def Value011(cls,
data: pd.DataFrame,
net_asset_ex: str = FBSN.Net_Asset_Ex.value,
total_mv: str = PVN.TOTAL_MV.value,
switch: bool = False) -> DataInfo:
"""
市净率倒数(TTM)(BP_TTM)：市净率的倒数
:param data:
:param net_asset_ex:
:param total_mv:
:param switch:
:return:
"""
func_name = sys._getframe().f_code.co_name
data.set_index([KN.TRADE_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
data[func_name] = data[net_asset_ex] / data[total_mv]
data_fact = data[func_name].copy(deep=True)
data = data.reset_index()
F = DataInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.data_type = data['type'][0]
F.data_category = cls().__class__.__name__
F.data_name = func_name
return F
def Momentum012(cls,
data: pd.DataFrame,
price: str = PVN.CLOSE.value,
n: int = 20):
"""
路径动量因子(MTM_PathLen)
"""
factor_name = sys._getframe().f_code.co_name + f'_{n}days'
data.set_index([KN.TRADE_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
data['stand'] = data[price].groupby(KN.STOCK_ID.value,
group_keys=False).apply(
lambda x: (x - x.rolling(n, min_periods=1).mean()) / x.rolling(n, min_periods=1).std(ddof=1))
data['stand'][np.isinf(data['stand'])] = np.nan
data['diff'] = data['stand'].groupby(KN.STOCK_ID.value).diff(1).abs()
data[factor_name] = data['diff'].groupby(KN.STOCK_ID.value,
group_keys=False).rolling(n, min_periods=1).sum()
F = DataInfo()
F.data = data[factor_name]
F.data_type = 'MTM'
F.data_category = cls().__class__.__name__
F.data_name = factor_name
return F
def Momentum017(cls,
data: pd.DataFrame,
open_price: str = PVN.OPEN.value,
close_price: str = PVN.CLOSE.value,
n: int = 1) -> DataInfo:
"""
动量OTC收益率均值(MTM_OTC):N日开盘价与收盘价收益率均值
:return:
"""
factor_name = sys._getframe().f_code.co_name + f'_{n}days'
data.set_index([KN.TRADE_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
data['return'] = data.groupby(KN.STOCK_ID.value,
group_keys=False).apply(
lambda x: (x[open_price] / x[close_price].shift(1) - 1).shift(-1))
data[factor_name] = data['return'].groupby(KN.STOCK_ID.value,
group_keys=False).rolling(n, min_periods=1).mean()
F = DataInfo()
F.data = data[factor_name]
F.data_type = 'MTM'
F.data_category = cls().__class__.__name__
F.data_name = factor_name
return F
def Value014(cls,
data: pd.DataFrame,
free_cash_flow: str = FCFSN.Free_Cash_Flow.value,
total_mv: str = PVN.TOTAL_MV.value,
switch: bool = False):
"""
市现率倒数(自由现金流，TTM)(FCFP_TTM)：市现率倒数（自由现金流）
:param data:
:param free_cash_flow:
:param total_mv:
:param switch:
:return:
"""
func_name = sys._getframe().f_code.co_name
data.set_index([KN.TRADE_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
data[func_name] = data[free_cash_flow] / data[total_mv]
data_fact = data[func_name].copy(deep=True)
data = data.reset_index()
F = DataInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.data_type = data['type'][0]
F.data_category = cls().__class__.__name__
F.data_name = func_name
return F
def Value004(cls,
data: pd.DataFrame,
operator_income: str = FISN.Op_Income.value,
total_mv: str = PVN.TOTAL_MV.value,
switch: bool = False):
"""
市销率倒数(TTM)(SP_TTM)：市销率倒数
:param data:
:param operator_income:
:param total_mv:
:param switch:
:return:
"""
func_name = sys._getframe().f_code.co_name
data.set_index([KN.TRADE_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
