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.

Hyundai Motor Company Stock Historical Price (005380.KS)

from January 2016 until Present

▶ Context 📝

The dataset is taken from Yahoo Finance website. It is about the historical stock price of Hyundai Motor Company.

▶ Acknowledgements 🙏

I'd like to clarify that I'm only making data about the historical stock price of Hyundai Motor Company available to Kaggle community.

▶ Inspiration 💭

• Forecasting stock price after July 2022
• Implementing machine learning models.
• Perform data analysis/data visualization of historical stock prices.

▶ Log 📰

• 11 November 2022: The datasets version now is fully automated update using Deepnote. Read more here.
• 19 August 2023: Deepnote schedule run will be deprecated after August 2023; Moving & updating script for update automation via Kaggle API using Kaggle schedule run.

📷 Image by The Punisher.

Stock Price Time Series for Daily Journal Corp. Daily Journal Corporation publishes newspapers and websites covering in California, Arizona, Utah, and Australia. It operates in two segments, Traditional Business and Journal Technologies. The company publishes 10 newspapers of general circulation, including Los Angeles Daily Journal, San Francisco Daily Journal, Daily Commerce, The Daily Recorder, The Inter-City Express, San Jose Post-Record, Orange County Reporter, Business Journal, The Daily Transcript, and The Record Reporter. It also provides specialized information services; and serves as an advertising and newspaper representative for commercial and public notice advertising. In addition, the company offers case management software systems and related products, including eCourt, eProsecutor, eDefender, and eProbation, which are browser-based case processing systems; eFile, a browser-based interface that allows attorneys and the public to electronically file documents with the court; and ePayIt, a service primarily for the online payment of traffic citations. It provides its software systems and related products to courts; prosecutor and public defender offices; probation departments; and other justice agencies, including administrative law organizations, city and county governments, and bar associations to manage cases and information electronically, to interface with other justice partners, and to extend electronic services to bar members and the public in 32 states and internationally. Daily Journal Corporation was incorporated in 1987 and is based in Los Angeles, California.

Stock Price Time Series for Expedia Group Inc.. Expedia Group, Inc. operates as an online travel company in the United States and internationally. The company operates through B2C, B2B, and trivago segments. The B2C segment includes Brand Expedia, a full-service online travel brand offers various travel products and services; Hotels.com for lodging accommodations; Vrbo, an online marketplace for the alternative accommodations; Orbitz, Travelocity, Wotif Group, ebookers, CheapTickets, Hotwire.com and CarRentals.com. The B2B segment provides various travel and non-travel companies including airlines, offline travel agents, online retailers, corporate travel management, and financial institutions who leverage its travel technology and tap into its diverse supply to augment their offerings and market Expedia Group rates and availabilities to its travelers. The trivago segment send referrals to online travel companies and travel service providers from hotel metasearch websites. In addition, the company provides brand advertising through online and offline channels, loyalty programs, mobile apps, and search engine marketing, as well as metasearch, social media, direct and personalized traveler communications on its websites, and through direct e-mail communication with its travelers. The company was formerly known as Expedia, Inc. and changed its name to Expedia Group, Inc. in March 2018. Expedia Group, Inc. was founded in 1996 and is headquartered in Seattle, Washington.

Stock Price Time Series for United Internet AG NA. United Internet AG, through its subsidiaries, operates as an Internet service provider worldwide. The company operates through Consumer Access, Business Access, Consumer Applications, and Business Applications segments. It offers landline-based broadband and mobile internet products, including home networks, online storage, smart home, and IPTV for private users; and telecommunication products ranging from fiber-optic direct connections to tailored ICT solutions, which include voice, data, and network solutions, as well as infrastructure services to national and international carriers and ISPs. The company also provides applications and services for home users, such as personal information management applications comprising email, to-do lists, appointments, and addresses; and online cloud storage and office software. In addition, it provides business applications for freelancers and small to medium enterprises, such as domains, websites, web hosting, servers, e-shops, group work, online cloud storage, and office software, as well as cloud solutions and infrastructure. It offers its access products through the yourfone, smartmobile.de, 1&1, and 1&1 Versatel brand names; and applications through GMX, mail.com, WEB.DE, home.pl, Arsys, STRATO, IONOS, Fasthosts, we22, InterNetX, united-domains, and World4You brand names. In addition, the company offers professional services in the fields of active domain management; performance-based advertising and sales services under the Sedo brand name; online advertising services under the United Internet Media brand name; and white-label website builder services under the we22 brand, as well as sells IT hardware. The company was founded in 1988 and is headquartered in Montabaur, Germany.

