Setting Up the Environment

Before we embark on our analysis, it's essential to establish our coding environment. We will import the libraries necessary for data handling and visualization.

import pandas as pd

import numpy as np

import yfinance as yf

import matplotlib.pyplot as plt

Data Acquisition

We will leverage the yfinance library to download historical stock data directly from Yahoo Finance. For our example, we will analyze Pepsico's stock.

# Stock symbol and date range

ticker = 'PEP' # Example with Pepsico

start_date = '2020-01-01'

end_date = '2024-03-27'

# Downloading data from Yahoo Finance

stock_data = yf.download(ticker, start=start_date, end=end_date)

Data Exploration

Once we have acquired the stock data, it is vital to understand its composition and features. We will conduct a descriptive analysis to gain initial insights into the stock's performance.

# Display the first few records of data

print(stock_data.head())

# Basic descriptive statistics

print(stock_data.describe())

# Visualizing historical closing prices

stock_data['Close'].plot(figsize=(10, 6))

plt.title('Historical Closing Price of {}'.format(ticker))

plt.xlabel('Date')

plt.ylabel('Closing Price (USD)')

plt.grid(True)

plt.show()

Trend Analysis

To gain a clearer picture of the stock's trajectory, we will apply methods such as moving averages and historical volatility.

# Calculate 50 and 200-day moving averages

stock_data['MA50'] = stock_data['Close'].rolling(window=50).mean()

stock_data['MA200'] = stock_data['Close'].rolling(window=200).mean()

# Calculate historical volatility

stock_data['Volatility'] = stock_data['Close'].rolling(window=50).std()

# Plotting moving averages and volatility

plt.figure(figsize=(12, 8))

plt.plot(stock_data.index, stock_data['Close'], label='Closing Price')

plt.plot(stock_data.index, stock_data['MA50'], label='MA50')

plt.plot(stock_data.index, stock_data['MA200'], label='MA200')

plt.fill_between(stock_data.index, stock_data['Close'] - stock_data['Volatility'],

stock_data['Close'] + stock_data['Volatility'], alpha=0.2, color='gray')

plt.title('Trend of {}'.format(ticker))

plt.xlabel('Date')

plt.ylabel('Price (USD)')

plt.legend()

plt.grid(True)

plt.show()

Performance Evaluation

To assess the stock's performance, we will calculate daily returns and the Sharpe ratio, which indicates the relationship between returns and risk.

# Calculate daily returns

stock_data['Daily_Return'] = stock_data['Close'].pct_change()

# Calculate Sharpe ratio (assuming a risk-free rate of 0%)

risk_free_rate = 0

sharpe_ratio = (stock_data['Daily_Return'].mean() - risk_free_rate) / stock_data['Daily_Return'].std()

# Print Sharpe ratio

print('Sharpe Ratio of {}: {:.2f}'.format(ticker, sharpe_ratio))

Result Visualization

Finally, we will create a histogram to visually represent our findings regarding daily returns.

# Plotting daily returns histogram

plt.figure(figsize=(10, 6))

stock_data['Daily_Return'].plot(kind='hist', bins=50, alpha=0.7)

plt.title('Histogram of Daily Returns for {}'.format(ticker))

plt.xlabel('Daily Return')

plt.ylabel('Frequency')

plt.grid(True)

plt.show()