[Numpy-discussion] How to get the prices of Moving Averages Crosses?

Andre Lopes lopes80andre@gmail....
Tue Mar 1 14:31:37 CST 2011


Hi,

Thanks for the reply.

I think my problem will be to get some knowledge on some math subjects.

What kind of subjects in math I need to get knowledge to be able to
work in this field(indicators for financial products)?

Best Regards,



On Tue, Mar 1, 2011 at 7:17 PM, Christopher Barker
<Chris.Barker@noaa.gov> wrote:
> On 3/1/11 10:55 AM, Andre Lopes wrote:
>> I'm not okay with linear interpolation.
>
> Well, odds are that the crossing point won't be exactly at a data point,
> so you need to do SOME kind of interpolation.
>
> You could use a higher order interpolation (cubic spline, etc): see the
> interpolation routines in scipy.
>
> However, for this use, perhaps rather than the crossing point, you could
> use the next time after the crossing point as your metric.
>
> What's appropriate depends entirely on your purpose.
>
> Do note that that key here is that you need to find a point where the
> difference changes sign -- it's not likely to be zero at any point.
>
> One more thought -- you're looking at a moving average anyway -- with
> that smoothing already in place linear interpolation is probably fine,
> after all the exact crossing point is going to be a function of your
> smoothing parameters.
>
> You'll also want to think about what it means when the prices cross,
> then cross right back at the next time step, or even match, then the one
> that was higher goes back up...
>
> -Chris
>
>
>> Can you suggest me some books
>> around this subject. I will mainly try to build some indicator for the
>> stock market.
>>
>> If you can give me a clue I would be appreciated.
>>
>> Best Regards,
>>
>>
>>
>> On Tue, Mar 1, 2011 at 5:23 PM, Joe Kington <jkington@wisc.edu
>> <mailto:jkington@wisc.edu>> wrote:
>>
>>     Hi Andre,
>>
>>     Assuming that you want the exact point (date and value) where each
>>     crossing occurs, you'll need to interpolate where they cross.
>>
>>     There are a number of different ways to do so, but assuming you're
>>     okay with linear interpolation, and everything's sampled on the same
>>     dates, you can simply do something like this:
>>
>>     import numpy as np
>>     import matplotlib.pyplot as plt
>>
>>     def main():
>>          x = np.linspace(0, 2*np.pi, 20)
>>          y1 = np.sin(2*x)
>>          y2 = np.cos(x)
>>          crossings = find_crossings(x, y1, y2)
>>          cross_x, cross_y = crossings.T
>>          plt.plot(x, y1, 'bx-')
>>          plt.plot(x, y2, 'gx-')
>>          plt.plot(cross_x, cross_y, 'ro')
>>          plt.show()
>>
>>     def find_crossings(x, y1, y2):
>>          diff = np.diff(np.sign(y1 - y2))
>>          indicies, = np.nonzero(diff)
>>          crossings = [interpolate_crossing(i, x, y1, y2) for i in indicies]
>>          return np.array(crossings)
>>
>>     def interpolate_crossing(i, x, y1, y2):
>>          slope = (       (y1[i] - y2[i])
>>                 / ((y2[i+1] - y2[i]) - (y1[i+1] - y1[i])))
>>          x = x[i] + slope * (x[i+1] - x[i])
>>          y = y1[i] + slope * (y1[i+1] - y1[i])
>>          return x, y
>>
>>     main()
>>
>>     VXsqp.png
>>
>>     On Tue, Mar 1, 2011 at 10:07 AM, Andre Lopes <lopes80andre@gmail.com
>>     <mailto:lopes80andre@gmail.com>> wrote:
>>
>>         Hi,
>>
>>         I'm new to Numpy. I'm doing some tests with some Stock Market Quotes
>>
>>         My struggle right now is "how to get the values of the moving
>>         averages
>>         crosses", I send an image in attach to illustrate what I'm trying to
>>         get.
>>
