def import_tushare_index_basic(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_index_basic'
logging.info("更新 %s 开始", table_name)
has_table = engine_md.has_table(table_name)
fields = [
'ts_code', 'name', 'fullname', 'market', 'publisher', 'index_type',
'category', 'base_date', 'base_point', 'list_date', 'weight_rule',
'desc', 'exp_date'
]
market_list = list(
['MSCI', 'CSI', 'SSE', 'SZSE', 'CICC', 'SW', 'CNI', 'OTH'])
for mkt in market_list:
# trade_date = datetime_2_str(trddate[i], STR_FORMAT_DATE_TS)
data_df = invoke_index_basic(market=mkt, fields=fields)
if len(data_df) > 0:
data_count = bunch_insert_p(data_df,
table_name=table_name,
dtype=DTYPE_TUSHARE_STOCK_INDEX_BASIC,
primary_keys=['ts_code'])
logging.info("%s 更新 %s 结束 %d 条信息被更新", mkt, table_name, data_count)
else:
logging.info("%s 无数据信息可被更新", mkt)
def import_info_table(type_name, insert_db=True) -> pd.DataFrame:
"""
调用 get_all_securities 获取指定 type 的信息
type: 'stock', 'fund', 'index', 'futures', 'etf', 'lof', 'fja', 'fjb'。types为空时返回所有股票, 不包括基金,指数和期货
:param type_name:
:return:
"""
table_name = f'jq_{type_name}_info'
logger.info("更新 %s 开始", table_name)
# has_table = engine_md.has_table(table_name)
param_list = [
('jq_code', String(20)),
('display_name', String(20)),
('name', String(20)),
('start_date', Date),
('end_date', Date),
]
# 设置 dtype
dtype = {key: val for key, val in param_list}
# 数据提取
# types: list: 用来过滤securities的类型, list元素可选:
# 'stock', 'fund', 'index', 'futures', 'etf', 'lof', 'fja', 'fjb'。types为空时返回所有股票, 不包括基金,指数和期货
# date: 日期, 一个字符串或者 [datetime.datetime]/[datetime.date] 对象,
# 用于获取某日期还在上市的股票信息. 默认值为 None, 表示获取所有日期的股票信息
stock_info_all_df = get_all_securities(types=type_name)
stock_info_all_df.index.rename('jq_code', inplace=True)
stock_info_all_df.reset_index(inplace=True)
if insert_db:
logger.info('%s 数据将被导入', stock_info_all_df.shape[0])
data_count = bunch_insert_p(stock_info_all_df, table_name=table_name, dtype=dtype, primary_keys=['jq_code'])
logger.info("更新 %s 完成 存量数据 %d 条", table_name, data_count)
return stock_info_all_df
def save_data_2_daily_table(data_new_s_list: list, table_name, dtype: dict):
df = pd.DataFrame(data_new_s_list)
data_count = bunch_insert_p(df,
table_name,
dtype=dtype,
primary_keys=['id', 'trade_date'])
return data_count
def import_tushare_suspend(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_daily_suspend'
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_suspend
# 下面一定要注意引用表的来源，否则可能是串，提取混乱！！！比如本表是tushare_daily_basic，所以引用的也是这个，如果引用错误，就全部乱了l
if has_table:
sql_str = """
select cal_date
FROM
(
select * from tushare_trade_date trddate
where( cal_date>(SELECT max(suspend_date) FROM {table_name} ))
)tt
where (is_open=1
and cal_date <= if(hour(now())<16, subdate(curdate(),1), curdate())
and exchange='SSE') """.format(table_name=table_name)
else:
sql_str = """
SELECT cal_date FROM tushare_trade_date trddate WHERE (trddate.is_open=1
AND cal_date <= if(hour(now())<16, subdate(curdate(),1), curdate())
AND exchange='SSE') ORDER BY cal_date"""
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:
# 获取交易日数据
table = session.execute(sql_str)
trade_date_list = list(row[0] for row in table.fetchall())
try:
trade_date_list_len = len(trade_date_list)
for num, trade_date in enumerate(trade_date_list, start=1):
trade_date = datetime_2_str(trade_date, STR_FORMAT_DATE_TS)
