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FactorCalculate
Commit 2353e349 authored by 李煜's avatar 李煜
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modify per share

parent 757bc40e
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Showing with 149 additions and 151 deletions
client.py
View file @ 2353e349
...@@ -504,7 +504,7 @@ def get_basic_earning(trade_date): ...@@ -504,7 +504,7 @@ def get_basic_earning(trade_date):
if __name__ == '__main__': if __name__ == '__main__':
session1 = str('156099868869460811') session1 = str('156099868869460811')
session2 = str('156099868869460812') session2 = str('156099868869460812')
# session3 = str('156099868869460813') session3 = str('156099868869460813')
session4 = str('156099868869460814') session4 = str('156099868869460814')
session5 = str('156099868869460815') session5 = str('156099868869460815')
session6 = str('156099868869460816') session6 = str('156099868869460816')
...@@ -525,9 +525,9 @@ if __name__ == '__main__': ...@@ -525,9 +525,9 @@ if __name__ == '__main__':
history_value = historical_value.HistoricalValue('factor_historical_value') history_value = historical_value.HistoricalValue('factor_historical_value')
history_value.create_dest_tables() history_value.create_dest_tables()
# scale # per shapre
# scale = factor_per_share_indicators.PerShareIndicators('factor_scale') per_share = factor_per_share_indicators.PerShareIndicators('factor_per_share')
# scale.create_dest_tables() per_share.create_dest_tables()
# cash flow # cash flow
cash_flow = factor_cash_flow.FactorCashFlow('factor_cash_flow') cash_flow = factor_cash_flow.FactorCashFlow('factor_cash_flow')
...@@ -538,8 +538,8 @@ if __name__ == '__main__': ...@@ -538,8 +538,8 @@ if __name__ == '__main__':
constrain.create_dest_tables() constrain.create_dest_tables()
# earning # earning
earning = factor_earning.FactorEarning('factor_earning') # earning = factor_earning.FactorEarning('factor_earning')
earning.create_dest_tables() # earning.create_dest_tables()
for date_index in trade_date_sets: for date_index in trade_date_sets:
# factor_growth # factor_growth
...@@ -561,14 +561,16 @@ if __name__ == '__main__': ...@@ -561,14 +561,16 @@ if __name__ == '__main__':
time2 = time.time() time2 = time.time()
print('history_cal_time:{}'.format(time2 - time1)) print('history_cal_time:{}'.format(time2 - time1))
# # scale # per share indicators
# valuation_sets, ttm_factor_sets, cash_flow_sets, income_sets, balance_sets = get_basic_scale_data(date_index) valuation_sets, ttm_factor_sets, cash_flow_sets, income_sets, balance_sets = get_basic_scale_data(date_index)
# valuation_sets = pd.merge(valuation_sets, income_sets, on='symbol') valuation_sets = pd.merge(valuation_sets, income_sets, on='symbol')
# valuation_sets = pd.merge(valuation_sets, ttm_factor_sets, on='symbol') valuation_sets = pd.merge(valuation_sets, ttm_factor_sets, on='symbol')
# valuation_sets = pd.merge(valuation_sets, cash_flow_sets, on='symbol') valuation_sets = pd.merge(valuation_sets, cash_flow_sets, on='symbol')
# valuation_sets = pd.merge(valuation_sets, balance_sets, on='symbol') valuation_sets = pd.merge(valuation_sets, balance_sets, on='symbol')
# cache_data.set_cache(session3, date_index, valuation_sets.to_json(orient='records')) cache_data.set_cache(session3, date_index, valuation_sets.to_json(orient='records'))
# factor_per_share_indicators.factor_calculate(date_index=date_index, session=session3) factor_per_share_indicators.factor_calculate(date_index=date_index, session=session3)
time3 = time.time()
print('per_share_cal_time:{}'.format(time3 - time2))
# cash flow # cash flow
tp_cash_flow, ttm_factor_sets = get_basic_cash_flow(date_index) tp_cash_flow, ttm_factor_sets = get_basic_cash_flow(date_index)
...@@ -576,7 +578,7 @@ if __name__ == '__main__': ...@@ -576,7 +578,7 @@ if __name__ == '__main__':
cache_data.set_cache(session4 + "2", date_index, ttm_factor_sets.to_json(orient='records')) cache_data.set_cache(session4 + "2", date_index, ttm_factor_sets.to_json(orient='records'))
factor_cash_flow.factor_calculate(date_index=date_index, session=session4) factor_cash_flow.factor_calculate(date_index=date_index, session=session4)
time4 = time.time() time4 = time.time()
print('cash_flow_cal_time:{}'.format(time4 - time2)) print('cash_flow_cal_time:{}'.format(time4 - time3))
# constrain # constrain
balance_sets, ttm_factors_sets = get_basic_constrain(date_index) balance_sets, ttm_factors_sets = get_basic_constrain(date_index)
...@@ -587,11 +589,11 @@ if __name__ == '__main__': ...@@ -587,11 +589,11 @@ if __name__ == '__main__':
print('constrain_cal_time:{}'.format(time5 - time4)) print('constrain_cal_time:{}'.format(time5 - time4))
# earning # earning
tp_earning, ttm_earning_5y, ttm_earning = get_basic_earning(date_index) # tp_earning, ttm_earning_5y, ttm_earning = get_basic_earning(date_index)
cache_data.set_cache(session6 + "1", date_index, tp_earning.to_json(orient='records')) # cache_data.set_cache(session6 + "1", date_index, tp_earning.to_json(orient='records'))
