Aggregations#
import onetick.py as otp
Let’s start with an unaggregated time series.
otp.config['tz'] = 'EST5EDT'
q = otp.DataSource('NYSE_TAQ', tick_type='TRD')
q = q[['PRICE','SIZE','COND','EXCHANGE']]
otp.run(q, start=otp.dt(2023,3,29,9,30), end=otp.dt(2023,3,29,16), symbols=['SPY'])
| Time | PRICE | SIZE | COND | EXCHANGE | |
|---|---|---|---|---|---|
| 0 | 2023-03-29 09:30:00.000877568 | 399.920 | 400 | T | P |
| 1 | 2023-03-29 09:30:00.001151232 | 399.920 | 1000 | T | T |
| 2 | 2023-03-29 09:30:00.001154304 | 399.920 | 1000 | T | T |
| 3 | 2023-03-29 09:30:00.001921280 | 399.930 | 657 | T | T |
| 4 | 2023-03-29 09:30:00.010831360 | 399.925 | 100 | F | Z |
| ... | ... | ... | ... | ... | ... |
| 537609 | 2023-03-29 15:59:59.994555136 | 401.350 | 643 | F | P |
| 537610 | 2023-03-29 15:59:59.995045376 | 401.350 | 900 | F | P |
| 537611 | 2023-03-29 15:59:59.997313024 | 401.340 | 100 | Z | |
| 537612 | 2023-03-29 15:59:59.997354752 | 401.340 | 498 | N | |
| 537613 | 2023-03-29 15:59:59.997406208 | 401.340 | 200 | T |
537614 rows × 5 columns
Let’s make a note of the total number of trades.
We can aggregate over the entire queried interval.
q = otp.DataSource('NYSE_TAQ', tick_type='TRD')
q = q[['PRICE','SIZE','COND','EXCHANGE']]
q = q.agg({
'volume': otp.agg.sum('SIZE'),
'vwap': otp.agg.vwap('PRICE','SIZE')
})
otp.run(q, start=otp.dt(2023,3,29,9,30), end=otp.dt(2023,3,29,16), symbols=['SPY'])
| Time | volume | vwap | |
|---|---|---|---|
| 0 | 2023-03-29 16:00:00 | 68233701 | 399.760148 |
Or over fixed buckets (aka bars or windows).
q = otp.DataSource('NYSE_TAQ', tick_type='TRD')
q = q[['PRICE','SIZE','COND','EXCHANGE']]
q = q.agg({
'volume': otp.agg.sum('SIZE'),
'vwap': otp.agg.vwap('PRICE','SIZE')
}, bucket_interval=1800)
otp.run(q, start=otp.dt(2023,3,29,9,30), end=otp.dt(2023,3,29,16), symbols=['SPY'])
| Time | volume | vwap | |
|---|---|---|---|
| 0 | 2023-03-29 10:00:00 | 11346391 | 398.805607 |
| 1 | 2023-03-29 10:30:00 | 5344474 | 399.544761 |
| 2 | 2023-03-29 11:00:00 | 5139562 | 399.146746 |
| 3 | 2023-03-29 11:30:00 | 2827218 | 399.166233 |
| 4 | 2023-03-29 12:00:00 | 3198533 | 399.313014 |
| 5 | 2023-03-29 12:30:00 | 4026223 | 399.077673 |
| 6 | 2023-03-29 13:00:00 | 2245602 | 399.872538 |
| 7 | 2023-03-29 13:30:00 | 2558837 | 400.080857 |
| 8 | 2023-03-29 14:00:00 | 3234347 | 399.745620 |
| 9 | 2023-03-29 14:30:00 | 3290063 | 399.376632 |
| 10 | 2023-03-29 15:00:00 | 3301416 | 400.263786 |
| 11 | 2023-03-29 15:30:00 | 7402762 | 400.131562 |
| 12 | 2023-03-29 16:00:00 | 14318273 | 400.934522 |
Or over a sliding window.
