def __init__(self, start=start_default, end=end_default):
# Midnight in UTC for each trading day.
# In pandas 0.18.1, pandas calls into its own code here in a way that
# fires a warning. The calling code in pandas tries to suppress the
# warning, but does so incorrectly, causing it to bubble out here.
# Actually catch and suppress the warning here:
with warnings.catch_warnings():
warnings.simplefilter('ignore')
_all_days = date_range(start, end, freq=self.day, tz='UTC')
# `DatetimeIndex`s of standard opens/closes for each day.
self._opens = days_at_time(_all_days, self.open_time, self.tz,
self.open_offset)
self._closes = days_at_time(
_all_days, self.close_time, self.tz, self.close_offset
)
# `DatetimeIndex`s of nonstandard opens/closes
_special_opens = self._calculate_special_opens(start, end)
_special_closes = self._calculate_special_closes(start, end)
# Overwrite the special opens and closes on top of the standard ones.
_overwrite_special_dates(_all_days, self._opens, _special_opens)
_overwrite_special_dates(_all_days, self._closes, _special_closes)
# In pandas 0.16.1 _opens and _closes will lose their timezone
# information. This looks like it has been resolved in 0.17.1.
# http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#datetime-with-tz # noqa
self.schedule = DataFrame(
index=_all_days,
columns=['market_open', 'market_close'],
data={
'market_open': self._opens,
'market_close': self._closes,
},
dtype='datetime64[ns]',
)
# Simple cache to avoid recalculating the same minute -> session in
# "next" mode. Analysis of current zipline code paths show that
# `minute_to_session_label` is often called consecutively with the same
# inputs.
self._minute_to_session_label_cache = LRU(1)
self.market_opens_nanos = self.schedule.market_open.values.\
astype(np.int64)
self.market_closes_nanos = self.schedule.market_close.values.\
astype(np.int64)
self._trading_minutes_nanos = self.all_minutes.values.\
astype(np.int64)
self.first_trading_session = _all_days[0]
self.last_trading_session = _all_days[-1]
self._early_closes = pd.DatetimeIndex(
_special_closes.map(self.minute_to_session_label)
)
def __init__(self, start=start_default, end=end_default):
# Midnight in UTC for each trading day.
# In pandas 0.18.1, pandas calls into its own code here in a way that
# fires a warning. The calling code in pandas tries to suppress the
# warning, but does so incorrectly, causing it to bubble out here.
# Actually catch and suppress the warning here:
with warnings.catch_warnings():
warnings.simplefilter('ignore')
_all_days = date_range(start, end, freq=self.day, tz='UTC')
# `DatetimeIndex`s of standard opens/closes for each day.
self._opens = days_at_time(_all_days, self.open_time, self.tz,
self.open_offset)
self._closes = days_at_time(
_all_days, self.close_time, self.tz, self.close_offset
)
# `DatetimeIndex`s of nonstandard opens/closes
_special_opens = self._calculate_special_opens(start, end)
_special_closes = self._calculate_special_closes(start, end)
# Overwrite the special opens and closes on top of the standard ones.
_overwrite_special_dates(_all_days, self._opens, _special_opens)
_overwrite_special_dates(_all_days, self._closes, _special_closes)
# In pandas 0.16.1 _opens and _closes will lose their timezone
# information. This looks like it has been resolved in 0.17.1.
# http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#datetime-with-tz # noqa
self.schedule = DataFrame(
index=_all_days,
columns=['market_open', 'market_close'],
data={
'market_open': self._opens,
'market_close': self._closes,
},
dtype='datetime64[ns]',
)
# Simple cache to avoid recalculating the same minute -> session in
# "next" mode. Analysis of current zipline code paths show that
# `minute_to_session_label` is often called consecutively with the same
# inputs.
self._minute_to_session_label_cache = LRU(1)
self.market_opens_nanos = self.schedule.market_open.values.\
astype(np.int64)
self.market_closes_nanos = self.schedule.market_close.values.\
astype(np.int64)
self._trading_minutes_nanos = self.all_minutes.values.\
astype(np.int64)
self.first_trading_session = _all_days[0]
self.last_trading_session = _all_days[-1]
self._early_closes = pd.DatetimeIndex(
_special_closes.map(self.minute_to_session_label)
)
def all_minutes(self):
"""
返回表示此日历中所有分钟的`DatetimeIndex`。
"""
opens_in_ns = self._opens.values.astype(
'datetime64[ns]',
).view('int64')
closes_in_ns = self._closes.values.astype(
'datetime64[ns]',
).view('int64')
# compute_all_minutes假设每天仅包含连续分钟块
dts = DatetimeIndex(
compute_all_minutes(opens_in_ns, closes_in_ns),
tz='utc',
)
# 如果有午休，则排除午休时段
if self.use_lunch_break:
# 需要使用utc时间
utc_start = days_at_time(
[dts[0].date()], self.lunch_break_start_time, self.tz).time[0]
utc_end = days_at_time(
[dts[0].date()], self.lunch_break_end_time, self.tz).time[0]
locs = dts.indexer_between_time(
utc_start, utc_end, include_start=True, include_end=True)
return dts.delete(locs)
else:
return dts
def __init__(self, start=start_default, end=end_default):
