karpet 0.3

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

Contents

• Karpet

  • Dependencies

  • Usage

    • fetch_historical_data()

    • fetch_exchanges()

    • fetch_tweets()

    • fetch_google_trends()

    • fetch_news()

    • fetch_top_news()

  • Changelog

    • 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

2. Import the library class first.

from karpet import Karpet

fetch_historical_data()

Retrieves historical data.

k = Karpet(date(2019, 1, 1), date(2019, 5, 1), cryptocompare_api_key="...")
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(cryptocompare_api_key="...")
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.
}

Changelog

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.