Variables and Data Structures

import onetick.py as otp

We can use variables to keep track of a state across trd. The example below shows how we may keep track of P&L.

trd = otp.Ticks({'PRICE': [13.5, 13.6, 13.3, 14.0],
                 'SIZE': [200, 100, 150, 200],
                 'SIDE': ['B', 'S', 'B', 'S']})

trd.state_vars['PROFIT'] = 0
trd.state_vars['PROFIT'] += trd.apply(
    lambda t: t['PRICE'] * t['SIZE'] if t['SIDE'] == 'S' else -(t['PRICE'] * t['SIZE'])
)

trd['PROFIT'] = trd.state_vars['PROFIT']

otp.run(trd)

	Time	PRICE	SIZE	SIDE	PROFIT
0	2003-12-01 00:00:00.000	13.5	200	B	-2700
1	2003-12-01 00:00:00.001	13.6	100	S	-1340
2	2003-12-01 00:00:00.002	13.3	150	B	-3335
3	2003-12-01 00:00:00.003	14.0	200	S	-535

Let’s take a look at what the code above does: the variable ‘PROFIT’ keeps a running total. In other words, it aggregates state across trd.

An alternative implementation might avoid using variables by keeping the running total in a separate column. Let’s try implementing that.

trd = otp.Ticks({'PRICE': [13.5, 13.6, 13.3, 14.0],
                 'SIZE': [200, 100, 150, 200],
                 'SIDE': ['B', 'S', 'B', 'S']})
trd['VALUE'] = trd.apply(
    lambda t: t['PRICE'] * t['SIZE'] if t['SIDE'] == 'S' else -(t['PRICE'] * t['SIZE'])
)
trd = trd.agg({'PROFIT': otp.agg.sum('VALUE')}, running=True)
otp.run(trd)

	Time	PROFIT
0	2003-12-01 00:00:00.000	-2700.0
1	2003-12-01 00:00:00.001	-1340.0
2	2003-12-01 00:00:00.002	-3335.0
3	2003-12-01 00:00:00.003	-535.0

Another use case is to store the value from the last tick during aggregation / grouping.

q = otp.Ticks(X=[-1, 3, -3, 4, 2], Y=[0, 1, 1, 0, 3])
q.state_vars['S'] = 0
q.state_vars['S'] = q['X']
q = q.high('X', group_by=['Y'])
q['S'] = q.state_vars['S']
otp.run(q)

	Time	X	Y	S
0	2003-12-01 00:00:00.001	3	1	2
1	2003-12-01 00:00:00.003	4	0	2
2	2003-12-01 00:00:00.004	2	3	2