About:

This dataset encompasses the historical data of major stock indices from around the world, sourced directly from Yahoo Finance. With data reaching back to the early 1920s (where available), it serves as an invaluable repository for academic researchers, financial analysts, and market enthusiasts. Users can delve into trends across decades, evaluate historical market behaviors, or even design and validate predictive financial models.

Photo by Tötös Ádám on Unsplash

Info on CSVs:

1. all_indices_data.csv:

   • Description: A consolidated dataset merging all the stock indices from Yahoo Finance.
   • Columns:
     • date: The date of the data point (formatted as YYYY-MM-DD).
     • open: The opening value of the index on that date.
     • high: The highest value of the index during the trading session.
     • low: The lowest value of the index during the trading session.
     • close: The closing value of the index.
     • volume: The trading volume of the index on that date.
     • ticker: The ticker symbol of the stock index.

2. individual_indices_data/[SYMBOL]_data.csv:

   • Description: Individual datasets for each stock index, where [SYMBOL] denotes the ticker symbol of the respective stock index. Each dataset is curated from Yahoo Finance's historical data archives.
   • Columns:
     • date: The date of the data point (formatted as YYYY-MM-DD).
     • open: The opening value of the index on that date.
     • high: The highest value of the index during the trading session.
     • low: The lowest value of the index during the trading session.
     • close: The closing value of the index.
     • volume: The trading volume of the index on that date.

Context:

The provided dataset is extracted from yahoo finance using pandas and yahoo finance library in python. This deals with stock market index of the world best economies. The code generated data from Jan 01, 2003 to Jun 30, 2023 that’s more than 20 years. There are 18 CSV files, dataset is generated for 16 different stock market indices comprising of 7 different countries. Below is the list of countries along with number of indices extracted through yahoo finance library, while two CSV files deals with annualized return and compound annual growth rate (CAGR) has been computed from the extracted data.

Number of Countries & Index:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F90ce8a986761636e3edbb49464b304d8%2FNumber%20of%20Index.JPG?generation=1688490342207096&alt=media" alt="">

Content:

Unit of analysis: Stock Market Index Analysis

This dataset is useful for research purposes, particularly for conducting comparative analyses involving capital market performance and could be used along with other economic indicators.

There are 18 distinct CSV files associated with this dataset. First 16 CSV files deals with number of indices and last two CSV file deals with annualized return of each year and CAGR of each index. If data in any column is blank, it portrays that index was launch in later years, for instance: Bse500 (India), this index launch in 2007, so earlier values are blank, similarly China_Top300 index launch in year 2021 so early fields are blank too.

The extraction process involves applying different criteria, like in 16 CSV files all columns are included, Adj Close is used to calculate annualized return. The algorithm extracts data based on index name (code given by the yahoo finance) according start and end date.

Annualized return and CAGR has been calculated and illustrated in below image along with machine readable file (CSV) attached to that.

To extract the data provided in the attachment, various criteria were applied:

1. Content Filtering: The data was filtered based on several attributes, including the index name, start and end date. This filtering process ensured that only relevant data meeting the specified criteria.

2. Collaborative Filtering: Another filtering technique used was collaborative filtering using yahoo finance, which relies on index similarity. This approach involves finding indices that are similar to other index or extended dataset scope to other countries or economies. By leveraging this method, the algorithm identifies and extracts data based on similarities between indices.

In the last two CSV files, one belongs to annualized return, that was calculated based on the Adj close column and new DataFrame created to store its outcome. Below is the image of annualized returns of all index (if unreadable, machine-readable or CSV format is attached with the dataset).

Annualized Return:

As far as annualised rate of return is concerned, most of the time India stock market indices leading, followed by USA, Canada and Japan stock market indices.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F37645bd90623ea79f3708a958013c098%2FAnnualized%20Return.JPG?generation=1688525901452892&alt=media" alt="">

Compound Annual Growth Rate (CAGR):

The best performing index based on compound growth is Sensex (India) that comprises of top 30 companies is 15.60%, followed by Nifty500 (India) that is 11.34% and Nasdaq (USA) all is 10.60%.

The worst performing index is China top300, however this is launch in 2021 (post pandemic), so would not possible to examine at that stage (due to less data availability). Furthermore, UK and Russia indices are also top 5 in the worst order.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F58ae33f60a8800749f802b46ec1e07e7%2FCAGR.JPG?generation=1688490409606631&alt=media" alt="">

Geography: Stock Market Index of the World Top Economies

Time period: Jan 01, 2003 – June 30, 2023

Variables: Stock Market Index Title, Open, High, Low, Close, Adj Close, Volume, Year, Month, Day, Yearly_Return and CAGR

File Type: CSV file

Inspiration:

• Time series prediction model
• Investment opportunities in world best economies
• Comparative Analysis of past data with other stock market indices or other indices

Disclaimer:

This is not a financial advice; due diligence is required in each investment decision.

