def ts_top_inst(trade_date: date) -> pd.DataFrame:
df: pd.DataFrame = ts_pro_api().top_inst(
trade_date=ts_date(trade_date), )
df['trade_date'] = df['trade_date'].apply(lambda x: to_datetime64(x))
df.sort_values(by='trade_date', inplace=True)
logging.info(colorama.Fore.YELLOW + '下载 [龙虎榜机构明细] 数据: %s, %s 条' %
(trade_date, df.shape[0]))
return df
def ts_concept_detail(self, concept_id: str,
concept_name: str) -> pd.DataFrame:
df: pd.DataFrame = ts_pro_api().concept_detail(
id=concept_id,
fields='id,concept_name,ts_code,name,in_date,out_date')
logging.info(colorama.Fore.YELLOW + '下载 [概念股列表] - %s 共 %s 条' %
(concept_name, df.shape[0]))
return df
def ts_margin_detail(trade_date: date) -> pd.DataFrame:
# 只取1个交易日的数据, 以保证数据记录数不会超过上限(经过测试观察为 2000 条)
df: pd.DataFrame = ts_pro_api().margin_detail(
start_date=ts_date(trade_date), end_date=ts_date(trade_date))
df['trade_date'] = df['trade_date'].apply(lambda x: to_datetime64(x))
df.sort_values(by='trade_date', inplace=True)
logging.info(colorama.Fore.YELLOW + '下载 [融资融券交易明细] %s 数据: 共 %s 条' %
(trade_date, df.shape[0]))
return df
def ts_margin(start_date: date, end_date: date) -> pd.DataFrame:
df: pd.DataFrame = ts_pro_api().margin(start_date=ts_date(start_date),
end_date=ts_date(end_date))
df['trade_date'] = df['trade_date'].apply(lambda x: to_datetime64(x))
df.sort_values(by='trade_date', inplace=True)
logging.info(colorama.Fore.YELLOW +
'下载 [融资融券每日交易汇总] 数据: %s - %s, 共 %s 条' %
(start_date, end_date, df.shape[0]))
return df
def ts_stock_basic() -> pd.DataFrame:
df: pd.DataFrame = ts_pro_api().stock_company(
exchange = '',
fields = ','.join(['ts_code', 'exchange', 'chairman', 'manager', 'secretary', 'reg_capital', 'setup_date',
'province', 'city', 'introduction', 'website', 'email', 'office', 'employees',
'main_business', 'business_scope'])
)
df['setup_date'] = pd.to_datetime(df['setup_date'], format = '%Y%m%d')
logging.info(colorama.Fore.YELLOW + '下载股票上市公司基本信息数据')
return df
def ts_money_flow(self, start_date: date, end_date: date) -> pd.DataFrame:
df: pd.DataFrame = ts_pro_api().moneyflow(
ts_code=self.stock_code,
start_date=ts_date(start_date),
end_date=ts_date(end_date))
df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')
df.set_index(keys='trade_date', drop=False, inplace=True)
df.sort_index(inplace=True)
logging.info(colorama.Fore.YELLOW + '下载 %s 个股资金流向数据: %s -- %s 共 %s 条' %
(self.stock_code, start_date, end_date, df.shape[0]))
return df
def ts_index_basic(self, market_code: str) -> pd.DataFrame:
df: pd.DataFrame = ts_pro_api().index_basic(
market=market_code,
fields=
'ts_code,name,fullname,market,publisher,index_type,category,base_date,base_point,list_date,weight_rule,desc,exp_date'
)
df['list_date'] = df['list_date'].apply(lambda x: to_datetime64(x))
df['exp_date'] = df['exp_date'].apply(lambda x: to_datetime64(x))
logging.info(colorama.Fore.YELLOW + '下载 %s [指数基本信息] 数据, 共 %s 条' %
(self.index_markets[market_code], df.shape[0]))
return df
def ts_stock_basic() -> pd.DataFrame:
df: pd.DataFrame = ts_pro_api().stock_basic(
exchange='',
list_status='L',
fields=
'ts_code,symbol,name,area,industry,fullname,market,exchange,list_status,list_date,delist_date,is_hs'
)
df['list_date'] = pd.to_datetime(df['list_date'], format='%Y%m%d')
df['delist_date'] = pd.to_datetime(df['delist_date'], format='%Y%m%d')
logging.info(colorama.Fore.YELLOW + '下载 [股票列表基础信息] 数据')
return df
def ts_pledge_stat(self) -> pd.DataFrame:
df: pd.DataFrame = ts_pro_api().pledge_stat(
ts_code = self.stock_code,
fields = 'ts_code,end_date,pledge_count,unrest_pledge,rest_pledge,total_share,pledge_ratio'
)
if not df.empty:
df['end_date'] = df['end_date'].apply(lambda x: to_datetime64(x))
df.sort_values(by = 'end_date', inplace = True)
logging.info(colorama.Fore.YELLOW + '下载 %s [股权质押统计] 数据 共 %s 条' % (self.stock_code, df.shape[0]))
else:
logging.info(colorama.Fore.YELLOW + '%s [股权质押统计] 无最新数据' % self.stock_code)
return df
def ts_block_trade(self, start_date: date, end_date: date) -> pd.DataFrame:
df: pd.DataFrame = ts_pro_api().block_trade(
start_date = ts_date(start_date),
end_date = ts_date(end_date)
)
if not df.empty:
df['trade_date'] = pd.to_datetime(df['trade_date'], format = '%Y%m%d')
