import numpy as np
import pandas as pd
from pandas_datareader import data as wb
import matplotlib.pyplot as plt
from scipy.stats import norm

ticker = 'TSLA'
data = pd.DataFrame() #can import stock returns here
data[ticker] = wb.DataReader(ticker, data_source = 'yahoo', start = '2020-1-1', end = '2020-12-31')['Adj Close']
#Plot
data.plot(figsize=(15,6))

log_returns = np.log(1 + data.pct_change())

u = log_returns.mean()
var = log_returns.var()
drift = u - (0.5*var)

stdev = log_returns.std()
days = 365
trials = 100 #can be modified with initial value of stock
Z = norm.ppf(np.random.rand(days, trials)) #days, trials
daily_returns = np.exp(drift.values + stdev.values * Z)

price_paths = np.zeros_like(daily_returns)
price_paths[0] = 
    ##data.iloc[-1]
for t in range(1, days):
    price_paths[t] = price_paths[t-1]*daily_returns[t]

plt.figure(figsize=(10,6))
plt.title("1 Year Monte Carlo simulation for " + ticker)
plt.ylabel("Price (P)")
plt.xlabel("Time (Days)")
plt.plot(price_paths)
plt.show()

fname=r'C:\Users\XXXX\MonteCarlosimu.pdf'
plt.savefig(fname)

df = pd.DataFrame(price_paths)
df.to_excel(r'C:\Users\XXXXE\MonteCarlosimu.xls')