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MAINT: Removing unused functions and related tests

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This commit is contained in:
Richard Frank
2016-02-11 18:46:43 -05:00
parent c11524fda1
commit 41231c5c59
3 changed files with 0 additions and 186 deletions
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tests/test_history.py
-125
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@@ -41,131 +41,6 @@ from zipline.sources import RandomWalkSource, DataFrameSource
import zipline.utils.factory as factory
from zipline.utils.test_utils import subtest
# Cases are over the July 4th holiday, to ensure use of trading calendar.
# March 2013
# Su Mo Tu We Th Fr Sa
# 1 2
# 3 4 5 6 7 8 9
# 10 11 12 13 14 15 16
# 17 18 19 20 21 22 23
# 24 25 26 27 28 29 30
# 31
# April 2013
# Su Mo Tu We Th Fr Sa
# 1 2 3 4 5 6
# 7 8 9 10 11 12 13
# 14 15 16 17 18 19 20
# 21 22 23 24 25 26 27
# 28 29 30
#
# May 2013
# Su Mo Tu We Th Fr Sa
# 1 2 3 4
# 5 6 7 8 9 10 11
# 12 13 14 15 16 17 18
# 19 20 21 22 23 24 25
# 26 27 28 29 30 31
#
# June 2013
# Su Mo Tu We Th Fr Sa
# 1
# 2 3 4 5 6 7 8
# 9 10 11 12 13 14 15
# 16 17 18 19 20 21 22
# 23 24 25 26 27 28 29
# 30
# July 2013
# Su Mo Tu We Th Fr Sa
# 1 2 3 4 5 6
# 7 8 9 10 11 12 13
# 14 15 16 17 18 19 20
# 21 22 23 24 25 26 27
# 28 29 30 31
#
# Times to be converted via:
# pd.Timestamp('2013-07-05 9:31', tz='US/Eastern').tz_convert('UTC')},
INDEX_TEST_CASES_RAW = {
'week of daily data': {
'input': {'bar_count': 5,
'frequency': '1d',
'algo_dt': '2013-07-05 9:31AM'},
'expected': [
'2013-06-28 4:00PM',
'2013-07-01 4:00PM',
'2013-07-02 4:00PM',
'2013-07-03 1:00PM',
'2013-07-05 9:31AM',
]
},
'five minutes on july 5th open': {
'input': {'bar_count': 5,
'frequency': '1m',
'algo_dt': '2013-07-05 9:31AM'},
'expected': [
'2013-07-03 12:57PM',
'2013-07-03 12:58PM',
'2013-07-03 12:59PM',
'2013-07-03 1:00PM',
'2013-07-05 9:31AM',
]
},
}
def to_timestamp(dt_str):
return pd.Timestamp(dt_str, tz='US/Eastern').tz_convert('UTC')
def convert_cases(cases):
"""
Convert raw strings to values comparable with system data.
"""
cases = cases.copy()
for case in cases.values():
case['input']['algo_dt'] = to_timestamp(case['input']['algo_dt'])
case['expected'] = pd.DatetimeIndex([to_timestamp(dt_str) for dt_str
in case['expected']])
return cases
INDEX_TEST_CASES = convert_cases(INDEX_TEST_CASES_RAW)
def get_index_at_dt(case_input, env):
history_spec = history.HistorySpec(
case_input['bar_count'],
case_input['frequency'],
None,
False,
env=env,
data_frequency='minute',
)
return history.index_at_dt(history_spec, case_input['algo_dt'], env=env)
class TestHistoryIndex(TestCase):
@classmethod
def setUpClass(cls):
cls.environment = TradingEnvironment()
@classmethod
def tearDownClass(cls):
del cls.environment
@parameterized.expand(
[(name, case['input'], case['expected'])
for name, case in INDEX_TEST_CASES.items()]
)
def test_index_at_dt(self, name, case_input, expected):
history_index = get_index_at_dt(case_input, self.environment)
history_series = pd.Series(index=history_index)
expected_series = pd.Series(index=expected)
pd.util.testing.assert_series_equal(history_series, expected_series)
class TestHistoryContainer(TestCase):
zipline/history/__init__.py
-4
View File
@@ -15,8 +15,6 @@
from . history import (
HistorySpec,
days_index_at_dt,
index_at_dt,
Frequency,
)
@@ -24,8 +22,6 @@ from . import history_container
__all__ = [
'HistorySpec',
'days_index_at_dt',
'index_at_dt',
'history_container',
'Frequency',
]
zipline/history/history.py
-57
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@@ -15,7 +15,6 @@
from __future__ import division
import numpy as np
import pandas as pd
import re
@@ -284,59 +283,3 @@ class HistorySpec(object):
def __repr__(self):
return ''.join([self.__class__.__name__, "('", self.key_str, "')"])
def days_index_at_dt(history_spec, algo_dt, env):
"""
Get the index of a frame to be used for a get_history call with daily
frequency.
"""
# Get the previous (bar_count - 1) days' worth of market closes.
day_delta = (history_spec.bar_count - 1) * history_spec.frequency.num
market_closes = env.open_close_window(
algo_dt,
day_delta,
offset=(-day_delta),
step=history_spec.frequency.num,
).market_close
if history_spec.frequency.data_frequency == 'daily':
market_closes = market_closes.apply(pd.tslib.normalize_date)
# Append the current algo_dt as the last index value.
# Using the 'rawer' numpy array values here because of a bottleneck
# that appeared when using DatetimeIndex
return np.append(market_closes.values, algo_dt)
def minutes_index_at_dt(history_spec, algo_dt, env):
"""
Get the index of a frame to be used for a get_history_call with minutely
frequency.
"""
# TODO: This is almost certainly going to be too slow for production.
return env.market_minute_window(
algo_dt,
history_spec.bar_count,
step=-1,
)[::-1]
def index_at_dt(history_spec, algo_dt, env):
"""
Returns index of a frame returned by get_history() with the given
history_spec and algo_dt.
The resulting index will have @history_spec.bar_count bars, increasing in
units of @history_spec.frequency, terminating at the given @algo_dt.
Note: The last bar of the returned frame represents an as-of-yet incomplete
time window, so the delta between the last and second-to-last bars is
usually always less than `@history_spec.frequency` for frequencies greater
than 1m.
"""
frequency = history_spec.frequency
if frequency.unit_str == 'd':
return days_index_at_dt(history_spec, algo_dt, env)
elif frequency.unit_str == 'm':
return minutes_index_at_dt(history_spec, algo_dt, env)