data[func_name] = data[operator_income] / data[total_mv]
data_fact = data[func_name].copy(deep=True)
data = data.reset_index()
F = DataInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.data_type = data['type'][0]
F.data_category = cls().__class__.__name__
F.data_name = func_name
return F
def Value009(cls,
data: pd.DataFrame,
operator_income: str = FISN.Op_Income.value,
total_mv: str = PVN.TOTAL_MV.value,
switch: bool = False):
"""
企业价值倍数倒数(最新财报，扣除现金)(EV2EBITDA_LR)：企业价值（扣除现金）/息税折旧摊销前利润
企业价值 = 总市值 + 负债总计 - 无息负债 - 货币资金
:param data:
:param operator_income:
:param total_mv:
:param switch:
:return:
"""
func_name = sys._getframe().f_code.co_name
data.set_index([KN.TRADE_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
data[func_name] = data[operator_income] / data[total_mv]
data_fact = data[func_name].copy(deep=True)
data = data.reset_index()
F = DataInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.data_type = data['type'][0]
F.data_category = cls().__class__.__name__
F.data_name = func_name
return F
def Value008(cls,
data: pd.DataFrame,
Surplus_Reserves: str = FBSN.Surplus_Reserves.value,
Undistributed_Profit: str = FBSN.Undistributed_Profit.value,
total_mv: str = PVN.TOTAL_MV.value,
switch: bool = False):
"""
股息率倒数(DP_TTM)：股息率（近12个月现金红利和）
股息 = 期末留存收益 - 期初留存收益
留存收益 = 盈余公积 + 未分配利润
"""
func_name = sys._getframe().f_code.co_name
data.set_index([KN.TRADE_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
data["RE"] = data[Surplus_Reserves] + data[Undistributed_Profit]
data[func_name] = data["RE"] / data[total_mv]
data_fact = data[func_name].copy(deep=True)
data = data.reset_index()
F = DataInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.data_type = data['type'][0]
F.data_category = cls().__class__.__name__
F.data_name = func_name
return F
def Profit013(cls,
data: pd.DataFrame,
net_profit_in: str = FISN.Net_Pro_In.value,
total_asset: str = FBSN.Total_Asset.value,
switch: bool = False):
"""
总资产净利率(TTM)(ROA_TTM)
"""
func_name = sys._getframe().f_code.co_name
data.set_index([SN.REPORT_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
data[func_name] = data[net_profit_in] / data[total_asset]
data[func_name][np.isinf(data[func_name])] = np.nan
if switch:
data_fact = cls()._switch_freq(data_=data, name=func_name)
else:
data_fact = None
data.reset_index(inplace=True)
F = DataInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.data_type = data['type'][0]
F.data_category = cls().__class__.__name__
F.data_name = func_name
return F
def Value002(cls,
data: pd.DataFrame,
net_profit_cut: str = FISN.Net_Pro_Cut.value,
total_mv: str = PVN.TOTAL_MV.value,
switch: bool = False):
"""
扣非市盈率倒数(EP_cut_TTM): 市盈率（扣除非经常性损益）倒数
:param data:
:param net_profit_cut:
:param total_mv:
:param switch:
:return:
"""
func_name = sys._getframe().f_code.co_name
data.set_index([KN.TRADE_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
data[func_name] = data[net_profit_cut] / data[total_mv]
data_fact = data[func_name].copy(deep=True)
data = data.reset_index()
F = DataInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.data_type = data['type'][0]
F.data_category = cls().__class__.__name__
F.data_name = func_name
return F
def Profit026(cls,
data: pd.DataFrame,
net_profit_in: str = FISN.Net_Pro_In.value,
operator_income: str = FISN.Op_Income.value,
