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.

Financial stock market index details with their stock exchanges, types and listing abbreviations.

Index Time Series for STOXX 600 Financial Services. The frequency of the observation is daily. Moving average series are also typically included.

Welcome to the Netflix Stock Prices and Performance Data dataset! This dataset is your go-to resource for analyzing the financial performance of Netflix, Inc. over time. Whether you’re a seasoned data scientist, a finance enthusiast, or a beginner looking to practice your time series analysis skills, this dataset provides all the key metrics you need.

Date: The trading day (YYYY-MM-DD format) Open: Opening price of the stock on that day High: Highest price reached during the trading day Low: Lowest price reached during the trading day Close: Closing price of the stock on that day Volume: Number of shares traded Potential Uses:

Trend Analysis: Examine how Netflix’s stock price has evolved over time and identify significant trends or events that impacted its performance.

Technical Analysis: Apply various technical indicators to forecast future stock movements.

Investment Strategy Development: Create and backtest trading strategies based on historical data.

Correlation Studies: Compare Netflix’s stock performance with other stocks or indices to uncover correlations.

Market Sentiment Analysis: Integrate with news or social media sentiment data to see how external factors influence stock prices.

Average ranks of TSVR with SVR on stock market index datasets using a linear and Gaussian kernel.

UPDATED EVERY WEEK Last Update - 26th July 2025

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

Context

• The NIFTY 50 is a well-diversified 50 stock index and it represents 13 important sectors of the economy.
• It is used for a variety of purposes such as benchmarking fund portfolios, index-based derivatives, and index funds.
• NIFTY 50 is owned and managed by NSE Indices Limited.
• The NIFTY 50 index has shaped up to be the largest single financial product in India.

This data contains all the indices of NSE. NIFTY 50, NIFTY BANK, NIFTY 100, NIFTY COMMODITIES, NIFTY CONSUMPTION, NIFTY FIN SERVICE, NIFTY IT, NIFTY INFRA, NIFTY ENERGY, NIFTY FMCG, NIFTY AUTO, NIFTY 200, NIFTY ALPHA 50, NIFTY 500, NIFTY CPSE, NIFTY GS COMPSITE, NIFTY HEALTHCARE, NIFTY CONSR DURBL, NIFTY LARGEMID250, NIFTY INDIA MFG, NIFTY IND DIGITAL, INDIA VIX

File Information and Column Descriptions.

Nifty 50 index data with 1 minute data. The dataset contains OHLC (Open, High, Low, and Close) prices from Jan 2015 to Aug 2024. - This dataset can be used for time series analysis, regression problems, and time series forecasting both for one step and multi-step ahead in the future. - Options data can be integrated with this minute data, to get more insight about this data. - Different backtesting strategies can be built on this data.

File Information

• This dataset contains 6 files, each file contains nifty 50 data with different intervals.
• Different intervals are - 1 min, 3 min, 5 min, 15 min, and 1 hour, Daily data from intervals of 2015 Jan to 2024 August.

Column Descriptors

• Each file contains OHLC (Open, High, Low, and Close) prices and Data time information. Since these are Nifty 50 index data, so volume is not present.

Inspiration

Time series forecasting - Predict stock price

• Predict future stock price one step ahead and multi-step ahead in time.
• Use different time series forecasting techniques for forecasting the future stock price. ### Machine learning and Deep learning techniques
• Possible ML and DL models include Neural networks, RNNs, LSTMs, Transformers, Attention networks, etc.
• Different error functions can be considered like RMSE, MAE, RMSEP etc. ### Feature engineering
• Different augmented features can be created and that can be used for forecasting.
• Correlation analysis, Feature importance to justify the important features.

RMSE is used for comparison. Time is used for the training in seconds.

RMSE is used for comparison. Time is used for the training in seconds.

Japan's main stock market index, the JP225, rose to 49300 points on October 24, 2025, gaining 1.35% from the previous session. Over the past month, the index has climbed 7.75% and is up 30.03% compared to the same time last year, 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 October of 2025.

