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 = FactorInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.factor_type = data['type'][0]
F.factor_category = cls().__class__.__name__
F.factor_name = func_name
return F
def HighFreq035(cls, data: pd.DataFrame, n: int = 21):
"""
博弈因子(Stren):存在涨停主卖为零情况，会导致分母为0，根据数值特征范围将分母为零的计算设置为2
"""
factor_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[
'buy_amount'] = data['BuyAll_AM_120min'] + data['BuyAll_PM_120min']
data['sale_amount'] = data['SaleAll_AM_120min'] + data[
'SaleAll_PM_120min']
# 升序
w = cls.Half_time(n)
data[['buy_amount_w',
'sale_amount_w']] = data[['buy_amount', 'sale_amount']].groupby(
KN.STOCK_ID.value, group_keys=False).rolling(
n, min_periods=n).apply(lambda x: (x * w).sum())
data[factor_name] = data['buy_amount_w'] / data['sale_amount_w']
# 无穷大值设置为2
data[factor_name][np.isinf(data[factor_name])] = 2
F = FactorInfo()
F.data = data[factor_name]
F.factor_type = 'HFD'
F.factor_category = cls().__class__.__name__
F.factor_name = factor_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 = FactorInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.factor_type = data['type'][0]
F.factor_category = cls().__class__.__name__
F.factor_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 = FactorInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.factor_type = data['type'][0]
F.factor_category = cls().__class__.__name__
F.factor_name = func_name
return F
def Distribution031(cls, data: pd.DataFrame, n: int = 20, **kwargs):
"""
N日上午收益与下午收益差和(AVP)
"""
factor_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)
# Calculate AM and PM returns on stocks
data['am_ret_stock'] = data['2hPrice'] / data['open'] - 1
data['pm_ret_stock'] = data['4hPrice'] / data['2hPrice'] - 1
data['diff'] = data['am_ret_stock'] - data['pm_ret_stock']
# filter momentum
data[factor_name] = data['diff'].groupby(
KN.STOCK_ID.value, group_keys=False).rolling(n).sum()
F = FactorInfo()
F.data = data[factor_name]
F.factor_type = 'HFD'
F.factor_category = cls().__class__.__name__
F.factor_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 = FactorInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.factor_type = data['type'][0]
F.factor_category = cls().__class__.__name__
F.factor_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}'
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 = FactorInfo()
F.data = data_new
F.factor_type = 'MTM'
F.factor_category = cls().__class__.__name__
F.factor_name = factor_name
return F
def Momentum013(cls,
data: pd.DataFrame,
close_price: str = PVN.CLOSE.value,
n: int = 1) -> FactorInfo:
"""
动量CTC收益率(MTM_CTC):N日收盘价计算的收益率均值
:return:
"""
factor_name = sys._getframe().f_code.co_name + f'_{n}'
data.set_index([KN.TRADE_DATE.value, KN.STOCK_ID.value], inplace=True)
data.sort_index(inplace=True)
data['return'] = data[close_price].groupby(
KN.STOCK_ID.value).pct_change()
data[factor_name] = data['return'].groupby(
KN.STOCK_ID.value, group_keys=False).rolling(n,
min_periods=1).mean()
F = FactorInfo()
F.data = data[factor_name]
F.factor_type = 'MTM'
F.factor_category = cls().__class__.__name__
F.factor_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) -> FactorInfo:
"""
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}'
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 = FactorInfo()
F.data = data[factor_name]
F.factor_type = 'MTM'
F.factor_category = cls().__class__.__name__
F.factor_name = factor_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 = FactorInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.factor_type = data['type'][0]
F.factor_category = cls().__class__.__name__
F.factor_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}'
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 = FactorInfo()
F.data = data[factor_name]
F.factor_type = 'MTM'
F.factor_category = cls().__class__.__name__
F.factor_name = factor_name
return F
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) -> FactorInfo:
"""
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 = FactorInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.factor_type = data['type'][0]
F.factor_category = cls().__class__.__name__
F.factor_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 = FactorInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.factor_type = data['type'][0]
F.factor_category = cls().__class__.__name__
F.factor_name = func_name
return F
def VolPrice017(cls, data: pd.DataFrame, n: int = 20, **kwargs):
"""PMA 特殊"""
factor_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)
