Introduction

Set-up Environment

mamba create -n da python=3.11
mamba activate da
pip install seaborn matplotlib pandas yfinance numpy

Coding

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import yfinance as yf

import matplotlib.patheffects as path_effects

companies = ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'TSLA']
start_date = '2020-01-01'
end_date = '2024-12-20'
stock_data = yf.download(companies, start=start_date, end=end_date)['Close']
stock_data.head()

stock_data.reset_index(inplace=True)
stock_data = stock_data.melt(id_vars='Date', var_name='Company', value_name='Close')
stock_data.head()

plt.figure(figsize=(6, 6))
sns.lineplot(data=stock_data, x='Date', y='Close', hue='Company')

custom_colors = {
    'AAPL': '#6929c4', 'MSFT': '#1192e8', 'GOOGL': '#005d5d',
    'AMZN': '#9f1853', 'TSLA': '#fa4d56'
}

plt.figure(figsize=(6, 6))
sns.lineplot(
    data=stock_data,
    x='Date',
    y='Close',
    hue='Company',
    palette=custom_colors
)

plt.rcParams.update({
    'axes.facecolor': '#000000',    # Plot background
    'axes.edgecolor': 'white',      # Border color
    'axes.labelcolor': 'white',     # Labels color
    'xtick.color': 'white',         # X-axis tick color
    'ytick.color': 'white',         # Y-axis tick color
    'grid.color': '#444444',        # Gridline color
    'text.color': 'white',          # Text color
    'figure.facecolor': '#000000',  # Overall figure background
    'figure.edgecolor': 'white',    # Figure border color
})

plt.figure(figsize=(6, 6))
sns.lineplot(
    data=stock_data,
    x='Date',
    y='Close',
    hue='Company',
    palette=custom_colors,
    linewidth=2
)
plt.savefig("./stock_price.jpg", dpi=300)