def get_k_data_JQ(stk,
count=None,
start_date=None,
end_date=None,
freq='daily'):
"""
使用JQData来下载stk的历史数据
:param stk_code:
:param amount:
:return:
"""
if 'm' in freq:
end_date = add_date_str(get_current_date_str(), 1)
if pd.isnull(end_date):
end_date = get_current_date_str()
try:
# 增加以兼容list的情况
if isinstance(stk, list):
stk_code = [jqdatasdk.normalize_code(x) for x in stk]
df = jqdatasdk.get_price(stk_code,
frequency=freq,
count=count,
end_date=end_date,
start_date=start_date)
elif stk in ['sh', 'sz', 'cyb', 'hs300', 'sz50', 'zz500']:
stk_code_normal = JQMethod.get_index_jq_code(stk)
df = jqdatasdk.get_price(stk_code_normal,
frequency=freq,
count=count,
start_date=start_date,
end_date=end_date)
else:
df = jqdatasdk.get_price(jqdatasdk.normalize_code(stk),
frequency=freq,
count=count,
end_date=end_date,
start_date=start_date)
if df.empty:
return df
df['datetime'] = df.index
df['date'] = df.apply(lambda x: str(x['datetime'])[:10], axis=1)
return df
except Exception as e:
print('函数get_k_data_JQ：出错！错误详情：\n' + str(e))
return pd.DataFrame()
def get_RT_price(stk_code, source='jq'):
if source == 'jq':
# 使用聚宽数据接口替代
if stk_code in ['sh', 'sz', 'cyb']:
stk_code_normal = {
'sh': '000001.XSHG',
'sz': '399001.XSHE',
'cyb': '399006.XSHE'
}[stk_code]
else:
stk_code_normal = jq.normalize_code(stk_code)
current_price = float(
jq.get_price(stk_code_normal,
count=1,
end_date=get_current_date_str())['close'].values[0])
elif source == 'ts':
# 获取实时价格
current_price = float(
ts.get_realtime_quotes(stk_code)['price'].values[0])
return current_price
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
def wrap_draw_line(stock, day, choice):
name,password = read_account(config_file)
authenticaiton(name, password)
stock_name = jqdatasdk.normalize_code(stock)
print(stock_name)
file_name = draw_line(stock_name, choice, days=day)
return file_name
def run(self):
# 抓取fund列表
df = finance.run_query(query(finance.FUND_MAIN_INFO))
df.index.name = 'entity_id'
df = df.reset_index()
# 上市日期
df.rename(columns={'start_date': 'timestamp'}, inplace=True)
df['timestamp'] = pd.to_datetime(df['timestamp'])
df['list_date'] = df['timestamp']
df['end_date'] = pd.to_datetime(df['end_date'])
df['entity_id'] = df.main_code.apply(lambda x: normalize_code(x))
df['entity_id'] = df['entity_id'].apply(
lambda x: to_entity_id(entity_type='fund', jq_code=x))
df['id'] = df['entity_id']
df['entity_type'] = 'fund'
df['exchange'] = df['entity_id'].apply(
lambda x: get_entity_exchange(x))
df['code'] = df['entity_id'].apply(lambda x: get_entity_code(x))
df['category'] = 'fund'
# df['choice_code'] = df.apply(lambda x:x.main_code+'.'+x.exchange.upper(),axis=1)
# loginResult = c.start("ForceLogin=1", '')
# df['underlying_index_code'] = df.apply(lambda x:c.css(x.choice_code, "BMINDEXCODE", "Rank=1").Data if x.operate_mode == 'ETF' else None,axis=1)
# df['underlying_index_code'] = df['underlying_index_code'].apply(lambda x:[i for i in x.values()][0][0].lower().replace(".","_") if x else None)
# c.stop()
df_to_db(df,
data_schema=FundDetail,
provider=self.provider,
force_update=self.force_update)
# self.logger.info(df_index)
self.logger.info("persist etf list success")
logout()
def run(self):
# 抓取fund列表
df = finance.run_query(query(finance.FUND_MAIN_INFO))
df.index.name = 'entity_id'
df = df.reset_index()
# 上市日期
df.rename(columns={'start_date': 'timestamp'}, inplace=True)
df['timestamp'] = pd.to_datetime(df['timestamp'])
df['list_date'] = df['timestamp']
df['end_date'] = pd.to_datetime(df['end_date'])
df['entity_id'] = df.main_code.apply(lambda x: normalize_code(x))
df['entity_id'] = df['entity_id'].apply(
lambda x: to_entity_id(entity_type='fund', jq_code=x))
df['id'] = df['entity_id']
df['entity_type'] = 'fund'
df['exchange'] = df['entity_id'].apply(
lambda x: get_entity_exchange(x))
df['code'] = df['entity_id'].apply(lambda x: get_entity_code(x))
df['category'] = 'fund'
df_to_db(df,
data_schema=FundDetail,
provider=self.provider,
force_update=self.force_update)
# self.logger.info(df_index)
self.logger.info("persist etf list success")
logout()
def get_1min_bars(self, stock_id: str, count: int, end_date: str):
return jq.get_bars(
jq.normalize_code(stock_id),
count,
unit='1m',
fields=['date', 'open', 'close', 'high', 'low', 'volume', 'money'],
include_now=False,
end_dt=end_date)
def init_light(self):
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!')
