def init_finance_table(self, start_date=0):
self.authenticate()
self.stock = jdk.normalize_code(self.stock)
self.stock_info = jdk.get_security_info(self.stock)
if self.stock_info is None:
print('Error: Cannot get data or stock code not correct!')
'''Internal usage. To get finance table'''
q = jdk.query(jdk.valuation).filter(jdk.valuation.code == self.stock)
end_date = datetime.now().date()
if start_date == 0:
df = jdk.get_fundamentals_continuously(q, end_date=end_date, count=5000)
else:
# get the days between start date and end date
date_span = jdk.get_trade_days(start_date=start_date, end_date=end_date)
if len(date_span) == 0:
return
df = jdk.get_fundamentals_continuously(q, end_date=end_date, count=len(date_span))
if df is None:
return
self.finance_data = df.swapaxes('items', 'minor_axis')
self.finance_data = self.finance_data[self.stock]
del self.finance_data['day.1']
del self.finance_data['code.1']
del self.finance_data['id']
return
async def get_valuation(self,
codes: Union[str, List[str]],
day: datetime.date,
n: int = 1) -> np.array:
if not self.connected:
logger.warning("not connected")
return None
"""get `n` of `code`'s valuation records, end at day.
对同一证券，返回的数据按升序排列（但取决于上游数据源）
Args:
code (str): [description]
day (datetime.date): [description]
n (int): [description]
Returns:
np.array: [description]
"""
if isinstance(codes, str):
codes = [codes]
if codes is None:
q = jq.query(jq.valuation)
else:
q = jq.query(jq.valuation).filter(jq.valuation.code.in_(codes))
records = jq.get_fundamentals_continuously(q,
count=n,
end_date=day,
panel=False)
return self._to_numpy(records)
def get_fundamentals_continuously(code, Date, Count):
myq = query(jqdatasdk.valuation).filter(jqdatasdk.valuation.code == code)
GL_df = jqdatasdk.get_fundamentals_continuously(myq,
end_date=Date,
count=Count,
panel=False)
return GL_df
def record(self, entity, start, end, size, timestamps):
q = query(valuation).filter(valuation.code == to_jq_entity_id(entity))
count: pd.Timedelta = now_pd_timestamp() - start
df = get_fundamentals_continuously(q,
end_date=now_time_str(),
count=count.days + 1,
panel=False)
df['entity_id'] = entity.id
df['timestamp'] = pd.to_datetime(df['day'])
df['code'] = entity.code
df['name'] = entity.name
df['id'] = df['timestamp'].apply(
lambda x: "{}_{}".format(entity.id, to_time_str(x)))
df = df.rename(
{
'pe_ratio_lyr': 'pe',
'pe_ratio': 'pe_ttm',
'pb_ratio': 'pb',
'ps_ratio': 'ps',
'pcf_ratio': 'pcf'
},
axis='columns')
df['market_cap'] = df['market_cap'] * 100000000
df['circulating_market_cap'] = df['circulating_market_cap'] * 100000000
df['capitalization'] = df['capitalization'] * 10000
df['circulating_cap'] = df['circulating_cap'] * 10000
df['turnover_ratio'] = df['turnover_ratio'] * 0.01
df_to_db(df=df,
data_schema=self.data_schema,
provider=self.provider,
force_update=self.force_update)
return None
def jq_get_fundamentals_continuously(query_object,
end_date=None,
count=1,
panel=True):
logger.info(
"HTTP GET: fundamentals_continuously, with end_date={}, count={}".
format(end_date, count))
return get_fundamentals_continuously(query_object,
end_date=end_date,
count=count,
panel=panel)
def maintask():
a=pd.DataFrame()
j=1
for i in indexs:
print('正在获取第%d家，股票代码%s.' % (j, i))
j+=1
q = jq.query(jq.valuation.turnover_ratio).filter(jq.valuation.code == i)
df = jq.get_fundamentals_continuously(q,end_date='2019-05-8', count=244)
dff=df.to_frame()
a=a.append(dff)
a.to_csv("C:/Users/Administrator/Desktop/1y.csv")
print(dff)
def financeData(self, stock_code):
q = jq.query(jq.valuation.turnover_ratio, jq.valuation.market_cap,
jq.valuation.pb_ratio, jq.valuation.pe_ratio,
jq.valuation.pcf_ratio, jq.indicator.eps).filter(
jq.valuation.code.in_([stock_code]))
#ROE = PB/PE
df = jq.get_fundamentals_continuously(q,
end_date=default_end,
count=20)
df = add_roe(df)
return df
def getTurnoverRatio(date_time, stock_code_list):
query = jq.query(jq.valuation.turnover_ratio).filter(
jq.valuation.code.in_(stock_code_list))
result = jq.get_fundamentals_continuously(query,
end_date=date_time,
count=1)
print(result)
#result = jq.get_fundamentals_continuously(query, start_date=date_time,end_date=date_time)
code = list(result['code'])
cps = list(result['turnover_ratio'])
code_cps_dict = dict(zip(code, cps))
return np.array([code_cps_dict[k] for k in stock_code_list])
def draw_line(stock_name, type, days=600):
info = jqdatasdk.get_security_info(stock_name)
if info is None:
return 'error'
q = jqdatasdk.query(jqdatasdk.valuation).filter(jqdatasdk.valuation.code == stock_name)
now = datetime.now()
date = '%d-%d-%d' % (now.year, now.month, now.day)
df = jqdatasdk.get_fundamentals_continuously(q, end_date=date, count=days)
print(df['pe_ratio'].index)
if type == 'PE':
return draw_diagram(df['pe_ratio'], 'PE-TTM', stock_name, info.display_name)
elif type == 'PB':
return draw_diagram(df['pb_ratio'], 'PB', stock_name, info.display_name)
final_stks = [x[:6] for x in res2.index.tolist()]
# 将放量上影线结果写入csv中
df_fl = df_pos[df_pos['股票代码'].isin(final_stks)]
df_fl.loc[:, '日期'] = datetime.datetime.now().strftime('%Y%m%d')
df_fl.to_csv('放量上影线.csv', mode='a', index=False, encoding='gbk', header=False)
print("=======今日放量上影线股票===============")
print(df_pos[df_pos['股票代码'].isin(final_stks)])
# ============计算换手率===========================================
q = jq.query(jq.valuation.turnover_ratio).filter(
jq.valuation.code.in_(holdings))
turnover = jq.get_fundamentals_continuously(q,
end_date=datetime.datetime.now(),
count=20)['turnover_ratio']
# 五日换手总和
tn_sum = turnover.iloc[-5:].sum() / 100
# 五日换手大于50%股票
tn_stks = tn_sum[tn_sum > 0.5].index.tolist()
# 100天内高点出现在最近5天
max5 = prices['close'].iloc[-5:].max()
max100 = prices['close'].iloc[-100:].max()
match_items = (max5 == max100)
match_stks = match_items[match_items].index.tolist()
final_match = list(set(tn_stks) & set(match_stks))
final_match = [x[:6] for x in final_match]
