The Virtual Events Market size was valued at USD 78.53 billion in 2023 and is projected to reach USD 262.27 billion by 2032, exhibiting a CAGR of 18.8 % during the forecasts period. Recent developments include: In August 2023, Zoom Communication, Inc., the video conferencing platform, launched 'Production Studio' for Zoom events and sessions. This innovative feature aims to empower event professionals to seamlessly create virtual event design elements, ensuring a polished, professional, and captivating event experience. , In July 2023, Cvent Inc. launched the Cvent Events+ solution at Cvent CONNECT. This new offering provides a continuously available branded event hub designed to promote upcoming webinars and events, as well as showcase video content from past events. , In May 2023, Vosmos, the technology startup launched VOSMOS.Events, a platform designed for user-generated virtual events. With VOSMOS.Events, individuals and organizations have the capability to organize secure and dynamic virtual events of varying sizes. This offering adopts a subscription-based business model, empowering customers to host virtual events ranging from 100 to over 100,000 participants. , In February 2023, Hubilo, a virtual and hybrid event technology provider, announced its acquisition of Fielddrive, a Belgian company specializing in on-site event technology, including check-in, badging, and access management. This acquisition reflects a growing trend among virtual event firms expanding their capabilities for in-person events. .

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.

The US_Stock_Data.csv dataset offers a comprehensive view of the US stock market and related financial instruments, spanning from January 2, 2020, to February 2, 2024. This dataset includes 39 columns, covering a broad spectrum of financial data points such as prices and volumes of major stocks, indices, commodities, and cryptocurrencies. The data is presented in a structured CSV file format, making it easily accessible and usable for various financial analyses, market research, and predictive modeling. This dataset is ideal for anyone looking to gain insights into the trends and movements within the US financial markets during this period, including the impact of major global events.

Key Features and Data Structure

The dataset captures daily financial data across multiple assets, providing a well-rounded perspective of market dynamics. Key features include:

• Commodities: Prices and trading volumes for natural gas, crude oil, copper, platinum, silver, and gold.
• Cryptocurrencies: Prices and volumes for Bitcoin and Ethereum, including detailed 5-minute interval data for Bitcoin.
• Stock Market Indices: Data for major indices such as the S&P 500 and Nasdaq 100.
• Individual Stocks: Prices and volumes for major companies including Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta.

The dataset’s structure is designed for straightforward integration into various analytical tools and platforms. Each column is dedicated to a specific asset's daily price or volume, enabling users to perform a wide range of analyses, from simple trend observations to complex predictive models. The inclusion of intraday data for Bitcoin provides a detailed view of market movements.

Applications and Usability

This dataset is highly versatile and can be utilized for various financial research purposes:

• Market Analysis: Track the performance of key assets, compare volatility, and study correlations between different financial instruments.
• Risk Assessment: Analyze the impact of commodity price movements on related stock prices and evaluate market risks.
• Educational Use: Serve as a resource for teaching market trends, asset correlation, and the effects of global events on financial markets.

The dataset’s daily updates ensure that users have access to the most current data, which is crucial for real-time analysis and decision-making. Whether for academic research, market analysis, or financial modeling, the US_Stock_Data.csv dataset provides a valuable foundation for exploring the complexities of financial markets over the specified period.

Acknowledgements:

This dataset would not be possible without the contributions of Dhaval Patel, who initially curated the US stock market data spanning from 2020 to 2024. Full credit goes to Dhaval Patel for creating and maintaining the dataset. You can find the original dataset here: US Stock Market 2020 to 2024.

