# top_dif['dff'] = top_dif['dff'].apply(lambda x: x * 2 if x > 0 else x) top_dif = top_dif[top_dif.lvol > ct.LvolumeSize] top_dif['dff'] = top_dif['dff'].apply(lambda x: x * 2 if x < 0 else x) if 'couts' in top_dif.columns.values: top_dif = top_dif.sort_values(by=['dff', 'percent', 'volume', 'couts', 'ratio'], ascending=[1, 0, 0, 1, 1]) 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 cct.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() > 915 and cct.get_now_time_int() < 1505) and ptype == 'low': top_dif = top_dif[top_dif.percent >= 0] top_temp = stf.filterPowerCount(top_dif,ct.PowerCount) 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 = stf.filterPowerCount(top_dif,ct.PowerCount) 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) else: # top_dif = top_dif[top_dif.percent >= 0] top_end = top_dif[:5].copy() top_temp = top_dif[-ct.PowerCount:].copy()
top_all = top_all.sort_values(by=ct.Monitor_sort_count, ascending=[0, 0, 0, 0, 1]) # top_all = top_all.sort_values(by=[ 'couts'], ascending=[0]) # top_all=top_all.sort_values(by=['dff','percent','couts','ratio'],ascending=[0,0,1,1]) # top_all=top_all.sort_values(by=['dff','couts'],ascending=[0,0]) # top_all=top_all.sort_values(by=['dff','percent','couts','ratio'],ascending=[0,0,1,1]) # print top_all # print pt.PrettyTable([''] + list(top_all.columns)) # print tbl.tabulate(top_all,headers='keys', tablefmt='psql') # print tbl.tabulate(top_all,headers='keys', tablefmt='orgtbl') # print cct.format_for_print(top_all) # print top_all[:10] top_temp = stf.filterPowerCount(top_all, ct.PowerCount) top_temp = pct.powerCompute_df(top_temp, dl=ct.PowerCountdl) goldstock = len( top_all[(top_all.buy >= top_all.lhigh * 0.99) & (top_all.buy >= top_all.llastp * 0.99)]) # print "G:%s Rt:%0.1f dT:%s N:%s" % (len(top_all),float(time.time() - # time_Rt),cct.get_time_to_date(time_s),cct.get_now_time()) cct.set_console(width, height, title=[ 'dT:%s' % cct.get_time_to_date(time_s), 'G:%s' % len(top_all), 'zxg: %s' % (blkname + '-' + market_blk) ]) top_all = tdd.get_powerdf_to_all(top_all, top_temp)