This is the name of the 38 global main stock indexes in the world. We collected from Yahoo! Finance. For the convenience of expression and computation later, we numbered it. For each item, the front is its serial number, followed by the corresponding stock 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.

THE FILES HAVE BEEN CREATED BY SZILÁRD ERHART FOR A RESEARCH: ERHART (2021): ESG RATINGS OF GENERAL

STOCK EXCHANGE INDICES, INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS

USERS OF THE FILES AGREE TO QUOTE THE ABOVE PAPER

THE PYTHON SCRIPT (PYTHONESG_ERHART.TXT) HELPS USERS TO GET TICKERS BY STOCK EXCHANGES AND EXTRACT ESG SCORES FOR THE UNDERLYING STOCKS FROM YAHOO FINANCE.

THE R SCRIPT (ESG_UA.TXT) HELPS TO REPLICATE THE MONTE CARLO EXPERIMENT DETAILED IN THE STUDY.

THE EXPORT_ALL CSV CONTAINS THE DOWNLOADED ESG DATA (SCORES, CONTROVERSIES, ETC) ORGANIZED BY STOCKS AND EXCHANGES.

DISCLAIMER

The author takes no responsibility for the timeliness, accuracy, completeness or quality of the information provided.

The author is in no event liable for damages of any kind incurred or suffered as a result of the use or non-use of the

information presented or the use of defective or incomplete information.

The contents are subject to confirmation and not binding.

The author expressly reserves the right to alter, amend, whole and in part,

without prior notice or to discontinue publication for a period of time or even completely.

##############################READ ME

BEFORE USING THE MONTE CARLO SIMULATIONS SCRIPT:

(1) COPY THE goascores.csv and goalscores_alt.csv FILES ONTO YOUR ON COMPUTER DRIVE. THE TWO FILES ARE IDENTICAL.

(2) SET THE EXACT FILE LOCATION INFORMATION IN THE 'Read in data' SECTION OF THE MONTE CARLO SCRIPT AND FOR THE OUTPUT FILES AT THE END OF THE SCRIPT

(3) LOAD MISC TOOLS AND MATRIXSTATS IN YOUR R APPLICATION

(4) RUN THE CODE.

##############################READ ME

This data-set has data spanning from 2013 till 2018. The S&P 500 stock market index, maintained by S&P Dow Jones Indices, comprises 505 common stocks issued by 500 large-cap companies and traded on American stock exchanges, and covers about 80 percent of the American equity market by capitalization. The index is weighted by free-float market capitalization, so more valuable companies account for relatively more of the index. The index constituents and the constituent weights are updated regularly using rules published by S&P Dow Jones Indices. Although the index is called the S&P "500", the index contains 505 stocks because it includes two share classes of stock from 5 of its component companies.

The dataset comprises of all the S&P 500 components with the records created for each stock's open and closing rate spanning from last 5 years.

yahoo finance

View market daily updates and historical trends for CBOE Equity Put/Call Ratio. from United States. Source: Chicago Board Options Exchange. Track economic…

Collected from Yahoo Finance, Investing.com and WJS, this dataset consists of the following indices ranging from July 17, 2017 to July 22, 2022:

1. DJI (Dow Jones Industrial Average)
2. SNP (Standard and Poor's 500)
3. IXIC (Nasdaq Composite)
4. VIX (CBOE Volatility Index)
5. FTSE (Financial Times Stock Exchange)
6. FCHI (CAC 40 Paris Index)
7. STOXX (The STOXX Europe 600)
8. AEX (Amsterdam Exchange Index)
9. IBEX (Iberian Index, Madrid)
10. MOEX (Russia Index)
11. BIST (Istanbul Index)
12. HSI (Hang Seng Index)
13. SSE (Shanghai Composite Index)
14. STI (Straits Times Index)
15. SZSE (Shenzhen Stock Exchange)
16. NIK (Nikkei 225 Index)
17. TWII (Taiwan Weighted)
18. JKSE (Jakarta Composite Index)

This data series of stock market indices includes FTSE 100(FTSE), AEX Index(AEX), DAX(GDAXI) and Straits Times Index(STI), from January 2007 to December 2017. And all these data is from a third party, downloaded with R software from 'Yahoo finance'.

Content

This dataset provides a comprehensive, consolidated collection of daily historical stock data for all companies included in the S&P 100 index. It is designed to be a clean and reliable resource for financial analysis, machine learning, and academic research.