data_df = pro.suspend(ts_code='',
suspend_date=trade_date,
resume_date='',
fields='')
if len(data_df) > 0:
data_count = bunch_insert_p(
data_df,
table_name=table_name,
dtype=DTYPE_TUSHARE_SUSPEND,
primary_keys=['ts_code', 'suspend_date'])
logging.info("%d/%d) %s 更新 %s 结束 %d 条信息被更新", num,
trade_date_list_len, trade_date, table_name,
data_count)
else:
logging.info("%s 当日无停牌股票", trade_date_list_len)
except:
logger.exception('更新 %s 表异常', table_name)
def import_tushare_hsgt_top10(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_hsgt_top10'
logging.info("更新 %s 开始", table_name)
param_list = [
('trade_date', Date),
('ts_code', String(20)),
('name', String(20)),
('close', DOUBLE),
('change', DOUBLE),
('rank', Integer),
('market_type', String(20)),
('amount', DOUBLE),
('net_amount', DOUBLE),
('buy', DOUBLE),
('sell', DOUBLE),
]
has_table = engine_md.has_table(table_name)
# 进行表格判断，确定是否含有tushare_daily_basic
if has_table:
sql_str = """
select cal_date
FROM
(
select * from tushare_trade_date trddate
where( cal_date>(SELECT max(trade_date) FROM {table_name}))
)tt
where (is_open=1
and cal_date <= if(hour(now())<16, subdate(curdate(),1), curdate())
and exchange='SSE') """.format(table_name=table_name)
else:
sql_str = """
SELECT cal_date FROM tushare_trade_date trddate WHERE (trddate.is_open=1
AND cal_date <= if(hour(now())<16, subdate(curdate(),1), curdate())
AND exchange='SSE' AND cal_date>='2014-11-17') ORDER BY cal_date"""
logger.warning('%s 不存在，仅使用 tushare_trade_date 表进行计算日期范围', table_name)
with with_db_session(engine_md) as session:
# 获取交易日数据
table = session.execute(sql_str)
trade_date_list = list(row[0] for row in table.fetchall())
# 设置 dtype
dtype = {key: val for key, val in param_list}
try:
trade_date_list_len = len(trade_date_list)
for num, trade_date in enumerate(trade_date_list, start=1):
trade_date = datetime_2_str(trade_date, STR_FORMAT_DATE_TS)
for market_type in list(['1', '3']):
data_df = invoke_hsgt_top10(trade_date=trade_date,
market_type=market_type)
if len(data_df) > 0:
data_count = bunch_insert_p(
data_df,
table_name=table_name,
dtype=dtype,
primary_keys=['ts_code', 'trade_date'])
logging.info("%d/%d) %s更新 %s 结束 %d 条信息被更新", num,
trade_date_list_len, trade_date, table_name,
data_count)
else:
logging.info("无数据信息可被更新")
break
except:
logger.exception('更新 %s 表异常', table_name)
def import_tushare_moneyflow_hsgt(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_moneyflow_hsgt'
logging.info("更新 %s 开始", table_name)
param_list = [
('trade_date', Date),
('ggt_ss', DOUBLE),
('ggt_sz', DOUBLE),
('hgt', DOUBLE),
('sgt', DOUBLE),
('north_money', DOUBLE),
('south_money', DOUBLE),
]
has_table = engine_md.has_table(table_name)
# 进行表格判断，确定是否含有tushare_daily_basic
# 下面一定要注意引用表的来源，否则可能是串，提取混乱！！！比如本表是tushare_daily_basic，所以引用的也是这个，如果引用错误，就全部乱了l
if has_table:
sql_str = """
select cal_date
FROM
(
select * from tushare_trade_date trddate
where( cal_date>(SELECT max(trade_date) FROM {table_name}))
)tt
where (is_open=1
and cal_date <= if(hour(now())<16, subdate(curdate(),1), curdate())
and exchange='SSE') """.format(table_name=table_name)
else:
sql_str = """
SELECT cal_date FROM tushare_trade_date trddate WHERE (trddate.is_open=1
AND cal_date <= if(hour(now())<16, subdate(curdate(),1), curdate())
AND exchange='SSE' AND cal_date>='2014-11-17') ORDER BY cal_date"""
