Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2015-12-02 to 2025-12-01 about stock market, average, industry, and USA.

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 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 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 statistic shows the worst days of the Dow Jones Industrial Average index from 1897 to 2024. The worst day in the history of the index was ****************, when the index value decreased by ***** percent. The largest single day loss in points was on ***********.

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 December 2 of 2025.

Following the announcement of sweeping tariffs on all countries by Donald Trump, ************* became the day with the third-highest point losses for the Dow Jones Industrial Average in history. Worse than the loss experienced on that day were only the losses that occurred following the beginning of the COVID-19 pandemic. The Dow Jones Industrial Average posted significant points losses due to the global impact of the coronavirus pandemic in 2020. With stocks falling sharply, the Dow recorded its worst single-day points drop ever, plunging ***** points – nearly ** percent – on **************.

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.

United Kingdom's main stock market index, the GB100, fell to 9690 points on December 2, 2025, losing 0.13% from the previous session. Over the past month, the index has declined 0.12%, though it remains 15.91% higher than a year ago, 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 December of 2025.

India's main stock market index, the SENSEX, fell to 85138 points on December 2, 2025, losing 0.59% from the previous session. Over the past month, the index has climbed 1.38% and is up 5.31% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from India. BSE SENSEX Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

The statistic shows the best days of the Dow Jones Industrial Average index from 1897 to 2024. The best day in the history of the index was October 6, 1931, when the index value increased by almost ** percent - although it should be noted that this occured one day after the Dow Jones experienced its fourth-worst day of all time, dropping over **** percent. The largest gain in points occurred on October 13, 2008.

April 9, 2025, saw the largest one-day gain in the history of the Dow Jones Industrial Average (DJIA), follwing Trump's announcement of 90-day delay in the introduction of tariffs imposed on imports from all countries. The second-largest one-day gain occurred on March 24, 2020, with the index increasing ******** points. This occurred approximately two weeks after the largest one-day point loss occurred on March 9, 2020, which was triggered by the growing panic about the coronavirus outbreak worldwide. Index fluctuations The DJIA is an index of ** large companies traded on the New York Stock Exchange. It is one of the numbers that financial analysts watch closely, using it as a bellwether for the United States economy. Seeing when these large gains occur, as well as the largest one-day point losses, gives insight to why these fluctuations may occur. The gains in 2009 are likely adjustments after major losses during the Financial Crisis, but those in 2018 are probably signs of high market volatility. Other leading financial indicators While the DJIA is closely watched, it only gives insight on the performance of thirty leading U.S. companies. An index like the S&P 500, tracking *** companies, can give a more comprehensive overview of the United States economy. Even so, this only reflects investment. Other parts of the economy, such as consumer spending or unemployment rate are not well reflected in stock market indices.

Graph and download economic data for Dow-Jones Industrial Stock Price Index for United States (M1109BUSM293NNBR) from Dec 1914 to Dec 1968 about stock market, industry, price index, indexes, price, and USA.

This dataset provides a comprehensive, pre-processed collection of U.S. stock market data, specifically curated for quantitative analysis, financial modeling, and machine learning applications focused on volatility and asset pricing. It is optimized to include essential price and volume change metrics, along with market fundamentals, to facilitate efficient research.

The data is collected into previous 1000 & 3500 market open days since 10/12/2025. Note for a stock to be in each dataset it must have at least 1000 & 3500 days of history. The source data is located at https://stooq.com/db/h/ and an extract script can be found in my accompanying notebook.

📊 Key Data Fields & Structure

The time-series data files (log_change.pkl) are optimized for quantitative modeling, where raw prices are replaced by daily change metrics to capture volatility and momentum efficiently.

Time-Series (3D NumPy Array Structure)

The 3D array (trimmed_market_data_log_change_1000.pkl) is structured as (Days, Features, Tickers) and contains the following 5 features per day:

ticker

date

log_Ret (Close-to-Close): Logarithmic return, ln(Closet​/Closet−1​). Used for overall volatility and total return.

log_Vol: Log change in volume, ln(Volt​/Volt−1​). Used to measure trading activity change.

OC_Log_Change (Open-to-Close): Intraday logarithmic return, ln(Closet​/Opent​). Used to isolate intraday volatility from overnight gaps.

HL_Range_Pct: Daily High-Low range normalized by previous close, (Hight​−Lowt​)/Closet−1​. Used as a proxy for realized daily volatility (Parkinson-like measure).

Fundamentals (market_fundamentals.csv)

This file contains point in time cross-sectional data, including fields like:

Ticker

Company Name (e.g., Agilent Technologies, Inc.)

marketCap

sector

industry

Read using pd.read_pickle('')

💡 Potential Use Cases

Volatility Forecasting: Use the historical time-series features (Log_Ret, HL_Range_Pct) to train models (e.g., GARCH, machine learning) to predict future volatility.

