Data Subscribers

Catalyst users may subscribe to data sets from the Enigma Data Marketplace and use them in their trading algorithms. Depending on their properties, data sets may be usable for backtesting as well as live trading.


The Enigma Data Marketplace enables data providers to publish data sets and make them directly available to subscribers. All published data is validated and hosted on the Enigma Data Marketplace. Your subscription payment is protected by the Enigma Data Marketplace Smart Contract. If a provider stops delivering data as per their agreement, the subscriber will be automatically refunded on a pro rata basis for the unfulfilled portion of their subscription.

The following command subscribes to the marketcap data set:

$ catalyst marketplace subscribe --dataset=marketcap

The price for a monthly subscription to this dataset is 10 ENG
Checking that the ENG balance in 0x..... is greater than 10 ENG... OK.
Please confirm that you agree to pay 10 ENG for a monthly subscription to the dataset "marketcap" starting today. [default: Y]
Ready to subscribe to dataset marketcap.

Catalyst will then provide you with instructions to sign two different transactions to process your subscription to the dataset.


The Catalyst ingest-data command downloads and makes the given data set available locally for backtesting or live trading with Catalyst.

The following command ingests the marketcap data set:

$ catalyst marketplace ingest --dataset=marketcap
Starting download of dataset for ingestion...
Dataset downloaded successfully. Processing dataset...
INFO: Marketplace: Processing file 0 of 1
INFO: Marketplace: Processing file 1 of 1

If you need to keep the data up to date while running a live algorithm, you can schedule a job (cron in Linux/MacOS) to run the above command at periodic intervals, and the live/paper trading algorithm will always retrieve the latest data available on disk on every iteration of the algorithm.

Use in an Algorithm

Once you have subscribed to a data set, and ingested the required data if appropriate, the data source is now usable in your algorithms.

This snippet of code below shows how to use our Marketcap data set in the initialize() function of an algorithm to rank the top-five asset by market cap:

from catalyst.api import get_dataset
from import set_print_settings

def initialize(context):
    # Get the marketcap data
    df = get_dataset(
        'marketcap', start=context.datetime
    )  # type: pd.DataFrame

    # Keep only the 100 top currencies by market cap
    df = df.loc[df['market_cap_usd'].isin(df['market_cap_usd'].nlargest(100))]
    # Sort the currencies by market cap
    df.sort_values(by=['market_cap_usd'], ascending=True, inplace=True)

    # Prepare the standard output for a wide DataFrame
    # Display the top 100 largest currencies
    print('the marketplace data:\n{}'.format(df))

    # Out of the 100 largest currencies, we want to trade the smallest
    # 5 markets with an Ether quote on the exchange of our algorithm
    quote_currency = 'eth'
    exchange = context.exchanges[next(iter(context.exchanges))]
    # Get a list of symbols only for assets tradable since the start
    # date of our algorithm.
    symbols = [a.symbol for a in exchange.assets
               if a.start_date < context.datetime]

    # Initialize an asset list which we will populate with the 5 smallest
    # markets
    context.assets = []
    for currency, price in df['market_cap_usd'].iteritems():
        if len(context.assets) >= 5:

        s = '{}_{}'.format(currency.decode('utf-8'), quote_currency)
        if s in symbols: