def fetch_daily_basic(self, ts_code: str = None, end_date: str = None, start_date: str = None,
trade_date: str = None) -> DataFrame:
"""
https://tushare.pro/document/2?doc_id=32
:param ts_code: 股票编码
:param end_date: 起始日期
:param start_date: 结束日期
:param trade_date: 交易日期
:return:
"""
df: DataFrame = self.__pro.daily_basic(ts_code=ts_code, start_date=start_date, end_date=end_date,
trade_date=trade_date)
if not df.empty:
# 按日期升序
df = df.sort_values(by='trade_date')
df.loc[:, 'turnover_rate'] = df.apply(lambda row: decimal_utils.none_to_zero(row.turnover_rate), axis=1)
df.loc[:, 'volume_ratio'] = df.apply(lambda row: decimal_utils.none_to_zero(row.volume_ratio), axis=1)
# 替换掉所有需要是0的之后，取最近的值填充
df = df.ffill()
df.loc[:, 'total_share'] = df.apply(lambda row:
decimal_utils.mul(row.total_share, 10000, err_default=None), axis=1)
df.loc[:, 'float_share'] = df.apply(lambda row:
decimal_utils.mul(row.float_share, 10000, err_default=None), axis=1)
df.loc[:, 'free_share'] = df.apply(lambda row:
decimal_utils.mul(row.free_share, 10000, err_default=None), axis=1)
df.loc[:, 'free_share'] = df.apply(lambda row:
row.float_share if row.free_share is None or math.isnan(row.free_share)
else row.free_share, axis=1)
df.loc[:, 'total_mv'] = df.apply(lambda row:
decimal_utils.mul(row.total_mv, 10000, err_default=None), axis=1)
return df
def fetch_cash_flow(self, ts_code: str = None, end_date: str = None, start_date: str = None,
ann_date: str = None, period: str = None) -> DataFrame:
"""
https://tushare.pro/document/2?doc_id=44
:param ts_code: 股票编码
:param end_date: 起始日期
:param start_date: 结束日期
:param ann_date: 公告日期
:param period: 报告期
:return:
"""
df1 = self.__pro.cashflow_vip(report_type=1, ts_code=ts_code, period=period,
start_date=start_date, end_date=end_date, ann_date=ann_date)
df2 = self.__pro.cashflow_vip(report_type=4, ts_code=ts_code, period=period,
start_date=start_date, end_date=end_date, ann_date=ann_date)
df = df1.append(df2)
if not df.empty:
self.fix_ann_date_with_list_date(df, 'ann_date')
self.fix_ann_date_with_list_date(df, 'f_ann_date')
df.loc[:, 'mq_ann_date'] = df.apply(lambda row: mini(row.ann_date, row.f_ann_date), axis=1)
df.loc[:, 'prov_depr_assets'] = df.apply(lambda row: decimal_utils.none_to_zero(row.prov_depr_assets),
axis=1)
df.loc[:, 'depr_fa_coga_dpba'] = df.apply(lambda row: decimal_utils.none_to_zero(row.depr_fa_coga_dpba),
axis=1)
df.loc[:, 'amort_intang_assets'] = df.apply(lambda row: decimal_utils.none_to_zero(row.amort_intang_assets),
axis=1)
df.loc[:, 'lt_amort_deferred_exp'] = df.apply(
lambda row: decimal_utils.none_to_zero(row.lt_amort_deferred_exp), axis=1)
df.loc[:, 'loss_scr_fa'] = df.apply(lambda row: decimal_utils.none_to_zero(row.loss_scr_fa), axis=1)
return df
def cal_dcf(self, fcf: Decimal, year: int, date: str) -> Decimal:
fcf = decimal_utils.none_to_zero(fcf)
if fcf <= 0:
return Decimal(0), Decimal(0)
inc_map: list = self._get_arr_by_name('inc_rate', date)
dr_map: list = self._get_arr_by_name('discount_rate', date)
mv = fcf
inc_rate = 0.07
discount_rate = 0.1
# 计算10年的
for i in range(0, 10):
y = year + i + 1
if y in inc_map:
inc_rate = inc_map[y].value
if y in dr_map:
discount_rate = dr_map[y].value
fcf = decimal_utils.div(decimal_utils.mul(fcf, (1 + inc_rate)), (1 + discount_rate))
if fcf < 0:
fcf = 0
mv = decimal_utils.add(mv, fcf)
inc_rate = inc_map[9999].value
discount_rate = dr_map[9999].value
mv_forever = decimal_utils.add(mv, decimal_utils.div(
decimal_utils.mul(fcf, (1 + inc_rate)),
(discount_rate - inc_rate)))
return mv, mv_forever
def fetch_income(self, ts_code: str = None, end_date: str = None, start_date: str = None,
ann_date: str = None, period: str = None) -> DataFrame:
"""
https://tushare.pro/document/2?doc_id=33
:param ts_code: 股票编码
:param end_date: 起始日期
