The main stock market index of United States, the US500, fell to 6369 points on March 27, 2026, losing 1.67% from the previous session. Over the past month, the index has declined 7.45%, though it remains 14.12% 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 March of 2026.

About:

This dataset encompasses the historical data of major stock indices from around the world, sourced directly from Yahoo Finance. With data reaching back to the early 1920s (where available), it serves as an invaluable repository for academic researchers, financial analysts, and market enthusiasts. Users can delve into trends across decades, evaluate historical market behaviors, or even design and validate predictive financial models.

Photo by Tötös Ádám on Unsplash

Info on CSVs:

1. all_indices_data.csv:

   • Description: A consolidated dataset merging all the stock indices from Yahoo Finance.
   • Columns:
     • date: The date of the data point (formatted as YYYY-MM-DD).
     • open: The opening value of the index on that date.
     • high: The highest value of the index during the trading session.
     • low: The lowest value of the index during the trading session.
     • close: The closing value of the index.
     • volume: The trading volume of the index on that date.
     • ticker: The ticker symbol of the stock index.

2. individual_indices_data/[SYMBOL]_data.csv:

   • Description: Individual datasets for each stock index, where [SYMBOL] denotes the ticker symbol of the respective stock index. Each dataset is curated from Yahoo Finance's historical data archives.
   • Columns:
     • date: The date of the data point (formatted as YYYY-MM-DD).
     • open: The opening value of the index on that date.
     • high: The highest value of the index during the trading session.
     • low: The lowest value of the index during the trading session.
     • close: The closing value of the index.
     • volume: The trading volume of the index on that date.

United Kingdom's main stock market index, the GB100, fell to 9972 points on March 26, 2026, losing 1.33% from the previous session. Over the past month, the index has declined 8.60%, though it remains 15.07% 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 March of 2026.

The PSX 100 Index is a key benchmark index of the Pakistan Stock Exchange, representing the top 100 companies by market capitalization. It includes a diverse range of industries and provides a comprehensive view of Pakistan's market performance.

Dataset Features: - Date: The trading date. - Open: The opening price of the stock on the given day. - High: The highest price of the stock on the given day. - Low: The lowest price of the stock on the given day. - Close: The closing price of the stock on the given day. - Volume: The number of shares traded on the given day.

Purpose: This dataset aims to offer detailed insights into the stock price trends of PSX 100 companies over the past decade. It is intended for financial analysis, academic research, and investment decision-making.

Dataset Description: 34-year Daily Stock Data (1990-2024)

Context and Inspiration

This dataset captures historical financial market data and macroeconomic indicators spanning over three decades, from 1990 onwards. It is designed for financial analysis, time series forecasting, and exploring relationships between market volatility, stock indices, and macroeconomic factors. This dataset is particularly relevant for researchers, data scientists, and enthusiasts interested in studying: - Volatility forecasting (VIX) - Stock market trends (S&P 500, DJIA, HSI) - Macroeconomic influences on markets (joblessness, interest rates, etc.) - The effect of geopolitical and economic uncertainty (EPU, GPRD)

Sources

The data has been aggregated from a mix of historical financial records and publicly available macroeconomic datasets: - VIX (Volatility Index): Chicago Board Options Exchange (CBOE). - Stock Indices (S&P 500, DJIA, HSI): Yahoo Finance and historical financial databases. - Volume Data: Extracted from official exchange reports. - Macroeconomic Indicators: Bureau of Economic Analysis (BEA), Federal Reserve, and other public records. - Uncertainty Metrics (EPU, GPRD): Economic Policy Uncertainty Index and Global Policy Uncertainty Database.

