Historical Stock Splits API provides financial data users with a rapid access to historical stock splits data. Company executive boards of public companies very often aim for stock splitting when circumstances are favourable. Stock-splitting leads to an increased number of shares sold at lower prices. In this way, prospective investors or company shareholders purchase more shares at attractive prices. If you need historical stock splitting data for your financial project, try out Finnworlds Historical Stock Splits API. In case you want to learn more about it, please, visit the website. https://finnworlds.com/historical-stock-splits-api/

Description

This project involves collecting and analyzing financial data for Electronic Arts (EA) using the Alpha Vantage API. The data includes historical stock prices, dividend payments, and stock splits. The project aims to provide a detailed view of EA’s financial performance and corporate actions over time.

Detail

The project consists of three main datasets:

1) Stock Price Data: Daily records of EA’s stock prices, including opening, high, low, and closing prices, as well as trading volume.

2) Dividend Data: Historical records of dividend payments by EA, detailing declaration dates, record dates, payment dates, and dividend amounts.

3) Stock Split Data: Records of stock split events, showing the date of each split and the split ratio.

The data is sourced from the Alpha Vantage API, which provides comprehensive financial market data. The datasets are cleaned and formatted to ensure consistency and accuracy. They are then saved in CSV files for easy access and analysis.

Usage

The collected data can be used for various financial analyses and insights:

Stock Price Analysis: Evaluate EA’s stock price trends, volatility, and trading volumes over time.

Dividend Analysis: Analyze dividend payment trends, yield, and changes in dividend policy.

Stock Split Analysis: Understand the impact of stock splits on EA’s stock price and overall market behavior.

This data can be used by investors, financial analysts, and researchers to make informed decisions or conduct further financial research. It can also be integrated into financial models or visualizations to provide a clearer picture of EA’s financial health and corporate actions.

Summary

The project provides a detailed dataset of Electronic Arts’ financial data, including stock prices, dividends, and stock splits. By sourcing data from the Alpha Vantage API and carefully formatting it, the project offers valuable insights into EA’s historical financial performance. The data is organized into CSV files, making it accessible for analysis, research, and decision-making purposes.

Context

I always wanted to have a program that fetch the whole stock market data at once without concerning about new companies that went public recently. So, here it is.

Content

This dataset contains 2 python scripts which one can fetch the data from on their own machine without any special requirements by just running the collect.py . I have done this part in May/21/2021 (Version 2). So, the data is available until then. If one wants to have extend that period, they can run the collect.py .

Columns Description

tickers.csv contains ticker names along with some additional data such as name of the company, sector, industry, and the country of the company.

Each CSV file in stocksData folder named as the company's ticker name. Each file has 8 columns: - Date: as an index. - Open, Close, High, Low: which is in dollars. - Volume: which is number of shares that traded in specific date. - Stock Splits: Show if there is a stock split in specific day as the split ratio. - Dividends: which is in dollars. If a company doesn’t provide dividends for their share holders, this column can be dropped.

Acknowledgements

I've used finviz site and yfinance package to gather this rich data.

Inspiration

I hope one can find this helpful and interesting. If you have any questions don't hesitate to contact me at milad@miladtabrizi.com .

Global Shares Data Reference data on more than 80K stocks worldwide. Historical data from 2000 onwards. Pay only for the parameters you need. Flexible in customizing our product to the customer's needs. Free test access as long as you need for integration. Reliable sources: issues documents, disclosure website, global depositories data and other open sources. The cost depends on the amount of required parameters and re-distribution right.

This dataset contains comprehensive historical trading data for Amazon, including daily open, high, low, and close prices, as well as trading volume, dividends, and stock splits. The data spans a significant time frame, offering insights into Amazon's stock performance over time. Ideal for investors, financial analysts, and data scientists, this dataset can be used for trend analysis, backtesting trading strategies, and understanding market behavior. Whether you're studying Amazon's stock history or developing predictive models, this dataset provides the essential data you need

Data Overview

1. Datetime: This column records the date and time when the stock prices were observed.

2. Open: This is the opening price of the stock for the given time period.

3. High: This represents the highest price at which the stock is traded during the specified time period

4. Low: This is the lowest price at which the stock is traded during the specified time period.

5. Close: This is the closing price of the stock for the given time period.

6. Volume: This column records the total number of shares of the stock that were traded during the specified time period.

