[Numpy-discussion] How to get the prices of Moving Averages Crosses?
Andre Lopes
lopes80andre at gmail.com
Tue Mar 1 15:31:37 EST 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 at 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 at wisc.edu
>> <mailto:jkington at 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 at gmail.com
>> <mailto:lopes80andre at 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,
>>
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