class Calendar(object):
"""
A calendar for manage trade date.
Attributes
----------
data_api :
"""
def __init__(self, data_api=None):
if data_api is not None:
self.data_api = data_api
else:
props = jutil.read_json(
jutil.join_relative_path('etc/data_config.json'))
address = props.get("remote.address", "")
username = props.get("remote.username", "")
password = props.get("remote.password", "")
if address is None or username is None or password is None:
raise ValueError("no address, username or password available!")
time_out = props.get("timeout", 60)
self.data_api = DataApi(address, use_jrpc=False)
self.data_api.set_timeout(timeout=time_out)
r, msg = self.data_api.login(username=username, password=password)
if not r:
print("DataAPI login failed: msg = '{}".format(msg))
else:
print "DataAPI login success : {}@{}".format(username, address)
@staticmethod
def _dic2url(d):
"""
Convert a dict to str like 'k1=v1&k2=v2'
Parameters
----------
d : dict
Returns
-------
str
"""
l = ['='.join([key, str(value)]) for key, value in d.viewitems()]
return '&'.join(l)
def get_trade_date_range(self, start_date, end_date):
"""
Get array of trade dates within given range.
Return zero size array if no trade dates within range.
Parameters
----------
start_date : int
YYmmdd
end_date : int
Returns
-------
trade_dates_arr : np.ndarray
dtype = int
"""
filter_argument = self._dic2url({
'start_date': start_date,
'end_date': end_date
})
df_raw, msg = self.data_api.query("jz.secTradeCal",
fields="trade_date",
filter=filter_argument,
orderby="")
if df_raw.empty:
return np.array([], dtype=int)
trade_dates_arr = df_raw['trade_date'].values.astype(int)
return trade_dates_arr
def get_last_trade_date(self, date):
"""
Parameters
----------
date : int
Returns
-------
res : int
"""
dt = jutil.convert_int_to_datetime(date)
delta = pd.Timedelta(weeks=2)
dt_old = dt - delta
date_old = jutil.convert_datetime_to_int(dt_old)
dates = self.get_trade_date_range(date_old, date)
mask = dates < date
res = dates[mask][-1]
return res
def is_trade_date(self, date):
"""
Check whether date is a trade date.
Parameters
----------
date : int
Returns
-------
bool
"""
dates = self.get_trade_date_range(date, date)
return len(dates) > 0
def get_next_trade_date(self, date):
"""
Parameters
----------
date : int
Returns
-------
res : int
"""
dt = jutil.convert_int_to_datetime(date)
delta = pd.Timedelta(weeks=2)
dt_new = dt + delta
date_new = jutil.convert_datetime_to_int(dt_new)
dates = self.get_trade_date_range(date, date_new)
mask = dates > date
res = dates[mask][0]
return res
class RemoteDataService(DataService):
"""
RemoteDataService is a concrete class using data from remote server's database.
"""
__metaclass__ = Singleton
# TODO no validity check for input parameters
def __init__(self):
DataService.__init__(self)
dic = fileio.read_json(
fileio.join_relative_path('etc/data_config.json'))
address = dic.get("remote.address", None)
username = dic.get("remote.username", None)
password = dic.get("remote.password", None)
if address is None or username is None or password is None:
raise ValueError("no address, username or password available!")
self.api = DataApi(address, use_jrpc=False)
self.api.set_timeout(60)
r, msg = self.api.login(username=username, password=password)
if not r:
print msg
else:
print "DataAPI login success.".format(address)
self.REPORT_DATE_FIELD_NAME = 'report_date'
def daily(self, symbol, start_date, end_date, fields="", adjust_mode=None):
df, err_msg = self.api.daily(symbol=symbol,
start_date=start_date,
end_date=end_date,
fields=fields,
adjust_mode=adjust_mode,
data_format="")
# trade_status performance warning
# TODO there will be duplicate entries when on stocks' IPO day
df = df.drop_duplicates()
return df, err_msg
def bar(self,
symbol,
start_time=200000,
end_time=160000,
trade_date=None,
freq='1m',
fields=""):
df, msg = self.api.bar(symbol=symbol,
fields=fields,
start_time=start_time,
end_time=end_time,
trade_date=trade_date,
freq='1m',
data_format="")
return df, msg
def query(self, view, filter="", fields="", **kwargs):
"""
Get various reference data.
