def import_tushare_stock_index_daily(chain_param=None, ts_code_set=None):
"""
插入股票日线数据到最近一个工作日-1。
如果超过 BASE_LINE_HOUR 时间，则获取当日的数据
:return:
"""
table_name = 'tushare_stock_index_daily_md'
logging.info("更新 %s 开始", table_name)
has_table = engine_md.has_table(table_name)
# 进行表格判断，确定是否含有tushare_stock_daily
if has_table:
sql_str = """
SELECT ts_code, date_frm, if(exp_date<end_date, exp_date, end_date) date_to
FROM
(
SELECT info.ts_code, ifnull(trade_date, base_date) date_frm, exp_date,
if(hour(now())<16, subdate(curdate(),1), curdate()) end_date
FROM
tushare_stock_index_basic info
LEFT OUTER JOIN
(SELECT ts_code, adddate(max(trade_date),1) trade_date FROM {table_name} GROUP BY ts_code) daily
ON info.ts_code = daily.ts_code
) tt
WHERE date_frm <= if(exp_date<end_date, exp_date, end_date)
ORDER BY ts_code""".format(table_name=table_name)
else:
sql_str = """
SELECT ts_code, date_frm, if(exp_date<end_date, exp_date, end_date) date_to
FROM
(
SELECT info.ts_code, base_date date_frm, exp_date,
if(hour(now())<16, subdate(curdate(),1), curdate()) end_date
FROM tushare_stock_index_basic info
) tt
WHERE date_frm <= if(exp_date<end_date, exp_date, end_date)
ORDER BY ts_code"""
logger.warning('%s 不存在，仅使用 tushare_stock_info 表进行计算日期范围', table_name)
with with_db_session(engine_md) as session:
# 获取每只股票需要获取日线数据的日期区间
table = session.execute(sql_str)
# 计算每只股票需要获取日线数据的日期区间
begin_time = None
# 获取date_from,date_to，将date_from,date_to做为value值
code_date_range_dic = {
ts_code:
(date_from if begin_time is None else min([date_from, begin_time]),
date_to)
for ts_code, date_from, date_to in table.fetchall()
if ts_code_set is None or ts_code in ts_code_set
}
# data_len = len(code_date_range_dic)
data_df_list, data_count, all_data_count, data_len = [], 0, 0, len(
code_date_range_dic)
logger.info('%d stocks will been import into tushare_stock_index_daily_md',
data_len)
# 将data_df数据，添加到data_df_list
try:
for num, (ts_code, (date_from,
date_to)) in enumerate(code_date_range_dic.items(),
start=1):
logger.debug('%d/%d) %s [%s - %s]', num, data_len, ts_code,
date_from, date_to)
data_df = invoke_index_daily(
ts_code=ts_code,
start_date=datetime_2_str(date_from, STR_FORMAT_DATE_TS),
end_date=datetime_2_str(date_to, STR_FORMAT_DATE_TS))
# data_df = df
if len(data_df) > 0:
while try_2_date(data_df['trade_date'].iloc[-1]) > date_from:
last_date_in_df_last, last_date_in_df_cur = try_2_date(
data_df['trade_date'].iloc[-1]), None
df2 = invoke_index_daily(
ts_code=ts_code,
start_date=datetime_2_str(date_from,
STR_FORMAT_DATE_TS),
end_date=datetime_2_str(
try_2_date(data_df['trade_date'].iloc[-1]) -
timedelta(days=1), STR_FORMAT_DATE_TS))
if len(df2 > 0):
last_date_in_df_cur = try_2_date(
df2['trade_date'].iloc[-1])
if last_date_in_df_cur < last_date_in_df_last:
data_df = pd.concat([data_df, df2])
# df = df2
elif last_date_in_df_cur == last_date_in_df_last:
break
if data_df is None:
logger.warning(
'%d/%d) %s has no data during %s %s', num,
data_len, ts_code, date_from, date_to)
continue
logger.info('%d/%d) %d data of %s between %s and %s',
num, data_len, data_df.shape[0], ts_code,
date_from, date_to)
else:
break
# 把数据攒起来
if data_df is not None and data_df.shape[0] > 0:
data_count += data_df.shape[0]
data_df_list.append(data_df)
# 仅调试使用
if DEBUG and len(data_df_list) > 5:
break
# 大于阀值有开始插入
if data_count >= 500:
data_df_all = pd.concat(data_df_list)
bunch_insert_on_duplicate_update(
data_df_all, table_name, engine_md,
DTYPE_TUSHARE_STOCK_INDEX_DAILY_MD)
all_data_count += data_count
data_df_list, data_count = [], 0
# # 数据插入数据库
# data_df_all = data_df
# data_count = bunch_insert_on_duplicate_update(data_df_all, table_name, engine_md,
# DTYPE_TUSHARE_STOCK_INDEX_DAILY_MD)
# logging.info("更新 %s 结束 %d 条信息被更新", table_name, data_count)
# data_df = []
finally:
# 导入数据库
if len(data_df_list) > 0:
data_df_all = pd.concat(data_df_list)
data_count = bunch_insert_on_duplicate_update(
data_df_all, table_name, engine_md,
DTYPE_TUSHARE_STOCK_INDEX_DAILY_MD)
logging.info("更新 %s 结束 %d 条信息被更新", table_name, data_count)
if not has_table and engine_md.has_table(table_name):
alter_table_2_myisam(engine_md, [table_name])
build_primary_key([table_name])
def import_tushare_stock_fina_audit(chain_param=None, ts_code_set=None):
"""
插入股票日线数据到最近一个工作日-1。
如果超过 BASE_LINE_HOUR 时间，则获取当日的数据
:return:
"""
table_name = 'tushare_stock_fin_audit'
logging.info("更新 %s 开始", table_name)
param_list = [
('ts_code', String(20)),
('ann_date', Date),
('end_date', Date),
('audit_result', Text),
('audit_fees', DOUBLE),
('audit_agency', String(100)),
('audit_sign', String(100)),
]
has_table = engine_md.has_table(table_name)
# 进行表格判断，确定是否含有tushare_stock_daily
if has_table:
sql_str = """
SELECT ts_code, date_frm, if(delist_date<end_date, delist_date, end_date) date_to
FROM
(
SELECT info.ts_code, ifnull(ann_date, subdate(list_date,365*8)) date_frm, delist_date,
if(hour(now())<16, subdate(curdate(),1), curdate()) end_date
FROM
tushare_stock_info info
LEFT OUTER JOIN
(SELECT ts_code, adddate(max(ann_date),1) ann_date
FROM {table_name} GROUP BY ts_code) fina_audit
ON info.ts_code = fina_audit.ts_code
) tt
WHERE date_frm <= if(delist_date<end_date, delist_date, end_date)
ORDER BY ts_code""".format(table_name=table_name)
else:
sql_str = """
SELECT ts_code, date_frm, if(delist_date<end_date, delist_date, end_date) date_to
FROM
(
SELECT info.ts_code, subdate(list_date,365*10) date_frm, delist_date,
if(hour(now())<16, subdate(curdate(),1), curdate()) end_date
FROM tushare_stock_info info
) tt
WHERE date_frm <= if(delist_date<end_date, delist_date, end_date)
ORDER BY ts_code DESC """
logger.warning('%s 不存在，仅使用 tushare_stock_info 表进行计算日期范围', table_name)
with with_db_session(engine_md) as session:
# 获取每只股票需要获取日线数据的日期区间
table = session.execute(sql_str)
# 计算每只股票需要获取日线数据的日期区间
begin_time = None
# 获取date_from,date_to，将date_from,date_to做为value值
code_date_range_dic = {
ts_code:
(date_from if begin_time is None else min([date_from, begin_time]),
date_to)
for ts_code, date_from, date_to in table.fetchall()
if ts_code_set is None or ts_code in ts_code_set
}
# 设置 dtype
dtype = {key: val for key, val in param_list}
data_len = len(code_date_range_dic)
logger.info('%d stocks will been import into wind_stock_daily', data_len)
Cycles = 1
try:
for num, (ts_code, (date_from,
date_to)) in enumerate(code_date_range_dic.items(),
start=1):
logger.debug('%d/%d) %s [%s - %s]', num, data_len, ts_code,
date_from, date_to)
df = invoke_fina_audit(
ts_code=ts_code,
start_date=datetime_2_str(date_from, STR_FORMAT_DATE_TS),
end_date=datetime_2_str(date_to, STR_FORMAT_DATE_TS))
data_df = df
if len(data_df) > 0:
while try_2_date(df['ann_date'].iloc[-1]) > date_from:
last_date_in_df_last, last_date_in_df_cur = try_2_date(
df['ann_date'].iloc[-1]), None
df2 = invoke_fina_audit(
ts_code=ts_code,
start_date=datetime_2_str(date_from,
STR_FORMAT_DATE_TS),
end_date=datetime_2_str(
try_2_date(df['ann_date'].iloc[-1]) -
timedelta(days=1), STR_FORMAT_DATE_TS))
if len(df2) > 0:
last_date_in_df_cur = try_2_date(
df2['ann_date'].iloc[-1])
if last_date_in_df_cur < last_date_in_df_last:
data_df = pd.concat([data_df, df2])
df = df2
elif last_date_in_df_cur == last_date_in_df_last:
break
if data_df is None:
logger.warning(
'%d/%d) %s has no data during %s %s', num,
data_len, ts_code, date_from, date_to)
continue
logger.info('%d/%d) %d data of %s between %s and %s',
num, data_len, data_df.shape[0], ts_code,
