The Bitcoin (BTC) price again reached an all-time high in 2025, as values exceeded over 118,003.3 USD on July 29, 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.

Analysis of ‘Crypto Fear and Greed Index’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/adelsondias/crypto-fear-and-greed-index on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Crypto Fear and Greed Index

Each day, the website https://alternative.me/crypto/fear-and-greed-index/ publishes this index based on analysis of emotions and sentiments from different sources crunched into one simple number: The Fear & Greed Index for Bitcoin and other large cryptocurrencies.

Why Measure Fear and Greed?

The crypto market behaviour is very emotional. People tend to get greedy when the market is rising which results in FOMO (Fear of missing out). Also, people often sell their coins in irrational reaction of seeing red numbers. With our Fear and Greed Index, we try to save you from your own emotional overreactions. There are two simple assumptions:

• Extreme fear can be a sign that investors are too worried. That could be a buying opportunity.
• When Investors are getting too greedy, that means the market is due for a correction.

Therefore, we analyze the current sentiment of the Bitcoin market and crunch the numbers into a simple meter from 0 to 100. Zero means "Extreme Fear", while 100 means "Extreme Greed". See below for further information on our data sources.

Data Sources

We are gathering data from the five following sources. Each data point is valued the same as the day before in order to visualize a meaningful progress in sentiment change of the crypto market.

First of all, the current index is for bitcoin only (we offer separate indices for large alt coins soon), because a big part of it is the volatility of the coin price.

But let’s list all the different factors we’re including in the current index:

Volatility (25 %)

We’re measuring the current volatility and max. drawdowns of bitcoin and compare it with the corresponding average values of the last 30 days and 90 days. We argue that an unusual rise in volatility is a sign of a fearful market.

Market Momentum/Volume (25%)

Also, we’re measuring the current volume and market momentum (again in comparison with the last 30/90 day average values) and put those two values together. Generally, when we see high buying volumes in a positive market on a daily basis, we conclude that the market acts overly greedy / too bullish.

Social Media (15%)

While our reddit sentiment analysis is still not in the live index (we’re still experimenting some market-related key words in the text processing algorithm), our twitter analysis is running. There, we gather and count posts on various hashtags for each coin (publicly, we show only those for Bitcoin) and check how fast and how many interactions they receive in certain time frames). A unusual high interaction rate results in a grown public interest in the coin and in our eyes, corresponds to a greedy market behaviour.

Surveys (15%) currently paused

Together with strawpoll.com (disclaimer: we own this site, too), quite a large public polling platform, we’re conducting weekly crypto polls and ask people how they see the market. Usually, we’re seeing 2,000 - 3,000 votes on each poll, so we do get a picture of the sentiment of a group of crypto investors. We don’t give those results too much attention, but it was quite useful in the beginning of our studies. You can see some recent results here.

Dominance (10%)

The dominance of a coin resembles the market cap share of the whole crypto market. Especially for Bitcoin, we think that a rise in Bitcoin dominance is caused by a fear of (and thus a reduction of) too speculative alt-coin investments, since Bitcoin is becoming more and more the safe haven of crypto. On the other side, when Bitcoin dominance shrinks, people are getting more greedy by investing in more risky alt-coins, dreaming of their chance in next big bull run. Anyhow, analyzing the dominance for a coin other than Bitcoin, you could argue the other way round, since more interest in an alt-coin may conclude a bullish/greedy behaviour for that specific coin.

Trends (10%)

We pull Google Trends data for various Bitcoin related search queries and crunch those numbers, especially the change of search volumes as well as recommended other currently popular searches. For example, if you check Google Trends for "Bitcoin", you can’t get much information from the search volume. But currently, you can see that there is currently a +1,550% rise of the query „bitcoin price manipulation“ in the box of related search queries (as of 05/29/2018). This is clearly a sign of fear in the market, and we use that for our index.

There's a story behind every dataset and here's your opportunity to share yours.

Copyright disclaimer

This dataset is produced and maintained by the administrators of https://alternative.me/crypto/fear-and-greed-index/.

This published version is an unofficial copy of their data, which can be also collected using their API (e.g., GET https://api.alternative.me/fng/?limit=10&format=csv&date_format=us).

