Use Cases#
import onetick.py as otp
Retrieving Tick Data#
s = otp.dt(2023, 5, 15, 9, 30)
e = otp.dt(2023, 5, 15, 9, 30, 1)
trd = otp.DataSource('NYSE_TAQ', tick_type='TRD')
otp.run(trd, start=s, end=e, symbols=['SPY'])
| 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 | 2023-05-15 09:30:00.000178688 | P | T | N | C | 0 | SPY | 412.22 | 1969-12-31 19:00:00 | 0 | 100 | 0 | 39195 | 52983525167153 | 2023-05-15 09:30:00.000150016 | 1969-12-31 19:00:00.000000000 | 0 | |
| 1 | 2023-05-15 09:30:00.000776704 | Z | N | C | 0 | SPY | 412.22 | 1969-12-31 19:00:00 | 0 | 247 | 0 | 39196 | 52983525035355 | 2023-05-15 09:30:00.000450000 | 1969-12-31 19:00:00.000000000 | 1 | ||
| 2 | 2023-05-15 09:30:00.003603456 | T | T | N | C | 0 | SPY | 412.22 | 1969-12-31 19:00:00 | 0 | 100 | 0 | 39212 | 62879133157950 | 2023-05-15 09:30:00.003165122 | 1969-12-31 19:00:00.000000000 | 0 | |
| 3 | 2023-05-15 09:30:00.006352128 | K | I | N | C | 0 | SPY | 412.24 | 1969-12-31 19:00:00 | 0 | 1 | 0 | 39227 | 52983525098301 | 2023-05-15 09:30:00.006091000 | 1969-12-31 19:00:00.000000000 | 0 | |
| 4 | 2023-05-15 09:30:00.007128064 | K | I | N | C | 0 | SPY | 412.24 | 1969-12-31 19:00:00 | 0 | 3 | 0 | 39231 | 52983525098302 | 2023-05-15 09:30:00.006873000 | 1969-12-31 19:00:00.000000000 | 0 | |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 310 | 2023-05-15 09:30:00.934032640 | T | T | N | C | 0 | SPY | 412.27 | 1969-12-31 19:00:00 | 0 | 160 | 0 | 40517 | 62879133545538 | 2023-05-15 09:30:00.933678033 | 1969-12-31 19:00:00.000000000 | 1 | |
| 311 | 2023-05-15 09:30:00.975609344 | D | I | N | C | T | 0 | SPY | 412.24 | 1969-12-31 19:00:00 | 0 | 2 | 0 | 40543 | 71675240595789 | 2023-05-15 09:30:00.661000000 | 2023-05-15 09:30:00.975241514 | 0 |
| 312 | 2023-05-15 09:30:00.980264448 | D | I | N | C | T | 0 | SPY | 412.27 | 1969-12-31 19:00:00 | 0 | 1 | 0 | 40545 | 71675240596294 | 2023-05-15 09:30:00.553501000 | 2023-05-15 09:30:00.979895700 | 0 |
| 313 | 2023-05-15 09:30:00.985391616 | T | N | C | 0 | SPY | 412.28 | 1969-12-31 19:00:00 | 0 | 100 | 0 | 40547 | 62879133552717 | 2023-05-15 09:30:00.984946104 | 1969-12-31 19:00:00.000000000 | 0 | ||
| 314 | 2023-05-15 09:30:00.985394944 | T | Q | N | C | 0 | SPY | 412.28 | 1969-12-31 19:00:00 | 0 | 100 | 0 | 40549 | 62879133552719 | 2023-05-15 09:30:00.984958312 | 1969-12-31 19:00:00.000000000 | 1 |
315 rows × 18 columns
Creating Bars#
We create 1-minute bars (bucket_interval=60 seconds) below.
