow = self.slow.Current.Value
self.is_uptrend = fast > slow * self.tolerance

if self.is_uptrend:
self.scale = (fast - slow) / ((fast + slow) / 2.0)
self.volume= volume

 

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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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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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