date_from, date_to)
elif len(df2) <= 0:
break
# 数据插入数据库
data_df_all = data_df
data_count = bunch_insert_on_duplicate_update(
data_df_all, table_name, engine_md, dtype)
logging.info("成功更新 %s 结束 %d 条信息被更新", table_name, data_count)
# 仅调试使用
Cycles = Cycles + 1
if DEBUG and Cycles > 10:
break
finally:
# 导入数据库
if len(data_df) > 0:
data_df_all = data_df
data_count = bunch_insert_on_duplicate_update(
data_df_all, table_name, engine_md, dtype)
logging.info("成功更新 %s 结束 %d 条信息被更新", table_name, data_count)
def import_tushare_stock_balancesheet(chain_param=None, ts_code_set=None):
"""
插入股票日线数据到最近一个工作日-1。
如果超过 BASE_LINE_HOUR 时间，则获取当日的数据
:return:
"""
table_name = 'tushare_stock_balancesheet'
logging.info("更新 %s 开始", table_name)
has_table = engine_md.has_table(table_name)
# 进行表格判断，确定是否含有tushare_stock_daily
if has_table:
sql_str = """
SELECT ts_code, date_frm, if(delist_date<end_date, delist_date, end_date) date_to
FROM
(
SELECT info.ts_code, ifnull(ann_date, list_date) date_frm, delist_date,
if(hour(now())<16, subdate(curdate(),1), curdate()) end_date
FROM
tushare_stock_info info
LEFT OUTER JOIN
(SELECT ts_code, adddate(max(ann_date),1) ann_date
FROM {table_name} GROUP BY ts_code) balancesheet
ON info.ts_code = balancesheet.ts_code
) tt
WHERE date_frm <= if(delist_date<end_date, delist_date, end_date)
ORDER BY ts_code""".format(table_name=table_name)
else:
sql_str = """
SELECT ts_code, date_frm, if(delist_date<end_date, delist_date, end_date) date_to
FROM
(
SELECT info.ts_code, list_date date_frm, delist_date,
if(hour(now())<16, subdate(curdate(),1), curdate()) end_date
FROM tushare_stock_info info
) tt
WHERE date_frm <= if(delist_date<end_date, delist_date, end_date)
ORDER BY ts_code DESC """
logger.warning('%s 不存在，仅使用 tushare_stock_info 表进行计算日期范围', table_name)
# ts_code_set = None
with with_db_session(engine_md) as session:
# 获取每只股票需要获取日线数据的日期区间
table = session.execute(sql_str)
# 计算每只股票需要获取日线数据的日期区间
begin_time = None
# 获取date_from,date_to，将date_from,date_to做为value值
code_date_range_dic = {
ts_code:
(date_from if begin_time is None else min([date_from, begin_time]),
date_to)
for ts_code, date_from, date_to in table.fetchall()
if ts_code_set is None or ts_code in ts_code_set
}
data_df_list, data_count, all_data_count, data_len = [], 0, 0, len(
code_date_range_dic)
logger.info(
'%d stock balancesheets will been import into tushare_stock_balancesheet',
data_len)
# 将data_df数据，添加到data_df_list
cycles = 1
try:
for num, (ts_code, (date_from,
date_to)) in enumerate(code_date_range_dic.items(),
start=1):
logger.debug('%d/%d) %s [%s - %s]', num, data_len, ts_code,
date_from, date_to)
data_df = invoke_balancesheet(
ts_code=ts_code,
start_date=datetime_2_str(date_from, STR_FORMAT_DATE_TS),
end_date=datetime_2_str(date_to, STR_FORMAT_DATE_TS))
if data_df is not None and data_df.shape[0] > 0 and data_df[
'ann_date'].iloc[-1] is not None:
last_date_in_df_last = try_2_date(data_df['ann_date'].iloc[-1])
while try_2_date(data_df['ann_date'].iloc[-1]) > date_from:
df2 = invoke_balancesheet(
ts_code=ts_code,
start_date=datetime_2_str(date_from,
STR_FORMAT_DATE_TS),
end_date=datetime_2_str(
try_2_date(data_df['ann_date'].iloc[-1]) -
timedelta(days=1), STR_FORMAT_DATE_TS))
if df2 is None:
break
if len(df2) > 0 and df2['ann_date'].iloc[-1] is not None:
last_date_in_df_cur = try_2_date(
df2['ann_date'].iloc[-1])
if last_date_in_df_cur < last_date_in_df_last:
data_df = pd.concat([data_df, df2])
# df = df2
elif last_date_in_df_cur == last_date_in_df_last:
break
elif len(df2) <= 0:
break
if data_df is None or data_df.shape[0] == 0:
logger.warning('%d/%d) %s has no data during %s %s', num,
data_len, ts_code, date_from, date_to)
continue
logger.debug('%d/%d)， %d 条 %s 资产负债表被提取，起止时间为 %s 和 %s', num,
data_len, data_df.shape[0], ts_code, date_from,
date_to)
# 把数据攒起来
if data_df.shape[0] > 0:
data_count += data_df.shape[0]
data_df_list.append(data_df)
# 大于阀值有开始插入
if data_count >= 1000 and len(data_df_list) > 0:
data_df_all = pd.concat(data_df_list)
bunch_insert_on_duplicate_update(
data_df_all, table_name, engine_md,
DTYPE_TUSHARE_STOCK_BALABCESHEET)
logger.info('%d 条资产负债表数据被插入 %s 表', data_count, table_name)
all_data_count += data_count
data_df_list, data_count = [], 0
# # 数据插入数据库
# data_count = bunch_insert_on_duplicate_update(data_df, table_name, engine_md,
# DTYPE_TUSHARE_STOCK_BALABCESHEET)
# logging.info("更新 %s 结束 %d 条信息被更新", table_name, data_count)
# data_df = []
# 仅调试使用
cycles = cycles + 1
if DEBUG and cycles > 10:
break
finally:
# 导入数据库
if len(data_df_list) > 0:
data_df_all = pd.concat(data_df_list)
data_count = bunch_insert_on_duplicate_update(
data_df_all,
table_name,
engine_md,
DTYPE_TUSHARE_STOCK_BALABCESHEET,
primary_keys=['ts_code', 'ann_date'],
schema=config.DB_SCHEMA_MD)
all_data_count = all_data_count + data_count
logging.info("更新 %s 结束 %d 条资产负债表信息被更新", table_name, all_data_count)
def import_tushare_stock_top10_holders(ts_code_set=None,chain_param=None):
"""
插入股票日线数据到最近一个工作日-1。
如果超过 BASE_LINE_HOUR 时间，则获取当日的数据
:return:
"""
table_name = 'tushare_stock_top10_holders'
logging.info("更新 %s 开始", table_name)
has_table = engine_md.has_table(table_name)
# 进行表格判断，确定是否含有tushare_stock_daily
if has_table:
sql_str = """
SELECT ts_code, date_frm, if(delist_date<end_date2, delist_date, end_date2) date_to
FROM
(
SELECT info.ts_code, ifnull(end_date, subdate(list_date,365*10)) date_frm, delist_date,
if(hour(now())<16, subdate(curdate(),1), curdate()) end_date2
FROM
tushare_stock_info info
LEFT OUTER JOIN
(SELECT ts_code, adddate(max(ann_date),1) end_date
FROM {table_name} GROUP BY ts_code) top10_holders
ON info.ts_code = top10_holders.ts_code
) tt
WHERE date_frm <= if(delist_date<end_date2, delist_date, end_date2)
ORDER BY ts_code""".format(table_name=table_name)
else:
sql_str = """
SELECT ts_code, date_frm, if(delist_date<end_date2, delist_date, end_date2) date_to
FROM
(
SELECT info.ts_code, subdate(list_date,365*10) date_frm, delist_date,
if(hour(now())<16, subdate(curdate(),1), curdate()) end_date2
FROM tushare_stock_info info
) tt
WHERE date_frm <= if(delist_date<end_date2, delist_date, end_date2)
ORDER BY ts_code """
logger.warning('%s 不存在，仅使用 tushare_stock_info 表进行计算日期范围', table_name)
with with_db_session(engine_md) as session:
# 获取每只股票需要获取日线数据的日期区间
table = session.execute(sql_str)
# 计算每只股票需要获取日线数据的日期区间
begin_time = None
# 获取date_from,date_to，将date_from,date_to做为value值
code_date_range_dic = {
ts_code: (date_from if begin_time is None else min([date_from, begin_time]), date_to)
for ts_code, date_from, date_to in table.fetchall() if
ts_code_set is None or ts_code in ts_code_set}
data_df_list, data_count, all_data_count, data_len = [], 0, 0, len(code_date_range_dic)
logger.info('%d stocks will been import into wind_stock_daily', data_len)
# 将data_df数据，添加到data_df_list
Cycles = 1
try:
for num, (ts_code, (date_from, date_to)) in enumerate(code_date_range_dic.items(), start=1):
logger.debug('%d/%d) %s [%s - %s]', num, data_len, ts_code, date_from, date_to)
data_df = invoke_top10_holders(ts_code=ts_code, start_date=datetime_2_str(date_from, STR_FORMAT_DATE_TS),
end_date=datetime_2_str(date_to, STR_FORMAT_DATE_TS))
if len(data_df) > 0 and data_df['ann_date'].iloc[-1] is not None:
last_date_in_df_last = try_2_date(data_df['ann_date'].iloc[-1])
while last_date_in_df_last > date_from:
df2 = invoke_top10_holders(ts_code=ts_code,
