API Examples

pip install artemis==0.1.0
pip install matplotlib

import os
import requests

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
from datetime import datetime

from artemis import Artemis
#from google.colab import userdata

# Set up Artemis API Client
client = Artemis(api_key=API_KEY)

# Get all assets available on Artemis

# Limiting to first 50 for display purposes
assets = client.asset.list()
print(f"There are {len(assets['assets'])} assets available on Artemis\n")

sorted([asset['symbol'] for asset in assets['assets']][:50])

# Get the supported metrics for select assets
# Get data for the first five supported metrics

filtered_assets = list(filter(lambda x: x['symbol'] in ['aave', 'mkr'], assets['assets']))

for i, asset in enumerate(filtered_assets):
    artemis_id, symbol = asset['artemis_id'], asset['symbol']
    supported_metrics_for_asset = client.asset.list_metrics(artemis_id).metrics

    first_five_metrics = ','.join(supported_metrics_for_asset[:5])

    metrics_for_asset = client.fetch_metrics(
        metric_names=first_five_metrics,
        symbols=symbol,
    ).data

    output = metrics_for_asset.symbols[symbol]

    print(f"Getting data and supported metrics for symbol: {symbol}")
    print(f" - Supported metrics: {supported_metrics_for_asset}")
    print(f" - Data for the first five metrics {output}\n")

# Calculate Net Income as Protocol Revenue - Total Expenses. Can use Net Income as part of DCF.
# Available ones to test: aave, maker, lido
symbol = 'eth'
start_date = '2024-01-01'
end_date = '2025-04-30'

metrics="protocol_revenue,total_expenses"

metrics_for_asset = client.fetch_metrics(
    metric_names=metrics,
    symbols=symbol,
    start_date=start_date,
    end_date=end_date,
)

data = metrics_for_asset.data.symbols[symbol]

df_revenue = pd.DataFrame(data["ecosystem_revenue"])
df_expenses = pd.DataFrame(data["total_expenses"])
df_revenue["date"] = pd.to_datetime(df_revenue["date"])
df_expenses["date"] = pd.to_datetime(df_expenses["date"])


# Monthly Aggregation (sum only 'val' column)
df_revenue_monthly = (
    df_revenue.groupby(df_revenue["date"].dt.to_period("M"))["val"]
    .sum()
    .reset_index()
)
df_expenses_monthly = (
    df_expenses.groupby(df_expenses["date"].dt.to_period("M"))["val"]
    .sum()
    .reset_index()
)

# Convert period to timestamp for plotting
df_revenue_monthly["date"] = df_revenue_monthly["date"].dt.to_timestamp()
df_expenses_monthly["date"] = df_expenses_monthly["date"].dt.to_timestamp()

# Calculate net income
df_net_income = df_revenue_monthly.copy()
df_net_income["val"] = df_revenue_monthly["val"] - df_expenses_monthly["val"]

# Pretty print values in $M
to_pretty_millions = lambda x: f"${x / 1e6:.1f}M"

df_revenue_monthly["val_pretty"] = df_revenue_monthly["val"].apply(to_pretty_millions)
df_expenses_monthly["val_pretty"] = df_expenses_monthly["val"].apply(to_pretty_millions)
df_net_income["val_pretty"] = df_net_income["val"].apply(to_pretty_millions)

# Print all 3 tables
print("\n📈 Revenue:")
print(df_revenue_monthly[["date", "val_pretty"]])

print("\n💸 Expenses:")
print(df_expenses_monthly[["date", "val_pretty"]])

print("\n🧾 Net Income:")
print(df_net_income[["date", "val_pretty"]])