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=60)
otp.run(bars, start=otp.dt(2023,5,15,9,30), end=otp.dt(2023,5,15,10), symbols=['SPY'])
| 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, start=otp.dt(2023,5,15,9,31), end=otp.dt(2023,5,19,16,1), symbols=['SPY'], 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
Prevailing quote at the time of a trade#
trd = otp.DataSource('NYSE_TAQ', tick_type='TRD')
trd = trd[['PRICE', 'SIZE']]
qte = otp.DataSource('TAQ_NBBO', tick_type='NBBO', back_to_first_tick=600)
qte = qte[['ASK_PRICE', 'BID_PRICE']]
qte['quote_time'] = qte['Time']
enriched_trades = otp.join_by_time([trd, qte])
otp.run(enriched_trades, start=s, end=e, symbols=['SPY'])
| Time | PRICE | SIZE | ASK_PRICE | BID_PRICE | quote_time | |
|---|---|---|---|---|---|---|
| 0 | 2023-05-15 09:30:00.000178688 | 412.22 | 100 | 412.25 | 412.22 | 2023-05-15 09:30:00.000174080 |
| 1 | 2023-05-15 09:30:00.000776704 | 412.22 | 247 | 412.24 | 412.21 | 2023-05-15 09:30:00.000715520 |
| 2 | 2023-05-15 09:30:00.003603456 | 412.22 | 100 | 412.24 | 412.22 | 2023-05-15 09:30:00.003562496 |
| 3 | 2023-05-15 09:30:00.006352128 | 412.24 | 1 | 412.25 | 412.22 | 2023-05-15 09:30:00.006343936 |
| 4 | 2023-05-15 09:30:00.007128064 | 412.24 | 3 | 412.25 | 412.22 | 2023-05-15 09:30:00.007110656 |
| ... | ... | ... | ... | ... | ... | ... |
| 310 | 2023-05-15 09:30:00.934032640 | 412.27 | 160 | 412.28 | 412.26 | 2023-05-15 09:30:00.934030080 |
| 311 | 2023-05-15 09:30:00.975609344 | 412.24 | 2 | 412.28 | 412.27 | 2023-05-15 09:30:00.970691840 |
| 312 | 2023-05-15 09:30:00.980264448 | 412.27 | 1 | 412.28 | 412.27 | 2023-05-15 09:30:00.979763456 |
| 313 | 2023-05-15 09:30:00.985391616 | 412.28 | 100 | 412.28 | 412.27 | 2023-05-15 09:30:00.985296640 |
| 314 | 2023-05-15 09:30:00.985394944 | 412.28 | 100 | 412.28 | 412.27 | 2023-05-15 09:30:00.985296640 |
315 rows × 6 columns
Point-in-time benchmarks: BBO at different markouts#
Now let’s find the prevailing quote at different time intervals (markouts) before/after each trade.
markouts = [-1, 0, 1, 5, 60, 600]
trd = otp.DataSource('NYSE_TAQ', tick_type='TRD')
trd = trd[['PRICE', 'SIZE']]
qte_by_markout = []
for m in markouts:
qte = otp.DataSource('TAQ_NBBO', tick_type='NBBO', back_to_first_tick=86400)
qte = qte[['ASK_PRICE', 'BID_PRICE']]
qte = qte.rename({'ASK_PRICE': f'ASK_PRICE_{m}',
'BID_PRICE': f'BID_PRICE_{m}'})
qte[f'quote_time_{m}'] = qte['Time']
# shift the data by m seconds
qte = qte.time_interval_shift(m * 1000)
qte_by_markout.append(qte)
trd = otp.join_by_time([trd] + qte_by_markout)
otp.run(trd, start=s, end=e, symbols=['SPY'], apply_times_daily=True)