7. Dividends: This column records any dividend payments that occurred on the specified date. Dividends are distributions of a company's earnings to its shareholders.

8. Stock Splits: This column records any stock splits that occurred on the specified date. A stock split is a corporate action in which a company increases the number of its outstanding shares by issuing more shares to its current shareholders.

🥤 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

Stock Price Time Series for E Split Corp Class A.

Daily end-of-day price data (OHLCV) with corporate actions processed correctly. CSV ready for Excel, Python, and TransStock.

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.

REST API access to corporate events including stock splits and IPO data. 100,000 requests/day. Historical and current corporate action data for stocks worldwide.

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.

Context

Huge stock market data set with historical data for all US stocks and ETF's.

Content

Most of the datasets that I searched in the internet has companies historical data in separate csv file which is hard to interpret while doing statistical analysis of market data. Here I have provided the historical data of all US stocks and ETF's in a single file along with dividends and stock splits.

Acknowledgements

Thanks to yahoo finance and iEX Cloud API's without which this data extraction would not be possible

Inspiration

I created this to find the optimal algorithm to predict the market outcomes. Good luck to you too!

Diluted-Average-Shares Time Series for Dividend 15 Split Corp. Dividend 15 Split Corp. is a close ended equity mutual fund launched by Quadravest Inc. The fund is managed by Quadravest Capital Management. It invests in public equity markets of Canada. The fund spreads its investments across diversified sectors. It benchmarks the performance of its portfolio against the S&P TSX 60 Index. Dividend 15 Split Corp. was formed on January 9, 2004 and is domiciled in Canada.

Stock Price Time Series for Daikin Industries Ltd.. Daikin Industries,Ltd. manufactures, distributes, and sells air-conditioning and refrigeration equipment, and chemical products in Japan, the Americas, China, Asia, Europe, Europe, and internationally. It offers air-conditioning and refrigeration equipment products, such as split/multi-split typeair conditioners, unitary, air to water heat pump systems, heating systems, air purifiers, skyair, multi-split type air conditioners, ventilations, control systems, packaged air-conditioning systems, air cooled chillers, water cooled chillers, rooftops, air side equipment, refrigeration, containers, marine HVAC, and air filters. The company's chemical products comprises fluoropolymers, fluoropolymers coatings, additives, films, anti-smudge coating, coating resin, fluorinated oil, refrigerants, fluorinated liquids, etching agents, battery materials, fine chemicals and intermediates, optical adhesive, fluorocarbons, fluoroplastics, fluoroelastomers, fluoropaints, fluoro coating agents, semiconductor-etching products, and water and oil repellent agents. It also provides oil hydraulics products, including oil hydraulic pumps and valves, cooling equipment and systems, inverter hydraulic power units, hydrostatic transmissions. In addition, the company offers after sales services. It serves automotive, semiconductor manufacturing, electronics, energy solutions, home and living, building and construction, oil and gas, aerospace, and life sciences industries. Daikin Industries, Ltd. was founded in 1924 and is headquartered in Osaka, Japan.

This dataset was maded to combine all the databases of the historical data of the companies that form the Sp&500 in a certain way.

The goal of this compilation is to have all the information compressed in every row that you can re-construct into a single or multiple companies databases in a easy way.

(All the information of how it was made is linked in the next Notebook: )

Usage - ⛏️Extract the historical data information of stocks of every company in the Sp&500. - 📈Analyze the stocks of the Sp&500 and compare with every company that it forms. - 🏦Use it as databank of 500 most important companies in the world. - 🔀Find interesting correlations and extra information between de companies taking advantage of the cronological sort of the data.

Column Description

• Columns: Every column has the name of the official ticker og that company.
• ** Every row has the information of the next columns:**
• Date: The trading date of the stock
• Open: Trading stock price of the day
• High: Higher trading stock of the day
• Low: Lower trading stock of the day
• Close: The stock's closing price on that day.
• Volume: The amount of stocks traded on that day. -Dividends: Dividends given by the company (In this case 0) -Stock Splits

** Extra details of the dataset ** Source: Yahoo Finance Ticker Format: CSV Date Range: 1962 - 2025 July File Size: 416.66 MB

You can see how it was maded, upload and making your own stocks datasets of official companies using yfinance in python (notebook example: )

EDI's history of corporate action events dates back to January 2007 and uses unique Security IDs that can track the history of events by issuer since January 2007.