self.finance_table = self.__generate_table_name() + '_finance'
self.price_table = self.__generate_table_name() + '_price'
def get_current_price_JQ(stk_code):
# 使用聚宽数据接口替代
if stk_code in ['sh', 'sz', 'cyb']:
stk_code_normal = {
'sh': '000001.XSHG',
'sz': '399001.XSHE',
'cyb': '399006.XSHE'
}[stk_code]
else:
stk_code_normal = jq.normalize_code(stk_code)
current_price = float(jq.get_price(stk_code_normal, count=1, end_date=get_current_date_str())['close'].values[0])
return current_price
def to_jq_symbol(code: str) -> str:
# 沪深300指数
if code.startswith("000300"):
return "000300.XSHG"
if code.endswith(".XSHG") \
or code.endswith(".XSHE"):
return code
jq_code = jq.normalize_code(code)
if jq_code is None:
if code.startswith("6"):
return f"{code}.XSHG"
else:
return f"{code}.XSHE"
return jq_code
def init(self):
'''
Get price, open, close, high, low, volume. And finantial data, TTM, PB, etc.
'''
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!')
self.finance_table = self.__generate_table_name() + '_finance'
self.price_table = self.__generate_table_name() + '_price'
price_data = jdk.get_price(self.stock, start_date=self.stock_info.start_date, end_date=datetime.now())
# remove NaN data
self.price_data = price_data.dropna()
self.price_data.index.name = 'date'
print('%s[%s] from %s - %s ' % (self.stock_info.display_name, self.stock,
self.stock_info.start_date, self.stock_info.end_date))
def _get_bar_range(jq: jq, code: str, season_date: datetime):
"""
返回前3个月的涨幅,后两个月的最大涨幅
"""
query_end_date = season_date + timedelta(days=63)
if datetime.now() < query_end_date:
return None
df = jq.get_bars(jq.normalize_code(code),
6,
unit='1M',
fields=['date', 'open', 'high', 'low', 'close', 'volume'],
include_now=True,
end_dt=query_end_date)
if df is None:
return None
size = df.shape[0]
if size != 6:
return None
##前3个月的涨幅
ranges = []
for i in range(1, 4):
_close = float(df.at[i, 'close'])
_close_pre = float(df.at[i - 1, 'close'])
v = (_close - _close_pre) / _close_pre
ranges.append(v)
###后两个月之内的最大涨幅
close = float(df.at[3, 'close'])
high = close
for i in range(4, 6):
_high = float(df.at[i, 'high'])
if _high > high:
high = _high
max_range = (high - close) / close
return ranges[0], ranges[1], ranges[2], max_range
def get_k_data_JQ(stk_code, count=None, start_date=None, end_date=get_current_date_str(), freq='daily'):
"""
使用JQData来下载stk的历史数据
:param stk_code:
:param amount:
:return:
"""
if stk_code in ['sh', 'sz', 'cyb']:
stk_code_normal = {
'sh': '000001.XSHG',
'sz': '399001.XSHE',
'cyb': '399006.XSHE'
}[stk_code]
df = jqdatasdk.get_price(stk_code_normal, frequency=freq, count=count, start_date=start_date,
end_date=end_date)
else:
df = jqdatasdk.get_price(jqdatasdk.normalize_code(stk_code), frequency=freq, count=count,
end_date=end_date, start_date=start_date)
df['datetime'] = df.index
df['date'] = df.apply(lambda x: str(x['datetime'])[:10], axis=1)
return df
'000760.SZ': ['2010-01-01', '', '被st了的股票', 1], '000752.SZ': ['2010-01-01', '', '被st了的股票', 1],
'000737.SZ': ['2010-01-01', '', '被st了的股票', 1], '000727.SZ': ['2010-01-01', '', '被st了的股票', 1],
'000707.SZ': ['2010-01-01', '', '被st了的股票', 1], '000611.SZ': ['2010-01-01', '', '被st了的股票', 1],
