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.

1) Data Introduction • The Finance Data Dataset is a survey-based dataset collected via Google Forms during the COVID-19 lockdown. It includes various questions related to individuals' investment behavior, preferences, information sources, and expected returns.

2) Data Utilization (1) Characteristics of the Finance Data Dataset: • The dataset reflects behavioral finance attributes such as preferences for investment instruments (e.g., stocks, bonds, gold, public provident funds), investment purposes, investment horizons, and information acquisition channels.

(2) Applications of the Finance Data Dataset: • Development of AI-based investment profiling and recommendation models: The survey data can be used to build classification models for predicting investment behavior, as well as personalized financial product recommendation systems. • Financial education and consumer behavior research: Insights into investment objectives, risk tolerance, and time preferences can be utilized for designing financial literacy programs and customized financial consulting services.

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.

This dataset provides comprehensive access to financial market data from Google Finance in real-time. Get detailed information on stocks, market quotes, trends, ETFs, international exchanges, forex, crypto, and related news. Perfect for financial applications, trading platforms, and market analysis tools. The dataset is delivered in a JSON format via REST API.

In the U.S. public companies, certain insiders and broker-dealers are required to regularly file with the SEC. The SEC makes this data available online for anybody to view and use via their Electronic Data Gathering, Analysis, and Retrieval (EDGAR) database. The SEC updates this data every quarter going back to January, 2009. To aid analysis a quick summary view of the data has been created that is not available in the original dataset. The quick summary view pulls together signals into a single table that otherwise would have to be joined from multiple tables and enables a more streamlined user experience. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets.詳細

Dataset

This dataset was created by Viraj Bhutada

Contents

This dataset is a combination of Stanford's Alpaca (https://github.com/tatsu-lab/stanford_alpaca) and FiQA (https://sites.google.com/view/fiqa/) with another 1.3k pairs custom generated using GPT3.5 Script for tuning through Kaggle's (https://www.kaggle.com) free resources using PEFT/LoRa: https://www.kaggle.com/code/gbhacker23/wealth-alpaca-lora GitHub repo with performance analyses, training and data generation scripts, and inference notebooks: https://github.com/gaurangbharti1/wealth-alpaca… See the full description on the dataset page: https://huggingface.co/datasets/gbharti/finance-alpaca.

In the U.S. public companies, certain insiders and broker-dealers are required to regularly file with the SEC. The SEC makes this data available online for anybody to view and use via their Electronic Data Gathering, Analysis, and Retrieval (EDGAR) database. The SEC updates this data every quarter going back to January, 2009. For more information please see this site.

To aid analysis a quick summary view of the data has been created that is not available in the original dataset. The quick summary view pulls together signals into a single table that otherwise would have to be joined from multiple tables and enables a more streamlined user experience.

DISCLAIMER: The Financial Statement and Notes Data Sets contain information derived from structured data filed with the Commission by individual registrants as well as Commission-generated filing identifiers. Because the data sets are derived from information provided by individual registrants, we cannot guarantee the accuracy of the data sets. In addition, it is possible inaccuracies or other errors were introduced into the data sets during the process of extracting the data and compiling the data sets. Finally, the data sets do not reflect all available information, including certain metadata associated with Commission filings. The data sets are intended to assist the public in analyzing data contained in Commission filings; however, they are not a substitute for such filings. Investors should review the full Commission filings before making any investment decision.

Band Protocol is a cross-chain data oracle platform that aggregates and connects real-world data and APIs to smart contracts. Band's flexible oracle design allows developers to query any data including real-world events, sports, weather, random numbers and more. Developers can create custom-made oracles using WebAssembly to connect smart contracts with traditional web APIs within minutes. BandChain is designed to be compatible with most smart contract and blockchain development frameworks. It does the heavy lifting jobs of pulling data from external sources, aggregating them, and packaging them into the format that’s easy to use and verified efficiently across multiple blockchains. This dataset is one of many crypto datasets that are available within the Google Cloud Public Datasets . As with other Google Cloud public datasets, you can query this dataset for free, up to 1TB/month of free processing, every month. Watch this short video to learn how to get started with the public datasets. Want to know how the data from these blockchains were brought into BigQuery, and learn how to analyze the data? Scopri di più

This dataset is composed of

• Twitter dataset of ~9M tweets mentioning stocks (cashtags) traded on the most important US markets, shared between May and September 2017 (users data enriched with bot classification label)
• Financial information about ~30k companies found in those tweets, retrieved from Google Finance

Refer to the paper below for more details.

