System imbalance prices applied if an imbalance is found between injections and offtakes in a balance responsible parties (BRPs) balance area. When imbalance prices are published on an one minute basis, the published prices have not yet been validated and can therefore only be used as an indication of the imbalance price.Only after the published prices have been validated can they be used for invoicing purposes. Contains the historical data and is refreshed daily.This dataset contains data until 21/05/2024 (before MARI local go-live).

Imbalance prices applied for balance responsible parties (BRPs) settlemnt. One minute imbalance prices are published as fast as possible and are never validated. The 1 min prices give an indication for the final imabalnce price of the ISP (imbalance settlement period which is 15 min). Contains the historical data and is refreshed daily.This dataset contains data from 22/05/2024 (MARI local go-live) on.

Context

nifty50.csv The NIFTY 50 index is National Stock Exchange of India's benchmark stock market index for Indian equity market. It is a well diversified 50 stock index accounting for 22 sectors of the economy. It is used for a variety of purposes such as bench-marking fund portfolios, index based derivatives and index funds.

banknifty.csv Bank Nifty represents the 12 most liquid and large capitalized stocks from the banking sector which trade on the National Stock Exchange (NSE). It provides investors and market intermediaries a benchmark that captures the capital market performance of Indian banking sector.

Content

A data frame with 8 variables: index, date, time, open, high, low, close and id. For each year from 2013 to 2016, the number of trading data of each minute of given each date. The currency of the price is Indian Rupee (INR).

• index : market id
• date: numerical value (Ex. 20121203- to be converted to 2012/12/03)
• time: factor (Ex. 09:16)
• open: numeric (opening price)
• high: numeric (high price)
• low: numeric (low price)
• close: numeric (closing price)

Inspiration

Initial raw data sets are very complex and mixed datatypes. These are processed properly using R libraries like dplyr, stringr and other data munging packages. The desired outputs are then converted into a CSV format to use for further analysis.

Dataset Information

This dataset includes minute bar price data for various stocks.

  Instruments Included

7000+ US Stocks

  Dataset Columns

symbol: The symbol of the stock. datetime: The date and time of the data. adj_open: The adjusted opening price of the stock. adj_high: The adjusted highest price of the stock. adj_low: The adjusted lowest price of the stock. adj_close: The adjusted closing price of the stock. volume: The volume of the stock.… See the full description on the dataset page: https://huggingface.co/datasets/paperswithbacktest/Stocks-1Min-Price.

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

Context

The dataset contains prices and volumes for different stocks

Here is an example: cat 201801_Amsterdam_AALB_NoExpiry.txt

01/02/2018,09:01:00, 42.39, 42.39, 42.21, 42.21, 737 01/02/2018,09:02:00, 42.28, 42.28, 42.27, 42.27, 277 01/02/2018,09:04:00, 42.24, 42.24, 42.24, 42.24, 177 01/02/2018,09:05:00, 42.23, 42.23, 42.22, 42.22, 1543 01/02/2018,09:06:00, 42.23, 42.23, 42.23, 42.23, 241

The dataset contains trading data for 2182 unique stocks, on 40 unique stock exchanges. The monthly data is provided by stocks with each stock being associated with a specific stock exchange and is initially stored in the .txt format. Each file contains a trading history of a stock in a particular month and has the following schema.

Content

The columns correspond to the following schema:

• Date (Calendar Year 2018)
• Time (in CET timezone) The following 5 columns describe the data, that came in until the last minute
• Opening price (The first price within this minute)
• Highest price (The highest price within this minute)
• Lowest price (The lowest price within this minute)
• Closing price (the last price within this minute)
• Sum of the volume of all transactions within this minute

Dataset is a zipped file of stocks from many stock markets and forex. It covers the whole of 2018. Notice the following: 1. All mentioned timestamps are CET. 2. There are missing records and irregularities on the updates – see the previous example. You need to decide how to handle the missing values/records. 3. Different stocks have different update frequencies.

These are matlab files. For the ShangaiMinuteDataPart1 to ShangaiMinuteDataPart6, the first column represents year, the second one represents month, the third one represents date, the forth one represents hour, the fifth one represents minute, the others represents different stocks, the code of which is in ShangaiMinuteDataStockName.mat. The data for stocks and index are from Jul. 27, 1999 to Nov. 5, 2003.

