Symbologies#
This is part of the OQD offering. Available in OneTick Cloud. Can be configured on prem.
We can query data using different symbologies.
import onetick.py as otp
trd = otp.DataSource('NYSE_TAQ', tick_type='TRD')
trd = trd[['PRICE','SIZE','EXCHANGE','COND']]
trd = trd.first(1)
merged = otp.merge([trd], symbols=['BTKR::::AAPL US','FGV::::btkr000BPH459'], identify_input_ts=True)
otp.run(merged, start=otp.dt(2022,3,1,10), end=otp.dt(2022,3,1,10,15), symbol_date=otp.dt(2022, 10, 1))
| Time | PRICE | SIZE | EXCHANGE | COND | SYMBOL_NAME | TICK_TYPE | |
|---|---|---|---|---|---|---|---|
| 0 | 2022-03-01 10:00:04.133843068 | 164.01 | 1 | P | @ TI | BTKR::::AAPL US | TRD |
We can create a mapping between symbologies.
figi = otp.Symbols('NYSE_TAQ', symbology='FGV', show_original_symbols=True, for_tick_type='TRD')
figi, _ = figi[figi['SYMBOL_NAME']!='']
otp.run(figi, start=otp.dt(2022, 12, 1), end=otp.dt(2022, 12, 2))
| Time | SYMBOL_NAME | ORIGINAL_SYMBOL_NAME | |
|---|---|---|---|
| 0 | 2022-12-01 | BBG000C2V3D6 | NYSE_TAQ::A |
| 1 | 2022-12-01 | BBG00B3T3HD3 | NYSE_TAQ::AA |
| 2 | 2022-12-01 | BBG01B0JRCS6 | NYSE_TAQ::AAA |
| 3 | 2022-12-01 | BBG00LPXX872 | NYSE_TAQ::AAAU |
| 4 | 2022-12-01 | BBG00YZC2Z91 | NYSE_TAQ::AAC |
| ... | ... | ... | ... |
| 10955 | 2022-12-01 | BBG00YZ7S2J6 | NYSE_TAQ::ZWRKW |
| 10956 | 2022-12-01 | BBG000H8R0N8 | NYSE_TAQ::ZWS |
| 10957 | 2022-12-01 | BBG019XSYC89 | NYSE_TAQ::ZYME |
| 10958 | 2022-12-01 | BBG007BBS8B7 | NYSE_TAQ::ZYNE |
| 10959 | 2022-12-01 | BBG000BJBXZ2 | NYSE_TAQ::ZYXI |
10960 rows × 3 columns
btkr = otp.Symbols('NYSE_TAQ', symbology='BTKR', show_original_symbols=True, for_tick_type='TRD')
btkr, _ = btkr[btkr['SYMBOL_NAME']!='']
otp.run(btkr, start=otp.dt(2022, 12, 1), end=otp.dt(2022, 12, 2))
| Time | SYMBOL_NAME | ORIGINAL_SYMBOL_NAME | |
|---|---|---|---|
| 0 | 2022-12-01 | A US | NYSE_TAQ::A |
| 1 | 2022-12-01 | AA US | NYSE_TAQ::AA |
| 2 | 2022-12-01 | AAA US | NYSE_TAQ::AAA |
| 3 | 2022-12-01 | AAAU US | NYSE_TAQ::AAAU |
| 4 | 2022-12-01 | AAC US | NYSE_TAQ::AAC |
| ... | ... | ... | ... |
| 10926 | 2022-12-01 | ZWRKW US | NYSE_TAQ::ZWRKW |
| 10927 | 2022-12-01 | ZWS US | NYSE_TAQ::ZWS |
| 10928 | 2022-12-01 | ZYME US | NYSE_TAQ::ZYME |
| 10929 | 2022-12-01 | ZYNE US | NYSE_TAQ::ZYNE |
| 10930 | 2022-12-01 | ZYXI US | NYSE_TAQ::ZYXI |
10931 rows × 3 columns
figi = otp.Symbols('NYSE_TAQ', symbology='FGV', show_original_symbols=True, for_tick_type='TRD')
btkr = otp.Symbols('NYSE_TAQ', symbology='BTKR', show_original_symbols=True, for_tick_type='TRD')
btkr, _ = btkr[btkr['SYMBOL_NAME']!='']
figi, _ = figi[figi['SYMBOL_NAME']!='']
