Context

Technology companies have become a dominant driver in recent years of economic growth, consumer tastes and the financial markets. The biggest tech stocks as a group, for example, have dramatically outpaced the broader market in the past decade.

That's because technology has reshaped in a major way how people communicate, consume information, shop, socialize, and work.

Broadly speaking, companies in the technology sector engage in the research, development, and manufacture of technologically based goods and services. They create software, and design and manufacture computers, mobile devices, and home appliances. They also provide products and services related to information technology.

Content

This dataset contains 3 files with the daily stock price and volume of the three companies: Google, Apple, and Facebook from 07/09/2017 to 07/09/2022. Source: Yahoo! Finance

Profile

Apple

Apple Inc. (AAPL) One Apple Park Way Cupertino, CA 95014 United States 408 996 1010 https://www.apple.com

Sector(s): Technology Industry: Consumer Electronics Full Time Employees: 154,000

Total Revenue (2021): $365,817,000
Net Income (2021):$94,680,000
Exchange: Nasdaq

Google

Alphabet Inc. (GOOG) 1600 Amphitheatre Parkway Mountain View, CA 94043 United States 650 253 0000 https://www.abc.xyz

Sector(s): Communication Services Industry: Internet Content & Information Full Time Employees: 174,014

Total Revenue (2021): $257,637,000 Net Income (2021):$76,033,000 Exchange: Nasdaq

Facebook

Meta Platforms, Inc. (META) 1601 Willow Road Menlo Park, CA 94025 United States 650 543 4800 https://investor.fb.com

Sector(s): Communication Services Industry: Internet Content & Information Full Time Employees: 83,553

Total Revenue (2021): $117,929,000 Net Income (2021):$39,370,000 Exchange: Nasdaq

Acknowledgements

Yahoo! Finance Investopedia Nasdaq

Start A New Notebook!

The price of Meta (former Facebook) shares traded on the Nasdaq stock exchange fluctuated significantly but increased overall during the period from May 2012 to January 2025. After peaking at ****** U.S. dollars per share in August 2021, the price of Meta shares started to fluctuate and exceeded its previous peak in 2025. The share price stood at ****** U.S. dollars as of the end of January 2025. Substantial fluctuations in the last few years Meta's stock prices have fluctuated particularly after the rebranding announcement in late 2021. Following the announcement and through 2022, Meta's revenue remained rather stagnant, and its net income decreased considerably. Moreover, the tech giant announced one of the industry's largest layoffs in late 2022. As a result, the share price hit a low of ***** U.S. dollars in October 2022, the lowest value observed since 2016. However, Meta's share price has been steadily recovering since then. Shift in strategy for the world’s first social network Meta has shifted its focus to the metaverse, virtual reality (VR), and augmented reality (AR), with the rebranding in late 2021. As a result, Reality Labs was established as a dedicated business and research unit to focus on developing metaverse and AR/VR technologies. However, as of early 2023, Meta still relies mainly on advertising and its Family of Apps to generate most of its revenue, despite having made significant investments in virtual reality. Reality Labs generated *** billion U.S. dollars in revenue in 2024 and has been consistently incurring operating losses since 2019.

Meta Platforms Stock Prices (Oct 28, 2021 - May 7, 2024)

This dataset contains daily stock price data for Meta Platforms (formerly Facebook) from October 28, 2021, to May 7, 2024. The data was collected from Yahoo Finance

Columns:

• Date: Date (DD/MM/YYYY)
• Open: The opening price of the stock on that day
• High: The highest price of the stock on that day
• Low: The lowest price of the stock on that day
• Close: The closing price of the stock on that day
• Adj Close: Adjusted closing price of the stock on that day (adjusted for stock splits)
• Volume: Number of shares traded on that day

Stock Data of Top USA Companies: Apple, Tesla, Amazon

👨‍💻 Author: Azhar Saleem

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Dataset Description

This dataset provides daily stock data for some of the top companies in the USA stock market, including major players like Apple, Microsoft, Amazon, Tesla, and others. The data is collected from Yahoo Finance, covering each company’s historical data from its starting date until today. This comprehensive dataset enables in-depth analysis of key financial indicators and stock trends for each company, making it valuable for multiple applications.

