The main stock market index of United States, the US500, rose to 6818 points on December 2, 2025, gaining 0.08% from the previous session. Over the past month, the index has declined 0.50%, though it remains 12.70% higher than a year ago, 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 December 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.

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.

Israel's main stock market index, the TA-125, rose to 3538 points on December 2, 2025, gaining 1.75% from the previous session. Over the past month, the index has climbed 4.40% and is up 50.06% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Israel. Israel Stock Market (TA-125) - values, historical data, forecasts and news - updated on December of 2025.

Overview

This dataset contains historical daily prices for all tickers currently trading on NASDAQ. The up to date list is available from nasdaqtrader.com. The historic data is retrieved from Yahoo finance via yfinance python package.

It contains prices for up to 01 of April 2020. If you need more up to date data, just fork and re-run data collection script also available from Kaggle.

Data Structure

The date for every symbol is saved in CSV format with common fields:

• Date - specifies trading date
• Open - opening price
• High - maximum price during the day
• Low - minimum price during the day
• Close - close price adjusted for splits
• Adj Close - adjusted close price adjusted for both dividends and splits.
• Volume - the number of shares that changed hands during a given day

All that ticker data is then stored in either ETFs or stocks folder, depending on a type. Moreover, each filename is the corresponding ticker symbol. At last, symbols_valid_meta.csv contains some additional metadata for each ticker such as full name.

In 2025, ** 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.

Spain's main stock market index, the ES35, rose to 16493 points on December 2, 2025, gaining 0.63% from the previous session. Over the past month, the index has climbed 2.84% and is up 38.90% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Spain. Spain Stock Market Index (ES35) - values, historical data, forecasts and news - updated on December of 2025.

Context

This is a comprehensive dataset including numerous financial metrics that many professionals and investing gurus often use to value companies. This data is a look at the companies that comprise the S&P 500 (Standard & Poor's 500). The S&P 500 is a capitalization-weighted index of the top 500 publicly traded companies in the United States (top 500 meaning the companies with the largest market cap). The S&P 500 index is a useful index to study because it generally reflects the health of the overall U.S. stock market. The dataset was last updated in July 2020.

Content

There are 14 rows included in this dataset: ``` - 4 character variables: - Symbol: Ticker symbol used to uniquely identify each company on a particular stock market - Name: Legal name of the company - Sector: An area of the economy where businesses share a related product or service - SEC Filings: Helpful documents relating to a company

- 10 numeric variables:
  - Price: Price per share of the company
  - Price to Earnings (PE): The ratio of a company’s share price to its earnings per share
  - Dividend Yield: The ratio of the annual dividends per share divided by the price per share
  - Earnings Per Share (EPS): A company’s profit divided by the number of shares of its stock
  - 52 week high and low: The annual high and low of a company’s share price
  - Market Cap: The market value of a company’s shares (calculated as share price x number of shares)
  - EBITDA: A company’s earnings before interest, taxes, depreciation, and amortization; often used as a proxy for its profitability
  - Price to Sales (PS): A company’s market cap divided by its total sales or revenue over the past year
  - Price to Book (PB): A company’s price per share divided by its book value

### Acknowledgements

I found this data on the website datahub at https://datahub.io/core/s-and-p-500-companies-financials/r/1.html. All references and citations should be given to them.


### Inspiration

What useful information can you gleam from this dataset? Are these fundamentals enough to predict a high-quality company? How can you determine high from low quality? What would you liked to have seen in this dataset?

Euro Area's main stock market index, the EU50, rose to 5684 points on December 2, 2025, gaining 0.27% from the previous session. Over the past month, the index has climbed 0.09% and is up 16.52% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Euro Area. Euro Area Stock Market Index (EU50) - values, historical data, forecasts and news - updated on December of 2025.

The "Stock Market Dataset for AI-Driven Prediction and Trading Strategy Optimization" is designed to simulate real-world stock market data for training and evaluating machine learning models. This dataset includes a combination of technical indicators, market metrics, sentiment scores, and macroeconomic factors, providing a comprehensive foundation for developing and testing AI models for stock price prediction and trading strategy optimization.

