The average price of one Bitcoin Cash reached its all-time high in 2017, although the price since then never came close to that position. As of November 16, 2025, one Bitcoin Cash token was worth 502.16 U.S. dollars, rather than the nearly 2,500 USD from the peak in 2017. Bitcoin Cash, abbreviated as BCH, is a variant of the much more known Bitcoin, or BTC, and is traded separately on online exchanges. That the two cryptocurrencies are different from each other already shows when looking at the price of a 'regular' Bitcoin: this was over 40,000 U.S. dollars during the same time frame.

All-time high price data for Bitcoin Cash, including the peak value, date achieved, and current comparison metrics.

Bitcoin Cash reached its all-time high price on December 20, 2017.

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.

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...

Context:

This dataset is an extra updating dataset for the G-Research Crypto Forecasting competition.

Introduction

This is a daily updated dataset, automaticlly collecting market data for G-Research crypto forecasting competition. The data is of the 1-minute resolution, collected for all competition assets and both retrieval and uploading are fully automated. see discussion topic.

The Data

For every asset in the competition, the following fields from Binance's official API endpoint for historical candlestick data are collected, saved, and processed.

1. **timestamp** - A timestamp for the minute covered by the row.
2. **Asset_ID** - An ID code for the cryptoasset.
3. **Count** - The number of trades that took place this minute.
4. **Open** - The USD price at the beginning of the minute.
5. **High** - The highest USD price during the minute.
6. **Low** - The lowest USD price during the minute.
7. **Close** - The USD price at the end of the minute.
8. **Volume** - The number of cryptoasset u units traded during the minute.
9. **VWAP** - The volume-weighted average price for the minute.
10. **Target** - 15 minute residualized returns. See the 'Prediction and Evaluation section of this notebook for details of how the target is calculated.
11. **Weight** - Weight, defined by the competition hosts [here](https://www.kaggle.com/cstein06/tutorial-to-the-g-research-crypto-competition)
12. **Asset_Name** - Human readable Asset name.

Indexing

The dataframe is indexed by timestamp and sorted from oldest to newest. The first row starts at the first timestamp available on the exchange, which is July 2017 for the longest-running pairs.

Usage Example

The following is a collection of simple starter notebooks for Kaggle's Crypto Comp showing PurgedTimeSeries in use with the collected dataset. Purged TimesSeries is explained here. There are many configuration variables below to allow you to experiment. Use either GPU or TPU. You can control which years are loaded, which neural networks are used, and whether to use feature engineering. You can experiment with different data preprocessing, model architecture, loss, optimizers, and learning rate schedules. The extra datasets contain the full history of the assets in the same format as the competition, so you can input that into your model too.

Baseline Example Notebooks:

• Neural Network Starter
• LightGBM Starter
• Catboost Starter
• XGBoost Starter
• TabNet Starter
• Reinforcement Learning (PPO) Starter

These notebooks follow the ideas presented in my "Initial Thoughts" here. Some code sections have been reused from Chris' great (great) notebook series on SIIM ISIC melanoma detection competition here

Loose-ends:

This is a work in progress and will be updated constantly throughout the competition. At the moment, there are some known issues that still needed to be addressed:

• VWAP: - At the moment VWAP calculation formula is still unclear. Currently the dataset uses an approximation calculated from the Open, High, Low, Close, Volume candlesticks. [Waiting for competition hosts input]
• Target Labeling: There exist some mismatches to the original target provided by the hosts at some time intervals. On all the others - it is the same. The labeling code can be seen here. [Waiting for competition hosts] input]
• Filtering: No filtration of 0 volume data is taken place.

Example Visualisations

Opening price with an added indicator (MA50): https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234678%2Fb8664e6f26dc84e9a40d5a3d915c9640%2Fdownload.png?generation=1582053879538546&alt=media" alt="">

Volume and number of trades: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234678%2Fcd04ed586b08c1576a7b67d163ad9889%2Fdownload-1.png?generation=1582053899082078&alt=media" alt="">

License

This data is being collected automatically from the crypto exchange Binance.

Context

Nowadays everybody is talking about cryptocurrencies and chances are this phenomenon will keep growing in the future. Not only Bitcoin and Ethereum, but also other cryptos are slowly attracting more and more investments. Here I have included all the available historical data of the top 50 cryptocurrencies listed on coinmarketcap.com (which at the time of writing is listing 1296 cryptocurrencies in total). Data is available at 1-day interval from when the crypto was listed on coinmarketcap.com up to 17 November 2017.

Good luck!

Content

As specified above, all the data was retrieved from coinmarketcap.com for the top 50 (at the time of writing) cryptocurrencies. Everytime marketcap was not available (listed as '-', usually during the first days after the launch) has been replaced with a "0"

Files are organized as follows: Date (dd/mm/yyyy) | Open ($) | High ($) | Low ($) | Close ($) | 24-hrs Volume ($) | MarketCap ($)