Webjet stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Webjet reported AUD1.87B in Market Capitalization this December of 2025, considering the latest stock price and the number of outstanding shares.Data for Webjet | WEB - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Stock Price Time Series for RH. RH, together with its subsidiaries, operates as a retailer and lifestyle brand in the home furnishings market in the United States, Canada, the United Kingdom, Germany, Belgium, and Spain. It operates through three segments: RH Segment, Waterworks, and Real Estate. The company offers products in various categories, including furniture, lighting, textiles, bathware, décor, outdoor and garden, baby, child, and teen furnishings. It sells its products through hospitality, websites, sourcebooks, and trade and contract channels, as well as operates RH galleries, RH outlet stores, RH guesthouses, RH Interior design office, and waterworks showrooms. The company was formerly known as Restoration Hardware Holdings, Inc. and changed its name to RH in January 2017. RH was founded in 1980 and is headquartered in Corte Madera, California.

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.

Weave (WEAV) Common Stock: A Web of Opportunities?

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

PT GoTo Gojek Tokopedia Tbk Stock Historical Price (GOTO.JK)

from April 2022 until present

▶ Context 📝

The dataset is taken from Yahoo Finance website. It is about the historical stock price of PT GoTo Gojek Tokopedia Tbk.

▶ Acknowledgements 🙏

I'd like to clarify that I'm only making data about the historical stock price of PT GoTo Gojek Tokopedia Tbk. available to Kaggle community.

▶ Inspiration 💭

• Forecasting stock price after April 2022
• Implementing machine learning models.
• Perform data analysis/data visualization of historical stock prices.

▶ Log 📰

• 18 January 2024: Initialization and fully automated update using Kaggle schedule run.

📷 Image by indiekraf.com.

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.

Does algo trading work? (LON:WEB Stock Forecast)

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

Stock Price Time Series for Next PLC. NEXT plc engages in the retail of clothing, homeware, and beauty products in the United Kingdom, rest of Europe, the Middle East, Asia, and internationally. It operates through NEXT Online, NEXT Retail, NEXT Finance, Total Platform, and Other Business Activities segments. The company offers NEXT branded products; and women's, men's, children's fashion clothing, and accessories; and third-party branded products. It also provides consumer credit; services to third-party brands, including websites, marketing, warehousing, distribution networks, and contact centers; and property management, which holds and leases properties. The company operates through retail stores, online retail platforms, and franchise stores. The company was formerly known as J Hepworth & Son and changed its name to NEXT plc in 1986. NEXT plc was founded in 1864 and is headquartered in Enderby, the United Kingdom.

PT Bank Mandiri (Persero) Tbk Stock Historical Price (BMRI.JK)

from January 2019 until present

▶ Context 📝

The dataset is taken from Yahoo Finance website. It is about the historical stock price of PT Bank Mandiri (Persero) Tbk.

▶ Acknowledgements 🙏

I'd like to clarify that I'm only making data about the historical stock price of PT Bank Mandiri (Persero) Tbk. available to Kaggle community.

▶ Inspiration 💭

• Forecasting stock price after January 2019
• Implementing machine learning models.
• Perform data analysis/data visualization of historical stock prices.

▶ Log 📰

• 4 January 2024: Initialization and fully automated update using Kaggle schedule run.

📷 Image by Liputan6.

Stock Price Time Series for Fortinet Inc. Fortinet, Inc. provides cybersecurity and convergence of networking and security solutions worldwide. The company offers secure networking solutions that focus on the convergence of networking and security; network firewall solutions, which consist of FortiGate data centers, hyperscale, and distributed firewalls, as well as encrypted applications; wireless local area network (LAN) solutions; and secure connectivity solutions, including FortiSwitch secure ethernet switches, FortiAP wireless LAN access points, and FortiExtender 5G connectivity gateways. It also provides the Fortinet Unified Secure Access Service Edge (SASE) solutions that include firewall, software-defined wide-area network, secure web gateway, cloud access services broker, data loss prevention, and zero trust network access; and web application firewalls, cloud network security with virtualized firewalls, cloud-native firewalls, cloud-native application protection, and code security. In addition the company offers artificial intelligence-driven security operation solutions, which provides a suite of cybersecurity solutions that identify, protect, detect, respond, and recover from threats, as well as FortiEDR, FortiXDR, FortiNDR, FortiSandbox, FortiDeceptor, FortiDLP, and FortiRecon that helps organizations ensuring attackers face multiple layers of detection and mitigation across endpoints, networks, and applications. Further, it offers FortiGuard application security, content security, device security, NOC/SOC security, and web security services; FortiCare technical support services; and training and certification programs, as well as operates FortiGuard Lab, a cybersecurity threat intelligence and research organization. The company serves large enterprises, communication service providers, government organizations, and small to medium-sized enterprises. Fortinet, Inc. was incorporated in 2000 and is headquartered in Sunnyvale, California.

