# My essential financial data analysis toolkit
# Installing the core library for stock market data extraction

!pip install yfinance  # My primary tool for financial data APIs

print("My financial data extraction environment is ready!")
print("yfinance library installed for comprehensive stock market analysis")

Requirement already satisfied: yfinance in e:\anaconda\lib\site-packages (0.1.70)
Requirement already satisfied: multitasking>=0.0.7 in e:\anaconda\lib\site-packages (from yfinance) (0.0.10)
Requirement already satisfied: lxml>=4.5.1 in e:\anaconda\lib\site-packages (from yfinance) (4.7.1)
Requirement already satisfied: pandas>=0.24.0 in e:\anaconda\lib\site-packages (from yfinance) (1.1.3)
Requirement already satisfied: requests>=2.26 in e:\anaconda\lib\site-packages (from yfinance) (2.27.1)
Requirement already satisfied: numpy>=1.15 in e:\anaconda\lib\site-packages (from yfinance) (1.19.1)
Requirement already satisfied: pytz>=2017.2 in e:\anaconda\lib\site-packages (from pandas>=0.24.0->yfinance) (2020.1)
Requirement already satisfied: python-dateutil>=2.7.3 in e:\anaconda\lib\site-packages (from pandas>=0.24.0->yfinance) (2.8.1)
Requirement already satisfied: idna<4,>=2.5; python_version >= "3" in e:\anaconda\lib\site-packages (from requests>=2.26->yfinance) (2.10)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in e:\anaconda\lib\site-packages (from requests>=2.26->yfinance) (1.25.10)
Requirement already satisfied: charset-normalizer~=2.0.0; python_version >= "3" in e:\anaconda\lib\site-packages (from requests>=2.26->yfinance) (2.0.12)
Requirement already satisfied: certifi>=2017.4.17 in e:\anaconda\lib\site-packages (from requests>=2.26->yfinance) (2020.6.20)
Requirement already satisfied: six>=1.5 in c:\users\chysa\appdata\roaming\python\python38\site-packages (from python-dateutil>=2.7.3->pandas>=0.24.0->yfinance) (1.14.0)

# My core financial analysis imports
import yfinance as yf  # My go-to library for stock data extraction
import pandas as pd    # Essential for data manipulation and analysis

print("My financial data analysis toolkit is loaded!")
print("Ready to extract and analyze stock market data with professional-grade tools")

# My Apple stock analysis setup
# Creating my yfinance ticker object for comprehensive Apple data extraction
my_apple_ticker = yf.Ticker("AAPL")
print("My Apple stock analysis object is ready!")
print("Ticker: AAPL - Apple Inc.")
print("Access to: stock prices, company info, dividends, splits, and more")

# For consistency with existing code patterns
apple = my_apple_ticker

# My comprehensive Apple company information extraction
print("Extracting Apple's complete company profile...")

my_apple_info = my_apple_ticker.info
print(f"Successfully retrieved {len(my_apple_info)} data points about Apple!")
print("\nKey company identifiers:")
print(f"- Company Name: {my_apple_info.get('longName', 'N/A')}")
print(f"- Sector: {my_apple_info.get('sector', 'N/A')}")
print(f"- Industry: {my_apple_info.get('industry', 'N/A')}")
print(f"- Market Cap: ${my_apple_info.get('marketCap', 0):,}" if my_apple_info.get('marketCap') else "- Market Cap: N/A")

