Please don't forget to vote

Context

Ethereum is a decentralized, open-source blockchain with smart contract functionality. Ether (ETH) is the native cryptocurrency of the platform. After Bitcoin, it is the second-largest cryptocurrency by market capitalization. Ethereum is the most actively used blockchain.

Content

CSV files for select bitcoin exchanges for the time period of Aug 2015 to May 2021, with day to day updates of OHLC (Open, High, Low, Close), Volume in ETH and indicated currency, and weighted ethereum price.

Acknowledgements

The Data was taken from Yahoo Finance.

This dataset provides comprehensive historical price data for Ethereum (ETH) from January 1, 2018, to September 26, 2024. It contains vital trading information that can help analyze market trends, investor behavior, and potential future price movements. The dataset is structured to include daily trading statistics, making it suitable for various analyses, including time series forecasting and algorithmic trading strategies.

Column Descriptions :

Time: This column indicates the specific date for each trading session. The dates are formatted in month/day/year (MM/DD/YYYY) style, allowing for easy chronological sorting and analysis of trends over time.

Open: The opening price of Ethereum for the day. This price reflects the market's initial valuation of Ethereum at the start of the trading day and is influenced by various factors, including previous day’s performance and market sentiment.

High: The highest price reached by Ethereum during the trading day. This value shows the peak demand for Ethereum within the session, indicating significant bullish activity and investor interest at that price point.

Low: The lowest price of Ethereum during the day. This metric represents the minimum value traders were willing to accept for Ethereum and can signify bearish pressure or selling activity during that trading session.

Close: The closing price of Ethereum at the end of the trading day. This is a crucial figure, as it serves as the reference point for assessing the performance of Ethereum in subsequent days. Analysts often use this price to calculate daily returns and overall market performance.

Volume: The total trading volume for Ethereum on that day, representing the number of Ethereum units traded. High volume indicates strong market activity and can signal investor confidence or a significant shift in market dynamics. Conversely, low volume may suggest a lack of interest or uncertainty among traders.

Conclusion : This Ethereum price dataset is a valuable resource for performing technical analysis, developing trading algorithms, and conducting price predictions. By examining the patterns and relationships within the data, analysts and traders can gain insights into market behavior and make informed decisions.

Context

This dataset contains 1-minute interval price data for Ethereum (ETH/USD), including the following columns: timestamp (Unix), open, high, low, close, and volume. The data is collected directly from the Bitstamp public API and is updated daily to provide the most recent market information.

This project was inspired by the work of the Kaggle user zielak, who created a similar dataset for Bitcoin (BTC/USD), and aims to provide a ready-to-use dataset for analysis, visualization, and machine learning applications related to Ethereum trading and market research.

Content

See https://github.com/ViniciusQroz/ethereum-1min-price-kaggle for the automation and scrapping script.

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

Find my notebook : Advanced EDA & Data Wrangling - Crypto Market Data where I cover the full EDA and advanced data wrangling to get beautiful dataset ready for analysis.

Find my Deep Reinforcement Learning v1 notebook: "https://www.kaggle.com/code/franoisgeorgesjulien/deep-reinforcement-learning-for-trading">Deep Reinforcement Learning for Trading

Find my Quant Analysis notebook:"https://www.kaggle.com/code/franoisgeorgesjulien/quant-analysis-visualization-btc-v1">💎 Quant Analysis & Visualization | BTC V1

Dataset Presentation:

This dataset provides a comprehensive collection of hourly price data for 34 major cryptocurrencies, covering a time span from January 2017 to the present day. The dataset includes Open, High, Low, Close, Volume (OHLCV), and the number of trades for each cryptocurrency for each hour (row).

Making it a valuable resource for cryptocurrency market analysis, research, and trading strategies. Whether you are interested in historical trends or real-time market dynamics, this dataset offers insights into the price movements of a diverse range of cryptocurrencies.

This is a pure gold mine, for all kind of analysis and predictive models. The granularity of the dataset offers a wide range of possibilities. Have Fun!

