Every time I try to use StaticAssets it returns the following error:

TypeError: MaximizeAlpha() expected a value with dtype 'float64' or 'int64' for argument 'alphas', but got 'object' instead.
There was a runtime error on line 100.

I've tried using universe=Filters.StaticAssets(context.stock) but that returns a key error 'stock'. How would I go about fixing this.

def make_pipeline(context):  
    """  
    A function to create our dynamic stock selector (pipeline). Documentation  
    on pipeline can be found here:  
    https://www.quantopian.com/help#pipeline-title  
    """  
    my_etfs = (StaticAssets(symbols('IVV','EFA','AGG','IJH','IWM','IWD','IWF','LQD','EEM','EZU',  
)))
    #first factor + winsorizing to prevent outliers  
    fac = Fundamentals.equity_per_share_growth.latest  
    fac_wins = fac.winsorize(min_percentile=0.05, max_percentile=0.95)

    #putting it into z-score form  
    finalfactor = fac_wins.zscore()  
    #deciding shorts and longs  
    longs = finalfactor.top(TOTAL_POSITIONS//2, mask=my_etfs)  
    shorts = finalfactor.bottom(TOTAL_POSITIONS//2, mask=my_etfs)  
    #adding another screen  
    finalscreen = (longs | shorts)  
    pipe = Pipeline(  
        columns={  
            'longs': longs,  
            'shorts': shorts,  
            'finalfactor': finalfactor  
        },  
        screen=(finalscreen & my_etfs)  
    )  
    return pipe


def before_trading_start(context, data):  
    """  
    Called every day before market open.  
    """  
    context.output = algo.pipeline_output('pipeline')  
    context.risk_loadings = algo.pipeline_output('risk_factors')

    # These are the securities that we are interested in trading each day.  
    context.security_list = context.output.index


def rebalance(context, data):  
    """  
    Execute orders according to our schedule_function() timing.  
    """  
    pipeline_data = context.output

    risk_loadings = context.risk_loadings

    objective = opt.MaximizeAlpha(pipeline_data.finalfactor)

    constraints = []  
    constraints.append(opt.MaxGrossExposure(MAX_GROSS_LEVERAGE))

    constraints.append(opt.DollarNeutral())

    neutralize_risk_factors = opt.experimental.RiskModelExposure(  
        risk_model_loadings=risk_loadings,  
        version=0)  
    constraints.append(neutralize_risk_factors)

    constraints.append(  
        opt.PositionConcentration.with_equal_bounds(min=-MAX_SHORT_POSITION_SIZE,  
            max=MAX_LONG_POSITION_SIZE))  
    algo.order_optimal_portfolio(objective=objective, constraints=constraints)