This comprehensive dataset curated by Stocksphi presents the 1-minute interval historical stock data for Apple Inc. (AAPL) spanning from 2006 to 2024. The dataset encapsulates key metrics such as opening price, high price, low price, closing price, adjusted close price, and trading volume for each minute of trading throughout this extensive period.

Insights and Applications:

Intraday Analysis: Delve into the intricate price movements and trading dynamics of AAPL stock on a minute-by-minute basis, gaining insights into short-term trends and patterns. Algorithmic Trading: Utilize the dataset to develop and backtest algorithmic trading strategies tailored for intraday trading, leveraging historical price and volume data. Quantitative Analysis: Conduct quantitative analysis to explore statistical properties, correlations, and anomalies within the dataset, facilitating data-driven decision-making. Financial Modeling: Employ the dataset for constructing predictive models and forecasting AAPL stock behavior at a fine-grained temporal resolution. Academic Research: Serve as a valuable resource for academic research in finance, enabling scholars to investigate market microstructure, liquidity dynamics, and other relevant topics. This meticulously curated dataset offers a wealth of information and opportunities for quantitative analysis, strategy development, financial research, and more, empowering traders, analysts, researchers, and enthusiasts to unlock valuable insights and enhance their understanding of AAPL stock dynamics over nearly two decades.

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.

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.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

Graph and download economic data for NASDAQ Composite Index (NASDAQCOM) from 1971-02-05 to 2025-12-01 about composite, NASDAQ, stock market, indexes, and USA.

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

TTF Gas fell to 27.92 EUR/MWh on December 3, 2025, down 0.17% from the previous day. Over the past month, TTF Gas's price has fallen 14.22%, and is down 40.94% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. EU Natural Gas - values, historical data, forecasts and news - updated on December of 2025.

This Dataset contains the Stock prices of Mastercard 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).

Mastercard D1 Daily timeframe

   datetime  open  high  low close  volume
0 2006-05-25 41.00 46.05 40.20 46.00 30181700
1 2006-05-26 46.30 46.74 44.11 44.92  9604700
2 2006-05-30 44.97 44.98 42.85 44.15  4908600
3 2006-05-31 44.35 45.36 44.35 44.93  2949300
4 2006-06-01 45.00 48.10 44.90 47.54  6169300


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

  datetime  open  high   low  close  volume


4522 2024-03-08 467.59 471.62 467.10 469.27  983015
4523 2024-03-09 469.28 469.34 469.23 469.26  294160
4524 2024-03-11 469.00 469.37 464.69 469.16  776020
4525 2024-03-12 470.53 474.37 468.71 472.87  873902
4526 2024-03-13 474.23 476.16 472.78 475.61 1028658

Mastercard H1 Hourly timeframe

       datetime  open  high  low close  volume
0 2006-05-25 17:00:00 41.00 44.16 40.20 44.15 14531500
1 2006-05-25 18:00:00 44.15 45.00 43.30 43.70  5178500
2 2006-05-25 19:00:00 43.70 43.91 43.11 43.84  2304100
3 2006-05-25 20:00:00 43.84 44.15 43.40 44.12  1950700
4 2006-05-25 21:00:00 44.15 44.83 44.03 44.63  2415400


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

       datetime  open  high   low  close volume


31969 2024-03-13 19:00:00 475.16 476.01 474.64 475.17  65395
31970 2024-03-13 20:00:00 475.25 475.49 474.80 475.33  58739
31971 2024-03-13 21:00:00 475.35 475.51 474.46 474.46  52727
31972 2024-03-13 22:00:00 474.55 475.98 473.36 475.78 158985
31973 2024-03-13 23:00:00 475.77 475.77 475.61 475.61 409383

UPDATED EVERY WEEK Last Update - 26th July 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

Context

• The NIFTY 50 is a well-diversified 50 stock index and it represents 13 important sectors of the economy.
• It is used for a variety of purposes such as benchmarking fund portfolios, index-based derivatives, and index funds.
• NIFTY 50 is owned and managed by NSE Indices Limited.
• The NIFTY 50 index has shaped up to be the largest single financial product in India.

