def peg(self):
# PEG = PE / 过去12个月的EPS增长率
pe_daily = self.pe_daily
basicepsyoy = self.basicepsyoy
basicepsyoy = adjust_months(basicepsyoy)
epsyoy = append_df(basicepsyoy, target_feq='D', fill_type='preceding')
pe_daily = CALFUNC.del_dat_early_than(pe_daily, START_YEAR)
epsyoy = CALFUNC.del_dat_early_than(epsyoy, START_YEAR)
[pe_daily, epsyoy] = align(pe_daily, epsyoy)
[h, l] = pe_daily.shape
pe_ar = pe_daily.values
eps_ar = epsyoy.values
res = np.zeros([h, l])
for i in range(0, h):
for j in range(0, l):
if pd.isna(eps_ar[i, j]) or eps_ar[i, j] == 0:
res[i, j] = np.nan
else:
res[i, j] = pe_ar[i, j] / eps_ar[i, j]
res_df = pd.DataFrame(data=res,
index=pe_daily.index,
columns=pe_daily.columns)
return res_df
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 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 grossprofitmargin_ttm(self):
gir = self.grossincomeratiottm
gir = adjust_months(gir)
# 用来扩展月度数据
gir = append_df(gir)
res = CALFUNC.del_dat_early_than(gir, START_YEAR)
return res
def totalassetturnover(self):
totalassettrate = self.totalassettrate
tmp0 = adjust_months(totalassettrate)
tmp1 = append_df(tmp0)
res = CALFUNC.del_dat_early_than(tmp1, START_YEAR)
return res
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 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 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 RDtosales(self):
data = Data()
rd_exp = data.rd_exp
revenue = data.operatingrevenue
rd_exp = CALFUNC.del_dat_early_than(rd_exp, 2018)
revenue = CALFUNC.del_dat_early_than(revenue, 2018)
res = rd_exp / revenue
res = adjust_months(res)
res = append_df(res)
to_del = res.columns[res.isna().sum() / len(res) > 0.9]
res.drop(to_del, axis=1, inplace=True)
return res
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 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
def position_daily(self, stock_pool):
data = Data()
main_net_buy_ratio = data.MAIN_NET_BUY_RATIO_SCALERED
main_net_buy_ratio = main_net_buy_ratio.shift(1, axis=1)
main_net_buy_ratio = main_net_buy_ratio.drop(
main_net_buy_ratio.columns[0], axis=1)
black_cond = float_2_bool_df(main_net_buy_ratio, min_para=5)
black_cond = cond_append_to_month(black_cond)
stock_pool_v = bool_2_ones(stock_pool, use_df=True)
pool = append_df(stock_pool_v, target_feq='D')
pool = pool.shift(1, axis=1)
pool.dropna(how='all', axis=1, inplace=True)
pool_boll = float_2_bool_df(pool, min_para=0.5)
# ttt = pool_boll.sum()
mu_col = [c for c in black_cond.columns if c in pool_boll.columns]
res = pool_boll.loc[:, mu_col] & black_cond.loc[pool.index, mu_col]
# tt = res.sum()
return res
def ROA_ttm(self):
roa_ttm = self.roattm
roa_ttm = adjust_months(roa_ttm)
roa_ttm = append_df(roa_ttm)
roa_ttm = CALFUNC.del_dat_early_than(roa_ttm, START_YEAR)
return roa_ttm
