The CAPIVIX Index gives crypto traders something traditional markets have long relied on - a clear measure of expected market volatility. Think of it as the VIX for Bitcoin and Ethereum, showing what the market anticipates for price swings over the next 30 days.

This crypto volatility index tracks market sentiment for BTC/USD and ETH/USD pairs by analyzing options data from major derivatives exchanges. When CAPIVIX rises, it signals increased uncertainty and potential turbulence ahead. When it falls, markets are expecting calmer conditions.

What makes CAPIVIX valuable is its methodology - we've adapted the widely-trusted VIX calculation approach to work specifically with cryptocurrency options. This gives you a standardized way to gauge market anxiety or confidence across different market conditions.

The index updates continuously throughout trading hours, incorporating real-time options pricing to reflect the market's evolving risk perception. For traders and investors looking to understand market sentiment beyond price movements alone, CAPIVIX provides that crucial additional dimension of market intelligence.

➡️ 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 ◦ Bitcoin Price Data

🔧 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 hedging positions, timing entries and exits, or just wanting to better understand market psychology, our Bitcoin and Ethereum volatility data offers valuable insights into what the market collectively expects in the weeks ahead.

In 2024, a country ranking that estimates crypto adoption based on transaction volume placed Malaysia in the top 50 in the world. Moreover, Malaysia was in the **** place based on its P2P exchange trade volume. Peer-to-peer (P2P) crypto exchanges are a type of crypto exchange that let users trade cryptocurrencies with one another without the influence of a mediator, such as banks or other regulatory bodies.

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.

In 2023, a country ranking that estimates crypto adoption based on transaction volume placed Indonesia in the top ten of the world. Indonesia ranked ******* in the world when it comes to retail value received from DeFi protocols or consumers who were buying certain DeFi protocols. In comparison, Indonesia was in the **** place based on its P2P exchange trade volume. Peer-to-peer (P2P) crypto exchanges are a type of crypto exchange that let users trade cryptocurrencies with one another without the influence of a mediator, such as banks or other regulatory bodies.

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

«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.

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.

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 for 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

Graph and download economic data for Coinbase Index (DISCONTINUED) (CBCCIND) from 2015-01-01 to 2020-05-26 about cryptocurrency, indexes, and USA.

CoinAPI's comprehensive set of crypto market indices gives traders and institutions the reliable price benchmarks they need. Our system tracks VWAP and PRIMKT indices data across more than 350 exchanges, updating every 100ms to ensure you always have the latest market information.

The VWAP (Volume-Weighted Average Price) index shows you what's happening across the entire market by combining prices and trading volumes from multiple exchanges. By weighting each trade by its size, VWAP reveals the true market consensus price, filtering out noise from low-liquidity venues. This makes it perfect for making informed trading decisions or valuing your crypto holdings accurately.

Meanwhile, our PRIMKT (Principal Market Price) index focuses specifically on the exchanges with the highest trading volumes for each cryptocurrency pair. This approach meets important accounting standards like IFRS 13 and FASB ASC 820, making it especially valuable for companies that need to report crypto assets on their financial statements.

Both real-time and historical crypto index data are available, giving you the complete picture of market movements over time. Whether you're trading actively, conducting research, or preparing financial reports, our crypto market indices provide the accurate price discovery tools you need.

Why work with us?

Market Coverage & Data Types: - Real-time and historical data since 2010 (for chosen assets) - Market indexes (VWAP, PRIMKT) - 13 Data Sources - +7k indexes tracked - +2k assets covered - Full order book depth (L2/L3) - Tick-by-tick data - OHLCV across multiple timeframes - Exchange rates with fiat pairs - Spot, futures, options, and perpetual contracts - Coverage of 90%+ global trading volume

Technical Excellence: - 99,9% uptime guarantee - 100ms update frequency - Multiple delivery methods: REST, WebSocket, FIX, S3 - Standardized data format across exchanges - Ultra-low latency data streaming - Detailed documentation - Custom integration assistance

From Wall Street trading desks to Silicon Valley analytics firms, financial professionals worldwide rely on our indices when accuracy matters most. We've built our reputation on delivering clean, consistent market benchmarks that stand up to scrutiny. When organizations need to know the true price of digital assets - not just what's displayed on a single exchange - they turn to CoinAPI. Join the community of professionals who've made our crypto market indices their gold standard for price discovery.

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 Cryptocurrency Adoption?

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

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.

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.

The dataset consists in cryptocurrency prices, sp500, epu and google trends statistics at daily frequency, as well as the matlab codes used for the analyses.

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

In the context of the COVID-19’s outbreak and its implications for the financial sector, this study analyses the aspect of hedging and safe-haven under pandemic. Drawing on the daily data from 02 August 2019 to 17 April 2020, our key findings suggest that the contagious effects in financial assets’ returns significantly increased under COVID-19, indicating exacerbated market risk. The connectedness spiked in the middle of March, consistent with lockdown timings in major economies. The effect became severe with the WHO’s declaration of a pandemic, confirming negative news effects. The return connectedness suggests that COVID-19 has been a catalyst of contagious effects on the financial markets. The crude oil and the government bonds are however not as much affected by the spillovers as their endogenous innovation. In term of spillovers, we do find the safe-haven function of Gold and Bitcoin. Comparatively, the safe-haven effectiveness of Bitcoin is unstable over the pandemic. Whereas, GOLD is the most promising hedge and safe-haven asset, as it remains robust during the current crisis of COVID-19 and thus exhibits superiority over Bitcoin and Tether. Our findings are useful for investors, portfolio managers and policymakers interested in spillovers and safe havens during the current pandemic.

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.

Sweden consistently ranked low compared to other countries worldwide when it comes to crypto adoption based on transaction volume. This is according to estimates based on web traffic toward individual websites that are used to perform cryptocurrency transactions. Sweden did not have a specific main area in which it preferred to use crypto. Initial estimates showcased an interest in P2P payments, but this ranking continuously declined as more research released.

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 Ethereum Index Reshape the Cryptocurrency Landscape?

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

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.

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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 ---