start_date=datetime_2_str(date_from, STR_FORMAT_DATE_TS),
end_date=datetime_2_str(last_date_in_df_last - timedelta(days=1),STR_FORMAT_DATE_TS))
if len(df2) > 0 and df2['ann_date'].iloc[-1] is not None:
last_date_in_df_cur = try_2_date(df2['ann_date'].iloc[-1])
if last_date_in_df_cur != last_date_in_df_last:
data_df = pd.concat([data_df, df2])
last_date_in_df_last = try_2_date(data_df['ann_date'].iloc[-1])
elif last_date_in_df_cur == last_date_in_df_last:
break
elif len(df2) > 0 and df2['ann_date'].iloc[-1] is None:
last_date_in_df_cur = try_2_date(df2['end_date'].iloc[-1])
if last_date_in_df_cur != last_date_in_df_last:
data_df = pd.concat([data_df, df2])
last_date_in_df_last = try_2_date(data_df['end_date'].iloc[-1])
elif last_date_in_df_cur == last_date_in_df_last:
break
else:
break
if data_df is None:
logger.warning('%d/%d) %s has no data during %s %s', num, data_len, ts_code, date_from,date_to)
elif data_df is not None:
logger.info('整体进度：%d/%d)， %d 条 %s 前10股东被提取，起止时间为 %s 和 %s', num, data_len, data_df.shape[0], ts_code,date_from, date_to)
# 把数据攒起来
if data_df is not None and data_df.shape[0] > 0:
data_count += data_df.shape[0]
data_df_list.append(data_df)
# 大于阀值有开始插入
if data_count >= 500 and len(data_df_list)>0 :
data_df_all = pd.concat(data_df_list)
bunch_insert_on_duplicate_update(data_df_all, table_name, engine_md, DTYPE_TUSHARE_STOCK_TOP10_HOLDERS)
all_data_count += data_count
data_df_list, data_count = [], 0
# 仅调试使用
Cycles = Cycles + 1
if DEBUG and Cycles > 25:
break
finally:
if len(data_df_list) > 0:
data_df_all = pd.concat(data_df_list)
data_count = bunch_insert_on_duplicate_update(data_df_all, table_name, engine_md,DTYPE_TUSHARE_STOCK_TOP10_HOLDERS)
all_data_count = all_data_count + data_count
logging.info("更新 %s 结束 %d 条信息被更新", table_name, all_data_count)
def import_tushare_stock_fina_indicator(chain_param=None, ts_code_set=None):
"""
插入股票日线数据到最近一个工作日-1。
如果超过 BASE_LINE_HOUR 时间，则获取当日的数据
:return:
"""
table_name = 'tushare_stock_fin_indicator'
logging.info("更新 %s 开始", table_name)
has_table = engine_md.has_table(table_name)
# 进行表格判断，确定是否含有tushare_stock_daily
if has_table:
sql_str = """
SELECT ts_code, date_frm, if(delist_date<end_date, delist_date, end_date) date_to
FROM
(
SELECT info.ts_code, ifnull(ann_date, list_date) date_frm, delist_date,
if(hour(now())<16, subdate(curdate(),1), curdate()) end_date
FROM
tushare_stock_info info
LEFT OUTER JOIN
(SELECT ts_code, adddate(max(ann_date),1) ann_date
FROM {table_name} GROUP BY ts_code) fina_indicator
ON info.ts_code = fina_indicator.ts_code
) tt
WHERE date_frm <= if(delist_date<end_date, delist_date, end_date)
ORDER BY ts_code""".format(table_name=table_name)
else:
sql_str = """
SELECT ts_code, date_frm, if(delist_date<end_date, delist_date, end_date) date_to
FROM
(
SELECT info.ts_code, list_date date_frm, delist_date,
if(hour(now())<16, subdate(curdate(),1), curdate()) end_date
FROM tushare_stock_info info
) tt
WHERE date_frm <= if(delist_date<end_date, delist_date, end_date)
ORDER BY ts_code"""
logger.warning('%s 不存在，仅使用 tushare_stock_info 表进行计算日期范围', table_name)
with with_db_session(engine_md) as session:
# 获取每只股票需要获取日线数据的日期区间
table = session.execute(sql_str)
# 计算每只股票需要获取日线数据的日期区间
begin_time = None
# 获取date_from,date_to，将date_from,date_to做为value值
code_date_range_dic = {
ts_code: (date_from if begin_time is None else min([date_from, begin_time]), date_to)
for ts_code, date_from, date_to in table.fetchall() if
ts_code_set is None or ts_code in ts_code_set}
fields = 'ts_code', 'ann_date', 'end_date', 'eps', 'dt_eps', 'total_revenue_ps', 'revenue_ps', 'capital_rese_ps', 'surplus_rese_ps', \
'undist_profit_ps', 'extra_item', 'profit_dedt', 'gross_margin', 'current_ratio', 'quick_ratio', 'cash_ratio', 'invturn_days', 'arturn_days', \
'inv_turn', 'ar_turn', 'ca_turn', 'fa_turn', 'assets_turn', 'op_income', 'valuechange_income', 'interst_income', 'daa', 'ebit', 'ebitda', 'fcff', \
'fcfe', 'current_exint', 'noncurrent_exint', 'interestdebt', 'netdebt', 'tangible_asset', 'working_capital', 'networking_capital', 'invest_capital', \
'retained_earnings', 'diluted2_eps', 'bps', 'ocfps', 'retainedps', 'cfps', 'ebit_ps', 'fcff_ps', 'fcfe_ps', 'netprofit_margin', 'grossprofit_margin', \
'cogs_of_sales', 'expense_of_sales', 'profit_to_gr', 'saleexp_to_gr', 'adminexp_of_gr', 'finaexp_of_gr', 'impai_ttm', 'gc_of_gr', 'op_of_gr', \
'ebit_of_gr', 'roe', 'roe_waa', 'roe_dt', 'roa', 'npta', 'roic', 'roe_yearly', 'roa2_yearly', 'roe_avg', 'opincome_of_ebt', 'investincome_of_ebt', \
'n_op_profit_of_ebt', 'tax_to_ebt', 'dtprofit_to_profit', 'salescash_to_or', 'ocf_to_or', 'ocf_to_opincome', 'capitalized_to_da', 'debt_to_assets', \
'assets_to_eqt', 'dp_assets_to_eqt', 'ca_to_assets', 'nca_to_assets', 'tbassets_to_totalassets', 'int_to_talcap', 'eqt_to_talcapital', 'currentdebt_to_debt', \
'longdeb_to_debt', 'ocf_to_shortdebt', 'debt_to_eqt', 'eqt_to_debt', 'eqt_to_interestdebt', 'tangibleasset_to_debt', 'tangasset_to_intdebt', \
'tangibleasset_to_netdebt', 'ocf_to_debt', 'ocf_to_interestdebt', 'ocf_to_netdebt', 'ebit_to_interest', 'longdebt_to_workingcapital', 'ebitda_to_debt', \
'turn_days', 'roa_yearly', 'roa_dp', 'fixed_assets', 'profit_prefin_exp', 'non_op_profit', 'op_to_ebt', 'nop_to_ebt', 'ocf_to_profit', 'cash_to_liqdebt', \
'cash_to_liqdebt_withinterest', 'op_to_liqdebt', 'op_to_debt', 'roic_yearly', 'total_fa_trun', 'profit_to_op', 'q_opincome', 'q_investincome', 'q_dtprofit', \
'q_eps', 'q_netprofit_margin', 'q_gsprofit_margin', 'q_exp_to_sales', 'q_profit_to_gr', 'q_saleexp_to_gr', 'q_adminexp_to_gr', 'q_finaexp_to_gr', \
'q_impair_to_gr_ttm', 'q_gc_to_gr', 'q_op_to_gr', 'q_roe', 'q_dt_roe', 'q_npta', 'q_opincome_to_ebt', 'q_investincome_to_ebt', 'q_dtprofit_to_profit', \
'q_salescash_to_or', 'q_ocf_to_sales', 'q_ocf_to_or', 'basic_eps_yoy', 'dt_eps_yoy', 'cfps_yoy', 'op_yoy', 'ebt_yoy', 'netprofit_yoy', 'dt_netprofit_yoy', \
'ocf_yoy', 'roe_yoy', 'bps_yoy', 'assets_yoy', 'eqt_yoy', 'tr_yoy', 'or_yoy', 'q_gr_yoy', 'q_gr_qoq', 'q_sales_yoy', 'q_sales_qoq', 'q_op_yoy', 'q_op_qoq', \
'q_profit_yoy', 'q_profit_qoq', 'q_netprofit_yoy', 'q_netprofit_qoq', 'equity_yoy', 'rd_exp'
data_df_list, data_count, all_data_count, data_len = [], 0, 0, len(code_date_range_dic)
logger.info('%d 财务指标信息将被插入 tushare_stock_fin_indicator 表', data_len)
# 将data_df数据，添加到data_df_list
Cycles = 1
try:
for num, (ts_code, (date_from, date_to)) in enumerate(code_date_range_dic.items(), start=1):
logger.debug('%d/%d) %s [%s - %s]', num, data_len, ts_code, date_from, date_to)
data_df = invoke_fina_indicator(ts_code=ts_code, start_date=datetime_2_str(date_from, STR_FORMAT_DATE_TS),
end_date=datetime_2_str(date_to, STR_FORMAT_DATE_TS), fields=fields)
# logger.info(' %d data of %s between %s and %s', df.shape[0], ts_code, date_from, date_to)
if len(data_df) > 0 and data_df['ann_date'].iloc[-1] is not None:
while try_2_date(data_df['ann_date'].iloc[-1]) > date_from:
last_date_in_df_last = try_2_date(data_df['ann_date'].iloc[-1])
df2 = invoke_fina_indicator(ts_code=ts_code,
start_date=datetime_2_str(date_from, STR_FORMAT_DATE_TS),
end_date=datetime_2_str(
try_2_date(data_df['ann_date'].iloc[-1]) - timedelta(days=1),STR_FORMAT_DATE_TS), fields=fields)
if len(df2) > 0 and df2['ann_date'].iloc[-1] is not None:
last_date_in_df_cur = try_2_date(df2['ann_date'].iloc[-1])
if last_date_in_df_cur < last_date_in_df_last:
data_df = pd.concat([data_df, df2])