Key Features

Consolidated Data: All data is combined into a single, easy-to-use CSV file, simplifying cross-company analysis.

Top U.S. Companies: Contains data for the 100 largest and most influential non-financial companies in the S&P 500.

Daily Updates: The dataset is updated daily.

Comprehensive Metrics: Each entry includes key OHLCV (Open, High, Low, Close, Volume) data points.

Data Dictionary

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

ticker: The standard ticker symbol for the company on Yahoo Finance.

name: The full name of the company.

Open: The opening price of the stock in USD at market open.

High: The highest price the stock reached during the trading day in USD.

Low: The lowest price the stock reached during the trading day in USD.

Close: The final price of the stock at market close in USD.

Volume: The total volume of shares traded during the day.

Data Collection

The data for this dataset is sourced from the Yahoo Finance API using the yfinance Python library. The list of S&P 100 companies is sourced from a reliable financial resource to ensure accuracy and relevance.

Potential Use Cases

Financial Analysis: Analyze market trends, performance correlations, and historical volatility.

Machine Learning: Train models to predict stock prices, identify trading patterns, or classify market regimes.

Time Series Modeling: Forecast future stock movements using historical price and volume data.

Educational Projects: Use as a practical, real-world dataset for learning data science and finance.

A collective dataset derived from Yahoo Finance for:

1. Stock prices
2. Raw material prices
3. Volatility indices

For multiple historical scenarios.

Context

Stocks and financial instrument trading is a lucrative proposition. Stock markets across the world facilitate such trades and thus wealth exchanges hands. Stock prices move up and down all the time and having ability to predict its movement has immense potential to make one rich. Stock price prediction has kept people interested from a long time. There are hypothesis like the Efficient Market Hypothesis, which says that it is almost impossible to beat the market consistently and there are others which disagree with it.

There are a number of known approaches and new research going on to find the magic formula to make you rich. One of the traditional methods is the time series forecasting. Fundamental analysis is another method where numerous performance ratios are analyzed to assess a given stock. On the emerging front, there are neural networks, genetic algorithms, and ensembling techniques.

Another challenging problem in stock price prediction is Black Swan Event, unpredictable events that cause stock market turbulence. These are events that occur from time to time, are unpredictable and often come with little or no warning.

A black swan event is an event that is completely unexpected and cannot be predicted. Unexpected events are generally referred to as black swans when they have significant consequences, though an event with few consequences might also be a black swan event. It may or may not be possible to provide explanations for the occurrence after the fact – but not before. In complex systems, like economies, markets and weather systems, there are often several causes. After such an event, many of the explanations for its occurrence will be overly simplistic.

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https://www.visualcapitalist.com/wp-content/uploads/2020/03/mm3_black_swan_events_shareable.jpg"> #
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New bleeding age state-of-the-art deep learning models stock predictions is overcoming such obstacles e.g. "Transformer and Time Embeddings". An objectives are to apply these novel models to forecast stock price.

Content

Stock price prediction is the task of forecasting the future value of a given stock. Given the historical daily close price for S&P 500 Index, prepare and compare forecasting solutions. S&P 500 or Standard and Poor's 500 index is an index comprising of 500 stocks from different sectors of US economy and is an indicator of US equities. Other such indices are the Dow 30, NIFTY 50, Nikkei 225, etc. For the purpose of understanding, we are utilizing S&P500 index, concepts, and knowledge can be applied to other stocks as well.

Dataset

The historical stock price information is also publicly available. For our current use case, we will utilize the pandas_datareader library to get the required S&P 500 index history using Yahoo Finance databases. We utilize the closing price information from the dataset available though other information such as opening price, adjusted closing price, etc., are also available. We prepare a utility function get_raw_data() to extract required information in a pandas dataframe. The function takes index ticker name as input. For S&P 500 index, the ticker name is ^GSPC. The following snippet uses the utility function to get the required data.(See Simple LSTM Regression)

Features and Terminology: In stock trading, the high and low refer to the maximum and minimum prices in a given time period. Open and close are the prices at which a stock began and ended trading in the same period. Volume is the total amount of trading activity. Adjusted values factor in corporate actions such as dividends, stock splits, and new share issuance.

Starter Kernel(s)

• Simple LSTM Regression

Acknowledgements

Mining and updating of this dateset will depend upon Yahoo Finance .

