coinmetrics-api-client 2022.8.29.6

Python client for Coin Metrics API v4.

Coin Metrics Python API v4 client library

This is an official Python API client for Coin Metrics API v4.

Installation and Updates

To install the client you can run the following command:

pip install coinmetrics-api-client

Note that the client is updated regularly to reflect the changes made in API v4. Ensure that your latest version matches with what's in pyPI

To update your version, run the following command:

pip install coinmetrics-api-client -U

Introduction

You can use this client for querying all kinds of data with your API.

To initialize the client you should use your API key, and the CoinMetricsClient class like the following.

from coinmetrics.api_client import CoinMetricsClient

client = CoinMetricsClient("<cm_api_key>")

# or to use community API:
client = CoinMetricsClient()

After that you can use the client object for getting stuff like available markets:

print(client.catalog_markets())

or to query all available assets along with what is available for those assets, like metrics, markets:

print(client.catalog_assets())

you can also use filters for the catalog endpoints like this:

print(client.catalog_assets(assets=['btc']))

in this case you would get all the information for btc only

You can use this client to connect to our API v4 and get catalog or timeseries data from python environment. It natively supports paging over the data so you can use it to iterate over timeseries entries seamlessly.

The client can be used to query both pro and community data.

The full list of methods can be found in the API Client Spec.

Examples

The API Client allows you to chain together workflows for importing, transforming, then exporting Coin Metrics data. Below are examples of common use-cases that can be altered to tailor your specific needs.

Example Notebooks

• walkthrough_community.ipynb: Walks through the basic functionality available using the community client.

Asset Metrics

• bbb_metrics_csv_exporter_using_plain_requests.py: Queries block-by-block metrics using the requests library and exports the output into a CSV file.
• bbb_metrics_json_exporter.py: Queries block-by-block metrics and exports the output into a JSON file.
• eod_metrics_csv_exporter.py: Exports a set of user-defined metrics and assets published at end-of-day and exports the output into a CSV file.
• reference_rates_json_exporter.py: Queries Coin Metrics Reference Rates at a user-defined frequency for a set of assets, then exports the output into a JSON file.

Market Data

• books_json_exporter.py: Queries market orderbook data then exports the output into a JSON file.
• candles_json_exporter.py: Queries market candles data then exports the output into a JSON file.
• funding_rates_json_exporter.py: Queries market funding rates data then exports the output into a JSON file.
• trades_csv_exporter.py: Queries market trades data then exports the output into a CSV file.
• trades_json_exporter.py: Queries market trades data then exports the output into a JSON file.

Getting timeseries data

For getting timeseries data you want to use methods of the client class that start with get_.

For example if you want to get a bunch of market data trades for coinbase btc-usd pair you can run something similar to the following:

for trade in client.get_market_trades(
    markets='coinbase-btc-usd-spot', 
    start_time='2020-01-01', 
    end_time='2020-01-03',
    limit_per_market=10
):
    print(trade)

Or if you want to see daily btc asset metrics you can use something like this:

for metric_data in client.get_asset_metrics(assets='btc', 
                                            metrics=['ReferenceRateUSD', 'BlkHgt', 'AdrActCnt',  
                                                     'AdrActRecCnt', 'FlowOutBFXUSD'], 
                                            frequency='1d',
                                            limit_per_asset=10):
    print(metric_data)

This will print you the requested metrics for all the days where we have any of the metrics present.

DataFrames

(New in >=2021.9.30.14.30)

Timeseries data can be transformed into a pandas dataframe by using the to_dataframe() method. The code snippet below shows how:

import pandas as pd
from coinmetrics.api_client import CoinMetricsClient
from os import environ

client = CoinMetricsClient()
trades = client.get_market_trades(
    markets='coinbase-btc-usd-spot', 
    start_time='2021-09-19T00:00:00Z', 
    limit_per_market=10
)
trades_df = trades.to_dataframe()
print(trades_df.head())

If you want to use dataframes, then you will need to install pandas

Notes

• This only works with requests that return the type DataCollection. Thus, catalog requests, which return lists cannot be returned as dataframes. Please see the API Client Spec for a full list of requests and their return types.
• API restrictions apply. Some requests may return empty results due to limited access to the API from you API key.

