Stock to flow model

stock_to_flow_btc.py

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import datetime
import locale

import matplotlib.pyplot as plt

locale.setlocale(locale.LC_ALL, 'en_CA.UTF-8')
reward = 50
total_btc = 0
halving = 0
days_per_year = 365
years_per_halving = 4
blocks_per_halving = 210000
years_per_block = datetime.timedelta(days=days_per_year * years_per_halving / blocks_per_halving)
blocks_per_day = int(blocks_per_halving / (days_per_year * years_per_halving))
sat = 1 / 100000000
history = []
start_time = datetime.datetime.strptime("03/01/2009", "%d/%m/%Y")
end_time = datetime.datetime.strptime("03/01/2030", "%d/%m/%Y")
time = []
halving_time = []

ordinal = lambda n: "%d%s" % (n, "tsnrhtdd"[(n / 10 % 10 != 1) * (n % 10 < 4) * n % 10::4])


def stock_to_flow(stocks):
    for i in range(blocks_per_day, len(stocks)):
        stock = stocks[i - blocks_per_day]
        stock2 = stocks[i]
        flow = (stock2 - stock) * 10000
        yield pow(stock2 / flow, 3.5) * 10000


print("Start, reward = %.08f" % reward)

while start_time < end_time and reward > 0:
    for i in range(blocks_per_halving):
        total_btc += reward
        history.append(total_btc)
        start_time = start_time + years_per_block
        time.append(start_time)

    halving_time.append(start_time)
    reward = int((reward / 2) / sat) * sat
    halving += 1
    if reward > 0:
        print("%s Halving, reward = %.08f, time (aprox): %s" % (ordinal(halving), reward, start_time.strftime("%d/%m/%Y")))

prices = list(stock_to_flow(history))

plt.yscale("log")
plt.plot(time[blocks_per_day:], prices)

for o in range(len(halving_time) - 1):
    ht = halving_time[o]
    u = time[blocks_per_day:].index(ht)
    price1 = prices[u + blocks_per_day]
    price2 = prices[u + blocks_per_halving]
    p1 = locale.currency(price1, grouping=True)
    p2 = locale.currency(price2, grouping=True)
    hts = ht.strftime("%d/%m/%y")
    plt.annotate("%s Halving\n%s\nmin: %s\nmax: %s" % (ordinal(o + 1), hts, p1, p2), xy=(ht, price2))

plt.show()

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