| Time | PRICE | SIZE | ASK_PRICE_-1 | BID_PRICE_-1 | quote_time_-1 | ASK_PRICE_0 | BID_PRICE_0 | quote_time_0 | ASK_PRICE_1 | BID_PRICE_1 | quote_time_1 | ASK_PRICE_5 | BID_PRICE_5 | quote_time_5 | ASK_PRICE_60 | BID_PRICE_60 | quote_time_60 | ASK_PRICE_600 | BID_PRICE_600 | quote_time_600 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2023-05-15 09:30:00.000178688 | 412.22 | 100 | 412.26 | 412.23 | 2023-05-15 09:29:59.000000000 | 412.25 | 412.22 | 2023-05-15 09:30:00.000174080 | 412.28 | 412.27 | 2023-05-15 09:30:01.000000000 | 412.23 | 412.21 | 2023-05-15 09:30:05.000000000 | 412.06 | 412.05 | 2023-05-15 09:31:00.000105984 | 411.45 | 411.44 | 2023-05-15 09:40:00.000000000 |
| 1 | 2023-05-15 09:30:00.000776704 | 412.22 | 247 | 412.26 | 412.23 | 2023-05-15 09:29:59.000000000 | 412.24 | 412.21 | 2023-05-15 09:30:00.000715520 | 412.28 | 412.27 | 2023-05-15 09:30:01.000000000 | 412.23 | 412.21 | 2023-05-15 09:30:05.000000000 | 412.06 | 412.05 | 2023-05-15 09:31:00.000772608 | 411.45 | 411.44 | 2023-05-15 09:40:00.000000000 |
| 2 | 2023-05-15 09:30:00.003603456 | 412.22 | 100 | 412.26 | 412.23 | 2023-05-15 09:29:59.000000000 | 412.24 | 412.22 | 2023-05-15 09:30:00.003562496 | 412.28 | 412.27 | 2023-05-15 09:30:01.002818816 | 412.23 | 412.21 | 2023-05-15 09:30:05.000000000 | 412.06 | 412.05 | 2023-05-15 09:31:00.001634816 | 411.45 | 411.44 | 2023-05-15 09:40:00.000000000 |
| 3 | 2023-05-15 09:30:00.006352128 | 412.24 | 1 | 412.26 | 412.23 | 2023-05-15 09:29:59.005259520 | 412.25 | 412.22 | 2023-05-15 09:30:00.006343936 | 412.28 | 412.27 | 2023-05-15 09:30:01.004251904 | 412.23 | 412.21 | 2023-05-15 09:30:05.000000000 | 412.06 | 412.05 | 2023-05-15 09:31:00.005624320 | 411.45 | 411.44 | 2023-05-15 09:40:00.006328576 |
| 4 | 2023-05-15 09:30:00.007128064 | 412.24 | 3 | 412.26 | 412.23 | 2023-05-15 09:29:59.007053824 | 412.25 | 412.22 | 2023-05-15 09:30:00.007110656 | 412.28 | 412.27 | 2023-05-15 09:30:01.004251904 | 412.23 | 412.21 | 2023-05-15 09:30:05.000000000 | 412.05 | 412.04 | 2023-05-15 09:31:00.007117824 | 411.45 | 411.44 | 2023-05-15 09:40:00.007125760 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 310 | 2023-05-15 09:30:00.934032640 | 412.27 | 160 | 412.26 | 412.22 | 2023-05-15 09:29:59.837682688 | 412.28 | 412.26 | 2023-05-15 09:30:00.934030080 | 412.28 | 412.26 | 2023-05-15 09:30:01.931170560 | 412.25 | 412.23 | 2023-05-15 09:30:05.898093824 | 412.07 | 412.06 | 2023-05-15 09:31:00.927816448 | 411.44 | 411.43 | 2023-05-15 09:40:00.928655104 |
| 311 | 2023-05-15 09:30:00.975609344 | 412.24 | 2 | 412.25 | 412.22 | 2023-05-15 09:29:59.970543872 | 412.28 | 412.27 | 2023-05-15 09:30:00.970691840 | 412.29 | 412.26 | 2023-05-15 09:30:01.972468480 | 412.25 | 412.24 | 2023-05-15 09:30:05.973397760 | 412.07 | 412.06 | 2023-05-15 09:31:00.972016640 | 411.44 | 411.43 | 2023-05-15 09:40:00.954621952 |