Snapshot img

Event Logistics Market Size 2025-2029

The event logistics market size is valued to increase USD 1.58 billion, at a CAGR of 5.9% from 2024 to 2029. Growth of large-scale events will drive the event logistics market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 33% growth during the forecast period.
By Event Type - Entertainment events segment was valued at USD 1.73 billion in 2023
By End-user - Corporates and enterprises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: 54.70 million
Market Future Opportunities: USD 1.58 billion 
CAGR : 5.9%
APAC: Largest market in 2023

Market Summary

The market encompasses the planning, coordination, and execution of logistical operations for various events, from small corporate gatherings to large-scale international conferences. This dynamic market is fueled by the growing demand for seamless event experiences, with core technologies and applications, such as digital and smart logistics solutions, playing a pivotal role. Service types, including transportation, accommodation, catering, and security, are continually evolving to meet the needs of event organizers. Regulations and geopolitical risks pose challenges, while the adoption of digital solutions and the growth of large-scale events offer significant opportunities.
According to recent studies, the digital transformation of event logistics is expected to reach a market share of over 30% by 2026. In related markets such as the transportation and hospitality industries, the integration of technology is also driving innovation and growth. The ongoing unfolding of these trends and patterns underscores the continuous evolution of the market.

What will be the Size of the Event Logistics Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Event Logistics Market Segmented and what are the key trends of market segmentation?

The event logistics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Event Type

  Entertainment events
  Sports events
  Trade fairs and expos
  Corporate events
  Others


End-user

  Corporates and enterprises
  Entertainment companies
  Government and public sector
  Sports organizations
  Others


Service Type

  Transportation and freight
  On-site setup and handling
  Warehousing and storage
  Customs and compliance
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Event Type Insights

The entertainment events segment is estimated to witness significant growth during the forecast period.

The market encompasses a significant and intricate segment dedicated to managing the complexities of various types of events, particularly entertainment events. This category comprises concerts, music festivals, film festivals, theater productions, live shows, and touring performances. These events necessitate the transportation of substantial volumes of equipment, including audio-visual gear, stage props, lighting rigs, costumes, instruments, and promotional materials, often across cities, countries, or even continents. The logistical challenges are amplified by tight turnaround times between shows. Effective execution of entertainment events hinges on the precise coordination of transport, customs clearance, setup, and dismantling within narrow timeframes. Logistics providers must be adept at handling last-minute changes, rerouting, and special cargo handling, as a considerable portion of the equipment is high-value, fragile, or custom-made.

Moreover, sustainability is increasingly becoming a crucial aspect of event planning, with a growing emphasis on reducing carbon footprints and minimizing waste. Event marketing automation, data privacy compliance, attendee engagement tools, and resource allocation models are essential components of modern event logistics. Contract negotiation strategies, event sponsorship acquisition, exhibitor management tools, accessibility event planning, digital ticketing solutions, company management platforms, supplier relationship management, crowd management strategies, event registration systems, lead generation strategies, venue management software, security management systems, virtual event platforms, emergency response planning, event staffing solutions, registration data analytics, event content management, post-event evaluation metrics, event technology integration, hybrid event management, transportation route planning, real-time event tracking, risk assessment pro

France's main stock market index, the FR40, rose to 8121 points on December 2, 2025, gaining 0.29% from the previous session. Over the past month, the index has climbed 0.13% and is up 11.93% 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 December of 2025.

China Warehouse Stock: Shanghai Future Exchange: Aluminum data was reported at 61,340.000 Ton in 13 May 2025. This records a decrease from the previous number of 62,114.000 Ton for 12 May 2025. China Warehouse Stock: Shanghai Future Exchange: Aluminum data is updated daily, averaging 144,041.000 Ton from Oct 2008 (Median) to 13 May 2025, with 4034 observations. The data reached an all-time high of 879,587.000 Ton in 24 Apr 2018 and a record low of 2,076.000 Ton in 11 Nov 2016. China Warehouse Stock: Shanghai Future Exchange: Aluminum data remains active status in CEIC and is reported by Shanghai Futures Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZB: Shanghai Futures Exchange: Commodity Futures: Stock.

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.