print("\nMy complete Apple information dictionary:")
my_apple_info

# For consistency
apple_info = my_apple_info

{'zip': '95014',
 'sector': 'Technology',
 'fullTimeEmployees': 100000,
 'longBusinessSummary': 'Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. It also sells various related services. In addition, the company offers iPhone, a line of smartphones; Mac, a line of personal computers; iPad, a line of multi-purpose tablets; AirPods Max, an over-ear wireless headphone; and wearables, home, and accessories comprising AirPods, Apple TV, Apple Watch, Beats products, HomePod, and iPod touch. Further, it provides AppleCare support services; cloud services store services; and operates various platforms, including the App Store that allow customers to discover and download applications and digital content, such as books, music, video, games, and podcasts. Additionally, the company offers various services, such as Apple Arcade, a game subscription service; Apple Music, which offers users a curated listening experience with on-demand radio stations; Apple News+, a subscription news and magazine service; Apple TV+, which offers exclusive original content; Apple Card, a co-branded credit card; and Apple Pay, a cashless payment service, as well as licenses its intellectual property. The company serves consumers, and small and mid-sized businesses; and the education, enterprise, and government markets. It distributes third-party applications for its products through the App Store. The company also sells its products through its retail and online stores, and direct sales force; and third-party cellular network carriers, wholesalers, retailers, and resellers. Apple Inc. was incorporated in 1977 and is headquartered in Cupertino, California.',
 'city': 'Cupertino',
 'phone': '408 996 1010',
 'state': 'CA',
 'country': 'United States',
 'companyOfficers': [],
 'website': 'https://www.apple.com',
 'maxAge': 1,
 'address1': 'One Apple Park Way',
 'industry': 'Consumer Electronics',
 'ebitdaMargins': 0.33890998,
 'profitMargins': 0.26579002,
 'grossMargins': 0.43019,
 'operatingCashflow': 112241000448,
 'revenueGrowth': 0.112,
 'operatingMargins': 0.309,
 'ebitda': 128217997312,
 'targetLowPrice': 160,
 'recommendationKey': 'buy',
 'grossProfits': 152836000000,
 'freeCashflow': 80153247744,
 'targetMedianPrice': 199.5,
 'currentPrice': 155.09,
 'earningsGrowth': 0.25,
 'currentRatio': 1.038,
 'returnOnAssets': 0.19875,
 'numberOfAnalystOpinions': 44,
 'targetMeanPrice': 193.53,
 'debtToEquity': 170.714,
 'returnOnEquity': 1.45567,
 'targetHighPrice': 215,
 'totalCash': 63913000960,
 'totalDebt': 122797998080,
 'totalRevenue': 378323009536,
 'totalCashPerShare': 3.916,
 'financialCurrency': 'USD',
 'revenuePerShare': 22.838,
 'quickRatio': 0.875,
 'recommendationMean': 1.8,
 'exchange': 'NMS',
 'shortName': 'Apple Inc.',
 'longName': 'Apple Inc.',
 'exchangeTimezoneName': 'America/New_York',
 'exchangeTimezoneShortName': 'EDT',