This data contains all the indices of NSE. NIFTY 50, NIFTY BANK, NIFTY 100, NIFTY COMMODITIES, NIFTY CONSUMPTION, NIFTY FIN SERVICE, NIFTY IT, NIFTY INFRA, NIFTY ENERGY, NIFTY FMCG, NIFTY AUTO, NIFTY 200, NIFTY ALPHA 50, NIFTY 500, NIFTY CPSE, NIFTY GS COMPSITE, NIFTY HEALTHCARE, NIFTY CONSR DURBL, NIFTY LARGEMID250, NIFTY INDIA MFG, NIFTY IND DIGITAL, INDIA VIX

File Information and Column Descriptions.

Nifty 50 index data with 1 minute data. The dataset contains OHLC (Open, High, Low, and Close) prices from Jan 2015 to Aug 2024. - This dataset can be used for time series analysis, regression problems, and time series forecasting both for one step and multi-step ahead in the future. - Options data can be integrated with this minute data, to get more insight about this data. - Different backtesting strategies can be built on this data.

File Information

• This dataset contains 6 files, each file contains nifty 50 data with different intervals.
• Different intervals are - 1 min, 3 min, 5 min, 15 min, and 1 hour, Daily data from intervals of 2015 Jan to 2024 August.

Column Descriptors

• Each file contains OHLC (Open, High, Low, and Close) prices and Data time information. Since these are Nifty 50 index data, so volume is not present.

Inspiration

Time series forecasting - Predict stock price

• Predict future stock price one step ahead and multi-step ahead in time.
• Use different time series forecasting techniques for forecasting the future stock price. ### Machine learning and Deep learning techniques
• Possible ML and DL models include Neural networks, RNNs, LSTMs, Transformers, Attention networks, etc.
• Different error functions can be considered like RMSE, MAE, RMSEP etc. ### Feature engineering
• Different augmented features can be created and that can be used for forecasting.
• Correlation analysis, Feature importance to justify the important features.

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 | | | |

Graph and download economic data for Market Yield on U.S. Treasury Securities at 2-Year Constant Maturity, Quoted on an Investment Basis (DGS2) from 1976-06-01 to 2025-12-01 about 2-year, maturity, Treasury, interest rate, interest, rate, and USA.

The NIFTY500 Stocks Data dataset contains comprehensive historical stock data for the NIFTY 500 index. This dataset includes detailed information on stock prices at various time intervals for each company listed in the NIFTY 500. The data is organized into individual Excel files, each representing a different stock, with multiple sheets within each file representing different timeframes.

File Naming Convention The Excel files are named according to the NSE code of each stock. For example, the file for the stock with the NSE code RELIANCE will be named RELIANCE.xlsx.

Data Description Each sheet within an Excel file contains the following columns:

• Date: The date and time of the data point.
• Open: The opening price of the stock at the given date and time.
• High: The highest price of the stock at the given date and time.
• Low: The lowest price of the stock at the given date and time.
• Close: The closing price of the stock at the given date and time.
• Volume: The number of shares traded at the given date and time

Usage This dataset is ideal for financial analysts, data scientists, and researchers who are interested in analyzing stock market trends, developing trading strategies, or conducting market research. The variety of timeframes allows for both long-term and short-term analysis.

Licensing This dataset is available under the CC0-1.0 License. This means it is free to use for any purpose, including commercial use, without needing to request permission.

How to Access the Data To access and use this dataset, follow these steps:

• Download the dataset from Kaggle.
• Unzip the downloaded file to access the individual Excel files.
• Open each Excel file to explore the historical stock data for each stock in the NIFTY 500 index.

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.

Full historical data for the S&P 500 (ticker ^GSPC), sourced from Yahoo Finance (https://finance.yahoo.com/).

Including Open, High, Low and Close prices in USD + daily volumes.

Info about S&P 500: https://en.wikipedia.org/wiki/S%26P_500

🥤 Coca-Cola (KO) Stock Price History (1919–2025)

This dataset provides daily historical stock price data for The Coca-Cola Company (ticker: KO) from January 2, 1962 to April 6, 2025. It captures Coca-Cola’s stock performance through decades of economic cycles, technological shifts, and global events — making it a rich resource for time-series analysis, investment research, and machine learning projects.