The Nifty 50 is an index that represents the performance of the top 50 companies listed on the National Stock Exchange (NSE) in India. The dataset you mentioned includes 3-minute interval data for the Nifty 50 index from January 2015 to October 2022, with the date and time combined in a single "Date" column.

Each entry in the dataset represents a 3-minute interval and includes the following information:

1. date: The specific date and time corresponding to the 3-minute interval.
2. open: The opening price of the Nifty 50 index during that 3-minute period.
3. high: The highest price reached by the Nifty 50 index during that 3-minute period.
4. low: The lowest price reached by the Nifty 50 index during that 3-minute period.
5. close: The closing price of the Nifty 50 index during that 3-minute period. 6.volume: NA

This dataset with combined date and time information in a single "Date" column can still be utilized for scalping strategies and general analysis purposes. Traders and analysts can analyze the open, high, low, and close prices at each 3-minute interval, along with the associated date and time, to identify short-term trends, measure volatility, and determine potential entry and exit points for trades.

Additionally, traders and analysts can perform technical analysis, identify patterns, and develop trading strategies based on this dataset. They can apply various technical indicators, such as moving averages, oscillators, and trend lines, to gain insights into market dynamics and make informed trading decisions, considering the combined date and time information for each 3-minute interval.

By studying the Nifty 50 3-minute dataset, traders and analysts can gain a deeper understanding of the price behavior of the top 50 companies in the Indian stock market. The dataset spans from January 2015 to October 2022, allowing for comprehensive analysis of historical trends, patterns, and market movements. The combined date and time information in the "Date" column provides a reference for each 3-minute interval, enabling traders and analysts to refine their trading strategies, enhance decision-making processes, and potentially extract valuable insights for successful trading.

Context

NIFTY 500 is India’s first broad-based stock market index of the Indian stock market. It contains the top 500 listed companies on the NSE. The NIFTY 500 index represents about 96.1% of free-float market capitalization and 96.5% of the total turnover on the National Stock Exchange (NSE).

NIFTY 500 companies are disaggregated into 72 industry indices. Industry weights in the index reflect industry weights in the market. For example, if the banking sector has a 5% weight in the universe of stocks traded on the NSE, banking stocks in the index would also have an approximate representation of 5% in the index. NIFTY 500 can be used for a variety of purposes such as benchmarking fund portfolios, launching index funds, ETFs, and other structured products.

• Other Notable Indices -
  • NIFTY 50: Top 50 listed companies on the NSE. A diversified 50-stock index accounting for 13 sectors of the Indian economy.
  • NIFTY Next 50: Also called NIFTY Juniors. Represents 50 companies from NIFTY 100 after excluding the NIFTY 50 companies.
  • NIFTY 100: Diversified 100 stock index representing major sectors of the economy. NIFTY 100 represents the top 100 companies based on full market capitalization from NIFTY 500.
  • NIFTY 200: Designed to reflect the behavior and performance of large and mid-market capitalization companies.

Content

The dataset comprises various parameters and features for each of the NIFTY 500 Stocks, including Company Name, Symbol, Industry, Series, Open, High, Low, Previous Close, Last Traded Price, Change, Percentage Change, Share Volume, Value in Indian Rupee, 52 Week High, 52 Week Low, 365 Day Percentage Change, and 30 Day Percentage Change.

Dataset Glossary (Column-Wise)

Company Name: Name of the Company.

Symbol: A stock symbol is a unique series of letters assigned to a security for trading purposes.

Industry: Name of the industry to which the stock belongs.

Series: EQ stands for Equity. In this series intraday trading is possible in addition to delivery and BE stands for Book Entry. Shares falling in the Trade-to-Trade or T-segment are traded in this series and no intraday is allowed. This means trades can only be settled by accepting or giving the delivery of shares.

Open: It is the price at which the financial security opens in the market when trading begins. It may or may not be different from the previous day's closing price. The security may open at a higher price than the closing price due to excess demand for the security.

High: It is the highest price at which a stock is traded during the course of the trading day and is typically higher than the closing or equal to the opening price.

Low: Today's low is a security's intraday low trading price. Today's low is the lowest price at which a stock trades over the course of a trading day.

Previous Close: The previous close almost always refers to the prior day's final price of a security when the market officially closes for the day. It can apply to a stock, bond, commodity, futures or option co-contract, market index, or any other security.

Last Traded Price: The last traded price (LTP) usually differs from the closing price of the day. This is because the closing price of the day on NSE is the weighted average price of the last 30 mins of trading. The last traded price of the day is the actual last traded price.

Change: For a stock or bond quote, change is the difference between the current price and the last trade of the previous day. For interest rates, change is benchmarked against a major market rate (e.g., LIBOR) and may only be updated as infrequently as once a quarter.

Percentage Change: Take the selling price and subtract the initial purchase price. The result is the gain or loss. Take the gain or loss from the investment and divide it by the original amount or purchase price of the investment. Finally, multiply the result by 100 to arrive at the percentage change in the investment.

Share Volume: Volume is an indicator that means the total number of shares that have been bought or sold in a specific period of time or during the trading day. It will also involve the buying and selling of every share during a specific time period.

Value (Indian Rupee): Market value—also known as market cap—is calculated by multiplying a company's outstanding shares by its current market price.

52-Week High: A 52-week high is the highest share price that a stock has traded at during a passing year. Many market aficionados view the 52-week high as an important factor in determining a stock's current value and predicting future price movement. 52-week High prices are adjusted for Bonus, Split & Rights Corporate actions.

52-Week Low: A 52-week low is the lowest ...

According to Cognitive Market Research, the global stock market size will be USD 3645.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 13% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 1458.1 million in 2024 and will grow at a compound annual growth rate (CAGR) of 11.2% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 1093.6 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 838.4 million in 2024 and will grow at a compound annual growth rate (CAGR) of 15% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 182.3 million in 2024 and will grow at a compound annual growth rate (CAGR) of 12.4% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 72.9 million in 2024 and will grow at a compound annual growth rate (CAGR) of 12.7% from 2024 to 2031.
The broker end users held the highest stock market revenue share in 2024.

Market Dynamics of Stock Market

Key Drivers for the Stock Market

Rising Demand for Real-Time Data and Analytics to be an Emerging Market Trend

The increasing need for real-time data and advanced analytics is a significant driver in the stock trading and investing market growth. Investors and traders require up-to-the-minute information on stock prices, market trends, and financial news to make informed decisions quickly. As financial markets become more dynamic and competitive, the ability to access and analyze real-time data becomes crucial for success. Trading applications that offer real-time updates, advanced charting tools, and detailed analytics provide users with a competitive edge by enabling them to react swiftly to market movements. This heightened demand for real-time insights fuels the development and adoption of sophisticated trading platforms that cater to both professional traders and retail investors seeking to maximize their investment opportunities.

Increasing Adoption of Mobile Trading Platforms to Boost Market Growth

The rapid adoption of mobile trading platforms is another key driver for the stock market expansion. With the proliferation of smartphones and mobile internet access, investors are increasingly favoring mobile platforms for their trading activities due to their convenience and accessibility. Mobile trading apps offer users the ability to trade, monitor portfolios, and access financial information on the go, which appeals to both active traders and casual investors. This shift towards mobile platforms is supported by innovations in-app functionality, user experience, and security features. As more investors seek flexibility and real-time engagement with their investments, the demand for sophisticated and user-friendly mobile trading applications continues to rise, propelling market growth.

Restraint Factor for the Stock Market

Stringent Rules and Regulations to Impede the Adoption of Online Trading Platforms

Regulatory compliance and legal challenges are major restraints for the stock trading and investing market share. The financial industry is heavily regulated, with strict rules governing trading practices, data protection, and financial disclosures. Compliance with these regulations requires substantial investment in legal expertise, technology, and administrative processes. Changes in regulations can also introduce uncertainty and additional compliance costs for application providers. For example, regulations such as the Markets in Financial Instruments Directive II (MiFID II) in Europe and the Dodd-Frank Act in the U.S. impose stringent requirements on trading practices and transparency. Failure to adhere to these regulations can result in legal penalties and damage to a company’s reputation, which can inhibit market growth and innovation in trading applications.