| 312 | 2023-05-15 09:30:00.980264448 | 412.27 | 1 | 412.25 | 412.22 | 2023-05-15 09:29:59.970543872 | 412.28 | 412.27 | 2023-05-15 09:30:00.979763456 | 412.29 | 412.27 | 2023-05-15 09:30:01.978472704 | 412.26 | 412.24 | 2023-05-15 09:30:05.978229504 | 412.07 | 412.06 | 2023-05-15 09:31:00.972016640 | 411.44 | 411.43 | 2023-05-15 09:40:00.954621952 |
| 313 | 2023-05-15 09:30:00.985391616 | 412.28 | 100 | 412.25 | 412.22 | 2023-05-15 09:29:59.970543872 | 412.28 | 412.27 | 2023-05-15 09:30:00.985296640 | 412.29 | 412.27 | 2023-05-15 09:30:01.985324032 | 412.26 | 412.24 | 2023-05-15 09:30:05.978229504 | 412.07 | 412.06 | 2023-05-15 09:31:00.972016640 | 411.44 | 411.43 | 2023-05-15 09:40:00.954621952 |
| 314 | 2023-05-15 09:30:00.985394944 | 412.28 | 100 | 412.25 | 412.22 | 2023-05-15 09:29:59.970543872 | 412.28 | 412.27 | 2023-05-15 09:30:00.985296640 | 412.29 | 412.27 | 2023-05-15 09:30:01.985324032 | 412.26 | 412.24 | 2023-05-15 09:30:05.978229504 | 412.07 | 412.06 | 2023-05-15 09:31:00.972016640 | 411.44 | 411.43 | 2023-05-15 09:40:00.954621952 |
315 rows × 21 columns
Interval Metrics (e.g., VWAP)#
q = otp.DataSource('NYSE_TAQ', tick_type='TRD')
q = q.agg({'market_vwap': otp.agg.vwap('PRICE', 'SIZE')})
otp.run(q, start=s, end=e, symbols=['SPY'])
| Time | market_vwap | |
|---|---|---|
| 0 | 2023-05-15 09:30:01 | 412.212012 |
Computing market VWAP for every order’s arrival/exit interval#
orders = otp.Ticks(arrival=[s, s+otp.Milli(7934)],
exit=[e, e+otp.Milli(2556)],
sym=['SPY', 'QQQ'])
otp.run(orders, start=s, end=s+otp.Day(1))
| Time | arrival | exit | sym | |
|---|---|---|---|---|
| 0 | 2023-05-15 09:30:00.000 | 2023-05-15 09:30:00.000 | 2023-05-15 09:30:01.000 | SPY |
| 1 | 2023-05-15 09:30:00.001 | 2023-05-15 09:30:07.934 | 2023-05-15 09:30:03.556 | QQQ |
def vwap(symbol):
q = otp.DataSource('NYSE_TAQ', tick_type='TRD')
q = q.agg({'market_vwap': otp.agg.vwap('PRICE','SIZE')})
return q
orders = otp.Ticks(arrival=[s, s+otp.Milli(7934)],
exit=[e, e+otp.Milli(9556)],
sym=['SPY', 'QQQ'])
orders = orders.join_with_query(vwap, start=orders['arrival'], end=orders['exit'], symbol=orders['sym'])
otp.run(orders, start=s, end=s+otp.Day(1))
| Time | market_vwap | arrival | exit | sym | |
|---|---|---|---|---|---|
| 0 | 2023-05-15 09:30:00.000 | 412.212012 | 2023-05-15 09:30:00.000 | 2023-05-15 09:30:01.000 | SPY |
| 1 | 2023-05-15 09:30:00.001 | 325.318988 | 2023-05-15 09:30:07.934 | 2023-05-15 09:30:10.556 | QQQ |
A more efficient implementation is also available with symbol parameters.