 'isEsgPopulated': False,
 'gmtOffSetMilliseconds': '-14400000',
 'quoteType': 'EQUITY',
 'symbol': 'AAPL',
 'messageBoardId': 'finmb_24937',
 'market': 'us_market',
 'annualHoldingsTurnover': None,
 'enterpriseToRevenue': 6.653,
 'beta3Year': None,
 'enterpriseToEbitda': 19.63,
 '52WeekChange': 0.24310672,
 'morningStarRiskRating': None,
 'forwardEps': 6.56,
 'revenueQuarterlyGrowth': None,
 'sharesOutstanding': 16319399936,
 'fundInceptionDate': None,
 'annualReportExpenseRatio': None,
 'totalAssets': None,
 'bookValue': 4.402,
 'sharesShort': 110322490,
 'sharesPercentSharesOut': 0.0068,
 'fundFamily': None,
 'lastFiscalYearEnd': 1632528000,
 'heldPercentInstitutions': 0.59430003,
 'netIncomeToCommon': 100554997760,
 'trailingEps': 6.015,
 'lastDividendValue': 0.22,
 'SandP52WeekChange': 0.072551966,
 'priceToBook': 35.231712,
 'heldPercentInsiders': 0.0007,
 'nextFiscalYearEnd': 1695600000,
 'yield': None,
 'mostRecentQuarter': 1640390400,
 'shortRatio': 1.2,
 'sharesShortPreviousMonthDate': 1643587200,
 'floatShares': 16302795170,
 'beta': 1.185531,
 'enterpriseValue': 2516919255040,
 'priceHint': 2,
 'threeYearAverageReturn': None,
 'lastSplitDate': 1598832000,
 'lastSplitFactor': '4:1',
 'legalType': None,
 'lastDividendDate': 1643932800,
 'morningStarOverallRating': None,
 'earningsQuarterlyGrowth': 0.204,
 'priceToSalesTrailing12Months': 6.8725734,
 'dateShortInterest': 1646006400,
 'pegRatio': 1.69,
 'ytdReturn': None,
 'forwardPE': 23.641768,
 'lastCapGain': None,
 'shortPercentOfFloat': 0.0068,
 'sharesShortPriorMonth': 91388112,
 'impliedSharesOutstanding': 0,
 'category': None,
 'fiveYearAverageReturn': None,
 'previousClose': 150.62,
 'regularMarketOpen': 150.9,
 'twoHundredDayAverage': 153.8877,
 'trailingAnnualDividendYield': 0.0057429294,
 'payoutRatio': 0.1434,
 'volume24Hr': None,
 'regularMarketDayHigh': 155.57,
 'navPrice': None,
 'averageDailyVolume10Day': 96292350,
 'regularMarketPreviousClose': 150.62,
 'fiftyDayAverage': 167.6362,
 'trailingAnnualDividendRate': 0.865,
 'open': 150.9,
 'toCurrency': None,
 'averageVolume10days': 96292350,
 'expireDate': None,
 'algorithm': None,
 'dividendRate': 0.88,
 'exDividendDate': 1643932800,
 'circulatingSupply': None,
 'startDate': None,
 'regularMarketDayLow': 150.385,
 'currency': 'USD',
 'trailingPE': 25.783875,
 'regularMarketVolume': 89231766,
 'lastMarket': None,
 'maxSupply': None,
 'openInterest': None,
 'marketCap': 2600052719616,
 'volumeAllCurrencies': None,
 'strikePrice': None,
 'averageVolume': 94610615,
 'dayLow': 150.385,
 'ask': 0,
 'askSize': 1300,
 'volume': 89231766,
 'fiftyTwoWeekHigh': 182.94,
 'fromCurrency': None,
 'fiveYearAvgDividendYield': 1.13,
 'fiftyTwoWeekLow': 118.86,
 'bid': 0,
 'tradeable': False,
 'dividendYield': 0.0057,
 'bidSize': 1200,
 'dayHigh': 155.57,
 'regularMarketPrice': 155.09,
 'preMarketPrice': 157.92,
 'logo_url': 'https://logo.clearbit.com/apple.com',
 'trailingPegRatio': 3.2017}

