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 50 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.

About Nifty 50

The NIFTY 50 index is a well-diversified 50 companies index reflecting overall market conditions. NIFTY 50 Index is computed using the free float market capitalization method.

NIFTY 50 can be used for a variety of purposes such as benchmarking fund portfolios, launching of index funds, ETFs and structured products.

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 33 GB (compressed here to 13 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 - 55 Technical indicator values are also present

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

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 | | | |

Global Stock Market Data. More than 150 pricing sources, including biggest world stock exchanges. Pay only for the stock exchanges, parameters or regions you need. Flexible in customizing our product to the customer's needs. Free test access as long as you need for integration. Reliable sources: stock exchanges and market participants. The cost depends on the amount of required parameters and re-distribution right.

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 August 31 of 2025.

This dataset offers both live (delayed) prices and End Of Day time series on equity options

1/ Live (delayed) prices for options on European stocks and indices including: Reference spot price, bid/ask screen price, fair value price (based on surface calibration), implicit volatility, forward Greeks : delta, vega Canari.dev computes AI-generated forecast signals indicating which option is over/underpriced, based on the holders strategy (buy and hold until maturity, 1 hour to 2 days holding horizon...). From these signals is derived a "Canari price" which is also available in this live tables.
Visit our website (canari.dev ) for more details about our forecast signals.

The delay ranges from 15 to 40 minutes depending on underlyings.

2/ Historical time series: Implied vol Realized vol Smile Forward
See a full API presentation here : https://youtu.be/qitPO-SFmY4 .

These data are also readily accessible in Excel thanks the provided Add-in available on Github: https://github.com/canari-dev/Excel-macro-to-consume-Canari-API

If you need help, contact us at: contact@canari.dev

User Guide: You can get a preview of the API by typing "data.canari.dev" in your web browser. This will show you a free version of this API with limited data.

Here are examples of possible syntaxes:

For live options prices: data.canari.dev/OPT/DAI data.canari.dev/OPT/OESX/0923 The "csv" suffix to get a csv rather than html formating, for example: data.canari.dev/OPT/DB1/1223/csv For historical parameters: Implied vol : data.canari.dev/IV/BMW

data.canari.dev/IV/ALV/1224

data.canari.dev/IV/DTE/1224/csv

Realized vol (intraday, maturity expressed as EWM, span in business days): data.canari.dev/RV/IFX ... Implied dividend flow: data.canari.dev/DIV/IBE ... Smile (vol spread between ATM strike and 90% strike, normalized to 1Y with factor 1/√T): data.canari.dev/SMI/DTE ... Forward: data.canari.dev/FWD/BNP ...

List of available underlyings: Code Name OESX Eurostoxx50 ODAX DAX OSMI SMI (Swiss index) OESB Eurostoxx Banks OVS2 VSTOXX ITK AB Inbev ABBN ABB ASM ASML ADS Adidas AIR Air Liquide EAD Airbus ALV Allianz AXA Axa BAS BASF BBVD BBVA BMW BMW BNP BNP BAY Bayer DBK Deutsche Bank DB1 Deutsche Boerse DPW Deutsche Post DTE Deutsche Telekom EOA E.ON ENL5 Enel INN ING IBE Iberdrola IFX Infineon IES5 Intesa Sanpaolo PPX Kering LOR L Oreal MOH LVMH LIN Linde DAI Mercedes-Benz MUV2 Munich Re NESN Nestle NOVN Novartis PHI1 Philips REP Repsol ROG Roche SAP SAP SNW Sanofi BSD2 Santander SND Schneider SIE Siemens SGE Société Générale SREN Swiss Re TNE5 Telefonica TOTB TotalEnergies UBSN UBS CRI5 Unicredito SQU Vinci VO3 Volkswagen ANN Vonovia ZURN Zurich Insurance Group

London Stock Exchange stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Open Text stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

In this paper, we explore the problem of establishing a network among the stocks of a market at high frequency level and give an application to program trading. Our work uses high frequency data from the National Stock Exchange, India, for the year 2014. To begin, we analyse the spectrum of the correlation matrix to establish the presence of linear relations amongst the stock returns. A comparison of correlations with pairwise mutual information shows the further existence of non-linear relations which are not captured by correlation. We also see that the non-linear relations are more pronounced at the high frequency level in comparison to the daily returns used in earlier work. We provide two applications of this approach. First, we construct minimal spanning trees for the stock network based on mutual information and study their topology. The year 2014 saw the conduct of general elections in India and the data allows us to explore their impact on aspects of the network, such as the scale-free property and sectorial clusters. Second, having established the presence of non-linear relations, we would like to be able to exploit them. Previous authors have suggested that peripheral stocks in the network would make good proxies for the Markowitz portfolio but with a much smaller number of stocks. We show that peripheral stocks selected using mutual information perform significantly better than ones selected using correlation.

Australian Securities Exchange stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

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 September 1 of 2025.

Projekt 1: The study conducts an online experiment employing a 2x2+1 between-subjects design. Participants assume the role of investors and have to invest a budget in two firms. For this purpose, the participants receive 1,000 coins they have to invest in two firms in steps of 1 coin. Before ending the experiment, participants answer follow-up questions. Participants are recruited from the crowdsourcing platform Prolific. Projekt 2: The study uses intraday stock returns in 5-minute intervals taken from kibot.com. Daily and monthly returns are taken from CRSP and data on company fundamentals from Compustat. Risk-free rates, the market factor, as well as the Fama-French factors are taken from the data library of Kenneth French. In addition, the study uses options data provided by Historical Option Data. Projekt 3: Monthly returns are taken from CRSP. Risk-free rates and the market factor are obtained from Kenneth French's data library. The study uses options data, which are sourced from Historical Option Data. In addition, the study employs firm characteristics, which can be downloaded from openassetpricing.com.

