The AAPL Stock dataset includes daily historical stock market data for Apple Inc. from the New York Stock Exchange (NYSE) from 12th December 1980 to April 1st 2023 The dataset includes six columns: Date, Open, High, Low, Close, Adjusted Close, and Volume.

The "Date" column contains the date of each trading day, while the "Open," "High," "Low," and "Close" columns represent the stock prices of Apple Inc. at the opening, highest, lowest, and closing points of each trading day, respectively.

The "Adjusted Close" column is the closing price adjusted to reflect any corporate actions, such as stock splits, dividends, or mergers and acquisitions. Finally, the "Volume" column indicates the number of shares of Apple Inc. that were traded on each trading day.

This dataset can be used to analyze the historical performance of Apple Inc. stock, to create predictive models, or to perform technical analysis to identify patterns or trends in the data. It is suitable for use in financial analysis, machine learning, and other data-driven applications.
license:openrail region:us
{"license": "openrail"}
TAGS
#license-openrail #region-us 

The AAPL Stock dataset includes daily historical stock market data for Apple Inc. from the New York Stock Exchange (NYSE) from 12th December 1980 to April 1st 2023 The dataset includes six columns: Date, Open, High, Low, Close, Adjusted Close, and Volume.

The "Date" column contains the date of each trading day, while the "Open," "High," "Low," and "Close" columns represent the stock prices of Apple Inc. at the opening, highest, lowest, and closing points of each trading day, respectively.

The "Adjusted Close" column is the closing price adjusted to reflect any corporate actions, such as stock splits, dividends, or mergers and acquisitions. Finally, the "Volume" column indicates the number of shares of Apple Inc. that were traded on each trading day.

This dataset can be used to analyze the historical performance of Apple Inc. stock, to create predictive models, or to perform technical analysis to identify patterns or trends in the data. It is suitable for use in financial analysis, machine learning, and other data-driven applications.
TAGS
#license-openrail #region-us 

passage: TAGS
#license-openrail #region-us 
