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.

This dataset captures YouTube's public discourse surrounding the United States government's seizure of $225 million in cryptocurrency. The topic was selected for data collection as it was trending on YouTube's News tab, indicating significant public interest and media coverage. Collected on June 19, 2025, it includes comprehensive video metadata and user comments from 453 relevant videos, totaling over 115,000 comments. This dataset provides valuable insights into public sentiment, media coverage patterns, and social media reactions to major cryptocurrency-related government actions.

Data Collection

Data was systematically collected using YouTube's public data with the query "United States seizes $225 million in cryptocurrency." The collection process targeted the most relevant and recent content to ensure comprehensive coverage of the topic across different YouTube channels and content creators.

Collection Date: June 19, 2025
Search Query: "United States seizes $225 million in cryptocurrency"
Total Videos Processed: 453
Total Comments Collected: 115,202

Dataset Contents

The dataset consists of three main components:

1. Video Details (453 records): Comprehensive metadata for each video including titles, descriptions, view counts, and channel information
2. Comments Data (115,202 records): User comments with timestamps, engagement metrics, and reply structures

Potential Use Cases

• Sentiment Analysis: Analyze public opinion on government cryptocurrency interventions
• Media Coverage Research: Study how different YouTube channels cover financial crime stories
• Social Network Analysis: Examine comment reply patterns and user engagement
• Temporal Analysis: Track how public discourse evolves over time
• Content Strategy: Understand what cryptocurrency-related content resonates with audiences
• Academic Research: Support studies on digital asset regulation and public perception
• Journalism: Identify trending topics and public concerns for news reporting

Data Quality Notes

• All data collected from publicly available YouTube content
• Video metadata includes standard YouTube API fields
• Data represents a snapshot from June 19, 2025, and may not reflect subsequent changes
• Some videos may have been deleted or made private after collection

Support & Updates

This dataset represents a comprehensive snapshot of YouTube discourse on a significant cryptocurrency news event. If you have questions about the data collection methodology, need clarification on any fields, or have suggestions for improvements, please don't hesitate to ask in the discussion forum - I'll be happy to help and provide additional context!

A league table of the 120 cryptocurrencies with the highest market cap reveals how diverse each crypto is and potentially how much risk is involved when investing in one. Bitcoin (BTC), for instance, had a so-called "high cap" - a market cap worth more than 10 billion U.S. dollars - indicating this crypto project has a certain track record or, at the very least, is considered a major player in the cryptocurrency space. This is different in Decentralize Finance (DeFi), where Bitcoin is only a relatively new player. A concentrated market The number of existing cryptocurrencies is several thousands, even if most have a limited significance. Indeed, Bitcoin and Ethereum account for nearly 75 percent of the entire crypto market capitalization. As crypto is relatively easy to create, the range of projects varies significantly - from improving payments to solving real-world issues, but also meme coins and more speculative investments. Crypto is not considered a payment method While often talked about as an investment vehicle, cryptocurrencies have not yet established a clear use case in day-to-day life. Central bankers found that usefulness of crypto in domestic payments or remittances to be negligible. A forecast for the world's main online payment methods took a similar stance: It predicts that cryptocurrency would only take up 0.2 percent of total transaction value by 2027.

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.

Context

This dataset was obtained by myself from the top crypto Telegram groups using the Telegram API and the Telethon Python module. More details on Telethon can be found here: https://docs.telethon.dev/en/latest/

The dataset is in JSON format. This particular JSON is easy to read and load into a Pandas data frame. Refer to my notebook here: https://www.kaggle.com/aagghh/trends-in-crypto-space-telegram-analysis

The size of the dataset is 2.8GB

Content

This dataset contains records of approximately 3+ million messages in the official top crypto exchanges Telegram groups from various users. Also, it contains data such as dates, number of views, shares, and many more.