cache_data.set_cache(session6 + "2", date_index, ttm_earning_5y.to_json(orient='records')) # cache_data.set_cache(session6 + "2", date_index, ttm_earning_5y.to_json(orient='records'))
cache_data.set_cache(session6 + "3", date_index, ttm_earning.to_json(orient='records')) # cache_data.set_cache(session6 + "3", date_index, ttm_earning.to_json(orient='records'))
factor_earning.factor_calculate(date_index=date_index, session=session6) # factor_earning.factor_calculate(date_index=date_index, session=session6)
time6 = time.time() # time6 = time.time()
print('earning_cal_time:{}'.format(time6 - time5)) # print('earning_cal_time:{}'.format(time6 - time5))
print('---------------------->') print('---------------------->')
factor/factor_per_share_indicators.py
View file @ 2353e349
...@@ -38,26 +38,26 @@ class PerShareIndicators(FactorBase): ...@@ -38,26 +38,26 @@ class PerShareIndicators(FactorBase):
`id` varchar(32) NOT NULL, `id` varchar(32) NOT NULL,
`symbol` varchar(24) NOT NULL, `symbol` varchar(24) NOT NULL,
`trade_date` date NOT NULL, `trade_date` date NOT NULL,
`eps_latest` decimal(19,4), `EPS` decimal(19,4),
`diluted_eps_ttm` decimal(19,4), `DilutedEPSTTM` decimal(19,4),
`cash_equivalent_ps_latest` decimal(19,4), `CashEquPS` decimal(19,4),
`dividend_ps_latest` decimal(19,4), `DivPS` decimal(19,4),
`eps_ttm` decimal(19,4), `EPSTTM` decimal(19,4),
`net_asset_ps_latest` decimal(19,4), `NetAssetPS` decimal(19,4),
`tor_ps_latest` decimal(19,4), `TotalRevPS` decimal(19,4),
`tor_ps_ttm` decimal(19,4), `TotalRevPSTTM` decimal(19,4),
`operating_revenue_ps_ttm` decimal(19,4), `OptRevPSTTM` decimal(19,4),
`operating_revenue_ps_latest` decimal(19,4), `OptRevPS` decimal(19,4),
`operating_profit_ps_ttm` decimal(19,4), `OptProfitPSTTM` decimal(19,4),
`operating_profit_ps_latest` decimal(19,4), `OptProfitPS` decimal(19,4),
`capital_surplus_fund_ps_latest` decimal(19,4), `CapticalSurplusPS` decimal(19,4),
`surplus_reserve_fund_ps_latest` decimal(19,4), `SurplusReservePS` decimal(19,4),
`undivided_pro_fit_ps_latest` decimal(19,4), `UndividedProfitPS` decimal(19,4),
`retained_earnings_ps_latest` decimal(19,4), `RetainedEarningsPS` decimal(19,4),
`oper_cash_flow_ps_ttm` decimal(19,4), `OptCFPSTTM` decimal(19,4),
`cash_flow_ps_ttm` decimal(19,4), `CFPSTTM` decimal(19,4),
`enterprise_fcfps_latest` decimal(19,4), `EnterpriseFCFPS` decimal(19,4),
`shareholder_fcfps_latest` decimal(19,4), `ShareholderFCFPS` decimal(19,4),
PRIMARY KEY(`id`,`trade_date`,`symbol`) PRIMARY KEY(`id`,`trade_date`,`symbol`)
)ENGINE=InnoDB DEFAULT CHARSET=utf8;""".format(self._name) )ENGINE=InnoDB DEFAULT CHARSET=utf8;""".format(self._name)
super(PerShareIndicators, self)._create_tables(create_sql, drop_sql) super(PerShareIndicators, self)._create_tables(create_sql, drop_sql)
...@@ -71,9 +71,9 @@ class PerShareIndicators(FactorBase): ...@@ -71,9 +71,9 @@ class PerShareIndicators(FactorBase):
""" """
columns_lists = ['symbol', 'basic_eps'] columns_lists = ['symbol', 'basic_eps']
share_indicators = tp_share_indicators.loc[:, columns_lists] share_indicators = tp_share_indicators.loc[:, columns_lists]
share_indicators['eps_latest'] = share_indicators['basic_eps'] share_indicators['EPS'] = share_indicators['basic_eps']
# share_indicators = share_indicators.drop(columns=['basic_eps'], axis=1) # share_indicators = share_indicators.drop(columns=['basic_eps'], axis=1)
share_indicators = share_indicators[['symbol', 'eps_latest']] share_indicators = share_indicators[['symbol', 'EPS']]
factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol') factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
return factor_share_indicators return factor_share_indicators
...@@ -86,9 +86,9 @@ class PerShareIndicators(FactorBase): ...@@ -86,9 +86,9 @@ class PerShareIndicators(FactorBase):
""" """
columns_lists = ['symbol', 'diluted_eps'] columns_lists = ['symbol', 'diluted_eps']
share_indicators = tp_share_indicators.loc[:, columns_lists] share_indicators = tp_share_indicators.loc[:, columns_lists]
share_indicators['diluted_eps_ttm'] = share_indicators['diluted_eps'] share_indicators['DilutedEPSTTM'] = share_indicators['diluted_eps']
# share_indicators = share_indicators.drop(columns=['diluted_eps'], axis=1) # share_indicators = share_indicators.drop(columns=['diluted_eps'], axis=1)
share_indicators = share_indicators[['symbol', 'diluted_eps_ttm']] share_indicators = share_indicators[['symbol', 'DilutedEPSTTM']]
factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol') factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
return factor_share_indicators return factor_share_indicators
...@@ -103,11 +103,11 @@ class PerShareIndicators(FactorBase): ...@@ -103,11 +103,11 @@ class PerShareIndicators(FactorBase):
columns_lists = ['symbol', 'capitalization', 'cash_and_equivalents_at_end'] columns_lists = ['symbol', 'capitalization', 'cash_and_equivalents_at_end']
