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 add_up(name: str, arr: list) -> MqQuarterMetric:
report_type = 0
sum = None
for i in arr: # type: MqQuarterMetric
if i is not None:
if sum is None:
sum = MqQuarterMetric(ts_code=i.ts_code,
period=i.period,
update_date=i.update_date,
name=name)
report_type = report_type | i.report_type
sum.value = decimal_utils.add(sum.value, i.value)
if sum is not None:
sum.report_type = report_type
return sum
def sub_from(name: str, arr: list) -> MqQuarterMetric:
report_type = 0
sum = None
for index, i in enumerate(arr): # type: MqQuarterMetric
if i is not None:
if sum is None:
sum = MqQuarterMetric(ts_code=i.ts_code,
period=i.period,
update_date=i.update_date,
name=name)
report_type = report_type | i.report_type
if index == 0:
sum.value = decimal_utils.add(sum.value, i.value)
else:
sum.value = decimal_utils.sub(sum.value, i.value)
if sum is not None:
sum.report_type = report_type
return sum
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
def cal_ltm_with_quarter(name: str, i1: MqQuarterMetric, i2: MqQuarterMetric,
i3: MqQuarterMetric, i4) -> MqQuarterMetric:
'''
用4个单季去计算LTM
'''
if i1 is not None and date_utils.get_quarter_num(i1.period) == 4:
return MqQuarterMetric(ts_code=i1.ts_code,
report_type=i1.report_type,
period=i1.period,
update_date=i1.update_date,
name=name,
value=i1.value)
elif i1 is None or i2 is None or i3 is None or i4 is None:
return None
else:
return MqQuarterMetric(ts_code=i1.ts_code,
report_type=i1.report_type | i2.report_type
| i3.report_type | i4.report_type,
period=i1.period,
update_date=i1.update_date,
name=name,
value=decimal_utils.add(i1.value, i2.value,
i3.value, i4.value))
def cal(daily_store: mq_daily_store.MqDailyStore,
quarter_store: mq_quarter_store.MqQuarterStore, ts_code: str,
update_date: str) -> MqDailyMetric:
daily_find = partial(daily_store.find_date_exact,
ts_code=ts_code,
update_date=update_date)
quarter_find = partial(quarter_store.find_latest,
ts_code=ts_code,
update_date=update_date)
score = -1
period = '00000000'
dividend_yields = daily_find(
name=mq_daily_metric_enum.dividend_yields.name)
risk_point = quarter_find(name=mq_quarter_metric_enum.risk_point.name)
revenue_quarter = quarter_find(
name=mq_quarter_metric_enum.revenue_quarter.name)
dprofit_quarter = quarter_find(
name=mq_quarter_metric_enum.dprofit_quarter.name)
if dividend_yields is None or \
calculate.gt(risk_point, 0, 'value', True) or \
calculate.lt(dividend_yields, 0.03, 'value', True) or \
not earn_and_dividend_in_year(quarter_store, ts_code, dividend_yields.period, update_date, 5) or \
not earn_in_period(quarter_store, ts_code, dividend_yields.period, update_date, 4) or \
calculate.lt(revenue_quarter, max_desc_yoy, 'yoy', True) or \
calculate.lt(revenue_quarter, dprofit_quarter, 'yoy', True):
score = -1
else:
period = dividend_yields.period
pe = daily_find(name=mq_daily_metric_enum.pe.name)
pb = daily_find(name=mq_daily_metric_enum.pb.name)
dividend_score = decimal_utils.mul(dividend_yields.value,
Decimal(1000)) # * 100 / 10 * 100
pe_score = decimal_utils.valid_score((1 - decimal_utils.div(
calculate.get_val(pe, 'value', max_pe), max_pe, err_default=0)) *
100)
pb_score = decimal_utils.valid_score((1 - decimal_utils.div(
calculate.get_val(pb, 'value', max_pb), max_pb, err_default=0)) *
100)
pepb_score = decimal_utils.valid_score((1 - decimal_utils.div(
decimal_utils.mul(calculate.get_val(pe, 'value', max_pe),
calculate.get_val(pb, 'value', max_pb)),
max_pepb)) * 100)
profit_yoy_score = history_profit_yoy_score(quarter_store, ts_code,
dividend_yields.period,
update_date, 5)
dividend_yoy_score = history_dividend_yoy_score(
quarter_store, ts_code, dividend_yields.period, update_date, 5)
if profit_yoy_score == 0 or dividend_yoy_score == 0:
score = 0
elif pe_score == 0 and pb_score == 0 and pepb_score == 0:
score = 0
else:
score = decimal_utils.add(
decimal_utils.mul(dividend_score, 0.3),
decimal_utils.mul(dividend_yoy_score, 0.2),
decimal_utils.mul((pe_score + pb_score + pepb_score), 0.1),
decimal_utils.mul(profit_yoy_score, 0.2))
val_score_metric = MqDailyMetric(ts_code=ts_code,
report_type=mq_report_type.mq_predict,
period=period,
update_date=update_date,
name=mq_daily_metric_enum.val_score.name,
value=decimal_utils.valid_score(score))
return [val_score_metric]