Tesla stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Prices for US 100 Tech Index including live quotes, historical charts and news. US 100 Tech Index was last updated by Trading Economics this September 1 of 2025.

An example of TRTH intraday top-of-book transaction data for a single Johannesburg Stock Exchange (JSE) listed equity. The data is for teaching, learning and research projects sourced from the legacy Tick History v1 SOAP API interface from https://tickhistory.thomsonreuters.com/TickHistory in May 2016. Related raw data and similar data-structures can now be accessed using Tick History v2 and the REST API https://hosted.datascopeapi.reuters.com/RestApi/v1.

Configuration control: the test dataset contains 16 CSV files with names: "

Attributes: The data set is for the ticker: AGLJ.J from May 2010 until May 2016. The files include the following attributes: RIC, Local Date-Time, Event Type, Price at the Event, Volume at the Event, Best Bid Changes, Best Ask Changes, and Trade Event Sign: RIC, DateTimeL, Type, Price, Volume, L1 Bid, L1 Ask, Trade Sign. The Local Date-Time (DateTimeL) is a serial date number where 1 corresponds to Jan-1-0000, for example, 736333.382013 corresponds to 4-Jan-2016 09:10:05 (or 20160104T091005 in ISO 8601 format). The trade event sign (Trade Sign) indicates whether the transaction was buyer (or seller) initiated as +1 (-1) and was prepared using the method of Lee and Ready (2008).

Disclaimer: The data is not up-to-date, is incomplete, it has been pre-processed; as such it is not fit for any other purpose than teaching and learning, and algorithm testing. For complete, up-to-date, and error-free data please use the Tick History v2 interface directly.

Research Objectives: The data has been used to build empirical evidence in support of hierarchical causality and universality in financial markets by considering price impact on different time and averaging scales, feature selection on different scales as inputs into scale dependent machine learning applications, and for various aspects of agent-based model calibration and market ecology studies on different time and averaging scales.

Acknowledgements to: Diane Wilcox, Dieter Hendricks, Michael Harvey, Fayyaaz Loonat, Michael Gant, Nicholas Murphy and Donovan Platt.

Snapshot img

Electricity Trading Market Size 2025-2029

The electricity trading market size is forecast to increase by USD 123.5 billion at a CAGR of 6.5% between 2024 and 2029.

The market is witnessing significant growth due to several key trends. The integration of renewable energy sources, such as solar panels and wind turbines, into the grid is a major driver. Energy storage systems are increasingly being adopted to ensure a stable power supply from these intermittent sources. Concurrently, the adoption of energy storage systems addresses key challenges like intermittency, enabling better integration of renewable sources, and bolstering grid resilience. Self-generation of electricity by consumers through microgrids is also gaining popularity, allowing them to sell excess power back to the grid. The entry of new players and collaborations among existing ones are further fueling market growth. These trends reflect the shift towards clean energy and the need for a more decentralized and efficient electricity system.

What will be the Size of the Electricity Trading Market During the Forecast Period?

Request Free Sample

The market, a critical component of the global energy industry, functions as a dynamic interplay between wholesale energy markets and traditional financial markets. As a commodity, electricity is bought and sold through various trading mechanisms, including equities, bonds, and real-time auctions. The market's size and direction are influenced by numerous factors, such as power station generation data, system operator demands, and consumer usage patterns. Participants in the market include power station owners, system operators, consumers, and ancillary service providers. Ancillary services, like frequency regulation and spinning reserves, help maintain grid stability. Market design and news reports shape the market's evolution, with initiatives like the European Green Paper and the Lisbon Strategy influencing the industry's direction towards increased sustainability and competition.
Short-term trading, through power purchase agreements and power distribution contracts, plays a significant role in the market's real-time dynamics. Power generation and power distribution are intricately linked, with the former influencing the availability and price of electricity, and the latter affecting demand patterns. Overall, the market is a complex, ever-evolving system that requires a deep understanding of both energy market fundamentals and financial market dynamics.

How is this Electricity Trading Industry segmented and which is the largest segment?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Day-ahead trading
  Intraday trading


Application

  Industrial
  Commercial
  Residential


Source

  Non-renewable energy
  Renewable energy


Geography

  Europe

    Germany
    UK
    France
    Italy
    Spain


  APAC

    China
    India
    Japan
    South Korea


  North America

    US


  South America



  Middle East and Africa

By Type Insights

The day-ahead trading segment is estimated to witness significant growth during the forecast period.

Day-ahead trading refers to the voluntary, financially binding forward electricity trading that occurs in exchanges such as the European Power Exchange (EPEX Spot) and Energy Exchange Austria (EXAA), as well as through bilateral contracts. This process involves sellers and buyers agreeing on the required volume of electricity for the next day, resulting in a schedule for everyday intervals. However, this schedule is subject to network security constraints and adjustments for real-time conditions and actual electricity supply and demand. Market operators, including ISOs and RTOs, oversee these markets and ensure grid reliability through balancing and ancillary services. Traders, including utilities, energy providers, and professional and institutional traders, participate in these markets to manage price risk, hedge against price volatility, and optimize profitability.

Key factors influencing electricity prices include weather conditions, fuel prices, availability, construction costs, and physical factors. Renewable energy sources, such as wind and solar power, also play a growing role in these markets, with the use of Renewable Energy Certificates and net metering providing consumer protection and incentives for homeowners and sustainable homes. Electricity trading encompasses power generators, power suppliers, consumers, and system operators, with contracts, generation data, and power station dispatch governed by market rules and regulations.

Get a glance at the Electricity Trading Industry report of share of various segments Request Free Sample

The day-ahead trading

Live intraday expected move chart with overlayed 0DTE option strategies and profit calculator for SPY, QQQ & SPX.

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