def test_data_api():
address = data_config.get("remote.data.address", None)
username = data_config.get("remote.data.username", None)
password = data_config.get("remote.data.password", None)
if address is None or username is None or password is None:
raise ValueError("no data service config available!")
api = DataApi(address, use_jrpc=False)
login_msg = api.login(username=username, password=password)
print login_msg
daily, msg = api.daily(symbol="600030.SH,000002.SZ", start_date=20170103, end_date=20170708,
fields="open,high,low,close,volume,last,trade_date,settle")
daily2, msg2 = api.daily(symbol="600030.SH", start_date=20170103, end_date=20170708,
fields="open,high,low,close,volume,last,trade_date,settle")
# err_code, err_msg = msg.split(',')
assert msg == '0,'
assert msg2 == '0,'
assert daily.shape == (248, 9)
assert daily2.shape == (124, 9)
df, msg = api.bar(symbol="600030.SH", trade_date=20170904, freq=common.QUOTE_TYPE.MIN, start_time=90000, end_time=150000)
print df.columns
assert df.shape == (240, 15)
print "test passed"
class TurtleTest(unittest.TestCase):
user_id = ''
def setUp(self):
self.api = DataApi(addr='tcp://data.tushare.org:8910')
self.api.login(
self.user_id,
"eyJhbGciOiJIUzI1NiJ9.eyJjcmVhdGVfdGltZSI6IjE1MjY5MDQ0NTgwODAiLCJpc3MiOiJhdXRoMCIsImlkIjoiMTg4MTAyOTkyMDAifQ.M9d07UvpEWVWugga0d2UtbWsRRZubS5mgMXaTxrieSk"
)
def tearDown(self):
pass
def test_api(self):
df, msg = self.api.daily(symbol="cu1801.SHF",
start_date="2017-10-26",
end_date="2018-11-06",
fields="",
adjust_mode="post")
self.assertEqual(msg, '0,')
self.assertTupleEqual(df.shape, (57, 16))
#! usr/bin/python
#coding=utf-8
from jaqs.data import DataApi
api = DataApi(addr="tcp://data.tushare.org:8910")
api.login(
"18852992009", "eyJhbGciOiJIUzI1NiJ9.eyJjcmV\
hdGVfdGltZSI6IjE1MTMxODExNzkxNDIiLCJpc3MiOiJhdXR\
oMCIsImlkIjoiMTg4NTI5OTIwMDkifQ.RVrI6_rndVcgwRvJYL\
fbPkBNuLEy65kG0lcOZSp4g8w")
# 士兰微(SH:600460)
# df,msg = api.quote("600460.SH", fields="open,high,low,last,volume")
df, msg = api.daily(symbol="600460.SH",
start_date=20171210,
end_date=20171212,
fields="",
adjust_mode="post")
print df
print msg
from jaqs.data import DataApi
api = DataApi(addr="tcp://data.tushare.org:8910")
api.login(
"18852992009", "eyJhbGciOiJIUzI1NiJ9.eyJjcmV\
hdGVfdGltZSI6IjE1MTMxODExNzkxNDIiLCJpc3MiOiJhdXR\
oMCIsImlkIjoiMTg4NTI5OTIwMDkifQ.RVrI6_rndVcgwRvJYL\
fbPkBNuLEy65kG0lcOZSp4g8w")
df, msg = api.daily(symbol="600832.SH, 600030.SH",
start_date=20121026,
end_date=20121130,
fields="",
adjust_mode="post")
_start_date = '2014-01-01'
_end_date = '2018-02-02'
phone = '13706519134'
token = 'eyJhbGciOiJIUzI1NiJ9.eyJjcmVhdGVfdGltZSI6IjE1MTM0ODE2NTM4MTAiLCJpc3MiOiJhdXRoMCIsImlkIjoiMTM3MDY1MTkxMzQifQ.6wOPdEDGnSBwMzb3dDVp_8LPBT0UbHqnFFvCc15bL_U'
api = DataApi("tcp://data.tushare.org:8910")
df, msg = api.login(phone, token)
#获取融资余额
df1 = ts.sh_margins(start=_start_date, end=_end_date)
df1.index = pd.to_datetime(df1.opDate)
df1.sort_index(inplace=True)
#获取上证日收盘价
df_sh, msg = api.daily(symbol='000001.SH', start_date=_start_date, end_date = _end_date, fields = '', freq = '1d')
df_sh.trade_date = pd.Series(df_sh.trade_date, index=df_sh.index, dtype=str)
df_sh.index = pd.to_datetime(df_sh.trade_date)
df_sh.sort_index(inplace=True)
df_a = df1['rzye']/100000000
df_b = df_sh['close']
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.set_xlabel('Daily')
ax1.set_ylabel('Amount(^9)')
ax1.set_title('SH RZYE & SH Index')
ax1.plot(df_a, label='RZYE', c='r')
ax1.legend()
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):
# do not initialize and login again
if self.data_api is not None and self.data_api._loggined:
return
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 = dict(
) # 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.data.address", "")
username = get_from_list_of_dict(dic_list, "remote.data.username", "")
password = get_from_list_of_dict(dic_list, "remote.data.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)
# -----------------------------------------------------------------------------------
# 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_OLD(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)
# ---------------------------------------------------------------------
# Calendar
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