def grossprofitmargin_diff(self):
revenue = self.operatingrevenue # 营业收入
cost = self.operatingcost # 营业成本
# 财务指标常规处理，移动月份，改月末日期
revenue_q = get_signal_season_value(revenue)
cost_q = get_signal_season_value(cost)
gross_q = (revenue_q - cost_q) / revenue_q
gir_d = CALFUNC.generate_diff(gross_q)
gir_d = adjust_months(gir_d)
# 用来扩展月度数据
gir_d = append_df(gir_d)
res = CALFUNC.del_dat_early_than(gir_d, START_YEAR)
return res
def Sales_G_q(self): # qfa_yoysales：单季度.营业收入同比增长率
operatingrevenue = self.operatingrevenue
sig_season_operatingrevenue = get_signal_season_value(operatingrevenue)
sales_g = CALFUNC.generate_yoygr(sig_season_operatingrevenue)
sales_g = adjust_months(sales_g)
sales_g = append_df(sales_g)
sales_g = CALFUNC.del_dat_early_than(sales_g, START_YEAR)
return sales_g
def ROE_G_q(self): # 单季度.ROE同比增长率
roe = self.roe
sig_season_roe = get_signal_season_value(roe)
roe_g = CALFUNC.generate_yoygr(sig_season_roe)
roe_g = adjust_months(roe_g)
roe_g = append_df(roe_g)
roe_g_q = CALFUNC.del_dat_early_than(roe_g, START_YEAR)
return roe_g_q
def Profit_G_q(self): # qfa_yoyprofit：单季度.净利润同比增长率
netprofit = self.netprofitcut # 扣除非经常损益后的净利润
sig_season_netprofit = get_signal_season_value(netprofit)
p_g = CALFUNC.generate_yoygr(sig_season_netprofit)
p_g = adjust_months(p_g)
p_g = append_df(p_g)
profit_g_q = CALFUNC.del_dat_early_than(p_g, START_YEAR)
return profit_g_q
def grossprofitmargin_q(self):
'''
计算公示：（营业收入 - 营业成本） / 营业收入 * 100 %
计算单季度指标，应该先对 营业收入 和 营业成本 分别计算单季度指标，再计算
'''
revenue = self.operatingrevenue # 营业收入
cost = self.operatingcost # 营业成本
# 财务指标常规处理，移动月份，改月末日期
revenue_q = get_signal_season_value(revenue)
cost_q = get_signal_season_value(cost)
gross_q = (revenue_q - cost_q) / revenue_q
# 调整为公告日期
tmp = adjust_months(gross_q)
# 用来扩展月度数据
tmp = append_df(tmp)
res = CALFUNC.del_dat_early_than(tmp, START_YEAR)
return res
def Profitmargin_q(self): # 单季度净利润率
'''
1.qfa_deductedprofit：单季度.扣除非经常损益后的净利润
2.qfa_oper_rev： 单季度.营业收入
:return:
'''
netprofit = self.netprofitcut # 扣除非经常损益后的净利润
operatingrevenue = self.operatingrevenue
sig_season_netprofit = get_signal_season_value(netprofit)
sig_season_operatingrevenue = get_signal_season_value(operatingrevenue)
profitmargin_q = sig_season_netprofit / sig_season_operatingrevenue
profitmargin_q = adjust_months(profitmargin_q)
profitmargin_q = append_df(profitmargin_q)
pq = CALFUNC.del_dat_early_than(profitmargin_q, START_YEAR)
return pq
def ROE_q(self):
totalshareholderequity = self.totalshareholderequity
netprofit = self.netprofit
# 得到单季度 净利润
sig_season_netprofit = get_signal_season_value(netprofit)
# 得到季度平均总资产
s_mean_equity = get_season_mean_value(totalshareholderequity)
roe_q = (sig_season_netprofit / s_mean_equity) * 100
roe_q = adjust_months(roe_q)
roe_q = append_df(roe_q)
roe_q = CALFUNC.del_dat_early_than(roe_q, START_YEAR)
return roe_q
def ROA_q(self):
totalassets = self.totalassets
netprofit = self.netprofit
# 得到单季度 净利润
sig_season_netprofit = get_signal_season_value(netprofit)
# 得到季度平均总资产
s_mean_totalassets = get_season_mean_value(totalassets)
roa_q = (sig_season_netprofit / s_mean_totalassets) * 100
roa_q = adjust_months(roa_q)
roa_q = append_df(roa_q)
roa_q = CALFUNC.del_dat_early_than(roa_q, START_YEAR)
return roa_q
def assetturnover_q(self):
totalassets = self.totalassets
revenue = self.operatingrevenue
# 得到单季度 净利润
sig_season_revenue = get_signal_season_value(revenue)
# 得到季度平均总资产
s_mean_totalassets = get_season_mean_value(totalassets)
turnover_q = (sig_season_revenue / s_mean_totalassets) * 100
turnover_q = adjust_months(turnover_q)
turnover_q = append_df(turnover_q)
turnover_q = CALFUNC.del_dat_early_than(turnover_q, START_YEAR)
return turnover_q
def REVSU(self):
netprofit = self.totaloperatingrevenueps
# 得到单季度的数据。
sig_season_va = get_signal_season_value(netprofit)
cols = pd.DataFrame([i for i in sig_season_va.columns])
revsu = pd.DataFrame()
rolling_cols = rolling_windows(cols, 6)
for roll in rolling_cols:
res = _calculate_su_simple(sig_season_va[roll])
res = pd.DataFrame(res.values, index=res.index, columns=[roll[-1]])
revsu = pd.concat([revsu, res], axis=1)
revsu.dropna(how='all', axis=0, inplace=True)
revsu = adjust_months(revsu)
revsu = append_df(revsu)
return revsu
def SUE(self):
# 使用原始的财务数据
eps = self.basiceps
# 得到单季度的数据。
sig_season_va = get_signal_season_value(eps)
cols = pd.DataFrame([i for i in sig_season_va.columns])
sue = pd.DataFrame()
rolling_cols = rolling_windows(cols, 6)
for roll in rolling_cols:
res = _calculate_su_simple(sig_season_va[roll])
res = pd.DataFrame(res.values, index=res.index, columns=[roll[-1]])
sue = pd.concat([sue, res], axis=1)
sue.dropna(how='all', axis=0, inplace=True)
sue = adjust_months(sue)
sue = append_df(sue)
return sue