# Calculate AM and PM returns on stocks
data['am_ret_stock'] = data['2hPrice'] / data['open'] - 1
data['pm_ret_stock'] = data['4hPrice'] / data['2hPrice'] - 1
# filter momentum
data[factor_name] = data.groupby(
KN.TRADE_DATE.value, group_keys=False).apply(
lambda x: cls._reg(x, 'am_ret_stock', 'pm_ret_stock'))
# data['mean'] = data['res'].groupby(KN.STOCK_ID.value,
# group_keys=False).rolling(n, min_periods=1).apply(np.nanmean)
# data['std'] = data['res'].groupby(KN.STOCK_ID.value,
# group_keys=False).rolling(n, min_periods=1).apply(np.nanstd)
# data[factor_name] = data['mean'] / data['std']
# data[factor_name][np.isinf(data[factor_name])] = 0
data[factor_name] = data['pm_ret_stock']
F = FactorInfo()
F.data = data[factor_name]
F.factor_type = 'HFD'
F.factor_category = cls().__class__.__name__
F.factor_name = factor_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) -> FactorInfo:
"""
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 = FactorInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.factor_type = data['type'][0]
F.factor_category = cls().__class__.__name__
F.factor_name = func_name
return F
def Momentum025(cls,
data: pd.DataFrame,
high_price: str = PVN.HIGH.value,
close_price: str = PVN.CLOSE.value,
n: int = 1) -> FactorInfo:
"""
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}'
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 = FactorInfo()
F.data = data[factor_name]
F.factor_type = 'MTM'
F.factor_category = cls().__class__.__name__
F.factor_name = factor_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 = FactorInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.factor_type = data['type'][0]
F.factor_category = cls().__class__.__name__
F.factor_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 = FactorInfo()
F.data_raw = data[[
SN.ANN_DATE.value, KN.STOCK_ID.value, SN.REPORT_DATE.value,
func_name
]]
F.data = data_fact
F.factor_type = data['type'][0]
F.factor_category = cls().__class__.__name__
F.factor_name = func_name
return F
def Distribution019(cls, data: pd.DataFrame, **kwargs):
"""成交量差分绝对值均值(Vol_diff_abs_mean)"""
F = FactorInfo()
F.data = data
F.factor_type = 'HFD'
F.factor_category = cls().__class__.__name__
F.factor_name = data.name
return F
def Distribution008(cls, data: pd.DataFrame, **kwargs):
"""高频量价相关性(HFD_Corr_Vol_P)"""
F = FactorInfo()
F.data = data
F.factor_type = 'HFD'
F.factor_category = cls().__class__.__name__
F.factor_name = data.name
return F
def VolPrice009(cls, data: pd.DataFrame, **kwargs):
"""改进反转(Rev_improve)"""
F = FactorInfo()
F.data = data
F.factor_type = 'HFD'
F.factor_category = cls().__class__.__name__
F.factor_name = data.name
return F
def VolPrice008(cls, data: pd.DataFrame, **kwargs):
"""大单驱动涨幅(MOM_bigOrder)"""
F = FactorInfo()
F.data = data
F.factor_type = 'HFD'
F.factor_category = cls().__class__.__name__
F.factor_name = data.name
return F
def Distribution027(cls, data: pd.DataFrame, **kwargs):
"""N日分钟振幅波动稳定性(HFD_ret_std)"""
F = FactorInfo()
F.data = data
F.factor_type = 'HFD'
F.factor_category = cls().__class__.__name__
F.factor_name = data.name
return F
def Distribution007(cls, data: pd.DataFrame, **kwargs):
"""高频下行波动占比(HFD_std_down_occupy)"""
F = FactorInfo()
F.data = data
F.factor_type = 'HFD'
F.factor_category = cls().__class__.__name__
F.factor_name = data.name
return F
def VolPrice011(cls, data: pd.DataFrame, **kwargs):
"""聪明钱因子(SmartQ)"""
F = FactorInfo()
F.data = data
F.factor_type = 'HFD'
F.factor_category = cls().__class__.__name__
F.factor_name = data.name
return F
def Distribution024(cls, data: pd.DataFrame, **kwargs):
"""均匀分布主动占比因子(Event_Amt_R)"""
F = FactorInfo()
F.data = data
F.factor_type = 'HFD'
F.factor_category = cls().__class__.__name__
F.factor_name = data.name
return F
def HighFreq036(cls, data: pd.DataFrame, **kwargs):
"""开盘X分钟成交占比(Open_X_vol)"""
F = FactorInfo()
F.data = data
F.factor_type = 'HFD'
F.factor_category = cls().__class__.__name__
F.factor_name = data.name
return F
def VolPrice013(cls, data: pd.DataFrame, **kwargs):
"""轨迹非流动因子(Illiq_Track)"""
F = FactorInfo()
F.data = data
F.factor_type = 'HFD'
F.factor_category = cls().__class__.__name__
F.factor_name = data.name
return F
def VolPrice012(cls, data: pd.DataFrame, **kwargs):
"""高频反转因子(HFD_Rev)"""
F = FactorInfo()
F.data = data
F.factor_type = 'HFD'
F.factor_category = cls().__class__.__name__
F.factor_name = data.name
return F
def Distribution009(cls, data: pd.DataFrame, **kwargs):
"""高频收益偏度(HFD_ret_skew)"""
F = FactorInfo()
F.data = data
F.factor_type = 'HFD'
F.factor_category = cls().__class__.__name__
F.factor_name = data.name
return F