Real-time processing: Signal Generation#
We’ll compute golden cross signals using 50-second and 200-second moving averages
‘Entries’ is set to 1 when the short-term moving average goes above the long term (i.e., a signal to buy)
‘Exits’ is set to 1 on when the short-term moving average goes below the long term (i.e., a signal to sell)
trd = otp.DataSource('NYSE_TAQ', tick_type='TRD')
trd = trd[['PRICE']]
trd = trd.agg({'short': otp.agg.mean('PRICE')}, bucket_interval=60, running=True, all_fields=True)
trd = trd.agg({'long': otp.agg.mean('PRICE')}, bucket_interval=60*5, running=True, all_fields=True)
trd['buy'] = (trd['short'][-1] < trd['long'][-1]) & (trd['short'] > trd['long'])
trd['sell'] = (trd['short'][-1] > trd['long'][-1]) & (trd['short'] < trd['long'])
We define a callback that for every tick (i.e., on every trade) will
print a ‘.’ if there is no signal
print out the tick followed by ‘BUY’ on an entry signal
print out the tick followed by ‘SELL’ on an exit signal
class GoldenCrossCallback(otp.CallbackBase):
def process_tick(self, tick, time):
if not tick['buy'] and not tick['sell']:
print('.', end='')
return
print()
print()
print(time, tick)
if tick['buy']:
print('BUY')
if tick['sell']:
print('SELL')
print()
The query will run continuously with the output printed as the events happen if you set start/end times accordingly (see the commented out line).
# timestamps appear in GMT
cb = GoldenCrossCallback()
otp.run(trd, symbols=['SPY'],
callback=cb, running=True,
# start=otp.dt.now(), end=otp.dt.now() + otp.Day(1),
start=otp.dt(2023, 3, 31, 10), end=otp.dt(2023, 3, 31, 10, 5),
)
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2023-03-31 14:02:16.717255 {'PRICE': 405.54, 'short': 405.70794973568997, 'long': 405.70836495181135, 'buy': 0.0, 'sell': 1.0}
SELL
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Upticks / Downticks#
Let’s mark each trade as an uptick if its price is above the last trade’s price and as a downtick if it’s below.
def uptick(t):
if t['PRICE'] == otp.nan or t['PRICE'][-1] == otp.nan:
return otp.nan
if t['PRICE'] > t['PRICE'][-1]:
return 1
elif t['PRICE'] < t['PRICE'][-1]:
return -1
else:
return 0
trd = otp.DataSource('NYSE_TAQ', tick_type='TRD')
trd = trd[['PRICE']]
trd['UPTICK'] = trd.apply(uptick)
otp.run(trd, start=s, end=e, symbols=['SPY'])
| Time | PRICE | UPTICK | |
|---|---|---|---|
| 0 | 2023-05-15 09:30:00.000178688 | 412.22 | NaN |
| 1 | 2023-05-15 09:30:00.000776704 | 412.22 | 0.0 |
| 2 | 2023-05-15 09:30:00.003603456 | 412.22 | 0.0 |
| 3 | 2023-05-15 09:30:00.006352128 | 412.24 | 1.0 |
| 4 | 2023-05-15 09:30:00.007128064 | 412.24 | 0.0 |
| ... | ... | ... | ... |
| 310 | 2023-05-15 09:30:00.934032640 | 412.27 | 0.0 |
| 311 | 2023-05-15 09:30:00.975609344 | 412.24 | -1.0 |
| 312 | 2023-05-15 09:30:00.980264448 | 412.27 | 1.0 |
| 313 | 2023-05-15 09:30:00.985391616 | 412.28 | 1.0 |
| 314 | 2023-05-15 09:30:00.985394944 | 412.28 | 0.0 |
315 rows × 3 columns