# 每个交易日UTC的午夜
# self.use_lunch_break = lunch_break # 标记对象是否使用午休时间
# In pandas 0.18.1, pandas calls into its own code here in a way that
# fires a warning. The calling code in pandas tries to suppress the
# warning, but does so incorrectly, causing it to bubble out here.
# Actually catch and suppress the warning here:
with warnings.catch_warnings():
warnings.simplefilter('ignore')
_all_days = date_range(start, end, freq=self.day, tz='UTC')
# 每天标准的开盘和收盘`DatetimeIndex`
self._opens = days_at_time(_all_days, self.open_time, self.tz,
self.open_offset)
self._closes = days_at_time(
_all_days, self.close_time, self.tz, self.close_offset
)
# 每天非标准的开盘和收盘`DatetimeIndex`
_special_opens = self._calculate_special_opens(start, end)
_special_closes = self._calculate_special_closes(start, end)
# 在标准集的基础上，重写特殊开盘与收盘
_overwrite_special_dates(_all_days, self._opens, _special_opens)
_overwrite_special_dates(_all_days, self._closes, _special_closes)
# In pandas 0.16.1 _opens and _closes will lose their timezone
# information. This looks like it has been resolved in 0.17.1.
# http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#datetime-with-tz # noqa
self.schedule = DataFrame(
index=_all_days,
columns=['market_open', 'market_close'],
data={
'market_open': self._opens,
'market_close': self._closes,
},
dtype='datetime64[ns]',
)
# 简单缓存以避免在“下一个”模式下重新计算相同的分钟 - >会话。
# 对当前zipline代码路径的分析显示，连续调用函数`minute_to_session_label`通常使用相
# 同的输入。
self._minute_to_session_label_cache = LRU(1)
# 转化为纳秒整数
self.market_opens_nanos = self.schedule.market_open.values.\
astype(np.int64)
self.market_closes_nanos = self.schedule.market_close.values.\
astype(np.int64)
# 所有交易分钟(纳秒整数)
self._trading_minutes_nanos = self.all_minutes.values.\
astype(np.int64)
self.first_trading_session = _all_days[0]
self.last_trading_session = _all_days[-1]
self._early_closes = pd.DatetimeIndex(
_special_closes.map(self.minute_to_session_label)
)
def data_query_cutoff_for_sessions(self, sessions):
return days_at_time(
sessions,
self._data_query_time,
self._data_query_time.tzinfo,
self._data_query_date_offset,
)
def data_query_cutoff_for_sessions(self, sessions):
return days_at_time(
sessions,
self._data_query_time,
self._data_query_time.tzinfo,
self._data_query_date_offset,
)
def _test_equity_calendar_domain(
self, domain, expected_cutoff_time, expected_cutoff_date_offset=0
):
sessions = pd.DatetimeIndex(domain.calendar.all_sessions[:50])
expected = days_at_time(
sessions,
expected_cutoff_time,
domain.calendar.tz,
expected_cutoff_date_offset,
)
actual = domain.data_query_cutoff_for_sessions(sessions)
assert_equal(actual, expected, check_names=False)
def _test_equity_calendar_domain(self,
domain,
expected_cutoff_time,
expected_cutoff_date_offset=0):
sessions = pd.DatetimeIndex(domain.calendar.all_sessions[:50])
expected = days_at_time(
sessions,
expected_cutoff_time,
domain.calendar.tz,
expected_cutoff_date_offset,
)
actual = domain.data_query_cutoff_for_sessions(sessions)
assert_equal(actual, expected, check_names=False)
def _special_dates(self, calendars, ad_hoc_dates, start_date, end_date):
"""
Union an iterable of pairs of the form (time, calendar)
and an iterable of pairs of the form (time, [dates])
(This is shared logic for computing special opens and special closes.)
"""
_dates = DatetimeIndex([], tz='UTC').union_many([
holidays_at_time(calendar, start_date, end_date, time_, self.tz)
for time_, calendar in calendars
] + [
days_at_time(datetimes, time_, self.tz)
for time_, datetimes in ad_hoc_dates
])
return _dates[(_dates >= start_date) & (_dates <= end_date)]
def _special_dates(self, calendars, ad_hoc_dates, start_date, end_date):
"""
联合一对以（时间，日历）格式的迭代和一对以（时间，[日期]）格式的迭代
(这是计算特殊开盘和特殊收盘的共享逻辑。)
"""
_dates = DatetimeIndex([], tz='UTC').union_many(
[
holidays_at_time(calendar, start_date, end_date, time_,
self.tz)
for time_, calendar in calendars
] + [
days_at_time(datetimes, time_, self.tz)
for time_, datetimes in ad_hoc_dates
]
)
return _dates[(_dates >= start_date) & (_dates <= end_date)]
def _special_dates(self, calendars, ad_hoc_dates, start_date, end_date):
"""
Union an iterable of pairs of the form (time, calendar)
and an iterable of pairs of the form (time, [dates])
(This is shared logic for computing special opens and special closes.)
"""
_dates = DatetimeIndex([], tz='UTC').union_many(
[
holidays_at_time(calendar, start_date, end_date, time_,
self.tz)
for time_, calendar in calendars
] + [
days_at_time(datetimes, time_, self.tz)
for time_, datetimes in ad_hoc_dates
]
)
return _dates[(_dates >= start_date) & (_dates <= end_date)]
def holidays_at_time(calendar, start, end, time, tz):
return days_at_time(
calendar.holidays(start, end),
time,
tz=tz,
)
def holidays_at_time(calendar, start, end, time, tz):
return days_at_time(
calendar.holidays(start, end),
time,
tz=tz,
)