This dataset includes (with starting date):

ardor.csv (23/07/2016) ark.csv (22/03/2017) attention-token-of-media.csv (04/10/2017) augur.csv (27/10/2015) basic-attention-token.csv (01/06/2017) binance-coin.csv (25/07/2017) bitcoin-cash.csv (23/07/2017) bitcoin.csv (28/04/2013) bitcoindark.csv (16/07/2014) bitconnect.csv (20/10/2017) bitshares.csv (21/07/2014) byteball.csv (27/12/2017) bytecoin-bcn.csv (17/06/2014) cardano.csv (01/10/2017) dash.csv (14/02/2014) decred.csv (10/02/2016) digixdao.csv (18/04/2016) dogecoin.csv (15/12/2013) eos.csv (01/07/2017) ethereum-classic.csv (24/07/2016) ethereum.csv (07/08/2015) factom.csv (06/10/2015) gas.csv (06/07/2017) golem.csv (19/10/2017) hshare.csv (20/08/2017) iota.csv (13/06/2017) komodo.csv (05/02/2017) kyber-network.csv (24/09/2017) lisk.csv (06/04/2016) litecoin.csv (28/04/2013) maidsafecoin.csv (28/04/2014) monacoin.csv (20/03/2014) monero.csv (21/05/2014) nem.csv (01/04/2015) neo.csv (09/09/2016) omisego.csv (14/07/2017) pivx.csv (13/02/2016) populous.csv (11/07/2017) qtum.csv (24/05/2017) ripple.csv (04/08/2013) salt.csv (29/09/2017) steem.csv (18/04/2016) stellar.csv (05/08/2014) stratis.csv (12/08/2016) tenx.csv (27/06/2017) tether.csv (25/02/2015) veritaseum.csv (08/06/2017) vertcoin.csv (20/01/2014) waves.csv (02/06/2016) zcash.csv (29/10/2016)

I will keep this collection up to date as much as I can. Please let me know if you are interested in additional cryptos.

Acknowledgements and Inspiration

If these files exist is thanks to CoinMarketCap (coinmarketcap.com) which is an awesome database of cryptocurrencies (and makes an awesome homepage).

Many have tried, few have succeeded. Can you predict tomorrow's price? What data patterns do you recognise? Can't wait to see what code/results you have to share! Comment below and please upvote this if you like it.

Bitcoin's transaction volume was at its highest in December 2023, when the network processed over ******* coins on the same day. Bitcoin generally has a higher transaction activity than other cryptocurrencies, except Ethereum. This cryptocurrency is often processed more than *********** times per day. Note that the transaction volume here refers to transactions registered within the Bitcoin blockchain. It should not be confused with Bitcoin's 24-hour trade volume, a metric associated with crypto exchanges. The more Bitcoin transactions, the more it is used in B2C payments? A Bitcoin transaction recorded in the blockchain can be any transaction, including B2C but also P2P. While it is possible to see in the blockchain which address sent Bitcoin to whom, details on who this person is and where they are from are typically missing. Bitcoin was designed to go against monetary authorities and prides itself on being anonymous. An important argument against Bitcoin replacing cash or cards in payments is that the cryptocurrency was not allowed for such a task: Bitcoin ranks among the slowest cryptocurrencies in terms of transaction speed. Are cryptocurrencies taking over payments? Cryptocurrency payments are set to grow at a CAGR of nearly ** percent between 2022 and 2029, although the market is relatively small. The forecast is according to a market estimate made in early 2023, based on various conditions and sources available at that time. Research across ** countries during the same time suggested that the market share of cryptocurrency in e-commerce transactions was "less than *** percent" in all surveyed countries, with predictions being this would not change in the future.

Bittensor reached its all-time high price on April 11, 2024.

In May 2022, over ***** billion Terra Classic – the 1.0 version of LUNA - tokens were issued and went into active circulation in a few days. The cryptocurrency’s built-in algorithm triggered this correction following the coin’s significant price drop that month. Terra’s algorithm would "burn" (permanently destroying) LUNA so that it created something else instead: TerraUSD (UST), a stablecoin within the same blockchain. This automated system was meant to keep the price of UST level, potentially avoiding economic sentiment. Because of this initial promise of stability, Terra and its two coins initially played a significant role in crypto lending. The poster child of algorithmic stablecoins Up until May 2022, TerraUSD (UST) was the biggest stablecoin that functioned with an algorithm. At the end of April 2022, the market cap of TerraUSD – now TerraClassicUSD – was *** times larger than what it was one month later, comparable in size to Binance USD. Algorithmic stablecoins are relatively new and, as most stablecoins, have an external asset as collateral. Several of the biggest stablecoins in the world, for example, are backed by real-world U.S. dollar assets, such as cash or securities. Others – such as DAI – rely on the backing of other cryptocurrencies, such as Ethereum (ETH). TerraUSD had little to no backing, relying on a closed ecosystem. Reset or revival: The LUNA (2.0) aftermath Terra got reset on May 28, 2022: A new *** coin released – taking over the Terra (LUNA) name - whilst the "original" crypto was abandoned and became Terra Classic (LUNC). TerraUSD (UST) remained but became TerraClassicUSD (USTC). Several of the *** LUNA holders, however, hoped for a different solution, rather seeing the (***) coin’s supply be bought back by the company who issued them. The company would then burn them, hopefully restoring the price of the original LUNA. Do Kown, the CEO of the Terra system, stated his company did not have the funds for such a big undertaking. He instead shared a blockchain address on Twitter where individuals could burn their tokens themselves. By June 2022, roughly **** LUNA tokens were destroyed that way – a burn rate of roughly ***** percent compared to the overall circulating supply.

All-time high price data for Aave, including the peak value, date achieved, and current comparison metrics.

Aave reached its all-time high price on May 18, 2021.