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_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_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_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_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_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