📂 Dataset Overview

🧮 Dataset Dimensions

• Total Rows: 15,922
• Total Columns: 7
• Missing Values: None ✅
• Date Range: 1962-01-02 to 2025-04-06

📊 Summary Statistics

• Highest Close Price: $73.18
• Lowest Close Price: $0.19
• Max Volume: 124M+ shares
• Average Close Price: ~$18.45
• Adjusted Prices: Range from $0.03 to $73.18

💡 Use Cases

• Time-series forecasting with LSTM, ARIMA, Prophet
• Volatility analysis and pattern detection
• Financial data visualization across decades
• Backtesting trading strategies on long-term data
• Comparing adjusted vs. raw stock prices

🧠 Project Ideas

• Predict future stock prices using ML models
• Visualize price trends during major economic events
• Analyze the effect of dividends and stock splits
• Build a financial dashboard using Plotly or Streamlit

📎 License

This dataset is for educational and research purposes only. For financial trading or commercial use, always consult a licensed data provider.

🙌 Acknowledgment

This dataset was compiled to support learning in data science, finance, and AI fields. Feel free to use it in your projects — and if you do, share your work! 📬 Contect info:

You can contect me for more data sets any type of data you want.

-E_mail

-Linkdin

-Kaggle

-X

-Github

NIFTY (National Stock Exchange Fifty) refers to a diversified index representing the weighted average of 50 of the largest and most liquid Indian stocks listed on the National Stock Exchange (NSE). Officially known as the NIFTY 50, it serves as a benchmark for the Indian equity market and is widely used by investors and analysts to gauge the performance of the Indian stock market. Content In the dataset we have hourly open, close, high and low price along with open, close, high and low price for previous hour.

This dataset provides comprehensive daily stock price data for the QQQ Trust, an exchange-traded fund (ETF) tracking the performance of the Nasdaq-100 Index. The dataset covers a five-year period from 2019 to 2024, offering insights into the ETF's trading activity and price movements over time.

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Context

This dataset contains historical data of stocks listed on IHSG with time ranges per minutes, hourly, and daily. The source of the dataset is taken from Yahoo Finance's public data and the IDX website which is listed in the metadata tab. This dataset was created with the intention of academic research purposes and not to be commercialized. If you have questions about the dataset, please ask in the discussion tab. Code snippet: https://github.com/muamkh/IHSGstockscraper

Content

Stock minutes data is taken from 1 November 2021 until 6 January 2023. Stock hourly data is taken from 16 April 2020 until 6 January 2023. Stock daily data is taken from 16 April 2001 until 6 January 2023. All of the data is using CSV format. Stock data isnt adjusted with dividend, stock split, and other corporate action.

Stocklist Structure

• Code = Stock code
• Name = Company name
• ListingDate = Listing date of stock on Indonesia Stock Exchange
• Shares = Amount of shares
• ListingBoard = Board category (Main Board, Development Board or Acceleration). More info: https://www.idx.co.id/en-us/products/stocks/
• Sector = Sector Category based on IDX-IC. More info: https://www.idx.co.id/en-us/products/stocks/
• LastPrice = Last stock price
• MarketCap = Market Capitalization.
• MinutesFirstAdded = Date the data first retrieved in minute range
• MinutesLastAdded = Date the data last retrieved in minute range
• HourlyFirstAdded = Date the data first retrieved in hourly range
• HourlyLastAdded = Date the data last retrieved in hourly range
• DailyFirstAdded = Date the data first retrieved in daily range
• DailyLastAdded = Date the data last retrieved in daily range

Struktur Data Saham

• timestamp = Date and time of stock transaction
• open = opening price
• low = lowest price in the timespan
• high = highest price in the timespan
• close = closing price
• volume = Total volume traded in the timespan

International Business Machines Corporation is an American multinational technology corporation headquartered in Armonk, New York, with operations in over 171 countries. Wikipedia

MetaData

Crude Oil Stock Price Data (2000-2024)

Dataset Description

This dataset contains historical stock price data for Crude Oil from 2000 to 2024. This data is extracted by using Python's yfinance library and it provides detailed insights into Crude Oil's stock performance over the years. It includes daily values for the stock's opening and closing prices, adjusted close price, high and low prices, and trading volume. This dataset is ideal for time series analysis, stock trend analysis, and financial machine learning projects such as price prediction models and volatility analysis.

The dataset is extracted from Yahoo Finance

Column Descriptions

Date: The trading date for each entry, in the format.

Adj_Close: Adjusted closing price of Crude Oil stock for each trading day, reflecting stock splits, dividends, and other adjustments.

Close: The raw closing price of Crude Oil stock at the end of each trading day.

High: The highest price reached by Crude Oil stock during the trading day.

Low: The lowest price reached by Crude Oil stock during the trading day.

Open: The price of Crude Oil stock at the start of the trading day.

Volume: The total number of shares traded during the trading day.