VaR_plot.py

# We will import the necessary libraries

import numpy as np
import pandas as pd
import yfinance as yf
from tabulate import tabulate
import scipy

# Plotting
import matplotlib.pyplot as plt
import seaborn
import matplotlib.mlab as mlab

#Statistical calculation
from scipy.stats import norm

# For warnings suppression
import warnings
warnings.filterwarnings("ignore")

# We will import the daily data of Amazon from yahoo finance
# Calculate daily returns
df = yf.download("AMZN", "2020-01-01", "2022-01-01")
df = df[['Close']]
df['returns'] = df.Close.pct_change()


# Now we will determine the mean and standard deviation of the daily returns 
# Plot the normal curve against the daily returns

mean = np.mean(df['returns'])
std_dev = np.std(df['returns'])

df['returns'].hist(bins=40, density=True, histtype='stepfilled', alpha=0.5)

x = np.linspace(mean - 3*std_dev, mean + 3*std_dev, 100)
plt.plot(x, scipy.stats.norm.pdf(x, mean, std_dev), "r")
plt.show()