Intro to Portfolio Risk Management in Python - course on Datacamp - Chapter 1

portfolio_risk_ch1.py

import pandas as pd 
import numpy as np
from scipy.stats import skew
from scipy.stats import kurtosis
from scipy import stats
from scipy.stats import shapiro

# Chapter 1: Univariate Investment Risk and Returns

StockPrices = pd.read_csv('StockData.csv', parse_dates=['Date'])
StockPrices = StockPrices.sort_values(by='Date')
StockPrices.set_index('Date', inplace=True)

# Read in the csv file
StockPrices = pd.read_csv(fpath_csv, parse_dates=['Date'])

# Ensure the prices are sorted by Date
StockPrices = StockPrices.sort_values(by='Date')

# Print the first five rows of StockPrices
print(StockPrices.head())

# Calculate the daily returns of the adjusted close price
StockPrices['Returns'] = StockPrices['Adjusted'].pct_change()

# Check the first five rows of StockPrices
print(StockPrices.head())

# Plot the returns over time
StockPrices['Returns'].plot()
plt.show()

# Convert the decimal returns into percentage returns
percent_return = StockPrices['Returns']*100

# Drop the missing values
returns_plot = percent_return.dropna()

# Plot the returns histogram
plt.hist(returns_plot, bins=75, density=False)
plt.show()

# Calculate the average daily return of the stock
mean_return_daily = np.mean(StockPrices['Returns'])
print(mean_return_daily)

# Calculate the implied annualized average return
mean_return_annualized = ((1 + mean_return_daily)**252)-1
print(mean_return_annualized)

# Calculate the standard deviation of daily return of the stock
sigma_daily = np.std( StockPrices['Returns'])
print(sigma_daily)

# Calculate the daily variance
variance_daily = sigma_daily**2
print(variance_daily)

sigma_annualized = sigma_daily*np.sqrt(252)
print(sigma_annualized)

# Calculate the annualized variance
variance_annualized = sigma_annualized**2
print(variance_annualized)

# Drop the missing values
clean_returns = StockPrices['Returns'].dropna()

# Calculate the third moment (skewness) of the returns distribution
returns_skewness = skew(clean_returns)
print(returns_skewness)

# Calculate the excess kurtosis of the returns distribution
excess_kurtosis = kurtosis(clean_returns)
print(excess_kurtosis)

# Derive the true fourth moment of the returns distribution
fourth_moment = excess_kurtosis + 3
print(fourth_moment)

# Run the Shapiro-Wilk test on the stock returns
shapiro_results = stats.shapiro(clean_returns)
print("Shapiro results:", shapiro_results)

# Extract the p-value from the shapiro_results
p_value = shapiro_results[1]
print("P-value: ", p_value)