Parameters
----------
view : str
data source.
fields : str
Separated by ','
filter : str
filter expressions.
kwargs
Returns
-------
df : pd.DataFrame
msg : str
error code and error message, joined by ','
Examples
--------
res3, msg3 = ds.query("lb.secDailyIndicator", fields="price_level,high_52w_adj,low_52w_adj",
filter="start_date=20170907&end_date=20170907",
orderby="trade_date",
data_format='pandas')
view does not change. fileds can be any field predefined in reference data api.
"""
df, msg = self.api.query(view,
fields=fields,
filter=filter,
data_format="",
**kwargs)
return df, msg
def get_suspensions(self):
return None
# TODO use Calendar instead
def get_trade_date(self,
start_date,
end_date,
symbol=None,
is_datetime=False):
if symbol is None:
symbol = '000300.SH'
df, msg = self.daily(symbol, start_date, end_date, fields="close")
res = df.loc[:, 'trade_date'].values
if is_datetime:
res = dtutil.convert_int_to_datetime(res)
return res
@staticmethod
def _dic2url(d):
"""
Convert a dict to str like 'k1=v1&k2=v2'
Parameters
----------
d : dict
Returns
-------
str
"""
l = ['='.join([key, str(value)]) for key, value in d.items()]
return '&'.join(l)
def query_lb_fin_stat(self,
type_,
symbol,
start_date,
end_date,
fields=""):
"""
Helper function to call data_api.query with 'lb.income' more conveniently.
Parameters
----------
type_ : {'income', 'balance_sheet', 'cash_flow'}
symbol : str
separated by ','
start_date : int
Annoucement date in results will be no earlier than start_date
end_date : int
Annoucement date in results will be no later than start_date
fields : str, optional
separated by ',', default ""
Returns
-------
df : pd.DataFrame
index date, columns fields
msg : str
"""
view_map = {
'income': 'lb.income',
'cash_flow': 'lb.cashFlow',
'balance_sheet': 'lb.balanceSheet',
'fin_indicator': 'lb.finIndicator'
}
view_name = view_map.get(type_, None)
if view_name is None:
raise NotImplementedError("type_ = {:s}".format(type_))
dic_argument = {
'symbol': symbol,
'start_date': start_date,
'end_date': end_date,
'update_flag': '0'
}
if view_name != 'lb.finIndicator':
dic_argument.update({
'report_type': '408001000'
}) # we do not use single quarter single there are zeros
"""
408001000: joint
408002000: joint (single quarter)
"""
filter_argument = self._dic2url(
dic_argument) # 0 means first time, not update
res, msg = self.query(view_name,
fields=fields,
filter=filter_argument,
order_by=self.REPORT_DATE_FIELD_NAME)
# change data type
try:
cols = list(
set.intersection({'ann_date', 'report_date'},
set(res.columns)))
dic_dtype = {col: int for col in cols}
res = res.astype(dtype=dic_dtype)
except:
pass
return res, msg
def query_lb_dailyindicator(self, symbol, start_date, end_date, fields=""):
"""
Helper function to call data_api.query with 'lb.secDailyIndicator' more conveniently.
Parameters
----------
symbol : str
separated by ','
start_date : int
end_date : int
fields : str, optional
separated by ',', default ""
Returns
-------
df : pd.DataFrame
index date, columns fields
msg : str
"""
filter_argument = self._dic2url({
'symbol': symbol,
'start_date': start_date,
'end_date': end_date
})
return self.query("lb.secDailyIndicator",
fields=fields,
filter=filter_argument,
orderby="trade_date")
def _get_index_comp(self, index, start_date, end_date):
"""
Return all securities that have been in index during start_date and end_date.
