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)
