def degm(ttm_earning, ttm_earning_p1y, factor_earning):
"""
毛利率增长，与去年同期相比
:param ttm_earning_p1y:
:param ttm_earning:
:param factor_earning:
:return:
"""
columns_list = ['operating_revenue', 'operating_cost']
earning = ttm_earning.loc[:, columns_list]
earning_p1y = ttm_earning_p1y.loc[:, columns_list]
earning['gross_income_ratio'] = np.where(
CalcTools.is_zero(earning.operating_revenue.values), 0,
(earning.operating_revenue.values - earning.operating_cost.values)
/ earning.operating_revenue.values)
earning_p1y['gross_income_ratio'] = np.where(
CalcTools.is_zero(earning_p1y.operating_revenue.values), 0,
(earning_p1y.operating_revenue.values -
earning_p1y.operating_cost.values) /
earning_p1y.operating_revenue.values)
earning["degm"] = earning["gross_income_ratio"] - earning_p1y[
"gross_income_ratio"]
columns_list.append('gross_income_ratio')
earning = earning.drop(columns_list, axis=1)
factor_earning = pd.merge(factor_earning, earning, on="symbol")
return factor_earning
def oper_cash_in_to_asset_ttm(ttm_cash_flow, factor_cash_flow):
columns_list = ['net_operate_cash_flow', 'total_assets']
cash_flow = ttm_cash_flow.loc[:, columns_list]
cash_flow['OptToAssertTTM'] = np.where(
CalcTools.is_zero(cash_flow.total_assets.values), 0,
cash_flow.net_operate_cash_flow.values /
cash_flow.total_assets.values)
cash_flow = cash_flow.drop(columns_list, axis=1)
factor_cash_flow = pd.merge(factor_cash_flow, cash_flow, on="symbol")
return factor_cash_flow
def nocf_to_net_debt_ttm(ttm_cash_flow, factor_cash_flow):
columns_list = ['net_operate_cash_flow', 'net_liability']
cash_flow = ttm_cash_flow.loc[:, columns_list]
cash_flow['OptCFToNetDebtTTM'] = np.where(
CalcTools.is_zero(cash_flow.net_liability.values), 0,
cash_flow.net_operate_cash_flow.values /
cash_flow.net_liability.values)
cash_flow = cash_flow.drop(columns_list, axis=1)
factor_cash_flow = pd.merge(factor_cash_flow, cash_flow, on="symbol")
return factor_cash_flow
def cash_rate_of_sales_latest(tp_cash_flow, factor_cash_flow):
columns_list = ['net_operate_cash_flow', 'operating_revenue']
cash_flow = tp_cash_flow.loc[:, columns_list]
cash_flow['CashOfSales'] = np.where(
CalcTools.is_zero(cash_flow.operating_revenue.values), 0,
cash_flow.net_operate_cash_flow.values /
cash_flow.operating_revenue.values)
cash_flow = cash_flow.drop(columns_list, axis=1)
factor_cash_flow = pd.merge(factor_cash_flow, cash_flow, on="symbol")
return factor_cash_flow
def net_profit_cash_cover_ttm(ttm_cash_flow, factor_cash_flow):
columns_list = ['net_operate_cash_flow', 'np_parent_company_owners']
cash_flow = ttm_cash_flow.loc[:, columns_list]
cash_flow['NetProCashCoverTTM'] = np.where(
CalcTools.is_zero(cash_flow.np_parent_company_owners.values), 0,
cash_flow.net_operate_cash_flow.values /
cash_flow.np_parent_company_owners.values)
cash_flow = cash_flow.drop(columns_list, axis=1)
factor_cash_flow = pd.merge(factor_cash_flow, cash_flow, on="symbol")
return factor_cash_flow
def acca_ttm(ttm_cash_flow, factor_cash_flow):
columns_list = ['net_operate_cash_flow', 'net_profit', 'total_assets']
cash_flow = ttm_cash_flow.loc[:, columns_list]
cash_flow['OptOnReToAssetTTM'] = np.where(
CalcTools.is_zero(cash_flow.total_assets.values), 0,
(cash_flow.net_operate_cash_flow.values -
cash_flow.net_profit.values) / (cash_flow.total_assets.values))
cash_flow = cash_flow.drop(columns_list, axis=1)
factor_cash_flow = pd.merge(factor_cash_flow, cash_flow, on="symbol")