Column Descriptions

The dataset contains the following columns, consistent across all companies:

• Date: The date of the stock data entry.
• Open: The stock's opening price for the day.
• High: The highest price reached during the trading day.
• Low: The lowest price during the trading day.
• Close: The stock’s closing price for the day.
• Volume: The total number of shares traded on that day.
• Dividends: Any dividends paid out on that day.
• Stock Splits: Records stock split events, if any, on that day.

Potential Use Cases

1. Machine Learning & Deep Learning:

   • Stock Price Prediction: Use historical prices to train models for forecasting future stock prices.
   • Sentiment Analysis and Price Correlation: Combine with external sentiment data to predict price movements based on market sentiment.
   • Anomaly Detection: Detect unusual price patterns or volume spikes using classification algorithms.

2. Data Science:

   • Trend Analysis: Identify long-term trends for each company or compare trends between companies.
   • Volatility Analysis: Calculate volatility to assess risk and return patterns over time.
   • Correlation Analysis: Compare stock performance across companies to study market relationships.

3. Data Analysis:

   • Historical Performance: Review historical data to understand growth trends, market impact of stock splits, and dividends.
   • Seasonal Patterns: Analyze data for seasonal trends or recurring patterns across years.
   • Investment Strategy Backtesting: Test various investment strategies based on historical data to assess potential profitability.

4. Financial Research:

   • Economic Impact Studies: Investigate how major events affected stock prices across top companies.
   • Sector-Specific Analysis: Identify performance differences across sectors, such as tech, healthcare, and retail.

This dataset is a powerful tool for analysts, researchers, and financial enthusiasts, offering versatility across multiple domains from stock analysis to algorithmic trading models.

The aim to reduce the risk of mistakes and omissions while managing processing costs drives the need for prompt, comprehensive, precise, and well-organized Corporate Actions notifications. We provide adaptable, efficient solutions, such as through DataScope Select, available in ISO 15022 MT 564/568 and Proprietary formats (custom user-defined XML/Delimited layouts). Data extractions can be performed either on an as-needed basis or according to a set schedule for specific portfolios. For scheduled extractions, the process can be executed once or recurrently at designated days and times. The foundational data undergo quality assurance and are published continuously throughout the day on a 15-minute cycle, operating 24x7.

Context

Facebook is a company that literally every kid is aware of. Its a household name. People from various age groups are there on this social media website. It has helped many in connecting with different people and also has helped some of the investors by earning them a good amount of money. This data set contains the details of the stock of Facebook Inc.

Content

This data set has 7 columns with all the necessary values such as opening price of the stock, the closing price of it, its highest in the day and much more. It has date wise data of the stock starting from 2012 to 2020(August).

Meta reported 67.32K in Employees for its fiscal year ending in December of 2023. Data for Meta | FB - Employees Total Number including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Meta reported $1.62T in Market Capitalization this December of 2025, considering the latest stock price and the number of outstanding shares.Data for Meta | FB - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Each company has it's own story behind their success. Here I just want to introduce when they came in the market for the time and issue first IPO.

IPO Term:- An initial public offering (IPO) refers to the process of offering shares of a private corporation to the public in a new stock issuance for the first time. An IPO allows a company to raise equity capital from public investors.

Meta:- Facebook (FB), now Meta Platforms, Inc. (META), went public with its initial public offering (IPO) on May 18, 2012. The popular social networking company had one of the largest and most anticipated IPOs in history. On that day, FB shares closed at $38.23, slightly above the $**38.00** IPO price.

Amazon:- Amazon went public on May 15, 1997, and the IPO price was $18.00, or $0.075 adjusted for the stocks splits that occurred on June 2, 1998 (2-for-1 split), January 5, 1999 (3-for-1 split), and September 1, 1999 (2-for-1 split), and June 3, 2022 (20-for-1 split).