--- Original source retains full ownership of the source dataset ---

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

It is estimated that the cumulative market cap of cryptocurrencies increased in early 2023 after the downfall in November 2022 due to FTX. That value declined in the summer of 2023, however, as international uncertainty grew over a potential recession. Bitcoin's market cap comprised the majority of the overall market capitalization. What is market cap? Market capitalization is a financial measure typically used for publicly traded firms, computed by multiplying the share price by the number of outstanding shares. However, cryptocurrency analysts calculate it as the price of the virtual currencies times the number of coins in the market. This gives cryptocurrency investors an idea of the overall market size, and watching the evolution of the measure tells how much money is flowing in or out of each cryptocurrency. Cryptocurrency as an investment The price of Bitcoin has been erratic, and most other cryptocurrencies follow its larger price swings. This volatility attracts investors who hope to buy when the price is low and sell at its peak, turning a profit. However, this does little for price stability. As such, few firms accept payment in cryptocurrencies. As of June 25, 2025, the cumulative market cap of cryptocurrencies reached a value of ******.

This dataset appears to be a time series dataset containing financial market data, specifically related to a certain asset or cryptocurrency. The columns in the dataset represent various financial metrics and market attributes. Here's a brief description of each column:

Date (Start-End): This column likely represents the time period for which the data is recorded, with a start and end date for each entry.

Open: The opening price of the asset or cryptocurrency at the beginning of the time period.

High: The highest price reached during the time period.

Low: The lowest price reached during the time period.

Close: The closing price of the asset or cryptocurrency at the end of the time period.

Volume: The trading volume, which typically represents the total number of units of the asset traded during the time period.

Market Cap: The market capitalization, which is often the product of the closing price and the total supply of the asset.

This dataset can be used for various financial and statistical analyses, including studying price trends, volatility, and trading volume over time. It may be particularly useful for analyzing the performance of the asset or cryptocurrency over the given time frame and identifying patterns or insights for investment or trading strategies.

«Datasets per la comparació de moviments i patrons entre els principals índexs borsatils espanyols i les crypto-monedes»

Context

En aquest cas el context és detectar o preveure els diferents moviments que es produeixen per una serie factors, tant de moviment interns (compra-venda), com externs (moviments polítics, econòmics, etc...), en els principals índexs borsatils espanyols i de les crypto-monedes.

Hem seleccionat diferents fonts de dades per generar fitxers «csv», guardar diferents valors en el mateix període de temps. És important destacar que ens interessa més les tendències alcistes o baixes, que podem calcular o recuperar en aquests períodes de temps.

Content

En aquest cas el contingut està format per diferents csv, especialment tenim els fitxers de moviments de cryptomoneda, els quals s’ha generat un fitxer per dia del període de temps estudiat.

Pel que fa als moviments del principals índexs borsatils s’ha generat una carpeta per dia del període, en cada directori un fitxer amb cadascun del noms dels índexs. Degut això s’han comprimit aquests últims abans de publicar-los en el directori de «open data» kaggle.com.

Pel que fa als camps, ens interessà detectar els moviments alcistes i baixistes, o almenys aquelles que tenen un patró similar en les cryptomonedes i els índexs. Els camps especialment destacats són:

• Data: Data de la observació
• Nom: Nom empresa o cryptomoneda, per identificar de quina moneda o index estem representant.
• Símbol: Símbol de la moneda o del index borsatil, per realitzar gràfic posteriorment d’una forma mes senzilla que el nom.
• Preu: Valor en euros d’una acció o una cryptomoneda (transformarem la moneda a euros en el cas de estigui en dòlars amb l'última cotització (un dollar a 0,8501 euro)
• Tipus_cotitzacio: Valor nou que agregarem per discretitzar entre la cotització: baix (0 i 1), normal (1 i 100), alt (100 i 1000), molt_alt (>1000)

Acknowledgements

En aquest cas les fonts de dades que s’han utilitzat per a la realització dels datasets corresponent a:

• http://www.eleconomista.es
• https://coinmarketcap.com

Per aquest fet, les dades de borsa i crypto-moneda estan en última instància sota llicència de les webs respectivament. Pel que fa a la terminologia financera podem veure vocabulari en renta4banco.
[https://www.r4.com/que-necesitas/formacion/diccionario]

Inspiration

Hi ha un estudi anterior on poder tenir primícies de com han enfocat els algoritmes:

• https://arxiv.org/pdf/1410.1231v1.pdf

En aquest cas el «trading» en cryptomoneda és relativament nou, força popular per la seva formulació com a mitja digital d’intercanvi, utilitzant un protocol que garanteix la seguretat, integritat i equilibri del seu estat de compte per mitjà d’un entramat d’agents.

