About the Google Stock Price Dataset

The Google Stock Price Dataset consists of two CSV (Comma Separated Values) files containing historical stock price data for training and evaluation. Each row in the dataset represents a trading day, and the columns provide various information related to Google's stock for that day.

Columns:

Date: The date of the trading day in the format "YYYY-MM-DD."

Open: The opening price of Google's stock on that trading day.

High: The highest price reached during the trading day.

Low: The lowest price reached during the trading day.

Close: The closing price of Google's stock on that trading day.

Adj Close: The adjusted closing price, accounting for any corporate actions (e.g., stock splits, dividends) that may affect the stock's value.

Volume: The trading volume, representing the number of shares traded on that trading day.

Time Period: The train dataset spans from January 1, 2010, to December 31, 2022, providing twelve years of daily stock price information for model training. The test dataset spans from January 1, 2023, to July 30, 2023, providing seven month of daily stock price data for model evaluation.

Data Source:

The dataset was collected from Yahoo Finance (finance.yahoo.com), a reputable and widely-used financial data platform.

Use Case:

The Google Stock Price Dataset can be utilized for various purposes, such as predicting future stock prices, analyzing historical stock trends, and building machine learning models for financial forecasting.

Potential Applications:

Time Series Analysis: Explore stock price patterns and seasonality. Financial Modeling: Develop predictive models to forecast stock prices. Algorithmic Trading: Create trading strategies based on historical stock data. Risk Management: Assess potential risks and volatilities in the stock market.

Citation:

If you use this dataset in your research or analysis, please provide proper attribution and citation to acknowledge the source.

License: This dataset is provided under the Creative Commons CC0 1.0 Universal (CC0 1.0) Public Domain Dedication, making it freely available for use without any restrictions or attribution requirements.

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.

Context

Alphabet Inc. is a listed US holding company of the former Google LLC, which continues to exist as a subsidiary. The headquarters is Mountain View in Silicon Valley. The company is led by Sundar Pichai as CEO.

With sales of $137 billion, a profit of $30.7 billion and a market value of $ 863.2 billion, Alphabet Inc. ranks 17th among the world's largest companies according to Forbes Global 2000 (as of 4th November 2019). The company had a market cap of $ 766.4 billion in early 2018. In 2019, Alphabet had annual sales of $161.9 billion and an annual profit of $34.3 billion.

Market capitalization of Alphabet (Google) (GOOG)

Market cap: $2.442 Trillion USD

As of August 2025 Alphabet (Google) has a market cap of $2.442 Trillion USD. This makes Alphabet (Google) the world's 4th most valuable company by market cap according to our data. The market capitalization, commonly called market cap, is the total market value of a publicly traded company's outstanding shares and is commonly used to measure how much a company is worth.

Content

Geography: USA

Time period: August 2004- August 2025

Unit of analysis: Google Stock Data 2025

Variables

Acknowledgements

This dataset belongs to me. I’m sharing it here for free. You may do with it as you wish.

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Overview

This dataset contains historical daily prices for all tickers currently trading on NASDAQ. The up to date list is available from nasdaqtrader.com. The historic data is retrieved from Yahoo finance via yfinance python package.

It contains prices for up to 01 of April 2020. If you need more up to date data, just fork and re-run data collection script also available from Kaggle.

Data Structure

The date for every symbol is saved in CSV format with common fields:

• Date - specifies trading date
• Open - opening price
• High - maximum price during the day
• Low - minimum price during the day
• 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

All that ticker data is then stored in either ETFs or stocks folder, depending on a type. Moreover, each filename is the corresponding ticker symbol. At last, symbols_valid_meta.csv contains some additional metadata for each ticker such as full name.

• This is the Historical Stock Market Data of five major Big Tech companies: NVIDIA (NVDA), Apple (AAPL), Microsoft (MSFT), Google (GOOGL), and Amazon (AMZN) over a 15 years from January 1, 2010 to January 1, 2025.
• It includes daily stock data with opening and closing prices, highs, lows and trading volume.
• This dataset serves as a valuable resource for analyzing long term growth trends, volatility and market behavior of leading tech giants.
• By analyzing this dataset, we can gain a deeper understanding of NVDA, AAPL, MSFT, GOOGL, and AMZN's historical stock behavior over 15 years and make predictions about their future performance.