return factor_cash_flow
def cash_to_current_liability_ttm(ttm_cash_flow, factor_cash_flow):
columns_list = ['cash_and_equivalents_at_end', 'total_current_assets']
cash_flow = ttm_cash_flow.loc[:, columns_list]
cash_flow['CashRatioTTM'] = np.where(
CalcTools.is_zero(cash_flow.total_current_assets.values), 0,
cash_flow.cash_and_equivalents_at_end.values /
cash_flow.total_current_assets.values)
cash_flow = cash_flow.drop(columns_list, axis=1)
factor_cash_flow = pd.merge(factor_cash_flow, cash_flow, on="symbol")
return factor_cash_flow
def oper_cash_in_to_current_liability_ttm(ttm_cash_flow, factor_cash_flow):
columns_list = ['net_operate_cash_flow', 'total_current_liability']
cash_flow = ttm_cash_flow.loc[:, columns_list]
cash_flow['OptCFToCurrLiabilityTTM'] = np.where(
CalcTools.is_zero(cash_flow.total_current_liability.values), 0,
cash_flow.net_operate_cash_flow.values /
cash_flow.total_current_liability.values)
cash_flow = cash_flow.drop(columns_list, axis=1)
factor_cash_flow = pd.merge(factor_cash_flow, cash_flow, on="symbol")
return factor_cash_flow
def debts_asset_ratio_latest(tp_contrarian, factor_contrarian):
columns_list = ['total_liability', 'total_assets']
contrarian = tp_contrarian.loc[:, columns_list]
contrarian['DA'] = np.where(
CalcTools.is_zero(contrarian['total_assets']), 0,
contrarian['total_liability'] / contrarian['total_assets'])
contrarian = contrarian.drop(columns_list, axis=1)
factor_contrarian = pd.merge(factor_contrarian,
contrarian,
on="symbol")
return factor_contrarian
def sales_cost_ratio_ttm(tp_contrarian, factor_contrarian):
columns_list = ['operating_cost', 'operating_revenue']
contrarian = tp_contrarian.loc[:, columns_list]
contrarian['SalesCostTTM'] = np.where(
CalcTools.is_zero(contrarian['operating_revenue']), 0,
contrarian['operating_cost'] / contrarian['operating_revenue'])
contrarian = contrarian.drop(columns_list, axis=1)
factor_contrarian = pd.merge(factor_contrarian,
contrarian,
on="symbol")
return factor_contrarian
def sale_service_cash_to_or_ttm(ttm_cash_flow, factor_cash_flow):
columns_list = [
'goods_sale_and_service_render_cash', 'operating_revenue'
]
cash_flow = ttm_cash_flow.loc[:, columns_list]
cash_flow['SaleServCashToOptReTTM'] = np.where(
CalcTools.is_zero(cash_flow.operating_revenue.values), 0,
cash_flow.goods_sale_and_service_render_cash.values /
cash_flow.operating_revenue.values)
cash_flow = cash_flow.drop(columns_list, axis=1)
factor_cash_flow = pd.merge(factor_cash_flow, cash_flow, on="symbol")
return factor_cash_flow
def financial_expense_rate_ttm(tp_contrarian, factor_contrarian):
columns_list = ['financial_expense', 'total_operating_cost']
contrarian = tp_contrarian.loc[:, columns_list]
contrarian['FinExpTTM'] = np.where(
CalcTools.is_zero(contrarian['total_operating_cost']), 0,
contrarian['financial_expense'] /
contrarian['total_operating_cost'])
contrarian = contrarian.drop(columns_list, axis=1)
factor_contrarian = pd.merge(factor_contrarian,
contrarian,
on="symbol")
return factor_contrarian
def tax_ratio_ttm(tp_contrarian, factor_contrarian):
columns_list = ['operating_tax_surcharges', 'operating_revenue']
contrarian = tp_contrarian.loc[:, columns_list]
contrarian['TaxRTTM'] = np.where(
CalcTools.is_zero(contrarian['operating_revenue']), 0,
contrarian['operating_tax_surcharges'] /
contrarian['operating_revenue'])
contrarian = contrarian.drop(columns_list, axis=1)