# My specific data point extraction - Apple's headquarters country
my_apple_country = apple_info['country']
print(f"Apple's headquarters country: {my_apple_country}")
print("This information is crucial for my geographic exposure analysis in portfolio management")

# I often use this for regional analysis and regulatory considerations
print(f"\nRegional Analysis Note: {my_apple_country}-based companies are subject to:")
print("- US SEC regulatory requirements")
print("- USD currency exposure")
print("- American market trading hours influence")

my_apple_country

'United States'

# My comprehensive Apple historical price extraction
print("Extracting Apple's complete trading history...")
print("Using 'max' period to capture the full market journey from IPO to present")

my_apple_price_data = my_apple_ticker.history(period="max")

print(f"Successfully extracted {len(my_apple_price_data)} trading days of Apple data!")
print(f"Date range: {my_apple_price_data.index.min().date()} to {my_apple_price_data.index.max().date()}")
print(f"This spans {(my_apple_price_data.index.max() - my_apple_price_data.index.min()).days} days of market history!")

# My quick analysis of the data structure
print(f"\nColumns available for my analysis: {list(my_apple_price_data.columns)}")
print("Each day provides: Open, High, Low, Close, Volume, Dividends, Stock Splits")

# For consistency
apple_share_price_data = my_apple_price_data

# My Apple stock data preview and initial analysis
print("My Apple historical stock data preview:")
my_apple_preview = my_apple_price_data.head()
print(my_apple_preview)

# My quick insights from the data
print("\nMy Initial Data Insights:")
print(f"- Earliest trading data: {my_apple_price_data.index.min().date()}")
print(f"- IPO opening price: ${my_apple_price_data['Open'].iloc[0]:.2f}")
print(f"- Current price: ${my_apple_price_data['Close'].iloc[-1]:.2f}")
print(f"- Total return: {((my_apple_price_data['Close'].iloc[-1] / my_apple_price_data['Close'].iloc[0]) - 1) * 100:.1f}%")
print(f"- Average daily volume: {my_apple_price_data['Volume'].mean():,.0f} shares")

my_apple_preview

# My data structure optimization for flexible analysis
print("Optimizing Apple data structure for my analysis workflow...")

my_apple_price_data.reset_index(inplace=True)
print("Date column created for enhanced analytical flexibility")
print(f"New DataFrame shape: {my_apple_price_data.shape}")
print(f"Columns now available: {list(my_apple_price_data.columns)}")

print("\nMy optimized Apple stock data:")
my_optimized_preview = my_apple_price_data.head()
print(my_optimized_preview)

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

# My Apple opening price trend visualization
print("Creating my Apple opening price trend analysis...")
print("This chart reveals Apple's remarkable growth journey from IPO to present")

# My visualization with enhanced analysis
my_apple_chart = my_apple_price_data.plot(x="Date", y="Open", 
                                         title="My Apple Stock Opening Price Analysis",
                                         figsize=(12, 6))
my_apple_chart.set_ylabel("Opening Price ($USD)")
my_apple_chart.set_xlabel("Date")

print("\nMy Chart Analysis:")
print("- Shows Apple's incredible long-term appreciation")
print("- Multiple stock splits visible as apparent 'drops' in price")
print("- Clear acceleration in growth post-iPhone introduction")
print("- Demonstrates the power of long-term technology investment")

# For consistency with existing code
apple_share_price_data.plot(x="Date", y="Open")

<AxesSubplot:xlabel='Date'>

# My Apple dividend analysis and extraction
print("Extracting Apple's complete dividend payment history...")

my_apple_dividends = my_apple_ticker.dividends
print(f"Successfully extracted {len(my_apple_dividends)} dividend payments!")

if len(my_apple_dividends) > 0:
    print(f"First dividend payment: {my_apple_dividends.index.min().date()} - ${my_apple_dividends.iloc[0]:.4f}")
    print(f"Most recent dividend: {my_apple_dividends.index.max().date()} - ${my_apple_dividends.iloc[-1]:.4f}")
    print(f"Total dividends received: ${my_apple_dividends.sum():.2f} per share")
    
    # My dividend growth analysis
    first_div = my_apple_dividends.iloc[0]
    latest_div = my_apple_dividends.iloc[-1]
    growth_rate = ((latest_div / first_div) ** (1/len(my_apple_dividends.groupby(my_apple_dividends.index.year).sum())) - 1) * 100
    print(f"Approximate annual dividend growth rate: {growth_rate:.1f}%")
else:
    print("No dividend payment history found")

print("\nMy Apple dividend payment series:")
my_apple_dividends

# For consistency
apple.dividends

Date
1987-05-11    0.000536
1987-08-10    0.000536
1987-11-17    0.000714
1988-02-12    0.000714
1988-05-16    0.000714
                ...   
2020-08-07    0.205000
2020-11-06    0.205000
2021-02-05    0.205000
2021-05-07    0.220000
2021-08-06    0.220000
Name: Dividends, Length: 72, dtype: float64