Ready to Use - Cleaned and arranged dataset less than 0.015% of missing data hour: crypto_data.csv

First Draft - Before External Sources Merge (to cover missing data points): crypto_force.csv

Original dataset merged from all individual token datasets: cryptotoken_full.csv

crypto_data.csv & cryptotoken_full.csv highly challenging wrangling situations: - fix 'Date' formats and inconsistencies - find missing hours and isolate them for each token - import external data source containing targeted missing hours and merge dataframes to fill missing rows

see notebook 'Advanced EDA & Data Wrangling - Crypto Market Data' to follow along and have a look at the EDA, wrangling and cleaning process.

Date Range: From 2017-08-17 04:00:00 to 2023-10-19 23:00:00

Date Format: YYYY-MM-DD HH-MM-SS (raw data to be converted to datetime)

Data Source: Binance API (some missing rows filled using Kraken & Poloniex market data)

Crypto Token in the dataset (also available as independent dataset): - 1INCH - AAVE - ADA (Cardano) - ALGO (Algorand) - ATOM (Cosmos) - AVAX (Avalanche) - BAL (Balancer) - BCH (Bitcoin Cash) - BNB (Binance Coin) - BTC (Bitcoin) - COMP (Compound) - CRV (Curve DAO Token) - DENT - DOGE (Dogecoin) - DOT (Polkadot) - DYDX - ETC (Ethereum Classic) - ETH (Ethereum) - FIL (Filecoin) - HBAR (Hedera Hashgraph) - ICP (Internet Computer) - LINK (Chainlink) - LTC (Litecoin) - MATIC (Polygon) - MKR (Maker) - RVN (Ravencoin) - SHIB (Shiba Inu) - SOL (Solana) - SUSHI (SushiSwap) - TRX (Tron) - UNI (Uniswap) - VET (VeChain) - XLM (Stellar) - XMR (Monero)

Date column presents some inconsistencies that need to be cleaned before formatting to datetime: - For column 'Symbol' and 'ETCUSDT' = '23-07-27': it is missing all hours (no data, no hourly rows for this day). I fixed it by using the only one row available for that day and duplicated the values for each hour. Can be fixed using this code:

start_timestamp = pd.Timestamp('2023-07-27 00:00:00')
end_timestamp = pd.Timestamp('2023-07-27 23:00:00')

hourly_timestamps = pd.date_range(start=start_timestamp, end=end_timestamp, freq='H')

hourly_data = {
  'Date': hourly_timestamps,
  'Symbol': 'ETCUSDT',
  'Open': 18.29,
  'High': 18.3,
  'Low': 18.17,
  'Close': 18.22,
  'Volume USDT': 127468,
  'tradecount': 623,
  'Token': 'ETC'
}

hourly_df = pd.DataFrame(hourly_data)
df = pd.concat([df, hourly_df], ignore_index=True)

df = df.drop(550341)

• Some rows for 'Date' have extra digits '.000' '.874' etc.. instead of the right format YYYY-MM-DD HH-MM-SS. To clean it you can use the following code:

# Count the occurrences of the pattern '.xxx' in the 'Date' column
count_occurrences_before = df['Date'].str.count(r'\.\d{3}')
print("Occurrences before cleaning:", count_occurrences_before.sum()) 

# Remove '.xxx' pattern from the 'Date' column
df['Date'] = df['Date'].str.replace(r'\.\d{3}', '', regex=True)

# Count the occurrences of the pattern '.xxx' in the 'Date' column after cleaning
count_occurrences_after = df['Date'].str.count(r'\.\d{3}')
print("Occurrences after cleaning:", count_occurrences_after.sum()) 

**Disclaimer: Any individual or entity choosing to engage in market analysis, develop predictive models, or utilize data for trading purposes must do so at their own discretion and risk. It is important to understand that trading involves potential financial loss, and decisions made in the financial mar...