mapping = otp.functions.join(figi, btkr, on=btkr['ORIGINAL_SYMBOL_NAME']==figi['ORIGINAL_SYMBOL_NAME'], how="inner")
mapping = mapping.rename({'RIGHT_SYMBOL_NAME' : 'BTKR',
'SYMBOL_NAME' : 'FIGI',
'ORIGINAL_SYMBOL_NAME': 'DB_SYMBOL'})
mapping = mapping[['FIGI', 'BTKR', 'DB_SYMBOL']]
otp.run(mapping, start=otp.dt(2022, 12, 1), end=otp.dt(2022, 12, 2))
| Time | FIGI | BTKR | DB_SYMBOL | |
|---|---|---|---|---|
| 0 | 2022-12-01 | BBG000C2V3D6 | A US | NYSE_TAQ::A |
| 1 | 2022-12-01 | BBG00B3T3HD3 | AA US | NYSE_TAQ::AA |
| 2 | 2022-12-01 | BBG01B0JRCS6 | AAA US | NYSE_TAQ::AAA |
| 3 | 2022-12-01 | BBG00LPXX872 | AAAU US | NYSE_TAQ::AAAU |
| 4 | 2022-12-01 | BBG00YZC2Z91 | AAC US | NYSE_TAQ::AAC |
| ... | ... | ... | ... | ... |
| 10953 | 2022-12-01 | BBG00YZ7S2J6 | ZWRKW US | NYSE_TAQ::ZWRKW |
| 10954 | 2022-12-01 | BBG000H8R0N8 | ZWS US | NYSE_TAQ::ZWS |
| 10955 | 2022-12-01 | BBG019XSYC89 | ZYME US | NYSE_TAQ::ZYME |
| 10956 | 2022-12-01 | BBG007BBS8B7 | ZYNE US | NYSE_TAQ::ZYNE |
| 10957 | 2022-12-01 | BBG000BJBXZ2 | ZYXI US | NYSE_TAQ::ZYXI |
10958 rows × 4 columns
And here is an example for a non-US market
# 'FGV' is venue FIGI
# 'FGC' is composite FIGI
fgv = otp.Symbols('LSE', symbology='FGV', show_original_symbols=True, for_tick_type='TRD')
tkr = otp.Symbols('LSE', symbology='TKR', show_original_symbols=True, for_tick_type='TRD')
mapping = otp.functions.join(fgv, tkr, on=fgv['ORIGINAL_SYMBOL_NAME']==tkr['ORIGINAL_SYMBOL_NAME'], how="inner")
mapping, _ = mapping[mapping['SYMBOL_NAME'] != '']
mapping = mapping.rename({'RIGHT_SYMBOL_NAME' : 'TICKER',
'SYMBOL_NAME' : 'FIGI',
'ORIGINAL_SYMBOL_NAME': 'DB_SYMBOL'})
mapping = mapping[['TICKER', 'FIGI', 'DB_SYMBOL']]
start = otp.dt(2022, 5, 20)
end = otp.dt(2022, 5, 25)
otp.run(mapping, start=otp.dt(2022, 12, 1), end=otp.dt(2022, 12, 2))
| Time | TICKER | FIGI | DB_SYMBOL | |
|---|---|---|---|---|
| 0 | 2022-12-01 | 020Y | BBG007XBDG85 | LSE::020Y |
| 1 | 2022-12-01 | 0A05 | BBG00SYG7Q00 | LSE::0A05 |
| 2 | 2022-12-01 | 0A07 | BBG00NWMW214 | LSE::0A07 |
| 3 | 2022-12-01 | 0A08 | BBG00NWMVL13 | LSE::0A08 |
| 4 | 2022-12-01 | 0A0B | BBG00NWMW143 | LSE::0A0B |
| ... | ... | ... | ... | ... |
| 5332 | 2022-12-01 | ZNWD | BBG00JG082G0 | LSE::ZNWD |
| 5333 | 2022-12-01 | ZOO | BBG000DBR279 | LSE::ZOO |
| 5334 | 2022-12-01 | ZPHR | BBG000DB8YK5 | LSE::ZPHR |
| 5335 | 2022-12-01 | ZTF | BBG000BGXLF2 | LSE::ZTF |
| 5336 | 2022-12-01 | ZYT | BBG000BZPV76 | LSE::ZYT |
5337 rows × 4 columns
Symbol name changes#
Symbol name changes are easy to handle with the symbol_date parameter of otp.run() which provides the “as of” date for the symbols. For example, we can pass META while setting symbol_date to any date after 2022-06-09 or FB and symbol_date before 2022-06-09.