La comunitat podrà respondre, entre altres preguntes, a:

• Està afectant o hi ha patrons comuns en les cotitzacions de cryptomonedes i el mercat de valors principals del país d'Espanya?
• Els efectes o agents externs afecten per igual a les accions o cryptomonedes?
• Hi ha relacions cause efecte entre les acciones i cryptomonedes?

Project repository

https://github.com/acostasg/scraping

Datasets

Els fitxers csv generats que componen el dataset s’han publicat en el repositori kaggle.com:

• https://www.kaggle.com/acostasg/stock-index/
• https://www.kaggle.com/acostasg/crypto-currencies

Per una banda, els fitxers els «stock-index» estan comprimits per carpetes amb la data d’extracció i cada fitxer amb el nom dels índexs borsatil. De forma diferent, les cryptomonedes aquestes estan dividides per fitxer on són totes les monedes amb la data d’extracció.

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

In March 2024 Bitcoin BTC reached a new all-time high with prices exceeding 73000 USD marking a milestone for the cryptocurrency market This surge was due to the approval of Bitcoin exchange-traded funds ETFs in the United States allowing investors to access Bitcoin without directly holding it This development increased Bitcoin’s credibility and brought fresh demand from institutional investors echoing previous price surges in 2021 when Tesla announced its 15 billion investment in Bitcoin and Coinbase was listed on the Nasdaq By the end of 2022 Bitcoin prices dropped sharply to 15000 USD following the collapse of cryptocurrency exchange FTX and its bankruptcy which caused a loss of confidence in the market By August 2024 Bitcoin rebounded to approximately 64178 USD but remained volatile due to inflation and interest rate hikes Unlike fiat currency like the US dollar Bitcoin’s supply is finite with 21 million coins as its maximum supply By September 2024 over 92 percent of Bitcoin had been mined Bitcoin’s value is tied to its scarcity and its mining process is regulated through halving events which cut the reward for mining every four years making it harder and more energy-intensive to mine The next halving event in 2024 will reduce the reward to 3125 BTC from its current 625 BTC The final Bitcoin is expected to be mined around 2140 The energy required to mine Bitcoin has led to criticisms about its environmental impact with estimates in 2021 suggesting that one Bitcoin transaction used as much energy as Argentina Bitcoin’s future price is difficult to predict due to the influence of large holders known as whales who own about 92 percent of all Bitcoin These whales can cause dramatic market swings by making large trades and many retail investors still dominate the market While institutional interest has grown it remains a small fraction compared to retail Bitcoin is vulnerable to external factors like regulatory changes and economic crises leading some to believe it is in a speculative bubble However others argue that Bitcoin is still in its early stages of adoption and will grow further as more institutions and governments recognize its potential as a hedge against inflation and a store of value 2024 has also seen the rise of Bitcoin Layer 2 technologies like the Lightning Network which improve scalability by enabling faster and cheaper transactions These innovations are crucial for Bitcoin’s wider adoption especially for day-to-day use and cross-border remittances At the same time central bank digital currencies CBDCs are gaining traction as several governments including China and the European Union have accelerated the development of their own state-controlled digital currencies while Bitcoin remains decentralized offering financial sovereignty for those who prefer independence from government control The rise of CBDCs is expected to increase interest in Bitcoin as a hedge against these centralized currencies Bitcoin’s journey in 2024 highlights its growing institutional acceptance alongside its inherent market volatility While the approval of Bitcoin ETFs has significantly boosted interest the market remains sensitive to events like exchange collapses and regulatory decisions With the limited supply of Bitcoin and improvements in its transaction efficiency it is expected to remain a key player in the financial world for years to come Whether Bitcoin is currently in a speculative bubble or on a sustainable path to greater adoption will ultimately be revealed over time.

The price of the cryptocurrency based on the famous internet meme broke its price decline in early November 2022, as people started buying the coin after FTX's collapse. This rally only lasted for a few days, however, as a Dogecoin was worth roughly 0.23 U.S. dollars on July 29, 2025. This is a different development than in 2021, when the crypto became very popular in a short amount of time. Between January 28 and January 29, 2021, Dogecoin's value grew by around 216 percent to 0.023535 U.S. dollars after comments from Tesla CEO Elon Musk. The digital coin quickly grew to become the most talked-about cryptocurrency available, not necessarily for its price - the prices of Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and several other virtual currencies were much higher than those of DOGE - but for its growth.

DOGE started it. SHIB took it mainstream. BONK and PEPE brought in the crowds. Now what?