switch: bool = False):
"""
当期净利润率(NP)
"""
func_name = sys._getframe().f_code.co_name
data.set_index([SN.REPORT_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
data[func_name] = data[net_profit_in] / data[operator_income]
data[np.isinf(data[func_name])] = 0
if switch:
data_fact = cls()._switch_freq(data_=data, name=func_name)
else:
data_fact = None
data = data.reset_index()
F = DataInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.data_type = data['type'][0]
F.data_category = cls().__class__.__name__
F.data_name = func_name
return F
def Momentum009(cls,
data: pd.DataFrame,
close_price: str = PVN.CLOSE.value,
bm_price: str = 'index_close',
n: int = 20):
"""
市场alpha因子
"""
factor_name = sys._getframe().f_code.co_name + f'_{n}days'
data.set_index([KN.TRADE_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
# ret
data = data.groupby(KN.STOCK_ID.value).pct_change().dropna()
data_new = data.groupby(KN.STOCK_ID.value,
group_keys=False).apply(
lambda x: cls._reg_rolling(x, bm_price, close_price, False, True, n))
data_new.name = factor_name
F = DataInfo()
F.data = data_new
F.data_type = 'MTM'
F.data_category = cls().__class__.__name__
F.data_name = factor_name
return F
def Momentum025(cls,
data: pd.DataFrame,
high_price: str = PVN.HIGH.value,
close_price: str = PVN.CLOSE.value,
n: int = 1) -> DataInfo:
"""
动量CTH收益率绝对值均值(MTM_CTH_abs):N日收盘价与最高价收益率绝对值均值
:return:
"""
factor_name = sys._getframe().f_code.co_name + f'_{n}days'
data.set_index([KN.TRADE_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
data['return'] = data.groupby(KN.STOCK_ID.value,
group_keys=False).apply(lambda x: abs(x[close_price] / x[high_price] - 1))
data[factor_name] = data['return'].groupby(KN.STOCK_ID.value,
group_keys=False).rolling(n, min_periods=1).mean()
F = DataInfo()
F.data = data[factor_name]
F.data_type = 'MTM'
F.data_category = cls().__class__.__name__
F.data_name = factor_name
return F
def Solvency017(cls,
data: pd.DataFrame,
total_asset: str = FBSN.Total_Asset.value,
currency: str = FBSN.Currency.value,
tradable_asset: str = FBSN.Tradable_Asset.value,
op_net_cash_flow: str = FCFSN.Op_Net_CF.value,
short_borrow: str = FBSN.ST_Borrow.value,
short_bond_payable: str = FBSN.ST_Bond_Payable.value,
short_iliq_liability_1y: str = FBSN.ST_IL_LB_1Y.value,
quarter: int = 8,
switch: bool = False):
"""
短期偿债能力指标1(ShortDebt2_CFPA_std)：（现金及现金等价物 + TTM经营性现金流 - 短期有息负债）/ 总资产
现金及现金等价物 = 货币资金 + 交易性金融资产
经营性现金流 = 经营性现金流量净额
短期有息负债 = 短期借款 + 短期应付债券 + 一年内到期的非流动负债
:param data:
:param total_asset:
:param currency:
:param tradable_asset:
:param op_net_cash_flow:
:param short_borrow:
:param short_bond_payable:
:param short_iliq_liability_1y:
:param quarter:
:param switch:
:return:
"""
func_name = sys._getframe().f_code.co_name
data.set_index([SN.REPORT_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
x1 = data[[currency, tradable_asset, op_net_cash_flow]].sum(skipna=True, axis=1)
x2 = data[[short_borrow, short_bond_payable, short_iliq_liability_1y]].sum(skipna=True, axis=1)
y = data[total_asset]
# 短期偿债能力指标
ShortDebt2_CFPA = (x1 - x2) / y
data[func_name] = ShortDebt2_CFPA.groupby(KN.STOCK_ID.value).apply(lambda x: - x.rolling(quarter).std())
# switch inf to Nan
data[func_name][np.isinf(data[func_name])] = np.nan
if switch:
data_fact = cls()._switch_freq(data_=data, name=func_name, limit=120)
else:
data_fact = None
data = data.reset_index()
F = DataInfo()
F.data_raw = data[[SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value, func_name]]
F.data = data_fact
F.data_type = data['type'][0]
F.data_category = cls().__class__.__name__
F.data_name = func_name
return F
def Solvency010(cls,
data: pd.DataFrame,
currency: str = FBSN.Currency.value,
tradable_asset: str = FBSN.Tradable_Asset.value,
op_net_cash_flow: str = FCFSN.Op_Net_CF.value,
short_borrow: str = FBSN.ST_Borrow.value,
short_bond_payable: str = FBSN.ST_Bond_Payable.value,
short_iliq_liability_1y: str = FBSN.ST_IL_LB_1Y.value,
switch: bool = False) -> DataInfo:
"""
短期偿债能力指标1(ShortDebt1_CFPA_qoq)：（现金及现金等价物 + TTM经营性现金流）/短期有息负债
现金及现金等价物 = 货币资金 + 交易性金融资产
经营性现金流 = 经营性现金流量净额
短期有息负债 = 短期借款 + 短期应付债券 + 一年内到期的非流动负债
:param data:
:param currency:
:param tradable_asset:
:param op_net_cash_flow:
:param short_borrow:
:param short_bond_payable:
:param short_iliq_liability_1y:
:param switch:
:return:
"""
func_name = sys._getframe().f_code.co_name
data.set_index([SN.REPORT_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
# 短期偿债能力指标
ShortDebt1_CFPA = data[[currency, tradable_asset, op_net_cash_flow]].sum(skipna=True,
axis=1) / \
data[[short_borrow, short_bond_payable, short_iliq_liability_1y]].sum(skipna=True,
axis=1)
# switch inf to Nan
ShortDebt1_CFPA[np.isinf(ShortDebt1_CFPA)] = np.nan
data[func_name] = ShortDebt1_CFPA.groupby(KN.STOCK_ID.value).apply(lambda x: x.diff(1) / abs(x.shift(1)))
if switch:
data_fact = cls()._switch_freq(data_=data, name=func_name)
else:
data_fact = None
data = data.reset_index()
F = DataInfo()
F.data_raw = data[[SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value, func_name]]
F.data = data_fact
F.data_type = data['type'][0]
F.data_category = cls().__class__.__name__
F.data_name = func_name
return F
def Solvency012(cls,
data: pd.DataFrame,
total_asset: str = FBSN.Total_Asset.value,
currency: str = FBSN.Currency.value,
tradable_asset: str = FBSN.Tradable_Asset.value,
short_borrow: str = FBSN.ST_Borrow.value,
short_bond_payable: str = FBSN.ST_Bond_Payable.value,
short_iliq_liability_1y: str = FBSN.ST_IL_LB_1Y.value,
switch: bool = False):
"""
短期偿债能力指标3(ShortDebt3_CFPA_qoq)：（现金及现金等价物 - 短期有息负债）/ 总资产
现金及现金等价物 = 货币资金 + 交易性金融资产
短期有息负债 = 短期借款 + 短期应付债券 + 一年内到期的非流动负债
:param data:
:param total_asset:
:param currency:
:param tradable_asset:
:param short_borrow:
:param short_bond_payable:
:param short_iliq_liability_1y:
:param switch:
:return:
"""
func_name = sys._getframe().f_code.co_name
data.set_index([SN.REPORT_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
x1 = data[[currency, tradable_asset]].sum(skipna=True, axis=1)
x2 = data[[short_borrow, short_bond_payable, short_iliq_liability_1y]].sum(skipna=True, axis=1)
y = data[total_asset]
# 短期偿债能力指标
ShortDebt2_CFPA = (x1 - x2) / y
data[func_name] = ShortDebt2_CFPA.groupby(KN.STOCK_ID.value).apply(lambda x: x.diff(1) / abs(x.shift(1)))
# switch inf to Nan
data[func_name][np.isinf(data[func_name])] = np.nan
if switch:
data_fact = cls()._switch_freq(data_=data, name=func_name)
else:
data_fact = None
data = data.reset_index()
F = DataInfo()
F.data_raw = data[[SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value, func_name]]
F.data = data_fact