elif last_date_in_df_cur == last_date_in_df_last:
break
elif len(df2) <= 0:
break
if data_df is None:
logger.warning('%d/%d) %s has no data during %s %s', num, data_len, ts_code, date_from, date_to)
continue
elif data_df is not None:
logger.info('整体进度：%d/%d)， %d 条 %s 财务指标已提取，起止时间 %s 和 %s', num, data_len, data_df.shape[0], ts_code,date_from, date_to)
# 把数据攒起来
if data_df is not None and data_df.shape[0] > 0:
data_count += data_df.shape[0]
data_df_list.append(data_df)
# 大于阀值有开始插入
if data_count >= 1000 and len(data_df_list) > 0:
data_df_all = pd.concat(data_df_list)
bunch_insert_on_duplicate_update(data_df_all, table_name, engine_md,DTYPE_STOCK_FINA_INDICATOR)
logger.info('%d 条财务指标将数据插入 %s 表', data_count,table_name)
all_data_count += data_count
data_df_list, data_count = [], 0
# 仅调试使用
Cycles = Cycles + 1
if DEBUG and Cycles > 10:
break
finally:
# 导入数据库
if len(data_df_list) > 0:
data_df_all = pd.concat(data_df_list)
data_count = bunch_insert_on_duplicate_update(data_df_all, table_name, engine_md, DTYPE_STOCK_FINA_INDICATOR)
all_data_count = all_data_count + data_count
logging.info("更新 %s 结束 %d 条信息被更新", table_name, all_data_count)
def import_tushare_stock_fina_indicator(ts_code_set=None):
"""
插入股票日线数据到最近一个工作日-1。
如果超过 BASE_LINE_HOUR 时间，则获取当日的数据
:return:
"""
table_name = 'tushare_stock_fin_indicator'
logging.info("更新 %s 开始", table_name)
param_list = [
('ts_code', String(20)),
('ann_date', Date),
('end_date', Date),
('eps', DOUBLE),
('dt_eps', DOUBLE),
('total_revenue_ps', DOUBLE),
('revenue_ps', DOUBLE),
('capital_rese_ps', DOUBLE),
('surplus_rese_ps', DOUBLE),
('undist_profit_ps', DOUBLE),
('extra_item', DOUBLE),
('profit_dedt', DOUBLE),
('gross_margin', DOUBLE),
('current_ratio', DOUBLE),
('quick_ratio', DOUBLE),
('cash_ratio', DOUBLE),
('invturn_days', DOUBLE),
('arturn_days', DOUBLE),
('inv_turn', DOUBLE),
('ar_turn', DOUBLE),
('ca_turn', DOUBLE),
('fa_turn', DOUBLE),
('assets_turn', DOUBLE),
('op_income', DOUBLE),
('valuechange_income', DOUBLE),
('interst_income', DOUBLE),
('daa', DOUBLE),
('ebit', DOUBLE),
('ebitda', DOUBLE),
('fcff', DOUBLE),
('fcfe', DOUBLE),
('current_exint', DOUBLE),
('noncurrent_exint', DOUBLE),
('interestdebt', DOUBLE),
('netdebt', DOUBLE),
('tangible_asset', DOUBLE),
('working_capital', DOUBLE),
('networking_capital', DOUBLE),
('invest_capital', DOUBLE),
('retained_earnings', DOUBLE),
('diluted2_eps', DOUBLE),
('bps', DOUBLE),
('ocfps', DOUBLE),
('retainedps', DOUBLE),
('cfps', DOUBLE),
('ebit_ps', DOUBLE),
('fcff_ps', DOUBLE),
('fcfe_ps', DOUBLE),
('netprofit_margin', DOUBLE),
('grossprofit_margin', DOUBLE),
('cogs_of_sales', DOUBLE),
('expense_of_sales', DOUBLE),
('profit_to_gr', DOUBLE),
('saleexp_to_gr', DOUBLE),
('adminexp_of_gr', DOUBLE),
('finaexp_of_gr', DOUBLE),
('impai_ttm', DOUBLE),
('gc_of_gr', DOUBLE),
('op_of_gr', DOUBLE),
('ebit_of_gr', DOUBLE),
('roe', DOUBLE),
('roe_waa', DOUBLE),
('roe_dt', DOUBLE),
('roa', DOUBLE),
('npta', DOUBLE),
('roic', DOUBLE),
('roe_yearly', DOUBLE),
('roa2_yearly', DOUBLE),
('roe_avg', DOUBLE),
('opincome_of_ebt', DOUBLE),
('investincome_of_ebt', DOUBLE),
('n_op_profit_of_ebt', DOUBLE),
('tax_to_ebt', DOUBLE),
('dtprofit_to_profit', DOUBLE),
('salescash_to_or', DOUBLE),
('ocf_to_or', DOUBLE),
('ocf_to_opincome', DOUBLE),
('capitalized_to_da', DOUBLE),
('debt_to_assets', DOUBLE),
('assets_to_eqt', DOUBLE),
('dp_assets_to_eqt', DOUBLE),
('ca_to_assets', DOUBLE),
('nca_to_assets', DOUBLE),
('tbassets_to_totalassets', DOUBLE),
('int_to_talcap', DOUBLE),
('eqt_to_talcapital', DOUBLE),
('currentdebt_to_debt', DOUBLE),
('longdeb_to_debt', DOUBLE),
('ocf_to_shortdebt', DOUBLE),
('debt_to_eqt', DOUBLE),
('eqt_to_debt', DOUBLE),
('eqt_to_interestdebt', DOUBLE),
('tangibleasset_to_debt', DOUBLE),
('tangasset_to_intdebt', DOUBLE),
('tangibleasset_to_netdebt', DOUBLE),
('ocf_to_debt', DOUBLE),
('ocf_to_interestdebt', DOUBLE),
('ocf_to_netdebt', DOUBLE),
('ebit_to_interest', DOUBLE),
('longdebt_to_workingcapital', DOUBLE),
('ebitda_to_debt', DOUBLE),
('turn_days', DOUBLE),
('roa_yearly', DOUBLE),
('roa_dp', DOUBLE),
('fixed_assets', DOUBLE),
('profit_prefin_exp', DOUBLE),
('non_op_profit', DOUBLE),
('op_to_ebt', DOUBLE),
('nop_to_ebt', DOUBLE),
('ocf_to_profit', DOUBLE),
('cash_to_liqdebt', DOUBLE),
('cash_to_liqdebt_withinterest', DOUBLE),
('op_to_liqdebt', DOUBLE),
('op_to_debt', DOUBLE),
('roic_yearly', DOUBLE),
('total_fa_trun', DOUBLE),
('profit_to_op', DOUBLE),
('q_opincome', DOUBLE),
('q_investincome', DOUBLE),
('q_dtprofit', DOUBLE),
('q_eps', DOUBLE),
('q_netprofit_margin', DOUBLE),
('q_gsprofit_margin', DOUBLE),
('q_exp_to_sales', DOUBLE),
('q_profit_to_gr', DOUBLE),
('q_saleexp_to_gr', DOUBLE),
('q_adminexp_to_gr', DOUBLE),
('q_finaexp_to_gr', DOUBLE),
('q_impair_to_gr_ttm', DOUBLE),
('q_gc_to_gr', DOUBLE),
('q_op_to_gr', DOUBLE),
('q_roe', DOUBLE),
('q_dt_roe', DOUBLE),
('q_npta', DOUBLE),
('q_opincome_to_ebt', DOUBLE),
('q_investincome_to_ebt', DOUBLE),
('q_dtprofit_to_profit', DOUBLE),
('q_salescash_to_or', DOUBLE),
('q_ocf_to_sales', DOUBLE),
('q_ocf_to_or', DOUBLE),
('basic_eps_yoy', DOUBLE),
('dt_eps_yoy', DOUBLE),
('cfps_yoy', DOUBLE),
('op_yoy', DOUBLE),
('ebt_yoy', DOUBLE),
('netprofit_yoy', DOUBLE),
('dt_netprofit_yoy', DOUBLE),
('ocf_yoy', DOUBLE),
('roe_yoy', DOUBLE),
('bps_yoy', DOUBLE),
('assets_yoy', DOUBLE),
('eqt_yoy', DOUBLE),
('tr_yoy', DOUBLE),
('or_yoy', DOUBLE),
('q_gr_yoy', DOUBLE),
('q_gr_qoq', DOUBLE),
('q_sales_yoy', DOUBLE),
('q_sales_qoq', DOUBLE),
('q_op_yoy', DOUBLE),
('q_op_qoq', DOUBLE),
('q_profit_yoy', DOUBLE),
('q_profit_qoq', DOUBLE),
('q_netprofit_yoy', DOUBLE),
('q_netprofit_qoq', DOUBLE),
('equity_yoy', DOUBLE),
('rd_exp', DOUBLE),
]
sql_str = """SELECT ts_code,subdate(list_date,365*10) date_frm,list_date date_to FROM tushare_stock_info"""
logger.warning('%s 打补丁，使用 tushare_stock_info 表进行计算需要补充提取的日期范围', table_name)
with with_db_session(engine_md) as session:
# 获取每只股票需要获取日线数据的日期区间
table = session.execute(sql_str)
# 计算每只股票需要获取日线数据的日期区间
begin_time = None
# 获取date_from,date_to，将date_from,date_to做为value值
code_date_range_dic = {
ts_code:
(date_from if begin_time is None else min([date_from, begin_time]),
date_to)
for ts_code, date_from, date_to in table.fetchall()
if ts_code_set is None or ts_code in ts_code_set
}
# 设置 dtype
dtype = {key: val for key, val in param_list}
# dtype['ts_code'] = String(20)
# dtype['trade_date'] = Date
fields = 'ts_code', 'ann_date', 'end_date', 'eps', 'dt_eps', 'total_revenue_ps', 'revenue_ps', 'capital_rese_ps', \
'surplus_rese_ps', 'undist_profit_ps', 'extra_item', 'profit_dedt', 'gross_margin', 'current_ratio', \
'quick_ratio', 'cash_ratio', 'invturn_days', 'arturn_days', 'inv_turn', 'ar_turn', 'ca_turn', 'fa_turn', \
'assets_turn', 'op_income', 'valuechange_income', 'interst_income', 'daa', 'ebit', 'ebitda', 'fcff', \
'fcfe', 'current_exint', 'noncurrent_exint', 'interestdebt', 'netdebt', 'tangible_asset', \
'working_capital', 'networking_capital', 'invest_capital', 'retained_earnings', 'diluted2_eps', 'bps', \
'ocfps', 'retainedps', 'cfps', 'ebit_ps', 'fcff_ps', 'fcfe_ps', 'netprofit_margin', 'grossprofit_margin', \
'cogs_of_sales', 'expense_of_sales', 'profit_to_gr', 'saleexp_to_gr', 'adminexp_of_gr', 'finaexp_of_gr', \
'impai_ttm', 'gc_of_gr', 'op_of_gr', 'ebit_of_gr', 'roe', 'roe_waa', 'roe_dt', 'roa', 'npta', 'roic', \
'roe_yearly', 'roa2_yearly', 'roe_avg', 'opincome_of_ebt', 'investincome_of_ebt', 'n_op_profit_of_ebt', \