Columns Description:

1. Date: The trading date of the stock data entry.
2. Close_AAPL: Apple’s stock price at market close at the end of the trading days.
3. Close_AMZN: Amazon’s stock price at market close at the end of the trading days.
4. Close_GOOGL: Google’s stock price at market close at the end of the trading days.
5. Close_MSFT: Microsoft’s stock price at the end of the trading days.
6. Close_NVDA: NVIDIA’s stock price at the end of the trading days.
7. High_AAPL: The highest price of Apple’s stock reached during the trading days.
8. High_AMZN: The highest price of Amazon’s stock reached during the trading days.
9. High_GOOGL: The highest price of Google’s stock reached during the trading days.
10. High_MSFT: The highest price of Microsoft’s stock reached during the trading days.
11. High_NVDA: The highest price of NVIDIA’s stock reached during the trading days.
12. Low_AAPL: The lowest price of Apple’s stock reached during the trading days.
13. Low_AMZN: The lowest price of Amazon’s stock reached during the trading days.
14. Low_GOOGL: The lowest price of Google’s stock reached during the trading days.
15. Low_MSFT: The lowest price of Microsoft’s stock reached during the trading days.
16. Low_NVDA: The lowest price NVIDIA’s stock reached during the trading days.
17. Open_AAPL: Apple’s opening stock price at the beginning of the trading days.
18. Open_AMZN: Amazon’s opening stock price at the beginning of the trading days.
19. Open_GOOGL: Google’s opening stock price at the beginning of the trading days.
20. Open_MSFT: Microsoft’s opening stock price at the beginning of the trading days.
21. Open_NVDA: NVIDIA’s opening stock price at the beginning of the trading days.
22. Volume_AAPL: The number of shares traded of Apple’s stock during the trading days.
23. Volume_AMZN: The number of shares traded of Amazon’s stock during the trading days.
24. Volume_GOOGL: The number of shares traded of Google’s stock during the trading days.
25. Volume_MSFT: The number of shares traded of Microsoft’s stock during the trading days.
26. Volume_NVDA: The number of shares traded of NVIDIA’s stock during the trading days.

Usefulness of Data:

1. Trend Analysis: This dataset can be used for the analysis of long term stock price trends for major 5 tech companies. By analyzing this dataset and taking deep insights about the data and stock patterns over 15 years, investors can identify potential opportunities.
2. Volatility and Risk Assessment: The data helps to assess the volatility of 5 big tech companies' stocks by comparing highs and lows and provides the management strategies to the investors.
3. Predictive Modeling: With stock prices, this dataset can be used for developing predictive models such as forecasting future stock prices using techniques such as ARIMA, SARIMAX, or Deep Learning Models.
4. Comparative Analysis: By analyzing this Dataset, researchers and analysts can compare the performance of NVIDIA, Apple, Microsoft, Google, and Amazon over 15 years, which helps to identify trends in the stock market and relative growth between these companies.
5. Market Behavior Understanding: By analyzing how each stock reacts to major market events (e.g., earnings reports & macroeconomic changes, etc.), we can understand the companies' growth & patterns.

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This dataset contains Apple's (AAPL) stock data for the last 10 years (from 2010 to date). I believe insights from this data can be used to build useful price forecasting algorithms to aid investment. I would like to thank Nasdaq for providing access to this rich dataset. I will make sure I update this dataset every few months.

Context

This dataset consists of the daily stock prices and volume of 14 different tech companies, including Apple (AAPL), Amazon (AMZN), Alphabet (GOOGL), and Meta Platforms (META), Adobe (ADBE), Cisco Systems (CSCO), IBM, Intel Corporation (INTC), Netflix (NFLX), Tesla (TSLA), NVIDIA (NVDA), and more!

Note: All stock_symbols have 3271 prices, except META (2688) and TSLA (3148) because they were not publicly traded for part of the period examined.

Content

Geography: Worldwide

Time period: Jan 2010- Jan 2023

Unit of analysis: Big Tech Giants Stock Price Data

Variables

Acknowledgements

Datasource: Yahoo Finance Credit: Evan Gower

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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

Title: Stock Prices of 500 Biggest Companies by Market Cap (Last 5 Years)

Description: This dataset comprises historical stock market data extracted from Yahoo Finance, spanning a period of five years. It includes daily records of stock performance metrics for the top 500 companies based on market capitalization.

