Can someone help with the error message shown below? I'm trying to exclude the symbol 'SHA', altogether, although if there's a global fix I'd rather use that. I thought the 'exclusion_filter' would exclude SHA but apparently something's not set up correctly.

Is there a simpler method to exclude single SIDs as part of the mask?

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ValueError: NaN or Inf values provided to FactorExposure for argument 'loadings'.  
Rows/Columns with NaNs:  
  row=Equity(32430 [SHA]) col='industrials'  
  row=Equity(32430 [SHA]) col='momentum'  
  row=Equity(32430 [SHA]) col='short_term_reversal'  
  row=Equity(32430 [SHA]) col='size'  
  row=Equity(32430 [SHA]) col='value'  
  ... (1 more)  
Rows/Columns with Infs:  
  None  
There was a runtime error on line 413.

Code for the exclusion list (note that SHA (32430) is included):

def initialize(context):  
    context.exclusion_list = [sid(34692), sid(19666), sid(46369), sid(3585), sid(32620), sid(42786),sid(32430)]  

Code for the exclusion filter, included with the pipeline screen.

exclusion_filter = ~StaticAssets(context.exclusion_list)  
    # Schedule Tasks  
    # --------------  
    # Create and register a pipeline computing our combined alpha and a sector  
    # code for every stock in our universe. We'll use these values in our  
    # optimization below.  
    pipe = Pipeline(  
        columns={  
            'alpha': combined_alpha,  
            'sector': Sector(),  
            'factor': factor,  
        },  
        # combined_alpha will be NaN for all stocks not in our universe,  
        # but we also want to make sure that we have a sector code for everything  
        # we trade.  
        screen=combined_alpha.notnull() & Sector().notnull()& mask & exclusion_filter,  
        #screen=exclusion_filter,  
    )  

Lines 411 through 414:

    constrain_sector_style_risk = opt.experimental.RiskModelExposure(  
        context.risk_loading_pipeline,  
        version=opt.Newest,  
    )  

My coding skills aren't great, so actual code examples to rectify this would be greatly appreciated.

Thanks,
Troy