factor_contrarian = pd.merge(factor_contrarian,
contrarian,
on="symbol")
return factor_contrarian
def cfo_to_ev_ttm(ttm_cash_flow, factor_cash_flow):
columns_list = [
'net_operate_cash_flow', 'longterm_loan', 'shortterm_loan',
'market_cap', 'cash_and_equivalents_at_end'
]
cash_flow = ttm_cash_flow.loc[:, columns_list]
cash_flow['OptToEnterpriseTTM'] = np.where(CalcTools.is_zero(
cash_flow.longterm_loan.values + cash_flow.shortterm_loan.values + \
cash_flow.market_cap.values - cash_flow.cash_and_equivalents_at_end.values), 0,
cash_flow.net_operate_cash_flow.values / (cash_flow.longterm_loan.values + cash_flow.shortterm_loan.values + \
cash_flow.market_cap.values - cash_flow.cash_and_equivalents_at_end.values))
cash_flow = cash_flow.drop(columns_list, axis=1)
factor_cash_flow = pd.merge(factor_cash_flow, cash_flow, on="symbol")
return factor_cash_flow
def nocf_to_operating_ni_latest(tp_cash_flow, factor_cash_flow):
columns_list = [
'net_operate_cash_flow', 'total_operating_revenue',
'total_operating_cost'
]
cash_flow = tp_cash_flow.loc[:, columns_list]
cash_flow['NOCFToOpt'] = np.where(
CalcTools.is_zero((cash_flow.total_operating_revenue.values -
cash_flow.total_operating_cost.values)), 0,
cash_flow.net_operate_cash_flow.values /
(cash_flow.total_operating_revenue.values -
cash_flow.total_operating_cost.values))
cash_flow = cash_flow.drop(columns_list, axis=1)
factor_cash_flow = pd.merge(factor_cash_flow, cash_flow, on="symbol")
return factor_cash_flow
def npcut_to_np(tp_earning, factor_earning):
"""
扣除非经常损益后的净利润/净利润
:param tp_earning:
:param factor_earning:
:return:
"""
columns_list = ['adjusted_profit', 'net_profit']
earning = tp_earning.loc[:, columns_list]
earning['npcut_to_np'] = np.where(
CalcTools.is_zero(earning.net_profit.values), 0,
earning.adjusted_profit.values / earning.net_profit.values)
earning = earning.drop(columns_list, axis=1)
factor_earning = pd.merge(factor_earning, earning, on="symbol")
return factor_earning
def roe_cut(tp_earning, factor_earning):
"""
:param tp_earning:
:param factor_earning:
:return:
"""
columns_list = ['adjusted_profit', 'equities_parent_company_owners']
earning = tp_earning.loc[:, columns_list]
earning['roe_cut'] = np.where(
CalcTools.is_zero(earning.equities_parent_company_owners.values),
0, earning.adjusted_profit.values /
earning.equities_parent_company_owners.values / 4)
earning = earning.drop(columns_list, axis=1)
factor_earning = pd.merge(factor_earning, earning, on="symbol")
return factor_earning
def operating_profit_ratio(ttm_earning, factor_earning):
"""
营业净利率
营业净利率=营业利润/营业收入
:param ttm_earning:
:param factor_earning:
:return:
"""
columns_list = ['operating_profit', 'operating_revenue']
earning = ttm_earning.loc[:, columns_list]
earning['operating_profit_ratio'] = np.where(
CalcTools.is_zero(earning.operating_revenue.values), 0,
earning.operating_profit.values / earning.operating_revenue.values)
earning = earning.drop(columns_list, axis=1)
factor_earning = pd.merge(factor_earning, earning, on="symbol")
return factor_earning
def np_to_tor(ttm_earning, factor_earning):
"""
净利润与营业总收入之比
净利润与营业总收入之比=净利润/营业总收入
:param ttm_earning:
:param factor_earning:
:return:
"""
columns_list = ['net_profit', 'total_operating_revenue']
earning = ttm_earning.loc[:, columns_list]
earning['np_to_tor'] = np.where(
CalcTools.is_zero(earning.total_operating_revenue.values), 0,
earning.net_profit.values / earning.total_operating_revenue.values)