This dataset contains ethereum (ETH) data since 2024-01-01 00:00. It is labeled by our team to make it easier for model to learn it well. it is labled based on the 20 previous candles. It covers all four data extracted from each candle like High, Low, Volume, Open, Close. wish it is helpful and you can use it to make the best out of it.💪

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

3MEth Dataset OverviewSection 1: Token TransactionsThis section provides 303 million transaction records from 3,880 tokens and 35 million users on the Ethereum blockchain. The data is stored in 3,880 CSV files, each representing a specific token. Each transaction includes the following information:Sender and receiver wallet addresses: Enables network analysis and user behavior studies.Token address: Links transactions to specific tokens for token-specific analysis.Transaction value: Reflects the number of tokens transferred, essential for liquidity studies.Blockchain timestamp: Captures transaction timing for temporal analysis.Apart from the large dataset, we also provide a smaller CSV file containing 267,242 transaction records from 29,164 wallet addresses. This smaller dataset involves a total of 1,194 tokens, covering the time period September 2016 to November 2023. This detailed transaction data is critical for studying user behavior, liquidity patterns, and tasks such as link prediction and fraud detection.Section 2: Token InformationThis section offers metadata for 3,880 tokens, stored in corresponding CSV files. Each file contains:Timestamp: Marks the time of data update.Token price: Useful for price prediction and volatility studies.Market capitalization: Reflects the token's market size and dominance.24-hour trading volume: Indicates liquidity and trading activity.Section 3: Global Market IndicesThis section provides macro-level data to contextualize token transactions, stored in separate CSV files. Key indicators include:Bitcoin dominance: Tracks Bitcoin's share of the cryptocurrency market.Total market capitalization: Measures the overall market's value, with breakdowns by token type.Stablecoin market capitalization: Highlights stablecoin liquidity and stability.24-hour trading volume: A key measure of market activity.These indices are essential for integrating global market trends into predictive models for volatility and risk-adjusted returns.Section 4: Textual IndicesThis section contains sentiment data from Reddit's Ethereum community, covering 7,800 top posts from 2014 to 2024. Each post includes:Post score (net upvotes): Reflects engagement and sentiment strength.Timestamp: Aligns sentiment with price movements.Number of comments: Gauges sentiment intensity.Sentiment indices: Sentiment scores computed using methods detailed in the data preprocessing section.The full Reddit textual dataset is available upon request; please contact us for access. Alternatively our open-source repository includes a tool to guide users in collecting Reddit data. Researchers are encouraged to apply for a Reddit API Key and adhere to Reddit's policies. This data is valuable for understanding social dynamics in the market and enhancing sentiment analysis models that can explain market movements and improve behavioral predictions.

Daily cryptocurrency data (transaction count, on-chain transaction volume, value of created coins, price, market cap, and exchange volume) in CSV format. The data sample stretches back to December 2013. Daily on-chain transaction volume is calculated as the sum of all transaction outputs belonging to the blocks mined on the given day. “Change” outputs are not included. Transaction count figure doesn’t include coinbase transactions. Zcash figures for on-chain volume and transaction count reflect data collected for transparent transactions only. In the last month, 10.5% (11/18/17) of ZEC transactions were shielded, and these are excluded from the analysis due to their private nature. Thus transaction volume figures in reality are higher than the estimate presented here, and NVT and exchange to transaction value lower. Data on shielded and transparent transactions can be found here and here. Decred data doesn’t include tickets and voting transactions. Monero transaction volume is impossible to calculate due to RingCT which hides transaction amounts.

This Dataset contains historical price data for 10 cryptocurrencies spanning from 2021 to 2024, in three different time frames: 1 day, 4 hours, and 1 hour. The data is sourced from the Binance API and stored in CSV (Comma Separated Values) format for easy accessibility and analysis.

Usage

You can use this data for various purposes such as backtesting trading strategies, conducting statistical analysis, or building predictive models related to cryptocurrency markets.

