Sweden's main stock market index, the Stockholm 30, rose to 2787 points on October 24, 2025, gaining 1.08% from the previous session. Over the past month, the index has climbed 6.23% and is up 7.51% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Sweden. Sweden Stock Market Index - values, historical data, forecasts and news - updated on October of 2025.

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

United Kingdom's main stock market index, the GB100, rose to 9423 points on October 21, 2025, gaining 0.21% from the previous session. Over the past month, the index has climbed 2.13% and is up 13.45% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United Kingdom. United Kingdom Stock Market Index (GB100) - values, historical data, forecasts and news - updated on October of 2025.

France's main stock market index, the FR40, fell to 8237 points on October 22, 2025, losing 0.26% from the previous session. Over the past month, the index has climbed 4.64% and is up 9.86% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from France. France Stock Market Index (FR40) - values, historical data, forecasts and news - updated on October of 2025.

This table contains 25 series, with data for years 1956 - present (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Toronto Stock Exchange Statistics (25 items: Standard and Poor's/Toronto Stock Exchange Composite Index; high; Standard and Poor's/Toronto Stock Exchange Composite Index; close; Toronto Stock Exchange; oil and gas; closing quotations; Standard and Poor's/Toronto Stock Exchange Composite Index; low ...).

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*

Analysis of ‘Time Series Forecasting with Yahoo Stock Price ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arashnic/time-series-forecasting-with-yahoo-stock-price on 30 September 2021.

--- Dataset description provided by original source is as follows ---

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.

#
#

https://www.visualcapitalist.com/wp-content/uploads/2020/03/mm3_black_swan_events_shareable.jpg"> #
#
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*

--- Original source retains full ownership of the source dataset ---

Graph and download economic data for CBOE Volatility Index: VIX (VIXCLS) from 1990-01-02 to 2025-10-20 about VIX, volatility, stock market, and USA.

View weekly updates and historical trends for US Investor Sentiment, % Bullish. from United States. Source: The American Association of Individual Investo…

View weekly updates and historical trends for US Investor Sentiment, % Bull-Bear Spread. from United States. Source: The American Association of Individua…

Poland's main stock market index, the WIG, rose to 109619 points on October 20, 2025, gaining 1.72% from the previous session. Over the past month, the index has climbed 4.08% and is up 33.54% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Poland. Warsaw Stock Exchange WIG Index - values, historical data, forecasts and news - updated on October of 2025.

Market Research Intellect's Data Matrix Barcode Reading System Market Report highlights a valuation of USD 1.5 billion in 2024 and anticipates growth to USD 3.2 billion by 2033, with a CAGR of 9.5% from 2026-2033.Explore insights on demand dynamics, innovation pipelines, and competitive landscapes.

The Reading Glasses Market Report is Segmented by Lens Type (Ready-Made Single-Vision Readers, Bifocal and More), Power Range (+0. 75 To +1. 75 D, +2. 00 To +2. 75 D, and More), Frame Material (Plastic, Metal Alloys, and More), Distribution Channel (Optical Retail Stores, Mass-Merchandisers, and More), and Geography (North America, Europe, Asia Pacific, and More). The Market Forecasts are Provided in Terms of Value (USD).

Report of Handheld Direct Reading Spectrometer Market is currently supplying a comprehensive analysis of many things which are liable for economy growth and factors which could play an important part in the increase of the marketplace in the prediction period. The record of Handheld Direct Reading Spectrometer Industry is providing the thorough study on the grounds of market revenue discuss production and price happened. The report also provides the overview of the segmentation on the basis of area, contemplating the particulars of earnings and sales pertaining to marketplace.

Point Reading Machine Market Outlook

In 2023, the global point reading machine market size was valued at approximately USD 1.5 billion and is anticipated to grow significantly, reaching around USD 3.8 billion by 2032, with an impressive compound annual growth rate (CAGR) of 11%. The robust growth trajectory of the market is underpinned by advancements in technology, particularly in optical character recognition (OCR) and text-to-speech technologies, which are critical components of point reading machines. These advancements are being accelerated by the increasing demand for accessibility tools among visually impaired individuals and the incorporation of such technologies in various sectors including education, healthcare, and retail. The burgeoning need for efficient and accurate reading aids has further propelled the demand, as these machines significantly enhance reading efficiency and accessibility.