This dataset contains historical stock price data for major banks from the year 2014 to 2024. The dataset includes daily stock prices, trading volume, and other relevant financial metrics for prominent banks. The stock prices are provided in IDR (Indonesian Rupiah) currency.

PT Bank Central Asia Tbk (BBCA.JK), more commonly recognized as Bank Central Asia (BCA). As one of Indonesia's largest privately-owned banks, BCA was founded in 1955 and provides a diverse array of banking services encompassing consumer banking, corporate banking, investment banking, and asset management. With a widespread presence throughout Indonesia, including numerous branches and ATMs, BCA is esteemed for its robust financial achievements, inventive banking offerings, and dedication to customer satisfaction.

Dataset Variables:

1. Date: The date of the stock price data.
2. Open Price: The opening price of the bank's stock on the given date.
3. Close Price: The closing price of the bank's stock on the given date.
4. High Price: The highest price reached by the bank's stock during the trading day.
5. Low Price: The lowest price reached by the bank's stock during the trading day.
6. Adjusted Low Price: The closing price on a given trading day, adjusted to reflect any corporate actions, such as stock splits, dividends, rights offerings, or other adjustments that may affect the stock price.
7. Volume: The number of shares traded on the given date.

Data Sources: The dataset is compiled from reliable financial sources, including stock exchanges, financial news websites, and reputable financial data providers. Data cleaning and preprocessing techniques have been applied to ensure accuracy and consistency. More info: https://finance.yahoo.com/quote/BBCA.JK/history/

Use Case: This dataset can be utilized for various purposes, including financial analysis, stock market forecasting, algorithmic trading strategies, and academic research. Researchers, analysts, and data scientists can explore the trends, patterns, and relationships within the data to derive valuable insights into the performance of the banking sector over the specified period. Additionally, this dataset can serve as a benchmark for evaluating the performance of machine learning models and quantitative trading strategies in the banking industry.

The dataset contains Walmart Inc historical stock price data for last 10 years. I obtained this data from the official Yahoo Website. It can found in the following link - https://finance.yahoo.com/quote/WMT/history?p=WMT. The purpose of this dataset is to understand and implement different forecasting models.

This dataset contains a total of 2516 observations and 6 features.

Each feature is described below: * Date - Date of the trading day * Close - Price of the stock at the end of the trading day * Volume - Total number of stocks sold on a given trading day * Open - Price of the stock at the beginning of the trading day * High - Highest stock price during the trading day * Low - Lowest stock price during the trading * Adj Close - Adjusted close is the closing price after adjustments for all applicable splits and dividend distributions.

This dataset is inspired by another dataset on Kaggle which can be found here

Last Update - 9th FEB 2025

Disclaimer!!! Data uploaded here are collected from the internet and some google drive. The sole purposes of uploading these data are to provide this Kaggle community with a good source of data for analysis and research. I don't own these datasets and am also not responsible for them legally by any means. I am not charging anything (either money or any favor) for this dataset. RESEARCH PURPOSE ONLY

THIS IS THE LARGEST DATASET ON NIFTY 100 STOCKS WITH EACH MINUTES AND DAILY DATA (2015 to 2025)

The NIFTY 50 is a benchmark Indian stock market index that represents the weighted average of 50 of the largest Indian companies listed on the National Stock Exchange. It is one of the two main stock indices used in India, the other being the BSE SENSEX.

Nifty 50 is owned and managed by NSE Indices (previously known as India Index Services & Products Limited), which is a wholly-owned subsidiary of the NSE Strategic Investment Corporation Limited.NSE Indices had a marketing and licensing agreement with Standard & Poor's for co-branding equity indices until 2013. The Nifty 50 index was launched on 22 April 1996, and is one of the many stock indices of Nifty.

The NIFTY 50 index is a free-float market capitalization-weighted index. The index was initially calculated on a full market capitalization methodology. On 26 June 2009, the computation was changed to a free-float methodology. The base period for the NIFTY 50 index is 3 November 1995, which marked the completion of one year of operations of the National Stock Exchange Equity Market Segment. The base value of the index has been set at 1000 and a base capital of ₹ 2.06 trillion.

Content This dataset contains Nifty 100 historical daily prices. The historical data are retrieved from the NSE India website. Each stock in this Nifty 500 and are of 1 minute itraday data.

