def mix_factor_mcap_(self, data_name, grow_1_name, grow_2_name, sector_df,
percent):
df = bt.AZ_Load_csv(f'{self.root_path}/EM_Funda/daily/R_{data_name}_TTM_First.csv') \
.reindex(index=self.xinx, columns=self.xnms)
mcap = bt.AZ_Load_csv(f'{self.root_path}/EM_Funda/LICO_YS_STOCKVALUE/AmarketCap.csv') \
.reindex(index=self.xinx, columns=self.xnms)
grow_1 = bt.AZ_Load_csv(f'{self.root_path}/EM_Funda/daily/R_{data_name}_TTM_{grow_1_name}.csv') \
.reindex(index=self.xinx, columns=self.xnms)
grow_2 = bt.AZ_Load_csv(f'{self.root_path}/EM_Funda/daily/R_{data_name}_TTM_{grow_2_name}.csv') \
.reindex(index=self.xinx, columns=self.xnms)
std = bt.AZ_Load_csv(f'{self.root_path}/EM_Funda/daily/R_{data_name}_TTM_QSD4Y.csv') \
.reindex(index=self.xinx, columns=self.xnms)
target_df = self.mix_factor_mcap(df, mcap, grow_1, grow_2, std,
sector_df, percent)
file_name = '{}_and_mcap_{}_{}_{}'.format(data_name, grow_1_name,
grow_2_name, percent)
fun = 'EM_Funda.EM_Funda_Deal.mix_factor_mcap'
raw_data_path = (f'EM_Funda/daily/R_{data_name}_TTM_First.csv',
'EM_Funda/LICO_YS_STOCKVALUE/AmarketCap.csv',
f'EM_Funda/daily/R_{data_name}_TTM_{grow_1_name}.csv',
f'EM_Funda/daily/R_{data_name}_TTM_{grow_2_name}.csv',
f'EM_Funda/daily/R_{data_name}_TTM_QSD4Y.csv')
args = (percent, )
self.judge_save_fun(target_df, file_name, self.save_root_path, fun,
raw_data_path, args)
def mix_factor_asset_(self, data_name, grow_1_name, grow_2_name, sector_df,
percent):
df = bt.AZ_Load_csv(f'{self.root_path}/EM_Funda/daily/R_{data_name}_TTM_First.csv') \
.reindex(index=self.xinx, columns=self.xnms)
asset = bt.AZ_Load_csv(f'{self.root_path}/EM_Funda/daily/R_SUMASSET_First.csv') \
.reindex(index=self.xinx, columns=self.xnms)
grow_1 = bt.AZ_Load_csv(f'{self.root_path}/EM_Funda/daily/R_{data_name}_TTM_{grow_1_name}.csv') \
.reindex(index=self.xinx, columns=self.xnms)
grow_2 = bt.AZ_Load_csv(f'{self.root_path}/EM_Funda/daily/R_{data_name}_TTM_{grow_2_name}.csv') \
.reindex(index=self.xinx, columns=self.xnms)
std = bt.AZ_Load_csv(f'{self.root_path}/EM_Funda/daily/R_{data_name}_TTM_QSD4Y.csv') \
.reindex(index=self.xinx, columns=self.xnms)
target_df = self.mix_factor_asset(df, asset, grow_1, grow_2, std,
sector_df, percent)
file_name = '{}_and_asset_{}_{}_{}'.format(data_name, grow_1_name,
grow_2_name, percent)
fun = 'EM_Funda.EM_Funda_Deal.mix_factor_asset'
raw_data_path = (f'EM_Funda/daily/R_{data_name}_TTM_First.csv',
'EM_Funda/daily/R_SUMASSET_First.csv',
f'EM_Funda/daily/R_{data_name}_TTM_{grow_1_name}.csv',
f'EM_Funda/daily/R_{data_name}_TTM_{grow_2_name}.csv',
f'EM_Funda/daily/R_{data_name}_TTM_QSD4Y.csv')
args = (percent, )
self.judge_save_fun(target_df, file_name, self.save_root_path, fun,
raw_data_path, args)
def get_corr_matrix(cut_date=None):
pos_file_list = [
x for x in os.listdir('/mnt/mfs/AAPOS') if x.startswith('WHS')
]
return_df = bt.AZ_Load_csv(
'/mnt/mfs/DAT_EQT/EM_Funda/DERIVED_14/aadj_r.csv').astype(float)
index_df_1 = load_index_data('000300', return_df.index).fillna(0)
index_df_2 = load_index_data('000905', return_df.index).fillna(0)
sum_pnl_df = pd.DataFrame()
for pos_file_name in pos_file_list:
pos_df = bt.AZ_Load_csv('/mnt/mfs/AAPOS/{}'.format(pos_file_name))
cond_1 = 'IF01' in pos_df.columns
cond_2 = 'IC01' in pos_df.columns
if cond_1 and cond_2:
hedge_df = 0.5 * index_df_1 + 0.5 * index_df_2
return_df_c = return_df.sub(hedge_df, axis=0)
elif cond_1:
hedge_df = index_df_1
return_df_c = return_df.sub(hedge_df, axis=0)
elif cond_2:
hedge_df = index_df_2
return_df_c = return_df.sub(hedge_df, axis=0)
else:
print('alpha hedge error')
continue
pnl_df = (pos_df.shift(2) * return_df_c).sum(axis=1)
pnl_df.name = pos_file_name
sum_pnl_df = pd.concat([sum_pnl_df, pnl_df], axis=1)
# plot_send_result(pnl_df, bt.AZ_Sharpe_y(pnl_df), 'mix_factor')
if cut_date is not None:
sum_pnl_df = sum_pnl_df[sum_pnl_df.index > cut_date]
return sum_pnl_df
def get_new_stock_info(self, xnms, xinx):
new_stock_data = bt.AZ_Load_csv(self.root_path.EM_Tab01.CDSY_SECUCODE /
'LISTSTATE.csv')
new_stock_data.fillna(method='ffill', inplace=True)
# 获取交易日信息
return_df = bt.AZ_Load_csv(self.root_path.EM_Funda.DERIVED_14 /
'aadj_r.csv').astype(float)
trade_time = return_df.index
new_stock_data = new_stock_data.reindex(index=trade_time).fillna(
method='ffill')
target_df = new_stock_data.shift(40).notnull().astype(int)
target_df = target_df.reindex(columns=xnms, index=xinx)
return target_df
def dev_row_extre2_(self, percent):
change_list = [
'R_ACCOUNTPAY_QYOY',
'R_ACCOUNTREC_QYOY',
'R_ASSETDEVALUELOSS_s_QYOY',
'R_Cashflow_s_YOY_First',
'R_CFO_s_YOY_First',
'R_CostSales_QYOY',
'R_EBITDA2_QYOY',
'R_EPSDiluted_YOY_First',
'R_ESTATEINVEST_QYOY',
'R_FairVal_TotProfit_QYOY',
'R_FINANCEEXP_s_QYOY',
'R_GrossProfit_TTM_QYOY',
'R_GSCF_sales_QYOY',
'R_IntDebt_Mcap_QYOY',
'R_INVESTINCOME_s_QYOY',
'R_LTDebt_WorkCap_QYOY',
'R_LOANREC_s_QYOY',
'R_NetAssets_s_YOY_First',
'R_NetInc_s_QYOY',
'R_NETPROFIT_s_QYOY',
'R_OPCF_TTM_QYOY',
'R_OperCost_sales_QYOY',
'R_OperProfit_YOY_First',
'R_OPEX_sales_QYOY',
'R_ROE1_QYOY',
'R_SUMLIAB_QYOY',
'R_TangAssets_IntDebt_QYOY',
'R_WorkCapital_QYOY',
'R_OTHERLASSET_QYOY',
]
for file_name in change_list:
data1_path = self.load_path / f'{file_name}.csv'
data2_path = self.load_path / (
'_'.join(file_name.split('_')[:-1]) + '_QTTM.csv')
data1 = bt.AZ_Load_csv(data1_path)
data2 = bt.AZ_Load_csv(data2_path)
target_df = self.dev_row_extre(data1, data2, self.sector_df,
percent)
file_name = '{}_and_{}_{}'.format(file_name, 'QTTM', percent)
fun = 'EM_Funda.EM_Funda_Deal.dev_row_extre'
raw_data_path = (data1_path, data2_path)
args = (percent, )
self.judge_save_fun(target_df, file_name, self.save_root_path, fun,
raw_data_path, args)
def __init__(self,
sector_df,
root_path,
table_num,
table_name,
data_name,
save_root_path,
if_replace=False):
xnms = sector_df.columns
xinx = sector_df.index
data = bt.AZ_Load_csv(
getattr(getattr(root_path, table_num), table_name) /
(data_name + '.csv'))
if if_replace:
self.raw_df = data.replace(0, np.nan).reindex(columns=xnms,
index=xinx)
else:
self.raw_df = data.reindex(columns=xnms, index=xinx)
self.sector_df = sector_df
self.table_num = table_num
self.table_name = table_name
self.data_name = data_name
self.save_root_path = save_root_path
