Creating Bars#
We create 1-minute bars (bucket_interval=60 seconds) below.
import onetick.py as otp
trd = otp.DataSource('NYSE_TAQ', tick_type='TRD')
trd, _ = trd[trd['COND'].str.match('^[^O6TUHILNRWZ47QMBCGPV]*$')]
bars = trd.agg({'VOLUME': otp.agg.sum('SIZE'),
'HIGH': otp.agg.max('PRICE'),
'LOW': otp.agg.min('PRICE'),
'OPEN': otp.agg.first('PRICE'),
'COUNT': otp.agg.count(),
'CLOSE': otp.agg.last('PRICE')},
bucket_interval=otp.Minute(1))
otp.run(
bars,
symbols=['SPY'],
start=otp.dt(2023, 5, 15, 9, 30),
end=otp.dt(2023, 5, 15, 10),
timezone='EST5EDT'
)
| Time | VOLUME | HIGH | LOW | OPEN | COUNT | CLOSE | |
|---|---|---|---|---|---|---|---|
| 0 | 2023-05-15 09:31:00 | 264719 | 412.2900 | 412.0350 | 412.2200 | 1446 | 412.0400 |
| 1 | 2023-05-15 09:32:00 | 218724 | 412.2600 | 412.0000 | 412.0500 | 1537 | 412.1600 |
| 2 | 2023-05-15 09:33:00 | 271364 | 412.2000 | 411.9400 | 412.1650 | 1649 | 412.0200 |
| 3 | 2023-05-15 09:34:00 | 312764 | 412.0200 | 411.6312 | 412.0100 | 1827 | 411.8200 |
| 4 | 2023-05-15 09:35:00 | 252569 | 411.8300 | 411.3600 | 411.8100 | 1241 | 411.3983 |
| 5 | 2023-05-15 09:36:00 | 202980 | 411.6250 | 411.3200 | 411.3900 | 1286 | 411.6250 |
| 6 | 2023-05-15 09:37:00 | 114774 | 411.6800 | 411.4400 | 411.6200 | 841 | 411.5900 |
| 7 | 2023-05-15 09:38:00 | 152180 | 411.6000 | 411.4100 | 411.5800 | 927 | 411.4300 |
| 8 | 2023-05-15 09:39:00 | 96940 | 411.5100 | 411.3600 | 411.4300 | 592 | 411.3600 |
| 9 | 2023-05-15 09:40:00 | 129613 | 411.4650 | 411.2300 | 411.3550 | 884 | 411.4471 |
| 10 | 2023-05-15 09:41:00 | 111231 | 411.6668 | 411.3600 | 411.4550 | 723 | 411.6300 |
| 11 | 2023-05-15 09:42:00 | 214502 | 411.7100 | 411.4300 | 411.6400 | 1395 | 411.4750 |
| 12 | 2023-05-15 09:43:00 | 262713 | 411.6600 | 411.4200 | 411.4700 | 1379 | 411.4700 |
| 13 | 2023-05-15 09:44:00 | 106379 | 411.5200 | 411.3520 | 411.4700 | 654 | 411.4400 |
| 14 | 2023-05-15 09:45:00 | 103743 | 411.5250 | 411.4000 | 411.4200 | 551 | 411.5016 |
| 15 | 2023-05-15 09:46:00 | 137092 | 411.6600 | 411.5200 | 411.5200 | 871 | 411.6300 |
| 16 | 2023-05-15 09:47:00 | 189016 | 411.6900 | 411.3600 | 411.6300 | 1201 | 411.3900 |
| 17 | 2023-05-15 09:48:00 | 121331 | 411.4780 | 411.3100 | 411.3900 | 612 | 411.3150 |
| 18 | 2023-05-15 09:49:00 | 108976 | 411.3900 | 411.1600 | 411.3200 | 664 | 411.2900 |
| 19 | 2023-05-15 09:50:00 | 112394 | 411.3000 | 411.1200 | 411.3000 | 637 | 411.1900 |