The growth factors contributing to the rise in the point reading machine market are manifold. Primarily, an increase in the prevalence of visual impairments and the rising awareness about accessibility rights worldwide have catalyzed the demand for point reading machines. Governments globally are implementing policies and programs that promote inclusive education and workplace environments, encouraging the adoption of assistive technologies. Additionally, the integration of artificial intelligence and machine learning in the development of point reading machines is allowing these devices to become more intuitive and accurate, thus enhancing their usability and appeal to a broader audience. Moreover, the rising disposable incomes and improved purchasing capacity in emerging economies have also played a crucial role in market expansion, as more individuals and institutions are able to afford these specialized devices.

Another significant growth factor is the increasing adoption of digital solutions across various sectors. In the education sector, for example, point reading machines are being adopted to facilitate learning for students with disabilities, improving their academic performance and access to educational materials. The healthcare sector also plays a pivotal role, where such devices are employed to aid in patient care management, helping visually impaired healthcare professionals and patients to access vital information effortlessly. The retail sector is not left behind, as point reading machines assist in enhancing customer experiences by allowing visually impaired individuals to independently access product information. This proliferation in different applications underscores the versatility of point reading machines and their crucial role in bridging the accessibility gap across industries.

On the regional front, North America is expected to dominate the point reading machine market, attributed to its advanced technological infrastructure and significant investments in research and development. The presence of major market players and supportive government policies aimed at enhancing accessibility also contribute to the region's market leadership. However, the Asia Pacific region is projected to witness the highest growth rate during the forecast period, driven by increasing awareness and adoption of assistive technologies in countries like China and India. The growing focus on inclusive education and technological advancements in these regions provides a fertile ground for the expansion of the point reading machine market. Europe also holds a substantial market share due to its supportive framework for digital accessibility and the presence of a considerable number of tech-savvy consumers.

Bar Code Reading Equipment plays a crucial role in the retail sector, where it is extensively utilized to streamline operations and enhance customer service. These devices are integral in inventory management, allowing retailers to efficiently track and manage stock levels, thereby reducing errors and improving accuracy. The ability to quickly scan and process barcodes ensures that checkout lines move swiftly, enhancing the overall shopping experience for customers. Moreover, the integration of barcode reading technology with point-of-sale systems enables retailers to gather valuable data on consumer behavior, which can be used to tailor marketing strategies and improve sales performance. As the retail industry continues to evolve with the advent of digital transformation, the demand for advanced Bar Code Reading Equipment is expected to rise, driving innovation and growth in this sector.

Product Type Analysis</h2

Report of Online Electronic Reading Service Market is currently supplying a comprehensive analysis of many things which are liable for economy growth and factors which could play an important part in the increase of the marketplace in the prediction period. The record of Online Electronic Reading Service Industry is providing the thorough study on the grounds of market revenue discuss production and price happened. The report also provides the overview of the segmentation on the basis of area, contemplating the particulars of earnings and sales pertaining to marketplace.

This dataset contains daily OHLCV data for ~ 2000 Indian Stocks listed on the National Stock Exchange for all time. The columns are multi-index columns, so this needs to be taken into account when reading and using the data. Source : Yahoo Finance Type: All files are CSV format. Currency : INR

All the tickers have been collected from here : https://www.nseindia.com/market-data/securities-available-for-trading

If using pandas, the following function is a utility to read any of the CSV files: ``` import pandas as pd def read_ohlcv(filename): "read a given ohlcv data file downloaded from yfinance" return pd.read_csv( filename, skiprows=[0, 1, 2], # remove the multiindex rows that cause trouble names=["Date", "Close", "High", "Low", "Open", "Volume"], index_col="Date", parse_dates=["Date"], )

dataset = read_ohlcv("ABCAPITAL.NS.csv")

Market Research Intellect's Comic Books Reading Apps Market Report highlights a valuation of USD 1.2 billion in 2024 and anticipates growth to USD 2.4 billion by 2033, with a CAGR of 8.7% from 2026-2033.Explore insights on demand dynamics, innovation pipelines, and competitive landscapes.

The global reading glasses market was valued at USD 138.77 Billion in 2024. It is expected to grow at a CAGR of 3.30% from 2025 to 2034, reaching approximately USD 192.00 Billion by 2034.

Prices for United States Stock Market Index (US30) including live quotes, historical charts and news. United States Stock Market Index (US30) was last updated by Trading Economics this October 21 of 2025.