Columns

1. dt: Date of observation in YYYY-MM-DD format.
2. vix: VIX (Volatility Index), a measure of expected market volatility.
3. sp500: S&P 500 index value, a benchmark of the U.S. stock market.
4. sp500_volume: Daily trading volume for the S&P 500.
5. djia: Dow Jones Industrial Average (DJIA), another key U.S. market index.
6. djia_volume: Daily trading volume for the DJIA.
7. hsi: Hang Seng Index, representing the Hong Kong stock market.
8. ads: Aruoba-Diebold-Scotti (ADS) Business Conditions Index, reflecting U.S. economic activity.
9. us3m: U.S. Treasury 3-month bond yield, a short-term interest rate proxy.
10. joblessness: U.S. unemployment rate, reported as quartiles (1 represents lowest quartile and so on).
11. epu: Economic Policy Uncertainty Index, quantifying policy-related economic uncertainty.
12. GPRD: Geopolitical Risk Index (Daily), measuring geopolitical risk levels.
13. prev_day: Previous day’s S&P 500 closing value, added for lag-based time series analysis.

Key Features

• Cross-Market Analysis: Compare U.S. markets (S&P 500, DJIA) with international benchmarks like HSI.
• Macroeconomic Insights: Assess how external factors like joblessness, interest rates, and economic uncertainty affect markets.
• Temporal Scope: Longitudinal data facilitates trend analysis and machine learning model training.

Potential Use Cases

• Forecasting market indices using machine learning or statistical models.
• Building volatility trading strategies with VIX Futures.
• Economic research on relationships between policy uncertainty and market behavior.
• Educational material for financial data visualization and analysis tutorials.

Feel free to use this dataset for academic, research, or personal projects.

Last Update -25th JAN 2026

Disclaimer!!! Data uploaded here are collected from the internet and some google drive. 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 money or any favor) for this dataset. RESEARCH PURPOSE ONLY

THIS IS THE LARGEST DATASET ON NIFTY 100 STOCKS WITH EACH MINUTES AND DAILY DATA (2015 to 2026)

The NIFTY 50 is a benchmark Indian stock market index that represents the weighted average of 50 of the largest Indian companies listed on the National Stock Exchange. It is one of the two main stock indices used in India, the other being the BSE SENSEX.

Nifty 50 is owned and managed by NSE Indices (previously known as India Index Services & Products Limited), which is a wholly-owned subsidiary of the NSE Strategic Investment Corporation Limited.NSE Indices had a marketing and licensing agreement with Standard & Poor's for co-branding equity indices until 2013. The Nifty 50 index was launched on 22 April 1996, and is one of the many stock indices of Nifty.

The NIFTY 50 index is a free-float market capitalization-weighted index. The index was initially calculated on a full market capitalization methodology. On 26 June 2009, the computation was changed to a free-float methodology. The base period for the NIFTY 50 index is 3 November 1995, which marked the completion of one year of operations of the National Stock Exchange Equity Market Segment. The base value of the index has been set at 1000 and a base capital of ₹ 2.06 trillion.

Content This dataset contains Nifty 100 historical daily prices. The historical data are retrieved from the NSE India website. Each stock in this Nifty 500 and are of 1 minute itraday data.

Every dataset contains the following fields. Open - Open price of the stock High - High price of the stock Low - Low price of the stock Close - Close price of the stock Volume - Volume traded of the stock in this time frame

Inspiration

• Data is uploaded for Research and Educational purposes.
• The data scientists and researchers can download and can build EDA, find Correlations, and perform Regression analysis on it.
• Quant researchers can build strategies and backtest their strategies with this dataset.