Market Volatility and Investor Uncertainty

The stock market is highly sensitive to global economic conditions, geopolitical tensions, interest rate fluctuations, and unexpected events (such as pandemics or wars). This inherent volatility can lead to sharp declines in investor confidence and capital outflows, especially among retai...

Prix du déséquilibre appliqués aux parties responsables de l'équilibre (BRP). Les prix de déséquilibre d'une minute sont publiés le plus rapidement possible et ne sont jamais validés. Les prix de 1 min donnent une indication pour le prix final de l'imabalnce du FAI (période de règlement du déséquilibre qui est de 15 min). Contient les données historiques et est actualisé quotidiennement.Cet ensemble de données contient des données du 22/05/2024 (MARI local go-live) sur.

Last Update - 9th FEB 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

THIS IS THE LARGEST DATASET ON NIFTY 100 STOCKS WITH EACH MINUTES AND DAILY DATA (2015 to 2025)

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 | | | |

1. The series of files named as ‘*_polarity.csv’ in folder ‘polarity’ includes the trading polarities of stocks listed on Shenzhen Stock Exchange from May 4 to July 31 2015. The eight numbers in the filenames specify the dates. The columns of these dataframes indicate the stock names, while the indices of dataframes indicate the time. The granularity of trading polarity is 1 minute for every stock. These trading polarities are calculated from the serial numbers for buyers and sellers in transactions data. The original transactions data is not publicly available due to the company’s license requirement.2. The files in the 'log_ret' folder cover the log returns of 1646 stocks listed on Shenzhen Stock Exchange from May 4 to July 31 2015. These data are calculated from the intraday price trends data provided by Thomson Reuters’ Tick History. The original price trends data is not publicly available due to the company’s license requirement.3. The file named as "stock_market_value.csv" gives the capitalization of stocks in June 31 2015, which is downloaded from Wind Information and we have converted the unit of measure from RMB into a dollar. Due to license requirements of the data companies, all of the above files have converted the names of stocks into integers in a consistent way. 4. Please cite the following paper:Shan Lu, Jichang Zhao and Huiwen Wang. Trading Imbalance in Chinese Stock Market—A High-Frequency View. Entropy, 2020, 22(8), 897.

Los precios de desequilibrio del sistema se aplican si se detecta un desequilibrio entre las inyecciones y las salidas en una zona de equilibrio de las partes responsables del equilibrio. Cuando los precios de desequilibrio se publican sobre una base de un minuto, los precios publicados aún no han sido validados y, por lo tanto, solo pueden utilizarse como indicación del precio de desequilibrio.Solo después de que se hayan validado los precios publicados, pueden utilizarse con fines de facturación. Contiene los datos históricos y se actualiza diariamente.

The Importance of Cryptocurrencies and the Impact of Prediction Projects

Cryptocurrencies have become one of the most groundbreaking innovations in the financial world in recent years. With their decentralized structure, transparency, and security features, they offer new opportunities for individuals and businesses alike. Leading cryptocurrencies like Bitcoin are not only investment vehicles but also catalysts for change in the global economy.

This dataset contains minute-level detailed information necessary for analyzing and predicting Bitcoin price movements. The volatile nature of cryptocurrencies amplifies the importance of developing accurate prediction models. Investors and analysts can use such data to develop various projects aimed at understanding market trends, minimizing risks, and making more informed decisions.

These projects include price prediction with machine learning models, trading strategies supported by technical indicators, and the development of risk management systems for long-term investments. AI-driven approaches, in particular, hold the potential to provide more effective and customizable solutions for both individual and institutional users.

Opening Time: The timestamp for when the candlestick (price data) begins.

Open : The price at which the first trade occurred in this time period.

High : The highest price reached during this time period.

Low : The lowest price reached during this time period.

Close : The price at which the last trade occurred in this time period.

Volume : The total amount of the base asset (e.g., Bitcoin) traded in this time period.

Quote Asset Volume : he total amount of the quote asset (e.g., USDT) traded in this time period.

Number of Trades : The total number of trades executed in this time period.

Taker Buy Base Asset Volume : The amount of the base asset bought via taker trades (market orders).