Apple:- Apple went public on **December 12, 1980 at $22.00 **per share. The stock has split five times since the IPO, so on a split-adjusted basis the IPO share price was $.10. The stock split on a 4-for-1 basis on August 28, 2020, a 7-for-1 basis on June 9, 2014, and split on a 2-for-1 basis on February 28, 2005, June 21, 2000, and June 16, 1987.

Netflix:- Since its initial public offering (IPO) in May 23, 2002, NFLX's stock price has skyrocketed. Consider this: Netflix currently has a market capitalization of $101 billion, compared to $3 billion in January 2010.

Google:- Google, now known as Alphabet Inc. Finally held its highly anticipated IPO in 2004, six years after it was founded. The company had already become a search juggernaut by that time, and IPO shares were priced at $85 per share for a valuation of $23 billion.

Column Description Of Each File

• Date: Trading Date
• Open: Opening Price
• High: It shows that maximum price during the day
• Low: It shows that minimum price during the day
• Close: close price adjust for calls
• Close : close price adjusted for splits
• Adj Close : adjusted close price adjusted for both dividends and splits.
• Volume : the number of shares that changed hands during a given day

This dataset contains the historical stock data for Meta Platforms Inc. (META), formerly known as Facebook Inc., sourced from Yahoo Finance, spanning from its initial public offering (IPO) on May 18, 2012 through to December 2024. The data includes daily stock prices and provides essential details such as:

Date: The specific trading date for each record. Open: The opening price of Meta’s stock for that day. High: The highest price reached by Meta’s stock on that day. Low: The lowest price of Meta’s stock on that day. Close: The closing price for Meta’s stock on that day. Adj Close: The adjusted closing price, accounting for stock splits, dividends, and other corporate actions. Volume: The number of shares traded on that particular day. This dataset is ideal for those interested in analyzing the financial journey of Meta Platforms, from its early days as a social media giant to its transformation into a broader technology company focused on virtual reality, artificial intelligence, and the "Metaverse." It can be used for time series analysis, stock price prediction, and financial modeling.

Source: The data has been sourced from Yahoo Finance, a trusted platform for obtaining accurate and reliable historical financial data.

Data Usage:

Time Series Analysis: Analyze Meta’s stock price trends and market behavior over time. Stock Price Prediction: Build predictive models to forecast Meta’s future stock price movements based on historical data. Financial Analysis: Perform detailed analysis of Meta’s stock performance, volatility, and market impact as it evolved from a social network to a global tech leader.

👨‍💻 Author: Azhar Saleem

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Description

This dataset represents the historical stock prices of Abu Dhabi National Oil Co. (ADNOC), a major player in the oil and gas industry headquartered in Abu Dhabi, United Arab Emirates. ADNOC operates through a variety of segments, including B2B (Commercial) and B2C (Retail), providing a broad spectrum of petroleum products and services. The data covers daily stock prices and includes various attributes from a specific period. This dataset is ideal for conducting detailed data analysis, predictive modeling in machine learning, deep learning applications, and comprehensive business or stock market analysis.

Columns in the Dataset

1. Date: The date on which the stock prices were recorded. Format: YYYY-MM-DD.
2. Open: The price at which the stock first traded upon the opening of an exchange on a given trading day (in AED).
3. High: The highest price at which the stock traded during the trading day (in AED).
4. Low: The lowest price at which the stock traded during the trading day (in AED).
5. Close: The price at which the stock last traded upon the close of an exchange on a given trading day (in AED).
6. Volume: The total number of shares or contracts traded for the stock in a single trading day.
7. Price_Change: The difference in the opening and closing price of the stock for the day (in AED).
8. Percentage_Change: The percentage change in the stock price from the opening to the closing of the trading day.
9. Average_Price: The average price of the stock for the day, calculated as the average of the high and low prices (in AED).
10. Range: The range between the high and low prices of the stock for the day (in AED).

Usage Notes

This data can be used for a variety of analytical purposes including but not limited to trend analysis, volatility studies, predictive analytics in financial markets, and comparative stock performance studies. The data is provided in CSV format for easy integration into various data analysis tools and platforms.

