import datetime  
import pytz

def initialize(context):  
    # Here we initialize each stock.  Note that we're not storing integers; by  
    # calling sid(24) we're storing the Security object.  
    context.stocks = [sid(24), sid(2), sid(2673), sid(5061)]

    # Setting our maximum position size, like previous example  
    context.max_notional = 1000000.1  
    context.min_notional = -1000000.0

    # Initializing the time variables we use for logging  
    # Convert timezone to US EST to avoid confusion  
    est = pytz.timezone('EST')  
    context.d=datetime.datetime(2000, 1, 1, 0, 0, 0, tzinfo=est)  


def handle_data(context, data):  
    # Initializing the position as zero at the start of each frame  
    notional=0  
    # This runs through each stock.  It computes  
    # our position at the start of each frame.  
    for stock in context.stocks:  
        price = data[stock].price  
        notional = notional + context.portfolio.positions[stock].amount * price  
        tradeday = data[stock].datetime  
    # This runs through each stock again.  It finds the price and calculates  
    # the volume-weighted average price.  If the price is moving quickly, and  
    # we have not exceeded our position limits, it executes the order and  
    # updates our position.  
    close_price = history(bar_count=21, frequency='1d',field='close_price')  
    ma_line = data[stock].mavg(24)  
    short_ma = data[stock].mavg(15)  
    long_ma = data[stock].mavg(30)  
    r = (close_price - ma_line)/ma_line  
    Bias = {}  
    highPrices  = history(25, '1d', 'high')  
    lowPrices   = history(25, '1d', 'low')  
    closePrices = history(25, '1d', 'close_price')

    for stock in context.stocks:  
        Bias[stock] = r  
        stochast = ta.STOCH(highPrices[context.stocks], lowPrices[context.stocks], closePrices[context.stocks],fastk_period=25)  
        slowk = stochast[0]  
        slowd = stochast[1]  
        if slowk > slowd and slowd < 10 and notional > context.min_notional:  
            order(stock,-100)  
            notional = notional - price*100  
        elif short_ma > long_ma and notional < context.max_notional:  
            order(stock,+100)  
            notional = notional + price*100

    # If this is the first trade of the day, it logs the notional.  
    if (context.d + datetime.timedelta(days=1)) < tradeday:  
        log.debug(str(notional) + ' - notional start ' + tradeday.strftime('%m/%d/%y'))  
        context.d = tradeday  

Hi, Quantapian company

I have several questions here for the code above.

1. I got an error saying that "Error Runtime exception: TypeError: init() got multiple values for keyword argument 'close'. " on the line ---> stochast = ta.STOCH(highPrices[context.stocks], lowPrices[context.stocks], closePrices[context.stocks],fastk_period=20)

2. For the ta.STOCH, what is the reason I need to specify the fastk_period even that I specify the data frequency in the previous high price/low price/close price?

3. I want to write the buy condition when slowk cross above slowd not just over it as it showed above. Also, I want to write the cross below condition when short_ma cross below long_ma?

Thanks!