'000585.SZ': ['2010-01-01', '', '被st了的股票', 1], '000571.SZ': ['2010-01-01', '', '被st了的股票', 1],
'000509.SZ': ['2010-01-01', '', '被st了的股票', 1], '000504.SZ': ['2010-01-01', '', '被st了的股票', 1],
'000422.SZ': ['2010-01-01', '', '被st了的股票', 1], '000410.SZ': ['2010-01-01', '', '被st了的股票', 1],
'000409.SZ': ['2010-01-01', '', '被st了的股票', 1], '000010.SZ': ['2010-01-01', '', '被st了的股票', 1],
'002290.SZ': ['2010-01-01', '', '被st了的股票', 1], '002207.SZ': ['2010-01-01', '', '被st了的股票', 1],
'002102.SZ': ['2010-01-01', '', '被st了的股票', 1], '000971.SZ': ['2010-01-01', '', '被st了的股票', 1],
'000572.SZ': ['2010-01-01', '', '被st了的股票', 1], '000939.SZ': ['2010-01-01', '', '被st了的股票', 1],
'000981.SZ': ['2010-01-01', '', '被st了的股票', 1], '002604.SZ': ['2010-01-01', '', '被st了的股票', 1],
'002450.SZ': ['2010-01-01', '', '被st了的股票', 1], '000670.SZ': ['2010-01-01', '', '被st了的股票', 1],
'002260.SZ': ['2010-01-01', '', '被st了的股票', 1], '000787.SZ': ['2010-01-01', '', '被st了的股票', 1]}
lst = []
n=0
for x, y in remove_st_data.items():
print(x)
code = jqdatasdk.normalize_code(x)
n+=1
# print()
# finance.run_query(query(finance.STK_NAME_HISTORY).filter(finance.STK_NAME_HISTORY.code == code))
q = query(finance.STK_STATUS_CHANGE).filter(finance.STK_STATUS_CHANGE.code == code)
df = finance.run_query(q)
lst.append(df)
ret = pd.concat(lst)
print(ret)
print(n)
ret.to_excel('c://e/data/change.xlsx', encoding='gbk')
# encoding=utf-8
try:
# 获取实时价格
current_price = float(ts.get_realtime_quotes(stk_code)['price'].values[0])
except:
# 使用聚宽数据接口替代
if stk_code in ['sh', 'sz', 'cyb']:
stk_code_normal = {
'sh': '000001.XSHG',
'sz': '399001.XSHE',
'cyb': '399006.XSHE'
}[stk_code]
else:
stk_code_normal = normalize_code(stk_code)
current_price = float(jq.get_price(stk_code_normal, count=1, end_date=get_current_date_str())['close'].values[0])
# 计算实时偏离度
list_history = dict['latest_data']
list_history.append(current_price)
M_diff = (current_price - np.mean(list_history)) / current_price
M_list = dict['history_M_diverge_data']
M_list.append(M_diff)
r_list = [relativeRank(dict['history_M_diverge_data'], x) for x in M_list]
def get_normalize_code(self, code):
return jqs.normalize_code(code)
# encoding=utf-8
'日期': str,
'股票代码': str
}).dropna(how='all')
except:
df_pos = pd.read_csv(open(fp, 'rb'),
encoding='gbk',
dtype={
'日期': str,
'股票代码': str
}).dropna(how='all')
df_pos = df_pos[~df_pos['股票代码'].isnull()]
df_pos['股票代码'] = ["{:0>6d}".format(int(x)) for x in df_pos['股票代码'].tolist()]
# ============行情数据===========================
holdings = df_pos['股票代码'].unique()
holdings = [jq.normalize_code(x) for x in holdings if x[0] in ['0', '6', '3']]
prices = jq.get_price(holdings, end_date=datetime.datetime.now(), count=100)
# series
close = prices['close'].iloc[-1]
hi = prices['high'].iloc[-1]
lo = prices['low'].iloc[-1]
open = prices['open'].iloc[-1]
# dataframe
vol = prices['volume']
# ===========计算放量上影线================================
# 上影线：(最高-收盘)/(最高-最低)
# 放量： 当日成交量/20日平均成交量
# 上影线*放量 > 3, 表明放量至少大于三倍，如果上影线为0.5，则需要放量6倍
syx = (hi - close) / (hi - lo)
fangda = vol.iloc[-1] / vol.iloc[-20:].mean()
futureID= 'IC2009.CCFX'