Attributes: 1. Date: The date corresponding to the recorded stock market data. 2. Open: The opening price of the stock on a given date. 3. High: The highest price of the stock reached during the trading day. 4. Low: The lowest price of the stock observed during the trading day. 5. Close: The closing price of the stock on a specific date. 6. Volume: The volume of shares traded on the given date. 7. Dividends: Any dividend payments made by the company on that date (if applicable). 8. Stock Splits: Information regarding any stock splits occurring on that date. 9. Company: Ticker symbol or identifier representing the respective company.

Usefulness: - Investors and analysts can leverage this dataset to conduct various analyses such as trend analysis, volatility assessment, and predictive modeling. - Researchers can explore correlations between stock prices of different companies, sector-wise performance, and market trends over the specified duration. - Machine learning enthusiasts can employ this dataset for developing predictive models for stock price forecasting or anomaly detection.

Note: Prior to using this dataset, it's recommended to perform data cleaning, handling missing values, and verifying the consistency of data across companies and time periods.

License: The dataset is sourced from Yahoo Finance and is provided for analytical purposes. Refer to Yahoo Finance's terms of use for further details on data usage and licensing.

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.

Description:

This dataset contains historical stock price data for International Business Machines Corporation (IBM) from [Jan/01/2020] to [May/01/2024]. The dataset includes daily closing prices, adjusted closing prices, and other relevant information.

Features:

• Date:
• Open:
• High:
• Low:
• Close:
• Adj Close:
• Volume:

Use Cases:

• Predicting stock prices
• Building stock forecasting models
• Analyzing stock market trends
• Backtesting investment strategies

• Comparing machine learning models for stock prediction

  This dataset is perfect for data scientists, analysts, and students looking to practice their skills in:

• Time series analysis

• Stock market analysis

• Predictive modeling

• Machine learning

Get started: Download the dataset and start exploring!

It is very difficult to find institutional quality equity pricing data with sufficient cross-sectional coverage for free. This dataset is the rare exception. The data was made freely available in the public domain by Quandl, a data aggregator and provider acquired by Nasdaq and now operating under the brand "Nasdaq Data Link." Unfortunately this data stopped being maintained by Quandl on April 11, 2018. Nonetheless, it remains an important and useful dataset for research.

TATA Motors

The TATA Motors Stock Price Dataset provides historical stock price and trading data for TATA Motors Limited, a prominent automotive company in India.

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This dataset spans from January 3, 2000, to September 2, 2023, offering insights into TATA Motors' stock performance over more than two decades.

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It includes daily records of open, high, low, close prices, adjusted close prices, and trading volumes. Investors, analysts, and researchers can use this dataset for various analyses, including trend identification, volatility assessment, and predictive modeling for stock price movements.

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Description:

The Open, High, Low and Close prices together form the price range for the stock on a given trading day. "Open" is the starting price, "High" is the highest price, "Low" is the lowest price, and Close is the final price at which the stock traded.

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The Adj Close price is particularly important for long-term analysis because it adjusts for events that can impact the stock's historical prices. This adjusted price allows you to assess the stock's true performance over time.

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The Volume column is essential for understanding the level of market activity on a specific day. High trading volumes can indicate increased market interest and potentially greater price volatility.

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By analyzing these columns and their historical trends, you can gain insights into how TATA Motors' stock has performed over time, identify patterns, and make informed investment decisions. Traders and investors often use this data to perform technical analysis, create trading strategies, and assess the stock's risk and potential for returns.

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Description

This Dataset contains the Details of Historical Daily Netflix Stock Price in USD in Nasdaq from 23-05-2002 to 25-02-2025.

Netflix, Inc. is an American media company founded in 1997 by Reed Hastings and Marc Randolph in Scotts Valley, California, and currently based in Los Gatos, California, with production offices and stages at the Los Angeles-based Hollywood studios (formerly Warner Brothers studios) and the Albuquerque Studios (formerly ABQ studios). It owns and operates an eponymous over-the-top subscription video on-demand service, which showcases acquired and original programming as well as third-party content licensed from other production companies and distributors. Netflix is also the first streaming media company to be a member of the Motion Picture Association.

Netflix's primary business is a streaming video on demand service now available in almost every country worldwide except China. Netflix delivers original and third-party digital video content to PCs, internet-connected TVs, and consumer electronic devices, including tablets, video game consoles, Apple TV, Roku, and Chromecast. In 2011, Netflix introduced DVD-only plans and separated the combined streaming and DVD plans, making it necessary for subscribers who want both to have separate plans.