Parameters
----------
index : str
separated by ','
start_date : int
end_date : int
Returns
-------
list
"""
filter_argument = self._dic2url({
'index_code': index,
'start_date': start_date,
'end_date': end_date
})
df_io, msg = self.query("lb.indexCons",
fields="",
filter=filter_argument,
orderby="symbol")
return df_io, msg
def get_index_comp(self, index, start_date, end_date):
"""
Return list of symbols that have been in index during start_date and end_date.
Parameters
----------
index : str
separated by ','
start_date : int
end_date : int
Returns
-------
list
"""
df_io, msg = self._get_index_comp(index, start_date, end_date)
if msg != '0,':
print msg
return list(np.unique(df_io.loc[:, 'symbol']))
def get_index_comp_df(self, index, start_date, end_date):
"""
Get index components on each day during start_date and end_date.
Parameters
----------
index : str
separated by ','
start_date : int
end_date : int
Returns
-------
res : pd.DataFrame
index dates, columns all securities that have ever been components,
values are 0 (not in) or 1 (in)
"""
df_io, msg = self._get_index_comp(index, start_date, end_date)
if msg != '0,':
print msg
def str2int(s):
if isinstance(s, (str, unicode)):
return int(s) if s else 99999999
elif isinstance(s, (int, np.integer, float, np.float)):
return s
else:
raise NotImplementedError("type s = {}".format(type(s)))
df_io.loc[:, 'in_date'] = df_io.loc[:, 'in_date'].apply(str2int)
df_io.loc[:, 'out_date'] = df_io.loc[:, 'out_date'].apply(str2int)
# df_io.set_index('symbol', inplace=True)
dates = self.get_trade_date(start_date=start_date,
end_date=end_date,
symbol=index)
dic = dict()
gp = df_io.groupby(by='symbol')
for sec, df in gp:
mask = np.zeros_like(dates, dtype=int)
for idx, row in df.iterrows():
bool_index = np.logical_and(dates > row['in_date'],
dates < row['out_date'])
mask[bool_index] = 1
dic[sec] = mask
res = pd.DataFrame(index=dates, data=dic)
return res
@staticmethod
def _group_df_to_dict(df, by):
gp = df.groupby(by=by)
res = {key: value for key, value in gp}
return res
def get_industry_daily(self, symbol, start_date, end_date, type_='SW'):
"""
Get index components on each day during start_date and end_date.
Parameters
----------
symbol : str
separated by ','
start_date : int
end_date : int
type_ : {'SW', 'ZZ'}
Returns
-------
res : pd.DataFrame
index dates, columns symbols
values are industry code
"""
df_raw = self.get_industry_raw(symbol, type_=type_)
dic_sec = self._group_df_to_dict(df_raw, by='symbol')
dic_sec = {
sec: df.sort_values(by='in_date', axis=0).reset_index()
for sec, df in dic_sec.viewitems()
}
df_ann = pd.concat([
df.loc[:, 'in_date'].rename(sec)
for sec, df in dic_sec.viewitems()
],
axis=1)
df_value = pd.concat([
df.loc[:, 'industry1_code'].rename(sec)
for sec, df in dic_sec.viewitems()
],
axis=1)
dates_arr = self.get_trade_date(start_date, end_date)
df_industry = align.align(df_value, df_ann, dates_arr)
# TODO before industry classification is available, we assume they belong to their first group.
df_industry = df_industry.fillna(method='bfill')
df_industry = df_industry.astype(str)
return df_industry
def get_industry_raw(self, symbol, type_='ZZ'):
"""
Get daily industry of securities from ShenWanHongYuan or ZhongZhengZhiShu.