Stay on top of the entire meme coin ecosystem through CoinAPI's comprehensive data feeds. We've connected to 350+ exchanges so you don't have to, bringing together every significant market into one unified API that actually works when you need it. Dig into historical patterns that shaped today's meme coin landscape. Compare volume spikes across different tokens during viral moments. Track institutional entry points that transformed joke coins into serious market movers.

From quick price checks to in-depth research projects, our institutional-grade precision helps you navigate this volatile but opportunity-rich corner of the crypto market. With Digital Asset Data complete market coverage, you'll never miss a beat. Serious data for not-so-serious coins. That's the CoinAPI difference

➡️ Why choose us?

📊 Market Coverage & Data Types: ◦ Real-time and historical data since 2010 (for chosen assets) ◦ Full order book depth (L2/L3) ◦ Trade-by-trade data ◦ OHLCV across multiple timeframes ◦ Market indexes (VWAP, PRIMKT) ◦ Exchange rates with fiat pairs ◦ Spot, futures, options, and perpetual contracts ◦ Coverage of 90%+ global trading volume ◦ Full Crypto Trade Data

🔧 Technical Excellence: ◦ 99,9% uptime guarantee ◦ Multiple delivery methods: REST, WebSocket, FIX, S3 ◦ Standardized data format across exchanges ◦ Ultra-low latency data streaming ◦ Detailed documentation ◦ Custom integration assistance

CoinAPI represents the gold standard in cryptocurrency data, trusted by leading financial institutions, technology providers, and market makers worldwide. By combining technology with rigorous data validation protocols, we provide the foundation upon which many financial products are being built.

The price of the native coin from BNB Chain (formerly BSC) grew by 50 percent in late 2021 but was much lower in 2022. On July 29, 2025, for example, a single BNB coin was worth more than 822.28 U.S. dollars - a value that is very different from the all-time high of 822.28 U.S. dollars in November 2021. Regardless, Binance Coin ranked in the top 10 most expensive cryptocurrencies in 2022. Noticeable is that the price increase of BNB in November 2021 coincides with a similar price change for Ethereum (ETH), a cryptocurrency where BNB initially originated in 2017 before coming to its own years later.BNB's history: From a reward token in 2017 to an ecosystem after 2019As the name suggests, Binance Coin, or BNB, originally started as an extension of the Binance.com trading platform, the most used cryptocurrency exchange in the world. It initially functioned on the Ethereum blockchain network as an ERC-20 token, offering incentives to owners like reduced trading fees, affiliate rewards, or a lottery ticket system ('Launchpad') that let users invest in new, Binance-selected crypto projects. In 2019, however, BNB moved away from the Ethereum network and migrated to Binance's self-developed blockchain: Binance Smart Chain, or BSC (called BNB Chain since February 2022). Here, BNB started to support a chain that initially did not focus on hosting decentralized apps but focused on high transaction speed and being able to handle large amounts of traffic.DeFi and GameFi: the main segments for BNBBSC, however, made significant strides in 2021, partly due to traffic overload and high gas prices on Ethereum, as well as the growing interest in both Decentralized Finance (DeFI) and NFTs. Much like Cardano, Solana, and Terra, Binance Smart Chain consequently became a valid alternative to Ethereum. The total value locked (TVL) of BNBs blockchain within DeFi, for example, ranked only behind that of Terra and Ethereum in early 2022. Another area where Binance's blockchain and token play a significant role is that of GameFi, or 'play-to-earn' blockchain games that are powered by cryptocurrencies. Some of the more well-known and most popular NFT games, like Alien Worlds and Axie Infinity, run on the blockchain behind BNB.

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

CoinAPI is a revolutionary platform delivering comprehensive currency data from over 350 global cryptocurrency exchanges. Our advanced system provides both real-time and historical market information with unprecedented precision and depth.

Traders, investors, and developers rely on our meticulously collected currency data to make informed decisions in the fast-moving digital asset landscape. Every data point is carefully verified and timestamped, ensuring the highest level of accuracy and reliability. From live price tracking to extensive volume metrics, CoinAPI offers an unparalleled window into the complex world of cryptocurrency markets.

Why work with us?