Type Conversion

(New in >=2021.12.17.18.00)

As of version 2021.12.17.18.00 or later, outputs from the to_dataframe function automatically convert the dtypes for a dataframe to the optimal pandas types.

metrics_list = ['volume_trusted_spot_usd_1d', 'SplyFF', 'AdrBalUSD1Cnt']
asset_list = ['btc','xmr']
start_time='2021-12-01'
df_metrics = client.get_asset_metrics(
  assets=asset_list, metrics=metrics_list, start_time=start_time, limit_per_asset=3
).to_dataframe()
print(df_metrics.dtypes)

asset                          string
time                           datetime64[ns, tzutc()]
AdrBalUSD1Cnt                   Int64
SplyFF                        Float64
volume_trusted_spot_usd_1d    Float64
dtype: object

This can be turned off by setting optimize_pandas_types=False

Alternatively, you can manually enter your own type conversion by passing in a dictionary for dtype_mapper. This can be done in conjunction with pandas' built in type optimizations.

mapper = {
  'SplyFF': 'Float64',
  'AdrBalUSD1Cnt': 'Int64',
}
df_mapped = client.get_asset_metrics(
  assets=asset_list, metrics=metrics_list, start_time=start_time, limit_per_asset=3
).to_dataframe(dtype_mapper=mapper, optimize_pandas_types=True)
print(df_mapped.dtypes)

asset                                          object
time                          datetime64[ns, tzutc()]
AdrBalUSD1Cnt                                   Int64
SplyFF                                        Float64
volume_trusted_spot_usd_1d                    float64
dtype: object

Or as strictly the only types in the dataframe

dtype_mapper = {
    'ReferenceRateUSD': np.float64,
    'time': np.datetime64
}
df = client.get_asset_metrics(
  assets='btc', metrics='ReferenceRateUSD', start_time='2022-06-15', limit_per_asset=1
).to_dataframe(dtype_mapper=dtype_mapper, optimize_pandas_types=False)
df.info()

RangeIndex: 1 entries, 0 to 0
Data columns (total 3 columns):
 #   Column            Non-Null Count  Dtype         
---  ------            --------------  -----         
 0   asset             1 non-null      object        
 1   time              1 non-null      datetime64[ns]
 2   ReferenceRateUSD  1 non-null      float64       
dtypes: datetime64[ns](1), float64(1), object(1)
memory usage: 152.0+ bytes

Note that in order to pass a custom datetime object, setting a dtype_mapper is mandatory.

Pandas type conversion tends to be more performant. But if there are custom operations that must be done using numpy datatypes, this option will let you perform them.

Paging

You can make the datapoints to iterate from start (default) or from end.

for that you should use a paging_from argument like the following:

from coinmetrics.api_client import CoinMetricsClient
from coinmetrics.constants import PagingFrom

client = CoinMetricsClient()

for metric_data in client.get_asset_metrics(assets='btc', metrics=['ReferenceRateUSD'],
                                            paging_from=PagingFrom.START):
    print(metric_data)

PagingFrom.END: is available but by default it will page from the start.

SSL Certs verification

Sometimes your organization network have special rules on SSL certs verification and in this case you might face the following error when running the script:

SSLError: HTTPSConnectionPool(host='api.coinmetrics.io', port=443): Max retries exceeded with url: <some_url_path> (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self signed certificate in certificate chain (_ssl.c:1123)')))

In this case, you can pass an option during client initialization to disable ssl verification for requests like this:

client = CoinMetricsClient(verify_ssl_certs=False)

We don't recommend setting it to False by default and you should make sure you understand the security risks of disabling SSL certs verification.

Additionally, you may choose to specify the path to the SSL certificates on your machine. This may cause errors where Python is unable to locate the certificates on your machine, particularly when using Python virtual environments.

from coinmetrics.api_client import CoinMetricsClient
SSL_CERT_LOCATION = '/Users/<USER_NAME>/Library/Python/3.8/lib/python/site-packages/certifi/cacert.pem'
client = CoinMetricsClient(verify_ssl_certs=SSL_CERT_LOCATION)

A quick way to find the certs on your machine is:
python3 -c "import requests; print(requests.certs.where())"
And note that this will change based on whether or not you are using a Python virtual environment or not

Installing and running coinmetrics package and other python packages behind a secure python network

Related to SSL Certs verification, you may have trouble installing and updating PyPi packages to your local environment. So you may need to choose the best solution for your company and environment - either using package managers or installing offline.

Installing using package managers

Full instructions for setting up your environment to use conda, pip, yarn, npm, etc. can be found here. Additionally, a workaround to disable SSL verification when installing a trusted Python package is this:

pip install --trusted-host pypi.python.org <packagename>

Although it is important to make sure you understand the risks associated with disabling SSL verification and ensure compliance with company policies.

Installing Python packages locally/ offline

It may be easier to download and install the package locally. Steps:

1. Download the files for the Coin Metrics API Client from PyPi
2. Install it locally

Requests Proxy

Sometimes your organization has special rules on making requests to third parties and you have to use proxies in order to comply with the rules.

For proxies that don't require auth you can specify them similar to this example:

client = CoinMetricsClient(proxy_url=f'http://<hostname>:<port>')

For proxies that require auth, you should be able to specify username and password similar to this example:

client = CoinMetricsClient(proxy_url=f'http://<username>:<password>@<hostname>:<port>')

Extended documentation

For more information about the available methods in the client please reference API Client Spec