Source

Abu Dhabi National Oil Co. (ADNOC) United Arab Emirates.

This dataset encapsulates a detailed examination of market dynamics over a five-year period, focusing on the fluctuation of prices and trading volumes across a diversified portfolio. It covers various sectors including energy commodities like natural gas and crude oil, metals such as copper, platinum, silver, and gold, cryptocurrencies including Bitcoin and Ethereum, and key stock indices and companies like the S&P 500, Nasdaq 100, Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta Platforms. This dataset serves as a valuable resource for analyzing trends and patterns in global markets.

Date: The date of the recorded data, formatted as DD-MM-YYYY. Natural_Gas_Price: Price of natural gas in USD per million British thermal units (MMBtu). Natural_Gas_Vol.: Trading volume of natural gas Crude_oil_Price: Price of crude oil in USD per barrel. Crude_oil_Vol.: Trading volume of crude oil Copper_Price: Price of copper in USD per pound. Copper_Vol.: Trading volume of copper Bitcoin_Price: Price of Bitcoin in USD. Bitcoin_Vol.: Trading volume of Bitcoin Platinum_Price: Price of platinum in USD per troy ounce. Platinum_Vol.: Trading volume of platinum Ethereum_Price: Price of Ethereum in USD. Ethereum_Vol.: Trading volume of Ethereum S&P_500_Price: Price index of the S&P 500. Nasdaq_100_Price: Price index of the Nasdaq 100. Nasdaq_100_Vol.: Trading volume for the Nasdaq 100 index Apple_Price: Stock price of Apple Inc. in USD. Apple_Vol.: Trading volume of Apple Inc. stock Tesla_Price: Stock price of Tesla Inc. in USD. Tesla_Vol.: Trading volume of Tesla Inc. stock Microsoft_Price: Stock price of Microsoft Corporation in USD. Microsoft_Vol.: Trading volume of Microsoft Corporation stock Silver_Price: Price of silver in USD per troy ounce. Silver_Vol.: Trading volume of silver Google_Price: Stock price of Alphabet Inc. (Google) in USD. Google_Vol.: Trading volume of Alphabet Inc. stock Nvidia_Price: Stock price of Nvidia Corporation in USD. Nvidia_Vol.: Trading volume of Nvidia Corporation stock Berkshire_Price: Stock price of Berkshire Hathaway Inc. in USD. Berkshire_Vol.: Trading volume of Berkshire Hathaway Inc. stock Netflix_Price: Stock price of Netflix Inc. in USD. Netflix_Vol.: Trading volume of Netflix Inc. stock Amazon_Price: Stock price of Amazon.com Inc. in USD. Amazon_Vol.: Trading volume of Amazon.com Inc. stock Meta_Price: Stock price of Meta Platforms, Inc. (formerly Facebook) in USD. Meta_Vol.: Trading volume of Meta Platforms, Inc. stock Gold_Price: Price of gold in USD per troy ounce. Gold_Vol.: Trading volume of gold

Image attribute : Image by Freepik

This dataset provides historical daily trading data for Meta Platforms, Inc. (formerly known as Facebook, Inc.), with the stock ticker symbol META. The data was collected from Yahoo Finance and consists of richly detailed records suitable for a wide variety of financial analyses and data science projects.

Data Source

• Source: Yahoo Finance (Meta Historical Data)
• Stock Ticker: META (Meta Platforms, Inc.)

Dataset Features

The dataset is structured as a CSV file, with each row corresponding to a single trading day. It covers a comprehensive time range, allowing users to examine Meta’s stock price evolution, volatility, and trading activity over time.

Columns Included

• Date: The trading date (YYYY-MM-DD).
• Open: Opening price of the stock for the day.
• High: Highest price reached during the day.
• Low: Lowest price reached during the day.
• Close: Closing price at the end of the trading session.
• Adj Close: Adjusted close price, accounting for corporate actions like splits and dividends.
• Volume: Total number of shares traded during the day.

Coverage

• Frequency: Daily (all trading days)
• Period: All available historical data for META stock as provided by Yahoo Finance.