'tax_to_ebt', 'dtprofit_to_profit', 'salescash_to_or', 'ocf_to_or', 'ocf_to_opincome', \
'capitalized_to_da', 'debt_to_assets', 'assets_to_eqt', 'dp_assets_to_eqt', 'ca_to_assets', \
'nca_to_assets', 'tbassets_to_totalassets', 'int_to_talcap', 'eqt_to_talcapital', 'currentdebt_to_debt', \
'longdeb_to_debt', 'ocf_to_shortdebt', 'debt_to_eqt', 'eqt_to_debt', 'eqt_to_interestdebt', \
'tangibleasset_to_debt', 'tangasset_to_intdebt', 'tangibleasset_to_netdebt', 'ocf_to_debt', \
'ocf_to_interestdebt', 'ocf_to_netdebt', 'ebit_to_interest', 'longdebt_to_workingcapital', \
'ebitda_to_debt', 'turn_days', 'roa_yearly', 'roa_dp', 'fixed_assets', 'profit_prefin_exp', \
'non_op_profit', 'op_to_ebt', 'nop_to_ebt', 'ocf_to_profit', 'cash_to_liqdebt', \
'cash_to_liqdebt_withinterest', 'op_to_liqdebt', 'op_to_debt', 'roic_yearly', 'total_fa_trun', \
'profit_to_op', 'q_opincome', 'q_investincome', 'q_dtprofit', 'q_eps', 'q_netprofit_margin', \
'q_gsprofit_margin', 'q_exp_to_sales', 'q_profit_to_gr', 'q_saleexp_to_gr', 'q_adminexp_to_gr', \
'q_finaexp_to_gr', 'q_impair_to_gr_ttm', 'q_gc_to_gr', 'q_op_to_gr', 'q_roe', 'q_dt_roe', 'q_npta', \
'q_opincome_to_ebt', 'q_investincome_to_ebt', 'q_dtprofit_to_profit', 'q_salescash_to_or', \
'q_ocf_to_sales', 'q_ocf_to_or', 'basic_eps_yoy', 'dt_eps_yoy', 'cfps_yoy', 'op_yoy', 'ebt_yoy', \
'netprofit_yoy', 'dt_netprofit_yoy', 'ocf_yoy', 'roe_yoy', 'bps_yoy', 'assets_yoy', 'eqt_yoy', \
'tr_yoy', 'or_yoy', 'q_gr_yoy', 'q_gr_qoq', 'q_sales_yoy', 'q_sales_qoq', 'q_op_yoy', 'q_op_qoq', \
'q_profit_yoy', 'q_profit_qoq', 'q_netprofit_yoy', 'q_netprofit_qoq', 'equity_yoy', 'rd_exp'
data_len = len(code_date_range_dic)
logger.info('%d data will been import into %s', data_len, table_name)
# 将data_df数据，添加到data_df_list
Cycles = 1
try:
for num, (ts_code, (date_from,
date_to)) in enumerate(code_date_range_dic.items(),
start=1):
logger.debug('%d/%d) %s [%s - %s]', num, data_len, ts_code,
date_from, date_to)
df = invoke_fina_indicator(
ts_code=ts_code,
start_date=datetime_2_str(date_from, STR_FORMAT_DATE_TS),
end_date=datetime_2_str(date_to, STR_FORMAT_DATE_TS),
fields=fields)
# logger.info(' %d data of %s between %s and %s', df.shape[0], ts_code, date_from, date_to)
data_df = df
if len(data_df) > 0:
while try_2_date(df['ann_date'].iloc[-1]) > date_from:
last_date_in_df_last, last_date_in_df_cur = try_2_date(
df['ann_date'].iloc[-1]), None
df2 = invoke_fina_indicator(
ts_code=ts_code,
start_date=datetime_2_str(date_from,
STR_FORMAT_DATE_TS),
end_date=datetime_2_str(
try_2_date(df['ann_date'].iloc[-1]) -
timedelta(days=1), STR_FORMAT_DATE_TS),
fields=fields)
if len(df2) > 0:
last_date_in_df_cur = try_2_date(
df2['ann_date'].iloc[-1])
if last_date_in_df_cur < last_date_in_df_last:
data_df = pd.concat([data_df, df2])
df = df2
elif last_date_in_df_cur == last_date_in_df_last:
break
if data_df is None:
logger.warning(
'%d/%d) %s has no data during %s %s', num,
data_len, ts_code, date_from, date_to)
continue
logger.info('%d/%d) %d data of %s between %s and %s',
num, data_len, data_df.shape[0], ts_code,
date_from, date_to)
elif len(df2) <= 0:
break
# 数据插入数据库
data_count = bunch_insert_on_duplicate_update(
data_df, table_name, engine_md, dtype)
logging.info("更新 %s 结束 %d 条信息被更新", table_name, data_count)
data_df = []
# 仅调试使用
Cycles = Cycles + 1
if DEBUG and Cycles > 10:
break
finally:
# 导入数据库
if len(data_df) > 0:
data_count = bunch_insert_on_duplicate_update(
data_df,
table_name,
engine_md,
dtype,
myisam_if_create_table=True,
primary_keys=['ts_code', 'ann_date', 'end_date'],
schema=config.DB_SCHEMA_MD)
logging.info("更新 %s 结束 %d 条信息被更新", table_name, data_count)
def import_tushare_stock_cashflow(ts_code_set=None):
"""
插入股票日线数据到最近一个工作日-1。
如果超过 BASE_LINE_HOUR 时间，则获取当日的数据
:return:
"""
table_name = 'tushare_stock_cashflow'
logging.info("更新 %s 开始", table_name)
sql_str = """SELECT ts_code,subdate(list_date,365*10) date_frm,list_date date_to FROM tushare_stock_info;"""
logger.warning('%s 打补丁，使用 tushare_stock_info 表进行计算需要补充提取的日期范围', table_name)
with with_db_session(engine_md) as session:
# 获取每只股票需要获取日线数据的日期区间
table = session.execute(sql_str)
# 计算每只股票需要获取日线数据的日期区间
begin_time = None
# 获取date_from,date_to，将date_from,date_to做为value值
code_date_range_dic = {
ts_code: (date_from if begin_time is None else min([date_from, begin_time]), date_to)
for ts_code, date_from, date_to in table.fetchall() if
ts_code_set is None or ts_code in ts_code_set}
data_len = len(code_date_range_dic)
logger.info('%d stocks will been import into wind_stock_daily', data_len)
# 将data_df数据，添加到data_df_list
Cycles = 1
try:
for num, (ts_code, (date_from, date_to)) in enumerate(code_date_range_dic.items(), start=1):
logger.debug('%d/%d) %s [%s - %s]', num, data_len, ts_code, date_from, date_to)
df = invoke_cashflow(ts_code=ts_code, start_date=datetime_2_str(date_from, STR_FORMAT_DATE_TS),
end_date=datetime_2_str(date_to, STR_FORMAT_DATE_TS))
# logger.info(' %d data of %s between %s and %s', df.shape[0], ts_code, date_from, date_to)
data_df = df
if len(data_df) > 0:
while try_2_date(df['ann_date'].iloc[-1]) > date_from:
last_date_in_df_last, last_date_in_df_cur = try_2_date(df['ann_date'].iloc[-1]), None
df2 = invoke_cashflow(ts_code=ts_code, start_date=datetime_2_str(date_from, STR_FORMAT_DATE_TS),
end_date=datetime_2_str(
try_2_date(df['ann_date'].iloc[-1]) - timedelta(days=1),
STR_FORMAT_DATE_TS))
if len(df2) > 0:
last_date_in_df_cur = try_2_date(df2['ann_date'].iloc[-1])
if last_date_in_df_cur < last_date_in_df_last:
data_df = pd.concat([data_df, df2])
df = df2
elif last_date_in_df_cur == last_date_in_df_last:
break
if data_df is None:
logger.warning('%d/%d) %s has no data during %s %s', num, data_len, ts_code, date_from,
date_to)
continue
logger.info('%d/%d) %d data of %s between %s and %s', num, data_len, data_df.shape[0], ts_code,
date_from, date_to)
elif len(df2) <= 0:
break
# 数据插入数据库
data_count = bunch_insert_on_duplicate_update(data_df, table_name, engine_md,
DTYPE_TUSHARE_STOCK_CASHFLOW)
logging.info("更新 %s 结束 %d 条信息被更新", table_name, data_count)
data_df = []
# 仅调试使用
Cycles = Cycles + 1
if DEBUG and Cycles > 10:
break
finally:
# 导入数据库
if len(data_df) > 0:
data_count = bunch_insert_on_duplicate_update(data_df, table_name, engine_md, DTYPE_TUSHARE_STOCK_CASHFLOW)
logging.info("更新 %s 结束 %d 条信息被更新", table_name, data_count)
def import_tushare_stock_balancesheet(ts_code_set=None):
"""
插入股票日线数据到最近一个工作日-1。
如果超过 BASE_LINE_HOUR 时间，则获取当日的数据
:return:
"""
table_name = 'tushare_stock_balancesheet'
logging.info("更新 %s 开始", table_name)
param_list = [
('ts_code', String(20)),
('ann_date', Date),
('f_ann_date', Date),
('end_date', Date),
('report_type', DOUBLE),
('comp_type', DOUBLE),
('total_share', DOUBLE),
('cap_rese', DOUBLE),
('undistr_porfit', DOUBLE),
('surplus_rese', DOUBLE),
('special_rese', DOUBLE),
('money_cap', DOUBLE),
('trad_asset', DOUBLE),
('notes_receiv', DOUBLE),
('accounts_receiv', DOUBLE),
('oth_receiv', DOUBLE),
('prepayment', DOUBLE),
('div_receiv', DOUBLE),
('int_receiv', DOUBLE),
('inventories', DOUBLE),
('amor_exp', DOUBLE),
('nca_within_1y', DOUBLE),
('sett_rsrv', DOUBLE),
('loanto_oth_bank_fi', DOUBLE),
('premium_receiv', DOUBLE),
('reinsur_receiv', DOUBLE),
('reinsur_res_receiv', DOUBLE),
('pur_resale_fa', DOUBLE),
('oth_cur_assets', DOUBLE),
('total_cur_assets', DOUBLE),
('fa_avail_for_sale', DOUBLE),