share_indicators = tp_share_indicators.loc[:, columns_lists] share_indicators = tp_share_indicators.loc[:, columns_lists]
fun = lambda x: (x[1] / x[0] if x[0] and x[0] != 0 else None) fun = lambda x: (x[1] / x[0] if x[0] and x[0] != 0 else None)
share_indicators['cash_equivalent_ps_latest'] = share_indicators[ share_indicators['CashEquPS'] = share_indicators[
['capitalization', 'cash_and_equivalents_at_end']].apply(fun, axis=1) ['capitalization', 'cash_and_equivalents_at_end']].apply(fun, axis=1)
# share_indicators = share_indicators.drop(columns=['cash_and_equivalents_at_end'], axis=1) # share_indicators = share_indicators.drop(columns=['cash_and_equivalents_at_end'], axis=1)
share_indicators = share_indicators[['symbol', 'cash_equivalent_ps_latest']] share_indicators = share_indicators[['symbol', 'CashEquPS']]
factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol') factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
return factor_share_indicators return factor_share_indicators
...@@ -121,9 +121,9 @@ class PerShareIndicators(FactorBase): ...@@ -121,9 +121,9 @@ class PerShareIndicators(FactorBase):
""" """
columns_lists = ['symbol', 'dividend_receivable'] columns_lists = ['symbol', 'dividend_receivable']
share_indicators = tp_share_indicators.loc[:, columns_lists] share_indicators = tp_share_indicators.loc[:, columns_lists]
share_indicators['dividend_ps_latest'] = share_indicators['dividend_receivable'] share_indicators['DivPS'] = share_indicators['dividend_receivable']
# share_indicators = share_indicators.drop(columns=['dividend_receivable'], axis=1) # share_indicators = share_indicators.drop(columns=['dividend_receivable'], axis=1)
share_indicators = share_indicators[['symbol', 'dividend_ps_latest']] share_indicators = share_indicators[['symbol', 'DivPS']]
factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol') factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
return factor_share_indicators return factor_share_indicators
...@@ -138,11 +138,11 @@ class PerShareIndicators(FactorBase): ...@@ -138,11 +138,11 @@ class PerShareIndicators(FactorBase):
columns_lists = ['symbol', 'np_parent_company_owners_ttm', 'capitalization'] columns_lists = ['symbol', 'np_parent_company_owners_ttm', 'capitalization']
share_indicators = tp_share_indicators.loc[:, columns_lists] share_indicators = tp_share_indicators.loc[:, columns_lists]
fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else 0) fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else 0)
share_indicators['eps_ttm'] = share_indicators[['np_parent_company_owners_ttm', 'capitalization']].apply(fun, share_indicators['EPSTTM'] = share_indicators[['np_parent_company_owners_ttm', 'capitalization']].apply(fun,
axis=1) axis=1)
# share_indicators = share_indicators.drop(columns=['np_parent_company_owners_ttm'], axis=1) # share_indicators = share_indicators.drop(columns=['np_parent_company_owners_ttm'], axis=1)
share_indicators = share_indicators[['symbol', 'eps_ttm']] share_indicators = share_indicators[['symbol', 'EPSTTM']]
factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol') factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
return factor_share_indicators return factor_share_indicators
...@@ -157,11 +157,11 @@ class PerShareIndicators(FactorBase): ...@@ -157,11 +157,11 @@ class PerShareIndicators(FactorBase):
columns_lists = ['symbol', 'total_owner_equities', 'capitalization'] columns_lists = ['symbol', 'total_owner_equities', 'capitalization']
share_indicators = tp_share_indicators.loc[:, columns_lists] share_indicators = tp_share_indicators.loc[:, columns_lists]
fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else None) fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else None)
share_indicators['net_asset_ps_latest'] = share_indicators[['total_owner_equities', 'capitalization']].apply(fun, share_indicators['NetAssetPS'] = share_indicators[['total_owner_equities', 'capitalization']].apply(fun,
axis=1) axis=1)
# share_indicators = share_indicators.drop(columns=['total_owner_equities'], axis=1) # share_indicators = share_indicators.drop(columns=['total_owner_equities'], axis=1)
share_indicators = share_indicators[['symbol', 'net_asset_ps_latest']] share_indicators = share_indicators[['symbol', 'NetAssetPS']]
factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol') factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
return factor_share_indicators return factor_share_indicators
...@@ -175,11 +175,11 @@ class PerShareIndicators(FactorBase): ...@@ -175,11 +175,11 @@ class PerShareIndicators(FactorBase):
columns_lists = ['symbol', 'total_operating_revenue_ttm', 'capitalization'] columns_lists = ['symbol', 'total_operating_revenue_ttm', 'capitalization']
share_indicators = tp_share_indicators.loc[:, columns_lists] share_indicators = tp_share_indicators.loc[:, columns_lists]
fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else None) fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else None)
