Author: Bernard Brenyah
Table of Content
- Frameworks Used
- Pending Features
- How To Use
- Release History
- How To Contribute
I spent the past couple of months on a graduate dissertation which required the use of both historical and current data on cryptocurrencies. After browsing the Python Packaging Index (PYPI), I was frustrated by the lack of a Python package that catered for such needs. As far as I know only cyrptoCMD came close to meeting my needs. The only drawback is the that package only delivers historical data. OK so "why not edit that project and make a pull request with your suggestions?"
That was the original plan until I realised that the scraping code could relatively be done quickly with the help of
pandas package. If I went with the original plan I would have to rewrite the whole code and implementation ideas for
cryptoCMD project. The only logical conclusion was starting a new project that I wish I had during my data collection process. A project inspired by scripts I generated for my dissertation project.
As a result, this project is the first Python project that supplies both historical and current data on cryptocurrency markets and assets in one coherent package.
This package leverages the power of the following packages:
The easiest way to install Coinsta is to use the default python package installer
pip install coinsta
and for the few brave ones who like bleeding edge technology, the latest source can be installed via with this command:
pip install git+git://github.com/PyDataBlog/Coinsta.git
- Current global information on cryptocurrency markets.
- Current market information on the top 100 cryptocurrencies.
- Current data on a specified cryptocurrency.
- Historical data on all active cryptocurrencies.
- Get historical snapshots of cryptocurrencies.
- Migrate the current class to the new CoinMarketCap API
- Support for Python 3.5
- test compliance with Python 3.7
- Improve documentation and doc strings
- Optimisation of code
- Support for CoinMarketCap's historical snapshots
How To Use
# import the Historical class from coinsta.core import Historical from datetime import date # specify dates considered start = date(2018, 3, 1) end = date(2018, 6,1) # get data coin_spec = Historical('btc', start=start, end=end) btc_data = coin_spec.get_data() print(btc_data.head()) #by default the end date is set to use the "today's" date # of the user unless otherwise specified like above
Alternative Constructors for Historical data from dates in the form of strings (YYYY-MM-DD) or (YYYY/MM/DD):
from coinsta.core import Historical # default alternative method for "-" formatted date strings alt_spec = Historical.from_strings('btc', '2018-3-1','2018-6-1', hyphen=True) alt_btc = alt_spec.get_data() print(alt_btc.head()) # another alternative method for "/" formated date strings other_spec = Historical.from_strings('btc', '2018/3/1','2018/6/1', hyphen=False) another_btc = other_spec.get_data() print(another_btc.head())
get_data() method and the
from_strings method from the Historical class returns a
pandas DataFrame object with sorted in an ascending order indexed the dates specified by the user:
Open High Low Close Volume Market_cap Date
So what was the top cryptocurrency (in terms of market capitalisation) on date XYZ?
Luckily, CoinMarketCap delivers periodic snapshots of the this type of rankings. The
HistoricalSnapshot class taps into data to supply users with such information.
The Historical Snapshot feature returns a Pandas DataFrame object with the following self describing columns:
Index(['Rank', 'Name', 'Symbol', 'Market Cap', 'Price', 'Circulating Supply', 'Volume (24h)', '% 1h', '% 24h', '% 7d'], dtype='object')
from coinsta.core import HistoricalSnapshot from datetime import date snap_date = date(2018, 7, 29) july_2018 = HistoricalSnapshot(snap_date) july_2018_snapshot = july_2018.get_snapshot() print(july_2018_snapshot.info())
# import the Current class and instantiate the current class object with specifications from coinsta.core import Current cur = Current(api_key='YOUR-API-KEY-HERE', currency='eur') # Default is usd # get current market information on a specified crypto btc_current = cur.get_current('btc') print(btc_current) # get the top 100 cryptos (in terms of market cap) current_100 = cur.top_100(limit=100) # Default limit is 100 but can be increased as a user wishes print(current_100.head()) # get global overview of crypto markets glo_info = cur.global_info() print(glo_info)
get_current() method from the current class returns a
pandas DataFrame object with one column representing the following named rows of information on the cryptocurrency specified:
dict_keys(['name', 'symbol', 'rank', 'circulating_supply', 'total_supply', 'max_supply', 'price', 'volume_24h', 'percent_change_1h', 'percent_change_24h', 'percent_change_7d', 'market_cap', 'last_updated'])
top_100 method in the current class returns a
pandas DataFrame object of the top 100 cryptocurrencies in terms of market capitalization. The following are the columns returned:
['id', 'name', 'symbol', 'slug', 'num_market_pairs', 'date_added', 'tags', 'max_supply', 'circulating_supply', 'total_supply', 'platform', 'cmc_rank', 'last_updated', '*currency*.price', '*currency*.volume_24h', '*currency*.percent_change_1h', '*currency*.percent_change_24h', '*currency*.percent_change_7d', '*currency*.market_cap', '*currency*.last_updated']
global_info() method in Current class returns a dictionary with the following keys as an overview of cryptocurrency markets as a whole
dict_keys(['active_cryptos', 'active_exchanges', 'btc_dominance', 'eth_dominance', 'total_market_cap', 'total_volume_24h', 'total_volume_24h_reported', 'altcoin_volume_24h', 'altcoin_volume_24h_reported', 'altcoin_market_cap', 'last_updated'])
- 0.1.5 - Updated historical snapshot to suit the new upstream changes from CoinMarketCap
- 0.1.4 - Re-wrote the Current classes to use the new CoinMarketCap API
- 0.1.3 - Added Historical Snapshot feature
- 0.1.2 - Added support for Python 3.5 and 3.7
- 0.1.1 - Added license info and improved documentation
- 0.1.0 - Initial Public Release
How to Contribute
This project welcomes contributions from anyone interested in this project. Guidelines for contribution is being drafted but for now a pull request with explanation of the contributions will suffice.
Shoutout to CoinMarketCap