When I use: history(2, '1d', 'high')
all of the sudden i get a bizarre error:cannot concatenate 'str' and 'NoneType' objects

if i only do 1 day history(1, '1d', 'high') , then it works. how can i prevent this?

Is this because some security isn't listed earlier or something?
I tried to find the stocks start dates but none of them seem to be in 2010: print {s.symbol:s.start_date for s in data.keys()}

My code picks the securities like this with a start date: 01/01/2010:

def before_trading_start(context):  
    """  
        Called before the start of each trading day (handle_data) and updates our universe with the securities and values found from fetch_fundamentals  
    """  
    #: Reference fundamentals in a shorter variable  
    f = fundamentals  
    #: SQLAlchemy  
    fundamental_df = get_fundamentals(  
        query(  
            f.valuation.market_cap,  
            f.valuation_ratios.pe_ratio,  
            f.asset_classification.morningstar_sector_code,  
            f.operation_ratios.roa,  
            f.cash_flow_statement.operating_cash_flow  
        )  
        # No no ADR or PINK, only USA  
        .filter(fundamentals.share_class_reference.is_depositary_receipt == False)  
        .filter(fundamentals.share_class_reference.is_primary_share == True)  
        .filter(fundamentals.company_reference.primary_exchange_id != "OTCPK")  
#        .filter(fundamentals.company_reference.country_id == "USA")  
         # Check for data sanity (i,e. avoid division by zero)  
        .filter(fundamentals.valuation.market_cap > 10000)  
        .filter(fundamentals.valuation.shares_outstanding > 0)  
#        .filter(fundamentals.balance_sheet.invested_capital > 0)  
        .filter(fundamentals.balance_sheet.common_stock_equity>0)  
#        .filter(fundamentals.balance_sheet.invested_capital != fundamentals.balance_sheet.cash_and_cash_equivalents)  
        .order_by(fundamentals.valuation.market_cap.desc())  
        .limit(100)  
    )  
    # the problem here is we get duplicate multple calsses of the shares  
    secs = np.array([s.symbol for s in fundamental_df.columns.values])  
    if 0:  
        for i, s in enumerate(secs):  
            if s[-2] is '_':  
                if s[:-2] in secs:  
                    raise(' drop duplicate class shares')  
                # drop duplicate class shares  
    #: Set our fundamentals into a context variable  
    context.fundamental_df = fundamental_df  
    #: Update our universe with the values  
    update_universe(fundamental_df.columns.values)