Market Coverage & Data Types: - Full Cryptocurrency Data - Real-time and historical data since 2010 (for chosen assets) - Full order book depth (L2/L3) - Tick-by-tick data - OHLCV across multiple timeframes - Market indexes (VWAP, PRIMKT) - Exchange rates with fiat pairs - Spot, futures, options, and perpetual contracts - Coverage of 90%+ global trading volume

Technical Excellence: - 99% uptime guarantee - Multiple delivery methods: REST, WebSocket, FIX, S3 - Standardized data format across exchanges - Ultra-low latency data streaming - Detailed documentation - Custom integration assistance

Whether you're building algorithmic trading systems, conducting research, or creating visualization tools, our real-time and historical candlesticks from exchanges worldwide provide the reliable market data you need

Consumers from countries in Africa, Asia, and South America were most likely to be an owner of cryptocurrencies, such as Bitcoin, in 2025. This conclusion can be reached after combining ** different surveys from the Statista's Consumer Insights over the course of that year. Nearly one out of three respondents to Statista's survey in Nigeria, for instance, mentioned they either owned or use a digital coin, rather than *** out of 100 respondents in the United States. This is a significant change from a list that looks at the Bitcoin (BTC) trading volume in ** countries: There, the United States and Russia were said to have traded the highest amounts of this particular virtual coin. Nevertheless, African and Latin American countries are noticeable entries in that list too. Daily use, or an investment tool? The survey asked whether consumers either owned or used cryptocurrencies but does not specify their exact use or purpose. Some countries, however, are more likely to use digital currencies on a day-to-day basis. Nigeria increasingly uses mobile money operations to either pay in stores or to send money to family and friends. Polish consumers could buy several types of products with a cryptocurrency in 2019. Opposed to this is the country of Vietnam: Here, the use of Bitcoin and other cryptocurrencies as a payment method is forbidden. Owning some form of cryptocurrency in Vietnam as an investment is allowed, however. Which countries are more likely to invest in cryptocurrencies? Professional investors looking for a cryptocurrency-themed ETF were more often found in Europe than in the United or China, according to a survey in early 2020. Most of the largest crypto hedge fund managers with a location in Europe in 2020, were either from the United Kingdom or Switzerland - the country with the highest cryptocurrency adoption rate in Europe according to Statista's Global Consumer Survey. Whether this had changed by 2025 was not yet clear.

The global user base of cryptocurrencies increased by nearly *** percent between 2018 and 2020, only to accelerate further in 2022. This is according to calculations from various sources, based on information from trading platforms and on-chain wallets. Increasing demographics might initially be attributed to a rise in the number of accounts and improvements in identification. In 2021, however, crypto adoption continued as companies like Tesla and Mastercard announced their interest in cryptocurrency. Consumers in Africa, Asia, and South America were most likely to be an owner of cryptocurrencies, such as Bitcoin, in 2022. How many of these users have Bitcoin? User figures for individual cryptocurrencies are unavailable. Bitcoin, for instance, was created not to be tracked by banks and governments. What comes closest is the trading volume of Bitcoin against domestic fiat currencies. The source assumed, however, that UK residents were the most likely to make Bitcoin transactions with British pounds. This assumption might not be accurate for popular fiat currencies worldwide. Moreover, coins such as Tether or Binance Coin - referred to as "stablecoins" - are often used to buy and sell Bitcoin. Those coins were not included in that particular statistic. Wallet usage declined Total crypto wallet downloads were significantly lower in 2022 than in 2021. The number of downloads of Coinbase, Blockchain.com, and MetaMask, among others, declined as the market hit a "crypto winter" over the year. The crypto market also suffered bad press when FTX - one of the largest crypto exchanges based on market share - collapsed in November 2022. Binance, on the other hand, regained some of the market share it had lost between September and October 2022, growing by *** percentage points in November. As of 2025, the highest forecast for the global user base of cryptocurrencies is projected to reach *** million.

Crypto Trading and Technical Indicators

Understanding the Market Dynamics of 600 Popular Cryptocurrencies

By [source]

About this dataset

This dataset provides an unprecedented overview of the crypto industry, offering comprehensive market analysis of more than 600 well-known cryptocurrencies. The data contained in this dataset is extremely up-to-date, ranging from trading statuses, price movements and volatility levels to technical indicators and market capitalization. Perfect for those interested in cryptocurrency trading, technical analysis or investing, this data can be used to facilitate informed decisions and fulfill respective requirements.
The 35 columns present in this dataset enable users to gain a greater understanding into price movements and distinguish between various levels of volatility. It also allows users to analyze oscillator ratings for each crypto asset listed within for accurate risk management. Banks, investors, data analysts as well as crypto exchanges could all benefit from utilizing this powerful dataset; its ability to provide a top level summary into the entire crypto industry has earned it a valuable place among the cryptocurrency community worldwide

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For more datasets, click here.