Potential Uses

• Time series forecasting and trend analysis
• Financial market research and academic projects
• Technical indicator calculations and trading strategy backtesting
• Visualization and exploratory data analysis
• Studying the impact of news and events on Meta’s stock price

License & Acknowledgements

• Data is provided for educational and research purposes only.
• Please cite Yahoo Finance as the original data source.

Saudi Arabian Oil Company Aramco, Stocks

👨‍💻 Author: Azhar Saleem

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Dataset Description

Welcome to the Enhanced Saudi Arabian Oil Company (Aramco) Stock Dataset! This dataset has been meticulously prepared from Yahoo Finance and further enriched with several engineered features to elevate your data analysis, machine learning, and financial forecasting projects. It captures the daily trading figures of Aramco stocks, presented in Saudi Riyal (SAR), providing a robust foundation for comprehensive market analysis.

Columns in the Dataset

• Date: The trading day for the data recorded (ISO 8601 format).
• Open: The price at which the stock first traded upon the opening of an exchange on a given trading day.
• High: The highest price at which the stock traded during the trading day.
• Low: The lowest price at which the stock traded during the trading day.
• Close: The price at which the stock last traded upon the close of an exchange on a given trading day.
• Volume: The total number of shares traded during the trading day.
• Dividends: The dividend value paid out per share on the trading day.
• Stock Splits: The number of stock splits occurring on the trading day.
• Lag Features (Lag_Close, Lag_High, Lag_Low): Previous day's closing, highest, and lowest prices.
• Rolling Window Statistics (e.g., Rolling_Mean_7, Rolling_Std_7): 7-day and 30-day moving averages and standard deviations of the Close price.
• Technical Indicators (RSI, MACD, Bollinger Bands): Key metrics used in trading to analyze short-term price movements.
• Change Features (Change_Close, Change_Volume): Day-over-day changes in Close price and trading volume.
• Date-Time Features (Weekday, Month, Year, Quarter): Extracted components of the trading day.
• Volume_Normalized: The standardized trading volume using z-score normalization to adjust for scale differences.

Potential Uses

This dataset is tailored for a wide array of applications:

• Financial Analysis: Explore historical performance, volatility, and market trends.
• Forecasting Models: Utilize features like lagged prices and rolling statistics to predict future stock prices.
• Machine Learning: Develop regression models or classification frameworks to predict market movements.
• Deep Learning: Leverage LSTM networks for more sophisticated time-series forecasting.
• Time-Series Analysis: Dive deep into trend analysis, seasonality, and cyclical behavior of stock prices.

Whether you are a data scientist, a financial analyst, or a hobbyist interested in the stock market, this dataset provides a rich playground for analysis and model building. Its comprehensive feature set allows for the development of robust predictive models and offers unique insights into one of the world’s most significant oil companies. Unlock the potential of financial data with this carefully crafted dataset.

Context

Time series modelling for the prediction of stocks prices is a challenging task. Political events, market expectations and economic factors are just a few known factors that can impact financial market behaviour. The financial market is a complex, noisy, evolutionary and chaotic field of study that attracts many enthusiasts and researches — the first, usually driven by the economic benefit of it, the latter, inspired by the challenge of handling such complex data.

This project aims to predict Facebook (FB) next day stock price direction with machine learning algorithms. Technical indicators and global market indexes are used, and their influence on the forecast accuracy is analysed.

Content

Daily values were retrieved (volume, open, close, low and high prices) from Yahoo! Finance website. For Facebook (FB), July 2012 was the earliest data available. The date range is July 2012 to November 2018.

The closing price of current day C(t) and closing price from the previous day C(t-1) are compared to build the initial dataset. The objective is to define if the price trend is going up or down by analysing these two values. For each instance, a comparison was made and recorded. If the price is going up, C(t) > C(t-1), class “1” is assigned. Class “0” is assigned for the opposite case.

• ID: Sample ID
• Close: Closing value of previous day
• Low: Lowest value of previous day
• High: Highest value of previous day
• Volume: Volume value of previous day

Research was initiated to understand which features could help the model to forecast the stock direction. Three main routes were found: Lag features, Technical Indicators and Global Market Indexes. Below is an explanation of each group of features.