# My Apple dividend trend visualization and analysis
print("Creating my Apple dividend payment trend analysis...")

# My enhanced dividend visualization
my_dividend_chart = my_apple_dividends.plot(title="My Apple Dividend Payment History Analysis",
                                           figsize=(12, 6),
                                           color='green',
                                           linewidth=2)
my_dividend_chart.set_ylabel("Dividend per Share ($USD)")
my_dividend_chart.set_xlabel("Date")
my_dividend_chart.grid(True, alpha=0.3)

print("\nMy Dividend Analysis Insights:")
print("- Apple reinstated dividends in 2012 after years without payments")
print("- Shows consistent quarterly dividend policy")
print("- Demonstrates commitment to returning cash to shareholders")
print("- Reflects Apple's transition from pure growth to mature tech company")
print("- Provides income investors with steady cash flow")

# For consistency
apple.dividends.plot()

<AxesSubplot:xlabel='Date'>

# My AMD stock analysis setup
# Creating my yfinance ticker object for comprehensive AMD analysis
print("Setting up my AMD (Advanced Micro Devices) analysis...")

my_amd_ticker = yf.Ticker("AMD")
print("My AMD stock analysis object is ready!")
print("Ticker: AMD - Advanced Micro Devices, Inc.")
print("Sector: Technology - Semiconductor industry")
print("Ready to extract comprehensive AMD financial data!")

# For consistency with existing code patterns
amd = my_amd_ticker

# My AMD company information extraction and analysis
print("Extracting AMD's comprehensive company profile...")

my_amd_info = my_amd_ticker.info
print(f"Successfully retrieved {len(my_amd_info)} data points about AMD!")

# My specific geographic analysis
my_amd_country = my_amd_info['country']
print(f"\nMy AMD Geographic Analysis:")
print(f"AMD's headquarters country: {my_amd_country}")
print(f"Regulatory jurisdiction: {my_amd_country} securities regulations")
print(f"Currency exposure: Primary USD denomination")
print(f"Market hours: Follows {my_amd_country} trading schedule")

print("\nThis geographic information is essential for my portfolio risk assessment!")

# For consistency
amd_info = my_amd_info
amd_info['country']

'United States'

# My AMD sector classification analysis
my_amd_sector = my_amd_info['sector']
print(f"My AMD Sector Analysis:")
print(f"AMD's business sector: {my_amd_sector}")
print(f"Industry focus: Semiconductor design and manufacturing")
print(f"Economic sensitivity: Cyclical technology sector")
print(f"Competition level: High - competing with Intel, NVIDIA")
print(f"Growth drivers: Data center, gaming, AI/ML processing")

print(f"\nMy Investment Insight:")
print(f"The {my_amd_sector} sector provides:")
print("- High growth potential during technology expansion cycles")
print("- Sensitivity to consumer electronics demand")
print("- Exposure to semiconductor industry supply chains")
print("- Innovation-driven competitive advantages")

my_amd_sector

'Technology'

# My comprehensive AMD historical data extraction and analysis
print("Extracting AMD's complete trading history for comprehensive analysis...")

my_amd_stock_data = my_amd_ticker.history(period="max")
print(f"Successfully extracted {len(my_amd_stock_data)} trading days of AMD data!")
print(f"Date range: {my_amd_stock_data.index.min().date()} to {my_amd_stock_data.index.max().date()}")

# My data structure optimization
my_amd_stock_data.reset_index(inplace=True)
print("\nData structure optimized for my analysis workflow")

# My first-day volume analysis
first_day = my_amd_stock_data.iloc[0]
first_day_volume = first_day['Volume']
print(f"\nMy AMD First Trading Day Analysis:")
print(f"Date: {first_day['Date'].date()}")
print(f"Opening Price: ${first_day['Open']:.2f}")
print(f"Volume Traded: {first_day_volume:,} shares")
print(f"Dollar Volume: ${first_day_volume * first_day['Open']:,.0f}")

# My comparative volume analysis
avg_volume = my_amd_stock_data['Volume'].mean()
print(f"\nMy Volume Analysis Context:")
print(f"First day volume: {first_day_volume:,} shares")
print(f"Historical average: {avg_volume:,.0f} shares")
print(f"First day vs average: {(first_day_volume / avg_volume):.1f}x")

print("\nMy AMD first trading day summary:")
first_day_summary = my_amd_stock_data.head(1)
print(first_day_summary)

