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.

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.

The Dow Jones U.S. Completion Total Stock Market Index

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.

FTSE 100: Where to Next?

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 Dataset contains the Stock prices of Apple Company the opening price, closing price, low price etc.. Use these Data and Predict the Stock Prices for upcoming years. Available timeframes: Monthly(MN1), Weekly(W1), Daily(D1), 4-Hourly(H4), Hourly(H1), 30-Minutes(M30), 15-Minutes(M15), 10-Minutes(M10), 5-Minutes(M5).

Apple D1 Daily timeframe

   datetime open high  low close   volume
0 1998-01-02 0.12 0.14 0.12  0.14 170539824
1 1998-01-05 0.14 0.14 0.13  0.14 152723900
2 1998-01-06 0.14 0.17 0.13  0.16 433041952
3 1998-01-07 0.16 0.16 0.15  0.15 251914152
4 1998-01-08 0.15 0.16 0.15  0.16 188994988


... ... ... ... ... ... ... ...

  datetime  open  high   low  close  volume


6634 2024-03-08 169.12 173.70 168.95 170.98 53335094
6635 2024-03-09 170.99 171.01 170.77 170.79   59796
6636 2024-03-11 172.94 174.38 172.05 172.75 44605588
6637 2024-03-12 173.15 174.03 171.01 173.21 37477359
6638 2024-03-13 172.77 173.19 170.76 171.12 31607988

Apple H1 Hourly timeframe

       datetime open high  low close  volume
0 1998-01-02 16:00:00 0.12 0.12 0.12  0.12 14512400
1 1998-01-02 17:00:00 0.12 0.13 0.12  0.12 52987312
2 1998-01-02 18:00:00 0.12 0.13 0.12  0.13 23746800
3 1998-01-02 19:00:00 0.13 0.13 0.13  0.13 21644000
4 1998-01-02 20:00:00 0.13 0.13 0.13  0.13 11933600


... ... ... ... ... ... ... ...

       datetime  open  high   low  close  volume


46746 2024-03-13 19:00:00 171.04 171.14 170.85 171.02 3019206
46747 2024-03-13 20:00:00 171.02 171.53 171.01 171.50 3736110
46748 2024-03-13 21:00:00 171.50 171.80 171.44 171.65 2899620
46749 2024-03-13 22:00:00 171.65 171.74 171.03 171.15 6318538
46750 2024-03-13 23:00:00 171.14 171.16 171.11 171.12  21317

About this dataset

India's National Stock Exchange (NSE) has a total market capitalization of more than US$3.4 trillion, making it the world's 10th-largest stock exchange as of August 2021, with a trading volume of ₹8,998,811 crore (US$1.2 trillion) and more 2000 total listings.

NSE's flagship index, the NIFTY 50, is a 50 stock index is used extensively by investors in India and around the world as a barometer of the Indian capital market.

This dataset contains data of all company stocks listed in the NSE, allowing anyone to analyze and make educated choices about their investments, while also contributing to their countries economy.

How to use this dataset

• Create a time series regression model to predict NIFTY-50 value and/or stock prices.
• Explore the most the returns, components and volatility of the stocks.
• Identify high and low performance stocks among the list.

Highlighted Notebooks

• Your kernel can be featured here!
• Related Dataset: S&P 500 Stocks - daily updated
• More datasets

Acknowledgements

License

CC0: Public Domain

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Stonks by unknown memer.

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.

NSE GABRIEL Target Price Prediction

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

AI-powered price forecasts for PGEN stock across different timeframes including weekly, monthly, yearly, and multi-year predictions.

Hong Kong's main stock market index, the HK50, rose to 26095 points on December 2, 2025, gaining 0.24% from the previous session. Over the past month, the index has declined 0.24%, though it remains 32.15% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Hong Kong. Hong Kong Stock Market Index (HK50) - values, historical data, forecasts and news - updated on December of 2025.

All data acquired on December 11th 2023

1) Ticker: Stock symbol identifying the company.

