def on_finish_entity(self, entity):
super().on_finish_entity(entity)
if not self.fetch_jq_timestamp:
return
# fill the timestamp for report published date
the_data_list = get_data(
data_schema=self.data_schema,
provider=self.provider,
entity_id=entity.id,
order=self.data_schema.timestamp.asc(),
return_type='domain',
session=self.session,
filters=[
self.data_schema.timestamp == self.data_schema.report_date,
self.data_schema.timestamp >= to_pd_timestamp('2005-01-01')
])
if the_data_list:
if self.data_schema == FinanceFactor:
for the_data in the_data_list:
self.fill_timestamp_with_jq(entity, the_data)
else:
df = FinanceFactor.query_data(
entity_id=entity.id,
columns=[
FinanceFactor.timestamp, FinanceFactor.report_date,
FinanceFactor.id
],
filters=[
FinanceFactor.timestamp != FinanceFactor.report_date,
FinanceFactor.timestamp >=
to_pd_timestamp('2005-01-01'),
FinanceFactor.report_date >=
the_data_list[0].report_date,
FinanceFactor.report_date <=
the_data_list[-1].report_date,
])
if pd_is_not_null(df):
index_df(df, index='report_date', time_field='report_date')
for the_data in the_data_list:
if (df is not None) and (
not df.empty) and the_data.report_date in df.index:
the_data.timestamp = df.at[the_data.report_date,
'timestamp']
self.logger.info(
'db fill {} {} timestamp:{} for report_date:{}'.
format(self.data_schema, entity.id,
the_data.timestamp, the_data.report_date))
self.session.commit()
else:
# self.logger.info(
# 'waiting jq fill {} {} timestamp:{} for report_date:{}'.format(self.data_schema,
# security_item.id,
# the_data.timestamp,
# the_data.report_date))
self.fill_timestamp_with_jq(entity, the_data)
def risky_company(region: Region,
the_date=None,
income_yoy=-0.1,
profit_yoy=-0.1,
entity_ids=None):
if not the_date:
the_date = to_pd_timestamp(now_time_str(region))
codes = []
start_timestamp = to_pd_timestamp(the_date) - datetime.timedelta(130)
# 营收降，利润降,流动比率低，速动比率低
finance_filter = or_(FinanceFactor.op_income_growth_yoy < income_yoy,
FinanceFactor.net_profit_growth_yoy <= profit_yoy,
FinanceFactor.current_ratio < 0.7,
FinanceFactor.quick_ratio < 0.5)
df = FinanceFactor.query_data(region=region,
entity_ids=entity_ids,
start_timestamp=start_timestamp,
filters=[finance_filter],
columns=['code'])
if pd_is_not_null(df):
codes = codes + df.code.tolist()
# 高应收，高存货，高商誉
balance_filter = (BalanceSheet.accounts_receivable + BalanceSheet.inventories + BalanceSheet.goodwill) \
> BalanceSheet.total_equity
df = BalanceSheet.query_data(region=region,
entity_ids=entity_ids,
start_timestamp=start_timestamp,
filters=[balance_filter],
columns=['code'])
if pd_is_not_null(df):
codes = codes + df.code.tolist()
# 应收>利润*1/2
df1 = BalanceSheet.query_data(
region=region,
entity_ids=entity_ids,
start_timestamp=start_timestamp,
columns=[BalanceSheet.code, BalanceSheet.accounts_receivable])
if pd_is_not_null(df1):
df1.drop_duplicates(subset='code', keep='last', inplace=True)
df1 = df1.set_index('code', drop=True).sort_index()
df2 = IncomeStatement.query_data(
region=region,
entity_ids=entity_ids,
start_timestamp=start_timestamp,
columns=[IncomeStatement.code, IncomeStatement.net_profit])
if pd_is_not_null(df2):
df2.drop_duplicates(subset='code', keep='last', inplace=True)
df2 = df2.set_index('code', drop=True).sort_index()
if pd_is_not_null(df1) and pd_is_not_null(df2):
codes = codes + df1[df1.accounts_receivable > df2.net_profit /
2].index.tolist()
return list(set(codes))
def filter_selector_long_targets(self, timestamp, selector: TargetSelector, long_targets: List[str]) -> List[str]:
# 选择器选出的个股，再做进一步处理
if selector.level == IntervalLevel.LEVEL_1DAY:
if not long_targets:
return None
# 选择营收有增长的，并排序
start_timestamp = timestamp - datetime.timedelta(100)
df = FinanceFactor.query_data(entity_ids=long_targets, order=FinanceFactor.op_income_growth_yoy.desc(),
filters=[FinanceFactor.op_income_growth_yoy > 0],
start_timestamp=start_timestamp,
end_timestamp=timestamp,
columns=[FinanceFactor.entity_id, FinanceFactor.op_income_growth_yoy])
if pd_is_not_null(df):
return df['entity_id'].iloc[:10].tolist()
return None
return long_targets
def test_000778_finance_factor():
correct_timestamps = [
'2018-09-30', '2018-06-30', '2018-03-31', '2017-12-31', '2017-09-30',
'2017-06-30', '2017-03-31', '2016-12-31', '2016-09-30', '2016-06-30',
'2016-03-31', '2015-12-31', '2015-09-30', '2015-06-30', '2015-03-31',
'2014-12-31', '2014-09-30', '2014-06-30', '2014-03-31', '2013-12-31',
'2013-09-30', '2013-06-30', '2013-03-31', '2012-12-31', '2012-09-30',
'2012-06-30', '2012-03-31', '2011-12-31', '2011-09-30', '2011-06-30',
'2011-03-31', '2010-12-31', '2010-09-30', '2010-06-30', '2010-03-31',
'2009-12-31', '2009-09-30', '2009-06-30', '2009-03-31', '2008-12-31',
'2008-09-30', '2008-06-30', '2008-03-31', '2007-12-31', '2007-09-30',
'2007-06-30', '2007-03-31', '2006-12-31', '2006-09-30', '2006-06-30',
'2006-03-31', '2005-12-31', '2005-09-30', '2005-06-30', '2005-03-31',
'2004-12-31', '2004-09-30', '2004-06-30', '2004-03-31', '2003-12-31',
'2003-09-30', '2003-06-30', '2003-03-31', '2002-12-31', '2002-09-30',
'2002-06-30', '2002-03-31', '2001-12-31', '2001-06-30', '2000-12-31',
'2000-06-30', '1999-12-31', '1999-06-30', '1998-12-31', '1998-06-30',
'1997-12-31', '1997-06-30', '1996-12-31', '1995-12-31', '1994-12-31'
]
result = FinanceFactor.query_data(session=session,
provider='eastmoney',
return_type='domain',
codes=['000778'],
end_timestamp='2018-12-30',
order=FinanceFactor.report_date.desc(),
time_field='report_date')
assert len(correct_timestamps) == len(result)
timestamps = [to_time_str(item.report_date) for item in result]
assert set(correct_timestamps) == set(timestamps)
latest: FinanceFactor = result[0]
assert latest.basic_eps == 0.4537
assert latest.diluted_eps == 0.4537
assert latest.bps == 5.0919
assert latest.capital_reserve_ps == 2.1769
assert latest.undistributed_profit_ps == 1.8132
assert latest.op_cash_flow_ps == 1.0148
assert latest.total_op_income == 31710000000
assert latest.gross_profit == 5491000000
assert latest.net_profit == 1811000000
assert latest.deducted_net_profit == 1897000000
assert latest.total_op_income_growth_yoy == -0.1024
assert latest.inc_net_profit_shareholders_yoy == 1.2404
assert latest.deducted_net_profit_growth_yoy == 1.4813
assert latest.op_income_growth_qoq == 0.0408
assert latest.net_profit_growth_qoq == 0.2143
assert latest.deducted_net_profit_growth_qoq == 0.2955
assert latest.roe == 0.0882
assert latest.rota == 0.0376
assert latest.gross_profit_margin == 0.1731
assert latest.net_margin == 0.0591
assert latest.advance_receipts_per_op_income == 0.1
assert latest.sales_net_cash_flow_per_op_income == 0.88
assert latest.op_net_cash_flow_per_op_income == 0.13
assert latest.actual_tax_rate == 0.2362
assert latest.current_ratio == 1
assert latest.quick_ratio == 0.84
assert latest.cash_flow_ratio == 0.17
assert latest.debt_asset_ratio == 0.5766
assert latest.em == 2.36
assert latest.equity_ratio == 1.43
assert latest.total_assets_turnover_days == 423.91
assert latest.inventory_turnover_days == 32.88
assert latest.receivables_turnover_days == 52.23
assert latest.total_assets_turnover == 0.64
assert latest.inventory_turnover == 8.21
assert latest.receivables_turnover == 5.17
def test_000001_finance_factor():