('htm_invest', DOUBLE),
('lt_eqt_invest', DOUBLE),
('invest_real_estate', DOUBLE),
('time_deposits', DOUBLE),
('oth_assets', DOUBLE),
('lt_rec', DOUBLE),
('fix_assets', DOUBLE),
('cip', DOUBLE),
('const_materials', DOUBLE),
('fixed_assets_disp', DOUBLE),
('produc_bio_assets', DOUBLE),
('oil_and_gas_assets', DOUBLE),
('intan_assets', DOUBLE),
('r_and_d', DOUBLE),
('goodwill', DOUBLE),
('lt_amor_exp', DOUBLE),
('defer_tax_assets', DOUBLE),
('decr_in_disbur', DOUBLE),
('oth_nca', DOUBLE),
('total_nca', DOUBLE),
('cash_reser_cb', DOUBLE),
('depos_in_oth_bfi', DOUBLE),
('prec_metals', DOUBLE),
('deriv_assets', DOUBLE),
('rr_reins_une_prem', DOUBLE),
('rr_reins_outstd_cla', DOUBLE),
('rr_reins_lins_liab', DOUBLE),
('rr_reins_lthins_liab', DOUBLE),
('refund_depos', DOUBLE),
('ph_pledge_loans', DOUBLE),
('refund_cap_depos', DOUBLE),
('indep_acct_assets', DOUBLE),
('client_depos', DOUBLE),
('client_prov', DOUBLE),
('transac_seat_fee', DOUBLE),
('invest_as_receiv', DOUBLE),
('total_assets', DOUBLE),
('lt_borr', DOUBLE),
('st_borr', DOUBLE),
('cb_borr', DOUBLE),
('depos_ib_deposits', DOUBLE),
('loan_oth_bank', DOUBLE),
('trading_fl', DOUBLE),
('notes_payable', DOUBLE),
('acct_payable', DOUBLE),
('adv_receipts', DOUBLE),
('sold_for_repur_fa', DOUBLE),
('comm_payable', DOUBLE),
('payroll_payable', DOUBLE),
('taxes_payable', DOUBLE),
('int_payable', DOUBLE),
('oth_payable', DOUBLE),
('acc_exp', DOUBLE),
('deferred_inc', DOUBLE),
('st_bonds_payable', DOUBLE),
('payable_to_reinsurer', DOUBLE),
('rsrv_insur_cont', DOUBLE),
('acting_trading_sec', DOUBLE),
('acting_uw_sec', DOUBLE),
('non_cur_liab_due_1y', DOUBLE),
('oth_cur_liab', DOUBLE),
('total_cur_liab', DOUBLE),
('bond_payable', DOUBLE),
('lt_payable', DOUBLE),
('specific_payables', DOUBLE),
('estimated_liab', DOUBLE),
('defer_tax_liab', DOUBLE),
('defer_inc_non_cur_liab', DOUBLE),
('oth_ncl', DOUBLE),
('total_ncl', DOUBLE),
('depos_oth_bfi', DOUBLE),
('deriv_liab', DOUBLE),
('depos', DOUBLE),
('agency_bus_liab', DOUBLE),
('oth_liab', DOUBLE),
('prem_receiv_adva', DOUBLE),
('depos_received', DOUBLE),
('ph_invest', DOUBLE),
('reser_une_prem', DOUBLE),
('reser_outstd_claims', DOUBLE),
('reser_lins_liab', DOUBLE),
('reser_lthins_liab', DOUBLE),
('indept_acc_liab', DOUBLE),
('pledge_borr', DOUBLE),
('indem_payable', DOUBLE),
('policy_div_payable', DOUBLE),
('total_liab', DOUBLE),
('treasury_share', DOUBLE),
('ordin_risk_reser', DOUBLE),
('forex_differ', DOUBLE),
('invest_loss_unconf', DOUBLE),
('minority_int', DOUBLE),
('total_hldr_eqy_exc_min_int', DOUBLE),
('total_hldr_eqy_inc_min_int', DOUBLE),
('total_liab_hldr_eqy', DOUBLE),
('lt_payroll_payable', DOUBLE),
('oth_comp_income', DOUBLE),
('oth_eqt_tools', DOUBLE),
('oth_eqt_tools_p_shr', DOUBLE),
('lending_funds', DOUBLE),
('acc_receivable', DOUBLE),
('st_fin_payable', DOUBLE),
('payables', DOUBLE),
('hfs_assets', DOUBLE),
('hfs_sales', DOUBLE),
]
# 进行表格判断，确定是否含有tushare_stock_daily
sql_str = """SELECT ts_code,subdate(list_date,365*10) date_frm,list_date date_to FROM tushare_stock_info"""
logger.warning('%s 打补丁，使用 tushare_stock_info 表进行计算需要补充提取的日期范围', table_name)
# ts_code_set = None
with with_db_session(engine_md) as session:
# 获取每只股票需要获取日线数据的日期区间
table = session.execute(sql_str)
# 计算每只股票需要获取日线数据的日期区间
begin_time = None
# 获取date_from,date_to，将date_from,date_to做为value值
code_date_range_dic = {
ts_code:
(date_from if begin_time is None else min([date_from, begin_time]),
date_to)
for ts_code, date_from, date_to in table.fetchall()
if ts_code_set is None or ts_code in ts_code_set
}
# 设置 dtype
dtype = {key: val for key, val in param_list}
# dtype['ts_code'] = String(20)
# dtype['trade_date'] = Date
data_len = len(code_date_range_dic)
logger.info('%d stocks will been import into wind_stock_daily', data_len)
# 将data_df数据，添加到data_df_list
Cycles = 1
try:
for num, (ts_code, (date_from,
date_to)) in enumerate(code_date_range_dic.items(),
start=1):
logger.debug('%d/%d) %s [%s - %s]', num, data_len, ts_code,
date_from, date_to)
df = invoke_balancesheet(
ts_code=ts_code,
start_date=datetime_2_str(date_from, STR_FORMAT_DATE_TS),
end_date=datetime_2_str(date_to, STR_FORMAT_DATE_TS))
# logger.info(' %d data of %s between %s and %s', df.shape[0], ts_code, date_from, date_to)
data_df = df
if len(data_df) > 0:
while try_2_date(df['ann_date'].iloc[-1]) > date_from:
last_date_in_df_last, last_date_in_df_cur = try_2_date(
df['ann_date'].iloc[-1]), None
df2 = invoke_balancesheet(
ts_code=ts_code,
start_date=datetime_2_str(date_from,
STR_FORMAT_DATE_TS),
end_date=datetime_2_str(
try_2_date(df['ann_date'].iloc[-1]) -
timedelta(days=1), STR_FORMAT_DATE_TS))
if len(df2) > 0:
last_date_in_df_cur = try_2_date(
df2['ann_date'].iloc[-1])
if last_date_in_df_cur < last_date_in_df_last:
data_df = pd.concat([data_df, df2])
df = df2
elif last_date_in_df_cur == last_date_in_df_last:
break
if data_df is None:
logger.warning(
'%d/%d) %s has no data during %s %s', num,
data_len, ts_code, date_from, date_to)
continue
logger.info('%d/%d) %d data of %s between %s and %s',
num, data_len, data_df.shape[0], ts_code,
date_from, date_to)
elif len(df2) <= 0:
break
# 数据插入数据库
data_count = bunch_insert_on_duplicate_update(
data_df, table_name, engine_md, dtype)
logging.info("更新 %s 结束 %d 条信息被更新", table_name, data_count)
data_df = []
# 仅调试使用
Cycles = Cycles + 1
if DEBUG and Cycles > 10:
break
finally:
# 导入数据库
if len(data_df) > 0:
data_count = bunch_insert_on_duplicate_update(
data_df,
table_name,
engine_md,
dtype,
myisam_if_create_table=True,
primary_keys=['ts_code', 'ann_date'],
schema=config.DB_SCHEMA_MD)
logging.info("更新 %s 结束 %d 条信息被更新", table_name, data_count)
def import_tushare_stock_top10_floatholders(chain_param=None,ts_code_set=None):
"""
插入股票日线数据到最近一个工作日-1。
如果超过 BASE_LINE_HOUR 时间，则获取当日的数据
:return:
"""
table_name = 'tushare_stock_top10_floatholders'
logging.info("更新 %s 开始", table_name)
has_table = engine_md.has_table(table_name)
# 进行表格判断，确定是否含有tushare_stock_daily
if has_table:
sql_str = """
SELECT ts_code, date_frm, if(delist_date<end_date2, delist_date, end_date2) date_to
FROM
(
SELECT info.ts_code, ifnull(end_date, subdate(list_date,365*10)) date_frm, delist_date,
if(hour(now())<16, subdate(curdate(),1), curdate()) end_date2
FROM
tushare_stock_info info
LEFT OUTER JOIN
(SELECT ts_code, adddate(max(ann_date),1) end_date
FROM {table_name} GROUP BY ts_code) top10_floatholders
ON info.ts_code = top10_floatholders.ts_code
) tt
WHERE date_frm <= if(delist_date<end_date2, delist_date, end_date2)
ORDER BY ts_code""".format(table_name=table_name)
else:
sql_str = """
SELECT ts_code, date_frm, if(delist_date<end_date2, delist_date, end_date2) date_to
FROM
(
SELECT info.ts_code, subdate(list_date,365*10) date_frm, delist_date,
if(hour(now())<16, subdate(curdate(),1), curdate()) end_date2
FROM tushare_stock_info info
) tt
WHERE date_frm <= if(delist_date<end_date2, delist_date, end_date2)
ORDER BY ts_code """
logger.warning('%s 不存在，仅使用 tushare_stock_info 表进行计算日期范围', table_name)
with with_db_session(engine_md) as session:
# 获取每只股票需要获取日线数据的日期区间
table = session.execute(sql_str)
# 计算每只股票需要获取日线数据的日期区间
begin_time = None
# 获取date_from,date_to，将date_from,date_to做为value值
code_date_range_dic = {
ts_code: (date_from if begin_time is None else min([date_from, begin_time]), date_to)
for ts_code, date_from, date_to in table.fetchall() if
ts_code_set is None or ts_code in ts_code_set}
data_len = len(code_date_range_dic)