Attribute Information

• Date: Date of trading
• Open: Open Price of the stock.
• High: High Price of the stock.
• Low: Low Price of the stock.
• Close: Close Price of the stock.
• Adj Close: Adjust Closing of the Stock
• Volume: Traded Volume of the Stock

Acknowledgements

https://finance.yahoo.com/

Photo by Thibault Penin on Unsplash

Photo by charlesdeluvio on Unsplash

The "Stock Market Dataset for AI-Driven Prediction and Trading Strategy Optimization" is designed to simulate real-world stock market data for training and evaluating machine learning models. This dataset includes a combination of technical indicators, market metrics, sentiment scores, and macroeconomic factors, providing a comprehensive foundation for developing and testing AI models for stock price prediction and trading strategy optimization.

Key Features Market Metrics:

Open, High, Low, Close Prices: Daily stock price movement. Volume: Represents the trading activity during the day. Technical Indicators:

RSI (Relative Strength Index): A momentum oscillator to measure the speed and change of price movements. MACD (Moving Average Convergence Divergence): An indicator to reveal changes in strength, direction, momentum, and duration of a trend. Bollinger Bands: Upper and lower bands around a stock price to measure volatility. Sentiment Analysis:

Sentiment Score: Simulated sentiment derived from financial news and social media, ranging from -1 (negative) to 1 (positive). Macroeconomic Factors:

GDP Growth: Indicates the overall health and growth of the economy. Inflation Rate: Reflects changes in purchasing power and economic stability. Target Variable:

Buy/Sell Signal: Binary classification (1 = Buy, 0 = Sell) based on price movement thresholds, simulating actionable trading decisions. Use Cases AI Model Training: Ideal for building stock prediction models using LSTM, Gradient Boosting, Random Forest, etc. Trading Strategy Optimization: Enables testing of trading algorithms and strategies in a simulated environment. Sentiment Analysis Research: Useful for understanding how sentiment influences stock movements. Feature Engineering and Selection: Provides a diverse set of features for experimentation with advanced techniques like PCA and LDA. Dataset Highlights Synthetic Yet Realistic: Carefully designed to mimic real-world financial data trends and relationships. Comprehensive Coverage: Includes key indicators and metrics used by traders and analysts. Scalable: Suitable for use in both small-scale academic projects and larger AI-driven trading platforms. Accessible for All Levels: The intuitive structure ensures that even beginners can utilize this dataset for financial machine learning applications. File Format The dataset is provided in CSV format, where:

Rows represent individual trading days. Columns represent features (technical indicators, market metrics, etc.) and the target variable. Acknowledgments This dataset is synthetically generated and is intended for research and educational purposes. It is not based on real market data and should not be used for actual trading.

The "yahoo_finance_dataset(2018-2023)" dataset is a financial dataset containing daily stock market data for multiple assets such as equities, ETFs, and indexes. It spans from April 1, 2018 to March 31, 2023, and contains 1257 rows and 7 columns. The data was sourced from Yahoo Finance, and the purpose of the dataset is to provide researchers, analysts, and investors with a comprehensive dataset that they can use to analyze stock market trends, identify patterns, and develop investment strategies. The dataset can be used for various tasks, including stock price prediction, trend analysis, portfolio optimization, and risk management. The dataset is provided in XLSX format, which makes it easy to import into various data analysis tools, including Python, R, and Excel.

The dataset includes the following columns:

Date: The date on which the stock market data was recorded. Open: The opening price of the asset on the given date. High: The highest price of the asset on the given date. Low: The lowest price of the asset on the given date. Close*: The closing price of the asset on the given date. Note that this price does not take into account any after-hours trading that may have occurred after the market officially closed. Adj Close**: The adjusted closing price of the asset on the given date. This price takes into account any dividends, stock splits, or other corporate actions that may have occurred, which can affect the stock price. Volume: The total number of shares of the asset that were traded on the given date.

This Dataset contains the Stock prices of Apple Company the opening price, closing price, low price etc.. Use these Data and Predict the Stock Prices for upcoming years. Available timeframes: Monthly(MN1), Weekly(W1), Daily(D1), 4-Hourly(H4), Hourly(H1), 30-Minutes(M30), 15-Minutes(M15), 10-Minutes(M10), 5-Minutes(M5).