Choose to receive accurate corporate actions data via an SFTP connection either 4x daily or end-of-day. Proprietary format. ISO 15022 message standard, providing MT564 & 568 announcements.

To support global trading schedules, EDI offers seven daily data feeds at 03:30, 07:00, 09:00, 11:00, 13:00, 15:00, and 17:15 GMT, ensuring continuous access to accurate, market-aligned data.

Overview

This dataset provides daily stock prices for all companies listed on the National Stock Exchange (NSE) of India. The data spans several years and includes essential trading information that can be used for various financial analyses, stock market research, and machine learning applications.

Content

The dataset includes the following columns:

• Date: The date of the trading day in YYYY-MM-DD format.
• Open: The opening price of the stock on the given date.
• High: The highest price of the stock on the given date.
• Low: The lowest price of the stock on the given date.
• Close: The closing price of the stock on the given date.
• Adj Close: The adjusted closing price of the stock on the given date, which accounts for dividends, stock splits, and other corporate actions.
• Volume: The number of shares traded on the given date.
• Symbol: The unique ticker symbol of the stock.

Data Source

The data has been sourced using the Yahoo Finance API, providing a reliable and comprehensive view of stock performance over time.

Usage

This dataset is ideal for:

• Time series analysis and forecasting of stock prices.
• Developing and testing trading algorithms.
• Financial market research and trend analysis.
• Machine learning projects related to finance and economics.

File Format

The dataset is available in CSV format, making it easy to load into data analysis and machine learning libraries such as pandas, scikit-learn, and TensorFlow.

This dataset provides comprehensive historical stock price data for Apple Inc. (AAPL) from 1980 to 2024. It offers invaluable insights for financial analysts, data scientists, and investors interested in studying Apple's stock performance, market trends, and potential investment strategies.

The data reflects Apple's stock performance over more than four decades, capturing significant events in the company's history.

The dataset includes over 10,000 daily records with the following columns:

Date: The date of the trading day. Open: The price of the stock at market open. High: The highest price reached during the trading day. Low: The lowest price reached during the trading day. Close: The price of the stock at market close. Adj Close: The closing price adjusted for dividends and stock splits. Volume: The total number of shares traded during the day.

Potential Use Cases:

-Financial Analysis: Analyzing stock price trends, volatility, and correlations.

-Predictive Modeling: Building models to forecast future stock prices, identify trading opportunities, or assess risk.

-Quantitative Trading: Developing algorithmic trading strategies based on historical data patterns.

-Research and Education: Studying market behaviour, economic indicators, and corporate events that impact stock prices.

Note: This dataset is intended for educational and research purposes. It is recommended to consult with a financial advisor before making any investment decisions based on this data.

Overview

This dataset collection contains detailed information related to Tesla's stock prices (USD) and stock splits. It is an excellent resource for analysts, researchers, and enthusiasts interested in studying the stock performance of one of the world's most innovative companies.

Stock Price Date Range: From June 29, 2010 to September 20, 2024.

Potential Uses:

These datasets can be used for various purposes, such as:

• Historical Analysis: Track and analyze stock price trends and the impact of stock splits over time.
• Financial Modeling: Develop predictive models for stock prices based on historical data.
• Educational Purposes: Use the data to understand the implications of stock splits and their effects on stock performance.

Datasets Included

1. TeslaStockPrice.csv
2. TeslaStockSplit.csv

1. TeslaStockPrice.csv

Description: This dataset provides daily stock prices of Tesla, Inc. Note: All prices are in USD.

Columns: - Date: Date of the trading day. - Open: Stock price at market open. - High: Highest stock price during the trading day. - Low: Lowest stock price during the trading day. - Close: Stock price at market close. - Adj Close: Adjusted closing price accounting for dividends and stock splits. - Volume: Number of shares traded.

2. TeslaStockSplit.csv:

Description: This dataset details the history of stock splits conducted by Tesla, Inc.

Columns: - Date: The date of the stock split. - Split Ratio: The ratio by which stock was split (e.g., 5:1).

Acknowledgment: Data is sourced from publicly available financial records and is provided as-is for educational and research purposes.