Key Features Market Metrics:

Open, High, Low, Close Prices: Daily stock price movement. Volume: Represents the trading activity during the day. Technical Indicators:

RSI (Relative Strength Index): A momentum oscillator to measure the speed and change of price movements. MACD (Moving Average Convergence Divergence): An indicator to reveal changes in strength, direction, momentum, and duration of a trend. Bollinger Bands: Upper and lower bands around a stock price to measure volatility. Sentiment Analysis:

Sentiment Score: Simulated sentiment derived from financial news and social media, ranging from -1 (negative) to 1 (positive). Macroeconomic Factors:

GDP Growth: Indicates the overall health and growth of the economy. Inflation Rate: Reflects changes in purchasing power and economic stability. Target Variable:

Buy/Sell Signal: Binary classification (1 = Buy, 0 = Sell) based on price movement thresholds, simulating actionable trading decisions. Use Cases AI Model Training: Ideal for building stock prediction models using LSTM, Gradient Boosting, Random Forest, etc. Trading Strategy Optimization: Enables testing of trading algorithms and strategies in a simulated environment. Sentiment Analysis Research: Useful for understanding how sentiment influences stock movements. Feature Engineering and Selection: Provides a diverse set of features for experimentation with advanced techniques like PCA and LDA. Dataset Highlights Synthetic Yet Realistic: Carefully designed to mimic real-world financial data trends and relationships. Comprehensive Coverage: Includes key indicators and metrics used by traders and analysts. Scalable: Suitable for use in both small-scale academic projects and larger AI-driven trading platforms. Accessible for All Levels: The intuitive structure ensures that even beginners can utilize this dataset for financial machine learning applications. File Format The dataset is provided in CSV format, where:

Rows represent individual trading days. Columns represent features (technical indicators, market metrics, etc.) and the target variable. Acknowledgments This dataset is synthetically generated and is intended for research and educational purposes. It is not based on real market data and should not be used for actual trading.

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.

Between March 4 and March 11, 2020, the S&P 500 index declined by ** percent, descending into a bear market. On March 12, 2020, the S&P 500 plunged *** percent, its steepest one-day fall since 1987. The index began to recover at the start of April and reached a peak in December 2021. As of December 29, 2024, the value of the S&P 500 stood at ******** points. Coronavirus sparks stock market chaos Stock markets plunged in the wake of the COVID-19 pandemic, with investors fearing its spread would destroy economic growth. Buoyed by figures that suggested cases were leveling off in China, investors were initially optimistic about the virus being contained. However, confidence in the market started to subside as the number of cases increased worldwide. Investors were deterred from buying stocks, and this was reflected in the markets – the values of the Dow Jones Industrial Average and the Nasdaq Composite also dived during the height of the crisis. What is a bear market? A bear market occurs when the value of a stock market suffers a prolonged decline of more than 20 percent over a period of at least 2 months. The COVID-19 pandemic caused severe concern and sent stock markets on a steep downward spiral. The S&P 500 achieved a record closing high of ***** on February 19, 2020. However, just over 3 weeks later, the market closed on *****, which represented a decline of around ** percent in only 16 sessions.

China's main stock market index, the SHANGHAI, fell to 3898 points on December 2, 2025, losing 0.42% from the previous session. Over the past month, the index has declined 1.98%, though it remains 15.36% higher than a year ago, 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 December of 2025.

United States Turnover: NYSE: Value: Avg Daily: Stocks data was reported at 74.151 USD bn in Sep 2018. This records an increase from the previous number of 60.740 USD bn for Aug 2018. United States Turnover: NYSE: Value: Avg Daily: Stocks data is updated monthly, averaging 52.690 USD bn from Jan 1991 (Median) to Sep 2018, with 333 observations. The data reached an all-time high of 144.530 USD bn in Jan 2008 and a record low of 5.262 USD bn in Jan 1991. United States Turnover: NYSE: Value: Avg Daily: Stocks data remains active status in CEIC and is reported by New York Stock Exchange. The data is categorized under Global Database’s United States – Table US.Z003: NYSE: Turnover.