Note

• All timestamps are in UTC timezone.
• Prices are quoted in USDT (Tether).

Cryptocurrency trading analysis and algorithmic strategy development rely on high-quality, high-frequency historical data. This dataset provides clean, structured OHLCV data for one of the most liquid and popular trading pairs, ETH/USDT, sourced directly from the Bybit exchange. It is ideal for quantitative analysts, data scientists, and trading enthusiasts looking to backtest strategies, perform market analysis, or build predictive models across different time horizons.

Content

The dataset consists of three separate CSV files, each corresponding to a different time frame:

BYBIT_ETHUSDT_15m.csv: Historical data in 15-minute intervals. BYBIT_ETHUSDT_1h.csv: Historical data in 1-hour intervals. BYBIT_ETHUSDT_4h.csv: Historical data in 4-hour intervals.

Each file contains the same six columns:

• Datetime: The UTC timestamp for the start of the candle/bar.
• Open: The opening price of ETH at the start of the interval.
• High: The highest price reached during the interval.
• Low: The lowest price reached during the interval.
• Close: The closing price at the end of the interval.
• Volume: The trading volume in the base asset (ETH) during the interval.

Methodology & Update Schedule

• Source: The data was collected using the public API of the Bybit cryptocurrency exchange via a Python script utilizing the ccxt library.
• Data Range: The dataset currently covers the period from July 5, 2021, to June 28, 2025.
• Update Frequency: This dataset is maintained locally and will be updated on a weekly basis to include the most recent trading data, ensuring its relevance for ongoing analysis.

Acknowledgements

This dataset is made possible by the publicly available data from the Bybit exchange. Please consider this when using the data for your projects.

Inspiration (Potential Use Cases)

• Backtesting Trading Strategies: Test the performance of strategies like moving average crossovers, RSI-based signals, or MACD indicators.
• Time Series Forecasting: Build models (e.g., ARIMA, LSTM, Prophet) to predict future price movements.
• Volatility Analysis: Analyze market volatility by calculating rolling standard deviations or other risk metrics.
• Feature Engineering: Create new technical indicators and features for machine learning models.
• Market Visualization: Plot candlestick charts and overlay them with various technical analysis tools.

Daily cryptocurrency data (transaction count, on-chain transaction volume, value of created coins, price, market cap, and exchange volume) in CSV format. The data sample stretches back to December 2013. Daily on-chain transaction volume is calculated as the sum of all transaction outputs belonging to the blocks mined on the given day. “Change” outputs are not included. Transaction count figure doesn’t include coinbase transactions. Zcash figures for on-chain volume and transaction count reflect data collected for transparent transactions only. In the last month, 10.5% (11/18/17) of ZEC transactions were shielded, and these are excluded from the analysis due to their private nature. Thus transaction volume figures in reality are higher than the estimate presented here, and NVT and exchange to transaction value lower. Data on shielded and transparent transactions can be found here and here. Decred data doesn’t include tickets and voting transactions. Monero transaction volume is impossible to calculate due to RingCT which hides transaction amounts.

This dataset shows the price of Ethereum over time, collected using Yahoo Finance API. It's useful for people who want to study Ethereum's past prices to make decisions about buying or selling. You can use it to see how Ethereum's price changed over the years and find patterns in the data. Whether you're a researcher, investor, or just curious about cryptocurrency, this dataset gives you valuable information to explore Ethereum's market history.

This dataset contains detailed daily price information for Bitcoin (BTC) and Ethereum (ETH) against USD, covering the period from 2014 to 2024. It includes key financial metrics such as Open, High, Low, Close prices, Volume, and Adjusted Close values for both cryptocurrencies. This dataset is ideal for cryptocurrency enthusiasts, financial analysts, and data scientists looking to explore trends, analyze market movements, and develop predictive models for Bitcoin’s and Ethereum’s performance over the last decade.