jqID= jq.normalize_code(futureID)
dataTable= myjqcfg.futureTickDT
tickPdData= myapi.getDateData(jqID, intDate)
indexID= '000001.XSHG'
jq.get_security_info(indexID).type
"""
codelist = [
'510500', '510300', '510330', 'IF2003', 'IF2006', 'IF2009', 'IC2003',
'IC2006', 'IC2009'
]
codelist = ['000300.SH', '000905.SH']
jqIDList = jq.normalize_code(codelist)
ld = jq.get_trade_days(start_date='20200101',
end_date='20200623',
count=None)
for dt in ld:
intDate = int(dt.strftime("%Y%m%d"))
print('dealing %d, time elapsed: %.2f min' % (intDate,
(ti.time() - t0) / 60))
for jqID in jqIDList:
tickPdData = myapi.getDateData(jqID, intDate)
myapi.close()
print('All done, time elapsed: %.2f min' % ((ti.time() - t0) / 60))
def get_k_data_JQ(start_date=None, end_date=None, freq='daily'):
"""
获得A股所有股票日数据
Args:
start_date (str): 起始日
end_date (str): 结束日
"""
#所有股票代码等
stk = open('./codes-joinquan.csv', 'r', encoding='utf-8').readlines()
stk_code = [jqdatasdk.normalize_code(x) for x in stk]
log.info("stk_code {}".format(stk_code))
#df = jqdatasdk.get_price(stk_code, frequency=freq,
# end_date=end_date, start_date=start_date)
#print(df.axes)
#print(df.head)
sleep_time=0.4
# 遍历所有股票ID
#for code in stock_list.ts_code:
for code in stk_code:
code = code.strip('\n')
log.info(code)
t0 = time.time()
df = jqdatasdk.get_price(code, frequency=freq,
end_date=end_date, start_date=start_date, fq='pre')
log.info(df.axes)
log.info(df.head())
log.info(df.tail())
log.info(df.columns)
log.info(df.index)
df['date'] = df.index#.to_pydatetime()
#log.info(df['date'])
log.info(df.axes)
log.info(df.head())
log.info(df.columns)
log.info(df.dtypes)
#log.info(df.info())
#查看缺失及每列数据类型
data = json.loads(df.T.to_json()).values()
#log.info(data)
for row in data:
#log.info(type(row['date']))
row['date'] = datetime.fromtimestamp(row['date']/1000 - 60*60*8)# ns to s
#row['date'] = datetime.utcfromtimestamp(row['date']/1000)# ns to s; same with up line
elapsed = time.time() - t0
log.info("get data {} rows for {} used {:.2f}s ".format(len(data), code, elapsed))
t1 = time.time()
# D5=MA(df.close,5)#5日均线
# df['D5']=pd.DataFrame({'D5':D5})
# D10=MA(df.close,10)#10日均线
# df['D10']=pd.DataFrame({'D10':D10})
# D20=MA(df.close,20)#20日均线
# df['D20']=pd.DataFrame({'D20':D20})
collection=database[code] #建立以股票名命名的集合
collection.insert_many(data) #存储到MongoDB数据库中
elapsed = time.time() - t1
log.info("collection.insert_many used: {:.2f}s".format(elapsed))
# 对股票每一天数据进行保存，注意唯一性
# 这里也可以批量创建，速度更快，但批量创建容易丢失数据
# 这里为了保证数据的绝对稳定性，选择一条条创建
#for row in data:
# daily.update({"_id": f"{row['ts_code']}-{row['trade_date']}"}, row, upsert=True)
time.sleep(sleep_time) #暂停，tushare对积分不足用户限制每分钟200次访问，积分很重要
if np.size(df,0)<60:
time.sleep(sleep_time)
continue
def importStockDataDailyJQ(codes=[], names=[], asset='E', adj='qfq'):
db = cx_Oracle.connect('c##stock', 'didierg160', 'myoracle') #创建连接
cr = db.cursor()
code_tail = ""
if asset == "INDEX":
code_tail = "zs"
for code, name in zip(codes, names):
start_date = ""
if adj == "qfq":
if isXR(code=code):
sql = "delete tb_stock_data_daily where code = '" + code + code_tail + "'"
cr.execute(sql)
#time.sleep(1)
