This Dataset contains the Stock prices of TESLA Company the opening price, closing price, low price etc.. Stock Details of the Year 29/09/2021 to 29/09/2022.

Use these Data and Predict the Stock Prices for upcoming years.

Tesla Stock Price Data (2019-2025)

Overview

This dataset contains daily historical stock price data for Tesla Inc. (TSLA) from January 30, 2019 to April 6, 2025. The data includes key metrics such as opening price, highest price, lowest price, closing price, adjusted closing price, and trading volume. It is ideal for financial analysis, machine learning models, and time-series forecasting.

Columns Description

The dataset has the following columns: - date: The date of the trading session. - open: The opening price of Tesla stock on the given day. - high: The highest price of Tesla stock during the trading session. - low: The lowest price of Tesla stock during the trading session. - close: The closing price of Tesla stock at the end of the trading session. - adj_close: Adjusted closing price accounting for stock splits and dividends. - volume: The number of shares traded during the session.

Key Features

• Covers over 6 years of Tesla stock trading data.
• Includes detailed daily metrics for in-depth analysis.
• Suitable for building predictive models on stock market trends.

Potential Use Cases

1. Time-Series Forecasting: Predict future stock prices using historical trends.
2. Financial Analysis: Analyze Tesla's performance over time.
3. Machine Learning Models: Train models to identify patterns in stock market behavior.
4. Trading Strategies: Backtest and develop trading algorithms.

File Information

The dataset is provided in CSV format with the filename: TSLA_2019-01-30_2025-04-06.csv

Example Rows

Here’s a preview of the dataset:

License

This dataset is provided for educational and research purposes only.

Feel free to use this description or modify it based on your specific needs! Let me know if you'd like additional sections or formatting changes.

Contect info:

You can contect me for more data sets if you want any type of data to scrape

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Dataset Description This dataset provides detailed daily stock data for Tesla Inc. (TSLA), covering a significant period. It includes essential financial metrics and market data, making it ideal for analysis and modeling in various financial and data science applications.

Data Source The data is sourced from [reliable financial data providers/market exchanges] and has been preprocessed for ease of use. Ensure to verify the data source and its reliability before use.

Columns Description Date: The trading date (Format: YYYY-MM-DD). Open: The opening price of Tesla stock on the given date. High: The highest price of Tesla stock during the trading day. Low: The lowest price of Tesla stock during the trading day. Close: The closing price of Tesla stock on the given date.

A digital record of all Tesla fires - including cars and other products, e.g. Tesla MegaPacks - that are corroborated by news articles or confirmed primary sources. Latest version hosted at https://www.tesla-fire.com.

This dataset contains historical data on Tesla stock prices over a specific period of time. Includes data on the opening price, closing price, the highest and lowest price for each day, as well as trading volume. Use this dataset to analyze and forecast Tesla stock price movements and other financial research.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Tesla Stock: An Analysis of its Growth, Volatility, and Future Prospects

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This dataset presents an extensive record of daily historical stock prices for Tesla, Inc. (TSLA), one of the world’s most innovative and closely watched electric vehicle and clean energy companies. The data was sourced from Yahoo Finance, a widely used and trusted provider of financial market data, and covers a significant period spanning from Tesla’s initial public offering (IPO) to the most recent date available at the time of extraction.

The dataset includes critical trading metrics for each market day, such as the opening price, highest and lowest prices of the day, closing price, adjusted closing price (accounting for dividends and splits), and total trading volume. This rich dataset supports a variety of use cases, including financial market analysis, investment research, time series forecasting, development and backtesting of trading algorithms, and educational projects in data science and finance.

Dataset Features

• Date: The calendar date for each trading session (in YYYY-MM-DD format)
• Open: The opening price of TSLA shares at the start of the trading day
• High: The highest price reached during the trading session
• Low: The lowest price reached during the trading session
• Close: The last price at which the stock traded during the day
• Adj Close: The closing price adjusted for corporate actions (splits, dividends, etc.)
• Volume: The total number of TSLA shares traded on that day

Source and Collection Details

• Source: Yahoo Finance - Tesla (TSLA) Historical Data
• Collection Method: Data was downloaded using Yahoo Finance's CSV export feature for accuracy and completeness.
• Time Range: Covers from Tesla’s IPO (June 2010) to the most recent available trading day.
• Data Integrity: Minimal cleaning was performed—dates were standardized, and any duplicate or empty rows were removed; all values remain as originally reported by Yahoo Finance.

