import requests
from urllib.parse import urljoin
from pprint import pprint
import json
import pandas as pd
import io
base_url = "https://api.clearmacro.com"
Follow the instructions for how to retrieve your personal API Key here: https://api.clearmacro.com/docs/authentication
api_key
Let's confirm the key is working, this should print out some further information about your API key.
url = urljoin(base_url, "/api/v3/key")
headers = {"accept": "application/json", "X-API-KEY": api_key}
response = requests.get(url, headers=headers)
response.raise_for_status() # Check for any errors in the response
response.json()
Ok, now let's list the signals in the library:
url = urljoin(base_url, "/api/v3/signal")
headers = {"accept": "application/json", "X-API-KEY": api_key}
response = requests.get(url=url, headers=headers)
signals = json.loads(response.content)
signals
Let's look at a particular signal, and find the latest version:
signal_code
ECNINF
signal = [signal for signal in signals if signal["code"] == signal_code][0]
print(signal)
latest_version = signal["versions"][-1]
print("Latest version:", latest_version)
Now let's look at what variants are available for this Signal version:
url = urljoin(base_url, f"/api/v3/signal/{signal_code}/{latest_version}")
headers = {"accept": "application/json", "X-API-KEY": api_key}
response = requests.get(url=url, headers=headers)
signal_variants = json.loads(response.content)
signal_variants
Now we have everything we need to query the full timeseries history for one or more of these variants. Let's pick two countries (using their ISO code to identify them) and pull the data for each:
country_isos = ["USA", "GBR", "CAN"]
for country_iso in country_isos:
print("Country:", country_iso)
url = urljoin(base_url, f"/api/v3/signal/{signal_code}/{latest_version}/data?country_iso={country_iso}&fmt=csv")
headers = {"accept": "application/csv", "X-API-KEY": api_key}
response = requests.get(url=url, headers=headers)
csv_file = io.BytesIO(response.content)
# Read CSV data into a pandas DataFrame
data = pd.read_csv(csv_file, encoding="utf-8")
print(data)
To save the data down locally, you can simply add the following line of code to the above for loop:
#pd.to_csv(data, f"{country_iso}.csv")