删除的东西

日的

#逐行检查后决定删除的变量
df1 = df1.drop([ 'open', 'high', 'low', 'close', 'pre_close', 'change', 'vol', 'amount', 'trade_date_1', 'index_x', 'pe', 'pb', 'total_share', 'total_mv', 'index_y', 'macd', 'macd_hist', 'macd_bidaxiao', 'macd_jincha', 'macd_jc_5count', 'macd_jc_cha', 'macd_jc_count', 'OBV', 'D_SLOW', 'shizi', 'RSI', 'SAR_up_flag', 'SAR_down_flag', 'shiti_l', 'shiti_flag', 'shiti_up', 'shiti_low', 'tiaokong_up', 'tiaokong_down', 'v_ma5', 'v_ma30', 'c_ma30', 'c_ma14', 'c_ma5', 'c_ma89', 'fangcha_4xian', 'hengcha_4xian', 'fangliang', 'fangliang_2', 'min', 'max', 'zhang_max40', 'xttupo_1', 'xttupo', 'shizi_panzheng_1', 'duotou', 'duotou_5', 'duotou_10', 'duotou_count', 'bianxian_1', 'bianxian_flag', 'min_12', 'max_12', 'shangtan_die_count', 'zhang_dsxy_count', 'tsf_89', 'c_tst', 'c_c5', 'c_c14', 'c_c30', 'bian_2_UP_count', 'blyytk_count', 'CDL3INSIDE_count', 'CDLCONCEALBABYSWALL_count', 'CDLCOUNTERATTACK_count', 'CDLDOJISTAR_count', 'CDLENGULFING_count', 'CDLHARAMICROSS_count', 'CDLHIKKAKEMOD_count', 'CDLHOMINGPIGEON_count', 'CDLKICKINGBYLENGTH_count', 'CDLLADDERBOTTOM_count', 'CDLLONGLEGGEDDOJI_count', 'CDLMARUBOZU_count', 'chuan_3xian_count', 'chuitou_count', 'chuitou_dao_count', 'cuorou_flag_count', 'dadidangqian_count', 'erzhichan_count', 'fangchui_count', 'fangliang_flag_count', 'fei5_count', 'fenglangda_count', 'KDJ_CHAOBUY_count', 'KDJ_CHAOSELL_count', 'pudian_count', 'qiang_hui_count', 'qiying_count', 'queyingxian_count', 'sanbaibing_count', 'sanxing_count', 'SAR_down_flag_count', 'SAR_up_flag_count', 'shangdiaoxian_count', 'shejizhixing_count', 'shizi_count', 'shizi_mubei_count', 'shizi_panzheng_count', 'suoliang_flag_count', 'tanshuigan_count', 'tuoli_count', 'Txing_count', 'wuya2_count', 'wuyun_count', 'xie_c_up_count', 'xie_h_down_count', 'xie_l_up_count', 'xie_v_up_count', 'xttupo_1_count', 'yuanhu_count', 'zaifei_count', 'zaochen_count', 'fangcha_4xian_count', 'h30_c_xielv_count', 'hengcha_4xian_count', 'hengcha_4xian_xielv_count', 'K_SLOW5xielv_count', 'l30_c_xielv_count', 'lisan_v_count', 'lisan_zhangfu_count', 'lisan_zhenfu_count', 'ma14xielv_count', 'ma30xielv_count', 'ma5xielv_count', 'ma89xielv_count', 'macd_xielv_count', 'OBV_xielv_count', 'pingjun_4xian_count', 'RSI_xielv_count', 'SAR_xielv_count', 'v30xielv_count', 'v5xielv_count', 'zhenfu_count', 'shift5_c'],axis=1)

周的

df4 = df4.drop(['rownum_W','macd_xielv_W','macd_jc_5count_W','macd_jc_count_W','OBV_xielv_W','qushi_xebt_W','K_SLOW5xielv_W','D_SLOW5xielv_W','KDJ_CHAOBUY_W','KDJ_CHAOSELL_W','shangdiaoxian_W','queyingxian_W','shizi_W','Txing_W','shizi_mubei_W','chuitou_W','fenglangda_W','chuitou_dao_W','tanshuigan_W','CDLDOJISTAR_W','CDLHARAMICROSS_W','CDLHIKKAKEMOD_W','CDLHOMINGPIGEON_W','CDLKICKINGBYLENGTH_W','CDLLADDERBOTTOM_W','CDLLONGLEGGEDDOJI_W','CDLMARUBOZU_W','CDL3INSIDE_W','CDLCONCEALBABYSWALL_W','CDLCOUNTERATTACK_W','CDLENGULFING_W','shejizhixing_W','fangchui_W','sanxing_W','tingdun_W','blyytk_W','pudian_W','wuya2_W','sanbaibing_W','qiying_W','dadidangqian_W','tuoli_W','wuyun_W','zaochen_W','RSI_W','RSI_2_W','SAR_W','SAR_xielv_W','SAR_up_flag_W','SAR_down_flag_W','zhenfu_W','tiaokong_up_W','tiaokong_down_W','tiaokong_up_count_W','tiaokong_down_count_W','erzhichan_W','ma5xielv_W','ma14xielv_W','ma30xielv_W','ma89xielv_W','v5xielv_W','v30xielv_W','l30_c_xielv_W','h30_c_xielv_W','RSI_xielv_W','hengcha_4xian_xielv_W','xttupo_W','shizi_panzheng_W','qiang_hui_W','xttupo_count_W','duotou_W','duotou_count_W','bian_2_UP_W','yuanhu_W','zaifei_W','shangtan_die_W','zhang_dashangxiaying_W','shangtan_die_count_W','zhang_dsxy_count_W','fei5_W','c_tst_W','c_c5_W','c_c14_W','c_c30_W','lisan_zhangfu_W','lisan_zhenfu_W','lisan_v_W','cuorou_flag_W','chuan_3xian_W'],axis=1)

#先排序，不然shift之类的就会错
df1 = df1.sort_values(by=['ts_code','trade_date'],ascending=True)
#通过日线来定义目标
df1['target']   =   np.where((df1['pct_chg']<9) & (df1['pct_chg']>(-10)) & (df1['pct_chg'].shift(1)<9) & (df1['pct_chg'].shift(1)>(-10))
                             & (df1['pct_chg'].shift(-1)>(-5)) & (df1['pct_chg'].shift(-2)>(-5)) & 
                             (df1['close'].shift(-5)/(df1['close']+0.01)>1.05) ,1,0) 

# #把预测用到的5天数据设为未知
df1['target'] = np.where(df1['rownum']==1,0,df1['target'])
df1['target'] = np.where(df1['rownum']==2,0,df1['target'])
df1['target'] = np.where(df1['rownum']==3,0,df1['target'])
df1['target'] = np.where(df1['rownum']==4,0,df1['target'])
df1['target'] = np.where(df1['rownum']==5,0,df1['target'])