simstock.py

import random
import statistics

# last qtr results
price = 229.52
market_cap = 222985351000

# this was fed into https://www.socscistatistics.com/tests/regression/default.aspx
q_rev_hist_mills = [5963, 5817, 5419, 5151, 4865, 4851, 4513, 3997, 3737, 3603, 3392, 3281, 3006, 2865, 2701, 2577, 2397, 2339]

# how many quarters to run the sim
sim_qtrs = 16


def apply_lreg_growth(base, qtrs):
    lreg_coeff = 222.79463
    lreg_base = 1802
    lreg_qtrs_min = 18
    return (lreg_coeff*(lreg_qtrs_min+qtrs))+lreg_base


def growth_pstdev(nums, offset):
    rates = [nums[n] / nums[n+offset] for n in range(len(nums)-offset)]
    return statistics.pstdev(rates)


def price_sim():
    base_rev = q_rev_hist_mills[0]
    growth_sd = growth_pstdev(q_rev_hist_mills, 4)
    outstanding = market_cap/price
    rev_mult = market_cap/(base_rev*4)
    rev_last_qtr = base_rev

    prices = []

    for q in range(sim_qtrs):
        rev_this_qtr = apply_lreg_growth(base_rev, q+1)
        growth_this_qtr = rev_this_qtr / rev_last_qtr
        rev_this_qtr = rev_last_qtr * random.gauss(growth_this_qtr, growth_sd)
        new_mkt_cap = (rev_this_qtr * 4) * rev_mult
        new_price = new_mkt_cap / outstanding
        prices.append(new_price)
        rev_last_qtr = rev_this_qtr

    return prices


def nsim(nsamples=10000):
    qtrs = [[] for _ in range(sim_qtrs)]

    for _ in range(nsamples):
        prices = price_sim()
        for i, p in enumerate(prices):
            qtrs[i].append(p)

    return qtrs


simout = nsim()

for qsim in simout:
    qmin = min(qsim)
    qmax = max(qsim)
    qmean = statistics.mean(qsim)
    print(qmin, qmax, qmean)