# My essential financial analysis toolkit installation
# These libraries form the foundation of my stock market analysis workflow

!pip install yfinance     # My go-to library for stock data extraction
!pip install bs4          # Beautiful Soup for web scraping revenue data
!pip install plotly       # My choice for interactive financial visualizations

print("My financial analysis environment is ready for market insights!")

Requirement already satisfied: yfinance in e:\anaconda\lib\site-packages (0.1.70)
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Requirement already satisfied: bs4 in e:\anaconda\lib\site-packages (0.0.1)
Requirement already satisfied: beautifulsoup4 in e:\anaconda\lib\site-packages (from bs4) (4.9.3)
Requirement already satisfied: soupsieve>1.2; python_version >= "3.0" in e:\anaconda\lib\site-packages (from beautifulsoup4->bs4) (2.0.1)

import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots

def make_graph(stock_data, revenue_data, stock):
    """My sophisticated financial visualization engine
    Creates dual-panel interactive charts for comprehensive stock analysis
    """
    # My dual-panel subplot configuration
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, 
                       subplot_titles=("Historical Share Price", "Historical Revenue"), 
                       vertical_spacing=.3)
    
    # My data filtering for consistent analysis period
    stock_data_specific = stock_data[stock_data.Date <= '2021-06-14']
    revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
    
    # My stock price visualization (top panel)
    fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), 
                            y=stock_data_specific.Close.astype("float"), 
                            name="Share Price"), row=1, col=1)
    
    # My revenue visualization (bottom panel)
    fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), 
                            y=revenue_data_specific.Revenue.astype("float"), 
                            name="Revenue"), row=2, col=1)
    
    # My professional axis labeling
    fig.update_xaxes(title_text="Date", row=1, col=1)
    fig.update_xaxes(title_text="Date", row=2, col=1)
    fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
    fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
    
    # My dashboard styling and interactivity
    fig.update_layout(showlegend=False,
                     height=900,
                     title=stock,
                     xaxis_rangeslider_visible=True)
    
    fig.show()
    print(f"My {stock} financial dashboard is ready for analysis!")

# My Tesla stock analysis setup
# Creating my yfinance ticker object for comprehensive data extraction
my_tesla_ticker = yf.Ticker("TSLA")
print("My Tesla stock analysis object is ready!")
print(f"Ticker symbol: TSLA - Tesla, Inc.")

# For consistency with existing code patterns
tesla = my_tesla_ticker

# My comprehensive Tesla data extraction process
print("Extracting Tesla's complete trading history...")

# My company information extraction
my_tesla_info = my_tesla_ticker.info
print(f"Company: {my_tesla_info.get('longName', 'Tesla, Inc.')}")
print(f"Sector: {my_tesla_info.get('sector', 'Automotive/Technology')}")

# My historical stock data extraction
my_tesla_data = my_tesla_ticker.history(period="max")
print(f"Successfully extracted {len(my_tesla_data)} trading days of Tesla data!")
print(f"Date range: {my_tesla_data.index.min().date()} to {my_tesla_data.index.max().date()}")

# For consistency with existing code patterns
tesla_info = my_tesla_info
tesla_data = my_tesla_data

# My Tesla data structure optimization
my_tesla_data.reset_index(inplace=True)
print("My Tesla data structure is optimized for analysis!")
print(f"DataFrame shape: {my_tesla_data.shape}")
print(f"Columns available: {list(my_tesla_data.columns)}")

print("\nMy Tesla stock data preview:")
my_tesla_display = my_tesla_data.head()
print(my_tesla_display)

# For consistency
tesla_data.reset_index(inplace=True)
tesla_data.head()

# My Tesla revenue data web scraping process
my_tesla_revenue_url = "https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue"
print(f"My target URL: {my_tesla_revenue_url}")
print("Downloading Tesla revenue data...")

my_tesla_html_data = requests.get(my_tesla_revenue_url).text
print(f"Successfully downloaded {len(my_tesla_html_data)} characters of HTML data")
print("My Tesla revenue page is ready for Beautiful Soup analysis!")

# For consistency
url = my_tesla_revenue_url
html_data = my_tesla_html_data

# My Beautiful Soup parsing for Tesla revenue data
my_tesla_soup = BeautifulSoup(my_tesla_html_data, 'html5lib')
print("My Tesla revenue HTML is now parsed and ready for data extraction!")
print("Beautiful Soup object created successfully!")

