About Dataset:

This dataset includes the daily historical stock prices for Google (GOOGL) spanning from 2020 to 2025. It features essential financial metrics such as opening and closing prices, daily highs and lows, adjusted close prices, and trading volumes. The information offers valuable insights into the stock's performance over a five-year timeframe.

Column Descriptions:

• Price: Date of the stock data (needs cleaning as the first two rows are headers).
• Adj Close: Adjusted closing price, accounting for events like dividends and splits.
• Close: Closing price of the stock at the end of the trading day.
• High: Highest price of the stock during the trading day.
• Low: Lowest price of the stock during the trading day.
• Open: Opening price of the stock at the start of the trading day.
• Volume: Number of shares traded during the day.

What Can You Achieve and Apply on This Data:

• Time Series Analysis: Examine trends and patterns over time.
• Stock Price Prediction: Use machine learning models to forecast future prices.
• Volatility Analysis: Measure the stock's price fluctuations.
• Technical Analysis: Calculate indicators like moving averages, RSI, and MACD.
• Correlation Analysis: Investigate the relationship between volume and price changes.
• Investment Strategy Backtesting: Test trading strategies like moving average crossovers.

Note: 1. This data is scraped from Yahoo Finance by me using python code. 2. Some of the About Data is generated from AI, but verified from me.

Analysis of ‘google stock price’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/medharawat/google-stock-price on 28 January 2022.

--- No further description of dataset provided by original source ---

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by Najmussakib21

Contents

This dataset contains the valuation template the researcher can use to retrieve real-time Excel stock price and stock price in Google Sheets. The dataset is provided by Finsheet, the leading financial data provider for spreadsheet users. To get more financial data, visit the website and explore their function. For instance, if a researcher would like to get the last 30 years of income statement for Meta Platform Inc, the syntax would be =FS_EquityFullFinancials("FB", "ic", "FY", 30) In addition, this syntax will return the latest stock price for Caterpillar Inc right in your spreadsheet. =FS_Latest("CAT") If you need assistance with any of the function, feel free to reach out to their customer support team. To get starter, install their Excel and Google Sheets add-on.

Hong Kong's main stock market index, the HK50, fell to 24508 points on August 1, 2025, losing 1.07% from the previous session. Over the past month, the index has climbed 1.18% and is up 44.63% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Hong Kong. Hong Kong Stock Market Index (HK50) - values, historical data, forecasts and news - updated on August of 2025.

Israel's main stock market index, the TA-125, fell to 3080 points on July 31, 2025, losing 0.21% from the previous session. Over the past month, the index has climbed 1.83% and is up 56.58% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Israel. Israel Stock Market (TA-125) - values, historical data, forecasts and news - updated on July of 2025.

Dataset Description

Overview:

This dataset contains the historical stock prices and related financial information for five major technology companies: Apple (AAPL), Microsoft (MSFT), Amazon (AMZN), Google (GOOGL), and Tesla (TSLA). The dataset spans a five-year period from January 1, 2019, to January 1, 2024. It includes key stock metrics such as Open, High, Low, Close, Adjusted Close, and Volume for each trading day.

Data Collection:

The data was sourced using the yfinance library in Python, which provides convenient access to historical market data from Yahoo Finance.

Contents:

The dataset contains the following columns:

Date: The trading date. Open: The opening price of the stock on that date. High: The highest price of the stock on that date. Low: The lowest price of the stock on that date. Close: The closing price of the stock on that date. Adj Close: The adjusted closing price, accounting for dividends and splits. Volume: The number of shares traded on that date. Ticker: The stock ticker symbol representing each company.

Analysis of ‘FAANG- Complete Stock Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/aayushmishra1512/faang-complete-stock-data on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

There are a few companies that are considered to be revolutionary. These companies also happen to be a dream place to work at for many many people across the world. These companies include - Facebook,Amazon,Apple,Netflix and Google also known as FAANG! These companies make ton of money and they help others too by giving them a chance to invest in the companies via stocks and shares. This data wass made targeting these stock prices.

