def bt_init_data(qx):
ksgn, df = qx.priceSgn, qx.wrkStkDat
df = df.sort_index(ascending=True)
#
if ksgn == 'avg':
df[ksgn] = df[zsys.ohlcLst].mean(axis=1)
else:
df[ksgn] = df[ksgn]
#
df['dprice'] = df[ksgn]
df['dpricek'] = df[ksgn].shift(-1) #trd-price.next,day
#
df['xtim'] = df.index
df = zdat.df_xtim2mtim(df, 'xtim', qx.priceDateFlag)
#
df = zta.mul_talib(zta.MA, df, ksgn, zsys.ma100Lst_var)
#
#df=df.round(2)
#df=df.dropna()
#qx.stkPools[xcod]=df.round(2)
qx.wrkStkDat = df.round(3)
#
return qx
#1 读取数据
fss = 'data/600663.csv'
print('\n#1，fss', fss)
df = pd.read_csv(fss, index_col=0)
df = df.sort_index(ascending=True)
print(df.tail())
#2 计算时间衍生参数
#
df['xtim'] = df.index
df['xyear'] = df['xtim'].apply(zstr.str_2xtim, ksgn='y')
df['xmonth'] = df['xtim'].apply(zstr.str_2xtim, ksgn='m')
df['xday'] = df['xtim'].apply(zstr.str_2xtim, ksgn='d')
#
df['xday_week'] = df['xtim'].apply(zstr.str_2xtim, ksgn='dw')
df['xday_year'] = df['xtim'].apply(zstr.str_2xtim, ksgn='dy')
#df['xday_month']=df['xtim'].apply(zstr.str_2xtim,ksgn='dm')
df['xweek_year'] = df['xtim'].apply(zstr.str_2xtim, ksgn='wy')
#
print('\n#2，dateLst:', zsys.dateLst)
print('\ndf.tail')
print(df.tail())
#3 计算时间衍生参数，使用ztools_data库函数
df = pd.read_csv(fss, index_col=0)
df = df.sort_index(ascending=True)
df['xtim'] = df.index
df2 = zdat.df_xtim2mtim(df, 'xtim', True)
print('\n#3，df2.tail')
print(df2.tail())
df = df.sort_index(ascending=True)
print(df.tail())
#2 计算时间衍生参数
#
df['xtim'] = df.index
df['xyear'] = df['xtim'].apply(zstr.str_2xtim, ksgn='y')
df['xmonth'] = df['xtim'].apply(zstr.str_2xtim, ksgn='m')
df['xday'] = df['xtim'].apply(zstr.str_2xtim, ksgn='d')
#
df['xday_week'] = df['xtim'].apply(zstr.str_2xtim, ksgn='dw')
df['xday_year'] = df['xtim'].apply(zstr.str_2xtim, ksgn='dy')
#df['xday_month']=df['xtim'].apply(zstr.str_2xtim,ksgn='dm')
df['xweek_year'] = df['xtim'].apply(zstr.str_2xtim, ksgn='wy')
#
#
df['xhour'] = df['xtim'].apply(zstr.str_2xtim, ksgn='h')
df['xminute'] = df['xtim'].apply(zstr.str_2xtim, ksgn='t')
#
print('\n#2，timeLst:', zsys.timeLst)
print('\ndf.tail')
print(df.tail())
#3 计算时间衍生参数，使用ztools_data库函数
df = pd.read_csv(fss, index_col=0)
df = df.sort_index(ascending=True)
df['xtim'] = df.index
df2 = zdat.df_xtim2mtim(df, 'xtim', False)
print('\n#3，df2.tail')
print(df2.tail())