Instead of 30, since fastk is 37, how about this (is also the up-to-date method)

    high  = data.history(context.stocks, 'high',  44, '1d')  
    low   = data.history(context.stocks, 'low',   44, '1d')  
    close = data.history(context.stocks, 'price', 44, '1d')  

Could also then check, skip each of them like this example, although nan should no longer occur

        if np.isnan(slowk ):  
            log.info('skip nan slowk  {}'.format(stock.symbol))  
            continue  

Benjamin,

You may also try this simplified bull market setup:

# TALib.STOCH portfolio bull market setup

import talib  
# -----------------------------------------  
stocks = symbols('UWM', 'UYG','SSO', 'QLD')  
fk, sk, sk_ma, sd, sd_ma = 36, 3, 0, 3, 0  
LB, UB, lev = 20, 80, 1.0  
# -----------------------------------------  
bars = fk + sk + sd  
wt = lev/len(stocks)

def initialize(context):  
    schedule_function(trade, date_rules.every_day(), time_rules.market_open(minutes = 65))

def trade(context, data):  
    if get_open_orders(): return

    for stock in stocks:  
        if not data.can_trade(stock): continue  
        H = data.history(stock, 'high', bars, '1d')  
        L = data.history(stock, 'low', bars, '1d')  
        C = data.history(stock, 'close', bars, '1d')  
        slowk, slowd = talib.STOCH(H, L, C, fk, sk, sk_ma, sd, sd_ma)    

        if (slowd[-1] > LB and slowd[-2] <= LB and slowk[-1] > slowd[-1]):  
            order_target_percent(stock, wt)  
        elif (slowd[-1] < UB and slowd[-2] >= UB and slowk[-1] < slowd[-1]):  
            order_target(stock, 0)  

    record(lev = context.account.leverage)  
'''
START  
06/01/2009  
END  
07/28/2017  
'''