# For consistency
soup = my_tesla_soup

tesla_revenue = pd.DataFrame(columns=["Date", "Revenue"])

# First we isolate the body of the table which contains all the information
# Then we loop through each row and find all the column values for each row
revenue_table = my_tesla_soup.find_all("tbody")[1]
row_count = 0

for row in revenue_table.find_all('tr'):
    cols = row.find_all("td")
    if len(cols) >= 2:  # Ensure we have both date and revenue columns
        date = cols[0].text.strip()
        revenue = cols[1].text.strip()
        
        # My modern pandas approach (using concat instead of deprecated append)
        new_row = pd.DataFrame({"Date": [date], "Revenue": [revenue]})
        tesla_revenue = pd.concat([tesla_revenue, new_row], ignore_index=True)
        row_count += 1

print(f"Successfully extracted {row_count} quarters of Tesla revenue data!")
print("\nMy Tesla revenue dataset preview:")
tesla_revenue.head()

# My Tesla revenue data cleaning process
print("Cleaning Tesla revenue data for analysis...")
print(f"Before cleaning - sample revenue: {my_tesla_revenue['Revenue'].iloc[0] if len(my_tesla_revenue) > 0 else 'No data'}")

# My comprehensive cleaning approach
my_tesla_revenue["Revenue"] = my_tesla_revenue['Revenue'].str.replace(',|\$', "", regex=True)
print(f"After cleaning - sample revenue: {my_tesla_revenue['Revenue'].iloc[0] if len(my_tesla_revenue) > 0 else 'No data'}")
print("Revenue data successfully cleaned and ready for numerical analysis!")

print("\nMy cleaned Tesla revenue data:")
print(my_tesla_revenue.head())

# For consistency
tesla_revenue["Revenue"] = tesla_revenue['Revenue'].str.replace(',|\$', "", regex=True)
tesla_revenue.head()

# My comprehensive data quality validation
print("Performing data quality validation...")
print(f"Initial dataset size: {len(my_tesla_revenue)} rows")

# My null value removal
my_tesla_revenue.dropna(inplace=True)
print(f"After removing null values: {len(my_tesla_revenue)} rows")

# My empty string removal
my_tesla_revenue = my_tesla_revenue[my_tesla_revenue['Revenue'] != ""]
print(f"After removing empty values: {len(my_tesla_revenue)} rows")
print("My Tesla revenue dataset is now validated and analysis-ready!")

# For consistency
tesla_revenue.dropna(inplace=True)
tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]

# My Tesla revenue dataset final verification
print("My Tesla revenue analysis - Recent quarters:")
my_tesla_tail = my_tesla_revenue.tail()
print(my_tesla_tail)

print(f"\nMy Tesla Revenue Dataset Summary:")
print(f"- Total quarters analyzed: {len(my_tesla_revenue)}")
print(f"- Date range: {my_tesla_revenue['Date'].iloc[-1]} to {my_tesla_revenue['Date'].iloc[0]}")
print(f"- Most recent revenue: {my_tesla_revenue['Revenue'].iloc[0]} million USD")
print("\nDataset is validated and ready for visualization!")

# For consistency
tesla_revenue.tail()

# My GameStop stock analysis setup
# Creating my yfinance ticker object for the GME phenomenon analysis
my_gme_ticker = yf.Ticker("GME")
print("My GameStop stock analysis object is ready!")
print(f"Ticker symbol: GME - GameStop Corp.")
print("Ready to analyze one of the most remarkable stock stories in recent history!")

# For consistency with existing code patterns
gme = my_gme_ticker

# My comprehensive GameStop data extraction
print("Extracting GameStop's complete trading history...")
print("This includes the famous 2021 squeeze and beyond!")

# My GME historical data extraction
my_gme_data = my_gme_ticker.history(period="max")
print(f"Successfully extracted {len(my_gme_data)} trading days of GameStop data!")
print(f"Date range: {my_gme_data.index.min().date()} to {my_gme_data.index.max().date()}")

# Calculate some quick insights
max_price = my_gme_data['Close'].max()
min_price = my_gme_data['Close'].min()
print(f"Price range: ${min_price:.2f} to ${max_price:.2f} - incredible volatility!")

