talib can be tough to wrap one's head around, it takes different inputs for different sets of indicators.
There are some examples in the Source tab at https://www.quantopian.com/posts/talib-indicators
and searches like this can be pretty productive, limiting the results to quantopian.com:

https://www.google.com/#q=%22import+talib%22+site:quantopian.com

If you want to use a simple moving average, you can use the built-in mavg() function:

context.stock = symbol('AAPL')

# get the 5 day simple moving average  
mavg = data[context.stock].mavg(5)  

One could diddle with the simple framework I proposed. (Never really heard from the group whether expando style properties was a bad pattern or not.)

See this post technical-framework.

The idea of the framework was to attach at runtime properties for any indicator one desired to calculate

   data[stock].SMA = sma[stock][-1]  

and then to use those properties at will:

    for stock in data:  
        if (data[stock].SMA < data[stock].close_price):  
            # do stuff  

But the essence for your use was the below code. Changing the MAType and providing any additional arguments based on the MAType should provide with a means of isolating the MA calculation.

LongMAPeriods = 200

def initialize(context):  
    schedule_function(CalculateLongMA)

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~  
def CalculateLongMA(context, data):  
    closes   = history(LongMAPeriods, "1d", "close_price")  
    closes   = closes.dropna(axis=1)  
    valid    = [sid for sid in closes if sid in data]  
    closes   = closes[valid]  
    sma      = closes.apply(talib.MA, timeperiod = LongMAPeriods, matype = MAType.SMA).dropna()  
    for stock in sma:  
        data[stock].Trigger += 1 if data[stock].close_price > sma[stock][-1] else 0  
        data[stock].SMA = sma[stock][-1]

class MAType():  
    SMA   = 0; EMA   = 1; WMA   = 2; DEMA  = 3; TEMA  = 4;  
    TRIMA = 5; KAMA  = 6; MAMA  = 7; T3    = 8