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How to use the dataset

This dataset provides comprehensive market analysis of more than 600 popular cryptocurrencies, including trading prices, volatility, technical indicators, and market capitalization. In this guide, we'll cover what kind of information you can obtain from the dataset, how to use it effectively to gain insight into the crypto industry, and how to analyze the results in order to make informed decisions regarding cryptocurrency trading.

The dataset consists of 35 columns that are divided into two main categories: Market Information and Technical Indicators. The Market Information section contains data about each cryptocurrency's price performance – including change percentages for 1 week/1 month/3 months/6 months/1 year – as well as its fully diluted market capitalization (FD Mkt Cap), traded volume (Traded Vol), last trading price in USD (Last_y), available coins (Avail Coins), total coins created with a max supply(Total Coins) and its respective rating out of 5 stars by moving averages(Moving Averages Rating). The Technical Indicators section includes data pertaining to oscillator ratings (Oscillators Rating) such as Average Directional Index (ADX), Awesome Oscillator(AO), Average True Range(ATR) , Commodity Channel Index20(CCI20) etc., moving averages such as Simple Moving Average 20 days /50 days / 200 days (SMA20/ SMA50 / SMA200) , Bollinger Bands upper & lower limit lines comprised of standard deviations known as BB Up & BB Low respectively , Momentum(MOM ), Relative Strength Index14 day time frame indicator denoted by RSI14 , Macd level & signal line along with Stochitic %K &%D indicators.

With all that knowledge now let’s take a look at some tips on how you can analyse this data effectively. For example: finding safety ranks based on volatility scores or locatingcryptocurrencies whose MACD line has recently crossed over its signal line thus giving buy signals or vice versa giving sell signals also mining through various time frames using multiple technical indicators like ADX +CCI20+RSI14 etc can help spot potential trends which may be indicative for a particular currency . Also moving averages assessments over several time periods might be useful for calculating trend based values in order for possible bullish or bearish orientations . Furthermore when examining long term trends across multiple currencies it might be suitable carry out simple comparisons between certain columns from one currency against

Research Ideas

• Utilizing the price movements and technical indicators, investors can analyze the different crypto industry trends and develop strategies to apply them in their portfolios.
• Governmental institutions and banks can use this dataset to monitor the industry’s activity from country to country, helping create regulatory policies when necessary.
• Crypto exchanges can design algorithms based on this data set which will assist with detecting any manipulation or malicious activities in trades occurring in their platform

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - Y...

The attached files enable the full replication of the empirical analysis. All .ipynb files ending in _retrieve contain the code for retrieving data via the respective APIs.

We manually obtained the total global crypto asset market capitalization, as well as the market capitalization of each individual cryptocurrency and stablecoin, from CoinGecko. The data is stored as individual .csv files, which are imported into the .ipynb files. Additionally, we retrieve the following daily variables from DataStream: the TRY/USD exchange rate, MSCI World Index, TR2YT, US2YT, BIST 100, and S&P 500.

The dummy variable indicating month-to-month inflation shocks exceeding 5% is based on the file AppendixB_Türkiye_Inflation_figures_CSV_format.csv. The data in this file was manually obtained from the website of the Central Bank of the Republic of Türkiye (CBRT).

Descriptive statistics and empirical analysis are conducted in Stata. All figures are generated using the .ipynb file P3_Code_Python_figures.

Note that the categorization used in the published paper (MegaCap, LargeCap, MidCap), which follows FINRA’s classification of stocks by market capitalization, differs from the terminology in the code (LargeCap, MidCap, SmallCap). This discrepancy is purely nominal and does not affect the results.

Dataset created for the Master Thesis "What drives cryptocurrency market dynamics? Analysing external variable influence on cryptocurrency prices" as part of the Management of Technologies Masters at the TPM Faculty, TUDelft. The dataset contains the combined data for a range of variables for a number of crypto currencies. Each variable is in a column. Variables included for each crypto currency are: Price, Volume, Empirical wavelet transform, Google Trends. Additional variables are: Twitter (and Influencer) sentiment, Open status of some stock exchanges and Close price and volume for SP500, Oil and Gold.

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.

Coinbase: Riding the Crypto Rollercoaster (COIN)

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

Explore the historical Whois records related to currency-crypto.com (Domain). Get insights into ownership history and changes over time.