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.bar...@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 <jking...@wisc.edu >> <mailto:jking...@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 <lopes80an...@gmail.com >> <mailto:lopes80an...@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.bar...@noaa.gov > _______________________________________________ > NumPy-Discussion mailing list > 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