Inspiration

Sort of variation of sequence modeling and bleeding age e.g. attention can be applied for research and forecasting

Some Readings

• Applications of deep learning in stock market prediction: recent progress
• Stock predictions with state-of-the-art Transformer and Time Embeddings
• https://arxiv.org/pdf/1712.02136.pdf
• https://catanacapital.com/blog/unpredictable-black-swan-event-stock-market/
• https://par.nsf.gov/servlets/purl/10149715
• https://arxiv.org/pdf/1712.02136.pdf

*If you download and find the data useful your upvote is an explicit feedback for future works*

Cotton futures showed resilience with gains despite early weakness, influenced by dollar and oil trends, CFTC positioning, and ICE stock changes.

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

Morocco's main stock market index, the CFG 25, fell to 18365 points on December 2, 2025, losing 0.38% from the previous session. Over the past month, the index has declined 6.83%, though it remains 24.69% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Morocco. Morocco Stock Market MASI - values, historical data, forecasts and news - updated on December of 2025.

Content

Daily price data for indexes tracking stock exchanges from all over the world (United States, China, Canada, Germany, Japan, and more). The data was all collected from Yahoo Finance, which had several decades of data available for most exchanges.

Prices are quoted in terms of the national currency of where each exchange is located.

Acknowledgements

Data collected from Yahoo Finance Photo by Jason Leung on Unsplash

Context

Stock market data can be interesting to analyze, strong predictive models can have large financial payoff. The amount of financial data on the web is seemingly endless. A large and well structured dataset on a wide array of companies can be hard to come by. Here I view a dataset with historical stock prices for all companies on the S&P 500 index.

Content

All the files have the following columns:

Date - in format: yy-mm-dd

Open - price of the stock at market open (this is NYSE data so all in USD)

High - highest price reached in the day

Low - lowest price reached in the day

Close - close price

Volume - number of shares traded

Acknowledgements

Thanks to Kaggle, Github, yahoo finance.

Purpose of creating the dataset

This dataset lends itself to a some very interesting visualizations. One can look at simple things like how prices change over time, graph an compare multiple stocks at once, or generate and graph new metrics from the data provided. From these data informative stock stats such as volatility and moving averages can be easily calculated. And people can build quantitative models such as: build portfoio, predict volatility, arbitrage, trading strategies.

The final dataset utilised for the publication "Investigating Reinforcement Learning Approaches In Stock Market Trading" was processed by downloading and combining data from multiple reputable sources to suit the specific needs of this project. Raw data were retrieved by downloading them using a Python finance API. Afterwards, Python and NumPy were used to combine and normalise the data to create the final dataset.The raw data was sourced as follows:Stock Prices of NVIDIA & AMD, Financial Indexes, and Commodity Prices: Retrieved from Yahoo Finance.Economic Indicators: Collected from the US Federal Reserve.The dataset was normalised to minute intervals, and the stock prices were adjusted to account for stock splits.This dataset was used for exploring the application of reinforcement learning in stock market trading. After creating the dataset, it was used in s reinforcement learning environment to train several reinforcement learning algorithms, including deep Q-learning, policy networks, policy networks with baselines, actor-critic methods, and time series incorporation. The performance of these algorithms was then compared based on profit made and other financial evaluation metrics, to investigate the application of reinforcement learning algorithms in stock market trading.The attached 'README.txt' contains methodological information and a glossary of all the variables in the .csv file.

As of October 2025, Google represented ***** percent of the global online search engine referrals on desktop devices. Despite being much ahead of its competitors, this represents a modest increase from the previous months. Meanwhile, its longtime competitor Bing accounted for ***** percent, as tools like Yahoo and Yandex held shares of over **** percent and **** percent respectively. Google and the global search market Ever since the introduction of Google Search in 1997, the company has dominated the search engine market, while the shares of all other tools has been rather lopsided. The majority of Google revenues are generated through advertising. Its parent corporation, Alphabet, was one of the biggest internet companies worldwide as of 2024, with a market capitalization of **** trillion U.S. dollars. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2024 with roughly ****** billion U.S. dollars. Search engine usage in different countries Google is the most frequently used search engine worldwide. But in some countries, its alternatives are leading or competing with it to some extent. As of the last quarter of 2023, more than ** percent of internet users in Russia used Yandex, whereas Google users represented little over ** percent. Meanwhile, Baidu was the most used search engine in China, despite a strong decrease in the percentage of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. By the end of 2024, nearly half of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over ** percent of users in Mexico said they used Yahoo.

Nigeria's main stock market index, the NSE All Share, fell to 143210 points on December 1, 2025, losing 0.22% from the previous session. Over the past month, the index has declined 6.85%, though it remains 46.53% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Nigeria. Nigeria Stock Market NSE - values, historical data, forecasts and news - updated on December of 2025.