Variables and Data Structures#
import onetick.py as otp
Variables can be used to keep track of state across ticks. The example below shows how we may keep track of P&L.
trd = otp.Ticks({'PRICE': [13.5, 13.6, 13.3, 14.0],
'SIZE': [200, 100, 150, 200],
'SIDE': ['B', 'S', 'B', 'S']})
trd.state_vars['PROFIT'] = 0
trd.state_vars['PROFIT'] += trd.apply(
lambda t: t['PRICE'] * t['SIZE'] if t['SIDE'] == 'S' else -(t['PRICE'] * t['SIZE'])
)
trd['PROFIT'] = trd.state_vars['PROFIT']
otp.run(trd)
Time | PRICE | SIZE | SIDE | PROFIT | |
---|---|---|---|---|---|
0 | 2003-12-01 00:00:00.000 | 13.5 | 200 | B | -2700 |
1 | 2003-12-01 00:00:00.001 | 13.6 | 100 | S | -1340 |
2 | 2003-12-01 00:00:00.002 | 13.3 | 150 | B | -3335 |
3 | 2003-12-01 00:00:00.003 | 14.0 | 200 | S | -535 |
The variable ‘PROFIT’ keeps a running total. In other words, it aggregates state across trd.
Note that the same can be accomplished without variables by keeping the running total in a separate column.
trd = otp.Ticks({'PRICE': [13.5, 13.6, 13.3, 14.0],
'SIZE': [200, 100, 150, 200],
'SIDE': ['B', 'S', 'B', 'S']})
trd['VALUE'] = trd.apply(
lambda t: t['PRICE'] * t['SIZE'] if t['SIDE'] == 'S' else -(t['PRICE'] * t['SIZE'])
)
trd = trd.agg({'PROFIT': otp.agg.sum('VALUE')}, running=True)
otp.run(trd)
Time | PROFIT | |
---|---|---|
0 | 2003-12-01 00:00:00.000 | -2700.0 |
1 | 2003-12-01 00:00:00.001 | -1340.0 |
2 | 2003-12-01 00:00:00.002 | -3335.0 |
3 | 2003-12-01 00:00:00.003 | -535.0 |
Another use case is to store the value from the last tick during aggregation / grouping.
q = otp.Ticks(X=[-1, 3, -3, 4, 2], Y=[0, 1, 1, 0, 3])
q.state_vars['S'] = 0
q.state_vars['S'] = q['X']
q = q.high('X', group_by=['Y'])
q['S'] = q.state_vars['S']
otp.run(q)
Time | X | Y | S | |
---|---|---|---|---|
0 | 2003-12-01 00:00:00.001 | 3 | 1 | 2 |
1 | 2003-12-01 00:00:00.003 | 4 | 0 | 2 |
2 | 2003-12-01 00:00:00.004 | 2 | 3 | 2 |
Dictionaries / Maps#
A map can be created with keys taken from one or more columns and holding entire ticks as values.
q = otp.DataSource('NYSE_TAQ', tick_type='TRD')
q = q[['PRICE','SIZE','COND','EXCHANGE']]
exchanges = otp.Ticks(EXCHANGE=['A', 'B', 'C', 'D', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'],
NAME=['NYSE American (Amex)', 'Nasdaq BX', 'NYSE National (NSX)', 'FINRA ADF + NYSE/Nasdaq TRFs', 'MIAX Pearl', 'International Securities Exchange', 'Cboe EDGA', 'Cboe EDGX', 'Long-Term Stock Exchange (LTSE)', 'NYSE Chicago', 'New York Stock Exchange', 'NYSE Arca', 'Nasdaq (Tape C securities)', 'Consolidated Tape System (CTS)', 'Nasdaq (Tape A,B securities)', 'Members Exchange (MEMX)', "The Investors' Exchange (IEX)", 'CBOE Stock Exchange (CBSX)', 'Nasdaq PSX', 'Cboe BYX', 'Cboe BZX'])
q.state_vars['exchanges'] = otp.state.tick_set('latest', 'EXCHANGE', otp.eval(exchanges))
d = q.state_vars['exchanges'].dump()
q['exchange_name'] = q.state_vars['exchanges'].find('NAME', 'unknown')
otp.run(q, start=otp.dt(2023,3,29,9,30), end=otp.dt(2023,3,29,10), symbols=['SPY'])
Time | PRICE | SIZE | COND | EXCHANGE | exchange_name | |
---|---|---|---|---|---|---|
0 | 2023-03-29 09:30:00.000877568 | 399.9200 | 400 | T | P | NYSE Arca |
1 | 2023-03-29 09:30:00.001151232 | 399.9200 | 1000 | T | T | Nasdaq (Tape A,B securities) |
2 | 2023-03-29 09:30:00.001154304 | 399.9200 | 1000 | T | T | Nasdaq (Tape A,B securities) |
3 | 2023-03-29 09:30:00.001921280 | 399.9300 | 657 | T | T | Nasdaq (Tape A,B securities) |
4 | 2023-03-29 09:30:00.010831360 | 399.9250 | 100 | F | Z | Cboe BZX |
... | ... | ... | ... | ... | ... | ... |
73249 | 2023-03-29 09:59:59.690966784 | 399.8582 | 3 | I | D | FINRA ADF + NYSE/Nasdaq TRFs |
73250 | 2023-03-29 09:59:59.697699840 | 399.8424 | 5 | I | D | FINRA ADF + NYSE/Nasdaq TRFs |
73251 | 2023-03-29 09:59:59.707425024 | 399.8600 | 1 | I | D | FINRA ADF + NYSE/Nasdaq TRFs |
73252 | 2023-03-29 09:59:59.928770304 | 399.8600 | 1 | I | D | FINRA ADF + NYSE/Nasdaq TRFs |
73253 | 2023-03-29 09:59:59.949504768 | 399.8500 | 35 | I | D | FINRA ADF + NYSE/Nasdaq TRFs |
73254 rows × 6 columns