This Stock Market Dataset is designed for predictive analysis and machine learning applications in financial markets. It includes 13647 records of simulated stock trading data with features commonly used in stock price forecasting.

🔹 Key Features Date – Trading day timestamps (business days only) Open, High, Low, Close – Simulated stock prices Volume – Trading volume per day RSI (Relative Strength Index) – Measures market momentum MACD (Moving Average Convergence Divergence) – Trend-following momentum indicator Sentiment Score – Simulated market sentiment from financial news & social media Target – Binary label (1: Price goes up, 0: Price goes down) for next-day prediction This dataset is useful for training hybrid deep learning models such as LSTM, CNN, and Attention-based networks for stock market forecasting. It enables financial analysts, traders, and AI researchers to experiment with market trends, technical analysis, and sentiment-based predictions.

In 2024, ** percent of adults in the United States invested in the stock market. This figure has remained steady over the last few years, and is still below the levels before the Great Recession, when it peaked in 2007 at ** percent. What is the stock market? The stock market can be defined as a group of stock exchanges, where investors can buy shares in a publicly traded company. In more recent years, it is estimated an increasing number of Americans are using neobrokers, making stock trading more accessible to investors. Other investments A significant number of people think stocks and bonds are the safest investments, while others point to real estate, gold, bonds, or a savings account. Since witnessing the significant one-day losses in the stock market during the Financial Crisis, many investors were turning towards these alternatives in hopes for more stability, particularly for investments with longer maturities. This could explain the decrease in this statistic since 2007. Nevertheless, some speculators enjoy chasing the short-run fluctuations, and others see value in choosing particular stocks.

The main stock market index of United States, the US500, fell to 6238 points on August 1, 2025, losing 1.60% from the previous session. Over the past month, the index has climbed 0.17% and is up 16.67% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on August of 2025.

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 value of the DJIA index amounted to ****** at the end of June 2025, up from ********* at the end of March 2020. Global panic about the coronavirus epidemic caused the drop in March 2020, which was the worst drop since the collapse of Lehman Brothers in 2008. Dow Jones Industrial Average index – additional information The Dow Jones Industrial Average index is a price-weighted average of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ, and includes companies like Goldman Sachs, IBM and Walt Disney. This index is considered to be a barometer of the state of the American economy. DJIA index was created in 1986 by Charles Dow. Along with the NASDAQ 100 and S&P 500 indices, it is amongst the most well-known and used stock indexes in the world. The year that the 2018 financial crisis unfolded was one of the worst years of the Dow. It was also in 2008 that some of the largest ever recorded losses of the Dow Jones Index based on single-day points were registered. On September 29, 2008, for instance, the Dow had a loss of ****** points, one of the largest single-day losses of all times. The best years in the history of the index still are 1915, when the index value increased by ***** percent in one year, and 1933, year when the index registered a growth of ***** percent.

Stock market forecasting remains a complex and challenging task to forecast, traditional technical analysis methods such as RSI, EMA, and Candlestick Patterns often fail to analyze the stock market time series pattern with many recent studies now exploring forecasting using machine learning or neural networks, other studies have improved accuracy or decreased regression losses by applying technical indicators and sentiment analysis. This dataset aims to be used to analyze the performance of machine learning models in predicting the next day's stock market trend by combining technical and sentiment-based features. The technical indicators are derived from historical price data focusing on swing trends in the market and sentiment features are extracted using FinBERT from Benzinga Pro as a reliable financial news source. There are limitations to the dataset especially financial news articles. Limitations such as the availability of news data remain a major challenge that has the potential to improve the performance of a machine learning model.

Overview: Welcome to my Kaggle profile! In this dataset, you will find a comprehensive collection of intraday activity data for USA's stocks, covering a single day of trading. As an avid enthusiast of the stock market and data analysis, I have meticulously curated this dataset to provide valuable insights and opportunities for further research and analysis.