Stock Names

| ACC | ADANIENT | ADANIGREEN | ADANIPORTS | AMBUJACEM | | -- | -- | -- | -- | -- | | APOLLOHOSP | ASIANPAINT | AUROPHARMA | AXISBANK | BAJAJ-AUTO | | BAJAJFINSV | BAJAJHLDNG | BAJFINANCE | BANDHANBNK | BANKBARODA | | BERGEPAINT | BHARTIARTL | BIOCON | BOSCHLTD | BPCL | | BRITANNIA | CADILAHC | CHOLAFIN | CIPLA | COALINDIA | | COLPAL | DABUR | DIVISLAB | DLF | DMART | | DRREDDY | EICHERMOT | GAIL | GLAND | GODREJCP | | GRASIM | HAVELLS | HCLTECH | HDFC | HDFCAMC | | HDFCBANK | HDFCLIFE | HEROMOTOCO | HINDALCO | HINDPETRO | | HINDUNILVR | ICICIBANK | ICICIGI | ICICIPRULI | IGL | | INDIGO | INDUSINDBK | INDUSTOWER | INFY | IOC | | ITC | JINDALSTEL | JSWSTEEL | JUBLFOOD | KOTAKBANK | | LICI | LT | LTI | LUPIN | M&M | | MARICO | MARUTI | MCDOWELL-N | MUTHOOTFIN | NAUKRI | | NESTLEIND | NIFTY 50 | NIFTY BANK | NMDC | NTPC | | ONGC | PEL | PGHH | PIDILITIND | PIIND | | PNB | POWERGRID | RELIANCE | SAIL | SBICARD | | SBILIFE | SBIN | SHREECEM | SIEMENS | SUNPHARMA | | TATACONSUM | TATAMOTORS | TATASTEEL | TCS | TECHM | | TITAN | TORNTPHARM | ULTRACEMCO | UPL | VEDL | | WIPRO | YESBANK | | | |

Poland's main stock market index, the WIG, fell to 119727 points on March 27, 2026, losing 1.01% from the previous session. Over the past month, the index has declined 4.50%, though it remains 22.40% 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 March of 2026.

Title: Historical Options Data for BANKNIFTY Index

Description: This dataset provides historical options data for the BANKNIFTY index, which is the benchmark index for the banking sector in India. The dataset includes information on the ticker, date, time, open, high, low, close, volume, and open interest for various call options contracts.

The data is provided in CSV format and covers the time period from March 1, 2021 to the present day. Each row in the dataset corresponds to a single options contract, and includes information on the opening and closing prices, as well as the trading volume and open interest for that contract.

Columns:

Ticker: the ticker symbol for the options contract (string) Date: the date when the contract was traded (date) Time: the time when the contract was traded (time) Open: the opening price for the contract (float) High: the highest price for the contract during the trading session (float) Low: the lowest price for the contract during the trading session (float) Close: the closing price for the contract (float) Volume: the total number of contracts traded during the session (int) Open Interest: the number of outstanding contracts at the end of the session (int) Example entry:

Ticker Date Time Open High Low Close Volume Open Interest BANKNIFTY01APR2130600CE 03/01/2021 12/31/1899 14:39 5057.2 5065 5057.2 5065 50 48000

This dataset can be used to perform various types of analysis on options trading for the BANKNIFTY index, such as calculating the daily trading volume and open interest, identifying trends and patterns in the price movements of options contracts, and developing models to predict future price movements based on historical data.

Trader Fear & Greed Index Dataset This dataset captures the psychological state of the market through the Fear and Greed Index, a sentiment analysis tool commonly used by traders and investors. It provides valuable insights into how fear and greed affect market behaviour, helping to identify potential opportunities and risks.

Dataset Overview The dataset comprises multiple time-series entries recorded between February 1, 2018 and May 2, 2025, capturing daily market sentiment classified as Extreme Fear, Fear, Neutral, Greed, and Extreme Greed.

Key Features timestamp: UNIX timestamp (in seconds) value: Numeric representation of the Fear & Greed Index (range: 0 to 100) classification: Sentiment label derived from the value (e.g., Fear, Greed) date: Human-readable date format corresponding to the timestamp

Data Summary Total Records: 2,649 daily entries Value Range: 5 (Extreme Fear) to 95 (Extreme Greed) Sentiment Distribution: Fear: 30% Greed: 24% Other/Neutral: 46%

Temporal Segmentation The data is evenly segmented over 10 periods, each approximately covering 9 months, making it suitable for training, testing, and validation purposes in time-series modeling.