Parameters
----------
symbol : str
separated by ','
type_ : {'SW', 'ZZ'}
Returns
-------
df : pd.DataFrame
"""
if type_ == 'SW':
src = u'申万研究所'.encode('utf-8')
elif type_ == 'ZZ':
src = u'中证指数有限公司'.encode('utf-8')
else:
raise ValueError("type_ must be one of SW of ZZ")
filter_argument = self._dic2url({
'symbol': symbol,
'industry_src': src
})
fields_list = ['symbol', 'industry1_code', 'industry1_name']
df_raw, msg = self.query("lb.secIndustry",
fields=','.join(fields_list),
filter=filter_argument,
orderby="symbol")
if msg != '0,':
print msg
df_raw = df_raw.astype(dtype={
'in_date': int,
# 'out_date': int
})
return df_raw.drop_duplicates()
def get_adj_factor_daily(self, symbol, start_date, end_date, div=False):
"""
Get index components on each day during start_date and end_date.
Parameters
----------
symbol : str
separated by ','
start_date : int
end_date : int
div : bool
False for normal adjust factor, True for diff.
Returns
-------
res : pd.DataFrame
index dates, columns symbols
values are industry code
"""
df_raw = self.get_adj_factor_raw(symbol)
dic_sec = self._group_df_to_dict(df_raw, by='symbol')
dic_sec = {
sec: df.loc[:, ['trade_date', 'adjust_factor']].set_index(
'trade_date').iloc[:, 0]
for sec, df in dic_sec.viewitems()
}
res = pd.concat(dic_sec, axis=1)
# align to every trade date
s, e = df_raw.loc[:,
'trade_date'].min(), df_raw.loc[:,
'trade_date'].max()
dates_arr = self.get_trade_date(s, e)
res = res.reindex(dates_arr)
res = res.fillna(method='ffill').fillna(method='bfill')
if div:
res = res.div(res.shift(1, axis=0)).fillna(1.0)
res = res.loc[start_date:end_date, :]
return res
def get_adj_factor_raw(self, symbol, start_date=None, end_date=None):
"""
Query adjust factor for symbols.
Parameters
----------
symbol : str
separated by ','
start_date : int
end_date : int
Returns
-------
df : pd.DataFrame
"""
if start_date is None:
start_date = ""
if end_date is None:
end_date = ""
filter_argument = self._dic2url({
'symbol': symbol,
'start_date': start_date,
'end_date': end_date
})
fields_list = ['symbol', 'trade_date', 'adjust_factor']
df_raw, msg = self.query("lb.secAdjFactor",
fields=','.join(fields_list),
filter=filter_argument,
orderby="symbol")
if msg != '0,':
print msg
df_raw = df_raw.astype(dtype={
'symbol': str,
'trade_date': int,
'adjust_factor': float
})
return df_raw.drop_duplicates()
def query_inst_info(self, symbol, inst_type="", fields=""):
if inst_type == "":
inst_type = "1,2,3,4,5,101,102,103,104"
filter_argument = self._dic2url({
'symbol': symbol,
'inst_type': inst_type
})
df_raw, msg = self.query("jz.instrumentInfo",
fields=fields,
filter=filter_argument,
orderby="symbol")
if msg != '0,':
print msg
dtype_map = {'symbol': str, 'list_date': int, 'delist_date': int}
cols = set(df_raw.columns)
dtype_map = {k: v for k, v in dtype_map.items() if k in cols}
df_raw = df_raw.astype(dtype=dtype_map)
return df_raw, msg
class RemoteDataService(DataService):
"""
RemoteDataService is a concrete class using data from remote server's database.
"""
__metaclass__ = Singleton
# TODO no validity check for input parameters
def __init__(self):
DataService.__init__(self)
self.data_api = None
self.REPORT_DATE_FIELD_NAME = 'report_date'
self.calendar = None
def __del__(self):
self.data_api.close()
def init_from_config(self, props=None):
if props is None:
props = dict()
if self.data_api is not None:
if len(props) == 0:
return
else:
self.data_api.close()
def get_from_list_of_dict(l, key, default=None):
res = None
for dic in l:
res = dic.get(key, None)
if res is not None:
break
if res is None:
res = default
return res
props_default = jutil.read_json(
jutil.join_relative_path('etc/data_config.json'))
dic_list = [props, props_default]
address = get_from_list_of_dict(dic_list, "remote.address", "")
username = get_from_list_of_dict(dic_list, "remote.username", "")
password = get_from_list_of_dict(dic_list, "remote.password", "")
if address is None or username is None or password is None:
raise ValueError("no address, username or password available!")