Taker Buy Quote Asset Volume : The amount of the quote asset spent in taker trades (market orders).

The Shanghai Composite Index (SSE), as a representative composite index of listed companies on the Shanghai Stock Exchange, is a core observation indicator of the systematic risk and price discovery mechanism in China's capital market. It includes various industries such as finance, energy, and industry, and can effectively depict the overall dynamic changes of the market This study selected intraday high-frequency data from January 2, 2024 to December 31, 2024. In order to accurately capture tail extreme events (such as liquidity shocks or policy driven jump risks) and overcome the discontinuity problem caused by low-frequency sampling, a balanced data frequency with 5-minute intervals was adopted The final dataset covers 48 observation points for each trading day, obtaining a total of 11656 observations of index returns within effective days Meanwhile, Monetary policy and real estate policy are the core tools of macroeconomic regulation. The former directly affects market liquidity, interest rates, and financing costs, while the latter, as a pillar industry of China's economy, directly affects market stability. Therefore, this article takes the release of information on monetary policy and real estate policy as representative events of macroeconomic policy, and adopts the event study method (Sorescu et al. (2017)) to ultimately determine 25 positive policies and 16 negative policies The price data of the Shanghai Composite Index was purchased from the financial data service of Jinshu Source（ http://www.jinshuyuan.net/pdt/196 ）, the monetary policy announcement was collected from the official website of the People's Bank of China（ http://www.pbc.gov.cn/zhengcehuobisi ）The real estate regulation policy documents are integrated from China Real Estate Network（ http://m.fangchan.com/data ).

Cryptocurrency historical datasets from January 2012 (if available) to October 2021 were obtained and integrated from various sources and Application Programming Interfaces (APIs) including Yahoo Finance, Cryptodownload, CoinMarketCap, various Kaggle datasets, and multiple APIs. While these datasets used various formats of time (e.g., minutes, hours, days), in order to integrate the datasets days format was used for in this research study. The integrated cryptocurrency historical datasets for 80 cryptocurrencies including but not limited to Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), Tether (USDT), Ripple (XRP), Solana (SOL), Polkadot (DOT), USD Coin (USDC), Dogecoin (DOGE), Tron (TRX), Bitcoin Cash (BCH), Litecoin (LTC), EOS (EOS), Cosmos (ATOM), Stellar (XLM), Wrapped Bitcoin (WBTC), Uniswap (UNI), Terra (LUNA), SHIBA INU (SHIB), and 60 more cryptocurrencies were uploaded in this online Mendeley data repository. Although the primary attribute of including the mentioned cryptocurrencies was the Market Capitalization, a subject matter expert i.e., a professional trader has also guided the initial selection of the cryptocurrencies by analyzing various indicators such as Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), MYC Signals, Bollinger Bands, Fibonacci Retracement, Stochastic Oscillator and Ichimoku Cloud. The primary features of this dataset that were used as the decision-making criteria of the CLUS-MCDA II approach are Timestamps, Open, High, Low, Closed, Volume (Currency), % Change (7 days and 24 hours), Market Cap and Weighted Price values. The available excel and CSV files in this data set are just part of the integrated data and other databases, datasets and API References that was used in this study are as follows: [1] https://finance.yahoo.com/ [2] https://coinmarketcap.com/historical/ [3] https://cryptodatadownload.com/ [4] https://kaggle.com/philmohun/cryptocurrency-financial-data [5] https://kaggle.com/deepshah16/meme-cryptocurrency-historical-data [6] https://kaggle.com/sudalairajkumar/cryptocurrencypricehistory [7] https://min-api.cryptocompare.com/data/price?fsym=BTC&tsyms=USD [8] https://min-api.cryptocompare.com/ [9] https://p.nomics.com/cryptocurrency-bitcoin-api [10] https://www.coinapi.io/ [11] https://www.coingecko.com/en/api [12] https://cryptowat.ch/ [13] https://www.alphavantage.co/ This dataset is part of the CLUS-MCDA (Cluster analysis for improving Multiple Criteria Decision Analysis) and CLUS-MCDAII Project: https://aimaghsoodi.github.io/CLUSMCDA-R-Package/ https://github.com/Aimaghsoodi/CLUS-MCDA-II https://github.com/azadkavian/CLUS-MCDA

Context:

This dataset is an extra updating dataset for the G-Research Crypto Forecasting competition.