earning = earning.drop(columns_list, axis=1)
factor_earning = pd.merge(factor_earning, earning, on="symbol")
return factor_earning
def roe5(ttm_earning_5y, factor_earning):
"""
5年权益回报率
:param ttm_earning_5y:
:param factor_earning:
:return:
"""
columns_list = ['net_profit', 'total_owner_equities']
earning = ttm_earning_5y.loc[:, columns_list]
earning['roe5'] = np.where(
CalcTools.is_zero(earning.total_owner_equities.values), 0,
earning.net_profit.values / earning.total_owner_equities.values /
4)
earning = earning.drop(columns_list, axis=1)
factor_earning = pd.merge(factor_earning, earning, on="symbol")
return factor_earning
def invest_r_associates_to_tp_latest(tp_earning, factor_earning):
"""
对联营和营公司投资收益/利润总额
:param tp_earning:
:param factor_earning:
:return:
"""
columns_list = ['invest_income_associates', 'total_profit']
earning = tp_earning.loc[:, columns_list]
earning['invest_r_associates_to_tp_latest'] = np.where(
CalcTools.is_zero(earning.total_profit.values), 0,
earning.invest_income_associates.values /
earning.total_profit.values)
earning = earning.drop(columns_list, axis=1)
factor_earning = pd.merge(factor_earning, earning, on="symbol")
return factor_earning
def roa(ttm_earning, factor_earning):
"""
资产回报率
资产回报率=净利润/总资产
:param ttm_earning:
:param factor_earning:
:return:
"""
columns_list = ['net_profit', 'total_assets']
earning = ttm_earning.loc[:, columns_list]
earning['roa'] = np.where(
CalcTools.is_zero(earning.total_assets.values), 0,
earning.net_profit.values / earning.total_assets.values)
earning = earning.drop(columns_list, axis=1)
factor_earning = pd.merge(factor_earning, earning, on="symbol")
return factor_earning
def debt_tangible_equity_ratio_latest(tp_contrarian, factor_contrarian):
columns_list = [
'total_liability', 'total_current_liability', 'fixed_assets'
]
contrarian = tp_contrarian.loc[:, columns_list]
contrarian['DTE'] = np.where(
CalcTools.is_zero(contrarian['total_current_liability'] +
contrarian['fixed_assets']), 0,
contrarian['total_current_liability'] /
(contrarian['total_current_liability'] +
contrarian['fixed_assets']))
contrarian = contrarian.drop(columns_list, axis=1)
factor_contrarian = pd.merge(factor_contrarian,
contrarian,
on="symbol")
return factor_contrarian
def period_costs_rate_ttm(tp_contrarian, factor_contrarian):
columns_list = [
'financial_expense', 'sale_expense', 'administration_expense',
'operating_revenue'
]
contrarian = tp_contrarian.loc[:, columns_list]
contrarian['PeridCostTTM'] = np.where(
CalcTools.is_zero(contrarian['operating_revenue']), 0,
(contrarian['financial_expense'] + contrarian['sale_expense'] + contrarian['administration_expense']) / \
contrarian['operating_revenue']
)
contrarian = contrarian.drop(columns_list, axis=1)
factor_contrarian = pd.merge(factor_contrarian,
contrarian,
on="symbol")
return factor_contrarian
def gross_income_ratio(ttm_earning, factor_earning):
"""
销售毛利率
销售毛利率=（营业收入-营业成本）/营业收入
:param ttm_earning:
:param factor_earning:
:return:
"""
columns_list = ['operating_revenue', 'operating_cost']
earning = ttm_earning.loc[:, columns_list]
earning['gross_income_ratio'] = np.where(
CalcTools.is_zero(earning.operating_revenue.values), 0,
(earning.operating_revenue.values - earning.operating_cost.values)
/ earning.operating_revenue.values)
earning = earning.drop(columns_list, axis=1)
factor_earning = pd.merge(factor_earning, earning, on="symbol")
return factor_earning