self.factor_to_fun = '/mnt/mfs/dat_whs/data/factor_to_fun'
self.if_replace = if_replace
def get_st_stock_info(self, xnms, xinx):
data = bt.AZ_Load_csv(self.root_path.EM_Tab01.CDSY_CHANGEINFO /
'CHANGEA.csv')
data = data.reindex(columns=xnms, index=xinx)
data.fillna(method='ffill', inplace=True)
data = data.astype(str)
target_df = data.applymap(lambda x: 0 if 'ST' in x or 'PT' in x else 1)
return target_df
def __init__(self, sector_df, root_path, save_root_path):
xnms = sector_df.columns
xinx = sector_df.index
self.sector_df = sector_df
self.load_path = root_path.EM_Funda.daily
self.part_load_path = 'EM_Funda/daily'
self.R_IntDebt_Y3YGR_path = self.load_path / 'R_IntDebt_Y3YGR.csv'
self.R_IntDebt_Y3YGR = bt.AZ_Load_csv(
self.R_IntDebt_Y3YGR_path).reindex(columns=xnms, index=xinx)
self.R_SUMASSET_First_path = self.load_path / 'R_SUMASSET_First.csv'
self.R_SUMASSET_First = bt.AZ_Load_csv(
self.R_SUMASSET_First_path).reindex(columns=xnms, index=xinx)
# self.R_EBITDA_QTTM_path = self.load_path / 'R_EBITDA_QTTM.csv'
# self.R_EBITDA_QTTM = bt.AZ_Load_csv(self.R_EBITDA_QTTM_path).reindex(columns=xnms, index=xinx)
self.MCAP_path = root_path.EM_Funda.LICO_YS_STOCKVALUE / 'AmarketCapExStri.csv'
self.MCAP = bt.AZ_Load_csv(self.MCAP_path).reindex(columns=xnms,
index=xinx)
self.R_EBITDA_QYOY_path = self.load_path / 'R_EBITDA_QYOY.csv'
self.R_EBITDA_QYOY = bt.AZ_Load_csv(self.R_EBITDA_QYOY_path).reindex(
columns=xnms, index=xinx)
self.R_EBIT2_Y3YGR_path = self.load_path / 'R_EBIT2_Y3YGR.csv'
self.R_EBIT2_Y3YGR = bt.AZ_Load_csv(self.R_EBIT2_Y3YGR_path).reindex(
columns=xnms, index=xinx)
self.R_SalesGrossMGN_QYOY_path = self.load_path / 'R_SalesGrossMGN_QYOY.csv'
self.R_SalesGrossMGN_QYOY = bt.AZ_Load_csv(
self.R_SalesGrossMGN_QYOY_path).reindex(columns=xnms, index=xinx)
self.R_SalesGrossMGN_QTTM_path = self.load_path / 'R_SalesGrossMGN_QTTM.csv'
self.R_SalesGrossMGN_QTTM = bt.AZ_Load_csv(
self.R_SalesGrossMGN_QTTM_path).reindex(columns=xnms, index=xinx)
self.R_OPCF_CurrentLiab_First_path = self.load_path / 'R_OPCF_CurrentLiab_First.csv'
self.R_OPCF_CurrentLiab_First = bt.AZ_Load_csv(self.R_OPCF_CurrentLiab_First_path) \
.reindex(columns=xnms, index=xinx)
self.R_CurrentRatio_QTTM_path = self.load_path / 'R_CurrentRatio_QTTM.csv'
self.R_CurrentRatio_QTTM = bt.AZ_Load_csv(
self.R_CurrentRatio_QTTM_path).reindex(columns=xnms, index=xinx)
self.save_root_path = save_root_path
def load_sector_data(self, begin_date, end_date, sector_name):
if sector_name.startswith('index'):
index_name = sector_name.split('_')[-1]
market_top_n = bt.AZ_Load_csv(
self.root_path.EM_Funda.IDEX_YS_WEIGHT_A /
f'SECURITYNAME_{index_name}.csv')
market_top_n[market_top_n == market_top_n] = 1
else:
market_top_n = bt.AZ_Load_csv(self.root_path.EM_Funda.DERIVED_10 /
f'{sector_name}.csv')
market_top_n = market_top_n[(market_top_n.index >= begin_date)
& (market_top_n.index < end_date)]
market_top_n.dropna(how='all', axis='columns', inplace=True)
xnms = market_top_n.columns
xinx = market_top_n.index
new_stock_df = self.get_new_stock_info(xnms, xinx)
st_stock_df = self.get_st_stock_info(xnms, xinx)
