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:
mr = str(m).replace('-', 'm')
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_{mr}',
'BID_PRICE': f'BID_PRICE_{mr}'})
qte[f'quote_time_{mr}'] = 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_m1 | BID_PRICE_m1 | quote_time_m1 | 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
Realized P&L on the FIFO basis#
Realized P&L is computed by keeping track of each buy and sell in the chronological order and updating the total using the oldest matching execution from the opposite side (FIFO). We’ll use the following sequence of trades for illustration.
import onetick.py as otp
trades = otp.Ticks(
SIDE=['B', 'B', 'B', 'S', 'S', 'S', 'S', 'S', 'B', 'B', 'S'],
PRICE=[1.0, 2.0, 3.0, 2.5, 4.0, 5.0, 6.0, 7.0, 3.0, 4.0, 1.0],
SIZE=[700, 20, 570, 600, 100, 100, 100, 100, 150, 10, 100],
)
otp.run(trades)
| Time | SIDE | PRICE | SIZE | |
|---|---|---|---|---|
| 0 | 2003-12-01 00:00:00.000 | B | 1.0 | 700 |
| 1 | 2003-12-01 00:00:00.001 | B | 2.0 | 20 |
| 2 | 2003-12-01 00:00:00.002 | B | 3.0 | 570 |
| 3 | 2003-12-01 00:00:00.003 | S | 2.5 | 600 |
| 4 | 2003-12-01 00:00:00.004 | S | 4.0 | 100 |
| 5 | 2003-12-01 00:00:00.005 | S | 5.0 | 100 |
| 6 | 2003-12-01 00:00:00.006 | S | 6.0 | 100 |
| 7 | 2003-12-01 00:00:00.007 | S | 7.0 | 100 |
| 8 | 2003-12-01 00:00:00.008 | B | 3.0 | 150 |
| 9 | 2003-12-01 00:00:00.009 | B | 4.0 | 10 |
| 10 | 2003-12-01 00:00:00.010 | S | 1.0 | 100 |
We define the deque variables to keep the buy and sell trades as they arrive. Note that the variables are associated with the trades time series.
trades.state_vars['BUY_DEQUE'] = otp.state.tick_deque()
trades.state_vars['SELL_DEQUE'] = otp.state.tick_deque()
As every trade arrives, we apply the following function, which updates the deques and computes the realized profit from the trade.
def fifo_computation_of_realized_profit(tick):
buy_tick = otp.tick_deque_tick()
sell_tick = otp.tick_deque_tick()
tick['PROFIT'] = 0.0
if tick['SIDE'] == 'B':
tick.state_vars['BUY_DEQUE'].push_back(tick)
else:
tick.state_vars['SELL_DEQUE'].push_back(tick)
while tick.state_vars['BUY_DEQUE'].get_size() > 0 and tick.state_vars['SELL_DEQUE'].get_size() > 0:
tick.state_vars['BUY_DEQUE'].get_tick(0, buy_tick)
tick.state_vars['SELL_DEQUE'].get_tick(0, sell_tick)
if buy_tick['SIZE'] > sell_tick['SIZE']:
tick['PROFIT'] += sell_tick['SIZE'] * (sell_tick['PRICE'] - buy_tick['PRICE'])
buy_tick['SIZE'] -= sell_tick['SIZE']
sell_tick['SIZE'] = 0
else:
tick['PROFIT'] += buy_tick['SIZE'] * (sell_tick['PRICE'] - buy_tick['PRICE'])
sell_tick['SIZE'] -= buy_tick['SIZE']
buy_tick['SIZE'] = 0
if buy_tick['SIZE'] == 0:
tick.state_vars['BUY_DEQUE'].pop_front()
if sell_tick['SIZE'] == 0:
tick.state_vars['SELL_DEQUE'].pop_front()
Total realized profit can now be calculated by applying the function to every trade.
trades = trades.script(fifo_computation_of_realized_profit)
trades = trades.agg({'TOTAL_PROFIT': otp.agg.sum('PROFIT')}, running=True, all_fields=True)
otp.run(trades)
| Time | SIDE | PRICE | SIZE | PROFIT | TOTAL_PROFIT | |
|---|---|---|---|---|---|---|
| 0 | 2003-12-01 00:00:00.000 | B | 1.0 | 700 | 0.0 | 0.0 |
| 1 | 2003-12-01 00:00:00.001 | B | 2.0 | 20 | 0.0 | 0.0 |
| 2 | 2003-12-01 00:00:00.002 | B | 3.0 | 570 | 0.0 | 0.0 |
| 3 | 2003-12-01 00:00:00.003 | S | 2.5 | 600 | 900.0 | 900.0 |
| 4 | 2003-12-01 00:00:00.004 | S | 4.0 | 100 | 300.0 | 1200.0 |
| 5 | 2003-12-01 00:00:00.005 | S | 5.0 | 100 | 220.0 | 1420.0 |
| 6 | 2003-12-01 00:00:00.006 | S | 6.0 | 100 | 300.0 | 1720.0 |
| 7 | 2003-12-01 00:00:00.007 | S | 7.0 | 100 | 400.0 | 2120.0 |
| 8 | 2003-12-01 00:00:00.008 | B | 3.0 | 150 | 0.0 | 2120.0 |
| 9 | 2003-12-01 00:00:00.009 | B | 4.0 | 10 | 0.0 | 2120.0 |
| 10 | 2003-12-01 00:00:00.010 | S | 1.0 | 100 | -200.0 | 1920.0 |