Use Cases Sentiment-based trading algorithms Market anomaly detection Financial forecasting models Backtesting strategies aligned with emotional sentiment shifts

Bonus Data (Included Separately) Alongside the Fear & Greed Index, this dataset pack also contains a sample trading dataset including: Account and coin metadata Execution price and trade size Trade direction (Buy/Sell) Position details and realised PnL This additional layer allows for deeper correlation analysis between sentiment and real-world trading behaviour.

Ideal For Quantitative analysts Machine learning practitioners in finance Behavioural economics researchers Algorithmic traders

Taiwan TWSE: Equity Market Index: Trading & Consumers' Goods data was reported at 265.650 31Dec1994=100 in Oct 2018. This records a decrease from the previous number of 276.610 31Dec1994=100 for Sep 2018. Taiwan TWSE: Equity Market Index: Trading & Consumers' Goods data is updated monthly, averaging 115.695 31Dec1994=100 from Jan 1995 (Median) to Oct 2018, with 286 observations. The data reached an all-time high of 276.680 31Dec1994=100 in Jun 2018 and a record low of 49.070 31Dec1994=100 in Apr 2003. Taiwan TWSE: Equity Market Index: Trading & Consumers' Goods data remains active status in CEIC and is reported by Taiwan Stock Exchange Corporation. The data is categorized under Global Database’s Taiwan – Table TW.Z001: Taiwan Stock Exchange (TWSE): Indices.

Israel Trading Volume: TASE: Avg Daily: Derivative: Option & Future: TA-35 Index data was reported at 104.000 Unit th in Oct 2018. This records an increase from the previous number of 94.000 Unit th for Sep 2018. Israel Trading Volume: TASE: Avg Daily: Derivative: Option & Future: TA-35 Index data is updated monthly, averaging 98.500 Unit th from May 2017 (Median) to Oct 2018, with 18 observations. The data reached an all-time high of 136.000 Unit th in Feb 2018 and a record low of 66.000 Unit th in Jul 2018. Israel Trading Volume: TASE: Avg Daily: Derivative: Option & Future: TA-35 Index data remains active status in CEIC and is reported by Tel Aviv Stock Exchange. The data is categorized under Global Database’s Israel – Table IL.Z005: Tel Aviv Stock Exchange: Trading Value and Trading Volume. The index was released under the name TA-25 Monthly Options and was broadened to TA-35 Monthly Options in May 2017.

Prices for United States Stock Market Index (US500) including live quotes, historical charts and news. United States Stock Market Index (US500) was last updated by Trading Economics this March 18 of 2026.

Discover the booming futures trading services market, projected to reach $28 billion by 2033 with an 8% CAGR. This in-depth analysis explores market drivers, trends, restraints, and key players across North America, Europe, Asia, and more. Learn about the growth of software-based trading and the increasing popularity of index and commodity futures.

In general, the stock prices of the same industry have a similar trend, but those of different industries do not. When investing in stocks of different industries, one should select the optimal model from lots of trading models for each industry because any model may not be suitable for capturing the stock trends of all industries. However, the study has not been carried out at present. In this paper, firstly we select 424 S&P 500 index component stocks (SPICS) and 185 CSI 300 index component stocks (CSICS) as the research objects from 2010 to 2017, divide them into 9 industries such as finance and energy respectively. Secondly, we apply 12 widely used machine learning algorithms to generate stock trading signals in different industries and execute the back-testing based on the trading signals. Thirdly, we use a non-parametric statistical test to evaluate whether there are significant differences among the trading performance evaluation indicators (PEI) of different models in the same industry. Finally, we propose a series of rules to select the optimal models for stock investment of every industry. The analytical results on SPICS and CSICS show that we can find the optimal trading models for each industry based on the statistical tests and the rules. Most importantly, the PEI of the best algorithms can be significantly better than that of the benchmark index and “Buy and Hold” strategy. Therefore, the algorithms can be used for making profits from industry stock trading.