The dataset shows average of Share Price Indices and Market Capitalisation

Note: 1. Market capitalisation data are as at end-December up to 1987-88 and at end-March from 1988-89 onwards. 2. Compilation of RBI index was discontinued by the Reserve Bank of India since 1999-2000. Similarly, the compilation of data on the All-India market capitalisation was discontinued by Bombay Stock Exchange Limited (BSE), since 1999-2000. 3. BSE 100 Index introduced from October 14, 1996 was previously known as BSE National Index. BSE National Index (Base: 1983-84 = 100) comprised 100 stocks listed at five major stock exchanges in India - Mumbai, Calcutta, Delhi, Ahmedabad and Madras, while BSE 100 index into account only the prices of stocks listed at BSE. 4. BSE 100 index has been re-based as 1983-84=58 with effect from June 04, 2012. Since 2009-10, data is based on re-based index value of 58.

China's main stock market index, the SHANGHAI, rose to 3949 points on October 24, 2025, gaining 0.69% from the previous session. Over the past month, the index has climbed 2.49% and is up 19.69% compared to the same time last year, 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 October of 2025.

Net-Income-Applicable-To-Common-Shares Time Series for Nanto Bank Ltd. The Nanto Bank, Ltd., together with its subsidiaries, provides banking, securities, leasing, and credit guarantee services in Japan. It operates through two segments, Banking and Leasing. The company provides lending, bills discounting, and remittance; guarantee of debt; acceptance of deposits; and acceptance of bills and other services related to the banking business. It also offers underwriting and dealing in securities; over-the-counter derivative transactions; and other related services, including security index future transactions. In addition, the company provides credit guarantees, real estate leasing and management, software development, and credit cards and securities; and consulting services. The Nanto Bank, Ltd. was incorporated in 1934 and is headquartered in Nara, Japan.

The data set comes from our working paper "Tweet Sentiments and Stock Market: New Evidence from China", including the stock prices, number of stock-related tweets with different emotions at different days.It shows the closing price of Shanghai composite index (SHCI), volumes of Tweets with different sentiments and two indices based on the Tweets. The first column shows the time, covering the period of 2014/06/03-2014/12/31. The second column is the SHCI of each trading day. The 3rd-8th columns are the numbers of Tweets with different sentiments, including anger, joyful, disgust, fear and sadness. The 9th column is the number of Tweets with negative sentiments. The last two columns show the indices of Agreement and Bullishness.Please cite the paper: Yingying Xu, Zhixin Liu, Jichang Zhao and Chiwei Su. Weibo sentiments and stock return: A time- frequency view. PLoS ONE 12(7): e0180723, 2017.

France's main stock market index, the FR40, fell to 8226 points on October 24, 2025, losing 0.00% from the previous session. Over the past month, the index has climbed 5.52% and is up 9.71% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from France. France Stock Market Index (FR40) - values, historical data, forecasts and news - updated on October of 2025.

Israel's main stock market index, the TA-125, rose to 3277 points on October 23, 2025, gaining 0.69% from the previous session. Over the past month, the index has climbed 6.77% and is up 50.86% 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 October of 2025.

Graph and download economic data for CBOE Volatility Index: VIX (VIXCLS) from 1990-01-02 to 2025-10-21 about VIX, volatility, stock market, and USA.

The US_Stock_Data.csv dataset offers a comprehensive view of the US stock market and related financial instruments, spanning from January 2, 2020, to February 2, 2024. This dataset includes 39 columns, covering a broad spectrum of financial data points such as prices and volumes of major stocks, indices, commodities, and cryptocurrencies. The data is presented in a structured CSV file format, making it easily accessible and usable for various financial analyses, market research, and predictive modeling. This dataset is ideal for anyone looking to gain insights into the trends and movements within the US financial markets during this period, including the impact of major global events.

Key Features and Data Structure

The dataset captures daily financial data across multiple assets, providing a well-rounded perspective of market dynamics. Key features include:

• Commodities: Prices and trading volumes for natural gas, crude oil, copper, platinum, silver, and gold.
• Cryptocurrencies: Prices and volumes for Bitcoin and Ethereum, including detailed 5-minute interval data for Bitcoin.
• Stock Market Indices: Data for major indices such as the S&P 500 and Nasdaq 100.
• Individual Stocks: Prices and volumes for major companies including Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta.

The dataset’s structure is designed for straightforward integration into various analytical tools and platforms. Each column is dedicated to a specific asset's daily price or volume, enabling users to perform a wide range of analyses, from simple trend observations to complex predictive models. The inclusion of intraday data for Bitcoin provides a detailed view of market movements.