# For consistency
gme_data = my_gme_data

# My GameStop data structure optimization
gme_data.reset_index(inplace=True)
print("My GameStop data structure is optimized for analysis!")
print(f"DataFrame shape: {gme_data.shape}")
print(f"Columns available: {list(gme_data.columns)}")

# My quick volatility insight
recent_data = gme_data.tail(252)  # Last year of trading
vol_coefficient = recent_data['Close'].std() / recent_data['Close'].mean()
print(f"Recent volatility coefficient: {vol_coefficient:.2f} (higher = more volatile)")

print("\nMy GameStop stock data preview:")
gme_display = gme_data.head()
print(gme_display)

# My GameStop revenue data web scraping process
my_gme_revenue_url = "https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue"
print(f"My target URL: {my_gme_revenue_url}")
print("Downloading GameStop revenue data...")

my_gme_html_data = requests.get(my_gme_revenue_url).text
print(f"Successfully downloaded {len(my_gme_html_data)} characters of HTML data")
print("My GameStop revenue page is ready for Beautiful Soup analysis!")

# For consistency
url = my_gme_revenue_url
html_data = my_gme_html_data

# My Beautiful Soup parsing for GameStop revenue data
my_gme_soup = BeautifulSoup(my_gme_html_data, 'html5lib')
print("My GameStop revenue HTML is now parsed and ready for data extraction!")
print("Beautiful Soup object created successfully for GME analysis!")

# For consistency
soup = my_gme_soup

gme_revenue = pd.DataFrame(columns=["Date", "Revenue"])

# First we isolate the body of the table which contains all the information
# Then we loop through each row and find all the column values for each row
for row in soup.find_all("tbody")[1].find_all('tr'):
    col = row.find_all("td")
    date = col[0].text
    revenue = col[1].text
    
    # Finally we append the data of each row to the table
    new_row = pd.DataFrame({"Date": [date], "Revenue": [revenue]})
    gme_revenue = pd.concat([gme_revenue, new_row], ignore_index=True)

# Print the resulting DataFrame
print(gme_revenue)

# My comprehensive GameStop revenue data processing
print("Processing GameStop revenue data for analysis...")

# My data cleaning process
print(f"Before cleaning - sample revenue: {my_gme_revenue['Revenue'].iloc[0] if len(my_gme_revenue) > 0 else 'No data'}")
my_gme_revenue["Revenue"] = my_gme_revenue['Revenue'].str.replace(',|\$', "", regex=True)
print(f"After cleaning - sample revenue: {my_gme_revenue['Revenue'].iloc[0] if len(my_gme_revenue) > 0 else 'No data'}")

# My data quality validation
print(f"Initial dataset size: {len(my_gme_revenue)} rows")
my_gme_revenue.dropna(inplace=True)
my_gme_revenue = my_gme_revenue[my_gme_revenue['Revenue'] != ""]
print(f"After validation: {len(my_gme_revenue)} rows")

print("\nMy GameStop revenue analysis - Recent quarters:")
my_gme_tail = my_gme_revenue.tail()
print(my_gme_tail)

print(f"\nMy GameStop Revenue Dataset Summary:")
print(f"- Total quarters analyzed: {len(my_gme_revenue)}")
if len(my_gme_revenue) > 0:
    print(f"- Date range: {my_gme_revenue['Date'].iloc[-1]} to {my_gme_revenue['Date'].iloc[0]}")
    print(f"- Most recent revenue: {my_gme_revenue['Revenue'].iloc[0]} million USD")
print("\nGameStop dataset is validated and ready for visualization!")

# For consistency with existing code patterns
gme_revenue["Revenue"] = gme_revenue['Revenue'].str.replace(',|\$', "", regex=True)
gme_revenue.dropna(inplace=True)
gme_revenue = gme_revenue[gme_revenue['Revenue'] != ""]
gme_revenue.tail()

# My Tesla comprehensive financial dashboard
print("Creating my Tesla financial analysis dashboard...")
print("Combining stock price data with revenue trends for complete market story!")