Example Use Cases

• Stock Price Prediction: Train and test time series models (ARIMA, LSTM, Prophet, etc.) to forecast Tesla’s stock prices.
• Algorithmic Trading: Backtest and evaluate trading strategies using historical price and volume data.
• Market Trend Analysis: Analyze price trends, volatility, and return rates over different periods.
• Event Study: Investigate the impact of major announcements (e.g., product launches, earnings releases) on TSLA stock price.
• Educational Projects: Use as a hands-on resource for learning finance, statistics, or machine learning.

License & Acknowledgments

• Intended Use: This dataset is provided for academic, research, and personal projects. For commercial or investment use, please verify data accuracy and consult Yahoo Finance’s terms of use.
• Acknowledgment: Data sourced from Yahoo Finance. All trademarks and copyrights belong to their respective owners.

In this Dataset you can get the data of **Tesla stock **of 2012 to 2018 This dataset is a dummy dataset that dummy dataset is Created by the Owner of the dataset You can download the dataset for exploring the Machine learning the Algorithm....... Like Regression,Clustering Algorithms or you can also use this dataset for classification algorithm.......

Thanks& Regards Faizan Naseem

For any Query Contact me on my email id (faizannaseem50@gmail.com)

The dataset contains a total of 25,161 rows, each row representing the stock market data for a specific company on a given date. The information collected through web scraping from www.nasdaq.com includes the stock prices and trading volumes for the companies listed, such as Apple, Starbucks, Microsoft, Cisco Systems, Qualcomm, Meta, Amazon.com, Tesla, Advanced Micro Devices, and Netflix.

Data Analysis Tasks:

1) Exploratory Data Analysis (EDA): Analyze the distribution of stock prices and volumes for each company over time. Visualize trends, seasonality, and patterns in the stock market data using line charts, bar plots, and heatmaps.

2)Correlation Analysis: Investigate the correlations between the closing prices of different companies to identify potential relationships. Calculate correlation coefficients and visualize correlation matrices.

3)Top Performers Identification: Identify the top-performing companies based on their stock price growth and trading volumes over a specific time period.

4)Market Sentiment Analysis: Perform sentiment analysis using Natural Language Processing (NLP) techniques on news headlines related to each company. Determine whether positive or negative news impacts the stock prices and volumes.

5)Volatility Analysis: Calculate the volatility of each company's stock prices using metrics like Standard Deviation or Bollinger Bands. Analyze how volatile stocks are in comparison to others.

Machine Learning Tasks:

1)Stock Price Prediction: Use time-series forecasting models like ARIMA, SARIMA, or Prophet to predict future stock prices for a particular company. Evaluate the models' performance using metrics like Mean Squared Error (MSE) or Root Mean Squared Error (RMSE).

2)Classification of Stock Movements: Create a binary classification model to predict whether a stock will rise or fall on the next trading day. Utilize features like historical price changes, volumes, and technical indicators for the predictions. Implement classifiers such as Logistic Regression, Random Forest, or Support Vector Machines (SVM).

3)Clustering Analysis: Cluster companies based on their historical stock performance using unsupervised learning algorithms like K-means clustering. Explore if companies with similar stock price patterns belong to specific industry sectors.

4)Anomaly Detection: Detect anomalies in stock prices or trading volumes that deviate significantly from the historical trends. Use techniques like Isolation Forest or One-Class SVM for anomaly detection.

5)Reinforcement Learning for Portfolio Optimization: Formulate the stock market data as a reinforcement learning problem to optimize a portfolio's performance. Apply algorithms like Q-Learning or Deep Q-Networks (DQN) to learn the optimal trading strategy.

The dataset provided on Kaggle, titled "Stock Market Stars: Historical Data of Top 10 Companies," is intended for learning purposes only. The data has been gathered from public sources, specifically from web scraping www.nasdaq.com, and is presented in good faith to facilitate educational and research endeavors related to stock market analysis and data science.

It is essential to acknowledge that while we have taken reasonable measures to ensure the accuracy and reliability of the data, we do not guarantee its completeness or correctness. The information provided in this dataset may contain errors, inaccuracies, or omissions. Users are advised to use this dataset at their own risk and are responsible for verifying the data's integrity for their specific applications.

This dataset is not intended for any commercial or legal use, and any reliance on the data for financial or investment decisions is not recommended. We disclaim any responsibility or liability for any damages, losses, or consequences arising from the use of this dataset.