:param start_date: 结束日期
:param ann_date: 公告日期
:param period: 报告期
:return:
"""
qpm = 30
if not self.__rt.get('fetch_income', seconds_per_minute / qpm, qpm, 2):
raise Exception('fetch_income %d per minute, waiting' % qpm)
df1: DataFrame = self.__pro.income_vip(report_type=1, ts_code=ts_code, period=period,
start_date=start_date, end_date=end_date, ann_date=ann_date)
df2: DataFrame = self.__pro.income_vip(report_type=4, ts_code=ts_code, period=period,
start_date=start_date, end_date=end_date, ann_date=ann_date)
df: DataFrame = df1.append(df2)
if not df.empty:
self.fix_ann_date_with_list_date(df, 'ann_date')
self.fix_ann_date_with_list_date(df, 'f_ann_date')
df.loc[:, 'mq_ann_date'] = df.apply(lambda row: mini(row.ann_date, row.f_ann_date), axis=1)
# 资产减值损失 - 2019Q2开始计入其他收益，应该为负数，不可转回。2019Q2之前为成本，所以需要取反
df.loc[:, 'assets_impair_loss'] = df.apply(lambda row: decimal_utils.none_to_zero(row.assets_impair_loss),
axis=1)
df.loc[:, 'assets_impair_loss'] = df.apply(
lambda row: decimal_utils.negative(row.assets_impair_loss)
if row.end_date < '20190630' or (row.end_date >= '20190630' and row.assets_impair_loss > 0)
else row.assets_impair_loss,
axis=1)
return df
def fetch_balance_sheet(self, ts_code: str = None, end_date: str = None, start_date: str = None,
ann_date: str = None, period: str = None) -> DataFrame:
"""
https://tushare.pro/document/2?doc_id=36
:param ts_code: 股票编码
:param end_date: 起始日期
:param start_date: 结束日期
:param ann_date: 公告日期
:param period: 报告期
:return:
"""
df1 = self.__pro.balancesheet_vip(report_type=1, ts_code=ts_code, period=period,
start_date=start_date, end_date=end_date, ann_date=ann_date)
df2 = self.__pro.balancesheet_vip(report_type=4, ts_code=ts_code, period=period,
start_date=start_date, end_date=end_date, ann_date=ann_date)
df = df1.append(df2)
if not df.empty:
self.fix_ann_date_with_list_date(df, 'ann_date')
self.fix_ann_date_with_list_date(df, 'f_ann_date')
df.loc[:, 'mq_ann_date'] = df.apply(lambda row: mini(row.ann_date, row.f_ann_date), axis=1)
df.loc[:, 'notes_receiv'] = df.apply(lambda row: decimal_utils.none_to_zero(row.notes_receiv), axis=1)
df.loc[:, 'accounts_receiv'] = df.apply(lambda row: decimal_utils.none_to_zero(row.accounts_receiv), axis=1)
df.loc[:, 'lt_rec'] = df.apply(lambda row: decimal_utils.none_to_zero(row.lt_rec), axis=1)
df.loc[:, 'oth_receiv'] = df.apply(lambda row: decimal_utils.none_to_zero(row.oth_receiv), axis=1)
df.loc[:, 'div_receiv'] = df.apply(lambda row: decimal_utils.none_to_zero(row.div_receiv), axis=1)
df.loc[:, 'int_receiv'] = df.apply(lambda row: decimal_utils.none_to_zero(row.int_receiv), axis=1)
df.loc[:, 'notes_payable'] = df.apply(lambda row: decimal_utils.none_to_zero(row.notes_payable), axis=1)
df.loc[:, 'acct_payable'] = df.apply(lambda row: decimal_utils.none_to_zero(row.acct_payable), axis=1)
df.loc[:, 'total_nca'] = df.apply(lambda row: decimal_utils.none_to_zero(row.total_nca), axis=1)
df.loc[:, 'fa_avail_for_sale'] = df.apply(lambda row: decimal_utils.none_to_zero(row.fa_avail_for_sale),
axis=1)
df.loc[:, 'total_cur_liab'] = df.apply(lambda row: decimal_utils.none_to_zero(row.total_cur_liab), axis=1)
df.loc[:, 'total_cur_assets'] = df.apply(lambda row: decimal_utils.none_to_zero(row.total_cur_assets),
axis=1)
df.loc[:, 'lt_borr'] = df.apply(lambda row: decimal_utils.none_to_zero(row.lt_borr), axis=1)
df.loc[:, 'st_borr'] = df.apply(lambda row: decimal_utils.none_to_zero(row.st_borr), axis=1)
df.loc[:, 'money_cap'] = df.apply(lambda row: decimal_utils.none_to_zero(row.money_cap), axis=1)
df.loc[:, 'oth_cur_assets'] = df.apply(lambda row: decimal_utils.none_to_zero(row.oth_cur_assets), axis=1)
# 待摊费用(新会计准则取消) -> 长期待摊费用
df.loc[:, 'lt_amor_exp'] = df.apply(lambda row: decimal_utils.add(row.amor_exp, row.lt_amor_exp), axis=1)
return df