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.
import onetick.py as otp
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,
symbols=['SPY'],
start=otp.dt(2023, 5, 15, 9, 30),
end=otp.dt(2023, 5, 15, 9, 30, 1),
)
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