Content: The dataset contains a wealth of information on various USA's stocks, each represented as individual data points. The key features of the dataset include:

Timestamp: The exact time when the data was recorded during the trading session. Stock Symbol: The unique identifier for each stock listed on the USA stock exchanges. Open Price: The opening price of the stock at the given timestamp. High Price: The highest price reached by the stock during the timestamp. Low Price: The lowest price reached by the stock during the timestamp. Close Price: The closing price of the stock at the given timestamp. Volume: The total trading volume of the stock at the given timestamp. Potential Insights: With this dataset, you can uncover various insights and trends related to intraday trading of USA's stocks. Some potential analysis opportunities include:

Stock Price Movement: Analyzing the price movement of individual stocks throughout the trading day. Volume Analysis: Investigating the relationship between trading volume and price fluctuations. Stock Correlations: Identifying correlations between different stocks during the day. Identifying Market Patterns: Discovering intraday market patterns or trends. Market Sentiment Analysis: Exploring the sentiment of investors during specific time intervals. Applications: The dataset can be beneficial for a wide range of applications, including:

Algorithmic Trading: Developing and testing intraday trading strategies using historical data. Predictive Modeling: Building models to predict stock price movements based on intraday activity. Financial Research: Conducting in-depth studies on specific stocks or sectors. Market Analysis: Gaining insights into broader market behavior and trends. Acknowledgment: I would like to express my gratitude to the financial community and Kaggle for providing an incredible platform to share and explore data. This dataset is a product of my passion for the stock market and data analytics. I hope it sparks curiosity and serves as a valuable resource for fellow data enthusiasts, traders, and researchers.

Happy exploring and may this dataset lead you to new discoveries and successful endeavors in the exciting world of stock trading!

Note: Please keep in mind that stock market data can be volatile and subject to fluctuations. Always exercise caution and perform thorough analysis before making any financial decisions based on this dataset.

United Kingdom's main stock market index, the GB100, fell to 9069 points on August 1, 2025, losing 0.70% from the previous session. Over the past month, the index has climbed 3.35% and is up 10.93% 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 August of 2025.

Japan's main stock market index, the JP225, fell to 40800 points on August 1, 2025, losing 0.66% from the previous session. Over the past month, the index has climbed 2.61% and is up 13.62% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on August of 2025.

It is forecast that the global online trading market will increase at a global compound annual growth rate of *** percent per year, increasing to an estimated **** billion U.S. dollars in 2026. This is from a base of around ***** billion U.S. dollars in 2022. Following the coronavirus pandemic beginning in 2020, online trading activity increased among millennial investors. Many online brokers, including Robinhood, experienced notable growth in the number of platform users from the second quarter of 2020 through to 2021. A low-cost business model, paired with technological integration and social media promotion were contributing factors to the popularity of online trading. What is an online trading platform? The online trading market is typically accessed through an online market broker, providing a platform for users to track market prices and execute buy and sell orders on financial securities. The user typically holds their portfolio through an online broker. The number of monthly downloads for leading online trading apps spiked in early 2021. While this was influenced by media attention to popular news stories such as the increase in the price of GameStop shares, online trading is expected to continue as an alternative to traditional investment methods. Factors driving online trading The integration of technology has improved investing activities. From a global survey, most respondents stated technology made investing easier, cheaper, and more efficient. The use of technology allowed information such as real-time data, industry and firm reports, and trading notifications to be more accessible directly to the investor. Online platforms had experienced an increase in the number of trades placed per day, in 2019, interactive brokers had an average of 1,380 trades placed per day. This number steadily increased to 3,905 trades per day in 2021. Technological integration allowed trading via online platforms to be an alternative to traditional methods of relying on an in-person full-service broker.

China's main stock market index, the SHANGHAI, fell to 3560 points on August 1, 2025, losing 0.37% from the previous session. Over the past month, the index has climbed 3.04% and is up 22.53% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on August of 2025.

The global day trading software market is experiencing robust growth, driven by the increasing popularity of online trading, the rise of mobile trading platforms, and the expansion of fintech innovations. While precise figures for market size and CAGR were not provided, based on industry analysis and the listed companies' market presence, a reasonable estimation places the 2025 market size at approximately $2.5 billion. Considering the consistent adoption of online trading and technological advancements, a conservative Compound Annual Growth Rate (CAGR) of 15% is projected for the forecast period 2025-2033. This growth is fueled by several key factors: the accessibility of online brokerage accounts, the development of sophisticated charting tools and algorithmic trading capabilities within the software, and a growing interest in financial markets among younger demographics. The market is segmented by deployment (cloud-based and on-premises) and application (personal and enterprise use). Cloud-based solutions are gaining traction due to their scalability, accessibility, and cost-effectiveness. The enterprise segment is expanding rapidly, with financial institutions and hedge funds increasingly adopting advanced day trading software to enhance their trading strategies. Geographic expansion also plays a crucial role in market growth. North America and Europe currently hold significant market share, but the Asia-Pacific region exhibits substantial growth potential, driven by rising internet penetration and a burgeoning middle class actively engaging in investment activities. However, regulatory challenges and security concerns surrounding online trading remain potential restraints. The competitive landscape is characterized by a mix of established players and emerging fintech companies, leading to innovation and continuous improvement in the features and functionalities offered by day trading software. The market's future trajectory hinges on adapting to evolving regulatory frameworks, cybersecurity enhancements, and the ongoing integration of artificial intelligence and machine learning capabilities to further automate and optimize trading strategies.

Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2015-08-03 to 2025-08-01 about stock market, average, industry, and USA.

The dataset contains a total of 25,161 rows, each row representing the stock market data for a specific company on a given date. The information collected through web scraping from www.nasdaq.com includes the stock prices and trading volumes for the companies listed, such as Apple, Starbucks, Microsoft, Cisco Systems, Qualcomm, Meta, Amazon.com, Tesla, Advanced Micro Devices, and Netflix.

Data Analysis Tasks:

1) Exploratory Data Analysis (EDA): Analyze the distribution of stock prices and volumes for each company over time. Visualize trends, seasonality, and patterns in the stock market data using line charts, bar plots, and heatmaps.

2)Correlation Analysis: Investigate the correlations between the closing prices of different companies to identify potential relationships. Calculate correlation coefficients and visualize correlation matrices.

3)Top Performers Identification: Identify the top-performing companies based on their stock price growth and trading volumes over a specific time period.

4)Market Sentiment Analysis: Perform sentiment analysis using Natural Language Processing (NLP) techniques on news headlines related to each company. Determine whether positive or negative news impacts the stock prices and volumes.

5)Volatility Analysis: Calculate the volatility of each company's stock prices using metrics like Standard Deviation or Bollinger Bands. Analyze how volatile stocks are in comparison to others.

Machine Learning Tasks:

1)Stock Price Prediction: Use time-series forecasting models like ARIMA, SARIMA, or Prophet to predict future stock prices for a particular company. Evaluate the models' performance using metrics like Mean Squared Error (MSE) or Root Mean Squared Error (RMSE).

2)Classification of Stock Movements: Create a binary classification model to predict whether a stock will rise or fall on the next trading day. Utilize features like historical price changes, volumes, and technical indicators for the predictions. Implement classifiers such as Logistic Regression, Random Forest, or Support Vector Machines (SVM).

3)Clustering Analysis: Cluster companies based on their historical stock performance using unsupervised learning algorithms like K-means clustering. Explore if companies with similar stock price patterns belong to specific industry sectors.

4)Anomaly Detection: Detect anomalies in stock prices or trading volumes that deviate significantly from the historical trends. Use techniques like Isolation Forest or One-Class SVM for anomaly detection.

5)Reinforcement Learning for Portfolio Optimization: Formulate the stock market data as a reinforcement learning problem to optimize a portfolio's performance. Apply algorithms like Q-Learning or Deep Q-Networks (DQN) to learn the optimal trading strategy.

The dataset provided on Kaggle, titled "Stock Market Stars: Historical Data of Top 10 Companies," is intended for learning purposes only. The data has been gathered from public sources, specifically from web scraping www.nasdaq.com, and is presented in good faith to facilitate educational and research endeavors related to stock market analysis and data science.

It is essential to acknowledge that while we have taken reasonable measures to ensure the accuracy and reliability of the data, we do not guarantee its completeness or correctness. The information provided in this dataset may contain errors, inaccuracies, or omissions. Users are advised to use this dataset at their own risk and are responsible for verifying the data's integrity for their specific applications.

This dataset is not intended for any commercial or legal use, and any reliance on the data for financial or investment decisions is not recommended. We disclaim any responsibility or liability for any damages, losses, or consequences arising from the use of this dataset.

By accessing and utilizing this dataset on Kaggle, you agree to abide by these terms and conditions and understand that it is solely intended for educational and research purposes.

Please note that the dataset's contents, including the stock market data and company names, are subject to copyright and other proprietary rights of the respective sources. Users are advised to adhere to all applicable laws and regulations related to data usage, intellectual property, and any other relevant legal obligations.

In summary, this dataset is provided "as is" for learning purposes, without any warranties or guarantees, and users should exercise due diligence and judgment when using the data for any purpose.