time_out = get_from_list_of_dict(dic_list, "timeout", 60)
self.data_api = DataApi(address, use_jrpc=False)
self.data_api.set_timeout(timeout=time_out)
print("\nDataApi login: {}@{}".format(username, address))
r, msg = self.data_api.login(username=username, password=password)
if not r:
print(" login failed: msg = '{}'\n".format(msg))
else:
print " login success \n"
self.calendar = Calendar(self.data_api)
# -----------------------------------------------------------------------------------
# Basic APIs
def daily(self, symbol, start_date, end_date, fields="", adjust_mode=None):
df, err_msg = self.data_api.daily(symbol=symbol,
start_date=start_date,
end_date=end_date,
fields=fields,
adjust_mode=adjust_mode,
data_format="")
# trade_status performance warning
# TODO there will be duplicate entries when on stocks' IPO day
df = df.drop_duplicates()
return df, err_msg
def bar(self,
symbol,
start_time=200000,
end_time=160000,
trade_date=None,
freq='1M',
fields=""):
df, msg = self.data_api.bar(symbol=symbol,
fields=fields,
start_time=start_time,
end_time=end_time,
trade_date=trade_date,
freq='1M',
data_format="")
return df, msg
def query(self, view, filter="", fields="", **kwargs):
"""
Get various reference data.
Parameters
----------
view : str
data source.
fields : str
Separated by ','
filter : str
filter expressions.
kwargs
Returns
-------
df : pd.DataFrame
msg : str
error code and error message, joined by ','
Examples
--------
res3, msg3 = ds.query("lb.secDailyIndicator", fields="price_level,high_52w_adj,low_52w_adj",\
filter="start_date=20170907&end_date=20170907",\
orderby="trade_date",\
data_format='pandas')
view does not change. fileds can be any field predefined in reference data api.
"""
df, msg = self.data_api.query(view,
fields=fields,
filter=filter,
data_format="",
**kwargs)
return df, msg
# -----------------------------------------------------------------------------------
# Convenient Functions
def get_trade_date_range(self, start_date, end_date):
return self.calendar.get_trade_date_range(start_date, end_date)
@staticmethod
def _dic2url(d):
"""
Convert a dict to str like 'k1=v1&k2=v2'
Parameters
----------
d : dict
Returns
-------
str
"""
l = ['='.join([key, str(value)]) for key, value in d.viewitems()]
return '&'.join(l)
def query_lb_fin_stat(self,
type_,
symbol,
start_date,
end_date,
fields="",
drop_dup_cols=None):
"""
Helper function to call data_api.query with 'lb.income' more conveniently.
Parameters
----------
type_ : {'income', 'balance_sheet', 'cash_flow'}
symbol : str
separated by ','
start_date : int
Annoucement date in results will be no earlier than start_date
end_date : int
Annoucement date in results will be no later than start_date
fields : str, optional
separated by ',', default ""
drop_dup_cols : list or tuple
Whether drop duplicate entries according to drop_dup_cols.
Returns
-------
df : pd.DataFrame
index date, columns fields
msg : str
"""
view_map = {
'income': 'lb.income',
'cash_flow': 'lb.cashFlow',
'balance_sheet': 'lb.balanceSheet',
'fin_indicator': 'lb.finIndicator'
}
view_name = view_map.get(type_, None)
if view_name is None:
raise NotImplementedError("type_ = {:s}".format(type_))
dic_argument = {
'symbol': symbol,
'start_date': start_date,
'end_date': end_date,
# 'update_flag': '0'
}
if view_name != 'lb.finIndicator':
dic_argument.update({
'report_type': '408001000'
}) # we do not use single quarter single there are zeros
"""
408001000: joint
408002000: joint (single quarter)
"""
filter_argument = self._dic2url(
dic_argument) # 0 means first time, not update
res, msg = self.query(view_name,
fields=fields,
filter=filter_argument,
order_by=self.REPORT_DATE_FIELD_NAME)
# change data type
try:
cols = list(
set.intersection({'ann_date', 'report_date'},
set(res.columns)))
dic_dtype = {col: int for col in cols}
res = res.astype(dtype=dic_dtype)
except:
pass
if drop_dup_cols is not None:
res = res.sort_values(by=drop_dup_cols, axis=0)
res = res.drop_duplicates(subset=drop_dup_cols, keep='first')
return res, msg
def query_lb_dailyindicator(self, symbol, start_date, end_date, fields=""):
"""
Helper function to call data_api.query with 'lb.secDailyIndicator' more conveniently.