The data provides insights into Bitcoin and Ethereum’s price fluctuations, from Bitcoin’s early adoption phase to Ethereum's rise as a dominant platform for decentralized applications and smart contracts. Whether you're interested in historical patterns, volatility analysis, or future price predictions, this comprehensive dataset serves as a valuable resource for your cryptocurrency research and analysis.

Contains daily high, low, close, open data from ethereumprice.org for Ethereum in USD up until 16 April.

From the site itself: This historical ETH price data is available for free and can be downloaded as a CSV using the button below. Data can be modified and published for commercial and non-commercial purposes under an attribution license – we only require that you link to ethereumprice.org when using this data publicly.

Candlestick data (ohlcv) of ETHUSDT.P prices from 2019 to 2024 March for 1h, 1d, 3d and 1w intervals. Data is fetched from Binance API. It contains 7 columns: timestamp, date, open, high, low, close, volume. Timestamp is in unix format (seconds).

Introduction: The "Cryptocurrency Price Analysis Dataset: BTC, ETH, XRP, LTC (2018-2023)" is a comprehensive dataset that captures the daily price movements of six popular cryptocurrencies. It covers a period from January 1, 2018, to May 31, 2023, providing a valuable resource for researchers, analysts, and enthusiasts interested in studying the historical price behavior of these digital assets.

Description: This dataset contains a wealth of information for six major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Litecoin (LTC). The data spans a time frame of over five years, enabling users to explore long-term trends, analyze volatility patterns, and gain insights into market dynamics.

Columns:

1. Crypto: This column specifies the name of the cryptocurrency (e.g., BTC, ETH, XRP, LTC).
2. Date: The date on which the price data was recorded.
3. Open: The opening price of the cryptocurrency at the beginning of the day.
4. High: The highest price reached by the cryptocurrency during the day.
5. Low: The lowest price reached by the cryptocurrency during the day.
6. Close: The closing price of the cryptocurrency at the end of the day.

Use Cases: The dataset offers numerous possibilities for analysis and research within the field of cryptocurrencies. Here are a few potential use cases:

1. Price Analysis: Researchers can investigate the historical price movements of each cryptocurrency to identify trends, patterns, and potential correlations between different assets.
2. Volatility Study: The dataset enables the study of volatility in cryptocurrency markets, helping users understand the frequency and magnitude of price fluctuations.
3. Market Performance: Analysts can analyze the performance of individual cryptocurrencies over time, comparing returns and risk measures to assess their investment potential.
4. Trading Strategies: Traders can utilize the dataset to develop and backtest trading strategies based on technical indicators, price patterns, or machine learning algorithms.
5. Sentiment Analysis: Combine this dataset with external sentiment data to explore the relationship between market sentiment and cryptocurrency price movements. By sharing this dataset on Kaggle, you are providing a valuable resource to the data science community, encouraging collaborative research, and enabling the development of innovative models and solutions within the cryptocurrency domain.

Please note that this dataset is for educational and research purposes only and should not be used for making financial decisions without thorough analysis and consultation with financial professionals.

Overview:

This dataset contains detailed information on cryptocurrency transactions, specifically focusing on Bitcoin (BTC) and Ethereum (ETH). The dataset includes transaction details such as sender and receiver addresses, transaction amounts, fees, timestamps, and mining pool information. It serves as a valuable resource for analyzing market trends, identifying patterns in trading behavior, and studying blockchain transaction dynamics across different mining pools.

Data Fields (Columns):

1. Transaction_ID:

• Description: A unique identifier for each cryptocurrency transaction.
• Type: String
• Example: TX2QW62Q5XM17K

2. Sender_Address:

• Description: The blockchain address of the sender initiating the transaction.
• Type: String
• Example: 0xd377b9203ad74038664c08f658c0245632651f55

3. Receiver_Address:

• Description: The blockchain address of the recipient receiving the transaction.
• Type: String
• Example: 0x4a3370c0f0b83d519ddf50892d006f64d7425880

4. Amount:

• Description: The total amount of cryptocurrency transferred in the transaction (in either BTC or ETH).
• Type: Float
• Example: 11.39618058 (BTC or ETH depending on the currency type)