q = otp.DataSource('NYSE_TAQ', tick_type='TRD')
q = q[['PRICE','SIZE','COND','EXCHANGE']]
q = q.agg({
'volume': otp.agg.sum('SIZE'),
'vwap': otp.agg.vwap('PRICE','SIZE')
}, bucket_interval=1800, running=True)
otp.run(q, start=otp.dt(2023,3,29,9,30), end=otp.dt(2023,3,29,16), symbols=['SPY'])
| Time | volume | vwap | |
|---|---|---|---|
| 0 | 2023-03-29 09:30:00.000877568 | 400 | 399.920000 |
| 1 | 2023-03-29 09:30:00.001151232 | 1400 | 399.920000 |
| 2 | 2023-03-29 09:30:00.001154304 | 2400 | 399.920000 |
| 3 | 2023-03-29 09:30:00.001921280 | 3057 | 399.922149 |
| 4 | 2023-03-29 09:30:00.010831360 | 3157 | 399.922239 |
| ... | ... | ... | ... |
| 946090 | 2023-03-29 15:59:59.994555136 | 14316575 | 400.934474 |
| 946091 | 2023-03-29 15:59:59.995045376 | 14317475 | 400.934500 |
| 946092 | 2023-03-29 15:59:59.997313024 | 14317575 | 400.934502 |
| 946093 | 2023-03-29 15:59:59.997354752 | 14318073 | 400.934517 |
| 946094 | 2023-03-29 15:59:59.997406208 | 14318273 | 400.934522 |
946095 rows × 3 columns
Note that the number of output ticks is more than the number of trades. This is due to the output tick being created not only when each input tick enters the window but also when it drops out.
We can display all fields of the incoming tick along with the current values of the sliding window metrics.
q = otp.DataSource('NYSE_TAQ', tick_type='TRD')
q = q[['PRICE','SIZE','COND','EXCHANGE']]
q = q.agg({
'volume': otp.agg.sum('SIZE'),
'vwap': otp.agg.vwap('PRICE','SIZE')
}, bucket_interval=1800, running=True, all_fields=True)
otp.run(q, start=otp.dt(2023,3,29,9,30), end=otp.dt(2023,3,29,16), symbols=['SPY'])
| Time | PRICE | SIZE | COND | EXCHANGE | volume | vwap | |
|---|---|---|---|---|---|---|---|
| 0 | 2023-03-29 09:30:00.000877568 | 399.920 | 400 | T | P | 400 | 399.920000 |
| 1 | 2023-03-29 09:30:00.001151232 | 399.920 | 1000 | T | T | 1400 | 399.920000 |
| 2 | 2023-03-29 09:30:00.001154304 | 399.920 | 1000 | T | T | 2400 | 399.920000 |
| 3 | 2023-03-29 09:30:00.001921280 | 399.930 | 657 | T | T | 3057 | 399.922149 |
| 4 | 2023-03-29 09:30:00.010831360 | 399.925 | 100 | F | Z | 3157 | 399.922239 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 537609 | 2023-03-29 15:59:59.994555136 | 401.350 | 643 | F | P | 14316575 | 400.934474 |
| 537610 | 2023-03-29 15:59:59.995045376 | 401.350 | 900 | F | P | 14317475 | 400.934500 |
| 537611 | 2023-03-29 15:59:59.997313024 | 401.340 | 100 | Z | 14317575 | 400.934502 | |
| 537612 | 2023-03-29 15:59:59.997354752 | 401.340 | 498 | N | 14318073 | 400.934517 | |
| 537613 | 2023-03-29 15:59:59.997406208 | 401.340 | 200 | T | 14318273 | 400.934522 |
537614 rows × 7 columns
In this case, we are back to the same number of ticks as the number trades as an output tick is only created on arrival of an input tick.
All of the aggregation operations support grouping.
q = otp.DataSource('NYSE_TAQ', tick_type='TRD')
q = q[['PRICE','SIZE','COND','EXCHANGE']]
q = q.agg({
'volume': otp.agg.sum('SIZE'),
'vwap': otp.agg.vwap('PRICE','SIZE')
}, group_by=['EXCHANGE'])
otp.run(q, start=otp.dt(2023,3,29,9,30), end=otp.dt(2023,3,29,16), symbols=['SPY'])
| Time | EXCHANGE | volume | vwap | |
|---|---|---|---|---|
| 0 | 2023-03-29 16:00:00 | A | 253162 | 400.322973 |
| 1 | 2023-03-29 16:00:00 | B | 516793 | 400.289029 |
| 2 | 2023-03-29 16:00:00 | C | 154697 | 400.230680 |
| 3 | 2023-03-29 16:00:00 | D | 24495779 | 398.999133 |
| 4 | 2023-03-29 16:00:00 | H | 1104733 | 400.336441 |
| 5 | 2023-03-29 16:00:00 | J | 598534 | 400.315377 |
| 6 | 2023-03-29 16:00:00 | K | 3899343 | 400.118436 |
| 7 | 2023-03-29 16:00:00 | L | 1948 | 401.266828 |
| 8 | 2023-03-29 16:00:00 | M | 1268484 | 400.170423 |
| 9 | 2023-03-29 16:00:00 | N | 2010830 | 400.202291 |
| 10 | 2023-03-29 16:00:00 | P | 13043077 | 400.191965 |
| 11 | 2023-03-29 16:00:00 | T | 10210762 | 400.208476 |
| 12 | 2023-03-29 16:00:00 | U | 1780588 | 400.185296 |
| 13 | 2023-03-29 16:00:00 | V | 1495374 | 400.174316 |
| 14 | 2023-03-29 16:00:00 | X | 597341 | 400.145003 |
| 15 | 2023-03-29 16:00:00 | Y | 1680262 | 400.025433 |
| 16 | 2023-03-29 16:00:00 | Z | 5121994 | 400.172607 |
Use case: Creating Bars#
The code below creates minute bars (bucket_interval=60 seconds).