Parameters
----------
symbol : str
separated by ','
start_date : int
end_date : int
fields : str, optional
separated by ',', default ""
Returns
-------
df : pd.DataFrame
index date, columns fields
msg : str
"""
filter_argument = self._dic2url({
'symbol': symbol,
'start_date': start_date,
'end_date': end_date
})
return self.query("lb.secDailyIndicator",
fields=fields,
filter=filter_argument,
orderby="trade_date")
def get_index_weights(self, index, trade_date):
"""
Return all securities that have been in index during start_date and end_date.
Parameters
----------
index : str
separated by ','
trade_date : int
Returns
-------
pd.DataFrame
"""
if index == '000300.SH':
index = '399300.SZ'
filter_argument = self._dic2url({
'index_code': index,
'trade_date': trade_date
})
df_io, msg = self.query("lb.indexWeight",
fields="",
filter=filter_argument)
if msg != '0,':
print msg
df_io = df_io.set_index('symbol')
df_io = df_io.astype({'weight': float, 'trade_date': int})
df_io.loc[:, 'weight'] = df_io['weight'] / 100.
return df_io
def get_index_weights_daily(self, index, start_date, end_date):
"""
Return all securities that have been in index during start_date and end_date.
Parameters
----------
index : str
start_date : int
end_date : int
Returns
-------
res : pd.DataFrame
Index is trade_date, columns are symbols.
"""
# TODO: temparary api
trade_dates = self.get_trade_date_range(start_date, end_date)
start_date, end_date = trade_dates[0], trade_dates[-1]
td = start_date
dic = dict()
symbols_set = set()
while True:
if td > end_date:
break
df = self.get_index_weights(index, td)
update_date = df['trade_date'].iat[0]
if update_date >= start_date and update_date <= end_date:
symbols_set.update(set(df.index))
dic[td] = df['weight']
td = jutil.get_next_period_day(td, 'month', 1)
merge = pd.concat(dic, axis=1).T
merge = merge.fillna(0.0) # for those which are not components
res = pd.DataFrame(index=trade_dates,
columns=sorted(list(symbols_set)),
data=np.nan)
res.update(merge)
res = res.fillna(method='ffill')
res = res.loc[start_date:end_date]
return res
def _get_index_comp(self, index, start_date, end_date):
"""
Return all securities that have been in index during start_date and end_date.
Parameters
----------
index : str
separated by ','
start_date : int
end_date : int
Returns
-------
list
"""
filter_argument = self._dic2url({
'index_code': index,
'start_date': start_date,
'end_date': end_date
})
df_io, msg = self.query("lb.indexCons",
fields="",
filter=filter_argument,
orderby="symbol")
return df_io, msg
def get_index_comp(self, index, start_date, end_date):
"""
Return list of symbols that have been in index during start_date and end_date.
Parameters
----------
index : str
separated by ','
start_date : int
end_date : int
Returns
-------
list
"""
df_io, msg = self._get_index_comp(index, start_date, end_date)
if msg != '0,':
print msg
return list(np.unique(df_io.loc[:, 'symbol']))
def get_index_comp_df(self, index, start_date, end_date):
"""
Get index components on each day during start_date and end_date.