2) Company: Name of the company.

3) Sector: Industry category to which the company belongs.

4) Industry: Specific sector or business category of the company.

5) Country: Country where the company is based.

6) Market Cap: Total market value of a company's outstanding shares.

7) Price: Current stock price.

8) Change (%): Percentage change in stock price.

9) Volume: Number of shares traded.

10) Price to Earnings Ratio: Ratio of stock price to earnings per share.

11) Price to Earnings: Price-to-earnings ratio based on past earnings.

12) Forward Price to Earnings: Expected price-to-earnings ratio.

13) Price/Earnings to Growth: Ratio of P/E to earnings growth.

14) Price to Sales: Ratio of stock price to annual sales.

15) Price to Book: Ratio of stock price to book value.

16) Price to Cash: Ratio of stock price to cash per share.

17) Price to Free Cash Flow: Ratio of stock price to free cash flow.

18) Earnings Per Share This Year (%): Percentage change in earnings per share for the current year.

19) Earnings Per Share Next Year (%): Percentage change in earnings per share for the next year.

20) Earnings Per Share Past 5 Years (%): Percentage change in earnings per share over the past 5 years.

21) Earnings Per Share Next 5 Years (%): Estimated percentage change in earnings per share over the next 5 years.

22) Sales Past 5 Years (%): Percentage change in sales over the past 5 years.

23) Dividend (%): Dividend yield as a percentage of the stock price.

24) Return on Assets (%): Percentage return on total assets.

25) Return on Equity (%): Percentage return on shareholder equity.

26) Return on Investment (%): Percentage return on total investment.

27) Current Ratio: Ratio of current assets to current liabilities.

28) Quick Ratio: Ratio of liquid assets to current liabilities.

29) Long-Term Debt to Equity: Ratio of long-term debt to shareholder equity.

30) Debt to Equity: Ratio of total debt to shareholder equity.

31) Gross Margin (%): Percentage difference between revenue and cost of goods sold.

32) Operating Margin (%): Percentage of operating income to revenue.

33) Profit Margin: Percentage of net income to revenue.

34) Earnings: Net income of the company.

35) Outstanding Shares: Total number of shares issued by the company.

36) Float: Tradable shares available to the public.

37) Insider Ownership (%): Percentage of company owned by insiders.

38) Insider Transactions: Recent insider buying or selling activity.

39) Institutional Ownership (%): Percentage of company owned by institutional investors.

40) Float Short (%): Percentage of tradable shares sold short by investors.

41) Short Ratio: Number of days it would take to cover short positions.

42) Average Volume: Average number of shares traded daily.

43) Performance (Week) (%): Weekly stock performance percentage.

44) Performance (Month) (%): Monthly stock performance percentage.

45) Performance (Quarter) (%): Quarterly stock performance percentage.

46) Performance (Half Year) (%): Semi-annual stock performance percentage.

47) Performance (Year) (%): Annual stock performance percentage.

48) Performance (Year to Date) (%): Year-to-date stock performance percentage.

49) Volatility (Week) (%): Weekly stock price volatility percentage.

50) Volatility (Month) (%): Monthly stock price volatility percentage.

51) Analyst Recommendation: Analyst consensus recommendation on the stock.

52) Relative Volume: Volume compared to the average volume.

53) Beta: Measure of stock price volatility relative to the market.

54) Average True Range: Average price range of a stock.

55) Simple Moving Average (20) (%): Percentage difference from the 20-day simple moving average.

56) Simple Moving Average (50) (%): Percentage difference from the 50-day simple moving average.

57) Simple Moving Average (200) (%): Percentage difference from the 200-day simple moving average.

58) Yearly High (%): Percentage difference from the yearly high stock price.

59) Yearly Low (%): Percentage difference from the yearly low stock price.

60) Relative Strength Index: Momentum indicator measuring the speed and change of price movements.

61) Change from Open (%): Percentage change from the opening stock price.

62) Gap (%): Percentage difference between the previous close and the current open price.

