Snapper
A Snapper represents a path dependent calculation, e.g. calculating returns in an equity cliquet. In the following timetable
- the first three events represent a snapper operation
addfix. These operations store results in a snap. - the last event represents a payment for the contract, which pays
ACC, the value of the snap.
time op quantity unit track
12/29/2023 NaN 0.0 addfix NaN
06/28/2024 NaN 0.0 addfix NaN
12/31/2024 NaN 0.0 addfix NaN
12/31/2024 + 100.0 ACC
A snapper is defined in the dataset, with four parameters.
- type, which must be "snapper"
- inp, a list of inputs to the snapper fn. These can be assets, such as "SPX", whose value comes from the model. These can also be snaps, such as "ACC" or "S_last", which has been stored as a result of previous snapper operation.
- fn, a python function that takes a list of inputs, and returns a list of outputs. The length of expected inputs list must match the length of inp, while the length of the output list must match the length of out.
- out, the list of name of snaps where the outputs are stored.
e.g.
# Define the function
def accumulator_update_fn(inputs):
[s, s_last, a] = inputs
ret = s / s_last - 1.0 # ret = S / S_last - 1
ret = np.maximum(local_floor, ret)
ret = np.minimum(local_cap, ret)
return [a + ret, s] # [A, S_last]
# Define the snapper
"CALCFIX": {
"type": "snapper",
"inp": ["SPX", "S_last", "ACC"],
"fn": accumulator_update_fn,
"out": ["ACC", "S_last"],
},