This dataset provides a comprehensive view of the daily trading activity and performance of the NIFTY50 index over the specified period. It enables various analyses, including trend analysis, volatility assessment, trading volume analysis, and correlation studies with other economic indicators or asset classes. Additionally, it can be used for backtesting trading strategies, risk management, and investment decision-making.

Features:

• Date: The date of the trading session.
• Open: The opening price of the NIFTY50 index at the beginning of the trading session.
• High: The highest price reached by the NIFTY50 index during the trading session.
• Low: The lowest price reached by the NIFTY50 index during the trading session.
• Close: The closing price of the NIFTY50 index at the end of the trading session.
• Shares Traded: The total number of shares of all companies in the NIFTY50 index that were traded during the trading session.
• Turnover (₹ Cr): The total value of shares traded during the trading session, measured in Indian Rupees (₹) in Crores (Cr).

Companies analysed and their ticker symbols.

Welcome to the NASDAQ-100 (NDX) Historical Data! This dataset provides a detailed look at the daily trading prices for the NASDAQ-100 index. The NASDAQ-100 is a prestigious stock market index featuring 100 of the largest non-financial companies listed on the NASDAQ stock exchange.

Why Use This Dataset?

• In-Depth Analysis: Get a comprehensive view of daily market trends for some of the biggest companies.
• Financial Forecasting: Utilize this data for predicting market movements and making informed trading decisions.
• Educational Purposes: Perfect for students, educators, and researchers studying market behaviors and financial markets.
• Trend Analysis: Analyze the highs and lows to understand market volatility and investor sentiment.

Dataset Features:

• Date: Specific trading date.
• Close/Last: The final trading price of the NASDAQ-100 index at the end of the trading day.
• Open: The initial trading price at the market open.
• High: The highest price reached during the trading day.
• Low: The lowest price reached during the trading day.

Sample Data:

Why This Data Matters:

The NASDAQ-100 index includes influential companies from various sectors such as technology, healthcare, consumer services, and more. Tracking the daily movements of this index can provide valuable insights into the overall market performance and economic conditions.

Potential Uses:

• Investment Strategies: Develop and test trading strategies based on historical price movements.
• Market Research: Conduct research to identify patterns and trends in the stock market.
• Educational Projects: Use this data for projects, case studies, and presentations.

Stay ahead of the market by exploring the NASDAQ-100 (NDX) Historical Data for Early July 2024. Gain insights, make predictions, and enhance your understanding of the stock market dynamics with this valuable dataset. Don't miss out—download now and start your analysis!

Japan's main stock market index, the JP225, fell to 52005 points on March 27, 2026, losing 2.98% from the previous session. Over the past month, the index has declined 10.42%, though it remains 40.10% 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 March of 2026.

Argentina Trading Value: BCBA: USD: Index Futures data was reported at 0.000 ARS mn in Feb 2021. This stayed constant from the previous number of 0.000 ARS mn for Jan 2021. Argentina Trading Value: BCBA: USD: Index Futures data is updated monthly, averaging 0.000 ARS mn from Jan 2000 (Median) to Feb 2021, with 254 observations. The data reached an all-time high of 35.488 ARS mn in May 2003 and a record low of 0.000 ARS mn in Feb 2021. Argentina Trading Value: BCBA: USD: Index Futures data remains active status in CEIC and is reported by Buenos Aires Stock Exchange. The data is categorized under Global Database’s Argentina – Table AR.Z003: Buenos Aires Stock Exchange: Trading Value (Discontinued).

US30 Historical Price Data - Multi-Timeframe OHLCV Trading Dataset

Overview

This dataset contains comprehensive historical price data for the US30 (Dow Jones Industrial Average) index, providing traders and analysts with multi-timeframe OHLCV (Open, High, Low, Close, Volume) data for technical analysis and algorithmic trading development.

Dataset Features

• 9 Different Timeframes: 1m, 5m, 15m, 30m, 1h, 4h, 1d, 1w, 1month
• Complete OHLCV Data: Open, High, Low, Close prices with Volume and Tick Volume