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Nathan,

I was looking for something similar check out this thread it says removing but there is a piece of code that appends symbols at the end of the fine filter is that what you tried?

Frank

Do the symbols have to be added directly to the fine universe? In order of your universe they are passed like this: Coarse -> Fine -> OnSecuritiesChanged -> OnData/Sceduled event.  Is it a list of symbols you just want to be available for trading along with those selected by the FineSelectionFunction?

Frank Giardina - Thanks for the link. I've gone through and retried this with success! The difference that solved it for me was using 

self.spy = Symbol.Create('SPY', SecurityType.Equity, Market.USA) 

rather than the usual 

self.spy = self.AddEquity("SPY", Resolution.Minute)

For anyone coming across this post in the future, here is a more complete example of a solution to refer to than the one in the thread linked above:

class MomentumEffAlgorithm(QCAlgorithm):

def Initialize(self):

self.SetStartDate(2020, 1, 1) # Set Start Date
self.SetEndDate(2020, 11, 1) # Set End Date
self.SetCash(100000) # Set Strategy Cash

# Create your symbols here
self.agg = Symbol.Create('AGG', SecurityType.Equity, Market.USA)
self.tlt = Symbol.Create('TLT', SecurityType.Equity, Market.USA)
self.ief = Symbol.Create('IEF', SecurityType.Equity, Market.USA)
self.iei = Symbol.Create('IEI', SecurityType.Equity, Market.USA)

# Add your symbols to a list
self.addedsymbols = [self.agg, self.tlt, self.ief, self.iei]

self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)

def CoarseSelectionFunction(self, coarse):

self.selected = sorted([x for x in coarse if x.HasFundamentalData and x.Price > 5],
key=lambda x: x.DollarVolume, reverse=True)

self.coarselist = [x.Symbol for x in self.selected[:100]]

for symbol in self.addedsymbols:
self.coarselist.append(symbol)

return self.coarselist

def FineSelectionFunction(self, fine):

fine = [f for f in fine if f.ValuationRatios.PERatio > 0
and f.EarningReports.BasicEPS.TwelveMonths > 0
and f.EarningReports.BasicAverageShares.ThreeMonths > 0
]

self.selectedfine = sorted(fine,
key=lambda f: f.ValuationRatios.PERatio *
f.EarningReports.BasicEPS.TwelveMonths *
f.EarningReports.BasicAverageShares.ThreeMonths,
reverse=True)

self.finesymbols = [x.Symbol for x in self.selectedfine[:50]]

for symbol in self.addedsymbols:
self.finesymbols.append(symbol)

return self.finesymbols

No updates to OnSecurities changed or OnData are necessary. The symbols are there!

*Note* - not sure why the formatting is screwed up, I'll repost it below with the correct indentation.

I am curious on how I would go about creating an algorithm that deals with a manual list of stocks. So say I created a list of APPL, MSFT and TSLA but only wanted to invest in the stock with the lowest pe ratio out of the three listed. How would I create an algo that picks only the security with the lowest pe ratio. Without distributing the portfolio equally so lets say MSFT has the lowest pe ratio the algo would select microsoft and Invest the whole portfolio in MSFT leaving the other securities untouched.

Hi Bryson Long,
To receive fundamental data during a backtest, we must use a fine universe filter. Just return the 3 assets during universe selection in this case. We now use OnData to scan for signal
Fundamental data can then be accessed through the stock's Security object. See the source code for reference. Refer to the attached backtest.
Best,
Varad Kabade

Thanks Varad, but how do you make it trade?

Hi Bryson Long,
Refer to the following algorithm where we buy the security with the highest ratio.
Best,
Varad Kabade