The Bitcoin (BTC) price again reached an all-time high in 2025, as values exceeded over 111,842.71 USD on August 27, 2025. Price hikes in early 2025 were connected to the approval of Bitcoin ETFs in the United States, while previous hikes in 2021 were due to events involving Tesla and Coinbase, respectively. Tesla's announcement in March 2021 that it had acquired 1.5 billion U.S. dollars' worth of the digital coin, for example, as well as the IPO of the U.S.'s biggest crypto exchange, fueled mass interest. The market was noticeably different by the end of 2022, however, after another crypto exchange, FTX, filed for bankruptcy.Is the world running out of Bitcoin?Unlike fiat currency like the U.S. dollar - as the Federal Reserve can simply decide to print more banknotes - Bitcoin's supply is finite: BTC has a maximum supply embedded in its design, of which roughly 89 percent had been reached in April 2021. It is believed that Bitcoin will run out by 2040, despite more powerful mining equipment. This is because mining becomes exponentially more difficult and power-hungry every four years, a part of Bitcoin's original design. Because of this, a Bitcoin mining transaction could equal the energy consumption of a small country in 2021.Bitcoin's price outlook: a potential bubble?Cryptocurrencies have few metrics available that allow for forecasting, if only because it is rumored that only a few cryptocurrency holders own a large portion of the available supply. These large holders - referred to as 'whales'-are' said to make up two percent of anonymous ownership accounts, while owning roughly 92 percent of BTC. On top of this, most people who use cryptocurrency-related services worldwide are retail clients rather than institutional investors. This means outlooks on whether Bitcoin prices will fall or grow are difficult to measure, as movements from one large whale are already having a significant impact on this market.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Will the S&P Bitcoin index redefine the crypto markets?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Description

This dataset, titled "Cryptocurrency Market Sentiment & Prediction," is a synthetic collection of real-time crypto market data designed for advanced analysis and predictive modeling. It captures a comprehensive range of features including price movements, social sentiment, news impact, and trading patterns for 10 major cryptocurrencies. Tailored for data scientists and analysts, this dataset is ideal for exploring market volatility, sentiment analysis, and price prediction, particularly in the context of significant events like the Bitcoin halving in 2024 and increasing institutional adoption.

Key Features Overview: - Price Movements: Tracks current prices and 24-hour price change percentages to reflect market dynamics. - Social Sentiment: Measures sentiment scores from social media platforms, ranging from -1 (negative) to 1 (positive), to gauge public perception. - News Sentiment and Impact: Evaluates sentiment from news sources and quantifies their potential impact on market behavior. - Trading Patterns: Includes data on 24-hour trading volumes and market capitalization, crucial for understanding market activity. - Technical Indicators: Features metrics like the Relative Strength Index (RSI), volatility index, and fear/greed index for in-depth technical analysis. - Prediction Confidence: Provides a confidence score for predictive models, aiding in assessing forecast reliability.

Purpose and Applications: - Perfect for machine learning tasks such as price prediction, sentiment-price correlation studies, and volatility classification. - Supports time series analysis for forecasting price movements and identifying volatility clusters. - Valuable for research into the influence of social media and news on cryptocurrency markets, especially during high-impact events.

Dataset Scope: - Covers a simulated 30-day period, offering a snapshot of market behavior under varying conditions. - Focuses on major cryptocurrencies including Bitcoin, Ethereum, Cardano, Solana, and others, ensuring relevance to current market trends.

Dataset Structure Table:

Dataset Statistics Table:

This dataset is a powerful resource for machine learning projects, sentiment analysis, and crypto market research, providing a robust foundation for AI/ML model development and testing.

Graph and download economic data for Nasdaq Brazil Bitcoin Futures TR Index (NASDAQNQBTCBRT) from 2025-06-27 to 2025-08-07 about cryptocurrency, NASDAQ, Brazil, indexes, and USA.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Will the S&P Bitcoin Index Revolutionize Cryptocurrency?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Prices for BTCUSD Bitcoin US Dollar including live quotes, historical charts and news. BTCUSD Bitcoin US Dollar was last updated by Trading Economics this August 28 of 2025.

About this Dataset

This dataset contains the prices of Bitcoin every minute over a period from 2017-11-06 03:00 to 2023-03-10 2:59 (YYYY-MM-DD). The data includes the time, close time, open, high, low, close prices, the volume exchanged per minute and the number of trades per minute. It represent Bitcoin prices over 2.8 millions values. This dataset is ideal for anyone who want to track, study and analyze BTC/USDT values over more than 5 years.

Time range: From 2017-11-06 04:00 to 2023-03-40 14:00

File format: Datas are in .csv format

Columns values: - time: Date in milliseconds where observation begins - open: Opening ETH price in the minute - high: Highest ETH price in the minute - low: Lowest ETH price in the minute - close: Closing ETH price in the minute - volume: Volume exchanges between time and close_time - close_time: Date in milliseconds were observation ends

Category

Economic

Keywords

Bitcoin,BTC,#btc,Cryptocurrency,Crypto

Row Count

2808000

Price

$149.00

This dataset contains a comprehensive collection of historical price records for the top 1000 cryptocurrencies. The data in this dataset is updated daily, providing a reliable and up-to-date source of information for cryptocurrency traders, researchers, and enthusiasts.

Each file in the dataset includes the following columns: date, open price, high price, low price, closing price, adjusted closing price, and trading volume. These columns provide a detailed picture of the daily price movements and trading activity of each cryptocurrency in the dataset.

