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_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_forecast(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=45
: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:
"""
df: DataFrame = self.__pro.forecast_vip(ts_code=ts_code, period=period,
start_date=start_date, end_date=end_date,
ann_date=ann_date)
df = df.where(pandas.notnull(df), None)
if not df.empty:
df.loc[:, 'net_profit_min'] = df.apply(lambda row:
decimal_utils.mul(row.net_profit_min, 10000, err_default=None),
axis=1)
df.loc[:, 'net_profit_max'] = df.apply(lambda row:
decimal_utils.mul(row.net_profit_max, 10000, err_default=None),
axis=1)
df.loc[:, 'last_parent_net'] = df.apply(lambda row:
decimal_utils.mul(row.last_parent_net, 10000, err_default=None),
axis=1)
self.fix_ann_date_with_list_date(df, 'ann_date')
df.loc[:, 'mq_ann_date'] = df.apply(lambda row: row.ann_date,
axis=1)
return df
def extract_from_dividend(result_list: list, store: MqQuarterStore,
period_dict: dict, d: TsDividend):
# 有可能有非固定分红的派钱
lp: str = date_utils.latest_period_date(d.end_date)
update_date: str = date_utils.format_delta(d.imp_ann_date, -1)
call_add_nx = partial(add_nx,
ts_code=d.ts_code,
period=lp,
update_date=update_date,
report_type=mq_report_type.report,
store=store,
result_list=result_list,
period_dict=period_dict)
# 叠加到最近一个季度的分红中
dividend: Decimal = decimal_utils.mul(d.cash_div_tax, d.base_share)
if lp != d.end_date:
ori_d: MqQuarterMetric = store.find_period_latest(
ts_code=d.ts_code,
period=lp,
update_date=update_date,
name=mq_quarter_metric_enum.dividend.name)
if ori_d is not None:
dividend = dividend + ori_d.value
call_add_nx(name='dividend', value=dividend)
def update_adj(self, adj_type: str, latest_adj: str):
"""
更新到想要的复权状态
:param adj_type: 复权类型 qfq-前复权 hfq-后复权 其他-不复权
:param latest_adj: 最新复权因子
:return:
"""
div_factor = 1
if self.adj_type == 'qfq' and not decimal_utils.equals(
self.adj, self.latest_adj):
div_factor = decimal_utils.div(self.adj, self.latest_adj)
elif self.adj_type == 'hfq':
div_factor = self.adj
mul_factor = 1
if adj_type == 'qfq' and not decimal_utils.equals(
self.adj, latest_adj):
mul_factor = decimal_utils.div(self.adj, latest_adj)
elif adj_type == 'hfq':
mul_factor = self.adj
factor = 1 if decimal_utils.equals(mul_factor,
div_factor) else decimal_utils.div(
mul_factor, div_factor)
if factor == 1:
return
self.open = decimal_utils.mul(self.open, factor)
self.high = decimal_utils.mul(self.high, factor)
self.low = decimal_utils.mul(self.low, factor)
self.close = decimal_utils.mul(self.close, factor)
self.up_limit = decimal_utils.mul(self.up_limit, factor)
self.down_limit = decimal_utils.mul(self.down_limit, factor)
self.pre_close = decimal_utils.mul(self.pre_close, factor)
def multiply(i1: MqQuarterMetric, i2: MqQuarterMetric,
name: str) -> MqQuarterMetric:
if i1 is None or i2 is None:
return None
return MqQuarterMetric(ts_code=i1.ts_code,
report_type=i1.report_type | i2.report_type,
period=i1.period,
update_date=i1.update_date,
name=name,
value=decimal_utils.mul(i1.value, i2.value))
def __init__(self, ts_code: str, trade_date: str, is_trade: int,
adj_type: str, adj: decimal, latest_adj: decimal,
open_price: decimal, high: decimal, low: decimal,
close: decimal, up_limit: decimal, down_limit: decimal,
pre_close: decimal):
factor = 1
if adj_type == 'qfq' and not decimal_utils.equals(adj, latest_adj):
factor = decimal_utils.div(adj, latest_adj)
elif adj_type == 'hfq':
factor = adj
self.ts_code = ts_code
self.trade_date = trade_date
self.is_trade = is_trade
self.adj_type = adj_type
self.adj = adj
self.latest_adj = latest_adj
self.open = decimal_utils.mul(open_price, factor)
self.high = decimal_utils.mul(high, factor)
self.low = decimal_utils.mul(low, factor)
self.close = decimal_utils.mul(close, factor)
self.up_limit = decimal_utils.mul(up_limit, factor)
self.down_limit = decimal_utils.mul(down_limit, factor)
self.pre_close = decimal_utils.mul(pre_close, factor)
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]