def result_dicts(some_mids,some_quotes,expiration_dict,trade_date,expiries):
vol_results = dict()
money_results = dict()
dte_info = dict() #could be appended to expiry info but...
basis_info = dict()
#loop through all your expiries
# -- append basis to your weighted mids table (we'll store this later)
# -- store vols/moneyness info into dicts temporarily
for exp in expiries:
und_expiry_code = underlying_code_by_two_digit_code(exp)
und_sym = underlying_code(und_expiry_code,underlyings(some_quotes)).values[0]
exp_dte = dte(exp,trade_date,expiration_dict)
print 'Expiry : ', exp, ' :: Corresponding Underlying: ',und_sym ,' :: DTE: ', exp_dte
basis_code = exp+'-'+und_expiry_code+'-'+str(exp_dte)
if und_expiry_code == exp: #quarterly
spot_filtered = pd.Series(some_mids[und_sym].values,index=some_mids.index)
syn_spread = pd.Series(np.zeros(len(spot_filtered)),index=some_mids.index)
else:
spot = synthetic_offset(exp,und_sym,some_mids)
if len(spot.valid()) == 0: #we were never able to calculate a synthetic basis and thus can't calc underlying
continue
syn_spread = univariate_kf(spot.values,spot[spot.first_valid_index()],1,500)
spot_filtered = pd.Series(syn_spread+some_mids[und_sym].values,index=spot.index)
some_mids[basis_code] = spot_filtered
vol_results[exp] = imp_vols_cython(some_mids.ix[:,options_expiry_mask(some_mids.columns,exp).values],spot_filtered,exp_dte)
money_results[exp] = pd.DataFrame(altmoneys(spot_filtered.fillna(method='ffill').fillna(method='bfill').values,
kospi_strikes_from_symbols(vol_results[exp].columns.values).values,exp_dte/260.0),
index = some_mids.index, columns = vol_results[exp].columns)
dte_info[exp] = exp_dte
basis_info[exp] = pd.Series(syn_spread,index=spot.index)
return [vol_results,money_results,dte_info,basis_info]
def save_supplementary(h5_pointer,pcap_info,vols_type):
s = pd.Series(h5_pointer.keys())
vol_series_list = []
basis_series_list = []
fut_bid_series_list = []
fut_ask_series_list = []
print 'Pcap Info Length entering: ',len(pcap_info)
for k in s[s.str.contains(vols_type)]:
two_digit_code = k[1:3]
my_und = underlying_code(underlying_code_by_two_digit_code(two_digit_code),underlyings(pcap_info)).values[0]
just_that_data = pcap_info[options_expiry_mask(pcap_info.symbol,two_digit_code)]
just_that_fut = pcap_info[pcap_info.symbol==my_und]
strikes = np.array(kospi_strikes_from_symbols(just_that_data.symbol.values),dtype=object)
just_those_vols = h5_pointer[k]
basis = h5_pointer['basis'][two_digit_code]
basis.index = basis.index.astype(np.int64)
just_that_data['vols'] = cross(strikes,just_that_data.index.astype(long),just_those_vols.index.astype(long),just_those_vols.columns.values,just_those_vols.values)
just_that_data['basis'] = basis.asof(just_that_data.index).fillna(method='ffill')
just_that_data['fut_bid'] = just_that_fut.bid1.asof(just_that_data.index).fillna(method='ffill')
just_that_data['fut_ask'] = just_that_fut.ask1.asof(just_that_data.index).fillna(method='ffill')
vol_series_list.append(just_that_data['vols'])
basis_series_list.append(just_that_data['basis'])
fut_bid_series_list.append(just_that_data['fut_bid'])
fut_ask_series_list.append(just_that_data['fut_ask'])
pcap_info['vols'] = pd.concat(vol_series_list).reindex_like(pcap_info)
pcap_info['basis'] = pd.concat(basis_series_list).reindex_like(pcap_info)
pcap_info['fut_bid'] = pd.concat(fut_bid_series_list).reindex_like(pcap_info)
pcap_info['fut_ask'] = pd.concat(fut_ask_series_list).reindex_like(pcap_info)
h5_pointer.remove('supplementary')
print 'Pcap Info Length exiting: ',len(pcap_info)
h5_pointer.append('supplementary',pcap_info.ix[:,['vols','basis','fut_bid','fut_ask']])
def save_vol_tables(h5_pointer,expiry_code,vol_results_dict,money_results_dict):
is_call = pd.Series(vol_results_dict[expiry_code].columns.values).str[2:4]=='42'
call_options = vol_results_dict[expiry_code].ix[:,is_call.values].sort_index(axis=1)
call_options.columns = kospi_strikes_from_symbols(call_options.columns)
call_moneys = money_results_dict[expiry_code].ix[:,is_call.values].sort_index(axis=1)
call_moneys.columns = call_options.columns
if not call_options.empty: #don't store empty tables (it's confusing for later analysis, no?