share_indicators['tor_ps_ttm'] = share_indicators[ share_indicators['TotalRevPSTTM'] = share_indicators[
['total_operating_revenue_ttm', 'capitalization']].apply(fun, axis=1) ['total_operating_revenue_ttm', 'capitalization']].apply(fun, axis=1)
# share_indicators = share_indicators.drop(columns=['total_operating_revenue_ttm'], axis=1) # share_indicators = share_indicators.drop(columns=['total_operating_revenue_ttm'], axis=1)
share_indicators = share_indicators[['symbol', 'tor_ps_ttm']] share_indicators = share_indicators[['symbol', 'TotalRevPSTTM']]
factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol') factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
return factor_share_indicators return factor_share_indicators
...@@ -194,10 +194,10 @@ class PerShareIndicators(FactorBase): ...@@ -194,10 +194,10 @@ class PerShareIndicators(FactorBase):
columns_lists = ['symbol', 'total_operating_revenue', 'capitalization'] columns_lists = ['symbol', 'total_operating_revenue', 'capitalization']
share_indicators = tp_share_indicators.loc[:, columns_lists] share_indicators = tp_share_indicators.loc[:, columns_lists]
fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else None) fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else None)
share_indicators['tor_ps_latest'] = share_indicators[['total_operating_revenue', 'capitalization']].apply(fun, axis=1) share_indicators['TotalRevPS'] = share_indicators[['total_operating_revenue', 'capitalization']].apply(fun, axis=1)
# share_indicators = share_indicators.drop(columns=['total_operating_revenue'], axis=1) # share_indicators = share_indicators.drop(columns=['total_operating_revenue'], axis=1)
share_indicators = share_indicators[['symbol', 'tor_ps_latest']] share_indicators = share_indicators[['symbol', 'TotalRevPS']]
factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol') factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
return factor_share_indicators return factor_share_indicators
...@@ -212,13 +212,13 @@ class PerShareIndicators(FactorBase): ...@@ -212,13 +212,13 @@ class PerShareIndicators(FactorBase):
columns_lists = ['symbol', 'operating_revenue_ttm', 'capitalization'] columns_lists = ['symbol', 'operating_revenue_ttm', 'capitalization']
share_indicators = tp_share_indicators.loc[:, columns_lists] share_indicators = tp_share_indicators.loc[:, columns_lists]
fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else None) fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else None)
share_indicators['operating_revenue_ps_ttm'] = share_indicators[ share_indicators['OptRevPSTTM'] = share_indicators[
['operating_revenue_ttm', 'capitalization']].apply( ['operating_revenue_ttm', 'capitalization']].apply(
fun, fun,
axis=1) axis=1)
# share_indicators = share_indicators.drop(columns=['operating_revenue_ttm'], axis=1) # share_indicators = share_indicators.drop(columns=['operating_revenue_ttm'], axis=1)
share_indicators = share_indicators[['symbol', 'operating_revenue_ps_ttm']] share_indicators = share_indicators[['symbol', 'OptRevPSTTM']]
factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol') factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
return factor_share_indicators return factor_share_indicators
...@@ -232,13 +232,13 @@ class PerShareIndicators(FactorBase): ...@@ -232,13 +232,13 @@ class PerShareIndicators(FactorBase):
columns_lists = ['symbol', 'operating_revenue', 'capitalization'] columns_lists = ['symbol', 'operating_revenue', 'capitalization']
share_indicators = tp_share_indicators.loc[:, columns_lists] share_indicators = tp_share_indicators.loc[:, columns_lists]
fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else None) fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else None)
share_indicators['operating_revenue_ps_latest'] = share_indicators[ share_indicators['OptRevPS'] = share_indicators[
['operating_revenue', 'capitalization']].apply( ['operating_revenue', 'capitalization']].apply(
fun, fun,
axis=1) axis=1)
# share_indicators = share_indicators.drop(columns=['operating_revenue'], axis=1) # share_indicators = share_indicators.drop(columns=['operating_revenue'], axis=1)
share_indicators = share_indicators[['symbol', 'operating_revenue_ps_latest']] share_indicators = share_indicators[['symbol', 'OptRevPS']]
factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol') factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
return factor_share_indicators return factor_share_indicators
...@@ -253,13 +253,13 @@ class PerShareIndicators(FactorBase): ...@@ -253,13 +253,13 @@ class PerShareIndicators(FactorBase):
columns_lists = ['symbol', 'operating_profit_ttm', 'capitalization'] columns_lists = ['symbol', 'operating_profit_ttm', 'capitalization']
share_indicators = tp_share_indicators.loc[:, columns_lists] share_indicators = tp_share_indicators.loc[:, columns_lists]
fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else None) fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else None)
share_indicators['operating_profit_ps_ttm'] = share_indicators[ share_indicators['OptProfitPSTTM'] = share_indicators[
['operating_profit_ttm', 'capitalization']].apply( ['operating_profit_ttm', 'capitalization']].apply(
fun, fun,
axis=1) axis=1)
# share_indicators = share_indicators.drop(columns=['operating_profit_ttm'], axis=1) # share_indicators = share_indicators.drop(columns=['operating_profit_ttm'], axis=1)
share_indicators = share_indicators[['symbol', 'operating_profit_ps_ttm']] share_indicators = share_indicators[['symbol', 'OptProfitPSTTM']]
factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol') factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
return factor_share_indicators return factor_share_indicators
...@@ -274,13 +274,10 @@ class PerShareIndicators(FactorBase): ...@@ -274,13 +274,10 @@ class PerShareIndicators(FactorBase):
columns_lists = ['symbol', 'operating_profit', 'capitalization'] columns_lists = ['symbol', 'operating_profit', 'capitalization']
share_indicators = tp_share_indicators.loc[:, columns_lists] share_indicators = tp_share_indicators.loc[:, columns_lists]
fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else None) fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else None)
share_indicators['operating_profit_ps_latest'] = share_indicators[ share_indicators['OptProfitPS'] = share_indicators[['operating_profit', 'capitalization']].apply(fun, axis=1)
['operating_profit', 'capitalization']].apply(
fun,
axis=1)
# share_indicators = share_indicators.drop(columns=['operating_profit'], axis=1) # share_indicators = share_indicators.drop(columns=['operating_profit'], axis=1)
share_indicators = share_indicators[['symbol', 'operating_profit_ps_latest']] share_indicators = share_indicators[['symbol', 'OptProfitPS']]
factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol') factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
return factor_share_indicators return factor_share_indicators
...@@ -294,13 +291,13 @@ class PerShareIndicators(FactorBase): ...@@ -294,13 +291,13 @@ class PerShareIndicators(FactorBase):
columns_lists = ['symbol', 'capital_reserve_fund', 'capitalization'] columns_lists = ['symbol', 'capital_reserve_fund', 'capitalization']
share_indicators = tp_share_indicators.loc[:, columns_lists] share_indicators = tp_share_indicators.loc[:, columns_lists]
fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else None) fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else None)
share_indicators['capital_surplus_fund_ps_latest'] = share_indicators[ share_indicators['CapticalSurplusPS'] = share_indicators[
['capital_reserve_fund', 'capitalization']].apply( ['capital_reserve_fund', 'capitalization']].apply(
fun, fun,
axis=1) axis=1)
# share_indicators = share_indicators.drop(columns=['capital_reserve_fund'], axis=1) # share_indicators = share_indicators.drop(columns=['capital_reserve_fund'], axis=1)
share_indicators = share_indicators[['symbol', 'capital_surplus_fund_ps_latest']] share_indicators = share_indicators[['symbol', 'CapticalSurplusPS']]
factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol') factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
return factor_share_indicators return factor_share_indicators
...@@ -314,10 +311,10 @@ class PerShareIndicators(FactorBase): ...@@ -314,10 +311,10 @@ class PerShareIndicators(FactorBase):
columns_lists = ['symbol', 'surplus_reserve_fund', 'capitalization'] columns_lists = ['symbol', 'surplus_reserve_fund', 'capitalization']
share_indicators = tp_share_indicators.loc[:, columns_lists] share_indicators = tp_share_indicators.loc[:, columns_lists]
fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else None) fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else None)
share_indicators['surplus_reserve_fund_ps_latest'] = share_indicators[['surplus_reserve_fund', 'capitalization']].apply(fun, axis=1) share_indicators['SurplusReservePS'] = share_indicators[['surplus_reserve_fund', 'capitalization']].apply(fun, axis=1)
# share_indicators = share_indicators.drop(columns=['surplus_reserve_fund'], axis=1) # share_indicators = share_indicators.drop(columns=['surplus_reserve_fund'], axis=1)
share_indicators = share_indicators[['symbol', 'surplus_reserve_fund_ps_latest']] share_indicators = share_indicators[['symbol', 'SurplusReservePS']]
factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol') factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
return factor_share_indicators return factor_share_indicators
...@@ -332,13 +329,13 @@ class PerShareIndicators(FactorBase): ...@@ -332,13 +329,13 @@ class PerShareIndicators(FactorBase):
columns_lists = ['symbol', 'retained_profit', 'capitalization'] columns_lists = ['symbol', 'retained_profit', 'capitalization']
share_indicators = tp_share_indicators.loc[:, columns_lists] share_indicators = tp_share_indicators.loc[:, columns_lists]
fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else None) fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else None)
share_indicators['undivided_pro_fit_ps_latest'] = share_indicators[ share_indicators['UndividedProfitPS'] = share_indicators[
['retained_profit', 'capitalization']].apply( ['retained_profit', 'capitalization']].apply(
fun, fun,
axis=1) axis=1)
# share_indicators = share_indicators.drop(columns=['retained_profit'], axis=1) # share_indicators = share_indicators.drop(columns=['retained_profit'], axis=1)
share_indicators = share_indicators[['symbol', 'undivided_pro_fit_ps_latest']] share_indicators = share_indicators[['symbol', 'UndividedProfitPS']]
factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol') factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
return factor_share_indicators return factor_share_indicators
...@@ -349,13 +346,12 @@ class PerShareIndicators(FactorBase): ...@@ -349,13 +346,12 @@ class PerShareIndicators(FactorBase):
:param factor_share_indicators: :param factor_share_indicators:
:return: :return:
""" """
columns_lists = ['symbol', 'surplus_reserve_fund_ps_latest', 'undivided_pro_fit_ps_latest'] columns_lists = ['symbol', 'SurplusReservePS', 'UndividedProfitPS']
share_indicators = tp_share_indicators.loc[:, columns_lists] share_indicators = tp_share_indicators.loc[:, columns_lists]
share_indicators['retained_earnings_ps_latest'] = share_indicators['undivided_pro_fit_ps_latest'] + \ share_indicators['RetainedEarningsPS'] = share_indicators['UndividedProfitPS'] + share_indicators['SurplusReservePS']
share_indicators['surplus_reserve_fund_ps_latest']
# share_indicators = share_indicators.drop(columns=['undivided_pro_fit_ps_latest', 'surplus_reserve_fund_ps_latest'], axis=1) # share_indicators = share_indicators.drop(columns=['UndividedProfitPS', 'SurplusReservePS'], axis=1)
share_indicators = share_indicators[['symbol', 'retained_earnings_ps_latest']] share_indicators = share_indicators[['symbol', 'RetainedEarningsPS']]
factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol') factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
return factor_share_indicators return factor_share_indicators
...@@ -369,13 +365,13 @@ class PerShareIndicators(FactorBase): ...@@ -369,13 +365,13 @@ class PerShareIndicators(FactorBase):
columns_lists = ['symbol', 'net_operate_cash_flow_ttm', 'capitalization'] columns_lists = ['symbol', 'net_operate_cash_flow_ttm', 'capitalization']
share_indicators = tp_share_indicators.loc[:, columns_lists] share_indicators = tp_share_indicators.loc[:, columns_lists]
fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else None) fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else None)
share_indicators['oper_cash_flow_ps_ttm'] = share_indicators[ share_indicators['OptCFPSTTM'] = share_indicators[
['net_operate_cash_flow_ttm', 'capitalization']].apply( ['net_operate_cash_flow_ttm', 'capitalization']].apply(
fun, fun,
axis=1) axis=1)
# share_indicators = share_indicators.drop(columns=['net_operate_cash_flow_ttm'], axis=1) # share_indicators = share_indicators.drop(columns=['net_operate_cash_flow_ttm'], axis=1)
share_indicators = share_indicators[['symbol', 'oper_cash_flow_ps_ttm']] share_indicators = share_indicators[['symbol', 'OptCFPSTTM']]
factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol') factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
return factor_share_indicators return factor_share_indicators
...@@ -390,10 +386,10 @@ class PerShareIndicators(FactorBase): ...@@ -390,10 +386,10 @@ class PerShareIndicators(FactorBase):
columns_lists = ['symbol', 'n_change_in_cash_ttm', 'capitalization'] columns_lists = ['symbol', 'n_change_in_cash_ttm', 'capitalization']
share_indicators = tp_share_indicators.loc[:, columns_lists] share_indicators = tp_share_indicators.loc[:, columns_lists]
fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else None) fun = lambda x: (x[0] / x[1] if x[1] and x[1] != 0 else None)
share_indicators['cash_flow_ps_ttm'] = share_indicators[['n_change_in_cash_ttm', 'capitalization']].apply(fun, axis=1) share_indicators['CFPSTTM'] = share_indicators[['n_change_in_cash_ttm', 'capitalization']].apply(fun, axis=1)
# share_indicators = share_indicators.drop(columns=['n_change_in_cash_ttm'], axis=1) # share_indicators = share_indicators.drop(columns=['n_change_in_cash_ttm'], axis=1)
share_indicators = share_indicators[['symbol', 'cash_flow_ps_ttm']] share_indicators = share_indicators[['symbol', 'CFPSTTM']]
factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol') factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
return factor_share_indicators return factor_share_indicators
...@@ -408,9 +404,9 @@ class PerShareIndicators(FactorBase): ...@@ -408,9 +404,9 @@ class PerShareIndicators(FactorBase):
""" """
columns_lists = ['symbol', 'enterprise_fcfps'] columns_lists = ['symbol', 'enterprise_fcfps']
