def _get_future_trading_minutes(self, trading_date):
trading_minutes = set()
universe = self._get_universe()
for order_book_id in universe:
if get_account_type(order_book_id) == DEFAULT_ACCOUNT_TYPE.STOCK.name:
continue
trading_minutes.update(self._env.data_proxy.get_trading_minutes_for(order_book_id, trading_date))
return set([convert_int_to_datetime(minute) for minute in trading_minutes])
def datetime(self):
"""
[datetime.datetime] 当前快照数据的时间戳
"""
if self._dt is not None:
return self._dt
if not self.isnan:
return convert_int_to_datetime(self._data['datetime'])
return datetime.datetime.min
def _get_future_trading_minutes(self, trading_date):
trading_minutes = set()
universe = self._get_universe()
for order_book_id in universe:
if self._env.get_account_type(
order_book_id) == DEFAULT_ACCOUNT_TYPE.STOCK:
continue
trading_minutes.update(
self._env.data_proxy.get_trading_minutes_for(
order_book_id, trading_date))
return set(
[convert_int_to_datetime(minute) for minute in trading_minutes])
def history(self, order_book_id, bar_count, frequency, field, dt):
data = self.history_bars(order_book_id,
bar_count,
frequency, ['datetime', field],
dt,
skip_suspended=False,
adjust_orig=dt)
if data is None:
return None
return pd.Series(
data[field],
index=[convert_int_to_datetime(t) for t in data['datetime']])
def datetime(self):
"""
[datetime.datetime] 当前快照数据的时间戳
"""
if self._dt is not None:
return self._dt
if not self.isnan:
dt = self._data['datetime']
if dt > 10000000000000000: # ms
return convert_ms_int_to_datetime(dt)
else:
return convert_int_to_datetime(dt)
return datetime.datetime.min
def datetime(self):
"""
[datetime.datetime] 当前快照数据的时间戳
"""
try:
dt = self._tick_dict['datetime']
except (KeyError, ValueError):
return datetime.datetime.min
else:
if not isinstance(dt, datetime.datetime):
if dt > 10000000000000000: # ms
return convert_ms_int_to_datetime(dt)
else:
return convert_int_to_datetime(dt)
return dt
def datetime(self):
"""
[datetime.datetime] 当前快照数据的时间戳
"""
try:
dt = self._tick_dict['datetime']
except (KeyError, ValueError):
return datetime.datetime.min
else:
if not isinstance(dt, datetime.datetime):
if dt > 10000000000000000: # ms
return convert_ms_int_to_datetime(dt)
else:
return convert_int_to_datetime(dt)
return dt
def get_future_trading_points(env, trading_date, frequency):
if frequency == "1m":
trading_minutes = set()
universe = env.get_universe()
for order_book_id in universe:
if get_account_type(
order_book_id) == DEFAULT_ACCOUNT_TYPE.STOCK:
continue
trading_minutes.update(
env.data_proxy.get_trading_minutes_for(
order_book_id, trading_date))
return set([
convert_int_to_datetime(minute) for minute in trading_minutes
])
# TODO future hours
return set()
def get_bars(self,
order_book_id,
dt,
bar_count=1,
frequency='1d',
fields=None,
skip_suspended=True,
include_now=False,
adjust_type='pre',
adjust_orig=None,
convert_to_dataframe=False,
time_format='datetime'):
order_book_id = to_order_book_id(order_book_id)
dt = to_date_object(dt) #TODO if we want minute bar?
