def get_pre_and_post_returns(args, day, prices):
"""
Append pre and post return data
"""
try:
pre_start = cruncher.get_end_index(
prices, day, index=-1 * args.interval
)
pre_end = cruncher.get_end_index(
prices, pre_start, index=args.interval - 1
)
post_start = cruncher.get_start_index(prices, day, index=1)
post_end = cruncher.get_end_index(
prices, post_start, index=args.interval - 1
)
except SkipEvaluationError:
logger.debug(
"The day {} does not support being evaluated".format(day)
)
else:
logger.debug("Appending to returns")
pre_rets.append(
(prices[pre_end] - prices[pre_start]) / prices[pre_start]
)
post_rets.append(
(prices[post_end] - prices[post_start]) / prices[post_start]
)
def test_get_end_index_for_close_to_end_of_range():
"""
Test discerner.get_end_index for cases where index is close to end of data
"""
data = range(8)
end = get_end_index(data, 0)
eq_(data[end], 7)
data = range(8)
data.append(NARealType())
end = get_end_index(data, 0)
eq_(data[end], 7)
def test_get_end_index():
"""
Test discerner.get_end_index for success cases
"""
data = range(15)
end = get_end_index(data, 0)
eq_(data[end], 13)
def test_get_end_index_for_negative_indexing():
"""
Test discerner.cruncher.get_end_index for negative indexing
"""
data = range(15)
end = get_end_index(data, 14, index=-1)
eq_(end, 13)
def test_get_end_index_with_nan():
"""
Test discerner.get_end_index for success case with nan value
"""
data = range(13)
data.append(NARealType())
data.extend([14, 15])
end = get_end_index(data, 0)
eq_(data[end], 12)
def test_get_end_index_for_error():
"""
Test discerner.get_end_index for error case
"""
data = [1, NARealType()]
get_end_index(data, 0)