By accessing and utilizing this dataset on Kaggle, you agree to abide by these terms and conditions and understand that it is solely intended for educational and research purposes.

Please note that the dataset's contents, including the stock market data and company names, are subject to copyright and other proprietary rights of the respective sources. Users are advised to adhere to all applicable laws and regulations related to data usage, intellectual property, and any other relevant legal obligations.

In summary, this dataset is provided "as is" for learning purposes, without any warranties or guarantees, and users should exercise due diligence and judgment when using the data for any purpose.

Overview

• Introduction
• Object Classes
  • Ally Barbarian
  • Ally Battle Ram
  • Ally Bomber
  • Ally Executioner
  • Ally Firecracker
  • Ally Goblin
  • Ally Goblin Brawler
  • Ally Goblin Cage
  • Ally Knight
  • Ally Mini Pekka
  • Ally Minion
  • Ally Pekka
  • Ally Ram Rider
  • Ally Skeleton
  • Ally Spear Goblin
  • Ally Tombstone
  • Enemy Archerer
  • Enemy Bat
  • Enemy Cannon
  • Enemy Closed Tesla
  • Enemy Evo Archerers
  • Enemy Evo Barbarians
  • Enemy Executioner
  • Enemy Fire Spirit
  • Enemy Flying Machine
  • Enemy Furnace
  • Enemy Goblin
  • Enemy Knight
  • Enemy Log
  • Enemy Minion
  • Enemy Mortar
  • Enemy Musketeer
  • Enemy Open Tesla
  • Enemy Skeleton

Introduction

The dataset focuses on identifying various characters and structures from the game Clash Royale. There are 34 classes, each representing a distinct unit or structure from the game, categorized into allies and enemies. Each class has unique visual traits that set it apart from others.

Object Classes

Ally Barbarian

Description

Barbarian characters are muscled warriors typically equipped with short swords. They have a rugged appearance with distinct armor pieces.

Instructions

Annotate the entire figure, including the swords and armor. Exclude shadows. Focus on capturing their physical build and distinctive attire, which typically includes visible metal and leather elements.

Ally Battle Ram

Description

The Battle Ram consists of two barbarians carrying a log on their shoulders, preparing to charge. The log is the focal point.

Instructions

Include both barbarians and the entire span of the log. Do not separate the barbarians from the log in the bounding box. Capture the motion posture of the barbarians and the horizontal alignment of the log.

Ally Bomber

Description

Bombers are small individuals carrying a large bomb. They are distinctive due to their exaggerated bomb size.

Instructions

Focus on the bomber and the bomb as a unit. Do not annotate the smoke trails. Consider their menacing gesture as they prepare to throw the bomb.

Ally Executioner

Description

Executioners are muscular figures wielding a large axe. They stand out due to their unique hood and broad shoulders.

Instructions

Encapsulate the figure and the entire length of the axe. Pay attention to the hood shape and the broad torso. Their stance is aggressive, amplifying their presence.

Ally Firecracker

Description

Firecrackers are agile, ranged attackers holding a long firework stick. They possess a distinct motion-ready stance.

Instructions

Include the character along with the whole length of the firework stick. Do not ignore the posture that suggests a shooting motion.

Ally Goblin

Description

Goblins are small, green creatures frequently seen with pointed ears and light armor.

Instructions

Capture their full character, focusing on their lively posture and armor details. Exclude any ornamental artifacts since they can introduce noise.

Ally Goblin Brawler

Description

Goblin Brawlers are green-skinned fighters, easily distinguishable by their bulkier armor compared to typical goblins.

Instructions

Focus on highlighting the robust armor and greenness of the character. Do not emphasize any background interference.

Ally Goblin Cage

Description

Goblin Cages are structures holding a goblin inside, often with wooden bars.

Instructions

Annotate the entire cage structure and the goblin inside. Ensure the bars are included to portray the 'cage' effectively.

Ally Knight

Description

Knights are armored figures with a broad sword and helmet with a plume.

Instructions

Capture the whole armored figure, including any distinctive helmet plumes. Exclude reflections on the armor that don’t add to shape recognition.

Ally Mini Pekka

Description

Mini Pekka is a small, sword-wielding knight with a metallic helmet and distinctive blue themes.

Instructions

Focus on the entire body and sword. Note the sleek metallic helmet and blue armor accents.

Ally Minion

Description

Minions are small flying creatures that resemble blobs with wings.

Instr

Quarterly data on vehicle registration by fuel type, vehicle type and number of vehicles, Canada, the provinces, census metropolitan areas and census sub-divisions.

