A dataset of key technical indicators for S&P 500 Index, including RSI and MACD, used for technical analysis.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

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.

Japan's main stock market index, the JP225, rose to 49553 points on December 2, 2025, gaining 0.51% from the previous session. Over the past month, the index has declined 3.78%, though it remains 26.25% higher than a year ago, 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 December of 2025.

A dataset of key technical indicators for Top 40 USD Net TRI Index, including RSI and MACD, used for technical analysis.

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.

This stock market dataset is designed for financial analysis and predictive modeling. It includes historical stock prices, technical indicators, macroeconomic factors, and sentiment scores to help in developing and testing machine learning models for stock trend prediction.

Dataset Features: Column Description Stock Random stock ticker (AAPL, GOOG, etc.) Date Random business date Open Open price High High price Low Low price Close Close price Volume Trading volume SMA_10 10-day Simple Moving Average RSI Relative Strength Index (10-90 range) MACD MACD indicator (-5 to 5) Bollinger_Upper Upper Bollinger Band Bollinger_Lower Lower Bollinger Band GDP_Growth Random GDP growth rate (2.5% to 3.5%) Inflation_Rate Inflation rate (1.5% to 3.0%) Interest_Rate Interest rate (0.5% to 5.0%) Sentiment_Score Random sentiment score (-1 to 1) Next_Close Next day's closing price (for regression) Target Binary classification (1: Price Increase, 0: Price Decrease)

Key Features: Stock Prices: Open, High, Low, Close, and Volume data. Technical Indicators: Simple Moving Average (SMA), Relative Strength Index (RSI), MACD, and Bollinger Bands. Macroeconomic Factors: Simulated GDP growth, inflation rate, and interest rates. Sentiment Scores: Randomized sentiment values between -1 and 1 to simulate market sentiment. Target Variables: Next-day close price (for regression) and price movement direction (for classification).

Disclaimer!!! Data uploaded here are collected from the internet. The sole purposes of uploading these data are to provide this Kaggle community with a good source of data for analysis and research. I don't own these datasets and am also not responsible for them legally by any means. I am not charging anything (either monetary or any favor) for this dataset.

For the first time, Nifty 100 stocks data and two indices data, along with 55 technical indicators used by Market experts are calculated and made available. Kindly download the data and make sure to share your code in public and if you like this data, do upvote. Thank you.

Overview

This dataset contains historical daily prices for Nifty 100 stocks and indices currently trading on the Indian Stock Market. - Data samples are of 5-minute intervals and the availability of data is from Jan 2015 to Feb 2022. - Along with OHLCV (Open, High, Low, Close, and Volume) data, we have created 55 technical indicators. - More details about these technical indicators are provided in the Data description file.

Content

The whole dataset is around 5 GB, and 100 stocks (Nifty 100 stocks) and 2 indices (Nifty 50 and Nifty Bank indices) are present in this dataset. Details about each file are - - OHLCV (Open, High, Low, Close, and Volume) data

Inspiration

• Data is uploaded for Research and Educational purposes.

Possible problem statements

• Univariate and Multi-variate time series forecasting of stock prices and index prices
• Multi-variate data can be used to predict the trend of the stock price (Buy or Sell or Hold)
• Different intraday or positional trading strategies can be built out of this multivariate data. [technical indicators are already added here]
• EDA on time series data

Turkey's main stock market index, the BIST 100, rose to 11132 points on December 2, 2025, gaining 0.14% from the previous session. Over the past month, the index has climbed 0.64% and is up 13.27% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Turkey. Turkey Stock Market - values, historical data, forecasts and news - updated on December of 2025.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

A dataset of key technical indicators for MSCI World Index, including RSI and MACD, used for technical analysis.

Kenya's main stock market index, the Nairobi 20, fell to 3024 points on December 2, 2025, losing 0.47% from the previous session. Over the past month, the index has declined 4.11%, though it remains 64.78% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Kenya. Kenya Stock Market (NSE20) - values, historical data, forecasts and news - updated on December of 2025.

Here’s a brief description of each field:

timestamp: This is the date and time of the data point. last: This is the last traded price of the security at the given timestamp. bbMiddle, bbLow, bbUp: These are likely related to Bollinger Bands, which are a type of statistical chart characterizing the prices and volatility over time of a financial instrument or commodity. icmkP, icmkten, icmkki: These fields are not immediately clear, they could be specific to the data source or represent some calculated metrics. stTrend, stNum: These could be short-term trend data and related numerical values. macdTrend, macdLine, macdSignal: These are related to the MACD (Moving Average Convergence Divergence) indicator, a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. rsEmTrend, rsEmRsi, rsEmEma: These could be related to the RSI (Relative Strength Index) and EMA (Exponential Moving Average), which are commonly used in technical analysis of stock prices. emSmTrend, emSmShort, emSmLong: These could also be trend data with short and long term values. cciValue: This is likely the Commodity Channel Index, an oscillator used in technical analysis to identify cyclical trends in a security.

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.

A dataset of key technical indicators for Dow Jones Global ex-U.S. Index, including RSI and MACD, used for technical analysis.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

Comprehensive corporate bond index aa data with real-time values, historical trends, charts, and economic analysis. Track corporate bond index aa indicators for informed investment decisions.

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.

Philadelphia Gold and Silver Index: The Future of Precious Metals?

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.

An In-depth Analysis of the S&P 500 Index: Performance, Composition, and Implications

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

ARLs of control charts when the underlying model is log-GARCH(1,1) with the specified parameters, where the dataset is contaminated with a Z3-distributed noise.