else: top_dif = top_dif.sort_values( by=['dff', 'percent', 'ratio'], ascending=[1, 0, 1]) # top_all=top_all.sort_values(by=['percent','dff','couts','ratio'],ascending=[0,0,1,1]) # print rl.format_for_print(top_dif[:10]) # top_dd = pd.concat([top_dif[:5],top_temp[:3],top_dif[-3:],top_temp[-3:]], axis=0) if percent_status == 'y' and (cct.get_now_time_int() > 935 or cct.get_now_time_int() < 900) and ptype == 'low': top_dif = top_dif[top_dif.percent >= 0] top_temp = top_dif[:ct.PowerCount].copy() top_end = top_dif[-5:].copy() top_temp = pct.powerCompute_df(top_temp, dl=ct.PowerCountdl, talib=True, newdays=newdays) top_end = pct.powerCompute_df(top_end, dl=ct.PowerCountdl, talib=True, newdays=newdays) # elif percent_status == 'y' and cct.get_now_time_int() > 935 and ptype == 'high' : elif ptype == 'low': # top_dif = top_dif[top_dif.percent >= 0] top_temp = top_dif[:ct.PowerCount].copy() top_end = top_dif[-5:].copy() top_temp = pct.powerCompute_df(top_temp, dl=ct.PowerCountdl, talib=True, newdays=newdays)
& (top_all.low >= top_all.ma51d) & (top_all.lasth1d > top_all.lasth2d)] # top_temp = top_all[((top_all.open > top_all.lastp1d)) & ( # top_all.low >= top_all.lastl1d) & (top_all.lasth1d > top_all.lasth2d)] # top_temp = top_all[ (top_all.low >= top_all.lastl1d) & (top_all.lasth1d > top_all.lasth2d) & (top_all.low >= top_all.nlow) & ((top_all.open >= top_all.nlow *0.998) & (top_all.open <= top_all.nlow*1.002)) ] # top_temp = top_all[ (top_all.volume >= 1.2 ) & (top_all.low >= top_all.lastl1d) & (top_all.lasth1d > top_all.lasth2d) & (top_all.close > top_all.lastp1d)] else: top_temp = top_all.copy() # if st_key_sort != '4': # top_temp = stf.filterPowerCount(top_temp, ct.PowerCount) top_end = top_all[-int((ct.PowerCount) / 10):].copy() top_temp = pct.powerCompute_df(top_temp, dl=ct.PowerCountdl, talib=True) top_end = pct.powerCompute_df(top_end, dl=ct.PowerCountdl) goldstock = len( top_dif[(top_dif.buy >= top_dif.lhigh * 0.99) & (top_dif.buy >= top_dif.llastp * 0.99)]) cct.set_console(width, height, title=[ 'dT:%s' % cct.get_time_to_date(time_s), 'G:%s' % len(top_dif), 'zxg: %s' % (blkname + '-' + market_blk) ])
else: if 'couts' in top_dif.columns.values: top_dif = top_dif.sort_values( by=ct.Monitor_sort_count, ascending=[0, 0, 0, 1, 1]) else: # print "Good Morning!!!" top_dif = top_dif.sort_values( by=['dff', 'percent', 'ratio'], ascending=[0, 0, 1]) # top_all=top_all.sort_values(by=['percent','dff','couts','ratio'],ascending=[0,0,1,1]) # print rl.format_for_print(top_dif[:10]) top_temp = top_dif[:ct.PowerCount].copy() top_temp = pct.powerCompute_df(top_temp, dl=ct.PowerCountdl) goldstock = len( top_dif[(top_dif.buy >= top_dif.lhigh * 0.99) & (top_dif.buy >= top_dif.llastp * 0.99)]) cct.set_console(width, height, title=[ 'dT:%s' % cct.get_time_to_date(time_s), 'G:%s' % len(top_dif), 'zxg: %s' % (blkname) ]) top_all = tdd.get_powerdf_to_all(top_all, top_temp) top_temp = stf.getBollFilter(df=top_temp, boll=ct.bollFilter,