logger.info('%d stocks will been import into top10_floatholders', data_len)
# 将data_df数据，添加到data_df_list
Cycles = 1
try:
for num, (ts_code, (date_from, date_to)) in enumerate(code_date_range_dic.items(), start=1):
logger.debug('%d/%d) %s [%s - %s]', num, data_len, ts_code, date_from, date_to)
df = invoke_top10_floatholders(ts_code=ts_code, start_date=datetime_2_str(date_from, STR_FORMAT_DATE_TS),
end_date=datetime_2_str(date_to, STR_FORMAT_DATE_TS))
# logger.info(' %d data of %s between %s and %s', df.shape[0], ts_code, date_from, date_to)
data_df = df
if len(data_df) > 0:
while try_2_date(df['ann_date'].iloc[-1]) > date_from:
last_date_in_df_last, last_date_in_df_cur = try_2_date(df['ann_date'].iloc[-1]), None
df2 = invoke_top10_floatholders(ts_code=ts_code, start_date=datetime_2_str(date_from, STR_FORMAT_DATE_TS),
end_date=datetime_2_str(
try_2_date(df['ann_date'].iloc[-1]) - timedelta(days=1),
STR_FORMAT_DATE_TS))
if len(df2) > 0:
last_date_in_df_cur = try_2_date(df2['ann_date'].iloc[-1])
if last_date_in_df_cur < last_date_in_df_last:
data_df = pd.concat([data_df, df2])
df = df2
elif last_date_in_df_cur == last_date_in_df_last:
break
if data_df is None:
logger.warning('%d/%d) %s has no data during %s %s', num, data_len, ts_code, date_from,
date_to)
continue
logger.info('%d/%d) %d data of %s between %s and %s', num, data_len, data_df.shape[0], ts_code,
date_from, date_to)
elif len(df2) <= 0:
break
# 数据插入数据库
data_count = bunch_insert_on_duplicate_update(data_df, table_name, engine_md, DTYPE_TUSHARE_STOCK_TOP10_FLOATHOLDERS)
logging.info("更新 %s 结束 %d 条信息被更新", table_name, data_count)
# 仅调试使用
Cycles = Cycles + 1
if DEBUG and Cycles > 5:
break
finally:
# 导入数据库
if len(data_df) > 0:
data_count = bunch_insert_on_duplicate_update(data_df, table_name, engine_md, DTYPE_TUSHARE_STOCK_TOP10_FLOATHOLDERS)
logging.info("更新 %s 结束 %d 条信息被更新", table_name, data_count)
def import_tushare_stock_income(chain_param=None, ts_code_set=None):
"""
插入股票日线数据到最近一个工作日-1。
如果超过 BASE_LINE_HOUR 时间，则获取当日的数据
:return:
"""
table_name = 'tushare_stock_income'
logging.info("更新 %s 开始", table_name)
# wind_indictor_str = ",".join([key for key, _ in param_list])
# rename_col_dic = {key.upper(): key.lower() for key, _ in param_list}
has_table = engine_md.has_table(table_name)
# 进行表格判断，确定是否含有tushare_stock_daily
if has_table:
sql_str = """
SELECT ts_code, date_frm, if(delist_date<end_date, delist_date, end_date) date_to
FROM
(
SELECT info.ts_code, ifnull(ann_date, subdate(list_date,365*10)) date_frm, delist_date,
if(hour(now())<16, subdate(curdate(),1), curdate()) end_date
FROM
tushare_stock_info info
LEFT OUTER JOIN
(SELECT ts_code, adddate(max(ann_date),1) ann_date
FROM {table_name} GROUP BY ts_code) income
ON info.ts_code = income.ts_code
) tt
WHERE date_frm <= if(delist_date<end_date, delist_date, end_date)
ORDER BY ts_code""".format(table_name=table_name)
else:
sql_str = """
SELECT ts_code, date_frm, if(delist_date<end_date, delist_date, end_date) date_to
FROM
(
SELECT info.ts_code, subdate(list_date,365*10) date_frm, delist_date,
if(hour(now())<16, subdate(curdate(),1), curdate()) end_date
FROM tushare_stock_info info
) tt
WHERE date_frm <= if(delist_date<end_date, delist_date, end_date)
ORDER BY ts_code DESC """
logger.warning('%s 不存在，仅使用 tushare_stock_info 表进行计算日期范围', table_name)
with with_db_session(engine_md) as session:
# 获取每只股票需要获取日线数据的日期区间
table = session.execute(sql_str)
# 计算每只股票需要获取日线数据的日期区间
begin_time = None
# 获取date_from,date_to，将date_from,date_to做为value值
code_date_range_dic = {
ts_code:
(date_from if begin_time is None else min([date_from, begin_time]),
date_to)
for ts_code, date_from, date_to in table.fetchall()
if ts_code_set is None or ts_code in ts_code_set
}
data_df_list, data_count, all_data_count, data_len = [], 0, 0, len(
code_date_range_dic)
logger.info('%d stocks will been import into wind_stock_daily', data_len)
# 将data_df数据，添加到data_df_list
Cycles = 1
try:
for num, (ts_code, (date_from,
date_to)) in enumerate(code_date_range_dic.items(),
start=1):
logger.debug('%d/%d) %s [%s - %s]', num, data_len, ts_code,
date_from, date_to)
df = invoke_income(ts_code=ts_code,
start_date=datetime_2_str(
date_from, STR_FORMAT_DATE_TS),
end_date=datetime_2_str(date_to,
STR_FORMAT_DATE_TS))
data_df = df
if len(data_df) > 0:
while try_2_date(df['ann_date'].iloc[-1]) > date_from:
last_date_in_df_last, last_date_in_df_cur = try_2_date(
df['ann_date'].iloc[-1]), None
df2 = invoke_income(
ts_code=ts_code,
start_date=datetime_2_str(date_from,
STR_FORMAT_DATE_TS),
end_date=datetime_2_str(
try_2_date(df['ann_date'].iloc[-1]) -
timedelta(days=1), STR_FORMAT_DATE_TS))
if len(df2) > 0:
last_date_in_df_cur = try_2_date(
df2['ann_date'].iloc[-1])
if last_date_in_df_cur < last_date_in_df_last:
data_df = pd.concat([data_df, df2])
df = df2
elif last_date_in_df_cur == last_date_in_df_last:
break
elif len(df2) <= 0:
break
if data_df is None:
logger.warning('%d/%d) %s has no data during %s %s', num,
data_len, ts_code, date_from, date_to)
continue
elif data_df is not None:
logger.info('整体进度：%d/%d)， %d 条 %s 的利润表数据被提取，起止时间为 %s 和 %s',
num, data_len, data_df.shape[0], ts_code,
date_from, date_to)
# # 数据插入数据库
# data_df_all = data_df
# data_count = bunch_insert_on_duplicate_update(data_df_all, table_name, engine_md,
# DTYPE_TUSHARE_STOCK_INCOME)
# logging.info("成功更新 %s 结束 %d 条信息被更新", table_name, data_count)
# 把数据攒起来
if data_df is not None and data_df.shape[0] > 0:
data_count += data_df.shape[0]
data_df_list.append(data_df)
# 大于阀值有开始插入
if data_count >= 1000 and len(data_df_list) > 0:
data_df_all = pd.concat(data_df_list)
bunch_insert_on_duplicate_update(data_df_all, table_name,
engine_md,
DTYPE_TUSHARE_STOCK_INCOME)
logger.info('%d 条财务指标将数据插入 %s 表', data_count, table_name)
all_data_count += data_count
data_df_list, data_count = [], 0
# 仅调试使用
Cycles = Cycles + 1
if DEBUG and Cycles > 10:
break
finally:
# 导入数据库
if len(data_df_list) > 0:
data_df_all = pd.concat(data_df_list)
data_count = bunch_insert_on_duplicate_update(
data_df_all, table_name, engine_md, DTYPE_TUSHARE_STOCK_INCOME)
all_data_count = all_data_count + data_count
logging.info("更新 %s 结束 %d 条信息被更新", table_name, all_data_count)
def import_tushare_stock_fina_mainbz(chain_param=None, ts_code_set=None):
"""
插入股票日线数据到最近一个工作日-1。
如果超过 BASE_LINE_HOUR 时间，则获取当日的数据
:return:
"""
table_name = 'tushare_stock_fin_mainbz'
logging.info("更新 %s 开始", table_name)
# param_list = [
# ('ts_code', String(20)),
# ('end_date', Date),
# ('bz_item', String(200)),
# ('bz_sales', DOUBLE),
# ('bz_profit', DOUBLE),
# ('bz_cost', DOUBLE),
# ('curr_type', String(20)),
# ('update_flag', String(20)),
# ('market_type', String(20)),
# ]
has_table = engine_md.has_table(table_name)
# 进行表格判断，确定是否含有tushare_stock_daily
if has_table:
sql_str = """
SELECT ts_code, date_frm, if(delist_date<end_date2, delist_date, end_date2) date_to
FROM
(
SELECT info.ts_code, ifnull(end_date, subdate(list_date,365*10)) date_frm, delist_date,
if(hour(now())<16, subdate(curdate(),1), curdate()) end_date2
FROM
tushare_stock_info info
LEFT OUTER JOIN
(SELECT ts_code, adddate(max(end_date),1) end_date
FROM {table_name} GROUP BY ts_code) mainbz
ON info.ts_code = mainbz.ts_code
) tt
WHERE date_frm <= if(delist_date<end_date2, delist_date, end_date2)
ORDER BY ts_code""".format(table_name=table_name)
else:
sql_str = """
SELECT ts_code, date_frm, if(delist_date<end_date2, delist_date, end_date2) date_to
