def get_stocks(cls,
code=None,
codes=None,
ids=None,
timestamp=now_pd_timestamp(),
provider=None):
from zvt.api.quote import get_etf_stocks
return get_etf_stocks(code=code,
codes=codes,
ids=ids,
timestamp=timestamp,
provider=provider)
def get_stocks(cls,
region: Region,
timestamp,
code=None,
codes=None,
ids=None,
provider: Provider = Provider.Default):
from zvt.api.quote import get_etf_stocks
return get_etf_stocks(region=region,
code=code,
codes=codes,
ids=ids,
timestamp=timestamp,
provider=provider)
def record(self, entity, start, end, size, timestamps):
if not end:
end = now_pd_timestamp()
date_range = pd.date_range(start=start, end=end, freq='1D').tolist()
for date in date_range:
# etf包含的个股和比例
etf_stock_df = get_etf_stocks(code=entity.code,
timestamp=date,
provider=self.provider)
if pd_is_not_null(etf_stock_df):
all_pct = etf_stock_df['proportion'].sum()
if all_pct >= 1.2 or all_pct <= 0.8:
self.logger.error(
f'ignore etf:{entity.id} date:{date} proportion sum:{all_pct}'
)
break
etf_stock_df.set_index('stock_id', inplace=True)
# 个股的估值数据
stock_valuation_df = StockValuation.query_data(
entity_ids=etf_stock_df.index.to_list(),
filters=[StockValuation.timestamp == date],
index='entity_id')
if pd_is_not_null(stock_valuation_df):
stock_count = len(etf_stock_df)
valuation_count = len(stock_valuation_df)
self.logger.info(
f'etf:{entity.id} date:{date} stock count: {stock_count},'
f'valuation count:{valuation_count}')
pct = abs(stock_count - valuation_count) / stock_count
if pct >= 0.2:
self.logger.error(
f'ignore etf:{entity.id} date:{date} pct:{pct}')
break
se = pd.Series({
'id': "{}_{}".format(entity.id, date),
'entity_id': entity.id,
'timestamp': date,
'code': entity.code,
'name': entity.name
})
for col in ['pe', 'pe_ttm', 'pb', 'ps', 'pcf']:
# PE=P/E
# 这里的算法为：将其价格都设为PE,那么Earning为1(亏钱为-1)，结果为 总价格(PE)/总Earning
value = 0
price = 0
# 权重估值
positive_df = stock_valuation_df[[
col
]][stock_valuation_df[col] > 0]
positive_df['count'] = 1
positive_df = positive_df.multiply(
etf_stock_df["proportion"], axis="index")
if pd_is_not_null(positive_df):
value = positive_df['count'].sum()
price = positive_df[col].sum()
negative_df = stock_valuation_df[[
col
]][stock_valuation_df[col] < 0]
if pd_is_not_null(negative_df):
negative_df['count'] = 1
negative_df = negative_df.multiply(
etf_stock_df["proportion"], axis="index")
value = value - negative_df['count'].sum()
price = price + negative_df[col].sum()
se[f'{col}1'] = price / value
# 简单算术平均估值
positive_df = stock_valuation_df[col][
stock_valuation_df[col] > 0]
positive_count = len(positive_df)
negative_df = stock_valuation_df[col][
stock_valuation_df[col] < 0]
negative_count = len(negative_df)
value = positive_count - negative_count
price = positive_df.sum() + abs(negative_df.sum())
se[col] = price / value
df = se.to_frame().T
self.logger.info(df)
df_to_db(df=df,
data_schema=self.data_schema,
provider=self.provider,
force_update=self.force_update)
return None