F.data_type = data['type'][0]
F.data_category = cls().__class__.__name__
F.data_name = func_name
return F
def Quality008(cls,
data: pd.DataFrame,
op_net_cash_flow: str = FCFSN.Op_Net_CF.value,
operator_profit: str = FISN.Op_Pro.value,
switch: bool = False):
"""
应计利润占比(APR) = 应计利润 / 营业利润
应计利润 = 营业利润 - 经营性现金流量净额
:param data:
:param op_net_cash_flow:
:param operator_profit:
:param switch:
:return:
"""
func_name = sys._getframe().f_code.co_name
data.set_index([SN.REPORT_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
# 缺失科目填补为0
data[op_net_cash_flow].fillna(0, inplace=True)
data[func_name] = (data[operator_profit] -
data[op_net_cash_flow]) / data[operator_profit]
data[func_name][np.isinf(data[func_name])] = np.nan
if switch:
data_fact = cls()._switch_freq(data_=data,
name=func_name,
limit=120)
else:
data_fact = None
data = data.reset_index()
F = DataInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.data_type = data['type'][0]
F.data_category = cls().__class__.__name__
F.data_name = func_name
return F
def Operate009(cls,
data: pd.DataFrame,
fixed_asset: str = FBSN.Fixed_Asset.value,
operator_total_cost: str = FISN.Op_Total_Cost.value,
quarter: int = 8,
switch: bool = False):
"""
产能利用率因子(OCFA)
"""
func_name = sys._getframe().f_code.co_name
data.set_index([SN.REPORT_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
# 回归取残差
data[func_name] = data[[fixed_asset, operator_total_cost]].groupby(
KN.STOCK_ID.value, group_keys=False).apply(
lambda x: cls._reg_rolling(x,
x_name=fixed_asset,
y_name=operator_total_cost,
has_cons=True,
win=quarter))
if switch:
data_fact = cls()._switch_freq(data_=data,
name=func_name,
limit=120)
else:
data_fact = None
data = data.reset_index()
F = DataInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.data_type = data['type'][0]
F.data_category = cls().__class__.__name__
F.data_name = func_name
return F
def Profit025(cls,
data: pd.DataFrame,
Surplus_Reserves: str = FBSN.Surplus_Reserves.value,
Undistributed_Profit: str = FBSN.Undistributed_Profit.value,
net_profit_in: str = FISN.Net_Pro_In.value,
switch: bool = False):
"""
股利支付率_TTM(DPR_TTM) = 每股股利/每股净利润 = （期末留存收益 - 期初留存收益） / 净利润
留存收益 = 盈余公积 + 未分配利润
:param data:
:param Surplus_Reserves:
:param Undistributed_Profit:
:param net_profit_in:
:param switch:
:return:
"""
func_name = sys._getframe().f_code.co_name
data.set_index([SN.REPORT_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
data["RE"] = data[Surplus_Reserves] + data[Undistributed_Profit]
data[func_name] = data['RE'] / data[net_profit_in]
data = data.reset_index()
if switch:
data_fact = cls()._switch_freq(data_=data, name=func_name)
else:
data_fact = None
data = data.reset_index()
F = DataInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.data_type = data['type'][0]
F.data_category = cls().__class__.__name__
F.data_name = func_name
return F
def Operate007(cls,
data: pd.DataFrame,
operator_income: str = FISN.Op_Income.value,
operator_cost: str = FISN.Op_Cost.value,
quarter: int = 8,
switch: bool = False):
"""
营业能力改善因子(RROC_N)
"""
func_name = sys._getframe().f_code.co_name
data.set_index([SN.REPORT_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
# 标准化
reg_input = data[[operator_income, operator_cost]].groupby(
KN.STOCK_ID.value).apply(lambda x: (x - x.mean()) / x.std())
