I'm performing a nested loop in python that is included below. This serves as a basic way of searching through existing financial time series and looking for periods in the time series that match certain characteristics. In this case there are two separate, equally sized, arrays representing the 'close' (i.e. the price of an asset) and the 'volume' (i.e. the amount of the asset that was exchanged over the period). For each period in time I would like to look forward at all future intervals with lengths between 1 and INTERVAL_LENGTH and see if any of those intervals have characteristics that match my search (in this case the ratio of the close values is greater than 1.0001 and less than 1.5 and the summed volume is greater than 100).

My understanding is that one of the major reasons for the speedup when using NumPy is that the interpreter doesn't need to type-check the operands each time it evaluates something so long as you're operating on the array as a whole (e.g. numpy_array * 2), but obviously the code below is not taking advantage of that. Is there a way to replace the internal loop with some kind of window function which could result in a speedup, or any other way using numpy/scipy to speed this up substantially in native python?

Alternatively, is there a better way to do this in general (e.g. will it be much faster to write this loop in C++ and use weave)?

ARRAY_LENGTH = 500000
INTERVAL_LENGTH = 15
close = np.array( xrange(ARRAY_LENGTH) )
volume = np.array( xrange(ARRAY_LENGTH) )
close, volume = close.astype('float64'), volume.astype('float64')

results = []
for i in xrange(len(close) - INTERVAL_LENGTH):
    for j in xrange(i+1, i+INTERVAL_LENGTH):
        ret = close[j] / close[i]
        vol = sum( volume[i+1:j+1] )
        if ret > 1.0001 and ret < 1.5 and vol > 100:
            results.append( [i, j, ret, vol] )
print results