logger.warning('%s 不存在，仅使用 tushare_trade_date 表进行计算日期范围', table_name)
with with_db_session(engine_md) as session:
# 获取交易日数据
table = session.execute(sql_str)
trade_date_list = list(row[0] for row in table.fetchall())
# 设置 dtype
dtype = {key: val for key, val in param_list}
try:
trade_date_list_len = len(trade_date_list)
for num, trade_date in enumerate(trade_date_list, start=1):
trade_date = datetime_2_str(trade_date, STR_FORMAT_DATE_TS)
data_df = invoke_moneyflow_hsgt(trade_date=trade_date)
if len(data_df) > 0:
data_count = bunch_insert_p(data_df,
table_name=table_name,
dtype=dtype,
primary_keys=['trade_date'])
logging.info("%d/%d) %s 更新 %s 结束 %d 条信息被更新", num,
trade_date_list_len, trade_date, table_name,
data_count)
else:
logging.info("无数据信息可被更新")
except:
logger.exception('更新 %s 表异常', table_name)
def import_tushare_margin(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_margin'
logging.info("更新 %s 开始", table_name)
param_list = [
('trade_date', Date),
('exchange_id', String(20)),
('rzye', DOUBLE),
('rzmre', DOUBLE),
('rzche', DOUBLE),
('rqye', DOUBLE),
('rqmcl', DOUBLE),
('rzrqye', DOUBLE),
]
has_table = engine_md.has_table(table_name)
# 进行表格判断，确定是否含有tushare_daily_basic
if has_table:
sql_str = """
select cal_date
FROM
(
select * from tushare_trade_date trddate
where( cal_date>(SELECT max(trade_date) FROM {table_name}))
)tt
where (is_open=1
and cal_date <= if(hour(now())<16, subdate(curdate(),1), curdate())
and exchange='SSE') """.format(
table_name=table_name)
else:
sql_str = """
SELECT cal_date FROM tushare_trade_date trddate WHERE (trddate.is_open=1
AND cal_date <= if(hour(now())<16, subdate(curdate(),1), curdate())
AND exchange='SSE' AND cal_date>='2010-03-31') ORDER BY cal_date"""
logger.warning('%s 不存在，仅使用 tushare_trade_date 表进行计算日期范围', table_name)
with with_db_session(engine_md) as session:
# 获取交易日数据
table = session.execute(sql_str)
trade_date_list = list(row[0] for row in table.fetchall())
# 设置 dtype
dtype = {key: val for key, val in param_list}
try:
trade_date_list_len = len(trade_date_list)
for num, trade_date in enumerate(trade_date_list, start=1):
trade_date = datetime_2_str(trade_date, STR_FORMAT_DATE_TS)
for exchange_id in list(['SSE', 'SZSE']):
data_df = invoke_margin(trade_date=trade_date,
exchange_id=exchange_id)
if len(data_df) > 0:
# data_count = bunch_insert_on_duplicate_update(data_df, table_name, engine_md, dtype)
# logging.info("%s更新 %s %s 结束 %d 条信息被更新", trade_date, table_name, exchange_id, data_count)
data_count = bunch_insert_p(
data_df,
table_name=table_name,
dtype=dtype,
primary_keys=['exchange_id', 'trade_date'])
logging.info("%d/%d) %s %s 更新 %s 结束 %d 条信息被更新", num,
trade_date_list_len, exchange_id, trade_date,
table_name, data_count)
else:
logging.info("%d/%d) %s %s 无数据信息可被更新 %s", num,
trade_date_list_len, exchange_id, trade_date,
table_name)
except:
logger.exception('更新 %s 表异常', table_name)
def import_tushare_top_inst(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_top_inst'
logging.info("更新 %s 开始", table_name)
has_table = engine_md.has_table(table_name)
if has_table:
sql_str = """
select cal_date
FROM
(
select * from tushare_trade_date trddate
where( cal_date>(SELECT max(trade_date) FROM {table_name} ))
)tt
where (is_open=1
and cal_date <= if(hour(now())<16, subdate(curdate(),1), curdate())
and exchange='SSE') """.format(table_name=table_name)
