Download Cryptocurrency Data in Python by Using Crypto Compare API

How to use Cryptocompare API to download cryptocurrency data in Python? Cryptocompare is a cryptocurrency market data provider. And this blog explains how you can go about it with super easy simple steps and readies you to apply it whenever you want!

If you are taking your first step toward Algorithmic Trading or within the cryptocurrency world, then this article is for you!

We cover:

  • Why CryptoCompare?
  • Import Libraries
  • Set API Key
  • Fetch All Cryptocurrency Tickers
  • Download Historical Data With a Specific Frequency
  • Download Bitcoin Hourly Historical Data
  • Plot the Data

Why CryptoCompare?

CryptoCompare allows users to get live prices, graph them and make market research analysis from most of the important crypto exchanges. It also allows us to get limited free data for downloading. That’s what we’re going to do in this article.

Import Libraries

First, let’s install the cryptocompare library in our Jupyter notebook:

Now, we are going to import the following libraries: cryptocompare, pandas, datetime (only its datetime method) and matplotlib.

Are you familiar with the last two code lines of the ‘matplotlib’ library? Don’t worry at all! Please check our great article on the ‘matplotlib’ library to know everything about it.

Set API Key

The API Key is used to have a valid authentication for the user. It serves to identify your account and access an automated program.

To download the data, you will need to authenticate yourself on First, you write your ‘cryptocompare_API_key’ and then you set the API key to the market provider. As the following:

Fetch All Cryptocurrency Tickers

You can do this easily. First, you get the ticker list, which is a dictionary. Then you transform the dictionary into a dataframe. Super easy, right?


Download Historical Data With a Specific Frequency

Contrary to the yahoo finance library, in you have for each frequency a specific method to download data. Let’s explain to you this:

You can fetch the historical data for the ticker symbol you want. The historical data in daily, hourly and minute frequency levels are available.


# For daily data
cryptocompare.get_historical_price_day(ticker_symbol, currency, limit=limit_value, exchange=exchange_name, toTs=data_before_timestamp)

# For hourly data
cryptocompare.get_historical_price_hour(ticker_symbol, currency, limit=limit_value, exchange=exchange_name, toTs=data_before_timestamp)

# For minute data
cryptocompare.get_historical_price_minute(ticker_symbol, currency, limit=limit_value, exchange=exchange_name, toTs=data_before_timestamp)

ticker_symbol: Ticker symbol whose data is required
currency: Currency in which the price is quoted
limit_value: The maximum number of bars to fetch (max. value is 2000)
exchange_name: The exchange to use when fetching the data
data_before_timestamp: Return the data before this timestamp (UNIX epoch time or a datetime object)

A dictionary containing the historical data.

Download Bitcoin Hourly Historical Data

For illustration purposes, let’s extract the hourly historical data for the ‘BTC’ before 01-May-2021.

First, you define the ticker symbol, and then you specify that the asset price will be in dollars. Besides, since Cryptocompare establishes a limit to download data, we set the limit number of data points to return as ‘limit_value’.

We choose the ‘CCCAGG’ as the crypto exchange, which means we are going to download the aggregate prices that Cryptocompare has.

Next, we are going to use the ‘get_historical_hour’ function to download hourly Bitcoin prices.

Then, since the download information comes as a dictionary type, we transform it into a dataframe. In addition, we set the ‘time’ column as our index. Finally, the method provides the ‘time’ column as seconds, that’s why we not only change its string type to a datetime type, but also specify the ‘unit’ argument as ‘s’.

timehighlowopenvolume fromvolume toclose
06/02/2021 21:004029627400924740141361264945.08675E+134009247
06/02/2021 22:004050397399919640092471688236.79449E+134004380
06/02/2021 23:004004419391321740043804298181.69581E+143926701
07/02/2021 0:003965158388027139267013982991.5625E+143960394
07/02/2021 1:003963317385573839603942625881.02679E+143870644

Visit QuantInsti Blog to learn how to plot the data:

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