Applications and Usability

This dataset is highly versatile and can be utilized for various financial research purposes:

• Market Analysis: Track the performance of key assets, compare volatility, and study correlations between different financial instruments.
• Risk Assessment: Analyze the impact of commodity price movements on related stock prices and evaluate market risks.
• Educational Use: Serve as a resource for teaching market trends, asset correlation, and the effects of global events on financial markets.

The dataset’s daily updates ensure that users have access to the most current data, which is crucial for real-time analysis and decision-making. Whether for academic research, market analysis, or financial modeling, the US_Stock_Data.csv dataset provides a valuable foundation for exploring the complexities of financial markets over the specified period.

Acknowledgements:

This dataset would not be possible without the contributions of Dhaval Patel, who initially curated the US stock market data spanning from 2020 to 2024. Full credit goes to Dhaval Patel for creating and maintaining the dataset. You can find the original dataset here: US Stock Market 2020 to 2024.

Stock Price Time Series for Principal Financial Group Inc. Principal Financial Group, Inc. provides retirement, asset management, and insurance products and services to businesses, individuals, and institutional clients worldwide. The company operates through Retirement and Income Solutions, Principal Asset Management, and Benefits and Protection segments. The Retirement and Income Solutions segment provides retirement, and related financial products and services. This segment offers products and services for defined contribution plans, including 401(k) and 403(b) plans, defined benefit plans, nonqualified executive benefit plans, employee stock ownership plans, equity compensation, and pension risk transfer services; individual retirement accounts; investment only products; and mutual funds, individual variable annuities, registered index-linked annuities, and bank products, as well as trust and custody services. The Principal Asset Management segment provides equity, fixed income, real estate, and other alternative investments, as well as fund offerings. This segment also offers pension accumulation products and services, mutual funds, asset management, income annuities, and life insurance accumulation products, as well as voluntary savings plans. The Benefits and Protection segment provides specialty benefits, such as group dental and vision insurance, group life and other insurance, and group and individual disability insurance, as well as administers group dental, disability, and vision benefits; and individual life insurance products comprising universal, variable universal, indexed universal, and term life insurance products. This segment also offers insurance solutions for small and medium-sized businesses and their owners, as well as employees. The company was founded in 1879 and is based in Des Moines, Iowa.

Diluted-Average-Shares Time Series for Amalgamated Bank. Amalgamated Financial Corp. operates as the bank holding company for Amalgamated Bank that provides commercial and retail banking, investment management, and trust and custody services in the United States. The company accepts various deposit products, including non-interest-bearing accounts, interest-bearing demand products, savings accounts, money market accounts, NOW accounts, time deposits, and certificates of deposit. It also provides residential real estate mortgage, commercial and industrial, commercial real estate, multifamily mortgage, consumer solar, and consumer and other loans. In addition, the company offers online banking, bill payment, online cash management, safe deposit box rentals, debit card, and ATM card services; and trust, custody, and investment management services, including asset safekeeping, corporate actions, income collections, proxy services, account transition, asset transfers, and conversion management; investment products, such as index and actively-managed funds, which include equity, fixed-income, real estate, and alternative investments; and investment, brokerage, asset management, and insurance products. Amalgamated Financial Corp. was founded in 1923 and is headquartered in New York, New York.

