def to_datamap(frame): temp = frame[['StockSymbol', 'ExpYear', 'ExpMonth', 'ExpDay', 'Cp', 'Strike', 'VWAV_sell']] temp['Strike'] = (temp['Strike']*1000.0) temp_dict = retro_dictify(temp) data_map = pycake.DataMap('SL_VWAV_SELL', True) data_map.clear() data_map.notify(data = temp_dict) data_map.close()
day_frac = (((datetime.datetime.now() - (datetime.datetime.combine( datetime.date.today(), datetime.time(8, 30)))).total_seconds()) / 60.0) / (6.5 * 60) day_frac = min(1, day_frac) if np.isnan(x): return 0 if x < (500 * day_frac): return 1 if x < (2500 * day_frac): return 2 return 3 frame['level'] = frame['qty'].map(add_level) frame['adj_level'] = frame['qty'].map(add_adj_level) data_map = pycake.DataMap('JG_PRINTVOLS_NEW', True) #data_map.svr1 = c data_map.clear() temp = frame[[ 'StockSymbol', 'ExpYear', 'ExpMonth', 'ExpDay', 'Strike', 'Cp', 'VWAV_sell', 'VWAV_buy', 'max_vol_sell', 'max_vol_buy', 'min_vol_sell', 'min_vol_buy', 'level', 'adj_level', 'lastupdated' ]] temp = pd.melt(temp, id_vars=[ 'StockSymbol', 'ExpYear', 'ExpMonth', 'ExpDay', 'Strike', 'Cp' ]) temp['variable'] = temp['variable'].apply(lambda x: x.upper()) temp['Strike'] = (temp['Strike'] * 1000.0)