ThredUp (TDUP) Stock: Analysts Project Future Growth Potential

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 market forecasting remains a complex and challenging task to forecast, traditional technical analysis methods like RSI, EMA, and Candlestick Patterns often fail to analyze the stock market time series pattern with many recent studies have now explored forecasting using machine learning or neural networks, other studies have improved the increase in accuracy or decrease in regression loss by applying technical indicator and sentiment analysis. This paper aims to analyze the performance of the combined reinforcement learning and machine learning models in predicting the stock market’s next day trend by incorporating both technical and sentiment-based features. Technical indicators were derived from historical price data focused on multi-timeframe trend and swing trend in the market, then sentiment features were extracted using FinBERT from Benzinga Pro as a reliable financial news source. The reinforcement learning model used is the Proximal Policy Optimization model, while a variety of machine learning models, such as XGBoost, Gradient Boosting, Random Forest, Decision Tree, K-Nearest Neighbor, Support Vector Machine, and Logistic Regression were trained to assess its predictive performance. Results indicate that the ensemble model outperformed the other tested machine learning models with an accuracy score of 69.97%. These reports highlight the effectiveness of the ensemble model combining sentiment and technical features to enhance stock market predictions accuracy. However, limitations such as news data availability and the small training time, remain a key challenge that could potentially increase the performance. Future research could experiment with alternative models, more training time, advance technical patterns, and more news datasets.

Snapshot img

Events Industry Market Size 2025-2029

The events industry market size is forecast to increase by USD 1.07 trillion, at a CAGR of 13.5% between 2024 and 2029. The market is experiencing significant growth, driven primarily by the increasing number of corporate events. Companies recognize the value of face-to-face interactions in fostering business relationships and driving sales.

Major Market Trends & Insights

Europe dominated the market and contributed 34% to the growth during the forecast period.
The market is expected to grow significantly in Noth America region as well over the forecast period.
Based on the Type, the corporate events and seminar segment led the market and was valued at USD 304.60 billion of the global revenue in 2023.
Based on the Source, the sponsorship segment accounted for the largest market revenue share in 2023.

Market Size & Forecast

Market Opportunities: USD 149.92 Billion
Future Opportunities: USD 1.07 Trillion
CAGR (2024-2029): 13.5%
Europe: Largest market in 2023

Another key trend is the growing popularity of events in education, as organizations leverage interactive learning experiences to engage their audiences and enhance brand awareness. However, this market faces a significant challenge: the emerging threat from open-source virtual events solutions. As technology advances, more businesses are turning to cost-effective virtual alternatives to traditional in-person events. This shift presents both opportunities and challenges for market players. Companies that can effectively adapt to this trend, offering innovative solutions that enhance the virtual event experience, will be well-positioned to capitalize on this market's potential. Conversely, those that fail to adapt may find themselves at a competitive disadvantage. To succeed in this dynamic market, companies must focus on delivering value-added services, leveraging technology to create engaging experiences, and continuously innovating to meet the evolving needs of their customers.

What will be the Size of the Events Industry Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The events industry continues to evolve, presenting numerous opportunities for businesses across various sectors. Emergency response planning remains a critical aspect of event management, ensuring the safety and well-being of attendees. Event networking opportunities are increasingly leveraged through virtual platforms, enabling global connectivity and expanded reach. Exhibitor management tools and event staff management systems streamline operations, while virtual event platform technology offers flexibility and cost savings. Event marketing automation and event data analytics provide valuable insights for targeted promotional strategies. Event ticketing systems facilitate seamless registration workflow automation, with a projected industry growth of 10.5% by 2026.

Event sustainability practices, such as virtual booth technology and accessibility features, are gaining traction, enhancing the overall event experience. Event risk assessment, event gamification strategies, and event feedback mechanisms ensure continuous improvement and attendee satisfaction. Registration workflow automation, speaker management platforms, and event sponsorship management tools further optimize event planning and execution. Live streaming technology and venue booking software enable hybrid event management, catering to diverse audience preferences. Post-event analysis reporting and attendee engagement tools provide valuable insights for future improvements. For instance, a leading event organizer reported a 30% increase in lead generation through the implementation of a lead retrieval system at a recent conference.

These advancements underscore the continuous dynamism of the events industry, with ongoing innovation shaping its future applications.

How is this Events Industry segmented?