Pakistan's main stock market index, the KSE 100, fell to 167838 points on December 2, 2025, losing 0.13% from the previous session. Over the past month, the index has climbed 3.09% and is up 60.52% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Pakistan. Pakistan Stock Market (KSE100) - values, historical data, forecasts and news - updated on December of 2025.

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.

UPDATE: This dataset is static and outdated. Get the latest data at https://www.kaggle.com/datasets/tesladeaths/tesla-deaths

Tesla Deaths is a record of Tesla accidents that involved a driver, occupant, cyclist, motorcyclist, or pedestrian death. We record information about Tesla fatalities that have been reported and as much related crash data as possible such as location of crash, names of deceased. This dataset also tallies claimed and confirmed Tesla autopilot crashes, that is instances when Autopilot was activated during a Tesla crash that resulted in death.

Latest version of dataset at https://www.tesladeaths.com.

The dataset contains information about world's biggest companies.

Among them you can find companies founded in the US, the UK, Europe, Asia, South America, South Africa, Australia.

The dataset contains information about the year the company was founded, its' revenue and net income in years 2018 - 2020, and the industry.

I have included 2 csv files: the raw csv file if you want to practice cleaning the data, and the clean csv ready to be analyzed.

The third dataset includes the name of all the companies included in the previous datasets and 2 additional columns: number of employees and name of the founder.

In addition there's tesla.csv file containing shares prices for Tesla.

Tesla Deaths

An In-depth Look into Driver, Occupant, and Pedestrian Deaths

By [source]

About this dataset

This dataset reveals an in-depth analysis of tragic Tesla vehicle accidents that have resulted in the death of a driver, occupant, cyclist, or pedestrian. It contains an extensive amount of information related to the fatal incidents including the date and location of each crash, model type involved and if Autopilot was enabled at the time. Every case is given its own unique identifier for easy reference and thorough review. Now is your chance to dive deep into these records to truly understand what happened during those tragic events and how we can prevent them from happening again

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides a comprehensive overview of the Tesla vehicle accidents that have resulted in fatalities. It includes details on the date and location of each incident, model involved, crash description, fatalities, and Autopilot usage. This dataset can be used to analyze the frequency and locations of these fatal accidents as well as gain valuable insights into potential safety risks associated with driving/operating Tesla vehicles.

To begin your analysis with this dataset, start by reading through the information contained in each column: Case # (unique identifier for each case), Year (year of incident), Date (date of incident), Country (country where the accident occurred), State (state where the accident occurred), Description (description of crash), Model (model of Tesla vehicle involved) Source(source). All columns are mandatory for analysis.

Once you have familiarized yourself with this data set, consider looking at how many fatal accidents there have been over time by creating line graphs to show trends over years or states. You may also decide to review incidents based on geographic location or model type to determine which locations or model types may require further investigation and testing in terms of Tesla's safety features. Additionally consider using descriptive analytics such as means and medians to determine if certain models are more prone to accidents than others compared against one another; while also exploring if Autopilot feature usage has any correlation to higher rates/ numbers involving fatalities .

Using this data set can help increase awareness about potential safety risk related issues associated with driving/ operating a Tesla vehicle allowing individuals involved production side decisions or investing decisions have a better understanding when entering such fields . We do recommend however that when conducting your analysis , it’s important understand proper ways for handling missing data points so that users can get an accurate picture related current issues surrounding vehicular mistakes involving teslas vehicles

Research Ideas

• Estimating the safety risk of Autopilot feature usage in different countries and states. By analyzing the differences in fatalities between Tesla vehicles operating with and without Autopilot, researchers can infer risks associated with Autopilot use.
• Examining the relation between driver / occupant fatalities and Tesla vehicle models over time. Through observation of trends in model-specific fatalities across years, engineers may be able to identify vulnerabilities or safety features that should be improved upon in the next version of a car model.
• Creating predictive models to assess crash probability per country or state based on uncontrollable factors such as road environment or traffic conditions by analyzing large numbers of reported accidents for which there were no fatalities but had similar characteristics (time of day, weather conditions, speed limit etc). Technological developments such as self-driving cars could potentially benefit from this type of predictive evaluation method to enhance their safety by improving preventive measures ahead of accidents occurring

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Tesla Deaths - Deaths (3).csv | Column name | Description ...

This dataset contains over 1 million rows of Apple Retail Sales data. It includes information on products, stores, sales transactions, and warranty claims across various Apple retail locations worldwide.

