quantopian | Sample algorithmic trading strategies for the Bachelors

def getVWAP(data):
    # if data is a pandas dataframe, just use data['o'].values instead
    open = numpy.array(data['o'])
    low = numpy.array(data['l'])
    high = numpy.array(data['h'])
    close = numpy.array(data['c'])
    volume = numpy.array(data['v'])

    # it's better to use logging.debug for debug output
    print(len(open), len(low), len(high), len(close), len(volume))

    # default dtype is float, but you can be more explicit
    typicalPrice = np.zeros(len(open), dtype=float)
    # another way to improve this is to use zeros_like,
    # which will generalize beyond 1D arrays
    VP = np.zeros_like(open)
    # but actually, we don't need to initialize these arrays at all
    # because we calculate them directly (see below)    

    # numpy arrays can be added/subtracted/... directly
    # it is actually quite a bit faster
    typicalPrice = (open + high + low)/3
    VP = volume * typicalPrice
    totalVolume = np.cumsum(volume)
    totalVP = np.cumsum(VP)
    VWAP = totalVP/totalVolume
    
    # this line is different. A typo?
    VWAP[0] = typicalPrice[0]