Introduction

This is a daily updated dataset, automaticlly collecting market data for G-Research crypto forecasting competition. The data is of the 1-minute resolution, collected for all competition assets and both retrieval and uploading are fully automated. see discussion topic.

The Data

For every asset in the competition, the following fields from Binance's official API endpoint for historical candlestick data are collected, saved, and processed.

1. **timestamp** - A timestamp for the minute covered by the row.
2. **Asset_ID** - An ID code for the cryptoasset.
3. **Count** - The number of trades that took place this minute.
4. **Open** - The USD price at the beginning of the minute.
5. **High** - The highest USD price during the minute.
6. **Low** - The lowest USD price during the minute.
7. **Close** - The USD price at the end of the minute.
8. **Volume** - The number of cryptoasset u units traded during the minute.
9. **VWAP** - The volume-weighted average price for the minute.
10. **Target** - 15 minute residualized returns. See the 'Prediction and Evaluation section of this notebook for details of how the target is calculated.
11. **Weight** - Weight, defined by the competition hosts [here](https://www.kaggle.com/cstein06/tutorial-to-the-g-research-crypto-competition)
12. **Asset_Name** - Human readable Asset name.

Indexing

The dataframe is indexed by timestamp and sorted from oldest to newest. The first row starts at the first timestamp available on the exchange, which is July 2017 for the longest-running pairs.

Usage Example

The following is a collection of simple starter notebooks for Kaggle's Crypto Comp showing PurgedTimeSeries in use with the collected dataset. Purged TimesSeries is explained here. There are many configuration variables below to allow you to experiment. Use either GPU or TPU. You can control which years are loaded, which neural networks are used, and whether to use feature engineering. You can experiment with different data preprocessing, model architecture, loss, optimizers, and learning rate schedules. The extra datasets contain the full history of the assets in the same format as the competition, so you can input that into your model too.

Baseline Example Notebooks:

• Neural Network Starter
• LightGBM Starter
• Catboost Starter
• XGBoost Starter
• TabNet Starter
• Reinforcement Learning (PPO) Starter

These notebooks follow the ideas presented in my "Initial Thoughts" here. Some code sections have been reused from Chris' great (great) notebook series on SIIM ISIC melanoma detection competition here

Loose-ends:

This is a work in progress and will be updated constantly throughout the competition. At the moment, there are some known issues that still needed to be addressed:

• VWAP: - At the moment VWAP calculation formula is still unclear. Currently the dataset uses an approximation calculated from the Open, High, Low, Close, Volume candlesticks. [Waiting for competition hosts input]
• Target Labeling: There exist some mismatches to the original target provided by the hosts at some time intervals. On all the others - it is the same. The labeling code can be seen here. [Waiting for competition hosts] input]
• Filtering: No filtration of 0 volume data is taken place.

Example Visualisations

Opening price with an added indicator (MA50): https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234678%2Fb8664e6f26dc84e9a40d5a3d915c9640%2Fdownload.png?generation=1582053879538546&alt=media" alt="">

Volume and number of trades: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234678%2Fcd04ed586b08c1576a7b67d163ad9889%2Fdownload-1.png?generation=1582053899082078&alt=media" alt="">

License

This data is being collected automatically from the crypto exchange Binance.

According to Cognitive Market Research, The Global Automatic Weigh-pricing Machines market size will grow at a compound annual growth rate (CAGR) of 3.9% from 2023 to 2030.

The growing retail and manufacturing sectors are rising due to increasing retail trade.
Demand for 100 packs/minute remains higher for the Automatic Weigh-pricing Machines Market.
The food processing held the highest revenue share in 2023 for the Automatic Weigh-pricing Machines Market.
Asia Pacific will continue to lead, whereas the North American Automatic Weigh-pricing Machines Market will experience the strongest growth until 2030.