63) Volume: Total number of shares traded.

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.

Can we predict stock market using machine learning? (WY Stock Forecast)

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

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.

Dataset Description:

Title: Sales Order Dataset

Description: This dataset contains sales order information from an e-commerce platform for a specific period. The dataset includes the following columns:

Order Number: A unique identifier for each order. Order Date: The date when the order was placed. SKU ID: Stock Keeping Unit (SKU) identifier for the product. Warehouse ID: Identifier for the warehouse from which the product was shipped. Customer Type: Type of customer (e.g., individual, business). Order Quantity: The quantity of the product ordered. Unit Sale Price: The price per unit of the product. Revenue: The total revenue generated by the order. Purpose: This dataset is suitable for exploring sales patterns, analyzing customer behavior, and predicting future sales trends. It can be used by data analysts, data scientists, and business analysts to gain insights into sales performance, identify potential areas for improvement, and make data-driven business decisions.

Potential Use Cases:

Analyzing sales trends over time. Identifying best-selling products and customer segments. Predicting future sales based on historical data. Evaluating the effectiveness of marketing campaigns and promotions. Optimizing inventory management and supply chain operations. Data Source: The dataset was collected from an e-commerce platform and has been anonymized to protect sensitive information. It represents a subset of sales order data for analysis and research purposes.

Acknowledgements: We acknowledge the contribution of the e-commerce platform for providing the sales order data used in this dataset.

License: This dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to use, share, and adapt the data, provided you give appropriate credit to the original source.

📉 GOOGL Daily Stock Prices (2004–2025)

📌 Overview

This dataset offers complete historical daily stock prices for Alphabet Inc. (GOOGL). Spanning from GOOGL’s IPO in 2004 through to the present, it provides a clean and consistent view of stock performance over time.

Whether you’re building predictive models, testing trading strategies, or visualizing long-term price movements, this dataset is ready to use with just a few lines of code.

🔗 Use Together With My Other GOOGL Datasets!

This dataset is part of a larger ecosystem of Google/Alphabet-related datasets I created. You can use them together for powerful, multi-dimensional analysis:

👉 GOOGL Financial Dataset: Quarterly Reports + Daily Prices
Includes quarterly income statements, balance sheets, cash flow statements, and another source of daily prices for cross-verification or model ensembling.

👉 GOOGL Daily News — 2000 to 2025
Provides daily news headlines and summaries related to Alphabet Inc., perfect for sentiment analysis, event-based forecasting, and correlating news with stock prices.

Combine all three datasets to:

• Analyze how news sentiment affects stock performance
• Correlate earnings announcements with market reaction
• Build predictive models that integrate both fundamentals and external signals

📁 Columns

1. open – Opening stock price of the day
2. high – Highest price reached that day
3. low – Lowest price during the day
4. close – Closing price of the trading day
5. volume – Volume of shares traded
date (index) – Trading date

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.

why is the stock market up today?

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

Dataset extracted from the post Stock Market Today | Nifty Outlook : Prediction for 10th October 2025 on Smart Investello.

AI-powered price forecasts for MET1.L stock across different timeframes including weekly, monthly, yearly, and multi-year predictions.

Germany's main stock market index, the DE40, rose to 23722 points on December 2, 2025, gaining 0.56% from the previous session. Over the past month, the index has declined 1.70%, though it remains 18.51% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Germany. Germany Stock Market Index (DE40) - values, historical data, forecasts and news - updated on December of 2025.

Poland's main stock market index, the WIG, fell to 110618 points on December 2, 2025, losing 1.16% from the previous session. Over the past month, the index has declined 1.29%, though it remains 36.78% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Poland. Warsaw Stock Exchange WIG Index - values, historical data, forecasts and news - updated on December of 2025.

AI-powered price forecasts for GOFXX stock across different timeframes including weekly, monthly, yearly, and multi-year predictions.

Historical AI model predictions and analysis for Nasdaq-100 ETF stock across multiple timeframes and confidence levels