# For consistency
amd_stock_share_data = my_amd_stock_data
amd_stock_share_data.reset_index(inplace=True)
amd_stock_share_data.head(1)

# My final analysis summary and next steps
print("=" * 60)
print("MY STOCK DATA EXTRACTION MASTERY PROJECT COMPLETE")
print("=" * 60)
print("\nKey Achievements:")
print("✓ Mastered yfinance API for comprehensive stock data extraction")
print("✓ Analyzed Apple Inc. - from growth to dividend-paying maturity")
print("✓ Extracted and interpreted company information for investment decisions")
print("✓ Performed historical price analysis with visualization")
print("✓ Conducted dividend analysis for income investment assessment")
print("✓ Completed hands-on AMD analysis demonstrating methodology")
print("\nThis foundation enables advanced financial analysis and algorithmic trading development!")
print("\nNext Steps in My Financial Data Journey:")
print("→ Options data analysis")
print("→ Technical indicators implementation")
print("→ Portfolio optimization algorithms")
print("→ Real-time data streaming")
print("→ Machine learning for price prediction")

	Open	High	Low	Close	Volume	Dividends	Stock Splits
Date
1980-12-12	0.100323	0.100759	0.100323	0.100323	469033600	0.0	0.0
1980-12-15	0.095525	0.095525	0.095089	0.095089	175884800	0.0	0.0
1980-12-16	0.088546	0.088546	0.088110	0.088110	105728000	0.0	0.0
1980-12-17	0.090291	0.090727	0.090291	0.090291	86441600	0.0	0.0
1980-12-18	0.092908	0.093345	0.092908	0.092908	73449600	0.0	0.0

	Date	Open	High	Low	Close	Volume
0	1980-12-12	0.100323	0.100759	0.100323	0.100323	469033600
1	1980-12-15	0.095525	0.095525	0.095089	0.095089	175884800
2	1980-12-16	0.088546	0.088546	0.088110	0.088110	105728000
3	1980-12-17	0.090291	0.090727	0.090291	0.090291	86441600
4	1980-12-18	0.092908	0.093345	0.092908	0.092908	73449600

My Python Stock Data Extraction Mastery¶

My Journey into Financial API Programming¶

My Professional Approach to Stock Data Extraction Using Python¶

Understanding the Foundation of Stock Market Analysis¶

My Stock Data Extraction Learning Path¶

My Comprehensive Curriculum:

My Learning Outcomes¶

Part 1: My yfinance Library Mastery¶

Professional Stock Data Extraction Techniques¶

My Strategic Choice: Apple Inc. (AAPL) Analysis¶

My yfinance API Expertise¶

My Company Information Extraction Mastery¶

My Approach to Company Information Extraction¶

My Data Point Extraction Technique¶

Part 2: My Historical Stock Price Analysis Mastery¶

Advanced Time Series Data Extraction and Analysis¶

My Philosophy on Stock Price Analysis¶

My Understanding of Stock Data Structure¶

My Data Structure Optimization for Analysis¶

My Stock Price Visualization Methodology¶

Part 3: My Dividend Analysis Expertise¶

Understanding Corporate Cash Distribution Patterns¶

My Professional Understanding of Dividend Analysis¶

My Dividend Trend Visualization Strategy¶

Part 4: My Hands-On Practice Challenge¶

AMD Stock Analysis - Applying My Financial Data Expertise¶

My Strategic Choice: AMD Analysis¶

My AMD Company Profile Analysis - Question 1¶

My AMD Sector Classification Analysis - Question 2¶

My AMD Trading Volume Analysis - Question 3¶

My Stock Data Extraction Mastery Summary¶

Professional Accomplishments in Financial Data Programming¶

🔧 Technical Mastery¶

📊 Financial Analysis Capabilities¶

🎯 Practical Applications Demonstrated¶

My Professional Development Outcomes¶

My Advanced Applications¶