else:
sql_str = """
SELECT cal_date FROM tushare_trade_date trddate WHERE (trddate.is_open=1
AND cal_date <= if(hour(now())<16, subdate(curdate(),1), curdate())
AND exchange='SSE'
and cal_date>'2012-01-03') ORDER BY cal_date"""
logger.warning('%s 不存在，仅使用 tushare_trade_date 表进行计算日期范围', table_name)
with with_db_session(engine_md) as session:
# 获取交易日数据
table = session.execute(sql_str)
trddate = list(row[0] for row in table.fetchall())
# 定义相应的中间变量
data_df_list, data_count, all_data_count, data_len, trade_date = [], 0, 0, len(
trddate), None
try:
for i in range(len(trddate)):
trade_date = datetime_2_str(trddate[i], STR_FORMAT_DATE_TS)
data_df = invoke_top_inst(trade_date=trade_date)
# 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 >= 10000:
data_df_all = pd.concat(data_df_list)
# bunch_insert_on_duplicate_update(data_df_all, table_name, engine_md, DTYPE_TUSHARE_STOCK_TOP_INST)
data_count = bunch_insert_p(data_df_all,
table_name=table_name,
dtype=DTYPE_TUSHARE_STOCK_TOP_INST,
primary_keys=[
'ts_code', 'trade_date',
'exalter', 'buy', 'sell'
])
logging.info("更新 %s 结束 ,截至%s日 %d 条信息被更新", table_name,
trade_date, all_data_count)
all_data_count += data_count
data_df_list, data_count = [], 0
finally:
if len(data_df_list) > 0:
data_df_all = pd.concat(data_df_list)
data_count = bunch_insert_p(data_df_all,
table_name=table_name,
dtype=DTYPE_TUSHARE_STOCK_TOP_INST,
primary_keys=[
'ts_code', 'trade_date', 'exalter',
'buy', 'sell'
])
all_data_count = all_data_count + data_count
logging.info("更新 %s 结束 ,截至%s日 %d 条信息被更新", table_name, trade_date,
all_data_count)
def import_jq_stock_daily(chain_param=None, 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_info = TABLE_NAME_INFO
table_name = TABLE_NAME
table_name_bak = get_bak_table_name(table_name)
logging.info("更新 %s 开始", table_name)
# 根据 info table 查询每只股票日期区间
sql_info_str = f"""
SELECT jq_code, date_frm, if(date_to<end_date, date_to, end_date) date_to
FROM
(
SELECT info.jq_code, start_date date_frm, end_date date_to,
if(hour(now())<16, subdate(curdate(),1), curdate()) end_date
FROM {table_name_info} info
) tt
WHERE date_frm <= if(date_to<end_date, date_to, end_date)
ORDER BY jq_code"""
has_table = engine_md.has_table(table_name)
has_bak_table = engine_md.has_table(table_name_bak)
# 进行表格判断，确定是否含有 jq_stock_daily_md
if has_table:
# 这里对原始的 sql语句进行了调整
# 以前的逻辑：每只股票最大的一个交易日+1天作为起始日期
# 现在的逻辑：每只股票最大一天的交易日作为起始日期
# 主要原因在希望通过接口获取到数据库中现有最大交易日对应的 factor因子以进行比对
sql_trade_date_range_str = f"""
SELECT jq_code, date_frm, if(date_to<end_date, date_to, end_date) date_to
FROM
(
SELECT info.jq_code, ifnull(trade_date, info.start_date) date_frm, info.end_date date_to,
if(hour(now())<16, subdate(curdate(),1), curdate()) end_date
FROM
{table_name_info} info
LEFT OUTER JOIN
(SELECT jq_code, max(trade_date) trade_date FROM {table_name} GROUP BY jq_code) daily
ON info.jq_code = daily.jq_code
) tt
WHERE date_frm < if(date_to<end_date, date_to, end_date)
ORDER BY jq_code"""
else:
sql_trade_date_range_str = sql_info_str
logger.warning('%s 不存在，仅使用 %s 表进行计算日期范围', table_name, table_name_info)
sql_trade_date_str = """SELECT trade_date FROM jq_trade_date trddate
WHERE trade_date <= if(hour(now())<16, subdate(curdate(),1), curdate()) ORDER BY trade_date"""
with with_db_session(engine_md) as session:
# 获取截至当期全部交易日前
table = session.execute(sql_trade_date_str)