The events industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Corporate events and seminar
  Music concert
  Festival
  Sports
  Others


Source

  Sponsorship
  Ticket sale
  Others


Revenue Type

  Ticket Sale
  Sponsorship
  Food and Beverage
  Advertising
  Merchandise Sales
  Membership Fees
  Participation Fees
  Media and Licensing Revenue
  Others


Age Group

  Below 20 Years
  21 to 40 Years
  Above 40 Years


Event Location

  Tier 1 Cities
  Tier 2 Cities
  Tier 3 Cities


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Spain
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of

ABSTRACT In 2015, the Financial Economics Research Center (NEFIN) of the University of São Paulo proposed an implicit volatility index for the Brazilian stock market based on the daily prices of options for the Bovespa index (Ibovespa) and that measures the expected volatility of the Ibovespa in the next two months. The aim of this study is to determine whether this implicit volatility index can be considered an antecedent indicator of future returns of the Brazilian stock market, given that it represents the expected volatility of the Ibovespa two months into the future. This study contributes to the literature on the implicit volatility index for the Brazilian stock market, which has been scarce until now. This happens due to the recent establishment of the index and due to the fact that there is not an official one published by the B3 S.A. - Brasil, Bolsa, Balcão (B3). Given the relationship found between the Brazilian implicit volatility index and the future returns of the Ibovespa, investors could anticipate instabilities in the Brazilian market by putting together strategies to protect their investment portfolios, as well as identifying opportunities to enter and exit the market. This research corroborates in disclosing the Brazilian implicit volatility index in order for it to become more widely used in academia and in the Brazilian financial market. The increase in studies on this index may also incentivize the launch of an official implicit volatility index by the B3. The relationship between the Brazilian implicit volatility index and the future returns of the Ibovespa is examined using least squares and quantile regressions. The implicit volatility index for the Brazilian stock market could help in predicting the future returns of the Ibovespa, especially for 20-, 60-, 120-, and 250-day future returns.

This study examines the market return spillovers from the US market to 10 Asia-Pacific stock markets, accounting for approximately 91 per cent of the region’s GDP from 1991 to 2022. Our findings indicate an increased return spillover from the US stock market to the Asia-Pacific stock market over time, particularly after major global events such as the 1997 Asian and the 2008 global financial crises, the 2015 China stock market crash, and the COVID-19 pandemic. The 2008 global financial crisis had the most substantial impact on these events. In addition, the findings also indicate that US economic policy uncertainty and US geopolitical risk significantly affect spillovers from the US to the Asia-Pacific markets. In contrast, the geopolitical risk of Asia-Pacific countries reduces these spillovers. The study also highlights the significant impact of information and communication technologies (ICT) on these spillovers. Given the increasing integration of global financial markets, the findings of this research are expected to provide valuable policy implications for investors and policymakers.

📊 Intel Stock Dataset (1980-2024)

🌟 This dataset contains daily stock trading data for Intel Corporation (ticker: INTC) from 1980 to 2024, sourced from Yahoo Finance. It provides a comprehensive view of Intel's stock performance over four decades, including key metrics like opening/closing prices, trading volume, dividends, and stock splits.

📄 Dataset Overview

• 🗓️ Time Period: 1980 to 2024
• 📈 Total Records: 11,289 rows
• 📂 File Size: ~989.25 KB

This dataset is ideal for financial analysis, stock trend forecasting, machine learning models, and portfolio optimization studies.

📋 Columns and Descriptions

🌟 Key Features

• Clean and Complete: No missing values across all columns.
• Rich Historical Data: Captures Intel's stock trends and major events over the years.
• Ready for Analysis: Ideal for time-series analysis, regression models, and financial forecasting.

🚀 Applications

1. 📈 Trend Analysis: Identify long-term trends and patterns in Intel's stock performance.
2. 🤖 Machine Learning: Train predictive models for stock price forecasting.
3. 💼 Portfolio Insights: Analyze Intel's stock as part of an investment portfolio.
4. 🧮 Statistical Research: Study correlations between market events and stock performance.

Feel free to dive into the dataset and unlock its potential! Let me know if you need help with analysis or visualization. 😄

Dataset Description

This dataset contains daily historical stock prices of Apple Inc. (AAPL) from January 2015 to January 2025, sourced from Yahoo Finance.