Every dataset contains the following fields. Open - Open price of the stock High - High price of the stock Low - Low price of the stock Close - Close price of the stock Volume - Volume traded of the stock in this time frame

Inspiration

• Data is uploaded for Research and Educational purposes.
• The data scientists and researchers can download and can build EDA, find Correlations, and perform Regression analysis on it.
• Quant researchers can build strategies and backtest their strategies with this dataset.

Stock Names

| ACC | ADANIENT | ADANIGREEN | ADANIPORTS | AMBUJACEM | | -- | -- | -- | -- | -- | | APOLLOHOSP | ASIANPAINT | AUROPHARMA | AXISBANK | BAJAJ-AUTO | | BAJAJFINSV | BAJAJHLDNG | BAJFINANCE | BANDHANBNK | BANKBARODA | | BERGEPAINT | BHARTIARTL | BIOCON | BOSCHLTD | BPCL | | BRITANNIA | CADILAHC | CHOLAFIN | CIPLA | COALINDIA | | COLPAL | DABUR | DIVISLAB | DLF | DMART | | DRREDDY | EICHERMOT | GAIL | GLAND | GODREJCP | | GRASIM | HAVELLS | HCLTECH | HDFC | HDFCAMC | | HDFCBANK | HDFCLIFE | HEROMOTOCO | HINDALCO | HINDPETRO | | HINDUNILVR | ICICIBANK | ICICIGI | ICICIPRULI | IGL | | INDIGO | INDUSINDBK | INDUSTOWER | INFY | IOC | | ITC | JINDALSTEL | JSWSTEEL | JUBLFOOD | KOTAKBANK | | LICI | LT | LTI | LUPIN | M&M | | MARICO | MARUTI | MCDOWELL-N | MUTHOOTFIN | NAUKRI | | NESTLEIND | NIFTY 50 | NIFTY BANK | NMDC | NTPC | | ONGC | PEL | PGHH | PIDILITIND | PIIND | | PNB | POWERGRID | RELIANCE | SAIL | SBICARD | | SBILIFE | SBIN | SHREECEM | SIEMENS | SUNPHARMA | | TATACONSUM | TATAMOTORS | TATASTEEL | TCS | TECHM | | TITAN | TORNTPHARM | ULTRACEMCO | UPL | VEDL | | WIPRO | YESBANK | | | |

PT Bank Rakyat Indonesia (Persero) Tbk Stock Historical Price (BBRI.JK)

from January 2019 until present

▶ Context 📝

The dataset is taken from Yahoo Finance website. It is about the historical stock price of PT Bank Rakyat Indonesia (Persero) Tbk.

▶ Acknowledgements 🙏

I'd like to clarify that I'm only making data about the historical stock price of PT Bank Rakyat Indonesia (Persero) Tbk. available to Kaggle community.

▶ Inspiration 💭

• Forecasting stock price after January 2019
• Implementing machine learning models.
• Perform data analysis/data visualization of historical stock prices.

▶ Log 📰

• 3 January 2024: Initialization
• 4 January 2024: Fully automated update using Kaggle schedule run.

📷 Image by marcopolis.net.

This is the stock historical prices dataset of Tesla which is downloaded from the yahoo finance website. It contains the daily stock prices data from Jun 2010 to Aug 2022

About the dataset

Historical Amazon stock prices in daily frequency, from 14 May, 1997 to 24 Sep, 2020.

Company Profile

Amazon.com, Inc. engages in the retail sale of consumer products and subscriptions in North America and internationally. The company operates through three segments: North America, International, and Amazon Web Services (AWS). It sells merchandise and content purchased for resale from third-party sellers through physical and online stores. The company also manufactures and sells electronic devices, including Kindle, Fire tablets, Fire TVs, Rings, and Echo and other devices; provides Kindle Direct Publishing, an online service that allows independent authors and publishers to make their books available in the Kindle Store; and develops and produces media content. In addition, it offers programs that enable sellers to sell their products on its Websites, as well as its stores; and programs that allow authors, musicians, filmmakers, skill and app developers, and others to publish and sell content. Further, the company provides compute, storage, database, and other AWS services, as well as fulfillment, advertising, publishing, and digital content subscriptions. Additionally, it offers Amazon Prime, a membership program, which provides free shipping of various items; access to streaming of movies and TV episodes; and other services. The company also operates in the food delivery business in Bengaluru, India. It serves consumers, sellers, developers, enterprises, and content creators. The company also has utility-scale solar projects in China, Australia, and the United States. Amazon.com, Inc. was founded in 1994 and is headquartered in Seattle, Washington.