Parameters
----------
index : str
separated by ','
start_date : int
end_date : int
Returns
-------
res : pd.DataFrame
index dates, columns all securities that have ever been components,
values are 0 (not in) or 1 (in)
"""
df_io, msg = self._get_index_comp(index, start_date, end_date)
if msg != '0,':
print msg
def str2int(s):
if isinstance(s, (str, unicode)):
return int(s) if s else 99999999
elif isinstance(s, (int, np.integer, float, np.float)):
return s
else:
raise NotImplementedError("type s = {}".format(type(s)))
df_io.loc[:, 'in_date'] = df_io.loc[:, 'in_date'].apply(str2int)
df_io.loc[:, 'out_date'] = df_io.loc[:, 'out_date'].apply(str2int)
# df_io.set_index('symbol', inplace=True)
dates = self.get_trade_date_range(start_date=start_date,
end_date=end_date)
dic = dict()
gp = df_io.groupby(by='symbol')
for sec, df in gp:
mask = np.zeros_like(dates, dtype=int)
for idx, row in df.iterrows():
bool_index = np.logical_and(dates > row['in_date'],
dates < row['out_date'])
mask[bool_index] = 1
dic[sec] = mask
res = pd.DataFrame(index=dates, data=dic)
return res
def get_industry_daily(self,
symbol,
start_date,
end_date,
type_='SW',
level=1):
"""
Get index components on each day during start_date and end_date.
Parameters
----------
symbol : str
separated by ','
start_date : int
end_date : int
type_ : {'SW', 'ZZ'}
Returns
-------
res : pd.DataFrame
index dates, columns symbols
values are industry code
"""
df_raw = self.get_industry_raw(symbol, type_=type_, level=level)
dic_sec = jutil.group_df_to_dict(df_raw, by='symbol')
dic_sec = {
sec: df.sort_values(by='in_date', axis=0).reset_index()
for sec, df in dic_sec.viewitems()
}
df_ann_tmp = pd.concat(
{sec: df.loc[:, 'in_date']
for sec, df in dic_sec.viewitems()},
axis=1)
df_value_tmp = pd.concat(
{
sec: df.loc[:, 'industry{:d}_code'.format(level)]
for sec, df in dic_sec.viewitems()
},
axis=1)
idx = np.unique(
np.concatenate([df.index.values for df in dic_sec.values()]))
symbol_arr = np.sort(symbol.split(','))
df_ann = pd.DataFrame(index=idx, columns=symbol_arr, data=np.nan)
df_ann.loc[df_ann_tmp.index, df_ann_tmp.columns] = df_ann_tmp
df_value = pd.DataFrame(index=idx, columns=symbol_arr, data=np.nan)
df_value.loc[df_value_tmp.index, df_value_tmp.columns] = df_value_tmp
dates_arr = self.get_trade_date_range(start_date, end_date)
df_industry = align.align(df_value, df_ann, dates_arr)
# TODO before industry classification is available, we assume they belong to their first group.
df_industry = df_industry.fillna(method='bfill')
df_industry = df_industry.astype(str)
return df_industry
def get_industry_raw(self, symbol, type_='ZZ', level=1):
"""
Get daily industry of securities from ShenWanZhiShu or ZhongZhengZhiShu.
Parameters
----------
symbol : str
separated by ','
type_ : {'SW', 'ZZ'}
level : {1, 2, 3, 4}
Use which level of industry index classification.
Returns
-------
df : pd.DataFrame
"""
if type_ == 'SW':
src = u'申万研究所'.encode('utf-8')
if level not in [1, 2, 3, 4]:
raise ValueError("For [SW], level must be one of {1, 2, 3, 4}")
elif type_ == 'ZZ':
src = u'中证指数有限公司'.encode('utf-8')
if level not in [1, 2, 3, 4]:
raise ValueError("For [ZZ], level must be one of {1, 2}")
else:
raise ValueError("type_ must be one of SW of ZZ")
filter_argument = self._dic2url({
'symbol': symbol,
'industry_src': src
})
fields_list = [
'symbol', 'industry{:d}_code'.format(level),
'industry{:d}_name'.format(level)
]
df_raw, msg = self.query("lb.secIndustry",
fields=','.join(fields_list),
filter=filter_argument,
orderby="symbol")
if msg != '0,':
print msg
df_raw = df_raw.astype(dtype={
'in_date': int,
# 'out_date': int
})
return df_raw.drop_duplicates()
def get_adj_factor_daily(self, symbol, start_date, end_date, div=False):
"""
Get index components on each day during start_date and end_date.