Lag features are features that contain the closing price and direction of previous days and it is a common strategy for Time Series models. The following features were added:

• C(t-5): Closing price of 5 days before
• C(t-4): Closing price of 4 days before
• C(t-3): Closing price of 3 days before
• C(t-2): Closing price of 2 days before
• C_up_4: Output 1 if closing price went up 4 days ago
• C_up_3: Output 1 if closing price went up 3 days ago
• C_up_2: Output 1 if closing price went up 2 days ago
• C_up_1: Output 1 if closing price went up 1 day ago

Technical indicators are used by researches and financial market analysts to support stock market trend forecasting. Common indicators retrieved from the literature were selected and calculated for Facebook stock. Techical Indicators added:

• MA-10: Moving Average considering previous 10 days
• MA-5: Moving Average considering previous 5 days
• WMA-10: Weighted Moving Average considering previous 10 days
• SO: Stochastic Oscillator
• M: Momentum as the difference in closing price in a 10 days interval
• SSO: Slow Stochastic Oscillator
• EMA: Exponential Moving Average for a 10 day period
• MACD_Sline_9: MACD Signal Line for a 9 day period
• RSI: Relative Strength Index
• CCI: Commodity Channel Index
• ADO: Accumulation Distribution Oscillator

Technical indicators provide a suggestion of the stock price movement. Additional features were created for each technical indicator by analysing its daily value and assigning a class according to their meaning. Class “1” is given if the indicator numerical value suggests upper trend, class “0” for a downtrend. In other words, financial market analysis is performed at a simplistic level, in the attempt to translate what the continuous value means.

• MA-10>C: If MA-10 is higher than Closing price output 1
• MA-5>C: If MA-5 is higher than Closing price output 1
• WMA-10>C: If WMA-10 is higher than Closing price output 1
• SO>SOt-1: Output is 1 if SO current value is higher than previous day
• M>0: A positive momentum outputs 1
• SSO>SSOt-1: SSO current value is higher than previous day
• EMA>C: If EMA is higher than Closing price output 1
• MACD>MACDt-1: If MACD current value is higher than previous day output 1
• RSI70-30: If RSI is above 70, output 0. Values below 30 output is one. For values within this range it compares to previous day and outputs 1 if value has increased
• CCI200-200: Similar to RSI, but if threshold set for 200 and -200.
• ADO>ADOt-1: Output is 1 if ADO current value is higher than previous day

For a given country or region, the stock market index characterises the performance of its financial market and the overall local economy. For this reason, the same day performance of these markets could contribute to the machine learning model predictions. Six global indexes were added as features, with their closing direction as up or down, class “1” or “0”, respectively. Data for these indexes (Nikkei, Hang Seng, All Ordinaries, Euronext 100, SSE and DAX) were also retrieved from Yahoo! Finance.

https://upload.wikimedia.org/wikipedia/commons/thumb/9/96/Microsoft_logo_%282012%29.svg/2560px-Microsoft_logo_%282012%29.svg.png" alt="Microsoft">

Microsoft Corporation is an American multinational technology corporation that produces computer software, consumer electronics, personal computers, and related services. Its best-known software products are the Microsoft Windows line of operating systems, the Microsoft Office suite, and the Internet Explorer and Edge web browsers. Its flagship hardware products are the Xbox video game consoles and the*** Microsoft Surface ***lineup of touchscreen personal computers. Microsoft ranked No. 21 in the 2020 Fortune 500 rankings of the largest United States corporations by total revenue; it was the world's largest software maker by revenue as of 2016. It is considered one of the Big Five companies in the U.S. information technology industry, along with Google, Apple, Amazon, and Facebook.

Context

Stock data is a very good tool for analysis in terms of EDA, Visualization a and predictions. Microsoft stock data is a brilliant one to consider

Content

Historic stock data downloaded from Yahoo! Finance

Acknowledgements

Yahoo! Finance Python API developers

Inspiration

All the Kagglers and budding data scientists!