The dataset is designed to reflect real-world business scenarios — including multiple product categories, regional sales variations, and customer service data — making it suitable for end-to-end data analytics and machine learning projects.

Important Note

This dataset is not based on real Apple Inc. data. It was created using Python and LLM-generated insights to simulate realistic sales patterns and business metrics.

Like most company-related datasets on Kaggle (e.g., Amazon, Tesla, or Samsung), this one is synthetic, as companies do not share their actual sales or confidential data publicly due to privacy and legal restrictions.

Purpose

This dataset is intended for: Practicing data analysis, visualization, and forecasting Building and testing machine learning models Learning ETL and data-cleaning workflows on large datasets

Usage You may freely use, modify, and share this dataset for learning, research, or portfolio projects.

What is inside dataset?

This dataset contains information about the stock of five high-tech companies: AMD, Intel, Tesla, Google, HP. Each file have sevral columns, such as open, close, high, low price and volume. For these data user can built his model and get other skills.

Who is dataset for?

For people, who want to learn create time-series model with ARIMA, RNN and other. In my opinion, it`s best choise for begginer data scientist.

Acknowledgments

Thanks for yahoo finance for free acces to prices.

This dataset contains Tesla’s quarterly financial reports from 2014 to 2025, compiled and structured for financial analysis, forecasting, and machine learning applications. It provides a detailed view of Tesla’s evolving performance through its most transformative decade.

The data includes key financial metrics such as revenue, gross profit, operating income, net income, EPS, assets, liabilities, and profitability ratios. It’s ideal for students, analysts, and investors interested in exploring Tesla’s financial trends over time.

Key Highlights

• 📅 Covers 44+ quarters from 2014 Q1 to 2025 Q4
• 💰 Includes core financial indicators and derived ratios
• 📈 Suitable for time series forecasting, financial modeling, and valuation analysis
• 🧠 Great for data visualization and machine learning tasks
• 🔍 Useful for studying macroeconomic impacts on corporate performance

Potential Use Cases

• Financial trend and ratio analysis
• Predicting revenue or profit growth
• Comparing Tesla’s performance across years
• Training models for earnings forecasting
• Visual storytelling with quarterly financial evolution

The US_Stock_Data.csv dataset offers a comprehensive view of the US stock market and related financial instruments, spanning from January 2, 2020, to February 2, 2024. This dataset includes 39 columns, covering a broad spectrum of financial data points such as prices and volumes of major stocks, indices, commodities, and cryptocurrencies. The data is presented in a structured CSV file format, making it easily accessible and usable for various financial analyses, market research, and predictive modeling. This dataset is ideal for anyone looking to gain insights into the trends and movements within the US financial markets during this period, including the impact of major global events.

Key Features and Data Structure

The dataset captures daily financial data across multiple assets, providing a well-rounded perspective of market dynamics. Key features include:

• Commodities: Prices and trading volumes for natural gas, crude oil, copper, platinum, silver, and gold.
• Cryptocurrencies: Prices and volumes for Bitcoin and Ethereum, including detailed 5-minute interval data for Bitcoin.
• Stock Market Indices: Data for major indices such as the S&P 500 and Nasdaq 100.
• Individual Stocks: Prices and volumes for major companies including Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta.

The dataset’s structure is designed for straightforward integration into various analytical tools and platforms. Each column is dedicated to a specific asset's daily price or volume, enabling users to perform a wide range of analyses, from simple trend observations to complex predictive models. The inclusion of intraday data for Bitcoin provides a detailed view of market movements.

Applications and Usability

This dataset is highly versatile and can be utilized for various financial research purposes:

• Market Analysis: Track the performance of key assets, compare volatility, and study correlations between different financial instruments.
• Risk Assessment: Analyze the impact of commodity price movements on related stock prices and evaluate market risks.
• Educational Use: Serve as a resource for teaching market trends, asset correlation, and the effects of global events on financial markets.

The dataset’s daily updates ensure that users have access to the most current data, which is crucial for real-time analysis and decision-making. Whether for academic research, market analysis, or financial modeling, the US_Stock_Data.csv dataset provides a valuable foundation for exploring the complexities of financial markets over the specified period.

Acknowledgements:

This dataset would not be possible without the contributions of Dhaval Patel, who initially curated the US stock market data spanning from 2020 to 2024. Full credit goes to Dhaval Patel for creating and maintaining the dataset. You can find the original dataset here: US Stock Market 2020 to 2024.