def linear_reg():
ibm_data = data_retrieval.get_time_series(
'download_yahoo_timeseries',
{
'symbol': 'IBM',
'start': datetime.datetime(2003, 11, 11),
'end': datetime.datetime(2013, 11, 11)
})
spx_data = data_retrieval.get_time_series(
'download_yahoo_timeseries',
{
'symbol': '^GSPC',
'start': datetime.datetime(2003, 11, 11),
'end': datetime.datetime(2013, 11, 11)
})
series = zip(*timeseries.common_dates(
ibm_data['IBM'][data_loader.TIMESERIES],
spx_data['^GSPC'][data_loader.TIMESERIES]))
ibm = [float(val) for val in series[1]]
spx = [float(val) for val in series[2]]
slope, intercept, r_value, _, _ = algorithms.linreg(ibm, spx)
print('Beta: {0}'.format(intercept))
print('Slope: {0}'.format(slope))
print('RSq: {0}'.format(r_value**2))
series_for_charting = [('IBM', ibm), ('SPX', spx)]
charts.line(series[0], series_for_charting)
charts.scatter(series_for_charting)
def test_common_dates(self):
'''
Test the common_dates correctly returns an ordered result on the
date intersection
'''
x = [
(datetime.datetime(2013, 10, 31), 4.53),
(datetime.datetime(2013, 11, 4), -2.89),
(datetime.datetime(2013, 11, 5), -0.18),
(datetime.datetime(2013, 11, 6), 1.36),
(datetime.datetime(2013, 11, 7), 6.32),
(datetime.datetime(2013, 11, 12), -6.30)
]
# Deliberately put dates slightly out of order
y = [
(datetime.datetime(2013, 10, 31), 8.53),
(datetime.datetime(2013, 11, 1), -9.87),
(datetime.datetime(2013, 11, 6), 11.36),
(datetime.datetime(2013, 11, 5), 1.18),
(datetime.datetime(2013, 11, 7), -6.32),
(datetime.datetime(2013, 11, 8), 3.51),
(datetime.datetime(2013, 11, 12), -2.30),
(datetime.datetime(2013, 11, 13), 3.51)
]
merged = timeseries.common_dates(x, y)
expected_result = [
(datetime.datetime(2013, 10, 31), 4.53, 8.53),
(datetime.datetime(2013, 11, 5), -0.18, 1.18),
(datetime.datetime(2013, 11, 6), 1.36, 11.36),
(datetime.datetime(2013, 11, 7), 6.32, -6.32),
(datetime.datetime(2013, 11, 12), -6.30, -2.30)
]
self.assertEqual(merged, expected_result)
