Context Things like Block chain, Bitcoin, Bitcoin cash, Ethereum, Ripple etc are constantly coming in the news articles I read. So I wanted to understand more about it and [this post][1] helped me get started. Once the basics are done, the data scientist inside me started raising questions like: 1. How many cryptocurrencies are there and what are their prices and valuations? 2. Why is there a sudden surge in the interest in recent days? For getting answers to all these questions (and if possible to predict the future prices ;)), I started collecting data from [coinmarketcap][2] about the cryptocurrencies. So what next? Now that we have the price data, I wanted to dig a little more about the factors affecting the price of coins. I started of with Bitcoin and there are quite a few parameters which affect the price of Bitcoin. Thanks to [Blockchain Info][3], I was able to get quite a few parameters on once in two day basis. This will help understand the other factors related to Bitcoin price and also help one make future predictions in a better way than just using the historical price. Content The dataset has one csv file for each currency. Price history is available on a daily basis from April 28, 2013. This dataset has the historical price information of some of the top crypto currencies by market capitalization. The currencies included are: - Bitcoin - Ethereum - Ripple - Bitcoin cash - Bitconnect - Dash - Ethereum Classic - Iota - Litecoin - Monero - Nem - Neo - Numeraire - Stratis - Waves - Date : date of observation - Open : Opening price on the given day - High : Highest price on the given day - Low : Lowest price on the given day - Close : Closing price on the given day - Volume : Volume of transactions on the given day - Market Cap : Market capitalization in USD **Bitcoin Dataset (bitcoin_dataset.csv) :** This dataset has the following features. - Date : Date of observation - btc_market_price : Average USD market price across major bitcoin exchanges. - btc_total_bitcoins : The total number of bitcoins that have already been mined. - btc_market_cap : The total USD value of bitcoin supply in circulation. - btc_trade_volume : The total USD value of trading volume on major bitcoin exchanges. - btc_blocks_size : The total size of all block headers and transactions. - btc_avg_block_size : The average block size in MB. - btc_n_orphaned_blocks : The total number of blocks mined but ultimately not attached to the main Bitcoin blockchain. - btc_n_transactions_per_block : The average number of transactions per block. - btc_median_confirmation_time : The median time for a transaction to be accepted into a mined block. - btc_hash_rate : The estimated number of tera hashes per second the Bitcoin network is performing. - btc_difficulty : A relative measure of how difficult it is to find a new block. - btc_miners_revenue : Total value of coinbase block rewards and transaction fees paid to miners. - btc_transaction_fees : The total value of all transaction fees paid to miners. - btc_cost_per_transaction_percent : miners revenue as percentage of the transaction volume. - btc_cost_per_transaction : miners revenue divided by the number of transactions. - btc_n_unique_addresses : The total number of unique addresses used on the Bitcoin blockchain. - btc_n_transactions : The number of daily confirmed Bitcoin transactions. - btc_n_transactions_total : Total number of transactions. - btc_n_transactions_excluding_popular : The total number of Bitcoin transactions, excluding the 100 most popular addresses. - btc_n_transactions_excluding_chains_longer_than_100 : The total number of Bitcoin transactions per day excluding long transaction chains. - btc_output_volume : The total value of all transaction outputs per day. - btc_estimated_transaction_volume : The total estimated value of transactions on the Bitcoin blockchain. - btc_estimated_transaction_volume_usd : The estimated transaction value in USD value. **Ethereum Dataset (ethereum_dataset.csv):** This dataset has the following features - Date(UTC) : Date of transaction - UnixTimeStamp : unix timestamp - eth_etherprice : price of ethereum - eth_tx : number of transactions per day - eth_address : Cumulative address growth - eth_supply : Number of ethers in supply - eth_marketcap : Market cap in USD - eth_hashrate : hash rate in GH/s - eth_difficulty : Difficulty level in TH - eth_blocks : number of blocks per day - eth_uncles : number of uncles per day - eth_blocksize : average block size in bytes - eth_blocktime : average block time in seconds - eth_gasprice : Average gas price in Wei - eth_gaslimit : Gas limit per day - eth_gasused : total gas used per day - eth_ethersupply : new ether supply per day - eth_chaindatasize : chain data size in bytes - eth_ens_register : Ethereal Name Service (ENS) registrations per day Acknowledgements This data is taken from [coinmarketcap][5] and it is [free][6] to use the data. Bitcoin dataset is obtained from [Blockchain Info][7]. Ethereum dataset is obtained from [Etherscan][8]. Cover Image : Photo by Thomas Malama on Unsplash Inspiration Some of the questions which could be inferred from this dataset are: 1. How did the historical prices / market capitalizations of various currencies change over time? 2. Predicting the future price of the currencies 3. Which currencies are more volatile and which ones are more stable? 4. How does the price fluctuations of currencies correlate with each other? 5. Seasonal trend in the price fluctuations Bitcoin / Ethereum dataset could be used to look at the following: 1. Factors affecting the bitcoin / ether price. 2. Directional prediction of bitcoin / ether price. (refer [this paper][9] for more inspiration) 3. Actual bitcoin price prediction. [1]: https://www.linkedin.com/pulse/blockchain-absolute-beginners-mohit-mamoria [2]: https://coinmarketcap.com/ [3]: https://blockchain.info/ [4]: https://etherscan.io/charts [5]: https://coinmarketcap.com/ [6]: https://coinmarketcap.com/faq/ [7]: https://blockchain.info/ [8]: https://etherscan.io/charts [9]: http://cs229.stanford.edu/proj2014/Isaac%20Madan,%20Shaurya%20Saluja,%20Aojia%20Zhao,Automated%20Bitcoin%20Trading%20via%20Machine%20Learning%20Algorithms.pdf