correct_timestamps = [
'2018-09-30', '2018-06-30', '2018-03-31', '2017-12-31', '2017-09-30',
'2017-06-30', '2017-03-31', '2016-12-31', '2016-09-30', '2016-06-30',
'2016-03-31', '2015-12-31', '2015-09-30', '2015-06-30', '2015-03-31',
'2014-12-31', '2014-09-30', '2014-06-30', '2014-03-31', '2013-12-31',
'2013-09-30', '2013-06-30', '2013-03-31', '2012-12-31', '2012-09-30',
'2012-06-30', '2012-03-31', '2011-12-31', '2011-09-30', '2011-06-30',
'2011-03-31', '2010-12-31', '2010-09-30', '2010-06-30', '2010-03-31',
'2009-12-31', '2009-09-30', '2009-06-30', '2009-03-31', '2008-12-31',
'2008-09-30', '2008-06-30', '2008-03-31', '2007-12-31', '2007-09-30',
'2007-06-30', '2007-03-31', '2006-12-31', '2006-09-30', '2006-06-30',
'2006-03-31', '2005-12-31', '2005-09-30', '2005-06-30', '2005-03-31',
'2004-12-31', '2004-09-30', '2004-06-30', '2004-03-31', '2003-12-31',
'2003-09-30', '2003-06-30', '2003-03-31', '2002-12-31', '2002-09-30',
'2002-06-30', '2002-03-31', '2001-12-31', '2001-09-30', '2001-06-30',
'2001-03-31', '2000-12-31', '2000-06-30', '1999-12-31', '1999-06-30',
'1998-12-31', '1998-06-30', '1997-12-31', '1997-06-30', '1996-12-31',
'1996-06-30', '1995-12-31', '1995-06-30', '1994-12-31', '1994-06-30',
'1993-12-31', '1993-06-30', '1992-12-31', '1991-12-31', '1990-12-31',
'1989-12-31'
]
result = FinanceFactor.query_data(session=session,
provider='eastmoney',
return_type='domain',
codes=['000001'],
end_timestamp='2018-12-30',
order=FinanceFactor.report_date.desc(),
time_field='report_date')
assert len(correct_timestamps) == len(result)
timestamps = [to_time_str(item.report_date) for item in result]
assert set(correct_timestamps) == set(timestamps)
latest: FinanceFactor = result[0]
assert latest.basic_eps == 1.14
assert latest.deducted_eps == 1.13
assert latest.diluted_eps == 1.14
assert latest.bps == 12.538
assert latest.capital_reserve_ps == 3.2886
assert latest.undistributed_profit_ps == 5.3566
assert latest.op_cash_flow_ps == -0.6587
assert latest.total_op_income == 86660000000
assert latest.net_profit == 20460000000
assert latest.deducted_net_profit == 20350000000
assert latest.total_op_income_growth_yoy == 0.0856
assert latest.inc_net_profit_shareholders_yoy == 0.068
assert latest.deducted_inc_net_profit_shareholders_yoy == 0.0636
assert latest.op_income_growth_qoq == 0.0336
assert latest.net_profit_growth_qoq == 0.0202
assert latest.deducted_net_profit_growth_qoq == 0.0168
assert latest.roe == 0.0948
assert latest.deducted_roe == 0.0943
assert latest.rota == 0.0062
assert latest.net_margin == 0.2360
assert latest.debt_asset_ratio == 0.9298
assert latest.em == 14.25
assert latest.equity_ratio == 13.25
assert latest.fi_total_deposit == 2130000000000
assert latest.fi_total_loan == 1920000000000
assert latest.fi_loan_deposit_ratio == 0.9004
assert latest.fi_npl_ratio == 0.0168
assert latest.fi_npl_provision_coverage == 1.6914
# arrowcolor='#030813',
ax=-10,
ay=-30,
bordercolor='#c7c7c7',
borderwidth=1,
bgcolor=color,
opacity=0.8))
return annotations
return None
if __name__ == '__main__':
# from zvt.factors.pattern import ZenFactor
#
# data_reader1 = ZenFactor(codes=['000338'], level='1d')
# data_reader2 = ZenFactor(codes=['000338'], level='1wk')
# print(data_reader2.data_df)
#
# stacked = StackedDrawer(data_reader1.drawer(), data_reader2.drawer()).draw_kline()
df = Stock1dHfqKdata.query_data(code='000338',
start_timestamp='2015-01-01')
sub_df = FinanceFactor.query_data(code='000338',
start_timestamp='2015-01-01',
columns=[
FinanceFactor.roe,
FinanceFactor.entity_id,
FinanceFactor.timestamp
])
Drawer(main_df=df, sub_df_list=[sub_df]).draw_kline(show=True)