5. Transaction_Fee:

• Description: The transaction fee paid to process the transaction.
• Type: Float
• Example: 6.226e-05

6. Timestamp:

• Description: The date and time when the transaction was processed, in ISO 8601 format.
• Type: Datetime (ISO 8601)
• Example: 2025-03-30T23:32:40.589676Z

7. Block_ID:

• Description: The unique identifier for the block that the transaction was included in.
• Type: String
• Example: f4A4D894b9Ee166B3F75F4Fb

8. Mining_Pool:

• Description: The name of the mining pool that confirmed the transaction.
• Type: String
• Example: Ethermine

9. Currency:

• Description: The cryptocurrency type involved in the transaction (either BTC or ETH).
• Type: String
• Example: ETH

10. Transaction_Type:

• Description: The type of the transaction. Typically, it is "Transfer" for regular cryptocurrency transfers.
• Type: String
• Example: Transfer

11. Transaction_Status:

• Description: The current status of the transaction, such as "Confirmed".
• Type: String
• Example: Confirmed

12. Gas_Price_Gwei:

• Description: The gas price for Ethereum transactions, measured in Gwei (used for ETH transactions only).
• Type: Integer
• Example: 50 (Only applicable to Ethereum transactions)

Use Cases:

1. Market Trend Analysis

Analyze transaction patterns to identify market trends and behaviors.
- Use the data to track spikes or drops in transaction volumes and correlate them with market events or price movements.

2. Blockchain Dynamics

Study how mining pools and transaction fees interact with blockchain dynamics.
- Investigate how different mining pools impact transaction confirmation times and fees across Bitcoin and Ethereum networks.

3. Trading Behavior

Investigate the behavior of users sending or receiving cryptocurrency.
- Identify patterns such as frequent senders/receivers, average transaction amounts, and transaction frequency.

4. Fee and Gas Price Optimization

Explore transaction fees and gas price fluctuations across different mining pools and blockchains.
- Examine how Ethereum’s gas prices and Bitcoin’s transaction fees fluctuate over time and how this affects user behavior.

Context

This data was collected for a dissertation project titled, "Forecasting cryptocurrency prices using machine learning".

Content

The three csv files contain the daily price data for Bitcoin, Ether and Ripple. The data was collected from https://coinmarketcap.com/
The datasets contain the following features:
* Open * Close * High * Low * Volume * Market Capitalisation * EMA 10 (Exponential moving average of 10 timesteps) * EMA 30 (Exponential moving average of 30 timesteps) * ATR (Average true range)

The collapse of Terra Luna and Terra USD (UST) shocked the broader cryptocurrency and financial markets. The aggregate market capitalization of large-cap cryptocurrencies dropped several hundreds of billions dollars in a matter of a week. Avana Wallet has aggregated 15-minute interval price data for stablecoins (US dollar pegs) and large-cap cryptocurrencies for you to analyze. The data span between 5/6/2022 and 5/17/2022, which captures the entire episode.

Several pricing discrepancies occurred during the volatility. You can analyze the data to find the discrepancies that occurred when the market panicked.

Data associated with Terra: - Anchor Protocol ANC (anchor-protocol.csv) - Terra Luna LUNA (terra-luna.csv) - Terra USD UST (terrausd.csv)

Large-cap cryptocurrencies: - Avalanche AVAX (avalanche.csv) - Bitcoin BTC (bitcoin.csv) - Binance BNB (bnb.csv) - Cardano ADA (cardano.csv) - Dogecoin DOGE (dogecoin.csv) - Ethereum ETH (ethereum.csv) - Polygon MATIC (polygon.csv) - Solana SOL (solana.csv) - XRP (xrp.csv)

Stablecoins: - Binance USD BUSD (binance-usd.csv) - DAI (dai.csv) - Tether USDT (tether.csv) - USD Coin UDSC (usd-coin.csv)