The "date" column indicates the day on which the price data was recorded, while the "open" column provides the opening price of the cryptocurrency for that day. The "high" and "low" columns indicate the highest and lowest prices of the cryptocurrency on that day, respectively. The "close" column represents the closing price of the cryptocurrency on that day, while the "adjusted close" column takes into account any dividends or other corporate actions that may have affected the price. Finally, the "volume" column shows the trading volume of the cryptocurrency on that day.

With this dataset, users can analyze and visualize the performance of individual cryptocurrencies, compare them to one another, and track trends over time. The data is ideal for use in machine learning models, predictive analytics, and other data-driven applications.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Will the S&P Bitcoin Index Usher in a New Era of Crypto Investment?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Comprehensive Bitcoin holdings, market data, and treasury information for Hashdex Nasdaq Crypto Index Fundo De Indice (HASH11.SA)

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

The following dataset contains the attributes: Date: Specific date to be observed for the corresponding price. Open: The opening price for the day High: The maximum price it has touched for the day Low: The minimum price it has touched for the day Close: The closing price for the day percent_change_24h: Percentage change for the last 24hours Volume: Volume of Bitcoin traded at the date Market Cap: Market Value of traded Bitcoin

The dataset was collected for the period spanning between 01/07/2019 and 31/12/2022.The historical Twitter volume were retrieved using ‘‘Bitcoin’’ (case insensitive) as the keyword from bitinfocharts.com. Google search volume was retrieved using library Gtrends. 2000 tweets per day using 4 times interval were crawled by employing Twitter API with the keyword “Bitcoin. The daily closing prices of Bitcoin, oil price, gold price, and U.S stock market indexes (S&P 500, NASDAQ, and Dow Jones Industrial Average) were collected using R libraries either Quantmod or Quandl.

OHLCV is an abbreviation for the five critical data points: Open, High, Low, Close, and Volume. It refers to the key points in analyzing an asset such as Bitcoin (BTC) in the market over a specified time. The dataset is important for not only traders and analysts but also for data scientists who work on BTC market prediction using artificial intelligence. The 'Open' and 'Close' prices represent the starting and ending price levels, while the 'High' and 'Low' are the highest and lowest prices during that period (a daily time frame (24h)). The 'Volume' is a measure of the total number of trades. This dataset provides five OHLCV data columns for BTC along with a column called "Next day close price" for regression problems and machine learning applications. The dataset includes daily information from 1/1/2012 to 8/6/2022.

This dataset contains the potential influencers of the bitcoin price. There are a total of 18 daily time series including hash rate, block size, mining difficulty etc. It also encompasses public opinion in the form of tweets and google searches mentioning the keyword bitcoin. The data is scraped from the interactive web-graphs available at https://bitinfocharts.com. The original dataset contains missing values and they have been replaced by carrying forward the corresponding last seen observations (LOCF method).

Between 2020 and 2023, a country ranking that estimates crypto adoption based on transaction volume consistently placed the U.S. in ********** of the world. The figure for 2022, especially, stands out as it broke a declining trend in 2021 and was likely caused by the change of the methodology to now include Decentralized Finance (DeFi) in the index. For example, the United States ranked second in the world when it comes to on-chain retail value received from DeFi protocols - or consumers who were buying certain DeFi protocols. This may refer to the growing use of OpenSea and other Web3 wallets within the U.S. particularly in the first months of 2022.

By 2025, the Bitcoin market cap had grown to over ***** billion USD as the cryptocurrency kept growing. Market capitalization is calculated by multiplying the total number of Bitcoins in circulation by the Bitcoin price. The Bitcoin market capitalization increased from approximately *** billion U.S. dollars in 2013 to several times this amount since its surge in popularity. Dominance The Bitcoin market cap takes up a significant portion of the overall cryptocurrency market cap. This is referred to as "dominance". Within the crypto world, this so-called "dominance" ratio is one of the oldest and most investigated metrics available. It measures the coin's market cap relative to the overall crypto market — effectively showing how strong Bitcoin compared to all the other cryptocurrencies that are not BTC, called "altcoins". The Bitcoin dominance was above ** percent. Maximum supply and scarcity Bitcoin is unusual from other cryptocurrencies in that its maximum supply is getting closer. By 2025, well over ** million out of all 21 million possible Bitcoin had been created. Bitcoin's supply is expected to reach its maximum around the year 2140, likely making mining more energy-intensive.

The selected variables were chosen base on the literature for time series of stock prices prediction or Forex (currency) prediction. The analises test only variables associated with the price like Bitcoin close, open, high and low price and volumn (for one representative exchange). Like Chen and Bahar, we used moving average of the variables to generate new variables in order to capture other information that could be hidden due the high noise generate characteristic of a high volatile asset. Also, like \cite{Bahar2016}, we use Gold an Death Cross, that are very common data for technical analysis.

In the year 2023, Vietnam occupied the third position in the Crypto Adoption Index, out of a total of *** nations worldwide. Within the **** metrics of this ranking, the nation achieved the second position for on-chain value received and the third position for on-chain retail value received.

The S&P Bitcoin index is anticipated to rise with moderate risk. Potential indicators include increased investor confidence, a favorable regulatory environment, and a positive correlation with traditional financial markets. However, risks associated with the index include volatility, exchange security issues, and regulatory uncertainties, which could impact its performance and value.