def cetop(tp_historical_value, factor_historical_value):
"""
# 经营活动产生的现金流量净额与市值比
:param tp_historical_value:
:param factor_historical_value:
:return:
"""
columns_lists = ['symbol', 'net_operate_cash_flow', 'market_cap']
historical_value = tp_historical_value.loc[:, columns_lists]
historical_value['CEToPTTM'] = np.where(CalcTools.is_zero(historical_value['market_cap']), 0,
historical_value['net_operate_cash_flow'] /
historical_value['market_cap'])
historical_value = historical_value.drop(columns=['net_operate_cash_flow', 'market_cap'], axis=1)
factor_historical_value = pd.merge(factor_historical_value, historical_value, on="symbol")
return factor_historical_value
def peg_5y(tp_historical_value, factor_historical_value):
"""
# 市盈率/归属于母公司所有者净利润 5 年复合增长率
:param tp_historical_value:
:param factor_historical_value:
:return:
"""
columns_lists = ['symbol', 'pe', 'np_parent_company_owners', 'np_parent_company_owners_5']
historical_value = tp_historical_value.loc[:, columns_lists]
tmp = np.where(CalcTools.is_zero(historical_value['np_parent_company_owners_5']), 0,
(historical_value['np_parent_company_owners'] / historical_value['np_parent_company_owners_5']))
historical_value['PEG5YChgTTM'] = tmp / abs(tmp) * pow(abs(tmp), 1 / 5.0) - 1
historical_value = historical_value.drop(
columns=['pe', 'np_parent_company_owners', 'np_parent_company_owners_5'], axis=1)
factor_historical_value = pd.merge(factor_historical_value, historical_value, on="symbol")
return factor_historical_value
def net_non_oi_to_tp_latest(tp_earning, factor_earning):
"""
营业外收支净额/利润总额
:param tp_earning:
:param factor_earning:
:return:
"""
columns_list = [
'total_profit', 'non_operating_revenue', 'non_operating_expense'
]
earning = tp_earning.loc[:, columns_list]
earning['net_non_oi_to_tp_latest'] = np.where(
CalcTools.is_zero(earning.total_profit.values), 0,
(earning.non_operating_revenue.values +
earning.non_operating_expense.values) /
earning.total_profit.values)
earning = earning.drop(columns_list, axis=1)
factor_earning = pd.merge(factor_earning, earning, on="symbol")
return factor_earning
def inte_bear_debt_to_total_capital_latest(tp_contrarian,
factor_contrarian):
columns_list = [
'interest_bearing_liability', 'fixed_assets',
'total_current_assets', 'total_current_liability'
]
contrarian = tp_contrarian.loc[:, columns_list]
contrarian['IntBDToCap'] = np.where(
CalcTools.is_zero(contrarian['fixed_assets'] + contrarian['total_current_assets'] + \
contrarian['total_current_liability']), 0,
contrarian['interest_bearing_liability'] / (contrarian['fixed_assets'] + \
contrarian['total_current_assets'] + contrarian[
'total_current_liability'])
)
contrarian = contrarian.drop(columns_list, axis=1)
factor_contrarian = pd.merge(factor_contrarian,
contrarian,
on="symbol")
return factor_contrarian
def etop(tp_historical_value, factor_historical_value):
"""
收益市值比
# 收益市值比= 净利润/总市值
:param tp_historical_value:
:param factor_historical_value:
:return:
"""
columns_lists = ['symbol', 'net_profit', 'market_cap']
historical_value = tp_historical_value.loc[:, columns_lists]
historical_value['EarnToPrice'] = np.where(CalcTools.is_zero(historical_value['market_cap']),
0,
historical_value['net_profit'] /
historical_value['market_cap'])
historical_value = historical_value.drop(columns=['net_profit', 'market_cap'], axis=1)
factor_historical_value = pd.merge(factor_historical_value, historical_value, on="symbol")
return factor_historical_value