h5_pointer.put(expiry_code+'/vols',call_options)
h5_pointer.put(expiry_code+'/moneys',call_moneys)
def save_implieds(h5_pointer, new_store,start_time,end_time,MAX_AGE=50,RISK_FREE=.03,GUESS=260):
s = pd.Series(h5_pointer['twmids'].columns)
front_syms = s[s.str.contains('-')].str[0:2].append(s[s.str.contains('-')].str[3:5]).unique()
dat = h5_pointer.select('pcap_data',[pd.Term('index','>=',start_time),pd.Term('index','<=',end_time)])
supp = h5_pointer.select('supplementary',[pd.Term('index','>=',start_time),pd.Term('index','<=',end_time)])
recombined_dat = (dat.join(supp,how='outer'))
recombined_dat = recombined_dat[is_kospi(dat.symbol).values]
expiries = recombined_dat.symbol.str[6:8]
recombined_dat = recombined_dat[expiries.isin(h5_pointer['dtes'].index).values]
recombined_dat['tte'] = recombined_dat.symbol.str[6:8].replace(h5_pointer['dtes'].to_dict()[0]).astype(float) / 260.
recombined_dat['strike'] = kospi_strikes_from_symbols(recombined_dat.symbol.values)
recombined_dat['type'] = kospi_types_from_symbols(recombined_dat.symbol.values)
recombined_dat = recombined_dat[recombined_dat.symbol.str[6:8].isin(front_syms).values]
recombined_dat = recombined_dat[kospi_fresh(recombined_dat.symbol,recombined_dat.tte.values,MAX_AGE/260.)]
implieds_frame = fast_implieds(recombined_dat.symbol,recombined_dat.bid1.astype(np.float64),recombined_dat.bidsize1,recombined_dat.bid2.astype(np.float64),recombined_dat.bidsize2,
recombined_dat.ask1.astype(np.float64),recombined_dat.asksize1,recombined_dat.ask2.astype(np.float64),recombined_dat.asksize2,
(recombined_dat.basis+recombined_dat.fut_bid),recombined_dat.basis,recombined_dat.vols,recombined_dat.tte,recombined_dat.strike.astype(np.float64),
recombined_dat.type.astype(long),RISK_FREE,GUESS)
implieds_frame.index = recombined_dat.index
implieds_frame['delta_effect'] = net_effect_cython(recombined_dat.symbol,recombined_dat.bid1.astype(np.float64),recombined_dat.tradeprice.astype(np.float64),recombined_dat.tradesize)
implieds_frame['delta'] = deltas(recombined_dat.fut_bid.astype(np.float64),recombined_dat.strike.astype(np.float64),recombined_dat.vols,recombined_dat.tte,recombined_dat.type.astype(long),.03)
implieds_frame['implied_trd'] = implied_futs(recombined_dat.tradeprice.astype(np.float64),recombined_dat.strike.astype(np.float64),recombined_dat.vols,recombined_dat.tte,recombined_dat.type.astype(long),.03,26410) - recombined_dat.basis
implieds_frame['implied_trd'][(implieds_frame.delta.abs()<.15).values] = np.NaN
implieds_frame['delta'][(implieds_frame.delta.abs()<.15).values] = np.NaN
implieds_frame['delta_effect'] *= implieds_frame['delta']
#implieds_frame = implieds_frame.drop_duplicates()
implieds_frame['symbol'] = recombined_data.symbol
implieds_frame['fut_bid'] = recombined_data.fut_bid
implieds_frame['fut_ask'] = recombined_data.fut_ask
newdat = pd.DataFrame(two_level_fix_a3s(recombined_dat.symbol,recombined_dat.msg_type.str[1:].astype(long),recombined_dat.ix[:,['bid1','bidsize1','ask1','asksize1','bid2','bidsize2','ask2','asksize2','tradeprice','tradesize']].values),columns = ['bid1','bidsize1','ask1','asksize1','bid2','bidsize2','ask2','asksize2','tradeprice','tradesize'], index = recombined_dat.index)
combo = implieds_frame.join(newdat,how='inner')
print 'Preparing to save implieds info... %d,%d' % (len(implieds_frame),len(combo))
if '/implieds' in new_store.keys():
print 'Deleteing old implieds frame...'
new_store.remove('implieds')
new_store.append('implieds',combo)
def filter_calls(some_df):
is_call = pd.Series(some_df.columns.values).str[2:4]=='42'
res = some_df.ix[:,is_call.values].sort_index(axis=1)
res.columns = kospi_strikes_from_symbols(res.columns)
return res