trd = otp.DataSource('NYSE_TAQ', tick_type='TRD')
trd = trd.first(5)
otp.run(trd, start=otp.dt(2023,2,2), end=otp.dt(2023,2,3), symbols='META', symbol_date=otp.dt(2023,2,2))
| Time | EXCHANGE | COND | STOP_STOCK | SOURCE | TRF | TTE | TICKER | PRICE | DELETED_TIME | TICK_STATUS | SIZE | CORR | SEQ_NUM | TRADE_ID | PARTICIPANT_TIME | TRF_TIME | OMDSEQ | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2023-02-02 00:00:00.100474511 | Q | @ TI | N | 0 | META | 183.13 | 1970-01-01 | 0 | 10 | 0 | 7286117 | 160978 | 2023-02-02 00:00:00.100458331 | 1970-01-01 00:00:00.000000000 | 0 | ||
| 1 | 2023-02-02 00:00:00.825173183 | Q | @FT | N | 1 | META | 183.13 | 1970-01-01 | 0 | 100 | 0 | 7286118 | 160979 | 2023-02-02 00:00:00.825157012 | 1970-01-01 00:00:00.000000000 | 0 | ||
| 2 | 2023-02-02 00:00:00.835111354 | K | @ TI | N | 1 | META | 183.18 | 1970-01-01 | 0 | 2 | 0 | 7286119 | 54129 | 2023-02-02 00:00:00.834865000 | 1970-01-01 00:00:00.000000000 | 0 | ||
| 3 | 2023-02-02 00:00:00.837461982 | Q | @ TI | N | 0 | META | 183.18 | 1970-01-01 | 0 | 5 | 0 | 7286123 | 160980 | 2023-02-02 00:00:00.837446438 | 1970-01-01 00:00:00.000000000 | 0 | ||
| 4 | 2023-02-02 00:00:00.877120054 | D | @ TI | N | Q | 0 | META | 183.13 | 1970-01-01 | 0 | 13 | 0 | 7286124 | 117854 | 2023-02-02 00:00:00.875000000 | 2023-02-02 00:00:00.877093881 | 0 |
trd = otp.DataSource('NYSE_TAQ', tick_type='TRD')
trd = trd.first(5)
otp.run(trd, start=otp.dt(2023,2,2), end=otp.dt(2023,2,3), symbols='FB', symbol_date=otp.dt(2021,2,2))
| Time | EXCHANGE | COND | STOP_STOCK | SOURCE | TRF | TTE | TICKER | PRICE | DELETED_TIME | TICK_STATUS | SIZE | CORR | SEQ_NUM | TRADE_ID | PARTICIPANT_TIME | TRF_TIME | OMDSEQ | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2023-02-02 00:00:00.100474511 | Q | @ TI | N | 0 | META | 183.13 | 1970-01-01 | 0 | 10 | 0 | 7286117 | 160978 | 2023-02-02 00:00:00.100458331 | 1970-01-01 00:00:00.000000000 | 0 | ||
| 1 | 2023-02-02 00:00:00.825173183 | Q | @FT | N | 1 | META | 183.13 | 1970-01-01 | 0 | 100 | 0 | 7286118 | 160979 | 2023-02-02 00:00:00.825157012 | 1970-01-01 00:00:00.000000000 | 0 | ||
| 2 | 2023-02-02 00:00:00.835111354 | K | @ TI | N | 1 | META | 183.18 | 1970-01-01 | 0 | 2 | 0 | 7286119 | 54129 | 2023-02-02 00:00:00.834865000 | 1970-01-01 00:00:00.000000000 | 0 | ||
| 3 | 2023-02-02 00:00:00.837461982 | Q | @ TI | N | 0 | META | 183.18 | 1970-01-01 | 0 | 5 | 0 | 7286123 | 160980 | 2023-02-02 00:00:00.837446438 | 1970-01-01 00:00:00.000000000 | 0 | ||
| 4 | 2023-02-02 00:00:00.877120054 | D | @ TI | N | Q | 0 | META | 183.13 | 1970-01-01 | 0 | 13 | 0 | 7286124 | 117854 | 2023-02-02 00:00:00.875000000 | 2023-02-02 00:00:00.877093881 | 0 |