if fields is None:
fields = [
'datetime', 'open', 'high', 'low', 'close', 'volume',
'total_turnover'
]
bars = super(DataSource,
self).history_bars(order_book_id=order_book_id,
bar_count=bar_count,
frequency=frequency,
field=fields,
dt=dt,
skip_suspended=skip_suspended,
include_now=include_now,
adjust_type=adjust_type,
adjust_orig=adjust_orig)
if convert_to_dataframe:
df = pd.DataFrame.from_dict(bars)
if 'datetime' in df.columns:
if time_format == 'datetime':
df['datetime'] = df['datetime'].map(
lambda x: convert_int_to_datetime(x))
if time_format == 'int' and frequency == '1d':
df['datetime'] = df['datetime'].map(lambda x: x / 1000000)
df.set_index('datetime', inplace=True)
# df.index.name = ''
# df.index[0] is like: Timestamp('2005-01-04 00:00:00'),type is pandas._libs.tslib.Timestamp
return df
return bars # type(bars): numpy.ndarray
def resample_week_bars(self, bars, bar_count, fields):
df_bars = pd.DataFrame(bars)
df_bars['datetime'] = df_bars.apply(lambda x: convert_int_to_datetime(x['datetime']), axis=1)
df_bars = df_bars.set_index('datetime')
nead_fields = fields
if isinstance(nead_fields, str):
nead_fields = [nead_fields]
hows = {field: BAR_RESAMPLE_FIELD_METHODS[field] for field in nead_fields if field in BAR_RESAMPLE_FIELD_METHODS}
df_bars = df_bars.resample('W-Fri').agg(hows)
df_bars.index = df_bars.index.map(self._update_weekly_trading_date_index)
df_bars = df_bars[~df_bars.index.duplicated(keep='first')]
df_bars.sort_index(inplace=True)
df_bars = df_bars[-bar_count:]
df_bars = df_bars.reset_index()
df_bars['datetime'] = df_bars.apply(lambda x: np.uint64(convert_date_to_int(x['datetime'].date())), axis=1)
df_bars = df_bars.set_index('datetime')
bars = df_bars.to_records()
return bars
def datetime(self):
if self._dt is not None:
return self._dt
enviroment = Environment.get_instance()
dict = enviroment.config.mod.tushare
#print(dict.__dict__.get('enabled'))
if dict.__dict__.get('enabled') == True:
print('enviroment.config.mod.tushare[\'enabled\'] == True')
return self._data['date']
dict2 = enviroment.config.mod.mysql
# print(dict2.__dict__.get('enabled'))
if dict2.__dict__.get('enabled') == True:
print('enviroment.config.mod.mysql[\'enabled\'] == True')
print(self._data)
return self._data['date']
#datetime1 = convert_int_to_datetime(self._data['date'])
#datetime1 = convert_int_to_datetime(self._data['datetime'])
return convert_int_to_datetime(self._data['datetime'])
def get_bars_all(self,
order_book_id,
dt=None,
frequency='1d',
fields=None,
skip_suspended=True,
include_now=False,
adjust_type='pre',
adjust_orig=None,
convert_to_dataframe=False):
order_book_id = to_order_book_id(order_book_id)
if dt is None:
dt = datetime.datetime.now()
dt = to_date_object(dt)
if fields is None:
fields = [
'datetime', 'open', 'high', 'low', 'close', 'volume',
'total_turnover'
]
bars = super(DataSource,
self).get_bars_all(order_book_id=order_book_id,
frequency=frequency,
field=fields,
dt=dt,
skip_suspended=skip_suspended,
include_now=include_now,
adjust_type=adjust_type,
adjust_orig=adjust_orig)
if convert_to_dataframe:
df = pd.DataFrame.from_dict(bars)
if 'datetime' in df.columns:
df['datetime'] = df['datetime'].map(
lambda x: convert_int_to_datetime(x))
df.set_index('datetime', inplace=True)
df.index.name = ''
return df
return bars
def last_dt(self):
if len(self):
return convert_int_to_datetime(self._data[-1]["datetime"])
else:
return None
def datetime(self):
if self._dt is not None:
return self._dt
return convert_int_to_datetime(self._data['datetime'])
def history(self, order_book_id, bar_count, frequency, field, dt):
data = self.history_bars(order_book_id, bar_count, frequency,
['datetime', field], dt, skip_suspended=False, adjust_orig=dt)
if data is None:
return None
return pd.Series(data[field], index=[convert_int_to_datetime(t) for t in data['datetime']])
def datetime(self):
if self._dt is not None:
return self._dt
return convert_int_to_datetime(self._data['datetime'])