share_indicators = tp_share_indicators.loc[:, columns_lists] share_indicators = tp_share_indicators.loc[:, columns_lists]
share_indicators['enterprise_fcfps_latest'] = share_indicators['enterprise_fcfps'] share_indicators['EnterpriseFCFPS'] = share_indicators['enterprise_fcfps']
# share_indicators = share_indicators.drop(columns=['enterprise_fcfps'], axis=1) # share_indicators = share_indicators.drop(columns=['enterprise_fcfps'], axis=1)
share_indicators = share_indicators[['symbol', 'enterprise_fcfps_latest']] share_indicators = share_indicators[['symbol', 'EnterpriseFCFPS']]
factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol') factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
return factor_share_indicators return factor_share_indicators
...@@ -424,17 +420,17 @@ class PerShareIndicators(FactorBase): ...@@ -424,17 +420,17 @@ class PerShareIndicators(FactorBase):
""" """
columns_lists = ['symbol', 'shareholder_fcfps'] columns_lists = ['symbol', 'shareholder_fcfps']
share_indicators = tp_share_indicators.loc[:, columns_lists] share_indicators = tp_share_indicators.loc[:, columns_lists]
share_indicators['shareholder_fcfps_latest'] = share_indicators['shareholder_fcfps'] share_indicators['ShareholderFCFPS'] = share_indicators['shareholder_fcfps']
# share_indicators = share_indicators.drop(columns=['shareholder_fcfps'], axis=1) # share_indicators = share_indicators.drop(columns=['shareholder_fcfps'], axis=1)
share_indicators = share_indicators[['symbol', 'shareholder_fcfps_latest']] share_indicators = share_indicators[['symbol', 'ShareholderFCFPS']]
factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol') factor_share_indicators = pd.merge(factor_share_indicators, share_indicators, on='symbol')
return factor_share_indicators return factor_share_indicators
def calculate(trade_date, valuation_sets, scale): def calculate(trade_date, valuation_sets, per_share):
""" """
规模 规模
:param scale: 规模类 :param per_share: 规模类
:param valuation_sets: 基础数据 :param valuation_sets: 基础数据
:param trade_date: 交易日 :param trade_date: 交易日
:return: :return:
...@@ -444,24 +440,24 @@ def calculate(trade_date, valuation_sets, scale): ...@@ -444,24 +440,24 @@ def calculate(trade_date, valuation_sets, scale):
return return
# psindu # psindu
factor_share_indicators = scale.eps(valuation_sets, valuation_sets) factor_share_indicators = per_share.eps(valuation_sets, valuation_sets)
factor_share_indicators = scale.diluted_eps(valuation_sets, factor_share_indicators) factor_share_indicators = per_share.diluted_eps(valuation_sets, factor_share_indicators)
factor_share_indicators = scale.cash_equivalent_ps(valuation_sets, factor_share_indicators) factor_share_indicators = per_share.cash_equivalent_ps(valuation_sets, factor_share_indicators)
factor_share_indicators = scale.dividend_ps(valuation_sets, factor_share_indicators) factor_share_indicators = per_share.dividend_ps(valuation_sets, factor_share_indicators)
factor_share_indicators = scale.eps_ttm(valuation_sets, factor_share_indicators) factor_share_indicators = per_share.eps_ttm(valuation_sets, factor_share_indicators)
factor_share_indicators = scale.net_asset_ps(valuation_sets, factor_share_indicators) factor_share_indicators = per_share.net_asset_ps(valuation_sets, factor_share_indicators)
factor_share_indicators = scale.tor_ps(valuation_sets, factor_share_indicators) factor_share_indicators = per_share.tor_ps(valuation_sets, factor_share_indicators)
factor_share_indicators = scale.tor_ps_latest(factor_share_indicators, factor_share_indicators) # memorydrror factor_share_indicators = per_share.tor_ps_latest(factor_share_indicators, factor_share_indicators) # memorydrror
factor_share_indicators = scale.operating_revenue_ps(factor_share_indicators, factor_share_indicators) # memoryerror factor_share_indicators = per_share.operating_revenue_ps(factor_share_indicators, factor_share_indicators) # memoryerror
factor_share_indicators = scale.operating_revenue_ps_latest(valuation_sets, factor_share_indicators) factor_share_indicators = per_share.operating_revenue_ps_latest(valuation_sets, factor_share_indicators)
factor_share_indicators = scale.operating_profit_ps(valuation_sets, factor_share_indicators) factor_share_indicators = per_share.operating_profit_ps(valuation_sets, factor_share_indicators)
factor_share_indicators = scale.operating_profit_ps_latest(valuation_sets, factor_share_indicators) factor_share_indicators = per_share.operating_profit_ps_latest(valuation_sets, factor_share_indicators)
factor_share_indicators = scale.capital_surplus_fund_ps(factor_share_indicators, factor_share_indicators) # memoryerror factor_share_indicators = per_share.capital_surplus_fund_ps(factor_share_indicators, factor_share_indicators) # memoryerror
factor_share_indicators = scale.surplus_reserve_fund_ps(factor_share_indicators, factor_share_indicators) # memorydrror factor_share_indicators = per_share.surplus_reserve_fund_ps(factor_share_indicators, factor_share_indicators) # memorydrror