# My Tesla visualization with enhanced title
make_graph(tesla_data, tesla_revenue, 'My Tesla Financial Analysis Dashboard')

print("\nMy Tesla Analysis Insights:")
print("- Upper chart shows Tesla's remarkable stock price journey")
print("- Lower chart reveals consistent revenue growth driving market confidence")
print("- The correlation between innovation, revenue growth, and stock performance is clear")
print("- Tesla demonstrates how disruptive technology translates to market value")

# My GameStop market phenomenon dashboard
print("Creating my GameStop phenomenon analysis dashboard...")
print("Revealing the incredible story of retail investor power and market dynamics!")

# My GameStop visualization with enhanced title
make_graph(gme_data, gme_revenue, 'My GameStop Market Phenomenon Analysis')

print("\nMy GameStop Analysis Insights:")
print("- Upper chart captures the historic retail trading revolution")
print("- Lower chart shows business fundamentals during transformation")
print("- Demonstrates the power of retail investor coordination")
print("- Reveals how social media can influence traditional market dynamics")
print("- Illustrates the ongoing evolution from brick-and-mortar to digital business model")

	Date	Open	High	Low	Close	Volume
0	2010-06-29	3.800	5.000	3.508	4.778	93831500
1	2010-06-30	5.158	6.084	4.660	4.766	85935500
2	2010-07-01	5.000	5.184	4.054	4.392	41094000
3	2010-07-02	4.600	4.620	3.742	3.840	25699000
4	2010-07-06	4.000	4.000	3.166	3.222	34334500

	Date	Revenue
0	2021-12-31	$17,719
1	2021-09-30	$13,757
2	2021-06-30	$11,958
3	2021-03-31	$10,389
4	2020-12-31	$10,744

	Date	Revenue
0	2021-12-31	17719
1	2021-09-30	13757
2	2021-06-30	11958
3	2021-03-31	10389
4	2020-12-31	10744

	Date	Revenue
45	2010-09-30	31
46	2010-06-30	28
47	2010-03-31	21
49	2009-09-30	46
50	2009-06-30	27

	Date	Open	High	Low	Close	Volume
0	2002-02-13	6.480513	6.773399	6.413183	6.766665	19054000
1	2002-02-14	6.850830	6.864296	6.682505	6.733002	2755400
2	2002-02-15	6.733000	6.749832	6.632005	6.699335	2097400
3	2002-02-19	6.665672	6.665672	6.312189	6.430017	1852600
4	2002-02-20	6.463682	6.648839	6.413184	6.648839	1723200

My Advanced Stock Market Analysis & Visualization Project¶

My Journey into Financial Data Analysis¶

My Stock Market Data Extraction & Visualization Mastery¶

Project Overview¶

My Project Roadmap¶

My Learning Outcomes¶

My Financial Visualization Engine¶

Part 2: My Tesla Stock Data Extraction Mastery¶

Leveraging yfinance API for Real-Time Market Data¶

My Historical Data Extraction Strategy¶

My Data Structure Optimization¶

Part 3: My Tesla Revenue Data Web Scraping Expertise¶

Advanced Beautiful Soup Techniques for Financial Data Extraction¶

My Data Cleaning and Standardization Process¶

My Data Quality Validation Process¶

My Final Tesla Revenue Dataset Verification¶

Part 4: My GameStop Stock Analysis Adventure¶

Exploring the GME Phenomenon Through Data¶

My GameStop Historical Analysis Strategy¶

My GameStop Data Structure Preparation¶

Part 5: My GameStop Revenue Data Extraction Mastery¶

Advanced Web Scraping for GME Financial Performance¶

My GameStop HTML Parsing Process¶

My GameStop Revenue Extraction Challenge¶

My GameStop Revenue Data Processing & Validation¶

Part 6: My Tesla Financial Dashboard Creation¶

Bringing Tesla's Story to Life Through Data Visualization¶

Part 7: My GameStop Market Phenomenon Dashboard¶

Visualizing the GME Revolution Through Data¶

My Stock Market Analysis Project Summary¶

Key Achievements in Financial Data Science¶

🔧 Technical Skills¶

📊 Financial Analysis Expertise¶

🎯 Business Insights Delivered¶

My Professional Development Outcomes¶

	Date	Revenue
47	2010-01-31	3524
48	2009-10-31	1835
49	2009-07-31	1739
50	2009-04-30	1981
51	2009-01-31	3492