# Creating Bars We create 1-minute bars (bucket_interval=60 seconds) below. ```ipython3 import onetick.py as otp trd = otp.DataSource('US_COMP_SAMPLE', tick_type='TRD') trd = trd.character_present(trd['COND'], 'O6TUHILNRWZ47QMBCGPV', discard_on_match=True) 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=['AAPL'], start=otp.dt(2024, 2, 1, 9, 30), end=otp.dt(2024, 2, 1, 10), timezone='EST5EDT' ) ``` ```myst-ansi Time VOLUME HIGH LOW OPEN COUNT CLOSE 0 2024-02-01 09:31:00 405208 184.3600 183.8200 183.9850 2147 184.2850 1 2024-02-01 09:32:00 269872 184.4600 183.9600 184.2850 1395 184.2099 2 2024-02-01 09:33:00 259842 184.8712 184.0100 184.2000 1330 184.4900 3 2024-02-01 09:34:00 214295 185.0800 184.3900 184.4800 1061 184.9593 4 2024-02-01 09:35:00 241206 185.2100 184.7900 184.9685 1042 185.0212 5 2024-02-01 09:36:00 186926 185.2900 184.9300 185.0300 966 185.2200 6 2024-02-01 09:37:00 176105 185.2250 184.8200 185.2250 970 184.9450 7 2024-02-01 09:38:00 154281 185.2200 184.7500 184.9500 898 185.0600 8 2024-02-01 09:39:00 146778 185.2100 184.8800 185.0650 878 184.9400 9 2024-02-01 09:40:00 201106 185.3389 184.9400 184.9500 841 185.1550 10 2024-02-01 09:41:00 154387 185.3400 184.8709 185.1500 832 185.3300 11 2024-02-01 09:42:00 165414 185.3500 185.0700 185.3173 668 185.3300 12 2024-02-01 09:43:00 226133 185.6400 185.3200 185.3400 956 185.4900 13 2024-02-01 09:44:00 121385 185.6200 185.3500 185.5000 634 185.5700 14 2024-02-01 09:45:00 129403 185.6400 185.2100 185.5700 590 185.2200 15 2024-02-01 09:46:00 262331 185.2262 184.6500 185.2100 1166 184.8800 16 2024-02-01 09:47:00 95166 185.1600 184.8900 184.8900 587 185.0201 17 2024-02-01 09:48:00 116609 185.0900 184.7000 185.0350 688 184.9400 18 2024-02-01 09:49:00 104638 185.0000 184.7200 184.9650 605 184.7900 19 2024-02-01 09:50:00 86386 184.9200 184.7300 184.7800 456 184.7950 20 2024-02-01 09:51:00 126998 184.8880 184.6850 184.8100 728 184.7100 21 2024-02-01 09:52:00 87979 184.9288 184.6200 184.7100 525 184.9000 22 2024-02-01 09:53:00 82921 185.0500 184.7700 184.9100 435 184.9600 23 2024-02-01 09:54:00 94808 185.1450 184.9400 184.9700 490 185.0200 24 2024-02-01 09:55:00 90658 185.2000 184.9900 185.0300 514 185.0300 25 2024-02-01 09:56:00 91304 185.1500 184.9550 185.0340 515 185.0500 26 2024-02-01 09:57:00 109770 185.1450 184.9749 185.0200 566 185.0419 27 2024-02-01 09:58:00 87780 185.1550 184.9900 185.0550 500 185.0600 28 2024-02-01 09:59:00 88513 185.2500 185.0400 185.0650 348 185.2150 29 2024-02-01 10:00:00 136385 185.5000 185.2000 185.2150 647 185.5000 ```
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](../daily_OHLCV.md). Note the use of [`apply_times_daily`](../../../api/run.md#onetick.py.run) 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). ```ipython3 bars = otp.DataSource('US_COMP_BARS', tick_type='TRD_1M') bars = bars[['FIRST', 'HIGH', 'LOW', 'LAST', 'VOLUME']] otp.run( bars, symbols=['AAPL'], start=otp.dt(2024, 2, 1, 9, 31), end=otp.dt(2024, 2, 1, 16, 1), timezone='EST5EDT', apply_times_daily=True) ``` ```myst-ansi Time FIRST HIGH LOW LAST VOLUME 0 2024-02-01 09:31:00 184.0100 184.3600 183.81 184.2850 1071527 1 2024-02-01 09:32:00 184.2850 184.4600 183.96 184.2099 269872 2 2024-02-01 09:33:00 184.2000 184.8712 184.01 184.4900 259842 3 2024-02-01 09:34:00 184.4800 185.0800 184.39 184.9593 214295 4 2024-02-01 09:35:00 184.9685 185.2100 184.79 185.0212 241206 .. ... ... ... ... ... ... 385 2024-02-01 15:56:00 186.5900 186.6900 186.58 186.6800 277485 386 2024-02-01 15:57:00 186.6850 186.6850 186.54 186.6000 316508 387 2024-02-01 15:58:00 186.6050 186.7900 186.57 186.7800 317427 388 2024-02-01 15:59:00 186.7850 186.7850 186.65 186.7600 366951 389 2024-02-01 16:00:00 186.7550 186.9500 186.69 186.8900 830791 [390 rows x 6 columns] ```