Description: The "Global Stock Price Archive" is a comprehensive dataset that provides a historical record of stock prices from a wide range of stock markets across the globe. This dataset is a valuable resource for researchers, investors, and analysts seeking to analyze trends, perform financial research, or develop trading strategies. Multi-Market Coverage: Historical stock price data from major stock exchanges worldwide, such as the New York Stock Exchange (NYSE), NASDAQ, London Stock Exchange (LSE), Tokyo Stock Exchange (TSE), and many others.

Time Series Data: Daily, weekly, or monthly stock price information over a significant timeframe, allowing users to track the performance of individual stocks or market indices.

Ticker Symbols: Ticker symbols or stock codes for easy identification of individual companies or securities.

Open, Close, High, Low Prices: Detailed pricing information, including opening prices, closing prices, daily highs, and lows.

Volume and Trading Data: Trading volumes, bid-ask spreads, and other relevant trading statistics.

Adjustments: Adjusted prices to account for factors like dividends, stock splits, and other corporate actions.

Data Formats: The dataset may be available in various formats, such as CSV, Excel, or API access, to accommodate different user needs

This table contains 14 series, with data starting from 1953 (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 ...), Stock market statistics (14 items: Toronto Stock Exchange; value of shares traded; United States common stocks; Dow-Jones industrials; high; United States common stocks; Dow-Jones industrials; low; Toronto Stock Exchange; volume of shares traded ...).

Sensitivity analysis of Dow Jones shares.

Graph and download economic data for Nasdaq Emerging Markets High Equity Income Total Return Index (NASDAQNQEMHEIT) from 2024-06-10 to 2025-11-07 about return, emerging markets, NASDAQ, equity, income, indexes, and USA.

Context

"Greed, for lack of a better word, is good"

The stock market has always intrigued me. Numbers, Charts, High-pressure environments, thinking on your feet, Wall Street, Hedge funds all of it!

To even begin to explore stocks, one needs to have a good amount of historical data and knowledge of a lot of technical details. Now, OHLC(Open High Low Close) data is available easily on many websites nowadays. But those as features are not enough to predict the stock prices. The stock market depends upon many many factors such as previous days performance, Global financial news, Public sentiment about the company, Mergers & Acquisitions, Moving Averages, etc

Feature Extraction is a tedious job to do, more so when we are talking about stocks. I have created this Pipeline to extract many Technical Indicators as well as lagged features for training Machine Learning algorithms for forecasting Stock Prices. One can also train multiple algorithms on multiple stocks and get an evaluation instantly on how did it perform.

Please check out the app below- https://stock-prediction-dashboard.herokuapp.com/

High-quality financial data especially which requires domain knowledge & expertise in quantitative methods is difficult to get and even if available it would be very costly. This was my motivation for creating and uploading this dataset on Kaggle, so anyone can leverage these extracted features and indicators to build and train their own machine learning models and identify patterns & trends.

Content

This dataset has around 64 features which include features extracted from OHLC, other index prices such as QQQ(Nasdaq-100 ETF) & S&P 500, technical indicators such as Bollinger bands, EMA(Exponential Moving Averages), Stochastic %K oscillator, RSI, etc.

Furthermore, It has lagged features from previous day price data as we know previous day prices affect the future stock price. Then, the data has date features which specify, if it's a leap year, if its month start or end, Quarter start or end, etc.

All of these features have something to offer for forecasting. Some tell us about the trend, some give us a signal if the stock is overbought or oversold, some portrays the strength of the price trend.

I will keep on adding all of the Nasdaq-100 companies to the dataset for the past 10 years approx. So when completed, this data will contain around 100 stocks.

Acknowledgements

This dataset belongs to me. I’m sharing it here so that people can build upon it and try and create some effective methods to predict the random walk.

Inspiration

Can you predict the unpredictable? Can you predict the Stock market movement using machine learning or deep learning techniques? To be precise, Can you generate realistic buy/sell signals for the next day based on future stock price estimates using time series modeling?