def available_data_range(self, frequency):
if frequency in ['tick', '1d']:
s, e = self._day_bars[
self.INSTRUMENT_TYPE_MAP['INDX']].get_date_range('000001.XSHG')
return convert_int_to_date(s).date(), convert_int_to_date(e).date()
raise NotImplementedError
def events(self, start_date, end_date, frequency):
s, e = self._env.data_source._day_bars[
INSTRUMENT_TYPE.INDX].get_date_range('000001.XSHG')
event_end_date = convert_int_to_date(e) if convert_int_to_date(
e).date() < self._end_date else self._end_date
return super(IncrementalEventSource,
self).events(self._event_start_date, event_end_date,
frequency)
def available_data_range(self, frequency):
# FIXME
from rqalpha.const import DEFAULT_ACCOUNT_TYPE
accounts = Environment.get_instance().config.base.accounts
if not (DEFAULT_ACCOUNT_TYPE.STOCK in accounts or DEFAULT_ACCOUNT_TYPE.FUTURE in accounts):
return date.min, date.max
if frequency in ['tick', '1d']:
s, e = self._day_bars[INSTRUMENT_TYPE.INDX].get_date_range('000001.XSHG')
return convert_int_to_date(s).date(), convert_int_to_date(e).date()
def available_data_range(self, frequency):
if self._market == MARKET.CN:
if frequency in ['tick', '1d']:
s, e = self._day_bars[self.INSTRUMENT_TYPE_MAP[
'INDX']].get_date_range('000001.XSHG')
return convert_int_to_date(s).date(), convert_int_to_date(
e).date()
elif self._market == MARKET.HK:
# FIXME
return datetime.date(2015, 1, 1), datetime.datetime.now().date()
raise NotImplementedError
def available_data_range(self, frequency):
"""
此数据源能提供数据的时间范围
:param str frequency: 周期频率，`1d` 表示日周期, `1m` 表示分钟周期
:return: (earliest, latest)
"""
if frequency == '1d':
mongo_data = self._day_bars[
self.INSTRUMENT_TYPE_MAP['INDX']]['000001.XSHG'].find({}, {
"_id": 0
}).sort('date', pymongo.ASCENDING)
mongo_data = list(mongo_data)
s, e = np.uint32(mongo_data[0]['date']), np.uint32(
mongo_data[-1]['date'])
return convert_int_to_date(s).date(), convert_int_to_date(e).date()
if frequency == '1m':
raise NotImplementedError
def available_data_range(self, frequency):
if frequency in ['tick', '1d']:
s, e = self._day_bars[INSTRUMENT_TYPE.INDX].get_date_range(
'000001.XSHG')
return convert_int_to_date(s).date(), convert_int_to_date(e).date()
def get_available_data_range(self):
min_date, max_date = self._table.attrs["min_date"], self._table.attrs["max_date"]
return convert_int_to_date(min_date).date(), convert_int_to_date(max_date).date()
def available_data_range(self, frequency):
if frequency in ['tick', '1d']:
s, e = self._day_bars[self.INSTRUMENT_TYPE_MAP['INDX']].get_date_range('000001.XSHG')
return convert_int_to_date(s).date(), convert_int_to_date(e).date()
raise NotImplementedError
def raw_history_bars(self,
instrument,
frequency,
start_dt=None,
end_dt=None,
length=None):
if frequency[-1] == "m":
days = []
if start_dt and end_dt:
assert start_dt <= end_dt, "start datetime later then end datetime!"
s_date, s_time = start_dt.date(), start_dt.time()
e_date, e_time = end_dt.date(), end_dt.time()
s_date_int = convert_date_to_int(s_date)
e_date_int = convert_date_to_int(e_date)
dates = self._dates_index(instrument)
s_pos = safe_searchsorted(dates, s_date_int)
e_pos = safe_searchsorted(dates, e_date_int, side="right") - 1
if s_pos == e_pos:
days.append(
dict(
trade_date=convert_int_to_date(dates[s_pos]),
start_time=s_time,
end_time=e_time,
))
else:
days.append(
dict(trade_date=convert_int_to_date(dates[s_pos]),
start_time=s_time))
days.extend(
map(lambda x: dict(trade_date=convert_int_to_date(x)),
dates[s_pos + 1:e_pos]))
days.append(
dict(trade_date=convert_int_to_date(dates[e_pos]),
end_time=e_time))
post_handler = lambda x: x
elif start_dt and length:
s_date, s_time = start_dt.date(), int(
start_dt.strftime("%H%M%S"))
dates = self._dates_index(instrument)
s_date_int = convert_date_to_int(s_date)
s_pos = safe_searchsorted(dates, s_date_int)
s_bar_count = self.get_bar_count_in_day(instrument,
frequency,
trade_date=s_date,
start_time=s_time)
total_bar_count = self.get_bar_count_in_day(
instrument, frequency)
extra_days = (max(length - s_bar_count, 0) -
1) // total_bar_count + 1
days.append(dict(trade_date=s_date, start_time=s_time))
days.extend(
map(lambda x: dict(trade_date=convert_int_to_date(x)),
dates[s_pos + 1:s_pos + 1 + extra_days]))
post_handler = lambda x: x[:length]
elif end_dt and length:
e_date, e_time = end_dt.date(), int(end_dt.strftime("%H%M%S"))
dates = self._dates_index(instrument)
e_date_int = convert_date_to_int(e_date)
e_pos = safe_searchsorted(dates, e_date_int, side="right") - 1
e_bar_count = self.get_bar_count_in_day(instrument,
frequency,
trade_date=e_date,
end_time=e_time)
total_bar_count = self.get_bar_count_in_day(
instrument, frequency)
extra_days = (max(length - e_bar_count, 0) -
1) // total_bar_count + 1
days.extend(
map(lambda x: dict(trade_date=convert_int_to_date(x)),
dates[max(e_pos - extra_days, 0):e_pos]))
days.append(dict(trade_date=e_date, end_time=e_time))
post_handler = lambda x: x[-length:]
else:
raise RuntimeError(
"At least two of [start_dt,end_dt,length] should be given."
)
data = post_handler(
self._get_bars_in_days(instrument, frequency, days))
return data
else:
return None