| 20 | 2023-05-15 09:51:00 | 94153 | 411.2700 | 411.0300 | 411.1900 | 469 | 411.0300 |
| 21 | 2023-05-15 09:52:00 | 112302 | 411.1900 | 410.9800 | 411.0300 | 687 | 410.9900 |
| 22 | 2023-05-15 09:53:00 | 114025 | 411.1179 | 410.9700 | 410.9850 | 599 | 410.9900 |
| 23 | 2023-05-15 09:54:00 | 80763 | 411.1299 | 410.9500 | 410.9850 | 458 | 411.0150 |
| 24 | 2023-05-15 09:55:00 | 66556 | 411.1099 | 411.0100 | 411.0190 | 435 | 411.0350 |
| 25 | 2023-05-15 09:56:00 | 131143 | 411.1100 | 410.9100 | 411.0350 | 790 | 410.9205 |
| 26 | 2023-05-15 09:57:00 | 128274 | 410.9300 | 410.8200 | 410.9205 | 679 | 410.8400 |
| 27 | 2023-05-15 09:58:00 | 162243 | 410.9500 | 410.8000 | 410.8200 | 953 | 410.8900 |
| 28 | 2023-05-15 09:59:00 | 105649 | 410.9300 | 410.8300 | 410.8800 | 572 | 410.8700 |
| 29 | 2023-05-15 10:00:00 | 145019 | 410.9100 | 410.7800 | 410.8600 | 911 | 410.8400 |
Note: OneTick Cloud has minute bars precomputed and available in *_BARS databases under the tick type TRD_1M.
Daily OHLCV data with the official closing prices is also available: see OHLCV.
Note the use of apply_times_daily to limit each day’s interval to 9:30-4:00pm (plus one minute is added as the minute bar for 9:30-9:31 has the timestamp of 9:31).
bars = otp.DataSource('NYSE_TAQ_BARS', tick_type='TRD_1M')
bars = bars[['FIRST', 'HIGH', 'LOW', 'LAST', 'VOLUME']]
otp.run(
bars,
symbols=['SPY'],
start=otp.dt(2023, 5, 15, 9, 31),
end=otp.dt(2023, 5, 19, 16, 1),
timezone='EST5EDT',
apply_times_daily=True)
| Time | FIRST | HIGH | LOW | LAST | VOLUME | |
|---|---|---|---|---|---|---|
| 0 | 2023-05-15 09:31:00 | 412.220 | 412.290 | 412.0350 | 412.0400 | 264719 |
| 1 | 2023-05-15 09:32:00 | 412.050 | 412.260 | 412.0000 | 412.1600 | 218724 |
| 2 | 2023-05-15 09:33:00 | 412.165 | 412.200 | 411.9400 | 412.0200 | 271364 |
| 3 | 2023-05-15 09:34:00 | 412.010 | 412.020 | 411.6312 | 411.8200 | 312764 |
| 4 | 2023-05-15 09:35:00 | 411.810 | 411.830 | 411.3600 | 411.3983 | 252569 |
| ... | ... | ... | ... | ... | ... | ... |
| 1945 | 2023-05-19 15:56:00 | 418.890 | 418.950 | 418.8501 | 418.9200 | 465446 |
| 1946 | 2023-05-19 15:57:00 | 418.910 | 418.915 | 418.7200 | 418.8700 | 618549 |
| 1947 | 2023-05-19 15:58:00 | 418.870 | 418.980 | 418.8400 | 418.9500 | 601161 |
| 1948 | 2023-05-19 15:59:00 | 418.950 | 418.990 | 418.9000 | 418.9600 | 711607 |
| 1949 | 2023-05-19 16:00:00 | 418.960 | 418.970 | 418.5700 | 418.6100 | 1565989 |
1950 rows × 6 columns