# 回归取残差
data[func_name] = reg_input.groupby(
KN.STOCK_ID.value,
group_keys=False).apply(lambda x: cls._reg_rolling(
x, operator_cost, operator_income, has_cons=True, win=quarter))
if switch:
data_fact = cls()._switch_freq(data_=data,
name=func_name,
limit=120)
else:
data_fact = None
data = data.reset_index()
F = DataInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.data_type = data['type'][0]
F.data_category = cls().__class__.__name__
F.data_name = func_name
return F
def Solvency006(cls,
data: pd.DataFrame,
short_borrow: str = FBSN.ST_Borrow.value,
short_bond_payable: str = FBSN.ST_Bond_Payable.value,
long_borrow: str = FBSN.LT_Borrow.value,
total_asset: str = FBSN.Total_Asset.value,
switch: bool = False):
"""
有息负债(Int_to_Asset) = 短期借款 + 短期应付债券 + 长期借款
:param data:
:param short_borrow:
:param short_bond_payable:
:param long_borrow:
:param total_asset:
:param switch:
:return:
"""
func_name = sys._getframe().f_code.co_name
data.set_index([SN.REPORT_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
data[func_name] = data[[short_borrow, short_bond_payable, long_borrow]].sum(skipna=True,
axis=1) / data[total_asset]
data[func_name][np.isinf(data[func_name])] = np.nan
if switch:
data_fact = cls()._switch_freq(data_=data, name=func_name, limit=120)
else:
data_fact = None
data = data.reset_index()
F = DataInfo()
F.data_raw = data[[SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value, func_name]]
F.data = data_fact
F.data_type = data['type'][0]
F.data_category = cls().__class__.__name__
F.data_name = func_name
return F
def Profit032(cls,
data: pd.DataFrame,
net_profit_in: str = FISN.Net_Pro_In.value,
total_asset: str = FBSN.Total_Asset.value,
switch: bool = False):
"""
总资产净利率(TTM,同比)(ROA_ttm_T)
:param data:
:param net_profit_in:
:param total_asset:
:param switch:
:return:
"""
func_name = sys._getframe().f_code.co_name
data.set_index([SN.REPORT_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
data['ROA_Q'] = data.groupby(
KN.STOCK_ID.value, group_keys=False).apply(
lambda x: x[net_profit_in].diff(1) / x[total_asset].shift(1))
data['ROA_Q'][np.isinf(data['ROA_Q'])] = np.nan
data[func_name] = data['ROA_Q'].groupby(KN.STOCK_ID.value).diff(1)
if switch:
data_fact = cls()._switch_freq(data_=data, name=func_name)
else:
data_fact = None
data.reset_index(inplace=True)
F = DataInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.data_type = data['type'][0]
F.data_category = cls().__class__.__name__
F.data_name = func_name
return F
def Operate010(cls,
data: pd.DataFrame,
operator_income: str = FISN.Op_Income.value,
total_asset: str = FBSN.Total_Asset.value,
switch: bool = False):
"""
总资产周转率(同比)(TA_Turn_ttm_T) = 本期营业收入 / 本期平均资产总额 - 上期营业收入 / 上期平均资产总额
:return:
"""
func_name = sys._getframe().f_code.co_name
data.set_index([SN.REPORT_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
data[total_asset] = data[total_asset].groupby(
KN.STOCK_ID.value, group_keys=False).rolling(2,
min_periods=1).mean()
data["TA_turn_ttm"] = data[operator_income] / data[total_asset]
data[func_name] = data["TA_turn_ttm"].groupby(
KN.STOCK_ID.value).diff(1)
if switch:
data_fact = cls()._switch_freq(data_=data,
name=func_name,
limit=120)
else:
data_fact = None
data = data.reset_index()
F = DataInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.data_type = data['type'][0]
F.data_category = cls().__class__.__name__
F.data_name = func_name
return F
def Profit031(cls,
data: pd.DataFrame,
net_profit_in: str = FISN.Net_Pro_In.value,
operator_income: str = FISN.Op_Income.value,
switch: bool = False):
"""
净利润率(同比)(NPM_T) = 本期净利润 / 本期主营业务收入 - 上期净利润 / 上期主营业务收入
:param data:
:param net_profit_in:
:param operator_income:
:param switch:
:return:
"""
func_name = sys._getframe().f_code.co_name
data.set_index([SN.REPORT_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
data['NP'] = data[net_profit_in] / data[operator_income]
data[np.isinf(data['NP'])] = 0
data[func_name] = data['NP'].groupby(KN.STOCK_ID.value).diff(1)
if switch:
data_fact = cls()._switch_freq(data_=data, name=func_name)
else:
data_fact = None
data = data.reset_index()
F = DataInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.data_type = data['type'][0]
F.data_category = cls().__class__.__name__
F.data_name = func_name
return F
def Quality010(cls,
data: pd.DataFrame,
cash_sales: str = FCFSN.Cash_From_Sales.value,
operator_income: str = FISN.Op_Income.value,
switch: bool = False):
"""
收现比(CSR) = 销售商品提供劳务收到的现金 / 营业收入
:param data:
:param cash_sales:
:param operator_income:
:param switch:
:return:
"""
func_name = sys._getframe().f_code.co_name
data.set_index([SN.REPORT_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
data[func_name] = data[cash_sales] / data[operator_income]
data[func_name][np.isinf(data[func_name])] = np.nan
if switch:
data_fact = cls()._switch_freq(data_=data,
name=func_name,
limit=120)
else:
data_fact = None
data = data.reset_index()
F = DataInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.data_type = data['type'][0]
F.data_category = cls().__class__.__name__
F.data_name = func_name
return F
def Solvency020(cls,
data: pd.DataFrame,
all_tax: str = FCFSN.All_Tax.value,
quarter: int = 8,
switch: bool = False):
"""
各项税费变化率标准化(PTCF_qoq_Z)
"""
func_name = sys._getframe().f_code.co_name
data.set_index([SN.REPORT_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
# 部分会计科目若缺失填充零
data.fillna(0, inplace=True)
PTCF_qoq = data[all_tax].groupby(KN.STOCK_ID.value).apply(lambda x: x.diff(1) / abs(x.shift(1)))
PTCF_qoq_mean = PTCF_qoq.groupby(KN.STOCK_ID.value).apply(lambda x: x.rolling(quarter).mean())
PTCF_qoq_std = PTCF_qoq.groupby(KN.STOCK_ID.value).apply(lambda x: x.rolling(quarter).std())
data[func_name] = (PTCF_qoq - PTCF_qoq_mean) / PTCF_qoq_std
# switch inf to nan
data[func_name][np.isinf(data[func_name])] = np.nan
if switch:
data_fact = cls()._switch_freq(data_=data, name=func_name, limit=120)
else:
data_fact = None
data = data.reset_index()
F = DataInfo()
F.data_raw = data[[SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value, func_name]]
F.data = data_fact
F.data_type = data['type'][0]
F.data_category = cls().__class__.__name__
F.data_name = func_name
return F
def Profit029(cls,
data: pd.DataFrame,
total_operator_income: str = FISN.Total_Op_Income.value,
operator_profit: str = FISN.Op_Pro.value,
switch: bool = False):
"""
营业利润率(TTM)(OPM_TTM) = 营业利润 / 总营业收入
:param data:
:param total_operator_income:
:param operator_profit:
:param switch:
:return:
"""
func_name = sys._getframe().f_code.co_name
data.set_index([SN.REPORT_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