SUMMARY & CONTEXTThis dataset aims to provide a comprehensive, rolling 20-year history of the constituent stocks and their corresponding weights in India's Nifty 50 index. The data begins on January 31, 2008, and is actively maintained with monthly updates. After hitting the 20-year mark, as new monthly data is added, the oldest month's data will be removed to maintain a consistent 20-year window. This dataset was developed as a foundational feature for a graph-based model analyzing the market structure of the Indian stock market. Unlike typical snapshots that only show the current 50 stocks, this dataset is a survivorship bias-free compilation that includes all stocks that have been part of the Nifty 50 index during this period. The data has been meticulously cleaned and adjusted for corporate actions, making it a robust feature set for financial analysis and quantitative modeling.DATA SOURCE & FREQUENCYPrimary Source: All raw data is sourced from the official historical data reports published by Nifty Indices (niftyindices.com), ensuring the highest level of accuracy.Data Frequency: The data is recorded on a monthly and event-driven basis. It includes end-of-month (EOM) weights but also captures intra-month data points for any date on which the Nifty 50 index was reshuffled or rebalanced. For periods between these data points, the weights can be considered static.METHODOLOGY & DATA INTEGRITYThe dataset was constructed based on official Nifty 50 rebalancing announcements. It relies on the observed assumption that on most reshuffles, the weights of stocks that aren’t being reshuffled stay almost the same before and after the change. Significant effort was made to handle exceptions and complex corporate actions:Corporate Actions: Adjustments were systematically made for major events like mergers (HDFC/HDFCBANK), demergers (Reliance/JIOFIN, ITC/ITCHOTELS), and dual listings (TATAMOTORS/TATAMTRDVR).Rebalancing Extrapolation: In cases where EOM weights did not align with beginning-of-month (BOM) realities post-reshuffle, a logarithmic-linear extrapolation method was used to estimate the weights of incoming/outgoing stocks.2013 Rebalancing Exception: For the second half rebalancing of 2013, due to significant discrepancies, all 50 stocks' weights were recalculated using the extrapolation method instead of carrying over previous values.Weight Normalization: On any given date, the sum of all 50 constituent weights is normalized to equal 100%. The weights are provided with a precision of up to 5 decimal places, and the sum for all observations is validated to a strict tolerance of 1e-6.TICKER & NAMING CONVENTIONSFor consistency across the time series, several historical stock tickers have been mapped to their modern or unified equivalents:INFOSYSTCH -> INFYHEROHONDA -> HEROMOTOCOBAJAJ-AUTO -> BAJAUTOSSTL -> VEDLREL -> RELINFRAZOMATO -> ETERNALCONTENTS & FILE STRUCTUREThis dataset is distributed as a collection of files. The primary data is contained in weights.csv, with several supplementary files provided for context, validation, and analysis.weights.csv: The main data file.Layout: This file is in a standard CSV format. The first row contains the headers, with DATE in the first column and stock tickers in the subsequent columns. Each row corresponds to a specific date.Values: The cells contain the stock's weight (as a percentage) in the Nifty 50 index on a given date. A value of 0 indicates the stock was not an index constituent at that time.sectors.csv: A helper file that maps each stock ticker to its corresponding industry sector.summary.csv: A simple summary file containing the first and last observed dates for each stock, along with a count of its non-zero weight observations.validate.py: A Python script to check weights.csv for data integrity issues (e.g., ensuring daily weights sum to 100).validation_report.txt: The output report generated by validate.py, showing the results of the latest data validation checks.analysis.ipynb: A Jupyter Notebook providing sample analyses that can be performed using this dataset, such as visualizing sector rotation and calculating HHI score over time.README.md: This file, containing the complete documentation for the dataset.CHANGELOG.md: A file for tracking all updates and changes made to the dataset over time.LICENSE.txt: The full legal text of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license, which is applicable to this dataset.POTENTIAL USE CASESAnalyzing historical sector rotation and weight concentration in the Indian market.Building features for quantitative models that aim to predict market movements.Backtesting investment strategies benchmarked against the Nifty 50.ACKNOWLEDGEMENTS & CITATIONThis dataset was created by Sukrit Bera. A permanent, versioned archive of this dataset is available on Figshare. If you use this dataset in your research, please use the following official citation, which includes the permanent DOI:Bera, S. (2025). Historical Nifty 50 Constituent Weights (Rolling 20-Year Window) [Data set]. figshare. https://doi.org/10.6084/m9.figshare.30217915LICENSINGThis dataset is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. The license selected in the metadata dropdown (CC BY 4.0) is the closest available option on this platform. The full terms of the applicable CC BY-NC-SA 4.0 license is available HERE, as well as in the uploaded LICENSE.txt file in the dataset. The CC BY-NC-SA 4.0 license DOES NOT permit commercial use. This dataset is FREE for academic and non-commercial research with attribution. If you wish to use this dataset for commercial purposes, please contact Sukrit Bera at sukritb2005@gmail.com to negotiate a separate, commercial license.DATA DICTIONARYColumn Name: DATEData Type: DateDescription: The date of the weight recording. This is the first column.Column Name: [Stock Ticker]Data Type: floatDescription: The percentage weight of the stock (e.g., 'RELIANCE', 'TCS') in the Nifty 50 index. A value of 0 indicates it was not an index constituent on that date.