sector_df = market_top_n * new_stock_df * st_stock_df
sector_df.replace(0, np.nan, inplace=True)
return sector_df
def __init__(self, sector_df, root_path, save_root_path):
xnms = sector_df.columns
xinx = sector_df.index
self.load_path = root_path.EM_Funda.DERIVED_14
self.part_load_path = 'EM_Funda/DERIVED_14'
self.open_path = self.load_path / 'aadj_p_OPEN.csv'
self.open = bt.AZ_Load_csv(self.open_path).reindex(columns=xnms,
index=xinx)
self.high_path = self.load_path / 'aadj_p_HIGH.csv'
self.high = bt.AZ_Load_csv(self.high_path).reindex(columns=xnms,
index=xinx)
self.low_path = self.load_path / 'aadj_p_LOW.csv'
self.low = bt.AZ_Load_csv(self.low_path).reindex(columns=xnms,
index=xinx)
self.close_path = self.load_path / 'aadj_p.csv'
self.close = bt.AZ_Load_csv(self.close_path).reindex(columns=xnms,
index=xinx)
self.volume_path = root_path.EM_Funda.TRAD_SK_DAILY_JC / 'TVOL.csv'
self.volume = bt.AZ_Load_csv(self.volume_path).reindex(columns=xnms,
index=xinx)
self.amount_path = root_path.EM_Funda.TRAD_SK_DAILY_JC / 'TVALCNY.csv'
self.amount = bt.AZ_Load_csv(self.amount_path).reindex(columns=xnms,
index=xinx)
self.turnrate_path = root_path.EM_Funda.TRAD_SK_DAILY_JC / 'TURNRATE.csv'
self.turnrate = bt.AZ_Load_csv(self.turnrate_path).reindex(
columns=xnms, index=xinx)
self.aadj_r_path = self.load_path / 'aadj_r.csv'
self.aadj_r = bt.AZ_Load_csv(self.aadj_r_path).reindex(columns=xnms,
index=xinx)
self.sector_df = sector_df
self.save_root_path = save_root_path
self.factor_to_fun = '/mnt/mfs/dat_whs/data/factor_to_fun'
def __init__(self, sector_df, root_path, save_root_path):
xnms = sector_df.columns
xinx = sector_df.index
self.load_path = root_path.EM_Funda.DERIVED_14
self.part_load_path1 = 'EM_Funda/TRAD_SK_DAILY_JC'
self.part_load_path = 'EM_Funda/DERIVED_14'
self.sector_open = bt.AZ_Load_csv(root_path.EM_Funda.DERIVED_14 / 'aadj_p_OPEN.csv')\
.reindex(columns=xnms, index=xinx)
self.sector_high = bt.AZ_Load_csv(root_path.EM_Funda.DERIVED_14 / 'aadj_p_HIGH.csv')\
.reindex(columns=xnms, index=xinx)
self.sector_low = bt.AZ_Load_csv(root_path.EM_Funda.DERIVED_14 / 'aadj_p_LOW.csv')\
.reindex(columns=xnms, index=xinx)
self.sector_close = bt.AZ_Load_csv(root_path.EM_Funda.DERIVED_14 / 'aadj_p.csv')\
.reindex(columns=xnms, index=xinx)
self.sector_volume = bt.AZ_Load_csv(root_path.EM_Funda.TRAD_SK_DAILY_JC / 'TVOL.csv')\
.reindex(columns=xnms, index=xinx)
self.sector_amount = bt.AZ_Load_csv(root_path.EM_Funda.TRAD_SK_DAILY_JC / 'TVALCNY.csv')\
.reindex(columns=xnms, index=xinx)
self.sector_adj_r = bt.AZ_Load_csv(root_path.EM_Funda.DERIVED_14 / 'aadj_r.csv')\
.reindex(columns=xnms, index=xinx)
self.sector_df = sector_df
self.save_root_path = save_root_path
self.factor_to_fun = '/mnt/mfs/dat_whs/data/factor_to_fun'
def load_daily_data(self, file_name):
return bt.AZ_Load_csv(f'{self.root_path}/EM_Funda/daily/{file_name}.csv')\
.reindex(index=self.xinx, columns=self.xnms)
def load_index_data(index_name, xinx):
data = bt.AZ_Load_csv(
os.path.join('/mnt/mfs/DAT_EQT', 'EM_Tab09/INDEX_TD_DAILYSYS/CHG.csv'))
target_df = data[index_name].reindex(index=xinx)
return target_df * 0.01