Apple D1 Daily timeframe

   datetime open high  low close   volume
0 1998-01-02 0.12 0.14 0.12  0.14 170539824
1 1998-01-05 0.14 0.14 0.13  0.14 152723900
2 1998-01-06 0.14 0.17 0.13  0.16 433041952
3 1998-01-07 0.16 0.16 0.15  0.15 251914152
4 1998-01-08 0.15 0.16 0.15  0.16 188994988


... ... ... ... ... ... ... ...

  datetime  open  high   low  close  volume


6634 2024-03-08 169.12 173.70 168.95 170.98 53335094
6635 2024-03-09 170.99 171.01 170.77 170.79   59796
6636 2024-03-11 172.94 174.38 172.05 172.75 44605588
6637 2024-03-12 173.15 174.03 171.01 173.21 37477359
6638 2024-03-13 172.77 173.19 170.76 171.12 31607988

Apple H1 Hourly timeframe

       datetime open high  low close  volume
0 1998-01-02 16:00:00 0.12 0.12 0.12  0.12 14512400
1 1998-01-02 17:00:00 0.12 0.13 0.12  0.12 52987312
2 1998-01-02 18:00:00 0.12 0.13 0.12  0.13 23746800
3 1998-01-02 19:00:00 0.13 0.13 0.13  0.13 21644000
4 1998-01-02 20:00:00 0.13 0.13 0.13  0.13 11933600


... ... ... ... ... ... ... ...

       datetime  open  high   low  close  volume


46746 2024-03-13 19:00:00 171.04 171.14 170.85 171.02 3019206
46747 2024-03-13 20:00:00 171.02 171.53 171.01 171.50 3736110
46748 2024-03-13 21:00:00 171.50 171.80 171.44 171.65 2899620
46749 2024-03-13 22:00:00 171.65 171.74 171.03 171.15 6318538
46750 2024-03-13 23:00:00 171.14 171.16 171.11 171.12  21317

Stock Market Data: 9,000+ Tickers (1962 - Present)

Dataset Overview

This dataset offers comprehensive historical stock market data covering over 9,000 tickers from 1962 to the present day. It includes essential daily trading information, making it suitable for various financial analyses, trend studies, and algorithmic trading model development.

Columns

• Date: The date of the recorded trading data.
• Ticker: The stock symbol of the company.
• Open: Opening price of the stock on the trading day.
• High: Highest price reached during the trading day.
• Low: Lowest price reached during the trading day.
• Close: Closing price of the stock on the trading day.
• Volume: The total number of shares traded during the day.
• Dividends: Cash dividends issued on the date, if applicable.
• Stock Splits: Stock split factor for the date, if any split occurred.

Usage

This dataset is ideal for: - Time-Series Analysis: Track stock price trends over time, examining daily, monthly, and yearly patterns across sectors. - Algorithmic Trading: Develop and backtest trading strategies using historical price movements and volume data. - Machine Learning Applications: Train models for stock price prediction, volatility forecasting, or portfolio optimization. - Quantitative Research: Perform event studies, analyze the impact of dividends and stock splits, and assess long-term investment strategies. - Comparative Analysis: Evaluate performance across industries or against broader market trends by analyzing multiple tickers in one dataset.

This dataset serves as a robust resource for academic research, quantitative finance studies, and financial technology development.

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.

About this dataset

India's National Stock Exchange (NSE) has a total market capitalization of more than US$3.4 trillion, making it the world's 10th-largest stock exchange as of August 2021, with a trading volume of ₹8,998,811 crore (US$1.2 trillion) and more 2000 total listings.

NSE's flagship index, the NIFTY 50, is a 50 stock index is used extensively by investors in India and around the world as a barometer of the Indian capital market.

This dataset contains data of all company stocks listed in the NSE, allowing anyone to analyze and make educated choices about their investments, while also contributing to their countries economy.

How to use this dataset

• Create a time series regression model to predict NIFTY-50 value and/or stock prices.
• Explore the most the returns, components and volatility of the stocks.
• Identify high and low performance stocks among the list.

Highlighted Notebooks

• Your kernel can be featured here!
• Related Dataset: S&P 500 Stocks - daily updated
• More datasets

Acknowledgements

License

CC0: Public Domain

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