karpet 0.3.2

Library for fetching coin/token historical data, trends, tweets and more.

Contents

• Karpet
  • Dependencies
  • Usage
    • fetch_crypto_historical_data()
    • fetch_exchanges()
    • fetch_tweets()
    • fetch_google_trends()
    • fetch_news()
    • fetch_top_news()
    • get_coin_ids()
  • Changelog
    • 0.3.2
    • 0.3.1
    • 0.3
    • 0.2.5
    • 0.2.4
    • 0.2.3
    • 0.2.2
    • 0.2.1
    • 0.2
    • 0.1
  • Credits

Karpet

Karpet is a tiny library with just a few dependencies for fetching coins/tokens metrics data from the internet.

It can provide following data:

• coin/token historical price data (no limits)
• google trends for the given list of keywords (longer period than official API)
• twitter scraping for the given keywords (no limits)
• much more info about crypto coins/tokens (no rate limits)

What is upcoming?

• Reddit metrics
• Have a request? Open an issue ;)

Dependencies

Library uses a few nifty dependencies and is Python 3.6+ only. There is no need to install dependencies you don’t need. Therefore this library utilizes extras which install optional dependencies:

• for Google trends - google
• for Twitter scraping - twitter

Usage

1. Install the library via pip.

pip install karpet  # Basics only
pip install karpet[twitter]  # For Twitter scraping
pip install karpet[google]  # For Google trends
pip install karpet[twitter,google]  # All features

1. Import the library class first.

from karpet import Karpet

fetch_crypto_historical_data()

Retrieves historical data.

k = Karpet(date(2019, 1, 1), date(2019, 5, 1))
df = k.fetch_crypto_historical_data(symbol="BTC")  # Dataframe with historical data.
df.head()

              close conversionSymbol conversionType     high      low     open  volumefrom      volumeto
date
2019-01-01  3880.15                          direct  3938.75  3696.94  3747.39    45104.29  1.705983e+08
2019-01-02  3961.01                          direct  3989.59  3826.29  3880.15    54034.73  2.108546e+08
2019-01-03  3835.86                          direct  3965.52  3778.76  3961.01    45585.19  1.764881e+08
2019-01-04  3874.06                          direct  3901.65  3783.88  3835.86    44398.90  1.705076e+08
2019-01-05  3855.39                          direct  3926.92  3841.13  3874.06    35766.65  1.394385e+08

fetch_exchanges()

Retrieves exchange list.

k = Karpet()
k.fetch_exchanges("nrg")
['DigiFinex', 'KuCoin', 'CryptoBridge', 'Bitbns', 'CoinExchange']

fetch_tweets()

Retrieves twitter tweets.

k = Karpet(date(2019, 1, 1), date(2019, 5, 1))
df = k.fetch_tweets(kw_list=["bitcoin"], lang="en")  # Dataframe with tweets.
df.head()

fetch_google_trends()

Retrieves Google Trends - in percents for the given date range.

k = Karpet(date(2019, 1, 1), date(2019, 5, 1))
df = k.fetch_google_trends(kw_list=["bitcoin"])  # Dataframe with trends.
df.head()

And with a few lines of code you can get a chart

df = df.set_index("date")
df.plot()

fetch_news()

Retrieves crypto news.

k = Karpet()
news = k.fetch_news("btc")  # Gets 10 news.
print(news[0])
{
   'url': 'https://cointelegraph.com/ ....',  # Truncated.
   'title': 'Shell Invests in Blockchain-Based Energy Startup',
   'description': 'The world’s fifth top oil and gas firm, Shell, has...',  # Truncated.
   'date': datetime.datetime(2019, 7, 28, 9, 24, tzinfo=datetime.timezone(datetime.timedelta(seconds=3600)))
   'image': 'https://images.cointelegraph.com/....jpg'  # Truncated.
}
news = k.fetch_news("btc", limit=30)  # Gets 30 news.

fetch_top_news()

Retrieves top crypto news in 2 categories:

• Editor’s choices - articles picked by editors
• Hot stories - articles with most views

k = Karpet()
editors_choices, top_stories = k.fetch_top_news()
print(len(editors_choices))
5
print(len(top_stories))
5
print(editors_choices[0])
{
   'url': 'https://cointelegraph.com/...',  # Truncated.
   'title': 'Bank of China’s New Infographic Shows Why Bitcoin Price Is Going Up',
   'date': datetime.datetime(2019, 7, 27, 10, 7, tzinfo=datetime.timezone(datetime.timedelta(seconds=3600))),
   'image': 'https://images.cointelegraph.com/images/740_aHR...', # Truncated.
   'description': 'The Chinese central bank released on its website an ...'  # Truncated.
}
print(top_stories[0])
{
   'url': 'https://cointelegraph.com/...',  # Truncated.
   'title': 'Bitcoin Price Shuns Volatility as Analysts Warn of Potential Drop to $7,000',
   'date': datetime.datetime(2019, 7, 27, 10, 7, tzinfo=datetime.timezone(datetime.timedelta(seconds=3600))),
   'image': 'https://images.cointelegraph.com/images/740_aHR0c...'  # Truncated.
   'description': 'Stability around $10,600 for Bitcoin price is ...'  # Truncated.
}

get_coin_ids()

Resolves coin ID’s based on the given symbol (there are coins out there with identical symbol).

Use this to get distinctive coin ID which can be used as id param for method fetch_crypto_historical_data().

k = Karpet()
print(k.get_coin_ids("sta"))
['statera']

Changelog

0.3.2

• new method get_coin_ids()
• method fetch_crypto_historical_data() has id param now

0.3.1

• migrated to coingecko.com API (no API key needed anymore)

0.3

• migrated to cryptocompare.com API (you need an API key now)
• requirements are now managed by Poetry

0.2.5

• added fetch_top_news() method for top crypto news separated in 2 categories

0.2.4

• fetch_news() adds new “description” item and renames “image_url” to “image”
• all fetch_news() item properties are now presented even if they are None

0.2.3

• simplified import from from karpet.karpet import Karpet to from karpet import Karpet

0.2.2

• added fetch_news() method for retrieving crypto news

0.2.1

• added fetch_exchanges() method for retrieving symbol exchange list
• removed obsolete library dependency

0.2

• twitter scraping added

0.1

• initial release

Credits

This is my personal library I use in my long-term project. I can pretty much guarantee it will live for a long time then. I will add new features over time and I more than welcome any help or bug reports. Feel free to open an issue or merge request.

The code is is licensed under MIT license.