A Python script for simulating a currency based on a couple of parameters. Requires `numpy` and `pandas` to be installed.

currency_simulation.py

#!/usr/bin/python3

"""
This is free and unencumbered software released into the public domain.

Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
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means.

In jurisdictions that recognize copyright laws, the author or authors
of this software dedicate any and all copyright interest in the
software to the public domain. We make this dedication for the benefit
of the public at large and to the detriment of our heirs and
successors. We intend this dedication to be an overt act of
relinquishment in perpetuity of all present and future rights to this
software under copyright law.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR
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"""

import numpy as np
import pandas as pd

# parameters
percentage_formatter = lambda n: f"{n * 100:.2f}%" if n else ""

initial_price = 1.00
initial_supply = 3600

sensitivity = 0.1
damping_factor = 1.0

buffer_factor = 0.1
buffer_size = 0.05

hours = 24 * 365

# arrays
np.random.seed(42)

prices = np.zeros(hours)
supplies = np.zeros(hours)
demands = np.random.normal(initial_supply, initial_supply * 0.5, hours)

daily_inflation = np.zeros(hours // 24)
monthly_inflation = np.zeros(hours // 24)

# initial values
prices[0] = initial_price
supplies[0] = initial_supply

# main simulation loop
for t in range(1, hours):
    # ensure demand is positive
    demands[t] = max(0, demands[t])

    # update the supply and price
    ratio = demands[t] / supplies[t - 1]
    prices[t] = prices[t - 1] * (1 + sensitivity * (ratio - 1))
    supplies[t] = max(0, supplies[t - 1] - demands[t])

    # calculate the inflation percentage
    if t % 24 == 0:
        current_cost = 1 / prices[t]
        previous_cost = 1 / prices[t - 24]

        daily_inflation[t // 24] = (current_cost - previous_cost) / previous_cost

    if t % (24 * 30) == 0:
        current_cost = 1 / prices[t]
        previous_cost = 1 / prices[t - (24 * 30)]

        monthly_inflation[t // 24] = (current_cost - previous_cost) / previous_cost

    # refill the supply
    supplies[t] += initial_supply * prices[t] * damping_factor

    if prices[t] > initial_price + buffer_factor:
        supplies[t] += initial_supply * buffer_size
    elif prices[t] < initial_price - buffer_factor:
        supplies[t] -= initial_supply * buffer_size

# create a DataFrame
pd.set_option("display.float_format", "{:.4f}".format)

df = pd.DataFrame({
    "Hour": np.arange(hours),
    "Price (USD)": prices,
    "Cost of $1": 1 / prices,
    "Supply": supplies,
    "Demand": demands
})
df2 = pd.DataFrame({
    "Day": np.arange(hours // 24),
    "Daily Inflation (%)": daily_inflation,
    "Monthly Inflation (%)": monthly_inflation
})

df.to_html(
    "rundown.html",
    justify="center",
    index=False
)
df2.to_html(
    "inflation.html",
    formatters={
        "Daily Inflation (%)": percentage_formatter,
        "Monthly Inflation (%)": percentage_formatter
    },
    justify="center",
    index=False
)

print(df, df2)