Parameters
----------
symbol : str
separated by ','
start_date : int
end_date : int
div : bool
False for normal adjust factor, True for diff.
Returns
-------
res : pd.DataFrame
index dates, columns symbols
values are industry code
"""
df_raw = self.get_adj_factor_raw(symbol,
start_date=start_date,
end_date=end_date)
dic_sec = jutil.group_df_to_dict(df_raw, by='symbol')
dic_sec = {
sec: df.set_index('trade_date').loc[:, 'adjust_factor']
for sec, df in dic_sec.viewitems()
}
# TODO: duplicate codes with dataview.py: line 512
res = pd.concat(dic_sec, axis=1) # TODO: fillna ?
idx = np.unique(
np.concatenate([df.index.values for df in dic_sec.values()]))
symbol_arr = np.sort(symbol.split(','))
res_final = pd.DataFrame(index=idx, columns=symbol_arr, data=np.nan)
res_final.loc[res.index, res.columns] = res
# align to every trade date
s, e = df_raw.loc[:,
'trade_date'].min(), df_raw.loc[:,
'trade_date'].max()
dates_arr = self.get_trade_date_range(s, e)
if not len(dates_arr) == len(res_final.index):
res_final = res_final.reindex(dates_arr)
res_final = res_final.fillna(method='ffill').fillna(method='bfill')
if div:
res_final = res_final.div(res_final.shift(1, axis=0)).fillna(1.0)
# res = res.loc[start_date: end_date, :]
return res_final
def get_adj_factor_raw(self, symbol, start_date=None, end_date=None):
"""
Query adjust factor for symbols.
Parameters
----------
symbol : str
separated by ','
start_date : int
end_date : int
Returns
-------
df : pd.DataFrame
"""
if start_date is None:
start_date = ""
if end_date is None:
end_date = ""
filter_argument = self._dic2url({
'symbol': symbol,
'start_date': start_date,
'end_date': end_date
})
fields_list = ['symbol', 'trade_date', 'adjust_factor']
df_raw, msg = self.query("lb.secAdjFactor",
fields=','.join(fields_list),
filter=filter_argument,
orderby="symbol")
if msg != '0,':
print msg
df_raw = df_raw.astype(dtype={
'symbol': str,
'trade_date': int,
'adjust_factor': float
})
return df_raw.drop_duplicates()
def query_inst_info(self, symbol, inst_type="", fields=""):
if inst_type == "":
inst_type = "1,2,3,4,5,101,102,103,104"
filter_argument = self._dic2url({
'symbol': symbol,
'inst_type': inst_type
})
df_raw, msg = self.query("jz.instrumentInfo",
fields=fields,
filter=filter_argument,
orderby="symbol")
if msg != '0,':
print msg
dtype_map = {
'symbol': str,
'list_date': int,
'delist_date': int,
'inst_type': int
}
cols = set(df_raw.columns)
dtype_map = {k: v for k, v in dtype_map.viewitems() if k in cols}
df_raw = df_raw.astype(dtype=dtype_map)
res = df_raw.set_index('symbol')
return res
# -----------------------------------------------------------------------------------
# subscribe for real time trading
def subscribe(self, symbols):
"""
Parameters
----------
symbols : str
Separated by ,
"""
self.data_api.subscribe(symbols, func=self.mkt_data_callback)
def mkt_data_callback(self, key, quote):
e = Event(EVENT_TYPE.MARKET_DATA)
# print quote
e.dic = {'quote': quote}
self.ctx.instance.put(e)