Content

The data contains information such as opening price of a stock, closing price, how much of these stocks were sold and many more things. There are 5 different CSV files in the data for each company.

--- Original source retains full ownership of the source dataset ---

Google Tokenized Stock Zipmex Ang pagsubaybay sa kasaysayan ng presyo ay nagbibigay-daan sa mga crypto investor na madaling masubaybayan ang performance ng kanilang pamumuhunan. Maginhawa mong masusubaybayan ang opening value, high, at close sa Google Tokenized Stock Zipmex sa paglipas ng panahon, pati na rin ang trade volume. Bukod pa rito, maaari mong agad na tingnan ang pang-araw-araw na pagbabago bilang isang porsyento, na ginagawang effortless na tukuyin ang mga araw na may significant fluctuations. Ayon sa aming data ng history ng presyo ng Google Tokenized Stock Zipmex, tumaas ang halaga nito sa hindi pa naganap na peak sa 2022-04-21, na lumampas sa $2,888.47 USD. Sa kabilang banda, ang pinakamababang punto sa trajectory ng presyo ni Google Tokenized Stock Zipmex, na karaniwang tinutukoy bilang "Google Tokenized Stock Zipmex all-time low", ay naganap noong 2023-12-14. Kung ang isa ay bumili ng Google Tokenized Stock Zipmex sa panahong iyon, kasalukuyan silang masisiyahan sa isang kahanga-hangang kita na -100%. Sa disenyo, ang Google Tokenized Stock Zipmex ay walang limitasyon sa kabuuang supply nito. Ang circulating supply nito ay kasalukuyang 0 coin. Ang lahat ng mga presyong nakalista sa pahinang ito ay nakuha mula sa Bitget, galing sa isang reliable source. Napakahalagang umasa sa iisang pinagmulan upang suriin ang iyong mga investment, dahil maaaring mag-iba ang mga halaga sa iba't ibang nagbebenta. Kasama sa aming makasaysayang Google Tokenized Stock Zipmex dataset ng presyo ang data sa pagitan ng 1 minuto, 1 araw, 1 linggo, at 1 buwan (bukas/mataas/mababa/close/volume). Ang mga dataset na ito ay sumailalim sa mahigpit na pagsubok upang matiyak ang consistency, pagkakumpleto, at accurancy. Ang mga ito ay partikular na idinisenyo para sa trade simulation at mga layunin ng backtesting, madaling magagamit para sa libreng pag-download, at na-update sa real-time.

Russia's main stock market index, the MOEX, fell to 2729 points on August 1, 2025, losing 0.12% from the previous session. Over the past month, the index has declined 3.07% and is down 5.87% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Russia. Russia Stock Market Index MOEX CFD - values, historical data, forecasts and news - updated on August of 2025.

The Google Stock Market Dataset is a comprehensive collection of historical stock market data specifically focused on Google's stock (ticker symbol: GOOGL). This dataset provides valuable information for analyzing and understanding the price range of Google's stock over time.

The dataset includes essential columns: "Price High" and "Price Low." The "Price High" column represents the highest recorded price for Google's stock on a given trading day. This value reflects the peak price reached during the trading session and provides insights into the stock's potential value and market sentiment.

Conversely, the "Price Low" column indicates the lowest recorded price for Google's stock on a specific trading day. This value signifies the minimum price reached during the trading session, revealing the stock's potential support level or market pressure.

With this dataset, researchers, analysts, and investors can track the historical fluctuations in Google's stock price and gain insights into its volatility, trend patterns, and potential trading strategies. It serves as a valuable resource for conducting in-depth technical analysis, developing predictive models, and making informed investment decisions related to Google's stock.