factor_share_indicators = scale.undivided_pro_fit_ps(factor_share_indicators, factor_share_indicators) # memorydrror factor_share_indicators = per_share.undivided_pro_fit_ps(factor_share_indicators, factor_share_indicators) # memorydrror
factor_share_indicators = scale.retained_earnings_ps(factor_share_indicators, factor_share_indicators) # memorydrror factor_share_indicators = per_share.retained_earnings_ps(factor_share_indicators, factor_share_indicators) # memorydrror
factor_share_indicators = scale.oper_cash_flow_ps(factor_share_indicators, factor_share_indicators) # memorydrror factor_share_indicators = per_share.oper_cash_flow_ps(factor_share_indicators, factor_share_indicators) # memorydrror
factor_share_indicators = scale.cash_flow_ps(factor_share_indicators, factor_share_indicators) # memorydrror factor_share_indicators = per_share.cash_flow_ps(factor_share_indicators, factor_share_indicators) # memorydrror
# factor_historical_value = factor_historical_value.drop(columns=['pb', 'pe', 'ps', 'pcf', 'market_cap', # factor_historical_value = factor_historical_value.drop(columns=['pb', 'pe', 'ps', 'pcf', 'market_cap',
# 'circulating_market_cap', 'isymbol', # 'circulating_market_cap', 'isymbol',
...@@ -472,50 +468,50 @@ def calculate(trade_date, valuation_sets, scale): ...@@ -472,50 +468,50 @@ def calculate(trade_date, valuation_sets, scale):
# 'goods_sale_and_service_render_cash']) # 'goods_sale_and_service_render_cash'])
# factor_share_indicators = factor_share_indicators[['symbol', # factor_share_indicators = factor_share_indicators[['symbol',
# 'eps_latest', # 'EPS',
# 'diluted_eps_ttm', # 'DilutedEPSTTM',
# 'cash_equivalent_ps_latest', # 'CashEquPS',
# 'dividend_ps_latest', # 'DivPS',
# 'eps_ttm', # 'EPSTTM',
# 'net_asset_ps_latest', # 'NetAssetPS',
# 'tor_ps_latest', # 'TotalRevPS',
# 'tor_ps_ttm', # 'TotalRevPSTTM',
# 'operating_revenue_ps_ttm', # 'OptRevPSTTM',
# 'operating_revenue_ps_latest', # 'OptRevPS',
# 'operating_profit_ps_ttm', # 'OptProfitPSTTM',
# 'operating_profit_ps_latest', # 'OptProfitPS',
# 'capital_surplus_fund_ps_latest', # 'CapticalSurplusPS',
# 'surplus_reserve_fund_ps_latest', # 'SurplusReservePS',
# 'undivided_pro_fit_ps_latest', # 'UndividedProfitPS',
# 'retained_earnings_ps_latest', # 'RetainedEarningsPS',
# 'oper_cash_flow_ps_ttm', # 'OptCFPSTTM',
# 'cash_flow_ps_ttm', # 'CFPSTTM',
# 'enterprise_fcfps_latest', # 'EnterpriseFCFPS',
# 'shareholder_fcfps_latest']] # 'ShareholderFCFPS']]
factor_share_indicators = factor_share_indicators[['symbol', factor_share_indicators = factor_share_indicators[['symbol',
'eps_latest', 'EPS',
'diluted_eps_ttm', 'DilutedEPSTTM',
'cash_equivalent_ps_latest', 'CashEquPS',
'dividend_ps_latest', 'DivPS',
'eps_ttm', 'EPSTTM',
'net_asset_ps_latest', 'NetAssetPS',
'tor_ps_latest', 'TotalRevPS',
'tor_ps_ttm', 'TotalRevPSTTM',
'operating_revenue_ps_ttm', 'OptRevPSTTM',
'operating_revenue_ps_latest', 'OptRevPS',
'operating_profit_ps_ttm', 'OptProfitPSTTM',
'operating_profit_ps_latest', 'OptProfitPS',
'capital_surplus_fund_ps_latest', 'CapticalSurplusPS',
'surplus_reserve_fund_ps_latest', 'SurplusReservePS',
'undivided_pro_fit_ps_latest', 'UndividedProfitPS',
'retained_earnings_ps_latest', 'RetainedEarningsPS',
'oper_cash_flow_ps_ttm', 'OptCFPSTTM',
'cash_flow_ps_ttm']] 'CFPSTTM']]
factor_share_indicators['id'] = factor_share_indicators['symbol'] + str(trade_date) factor_share_indicators['id'] = factor_share_indicators['symbol'] + str(trade_date)
factor_share_indicators['trade_date'] = str(trade_date) factor_share_indicators['trade_date'] = str(trade_date)
scale._storage_data(factor_share_indicators, trade_date) per_share._storage_data(factor_share_indicators, trade_date)
def do_update(self, start_date, end_date, count): def do_update(self, start_date, end_date, count):
...@@ -529,11 +525,11 @@ def do_update(self, start_date, end_date, count): ...@@ -529,11 +525,11 @@ def do_update(self, start_date, end_date, count):
# @app.task() # @app.task()
def factor_calculate(**kwargs): def factor_calculate(**kwargs):
print("scale_kwargs: {}".format(kwargs)) print("per_share_kwargs: {}".format(kwargs))
date_index = kwargs['date_index'] date_index = kwargs['date_index']
session = kwargs['session'] session = kwargs['session']
scale = PerShareIndicators('factor_scale') # 注意, 这里的name要与client中新建table时的name一致, 不然回报错 per_share = PerShareIndicators('factor_per_share') # 注意, 这里的name要与client中新建table时的name一致, 不然回报错
content = cache_data.get_cache(session, date_index) content = cache_data.get_cache(session, date_index)
total_growth_data = json_normalize(json.loads(str(content, encoding='utf8'))) total_growth_data = json_normalize(json.loads(str(content, encoding='utf8')))
print("len_total_growth_data {}".format(len(total_growth_data))) print("len_total_per_share_data {}".format(len(total_growth_data)))
calculate(date_index, total_growth_data, scale) calculate(date_index, total_growth_data, per_share)
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