Python normal_ad Examples

Example #1

File: test_statstools.py Project: zhisheng/statsmodels

def test_adnorm():
    #tests against R fBasics
    st_pv = []
    st_pv_R = np.array([0.5867235358882148, 0.1115380760041617])
    ad = normal_ad(x)
    assert_almost_equal(ad, st_pv_R, 12)
    st_pv.append(st_pv_R)

    st_pv_R = np.array([2.976266267594575e+00, 8.753003709960645e-08])
    ad = normal_ad(x**2)
    assert_almost_equal(ad, st_pv_R, 11)
    st_pv.append(st_pv_R)

    st_pv_R = np.array([0.4892557856308528, 0.1968040759316307])
    ad = normal_ad(np.log(x**2))
    assert_almost_equal(ad, st_pv_R, 12)
    st_pv.append(st_pv_R)

    st_pv_R = np.array([1.4599014654282669312, 0.0006380009232897535])
    ad = normal_ad(np.exp(-x**2))
    assert_almost_equal(ad, st_pv_R, 12)
    st_pv.append(st_pv_R)

    ad = normal_ad(np.column_stack((x,x**2, np.log(x**2),np.exp(-x**2))).T,
                   axis=1)
    assert_almost_equal(ad, np.column_stack(st_pv), 11)

Example #2

File: test_normality.py Project: wrightm/NumpyCookbook

    def test(self):
        #1. Download price data

        # 2011 to 2012
        start = datetime.datetime(2011, 01, 01)
        end = datetime.datetime(2012, 01, 01)

        quotes = finance.quotes_historical_yahoo('AAPL', start, end, asobject=True)

        close = numpy.array(quotes.close).astype(numpy.float)
        self.assertAlmostEqual(normal_ad(numpy.diff(numpy.log(close)))[0], 0.57, 2)
        self.assertAlmostEqual(normal_ad(numpy.diff(numpy.log(close)))[1], 0.13, 2)

Example #3

    def test(self):
        #1. Download price data

        # 2011 to 2012
        start = datetime.datetime(2011, 01, 01)
        end = datetime.datetime(2012, 01, 01)

        quotes = finance.quotes_historical_yahoo('AAPL',
                                                 start,
                                                 end,
                                                 asobject=True)

        close = numpy.array(quotes.close).astype(numpy.float)
        self.assertAlmostEqual(
            normal_ad(numpy.diff(numpy.log(close)))[0], 0.57, 2)
        self.assertAlmostEqual(
            normal_ad(numpy.diff(numpy.log(close)))[1], 0.13, 2)

Example #4

import datetime
import numpy
from matplotlib import finance
from statsmodels.stats.adnorm import normal_ad
import sys

#1. Download price data

# 2011 to 2012
start = datetime.datetime(2011, 01, 01)
end = datetime.datetime(2012, 01, 01)

print "Retrieving data for", sys.argv[1]
quotes = finance.quotes_historical_yahoo(sys.argv[1],
                                         start,
                                         end,
                                         asobject=True)

close = numpy.array(quotes.close).astype(numpy.float)
print close.shape

print normal_ad(numpy.diff(numpy.log(close)))

#Retrieving data for AAPL
#(252,)
#(0.57103805516803163, 0.13725944999430437)

Example #5

File: normality.py Project: hellios78/numpy-cookbook-code-sample

import datetime
import numpy
from matplotlib import finance
from statsmodels.stats.adnorm import normal_ad
import sys

#1. Download price data

# 2011 to 2012
start = datetime.datetime(2011, 01, 01)
end = datetime.datetime(2012, 01, 01)

print "Retrieving data for", sys.argv[1]
quotes = finance.quotes_historical_yahoo(sys.argv[1], start, end, asobject=True)

close = numpy.array(quotes.close).astype(numpy.float)
print close.shape

print normal_ad(numpy.diff(numpy.log(close)))

#Retrieving data for AAPL
#(252,)
#(0.57103805516803163, 0.13725944999430437)

Example #6

from __future__ import print_function
import datetime
import numpy as np
from matplotlib import finance
from statsmodels.stats.adnorm import normal_ad

#1. Download price data

# 2011 to 2012
start = datetime.datetime(2011, 01, 01)
end = datetime.datetime(2012, 01, 01)

quotes = finance.quotes_historical_yahoo('AAPL', start, end, asobject=True)

close = np.array(quotes.close).astype(np.float)
print(close.shape)

print(normal_ad(np.diff(np.log(close))))

#Retrieving data for AAPL
#(252,)
#(0.57103805516803163, 0.13725944999430437)