FROM
(
SELECT info.ts_code, subdate(list_date,365*10) date_frm, delist_date,
if(hour(now())<16, subdate(curdate(),1), curdate()) end_date2
FROM tushare_stock_info info
) tt
WHERE date_frm <= if(delist_date<end_date2, delist_date, end_date2)
ORDER BY ts_code """
logger.warning('%s 不存在，仅使用 tushare_stock_info 表进行计算日期范围', table_name)
with with_db_session(engine_md) as session:
# 获取每只股票需要获取日线数据的日期区间
table = session.execute(sql_str)
# 计算每只股票需要获取日线数据的日期区间
begin_time = None
# 获取date_from,date_to，将date_from,date_to做为value值
code_date_range_dic = {
ts_code:
(date_from if begin_time is None else min([date_from, begin_time]),
date_to)
for ts_code, date_from, date_to in table.fetchall()
if ts_code_set is None or ts_code in ts_code_set
}
# 设置 dtype
# dtype = {key: val for key, val in param_list}
# dtype['ts_code'] = String(20)
# dtype['trade_date'] = Date
data_df_list, data_count, all_data_count, data_len = [], 0, 0, len(
code_date_range_dic)
logger.info('%d data will been import into %s', data_len, table_name)
# 将data_df数据，添加到data_df_list
Cycles = 1
try:
for num, (ts_code, (date_from,
date_to)) in enumerate(code_date_range_dic.items(),
start=1):
for mainbz_type in list(['P', 'D']):
logger.debug('%d/%d) %s [%s - %s] %s', num, data_len, ts_code,
date_from, date_to, mainbz_type)
data_df = invoke_fina_mainbz(
ts_code=ts_code,
start_date=datetime_2_str(date_from, STR_FORMAT_DATE_TS),
end_date=datetime_2_str(date_to, STR_FORMAT_DATE_TS),
type=mainbz_type)
data_df['market_type'] = mainbz_type
# logger.info(' %d data of %s between %s and %s', df.shape[0], ts_code, date_from, date_to)
# data_df = df
if len(data_df) > 0:
while try_2_date(data_df['end_date'].iloc[-1]) > date_from:
last_date_in_df_last, last_date_in_df_cur = try_2_date(
data_df['end_date'].iloc[-1]), None
df2 = invoke_fina_mainbz(
ts_code=ts_code,
start_date=datetime_2_str(date_from,
STR_FORMAT_DATE_TS),
end_date=datetime_2_str(
try_2_date(data_df['end_date'].iloc[-1]),
STR_FORMAT_DATE_TS),
type=mainbz_type)
df2['market_type'] = mainbz_type
if len(df2) > 0:
last_date_in_df_cur = try_2_date(
df2['end_date'].iloc[-1])
if last_date_in_df_cur < last_date_in_df_last:
data_df = pd.concat([data_df, df2])
# df = df2
elif last_date_in_df_cur <= last_date_in_df_last:
break
elif len(df2) <= 0:
break
if data_df is None:
logger.warning('%d/%d) %s 在 %s 到 %s 这段时间如数据', num,
data_len, ts_code, date_from, date_to)
continue
elif data_df is not None:
logger.info(
'整体进度：%d/%d)， 提取出%d 条 %s 的主营业务数据，类型为%s,起止时间为 %s 和 %s',
num, data_len, data_df.shape[0], ts_code, mainbz_type,
date_from, date_to)
# # 数据插入数据库
# data_count = bunch_insert_on_duplicate_update(data_df, table_name, engine_md, dtype)
# logging.info("更新 %s 结束 %d 条信息被更新", table_name, data_count)
# 把数据攒起来
if data_df is not None and data_df.shape[0] > 0:
data_count += data_df.shape[0]
data_df_list.append(data_df)
# 大于阀值有开始插入
if data_count >= 100 and len(data_df_list) > 0:
data_df_all = pd.concat(data_df_list)
bunch_insert_on_duplicate_update(
data_df_all,
table_name,
engine_md,
DTYPE_TUSHARE_STOCK_FINA_MAINBZ,
myisam_if_create_table=True,
primary_keys=['ts_code', 'ann_date'],
schema=config.DB_SCHEMA_MD)
all_data_count += data_count
data_df_list, data_count = [], 0
# 仅调试使用
Cycles = Cycles + 1
if DEBUG and Cycles > 2:
break
finally:
# 导入数据库
if len(data_df_list) > 0:
data_df_all = pd.concat(data_df_list)
data_count = bunch_insert_on_duplicate_update(
data_df_all,
table_name,
engine_md,
DTYPE_TUSHARE_STOCK_FINA_MAINBZ,
myisam_if_create_table=True,
primary_keys=['ts_code', 'ann_date'],
schema=config.DB_SCHEMA_MD)
all_data_count = all_data_count + data_count
if not has_table and engine_md.has_table(table_name):
alter_table_2_myisam(engine_md, [table_name])
build_primary_key([table_name])
def import_tushare_tmt_twincome(chain_param=None, ts_code_set=None):
"""
插入股票日线数据到最近一个工作日-1。
如果超过 BASE_LINE_HOUR 时间，则获取当日的数据
:return:
"""
table_name = 'tushare_tmt_twincome'
logging.info("更新 %s 开始", table_name)
has_table = engine_md.has_table(table_name)
# 进行表格判断，确定是否含有tushare_stock_daily
if has_table:
sql_str = """
SELECT ts_code, date_frm start_date, end_date
FROM
(
SELECT info.ts_code, ifnull(date, start_date) date_frm,
if(hour(now())<16, subdate(curdate(),1), curdate()) end_date
FROM
tushare_tmt_twincome_info info
LEFT OUTER JOIN
(SELECT item, adddate(max(date),1) date FROM {table_name} GROUP BY item ) income
ON info.ts_code = income.item
) tt
order by ts_code""".format(table_name=table_name)
else:
sql_str = """SELECT ts_code, start_date ,
if(hour(now())<16, subdate(curdate(),1), curdate()) end_date
FROM tushare_tmt_twincome_info info """
logger.warning('%s 不存在，仅使用 tushare_tmt_twincome_info 表进行计算日期范围',
table_name)
# ts_code_set = None
with with_db_session(engine_md) as session:
# 获取每只股票需要获取日线数据的日期区间
table = session.execute(sql_str)
# 计算每只股票需要获取日线数据的日期区间
begin_time, ts_code_set = None, None
# 获取date_from,date_to，将date_from,date_to做为value值
code_date_range_dic = {
ts_code:
(date_from if begin_time is None else min([date_from, begin_time]),
date_to)
for ts_code, date_from, date_to in table.fetchall()
if ts_code_set is None or ts_code in ts_code_set
}
data_df_list, data_count, all_data_count, data_len = [], 0, 0, len(
code_date_range_dic)
logger.info(
'%d Taiwan TMT information will been import into tushare_tmt_twincome',
data_len)
# 将data_df数据，添加到data_df_list
Cycles = 1
try:
for num, (ts_code,
(start_date,
end_date)) in enumerate(code_date_range_dic.items(),
start=1):
logger.debug('%d/%d) %s [%s - %s]', num, data_len, ts_code,
start_date, end_date)
data_df = invoke_tmt_twincome(
item=ts_code,
start_date=datetime_2_str(start_date, STR_FORMAT_DATE_TS),
end_date=datetime_2_str(end_date, STR_FORMAT_DATE_TS))
# logger.info(' %d data of %s between %s and %s', df.shape[0], ts_code, start_date, date_to)
if len(data_df) > 0 and data_df['date'] is not None:
while try_2_date(
data_df['date'].iloc[-1]) > try_2_date(start_date):
last_date_in_df_last, last_date_in_df_cur = try_2_date(
data_df['date'].iloc[-1]), None
df2 = invoke_tmt_twincome(
item=ts_code,
start_date=datetime_2_str(start_date,
STR_FORMAT_DATE_TS),
end_date=datetime_2_str(
try_2_date(data_df['date'].iloc[-1]) -
timedelta(days=1), STR_FORMAT_DATE_TS))
if len(df2) > 0 and df2['date'] is not None:
last_date_in_df_cur = try_2_date(df2['date'].iloc[-1])
if last_date_in_df_cur < last_date_in_df_last:
data_df = pd.concat([data_df, df2])
elif last_date_in_df_cur == last_date_in_df_last:
break
if data_df is None:
logger.warning(
'%d/%d) %s has no data during %s %s', num,
data_len, ts_code, start_date, end_date)
continue
logger.info('%d/%d) %d data of %s between %s and %s',
num, data_len, data_df.shape[0], ts_code,
start_date, end_date)
elif len(df2) <= 0:
break
# 把数据攒起来
if data_df is not None and data_df.shape[0] > 0:
data_count += data_df.shape[0]
data_df_list.append(data_df)
# 大于阀值有开始插入
if data_count >= 1000:
data_df_all = pd.concat(data_df_list)
bunch_insert_on_duplicate_update(data_df_all, table_name,
engine_md,
DTYPE_TUSHARE_TMT_TWINCOME)
all_data_count += data_count
data_df_list, data_count = [], 0
finally:
# 导入数据库
if len(data_df_list) > 0:
data_df_all = pd.concat(data_df_list)
data_count = bunch_insert_on_duplicate_update(
data_df_all, table_name, engine_md, DTYPE_TUSHARE_TMT_TWINCOME)
all_data_count = all_data_count + data_count
logging.info("更新 %s 结束 %d 条信息被更新", table_name, all_data_count)