slot simulator

casino.py

import math
import pandas as pd
import time
import matplotlib.pyplot as plt
import slots

# start timer to calculate duration
start = time.time()

# TODO: let user choose table

# USER INPUT
# slot, choices are slots.bluejay, slots.inthemoney, slots.blazing7s, slots.rwb_reno, slots.dd_deluxe, slots.ll_lobster, slots.rwb_wizard
selected_slot = slots.dd_deluxe
# should rtp be adjusted? select rtp to adjust probability tables (0-1)
adjust = False
rtp = 0.98
# how much did we deposit? how much bonus money?
deposit = 100
bonus = 50
bank = deposit + bonus
# how much do we need to wager?
w_requirement = 3000
# stake size
stake = 5
# number of spins necessary rounded up
spins = math.ceil(w_requirement / stake)
# number of simulations
universes = 1000
# END USER INPUT


# Calculate one run of our slot from probability table, stored in slots.py, and stake
def result():
    multi = adjusted_paytable.Multiplier.sample(weights=adjusted_paytable.Probability)
    # TODO sample returns series we only want value
    multi = multi.iloc[0]
    win = stake * multi - stake
    return win


# Play until we are broke or wagering requirement is met
def simulate(spins_s, bank_s):
    for i in range(spins_s):
        if bank_s >= stake:
            bank_s += result()
        else:
            break
    return bank_s

# calculate the rtp of a probability dataframe rtp = 1 - house edge
def calc_rtp(dataframe):
    dataframe["pxp"] = dataframe["Multiplier"] * dataframe["Probability"]
    return dataframe["pxp"].sum()

# calculate the standard deviation of a dataframe as measurement of volatility
def calc_sd(dataframe):
    dataframe["pxp"] = dataframe["Multiplier"] * dataframe["Probability"]
    rtp = dataframe["pxp"].sum()
    dataframe["Variance"] = ((dataframe["Multiplier"] - 1) - (1 - rtp)) ** 2 * dataframe["Probability"]
    return math.sqrt(dataframe["Variance"].sum())

# Adjust paytable to new rtp by increasing/decreasing every win chance by the same value
def set_rtp(rtp_new, dataframe):
    rtp_old = calc_rtp(dataframe)
    if rtp_old != rtp_new:
        dataframe["pxp"] = dataframe["Multiplier"] * dataframe["Probability"]
        rtp_diff = abs(rtp_new - rtp_old)
        rtp_category = rtp_diff / len(dataframe.index)
        if rtp_new > rtp_old:
            print("Increasing RTP from", rtp_old, "to", rtp_new, "SD was", calc_sd(dataframe))
            dataframe["pxp"] += rtp_category
            dataframe["Probability"] = dataframe["pxp"] / dataframe["Multiplier"]
            dataframe.iat[0, 1] = 1 - dataframe.iloc[1:, [1]].sum()
            print("new rtp", round(calc_rtp(dataframe), 4), "new sd", calc_sd(dataframe))
            return dataframe
        else:
            print("Lowering RTP from", rtp_old, "to", rtp_new, "SD was", calc_sd(dataframe))
            dataframe["pxp"] -= rtp_category
            dataframe["Probability"] = dataframe["pxp"] / dataframe["Multiplier"]
            # reducing probabilities can lead to negative values, set them = 0
            dataframe[dataframe < 0] = 0
            dataframe.iat[0, 1] = 1 - dataframe.iloc[1:, [1]].sum()
            print("new rtp", round(calc_rtp(dataframe), 4), "new sd", calc_sd(dataframe))
            return dataframe
    else:
        print("RTP is already", rtp_new, "nothing to be done..")
        return dataframe


# initialize variables
bust_nr = 0
loss_nr = 0
result_list = []

print("Slot =", selected_slot.name)

# Adjust paytable if flag is set
if adjust is True:
    adjusted_paytable = set_rtp(rtp,selected_slot)
else:
    print("Not adjusting rtp")
    adjusted_paytable = selected_slot


# Simulate the number of universes TODO: need to speed this up
for i in range(universes):
    value = simulate(spins, bank)
    result_list.append(value)


# Present results and do statistics

# Calculate average win
ev = (sum(result_list) / universes) - deposit

# Count number of losses and busts
for i in result_list:
    if i < deposit:
        loss_nr += 1
    if i < stake:
        bust_nr += 1

# Create lists of wins for all universes
win_list = [x - deposit for x in result_list]

# Explain outcome to user
print("Deposit", deposit, "Bonus", bonus,  "Wagering Requirement", w_requirement)
print("After", universes, "simulations with stake size", stake )
print("EV is", round(ev, 2))
bust_percent = bust_nr / universes * 100
print(round(bust_percent, 2), "% percent bust rate")
loss_percent = loss_nr / universes * 100
print(round(loss_percent, 2), "% percent loss rate")

#Create histograms of winnings TODO: better understand and choose axis/bin size
histogram = plt.hist(win_list, bins='auto')  # arguments are passed to np.histogram
plt.title("Histogram of Winnings")

end = time.time()
elapsed = end - start
print("Finished in", elapsed, "seconds")
plt.show()

# TODO: plot graphs