The global day trading software market is experiencing robust growth, driven by increasing retail investor participation and advancements in trading technologies. The market's size in 2025 is estimated at $5 billion, projected to reach $8 billion by 2033, exhibiting a Compound Annual Growth Rate (CAGR) of approximately 5%. This expansion is fueled by several key factors. The rise of mobile trading, coupled with the accessibility of online brokerage accounts, has democratized day trading, attracting a wider range of investors. Furthermore, the continuous development of sophisticated algorithms and artificial intelligence (AI)-powered trading tools within software platforms enhances trading efficiency and profitability, encouraging further adoption. Cloud-based solutions are gaining significant traction, offering scalability, accessibility, and cost-effectiveness compared to on-premises options. However, regulatory changes and concerns surrounding cybersecurity and data privacy represent potential constraints to market growth. The market is segmented by application (personal and enterprise use) and type (cloud-based and on-premises), with cloud-based solutions dominating the market share. North America and Europe currently represent the largest regional markets, but the Asia-Pacific region demonstrates high growth potential owing to increasing internet and smartphone penetration. The competitive landscape is characterized by a mix of established players like Plus500, eToro, and MetaTrader, alongside emerging fintech companies offering innovative trading solutions. The success of individual companies within this dynamic market hinges on factors such as user-friendly interfaces, advanced charting tools, robust security measures, and competitive pricing strategies. The ongoing integration of AI and machine learning is expected to significantly shape the future of day trading software, leading to more personalized trading experiences and the development of sophisticated predictive analytics tools. The market is anticipated to witness further consolidation as larger players acquire smaller firms, and the demand for specialized trading software catering to niche market segments will likely increase. Regulatory scrutiny is expected to remain a key factor influencing market development, with a focus on protecting investors from fraudulent activities and ensuring market integrity. The expansion of high-speed internet access and the increasing adoption of advanced technologies in developing economies are expected to further fuel market growth in the coming years.

Day Trading Software Market Outlook

The global day trading software market size is currently experiencing a robust expansion, with an estimated valuation of USD 1.2 billion in 2023. It is projected to grow at a compound annual growth rate (CAGR) of 7.5% to reach approximately USD 2.4 billion by 2032. This growth is fueled by the increasing adoption of digital trading platforms and the growing popularity of online trading among both individual and institutional investors. The rising demand for real-time, data-driven trading analysis and automation solutions is a key factor driving the market forward.

One of the primary growth factors in the day trading software market is the surge in technological advancements, particularly in artificial intelligence and machine learning. These technologies have revolutionized trading platforms by enabling automated trading strategies, predictive analytics, and risk management tools, which are highly valued by traders seeking to maximize returns and minimize risks. Moreover, the increasing availability of high-speed internet and mobile connectivity has facilitated access to trading platforms, making day trading more accessible to a broader audience. With the ability to execute trades swiftly and efficiently, traders are increasingly relying on sophisticated software solutions to gain a competitive edge in the financial markets.

Another significant growth factor is the growing interest in alternative investment opportunities such as cryptocurrencies and forex trading. As cryptocurrencies continue to gain mainstream acceptance, traders are increasingly turning to specialized software that supports cryptocurrency trading. This trend is further supported by the volatility and lucrative potential of crypto markets, attracting both novice and experienced traders. Forex trading, on the other hand, remains a popular choice due to its liquidity and 24/7 market access. Day trading software that offers comprehensive support for stocks, commodities, and other trading types is becoming indispensable for traders looking to diversify their portfolios and capitalize on market fluctuations.

The proliferation of digital financial services and the rise of retail investing have also contributed to the market's growth. With the advent of commission-free trading platforms and easy-to-use interfaces, more individuals are participating in day trading activities. This democratization of trading has led to an increased demand for user-friendly software that caters to individual traders, empowering them with tools to execute informed decisions. Additionally, the growing emphasis on financial literacy and education is propelling the adoption of trading software among retail investors seeking to enhance their trading skills and knowledge.

Regionally, North America currently dominates the day trading software market, driven by the presence of major financial markets and a highly developed technological infrastructure. However, Asia Pacific is expected to witness the highest growth rate during the forecast period. The region's burgeoning middle class, increasing internet penetration, and rapid adoption of digital financial technologies are key factors contributing to this growth. Meanwhile, Europe and Latin America are also experiencing steady growth due to the rising popularity of online trading and favorable regulatory environments. The Middle East & Africa, although smaller in market share, is gradually embracing digital trading solutions as financial markets in the region continue to mature.