start_date = gsd.get_code_max_shi_jian(code=code + code_tail)
if start_date == "None":
start_date = ""
tsFaile = 1
while tsFaile != 0:
try:
#if tsFaile >=2 :
# cons = ts.get_apis()
#df = ts.bar(code, conn=cons, adj=adj,asset=asset, start_date=start_date, end_date='',retry_count = 2)
#print(df)
import datetime
df = jq.get_price(
jq.normalize_code(code),
start_date=start_date,
end_date=datetime.datetime.now().strftime("%Y-%m-%d"),
skip_paused=True)
df = df.dropna()
df = df.sort_index(ascending=False)
#print(df)
tsFaile = 0
except Exception as e:
# ts.close_apis(conn=cons)
if "找不到" in str(e):
tsFaile = 4
else:
time.sleep(5)
print(code, start_date, "importStockDataDaily fail:", tsFaile,
e)
if tsFaile <= 3:
tsFaile += 1
else:
break
if tsFaile > 0:
continue
df["datetime"] = df.index
df = df.reset_index(drop=True)
df_shift = df.shift(-1)
for row, row_s in zip(df.itertuples(), df_shift.itertuples()):
if round(float(getattr(row, "volume"))) < 1:
continue
# if len(getattr(row,"datetime")) <10 :
# continue
shi_jian = str(getattr(row, "datetime"))[0:10]
if len(shi_jian) < 10:
continue
if shi_jian == start_date:
continue
if str(getattr(row_s, "close")) == "nan":
price_last_day = "null"
amount_last_day = "null"
zhang_die = "null"
zhang_die_rate = "null"
else:
price_last_day = getattr(row_s, "close")
amount_last_day = str(getattr(row_s, "money"))
zhang_die = getattr(row, "close") - getattr(row_s, "close")
zhang_die_rate = getattr(row, "close") / getattr(
row_s, "close") - 1
sql = "insert into tb_stock_data_daily "
sql += "( "
sql += " code , "
sql += " name , "
sql += " price_today_open, "
sql += " price , "
sql += " max_price , "
sql += " min_price , "
sql += " vol , "
sql += " amount , "
sql += " amount_last_day , "
sql += " shi_jian , "
sql += " price_last_day , "
sql += " zhang_die , "
sql += " zhang_die_rate "
sql += ") "
sql += "values "
sql += "( "
sql += " '" + code + code_tail + "',"
sql += " '" + name + "',"
sql += " " + str(getattr(row, "open")) + ","
sql += " " + str(getattr(row, "close")) + ","
sql += " " + str(getattr(row, "high")) + ","
sql += " " + str(getattr(row, "low")) + ","
sql += " " + str(getattr(row, "volume")) + ","
sql += " " + str(getattr(row, "money")) + ","
sql += " " + amount_last_day + ","
sql += " to_date('" + shi_jian + " 15:00:00','yyyy-mm-dd hh24:mi:ss'),"
sql += " " + str(price_last_day) + ","
sql += " " + str(zhang_die) + ","
sql += " " + str(zhang_die_rate) + " "
sql += ")"
try:
cr.execute(sql)
except Exception as e:
print(sql)
#time.sleep(1)
db.commit()
now = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
print(now, code, "done")
db.commit()
cr.close()
db.close()
def normalize_code(self, code):
'''Normalize code'''
return normalize_code(code)
def normalizecode(self, codes):
return jq.normalize_code(codes)
def record(self, entity, start, end, size, timestamps):
if not end:
end = to_time_str(now_pd_timestamp())
fq = fq_ref_date = None
if self.adjust_type == AdjustType.hfq:
fq = 'post'
fq_ref_date = '2000-01-01'
elif self.adjust_type == AdjustType.qfq:
fq = 'pre'
fq_ref_date = to_time_str(now_pd_timestamp())
security = normalize_code(entity.code)