Alpha Generation: Develop trading signals based on the cross-sectional fundamentals combined with recent momentum/volatility changes.

Anomaly Detection: Use the difference between overnight return (implied by CC minus OC) to detect potential mispricings or significant after-hours news impact.

Factor Modeling: Construct stock factors based on market capitalization, price levels, and the novel volatility features provided.

Graph and download economic data for NASDAQ Composite Index (NASDAQCOM) from 1971-02-05 to 2025-12-01 about composite, NASDAQ, stock market, indexes, and USA.

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.

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.

Meta stock price for past 10 years. Following technical indicators added.

1. Date: This column represents the date for which the data is recorded.
2. Open: The opening price of a stock on a particular trading day.
3. High: The highest price at which a stock traded during the trading day.
4. Low: The lowest price at which a stock traded during the trading day.
5. Close: The closing price of a stock on a particular trading day. This is the final price at which the stock is valued for the day.
6. Volume: The number of shares or contracts traded in a security or an entire market during a given period, usually one trading day.
7. RSI_7: 7-day Relative Strength Index. It's a momentum indicator measuring the magnitude of recent price changes to evaluate overbought or oversold conditions in the price of a stock.
8. RSI_14: 14-day Relative Strength Index. Similar to RSI_7 but calculated over 14 days.
9. CCI_7: 7-day Commodity Channel Index. It’s a technical indicator that measures the difference between the current price and the historical average price. When calculated over 7 days, it gives short-term trends.
10. CCI_14: 14-day Commodity Channel Index. Like CCI_7, but over 14 days for more medium-term trends.
11. SMA_50: 50-day Simple Moving Average. It averages the closing prices of a stock over the past 50 days.
12. EMA_50: 50-day Exponential Moving Average. Similar to SMA_50, but gives more weight to recent prices, making it more responsive to new information.
13. SMA_100: 100-day Simple Moving Average. It averages the closing prices over the past 100 days.
14. EMA_100: 100-day Exponential Moving Average. Like SMA_100, but more responsive to recent price changes.
15. MACD: Moving Average Convergence Divergence. This indicator shows the relationship between two moving averages of a stock’s price.
16. Bollinger: Bollinger Bands. A type of price envelope developed by John Bollinger.
17. TrueRange: Typically used in calculating the Average True Range (ATR), it is a measure of volatility that considers the range between the high, low, and previous close of a stock.
18. ATR_7: 7-day Average True Range. It measures market volatility by decomposing the entire range of a stock for that period.
19. ATR_14: 14-day Average True Range. Similar to ATR_7, but calculated over 14 days.

Target

Next_Day_Close: Represents the closing price of the stock for the next day. It is useful for predictive models trying to forecast future prices.

The .csv file contains raw features & targeted features as well. This is suitable for predicting values with 1, 2, 3, 4, 5, 10, 15 & 20 days in the future without any additional labor. For more than that you have to do some preprocessing.

Columns of SP500_exhaustiveData.csv are: - Date: Date of the day - OpenPrice: Opening Price - HighPrice: High price of the day - LowPrice: Low price of the day - ClosedPrice: Closed Price of the day - AdjustedClosedPrice: Adjusted closed of the day - VolumeOfTransactions: Number of transactions taken place for the day - OPN1P_open_price_next_1_period: Open Price target of the next day - HPN1P_high_price_next_1_period: High Price target of the next day - LPN1P_low_price_next_1_period: Low Price target of the next day - CPN1P_close_price_next_1_period: Close Price target of the next day - close_price_next_2_periods: Close Price target of the 2nd day - CPN3P_close_price_next_3_periods: Close Price target of the 3rd day - CPN4P_close_price_next_4_periods: Close Price target of the 4th day - CPN5P_close_price_next_5_periods: Close Price target of the 5th day - APN5P_average_price_next_5_periods: Average Price target of the 5th day - APN10P_average_price_next_10_periods: Average Price target of the 10th day - LPN5P_lowest_price_next_5_periods: Lowest Price target of the next 5 day - MPN5P_median_price_next_5_periods: Median Price target of the next 5 day - HPN5P_highest_price_next_5_periods: Highest Price target of the next 5 day - LPN10P_lowest_price_next_10_periods: Lowest Price target of the next 10 day - MPN10P_median_price_next_10_periods: Median Price target of the 10 day - HPN10P_highest_price_next_10_periods: Highest Price target in next 10 days - LPN20P_lowest_price_next_20_periods: Lowest Price target in the next 20 days - MPN20P_median_price_next_20_periods: Median Price target in the next 20 days - HPN20P_highest_price_next_20_periods: Highest target in the next 20 days