Inspiration

A potential use-case for this data could be a practice in some sentiment analysis in order to predict the popularity/price of some crypto assets

Also good to try using this dataset along with various time-series price data in an attempt to see if there is a correlation between the price of an asset and how the community sees it (Bitcoin for instance)

Some other EDA can be applied to this dataset. For instance, I am gonna use this data for the crypto space trends analysis (hello GameStop)

Please refer to my notebook on the trends here: https://www.kaggle.com/aagghh/trends-in-crypto-space-telegram-analysis

🧠 Overview This dataset offers a meticulously collected and organized set of Ethereum (ETH) OHLCV data against Tether (USDT), spanning over 4 years — from December 1, 2019 to January 1, 2024. It is derived from Binance, one of the most trusted and liquid cryptocurrency exchanges globally. The dataset is offered in multiple timeframes — from 1-minute to 1-day intervals — allowing it to serve as a comprehensive base for a wide variety of quantitative, financial, machine learning, and research use cases.

ETH/USDT is one of the most traded crypto pairs and reflects significant market sentiment, liquidity, and volatility patterns across all time horizons.

🎯 Who is This Dataset For? This dataset was built with a wide range of users in mind:

Quant researchers and traders building trading strategies and backtests

Data scientists and ML practitioners training deep learning models (LSTM, CNN, Transformer) on price prediction, volatility forecasting, or anomaly detection

Finance students and educators looking for clean, real-world time-series data

Crypto developers and bot builders designing arbitrage, scalping, or HFT strategies

Technical analysts and chartists needing high-quality historical data for visual analysis

⚙️ Why This Dataset? (The Motivation) While there are countless crypto datasets available, most have limitations:

They cover only one timeframe (e.g., just 1d or 1h)

They include missing or irregular data

They are not structured for use in modeling pipelines or financial toolkits

This dataset solves all of that by:

Providing 12 distinct timeframes (1m to 1d)

Ensuring clean, sorted, and complete records

Using a standard OHLCV schema that aligns with trading systems and model inputs

Covering an extensive historical range, long enough for statistically valid backtests and long-term model training

Being stored in easy-to-read Excel files, compatible with Python, R, Excel, Power BI, etc.

🔍 Data Breakdown Each record (row) represents one candlestick (also called a "kline") for a specific time interval.

Columns (Features): Column Description Timestamp Start time of the candlestick interval (UTC). It's the reference time for the open of that candle. Format: YYYY-MM-DD HH:MM:SS. Open ETH/USDT price at the start of the interval (i.e., opening price). High Maximum price reached by ETH during the interval. Low Minimum price reached by ETH during the interval. Close ETH/USDT price at the end of the interval (i.e., closing price). Volume The total volume of ETH traded during the interval (base asset volume, not USDT).

All values are as recorded from the Binance exchange without transformation, preserving their integrity for accurate modeling and testing.

🗃️ Timeframes Provided The dataset is divided into 12 separate Excel files, each corresponding to a distinct time resolution:

Filename Interval Description ETHUSDT_1m.xlsx 1 Minute High-frequency data for scalping or HFT models ETHUSDT_3m.xlsx 3 Minute Good compromise between noise and resolution ETHUSDT_5m.xlsx 5 Minute Widely used in intraday strategies ETHUSDT_15m.xlsx 15 Minute Ideal for swing trading and signal generation ETHUSDT_30m.xlsx 30 Minute Captures broader intraday patterns ETHUSDT_1h.xlsx 1 Hour Balanced view of short and mid-term trends ETHUSDT_2h.xlsx 2 Hour Smooths volatility for multi-hour trend analysis ETHUSDT_4h.xlsx 4 Hour Macro movement visualization across days ETHUSDT_6h.xlsx 6 Hour Suited for overnight or global market overlap patterns ETHUSDT_8h.xlsx 8 Hour Large-window analysis for swing traders ETHUSDT_12h.xlsx 12 Hour Captures morning-evening cycle globally ETHUSDT_1d.xlsx 1 Day Ideal for backtesting, long-term trends, feature creation

Each file is structured with consistent formatting and identical columns, making merging, resampling, or comparative analysis simple.