trd = otp.DataSource('NYSE_TAQ', tick_type='TRD')
trd, _ = trd[trd['COND'].str.match('^[^O6TUHILNRWZ47QMBCGPV]*$')]
bars = trd.agg({
'OPEN': otp.agg.first('PRICE'),
'HIGH': otp.agg.max('PRICE'),
'LOW': otp.agg.min('PRICE'),
'CLOSE': otp.agg.last('PRICE'),
'VOLUME': otp.agg.sum('SIZE'),
},
bucket_interval=60)
otp.run(bars, start=otp.dt(2022,11,28,9,30), end=otp.dt(2022,11,30,16), symbols=['SPY'], apply_times_daily=True)
| Time | OPEN | HIGH | LOW | CLOSE | VOLUME | |
|---|---|---|---|---|---|---|
| 0 | 2022-11-28 09:31:00 | 399.09 | 399.750 | 399.000 | 399.6500 | 289791 |
| 1 | 2022-11-28 09:32:00 | 399.69 | 400.000 | 399.600 | 399.8900 | 295441 |
| 2 | 2022-11-28 09:33:00 | 399.89 | 400.175 | 399.840 | 400.1750 | 233032 |
| 3 | 2022-11-28 09:34:00 | 400.16 | 400.380 | 400.000 | 400.1600 | 208954 |
| 4 | 2022-11-28 09:35:00 | 400.13 | 400.360 | 400.120 | 400.3493 | 112015 |
| ... | ... | ... | ... | ... | ... | ... |
| 1165 | 2022-11-30 15:56:00 | 406.68 | 406.830 | 406.460 | 406.8100 | 1311316 |
| 1166 | 2022-11-30 15:57:00 | 406.80 | 406.830 | 406.525 | 406.6000 | 1319798 |
| 1167 | 2022-11-30 15:58:00 | 406.60 | 406.850 | 406.550 | 406.7300 | 1691348 |
| 1168 | 2022-11-30 15:59:00 | 406.73 | 406.950 | 406.680 | 406.7100 | 2592503 |
| 1169 | 2022-11-30 16:00:00 | 406.71 | 407.550 | 406.680 | 407.4700 | 6700757 |
1170 rows × 6 columns
Note that we have minute bars precomputed for your use.
bars = otp.DataSource('NYSE_TAQ_BARS', tick_type='TRD_1M')
bars = bars[['FIRST','HIGH','LOW','LAST','VOLUME']]
otp.run(bars, start=otp.dt(2022,11,28,9,31), end=otp.dt(2022,11,30,16,1), symbols=['SPY'], apply_times_daily=True)
| Time | FIRST | HIGH | LOW | LAST | VOLUME | |
|---|---|---|---|---|---|---|
| 0 | 2022-11-28 09:31:00 | 399.09 | 399.750 | 399.000 | 399.6500 | 289791 |
| 1 | 2022-11-28 09:32:00 | 399.69 | 400.000 | 399.600 | 399.8900 | 295441 |
| 2 | 2022-11-28 09:33:00 | 399.89 | 400.175 | 399.840 | 400.1750 | 233032 |
| 3 | 2022-11-28 09:34:00 | 400.16 | 400.380 | 400.000 | 400.1600 | 208954 |
| 4 | 2022-11-28 09:35:00 | 400.13 | 400.360 | 400.120 | 400.3493 | 112015 |
| ... | ... | ... | ... | ... | ... | ... |
| 1165 | 2022-11-30 15:56:00 | 406.68 | 406.830 | 406.460 | 406.8100 | 1311316 |
| 1166 | 2022-11-30 15:57:00 | 406.80 | 406.830 | 406.525 | 406.6000 | 1319798 |
| 1167 | 2022-11-30 15:58:00 | 406.60 | 406.850 | 406.550 | 406.7300 | 1691348 |
| 1168 | 2022-11-30 15:59:00 | 406.73 | 406.950 | 406.680 | 406.7100 | 2592503 |
| 1169 | 2022-11-30 16:00:00 | 406.71 | 407.550 | 406.680 | 407.4700 | 6700757 |
1170 rows × 6 columns