df.sort_values(by = 'trade_date', inplace = True)
row_count = df.shape[0]
if row_count == 1000:
raise Exception('超出记录条数上限[1000], %s -- %s' % (start_date, end_date))
logging.info(colorama.Fore.YELLOW + '下载 [大宗交易] 数据: %s -- %s %s条' % (start_date, end_date, df.shape[0]))
else:
logging.info(colorama.Fore.YELLOW + '[大宗交易] : %s -- %s 无数据' % (start_date, end_date))
return df
def ts_pledge_detail(self) -> pd.DataFrame:
df: pd.DataFrame = ts_pro_api().pledge_detail(
ts_code = self.stock_code,
fields = 'ts_code,ann_date,holder_name,pledge_amount,start_date,end_date,is_release,release_date,pledgor,holding_amount,pledged_amount,p_total_ratio,h_total_ratio,is_buyback'
)
if not df.empty:
df['ann_date'] = df['ann_date'].apply(lambda x: to_datetime64(x))
df['start_date'] = df['start_date'].apply(lambda x: to_datetime64(x))
df['end_date'] = df['end_date'].apply(lambda x: to_datetime64(x))
df['release_date'] = df['release_date'].apply(lambda x: to_datetime64(x))
df.sort_values(by = 'end_date', inplace = True)
logging.info(colorama.Fore.YELLOW + '下载 %s [股权质押明细] 数据 共 %s 条' % (self.stock_code, df.shape[0]))
else:
logging.info(colorama.Fore.YELLOW + '%s [股权质押明细] 无最新数据' % self.stock_code)
return df
def ts_top10_holders(self, start_date: date,
end_date: date) -> pd.DataFrame:
df: pd.DataFrame = ts_pro_api().top10_floatholders(
ts_code=self.stock_code,
start_date=ts_date(start_date),
end_date=ts_date(end_date))
if not df.empty:
df['ann_date'] = pd.to_datetime(df['ann_date'], format='%Y%m%d')
df['end_date'] = pd.to_datetime(df['end_date'], format='%Y%m%d')
df.sort_values(by='end_date', inplace=True)
logging.info(colorama.Fore.YELLOW +
'下载 %s 前十大流通股东数据: %s -- %s, 共 %s 条' %
(self.stock_code, start_date, end_date, df.shape[0]))
else:
logging.info(colorama.Fore.YELLOW + '%s 前十大流通股东数据: %s -- %s 无数据' %
(self.stock_code, start_date, end_date))
return df
def ts_top10_holders(self, start_date: date,
end_date: date) -> pd.DataFrame:
df: pd.DataFrame = ts_pro_api().stk_holdernumber(
ts_code=self.stock_code,
start_date=ts_date(start_date),
end_date=ts_date(end_date))
if not df.empty:
df['ann_date'] = df['ann_date'].apply(lambda x: to_datetime64(x))
df['end_date'] = df['end_date'].apply(lambda x: to_datetime64(x))
df.sort_values(by='end_date', inplace=True)
logging.info(colorama.Fore.YELLOW +
'下载 %s [股东人数] 数据: %s -- %s, 共 %s 条' %
(self.stock_code, start_date, end_date, df.shape[0]))
else:
logging.info(colorama.Fore.YELLOW + '%s [股东人数] 数据: %s -- %s 无数据' %
(self.stock_code, start_date, end_date))
return df
def ts_suspend(self) -> pd.DataFrame:
df: pd.DataFrame = ts_pro_api().suspend(
ts_code=self.stock_code,
fields=
'ts_code,suspend_date,resume_date,ann_date,suspend_reason,reason_type'
)
if not df.empty:
df['suspend_date'] = df['suspend_date'].apply(
lambda x: to_datetime64(x))
df['resume_date'] = df['resume_date'].apply(
lambda x: to_datetime64(x))
df['ann_date'] = df['ann_date'].apply(lambda x: to_datetime64(x))
df.sort_values(by='ann_date', inplace=True)
logging.info(colorama.Fore.YELLOW + '下载 %s [停复牌信息] 数据 共 %s 条' %
(self.stock_code, df.shape[0]))
else:
logging.info(colorama.Fore.YELLOW +
'%s [停复牌信息] 数据: 无数据' % self.stock_code)
return df
def ts_top10_holders(self, start_date: date,
end_date: date) -> pd.DataFrame:
df: pd.DataFrame = ts_pro_api().stk_holdertrade(
ts_code=self.stock_code,
start_date=ts_date(start_date),
end_date=ts_date(end_date),
fields=
'ts_code,ann_date,holder_name,holder_type,in_de,change_vol,change_ratio,after_share,after_ratio,avg_price,total_share,begin_date,close_date'
)
if not df.empty:
df['ann_date'] = df['ann_date'].apply(lambda x: to_datetime64(x))
df['begin_date'] = df['begin_date'].apply(
lambda x: to_datetime64(x))
df['close_date'] = df['close_date'].apply(
lambda x: to_datetime64(x))
df.sort_values(by='ann_date', inplace=True)
logging.info(colorama.Fore.YELLOW +
'下载 %s [股东增减持] 数据: %s -- %s, 共 %s 条' %
(self.stock_code, start_date, end_date, df.shape[0]))
else:
logging.info(colorama.Fore.YELLOW + '%s [股东增减持] 数据: %s -- %s 无数据' %
(self.stock_code, start_date, end_date))
return df
def ts_concept(self) -> pd.DataFrame:
df: pd.DataFrame = ts_pro_api().concept(src='ts')
return df