>>         I'm using the this computation to get when the moving averages
>>         crosses, but when I look at the graph, the values doesn't seem ok.
>>
>>         [quote]
>>         # Get when the ma20 cross ma50
>>         equal = np.round(ma20,2)==np.round(ma50,2)
>>         dates_cross  = (dates[equal])
>>         prices_cross = (prices[equal])
>>         [/quote]
>>
>>
>>         The full code is this:
>>         [quote]
>>         # Modules
>>         import datetime
>>         import numpy as np
>>         import matplotlib.finance as finance
>>         import matplotlib.mlab as mlab
>>         import matplotlib.pyplot as plot
>>
>>         # Define quote
>>         startdate = datetime.date(2008,10,1)
>>         today = enddate = datetime.date.today()
>>         ticker = 'uso'
>>
>>         # Catch CSV
>>         fh = finance.fetch_historical_yahoo(ticker, startdate, enddate)
>>
>>         # From CSV to REACARRAY
>>         r = mlab.csv2rec(fh); fh.close()
>>         # Order by Desc
>>         r.sort()
>>
>>
>>         ### Methods Begin
>>         def moving_average(x, n, type='simple'):
>>         """
>>             compute an n period moving average.
>>
>>             type is 'simple' | 'exponential'
>>
>>         """
>>             x = np.asarray(x)
>>             if type=='simple':
>>                 weights = np.ones(n)
>>             else:
>>                 weights = np.exp(np.linspace(-1., 0., n))
>>
>>             weights /= weights.sum()
>>
>>
>>             a =  np.convolve(x, weights, mode='full')[:len(x)]
>>             a[:n] = a[n]
>>             return a
>>         ### Methods End
>>
>>
>>         prices = r.adj_close
>>         dates = r.date
>>         ma20 = moving_average(prices, 20, type='simple')
>>         ma50 = moving_average(prices, 50, type='simple')
>>
>>         # Get when the ma20 cross ma50
>>         equal = np.round(ma20,2)==np.round(ma50,2)
>>         dates_cross  = (dates[equal])
>>         prices_cross = (prices[equal])
>>
>>         # Ver se a ma20 > ma50
>>         # ma20_greater_than_ma50 = np.round(ma20,2) > np.round(ma50,2)
>>         # dates_ma20_greater_than_ma50  = (dates[ma20_greater_than_ma50])
>>         # prices_ma20_greater_than_ma50 = (prices[ma20_greater_than_ma50])
>>
>>         print dates_cross
>>         print prices_cross
>>         #print dates_ma20_greater_than_ma50
>>         #print prices_ma20_greater_than_ma50
>>
>>
>>         plot.plot(prices)
>>         plot.plot(ma20)
>>         plot.plot(ma50)
>>         plot.show()
>>         [/quote]
>>
>>         Someone can give me some clues?
>>
>>         Best Regards,
>>
>>         _______________________________________________
>>         NumPy-Discussion mailing list
>>         NumPy-Discussion@scipy.org <mailto:NumPy-Discussion@scipy.org>
>>         http://mail.scipy.org/mailman/listinfo/numpy-discussion
>>
>>
>>
>>     _______________________________________________
>>     NumPy-Discussion mailing list
>>     NumPy-Discussion@scipy.org <mailto:NumPy-Discussion@scipy.org>
>>     http://mail.scipy.org/mailman/listinfo/numpy-discussion
>>
>>
>>
>>
>> _______________________________________________
>> NumPy-Discussion mailing list
>> NumPy-Discussion@scipy.org
>> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
> --
> Christopher Barker, Ph.D.
> Oceanographer
>
> Emergency Response Division
> NOAA/NOS/OR&R            (206) 526-6959   voice
> 7600 Sand Point Way NE   (206) 526-6329   fax
> Seattle, WA  98115       (206) 526-6317   main reception
>
> Chris.Barker@noaa.gov
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>


More information about the NumPy-Discussion mailing list