Cresci, S., Lillo, F., Regoli, D., Tardelli, S., & Tesconi, M. (2019). Cashtag Piggybacking: Uncovering Spam and Bot Activity in Stock Microblogs on Twitter. ACM Transactions on the Web (TWEB), 13(2), 11.

These three datasets provide closing price information for the following assets: Google, Apple, Microsoft, Netflix, Amazon, Pfizer, Astra Zeneca, Johnson & Johnson, ETH, BTC and LTC.The time period spans from 2012 to the end of 2020.

Dataset

This dataset was created by Venessa

Released under Database: Open Database, Contents: Database Contents

Contents

This dataset contains financial fundamentals of Alphabet (Google Inc), which includes balance sheets, income statement and cashflow. The data in this dataset only contains 10 years of data. To get full 30+ years of historical fundamental data, check out our website Finnhub.

Tezos is a technology for deploying a blockchain capable of modifying its own set of rules with minimal disruption to the network through an on-chain governance model. Learn more... This dataset is one of many crypto datasets that are available within the Google Cloud Public Datasets . As with other Google Cloud public datasets, you can query this dataset for free, up to 1TB/month of free processing, every month. Watch this short video to learn how to get started with the public datasets. Want to know how the data from these blockchains were brought into BigQuery, and learn how to analyze the data? 瞭解詳情

4974 Stock Symbols End of day data. Includes close open high low volume and date. Data was collected from Google finance public data. +—————+——————+ | Table | Size in MB | +—————+——————+ | surf_eod | 1109.00 | +—————+——————+ 1 row in set (0.00 sec) mysql> SELECT COUNT(DISTINCT( ticker )) FROM surf_eod; +—————————————-+ | COUNT(DISTINCT( ticker )) | +—————————————-+ | 4974 | +—————————————-+ 1 row in set (6.31 sec) mysql> describe surf_eod; +————+——————-+—&mdash

Dataset Card for Dataset Name

The FinancialNewsSentiment_26000 dataset comprises 26,000 rows of financial news articles related to the Indian market. It features four columns: URL, Content (scrapped content), Summary (generated using the T5-base model), and Sentiment Analysis (gathered using the GPT add-on for Google Sheets). The dataset is designed for sentiment analysis tasks, providing a comprehensive view of sentiments expressed in financial news.

  Dataset… See the full description on the dataset page: https://huggingface.co/datasets/kdave/Indian_Financial_News.

FiQA2018 An MTEB dataset Massive Text Embedding Benchmark

Financial Opinion Mining and Question Answering

Task category t2t

Domains Written, Financial

Reference https://sites.google.com/view/fiqa/

  How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code: import mteb

task = mteb.get_tasks(["FiQA2018"]) evaluator = mteb.MTEB(task)

model = mteb.get_model(YOUR_MODEL) evaluator.run(model)

To learn more… See the full description on the dataset page: https://huggingface.co/datasets/mteb/fiqa.

Solana is designed as a high performance blockchain optimized for use cases across finance, NFTs, payments, and gaming. This dataset, built and maintained by the Solana Community as part of the Google Cloud Public Datasets program, captures and publishes block data in near real-time. Data freshness can range between minutes to hours depending on chain activity and transaction volumes.

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.

This is a dataset of stocks of the four giants Apple, Amazon, Microsoft, and Google. Some suggestions for you to start with is to analyze the closing price and trading volume Daily stock changes v.v Gojo and Getou DA