Technological Advancements and Automation Integration to Augment Market Value

The functionality of weigh-pricing machines has evolved from simple mechanical devices to sophisticated automated systems thanks to the quick development of technology. Thanks to IoT integration, this equipment can connect to the internet and communicate with other systems and devices, making it easier to exchange real-time data and conduct remote monitoring. Organizations can reduce operational inefficiencies by centralizing management, updating pricing data remotely, and properly tracking inventory levels. Additionally, these devices can adjust to dynamic pricing schemes based on variables like demand, the time of day, and even competition pricing, thanks to machine learning and AI algorithms. This gives companies the option to maximize profits while maintaining competitive pricing. AI-driven analytics can also offer information on customer behavior trends, assisting companies in making wise choices on product placement, marketing, and inventory control.

Market Dynamics of Automatic Weigh-pricing Machines

Initial Investment Costs and Return on Investment (ROI) Concerns to Hinder Market Growth

The significant initial capital outlay necessary for purchasing and operating these cutting-edge technologies is one prominent barrier impeding the market for automatic weigh-pricing machines. Businesses, particularly small and medium-sized organizations (SMEs), may face financial difficulties when using automatic weigh-pricing equipment. The machines' up-front expenditures, the price of integrating them with the current infrastructure, and staff training costs may be substantial. Additionally, although enhanced efficiency and accuracy are among the long-term advantages, some firms can be hesitant due to worries about the time frame for obtaining a sufficient Return on Investment (ROI). The adoption rate of automatic weigh-pricing devices may be slowed by these investment-related concerns, especially among businesses concerned about their bottom line.

Impact of COVID–19 on the Automatic Weigh-pricing Machines Market

The sales of automatic weigh-pricing machines were considerably impacted by COVID-19. Increased hygiene and safety concerns brought on by the pandemic accelerated the need for touchless and automated operations in retail settings. This encouraged the use of such devices to reduce physical contact when paying. Sales growth was restrained, nonetheless, by the slowdown in non-essential equipment purchases and the uncertain state of the economy. Despite initial difficulties, the need for Automatic weight-pricing machines in the post-pandemic environment is anticipated to be sustained by the long-term trend toward effective and frictionless retail operations. Introduction of Automatic Weigh-pricing Machines

Several important factors are driving the market for automatic weigh-pricing machines. The adopting these devices is driven by the growing need for simplified and effective retail operations, improving product pricing and labeling accuracy and speed. Regulations requiring precise weight and pricing information on consumer items also expand the market. Demand in the market is also influenced by the growth of the retail industry and the requirement to improve customer experience by reducing checkout time and mistakes. Additionally, technological developments like IoT integration and data analytics are propelling the creation of more advanced and effective automatic weigh-pricing equipment, luring companies looking to upgrade their processes. Finally, adopting these devices is fueled by enterprises' quest for cost savings and operational efficiency, eventually shaping the market's growth for automatic weigh-pricing machines.

The IvyDB Signed Volume dataset, offered as an add-on to IvyDB US, provides historical and intraday records of detailed options trading volume. Trades within this dataset are classified as either buyer-initiated or seller-initiated, determined by the trade price and bid-ask quote at the transaction time. It encompasses extensive options data, derivatives data, trading data, stock market data, and quantitative model data, refreshed nightly and throughout the trading day. This dataset enriches your access to comprehensive options data, trading data, and derivatives data, along with robust stock market data and quantitative model data. Utilizing IvyDB Signed Volume ensures you have detailed insights into options data, derivatives data, and trading data, supported by accurate stock market data and quantitative model data, enhancing your decision-making capabilities.

Key Insights: - Tracking Market Maker Inventory: Gain real-time insights into market maker activities. - Analyzing Option Order Flows: Analyze option order flows during critical announcement periods, such as FOMC decisions, M&A activity, and analyst revisions. - Evaluating Institutional Options Participation: Identify institutional options participation through the observation of large lot sizes.

Features: • Tracks whether a trade occurred at the bid, ask, or midpoint • Every option, every 5/30 minutes since January 2016 • Clean and reliable data • Available intraday at 5-minute or 30-minute intervals • Symbols and security IDs link to IvyDB US • Volume lot size fields beginning in 2021 to help identify retail and institutional flow • Used in conjunction with IvyDB US, Signed Volume can help determine net gamma exposure by strike on a T-0 timeframe, as opposed to the traditional T-1