romanmichaelpaolucci

import matplotlib.pyplot as plt
import numpy as np

payouts = []

for i in range(10000):
  n = np.random.geometric(0.5)
  payouts.append(2 ** n)

plt.title("Payout distribution")

romanmichaelpaolucci

import random

total = 0
num_rolls = 10000

for _ in range(num_rolls):
    roll = random.randint(1, 6)
    total += roll

average_naive = total / num_rolls

romanmichaelpaolucci

romanmichaelpaolucci

import numpy as np

def HHH(p):
  return 1/p + 1/(p**2) + 1/(p**3)

tosses = []
for i in range(10000):
  chain = []
  while True:
    chain.append(np.random.choice([0, 1], p=[.5, .5]))

romanmichaelpaolucci

import numpy as np
import random

# Define the state space and number of days
state_space = range(101)
num_days = 365*20

# Generate fake daily data for the Markov chain
# For simplicity, we'll use a random choice for transitions between states
daily_data = [random.choice(state_space) for _ in range(num_days)]

romanmichaelpaolucci

import numpy as np

# transition matrix
P = np.array([[.5, .5, 0, 0],
              [.5, 0, .5, 0],
              [.5, 0, 0, .5],
              [.5, 0, 0, .5]])

# Simulation
hits = []

romanmichaelpaolucci

import numpy as np

N = 10

P0 = np.array([[.5, .5, 0, 0], [.5, 0, .5, .0], 
[.5, 0, 0, .5], [.5, 0, 0, .5]])

P1 = np.array([[.5, .5, 0, 0], [.5, 0, .5, .0], 
[.5, 0, 0, .5], [0, 0, 0, 1]])

romanmichaelpaolucci

import numpy as np


P = np.array([[.7, .3],[.6, .4]])

P26 = np.linalg.matrix_power(P, 26)

print(P26[1][0]*.5 + P26[0][0]*.5)

day27 = []

romanmichaelpaolucci

import numpy as np


day2 = []
for i in range(10000):
    
    # Randomly select coin 1 or 2 on day 1
    X = np.random.randint(0, 2)
    
    # Flip either coin and append the outcome to day2 vector

romanmichaelpaolucci

import matplotlib.pyplot as plt
import numpy as np

# Assuming df is your DataFrame and it has 'salary' and 'stock_return' columns
# df = pd.read_csv('your_data.csv')  # Replace with your data source

# Creating the scatter plot
plt.figure(figsize=(10, 6))
plt.scatter(fr, np.log(stock_comp), color='blue', edgecolor='black', s=50)