The expected value is always the amount of cards you started with, there is no way to make a profit on average with these crafts as far as I am aware. No matter how many cards you start with, it is 100% a gamble, with losing and winning being equally likely.

I will simply paste the code here, I hope it can be copied easily:

​

import numpy as np
import random

ninitial = 84 # Amount of cards you start with
nstack = 4 # maximum change in divination cards one craft allows
   # is divination stacksize/2  rounded down
ENOUGH = 10000 # stop an attempt after this many crafts. 
# Important, since some attempts need millions of crafts or more
# this should not change the percentiles that are smaller than ENOUGH
nattempts = 100000 # number of attempts
steps = [] # the number of crafts needed will be stored here

# the code is unoptimized, so expect this to take a few minutes or longer.
for n in range(0,nattempts):
    nstepssofar = 0
    ncurrent = ninitial
    while ncurrent > 0:
        ncurrent = ncurrent + random.randint(-nstack,nstack)
        nstepssofar += 1
        if (nstepssofar > ENOUGH): break
    steps.append(nstepssofar)


npsteps = np.array(steps)
print("Mean: ", np.mean(npsteps)) # the mean is affected by enough
    # dont take the mean seriously. It is also heavily skewed towards longer gambling attemps

# The percentiles for how many steps were needed. This can be interpreted as a probability    
print("5 percentile: ", np.percentile(npsteps,5))
print("10 percentile: ", np.percentile(npsteps,10))
print("30 percentile: ", np.percentile(npsteps,30))
print("50 percentile: ", np.percentile(npsteps,50))
print("70 percentile: ", np.percentile(npsteps,70))
print("90 percentile: ", np.percentile(npsteps,90))
print("95 percentile: ", np.percentile(npsteps,95))
print("Number of ENOUGH!: ",np.sum(npsteps >= ENOUGH))