Apple is one of the most valuable companies globally, and its stock price reflects market trends, investor sentiment, and global events. This dataset is suitable for:

• 📈 Time-Series Forecasting (ARIMA, Prophet, LSTM, etc.)
• 📊 Financial Analytics & Visualization
• 🤖 Machine Learning / Deep Learning Models
• 🎯 Educational Purposes (forecasting exercises, ML/AI practice)

Data Sources

• Website: Yahoo Finance – AAPL

  • Downloaded using: yfinanceb - Python library

File Information

File: AAPL_stock_2015_2025.csv

Columns:

Date → Trading day (YYYY-MM-DD) Open → Price at market open High → Highest price of the day Low → Lowest price of the day Close → Closing price of the day Adj Close → Adjusted closing price (adjusted for splits/dividends) Volume → Number of shares traded

Acknowledgement

Data is publicly available on Yahoo Finance and accessed through the open-source yfinance library.

Usability Tags (Kaggle)

Stock Market Finance Time-Series Forecasting Machine Learning Deep Learning ARIMA / Prophet / LSTM

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.

Israel Trading Volume: TASE: Avg Daily: Derivative: Option & Future: Currencies data was reported at 46.000 Unit th in Oct 2018. This records a decrease from the previous number of 69.000 Unit th for Sep 2018. Israel Trading Volume: TASE: Avg Daily: Derivative: Option & Future: Currencies data is updated monthly, averaging 45.000 Unit th from Nov 2006 (Median) to Oct 2018, with 144 observations. The data reached an all-time high of 102.000 Unit th in Dec 2014 and a record low of 24.000 Unit th in Aug 2010. Israel Trading Volume: TASE: Avg Daily: Derivative: Option & Future: Currencies data remains active status in CEIC and is reported by Tel Aviv Stock Exchange. The data is categorized under Global Database’s Israel – Table IL.Z005: Tel Aviv Stock Exchange: Trading Value and Trading Volume.

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.

Videndum (VID): Visualizing the Future of Entertainment?

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

Gain access to LSEG's National Stock Exchange of India data, India's largest stock exchange with more than 180,000 terminals across 600 districts.

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.

Arvinas Faces Uncertain Future Amidst Clinical Trial Data. (ARVN)

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

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.

DarioHealth Sees Bright Future Ahead for DRIO Stock

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

API Overview The Indian Stock Exchange API provides detailed financial data for companies listed on the Bombay Stock Exchange (BSE) and National Stock Exchange (NSE), empowering users with comprehensive insights into the dynamic Indian stock market. This powerful API allows investors, financial analysts, and developers to access a wealth of information essential for making informed investment decisions and conducting thorough research.

Check out our Indian API Marketplace here: https://indianapi.in/

Unlock the potential of the Indian stock market with Indian Stock Exchange API's extensive features, including:

Company Profiles: Dive deep into the profiles of Indian companies, gaining valuable insights into their background, history, and industry presence. Stock Prices: Stay up-to-date with real-time stock prices for both BSE and NSE listings, ensuring you never miss a market movement. Technical Data: Access detailed technical analysis data for Indian stocks, enabling you to assess performance and trends with precision. Financials: Explore financial statements and data for Indian companies, including income statements, balance sheets, and cash flow statements. Key Metrics: Evaluate key financial ratios and metrics specific to the Indian stock market, such as profitability, liquidity, and solvency. Analyst Views: Stay informed with expert analyst views and recommendations tailored to Indian stocks, helping you understand market sentiment and investment opportunities. Shareholding Patterns: Gain insights into shareholding patterns of Indian companies, including institutional holdings, promoter holdings, and public shareholding structures. Corporate Actions: Track corporate actions such as dividends, stock splits, mergers, and acquisitions in the Indian market, staying informed about events that may impact stock prices. Recent News: Access the latest news articles related to Indian companies, industries, and market developments, ensuring you're always in the know. mail: contact@indianapi.in

We also offer custom endpoints and a dedicated server for your needs!

The Indian Stock Exchange API provides detailed financial data for companies listed on the BSE and NSE. This API allows users to retrieve company profiles, stock prices, technical data, financials, key metrics, analyst views, shareholding patterns, corporate actions, and recent news.

Indian Stock Exchange API Documentation Welcome to the Indian Stock Exchange API! This API is built with FastAPI to provide real-time stock market data. Below, you will find detailed descriptions of the available endpoints, their methods, required parameters, and usage examples.