Component Analysis

The day trading software market is segmented into two primary components: software and services. The software segment encompasses the various applications and platforms used by traders to conduct transactions and analyze market data. This segment is the cornerstone of the day trading ecosystem, providing the necessary tools for executing trades, managing portfolios, and generating real-time analytics. The software component is predominantly driven by advancements in technology, particularly in areas such as artificial intelligence, machine learning, and big data analytics. These technologies are crucial in developing sophisticated trading algorithms and predictive models that help traders make informed decisions.

The demand for customizable and scalable software solutions is rising among both individual and institutional traders. Traders are increasingly seeking platforms that offer a seamless user experience, real-time data feed, and integration with multiple asset classes. Customizability is particularly important for institutional trad

The Dow Jones Industrial Average (DJIA) index dropped around ***** points in the four weeks from February 12 to March 11, 2020, but has since recovered and peaked at ********* points as of November 24, 2024. In February 2020 - just prior to the global coronavirus (COVID-19) pandemic, the DJIA index stood at a little over ****** points. U.S. markets suffer as virus spreads The COVID-19 pandemic triggered a turbulent period for stock markets – the S&P 500 and Nasdaq Composite also recorded dramatic drops. At the start of February, some analysts remained optimistic that the outbreak would ease. However, the increased spread of the virus started to hit investor confidence, prompting a record plunge in the stock markets. The Dow dropped by more than ***** points in the week from February 21 to February 28, which was a fall of **** percent – its worst percentage loss in a week since October 2008. Stock markets offer valuable economic insights The Dow Jones Industrial Average is a stock market index that monitors the share prices of the 30 largest companies in the United States. By studying the performance of the listed companies, analysts can gauge the strength of the domestic economy. If investors are confident in a company’s future, they will buy its stocks. The uncertainty of the coronavirus sparked fears of an economic crisis, and many traders decided that investment during the pandemic was too risky.

The size of the Middle East And Africa ETF Market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 9.00">> 9.00% during the forecast period. The ETF (Exchange-Traded Fund) market refers to the financial industry focused on creating, managing, and trading ETFs, which are investment funds that track the performance of a specific index, sector, commodity, or asset class. ETFs combine the diversification of mutual funds with the liquidity and convenience of stocks, allowing investors to buy or sell shares throughout the trading day at market prices. This industry is a key segment of the broader financial markets and has grown rapidly due to its accessibility, cost efficiency, and flexibility for both retail and institutional investors. ETFs are often classified based on the assets they track, such as equities, bonds, commodities, or currencies. The ETF market offers a wide variety of products, including index-based ETFs, which mirror well-known indices like the S&P 500, sector-specific ETFs that focus on industries like technology or healthcare, and thematic ETFs, which center around global trends like clean energy or artificial intelligence. These products are usually managed by large financial institutions like BlackRock, Vanguard, and State Street Global Advisors. Recent developments include: In March 2024, Abu Dhabi Securities Exchange and HSBC Bank have entered into a partnership to expand the availability of digital fixed-income securities in the capital markets of the region. In collaboration with HSBC, ADX will investigate a framework that would allow digital assets, such digital bonds, to be listed on ADX and accessible via HSBC Orion, the bank's digital assets platform., In September 2023, the Ministry of Investment signed agreements with Al-Rajhi Bank, Alinma Bank, and Banque Saudi Fransi to strengthen the position of the digital banking industry and aid these institutions provide investors with better service.. Key drivers for this market are: Decline in Cost of Service Providers, Availiblity of New distribution platform in the region. Potential restraints include: Market Saturation (lack of Availiblity of new asset class), Extreme market events increasing risk associate with ETF, dampening their demand.. Notable trends are: Equity ETFs a Gateway to Diversified Exposure in the Region's Stock Markets.

Dive into Market Research Intellect's report_name, valued at current_value in 2024, and forecast to reach forecast_value by 2033, growing at a CAGR of cagr_value from 2026 to 2033.

France's main stock market index, the FR40, fell to 7546 points on August 1, 2025, losing 2.91% from the previous session. Over the past month, the index has declined 2.48%, though it remains 4.06% higher than a year ago, 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 August of 2025.