trade_date_list = [row[0] for row in table.fetchall()]
trade_date_list.sort()
# 获取每只股票日线数据的日期区间
table = session.execute(sql_trade_date_range_str)
# 计算每只股票需要获取日线数据的日期区间
# 获取date_from,date_to，将date_from,date_to做为value值
code_date_range_dic = {
key_code: (date_from, date_to)
for key_code, date_from, date_to in table.fetchall()
if code_set is None or key_code in code_set
}
# 从 info 表中查询全部日期区间
if sql_info_str == sql_trade_date_range_str:
code_date_range_from_info_dic = code_date_range_dic
else:
# 获取每只股票日线数据的日期区间
table = session.execute(sql_info_str)
# 计算每只股票需要获取日线数据的日期区间
# 获取date_from,date_to，将date_from,date_to做为value值
code_date_range_from_info_dic = {
key_code: (date_from, date_to)
for key_code, date_from, date_to in table.fetchall()
if code_set is None or key_code in 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 %s', data_len, table_name)
# 将data_df数据，添加到data_df_list
try:
for num, (key_code,
(date_from_tmp,
date_to_tmp)) in enumerate(code_date_range_dic.items(),
start=1):
data_df = None
try:
for loop_count in range(2):
# 根据交易日数据取交集，避免不用的请求耽误时间
date_from = get_first(trade_date_list,
lambda x: x >= date_from_tmp)
date_to = get_last(trade_date_list,
lambda x: x <= date_to_tmp)
if date_from is None or date_to is None or date_from >= date_to:
logger.debug('%d/%d) %s [%s - %s] 跳过', num, data_len,
key_code, date_from, date_to)
break
logger.debug('%d/%d) %s [%s - %s] %s', num, data_len,
key_code, date_from, date_to,
'第二次查询' if loop_count > 0 else '')
data_df = invoke_daily(key_code=key_code,
start_date=date_2_str(date_from),
end_date=date_2_str(date_to))
# 该判断只在第一次循环时执行
if loop_count == 0 and has_table:
# 进行 factor 因子判断，如果发现最小的一个交易日的因子不为1,则删除数据库中该股票的全部历史数据，然后重新下载。
# 因为当期股票下载的数据为前复权价格，如果股票出现复权调整，则历史数据全部需要重新下载
factor_value = data_df.sort_values('trade_date').iloc[
0, :]['factor']
if factor_value != 1 and (
code_date_range_from_info_dic[key_code][0] !=
code_date_range_dic[key_code][0]):
# 删除该股屏历史数据
sql_str = f"delete from {table_name} where jq_code=:jq_code"
row_count = execute_sql_commit(
sql_str, params={'jq_code': key_code})
date_from_tmp, date_to_tmp = code_date_range_from_info_dic[
key_code]
if has_bak_table:
sql_str = f"delete from {table_name_bak} where jq_code=:jq_code"
row_count = execute_sql_commit(
sql_str, params={'jq_code': key_code})
date_from_tmp, date_to_tmp = code_date_range_from_info_dic[
key_code]
logger.info(
'%d/%d) %s %d 条历史记录被清除，重新加载前复权历史数据 [%s - %s] 同时清除bak表中相应记录',
num, data_len, key_code, row_count,
date_from_tmp, date_to_tmp)
else:
logger.info(
'%d/%d) %s %d 条历史记录被清除，重新加载前复权历史数据 [%s - %s]',
num, data_len, key_code, row_count,
date_from_tmp, date_to_tmp)
# 重新设置起止日期，进行第二次循环
continue
# 退出 for _ in range(2): 循环
break
except Exception as exp:
data_df = None
logger.exception('%s [%s - %s]', key_code,
date_2_str(date_from_tmp),
date_2_str(date_to_tmp))
if exp.args[0].find('超过了每日最大查询限制'):
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 >= 500:
data_df_all = pd.concat(data_df_list)
bunch_insert_p(data_df_all,
table_name,
dtype=DTYPE,
primary_keys=['jq_code', 'trade_date'])
all_data_count += data_count
data_df_list, data_count = [], 0
if DEBUG and num >= 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_p(data_df_all,
table_name,
dtype=DTYPE,
primary_keys=['jq_code', 'trade_date'])
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)