if not self.end_timestamp:
df = get_price(
security,
start_date=start,
end_date=end,
frequency=self.jq_trading_level,
fields=['open', 'close', 'low', 'high', 'volume', 'money'],
fq=fq)
else:
end_timestamp = to_time_str(self.end_timestamp)
df = get_price(
security,
count=size,
frequency=self.jq_trading_level,
fields=['open', 'close', 'low', 'high', 'volume', 'money'],
end_date=end_timestamp,
fq=fq)
if pd_is_not_null(df):
df['name'] = entity.name
df.index.name = 'timestamp'
df.rename(columns={
'money': 'turnover',
'date': 'timestamp'
},
inplace=True)
df['entity_id'] = entity.id
df['timestamp'] = pd.to_datetime(df.index)
df['provider'] = 'joinquant'
df['level'] = self.level.value
df['code'] = entity.code
# 判断是否需要重新计算之前保存的前复权数据
if self.adjust_type == AdjustType.qfq and df.open.dropna(
).shape[0] != 0:
check_df = df.head(1)
check_date = check_df['timestamp'][0]
current_df = get_kdata(entity_id=entity.id,
provider=self.provider,
start_timestamp=check_date,
end_timestamp=check_date,
limit=1,
level=self.level,
adjust_type=self.adjust_type)
if pd_is_not_null(current_df):
old = current_df.iloc[0, :]['close']
new = check_df['close'][0]
# 相同时间的close不同，表明前复权需要重新计算
if round(old, 2) != round(new, 2):
qfq_factor = new / old
last_timestamp = pd.Timestamp(check_date)
self.recompute_qfq(entity,
qfq_factor=qfq_factor,
last_timestamp=last_timestamp)
def generate_kdata_id(se):
if self.level >= IntervalLevel.LEVEL_1DAY:
return "{}_{}".format(
se['entity_id'],
to_time_str(se['timestamp'], fmt=TIME_FORMAT_DAY))
else:
return "{}_{}".format(
se['entity_id'],
to_time_str(se['timestamp'], fmt=TIME_FORMAT_ISO8601))
df['id'] = df[['entity_id', 'timestamp']].apply(generate_kdata_id,
axis=1)
df_to_db(df=df,
data_schema=self.data_schema,
provider=self.provider,
force_update=self.force_update)
return None
###### 有息负债 ################
###### 有息负债 ################
###### 有息负债 ################
# shortterm_loan 短期借款 decimal(20,4)
# non_current_liability_in_one_year 一年内到期的非流动负债 decimal(20,4)
# longterm_loan 长期借款 decimal(20,4)
# bonds_payable 应付债券 decimal(20,4)
import pandas
# qianyuandianli=pandas.DataFrame(columns=['A', 'B', 'C', 'D','A', 'B', 'C', 'D','A'])
from jqdatasdk import finance
codeshuidian = [
'sh600900', 'sh600025', 'sh600886', 'sh600236', '002039', 'sh600674',
'sz000722', 'sz000883', '000791.SZ', '000993', '000601', '600868'
]
for code in codeshuidian:
codeqianyuandianli = jqdatasdk.normalize_code(code)
# print("\n*********",code,"***********************")
qianyuandianli1 = finance.run_query(
jqdatasdk.query(
finance.STK_BALANCE_SHEET.shortterm_loan,
finance.STK_BALANCE_SHEET.longterm_loan,
finance.STK_BALANCE_SHEET.non_current_liability_in_one_year,
finance.STK_BALANCE_SHEET.bonds_payable,
finance.STK_BALANCE_SHEET.total_assets,
finance.STK_BALANCE_SHEET.code, finance.STK_BALANCE_SHEET.pub_date,
finance.STK_BALANCE_SHEET.start_date,
finance.STK_BALANCE_SHEET.end_date,
finance.STK_BALANCE_SHEET.company_name).filter(
finance.STK_BALANCE_SHEET.code == codeqianyuandianli,
finance.STK_BALANCE_SHEET.pub_date >= '2010-01-01',
finance.STK_BALANCE_SHEET.report_type == 0).limit(50))