data[func_name] = data[operator_profit] / data[total_operator_income]
data[np.isinf(data[func_name])] = 0
if switch:
data_fact = cls()._switch_freq(data_=data, name=func_name)
else:
data_fact = None
data = data.reset_index()
F = DataInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.data_type = data['type'][0]
F.data_category = cls().__class__.__name__
F.data_name = func_name
return F
def Momentum021(cls,
data: pd.DataFrame,
price: str = PVN.CLOSE.value,
n: int = 1):
"""
最低价格时间因子(LT):1-index(Min(P, N))/L
"""
factor_name = sys._getframe().f_code.co_name + f'_{n}days'
data.set_index([KN.TRADE_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
data[factor_name] = data[price].groupby(KN.STOCK_ID.value,
group_keys=False).rolling(n).apply(
lambda x: 1 - (list(x).index(np.nanmin(x)) - 1) / len(x))
F = DataInfo()
F.data = data[factor_name]
F.data_type = 'MTM'
F.data_category = cls().__class__.__name__
F.data_name = factor_name
return F
def Momentum008(cls,
data: pd.DataFrame,
close_price: str = PVN.CLOSE.value,
n: int = 1):
"""
最低价格因子(LPTP):Max(P,N) / P
"""
factor_name = sys._getframe().f_code.co_name + f'_{n}days'
data.set_index([KN.TRADE_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
data[factor_name] = data[close_price].groupby(KN.STOCK_ID.value,
group_keys=False).apply(
lambda x: x.rolling(n, min_periods=1).min() / x)
F = DataInfo()
F.data = data[factor_name]
F.data_type = 'MTM'
F.data_category = cls().__class__.__name__
F.data_name = factor_name
return F
def Momentum027(cls,
data: pd.DataFrame,
close_price: str = PVN.CLOSE.value,
n: int = 1) -> DataInfo:
"""
收益率标准差(MTM_RankRet_std)
"""
factor_name = sys._getframe().f_code.co_name + f'_{n}days'
data.set_index([KN.TRADE_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
data['ret'] = data[close_price].groupby(KN.STOCK_ID.value).pct_change()
data[factor_name] = data['ret'].groupby(KN.TRADE_DATE.value, group_keys=False).rolling(n,
min_periods=2).std(ddof=1)
F = DataInfo()
F.data = data[factor_name]
F.data_type = 'MTM'
F.data_category = cls().__class__.__name__
F.data_name = factor_name
return F
def Solvency022(cls,
data: pd.DataFrame,
tax_surcharges: str = FISN.Tax_Surcharges.value,
net_pro_in: str = FISN.Net_Pro_In.value,
quarter: int = 8,
switch: bool = False):
"""
税金及附加占比变化率标准化(OT2NP_qoq_Z)
"""
func_name = sys._getframe().f_code.co_name
data.set_index([SN.REPORT_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
OT2NP = data[tax_surcharges] / data[net_pro_in]
OT2NP_qoq = OT2NP.groupby(KN.STOCK_ID.value).apply(lambda x: x.diff(1) / abs(x.shift(1)))
OT2NP_qoq_mean = OT2NP_qoq.groupby(KN.STOCK_ID.value).apply(lambda x: x.rolling(quarter).mean())
OT2NP_qoq_std = OT2NP_qoq.groupby(KN.STOCK_ID.value).apply(lambda x: x.rolling(quarter).std())
data[func_name] = (OT2NP_qoq - OT2NP_qoq_mean) / OT2NP_qoq_std
# switch inf to nan
data[func_name][np.isinf(data[func_name])] = np.nan
data = data.reset_index()
if switch:
data_fact = cls()._switch_freq(data_=data, name=func_name, limit=120)
else:
data_fact = None
F = DataInfo()
F.data_raw = data[[SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value, func_name]]
F.data = data_fact
F.data_type = data['type'][0]
F.data_category = cls().__class__.__name__
F.data_name = func_name
return F
