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)
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)
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)
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)
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)
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)