Description: ChatGPT

Analysis of ‘Historical Stock Price of (FAANG + 5) companies’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/suddharshan/historical-stock-price-of-10-popular-companies on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The subject matter of this dataset contains the stock prices of the 10 popular companies ( Apple, Amazon, Netflix, Microsoft, Google, Facebook, Tesla, Walmart, Uber and Zoom)

Content

Within the dataset one will encounter the following: The date - "Date" The opening price of the stock - "Open" The high price of that day - "High" The low price of that day - "Low" The closed price of that day - "Close" The amount of stocks traded during that day - "Volume" The stock's closing price that has been amended to include any distributions/corporate actions that occurs before next days open - "Adj[usted] Close" Time period - 2015 to 2021 (day level)

Tasks - Exploratory Data Analysis - Tell a visualization story - Compare stock price growth between companies - Stock price prediction - Time series analysis

--- Original source retains full ownership of the source dataset ---

This dataset contains Bitcoin price and social economic factors that influence Bitcoin price.

Dataset

This dataset was created by Sivaram

Contents

It contains the following files:

5 years Stock Price of Apple, Amazon, Google, Microsoft, and Netflix. Easy to do stock price prediction, time-series data analysis, and data visualization! Date, Company name, Open price, Close price, Volume, High price, Low price, Stock Split, Dividends

This dataset facilitates an analysis of the impact of the recent Israel-Hamas conflict on the stock market performance of U.S. defense companies, as measured by the returns of defense-sector Exchange-Traded Funds (ETFs). The conflict is quantified using variables such as a binary "attack" indicator, casualty counts, and the intensity of Google search activity related to the war. Additionally, the dataset incorporates a comprehensive set of control variables, including interest rates, exchange rates, oil prices, inflation rates, and factors related to the Ukraine conflict, ensuring a robust framework for evaluating the effects of this geopolitical event.

Internet searching, retail investors and the stock price informativeness.

Dataset

This dataset was created by sudhanshu

Contents

Context

Analyzing stock price is interesting.

Content

Data from yahoo.com/finance AMD and GOOGLE historical price 5/22/2009 ~ 5/03/2017 daily price and volume. There are 7 columns; Date, open, high, low, close, volume, adj close (2001, 7) each of stock

Acknowledgements

Yahoo/finance

Inspiration

I want to find relationship between volume and price.

Context

Stock market data can be interesting to analyze and as a further incentive, strong predictive models can have large financial payoff. The amount of financial data on the web is seemingly endless. A large and well structured dataset on a wide array of companies can be hard to come by. Here I provide a dataset with historical stock prices (last 5 years) for all companies currently found on the S&P 500 index.

The script I used to acquire all of these .csv files can be found in this GitHub repository In the future if you wish for a more up to date dataset, this can be used to acquire new versions of the .csv files.

Content

The data is presented in a couple of formats to suit different individual's needs or computational limitations. I have included files containing 5 years of stock data (in the all_stocks_5yr.csv and corresponding folder) and a smaller version of the dataset (all_stocks_1yr.csv) with only the past year's stock data for those wishing to use something more manageable in size.

The folder individual_stocks_5yr contains files of data for individual stocks, labelled by their stock ticker name. The all_stocks_5yr.csv and all_stocks_1yr.csv contain this same data, presented in merged .csv files. Depending on the intended use (graphing, modelling etc.) the user may prefer one of these given formats.

All the files have the following columns: Date - in format: yy-mm-dd Open - price of the stock at market open (this is NYSE data so all in USD) High - Highest price reached in the day Low Close - Lowest price reached in the day Volume - Number of shares traded Name - the stock's ticker name

Acknowledgements

I scraped this data from Google finance using the python library 'pandas_datareader'. Special thanks to Kaggle, Github and The Market.

Inspiration

This dataset lends itself to a some very interesting visualizations. One can look at simple things like how prices change over time, graph an compare multiple stocks at once, or generate and graph new metrics from the data provided. From these data informative stock stats such as volatility and moving averages can be easily calculated. The million dollar question is: can you develop a model that can beat the market and allow you to make statistically informed trades!