Applying Different Types of Marketcap Metrics

This notebook demonstrates basic functionality offered by the Coin Metrics Python API Client and Network Data Pro.

Coin Metrics offers a vast assortment of data for hundreds of cryptoassets. The Python API Client allows for easy access to this data using Python without needing to create your own wrappers using requests and other such libraries.

Resources

To understand the data that Coin Metrics offers, feel free to peruse the resources below.

• The Coin Metrics API v4 website contains the full set of endpoints and data offered by Coin Metrics.

• The Coin Metrics Product Documentation gives detailed, conceptual explanations of the data that Coin Metrics offers.

• The API Spec contains a full list of functions.

File Download

Download the entire notebook as either a jupyter notebook to run yourself or as a pdf from the two links below

1MB

NDP - Market Cap Metrics.pdf

pdf

267KB

NDP_marketcap_metrics.ipynb

Setup

from os import environ
import sys
import pandas as pd
import numpy as np
import seaborn as sns
import logging
from datetime import date, datetime, timedelta
from coinmetrics.api_client import CoinMetricsClient
import json
import logging
import matplotlib.ticker as mticker
from matplotlib.dates import DateFormatter
from matplotlib.ticker import FuncFormatter
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
%matplotlib inline

logging.basicConfig(
    format='%(asctime)s %(levelname)-8s %(message)s',
    level=logging.INFO,
    datefmt='%Y-%m-%d %H:%M:%S'
)

end_time = datetime.today().date()
start_time = end_time - timedelta(days=90)

# We recommend privately storing your API key in your local environment.
try:
    api_key = environ["CM_API_KEY"]
    logging.info("Using API key found in environment")
except KeyError:
    api_key = ""
    logging.info("API key not found. Using community client")


client = CoinMetricsClient(api_key)

2024-09-16 15:00:54 INFO     Using API key found in environment

Market Cap Based on Verified On-Chain Supply

In order to trustlessly verify market capitalization, Coin Metrics directly indexes the blockchain to independently validate the amount of circulating supply.

The CapMrktCurUSD metric offers the most reliable measure of asset supply, with the trade-off of slightly limited asset coverage.

df_reference_capmkrtcur = client.reference_data_asset_metrics(metrics='CapMrktCurUSD').to_dataframe()

df_reference_capmkrtcur

	metric	full_name	description	product	category	subcategory	unit	data_type	type
0	CapMrktCurUSD	Capitalization, market, current supply, USD	The sum USD value of the current supply. Also ...	Network Data	Market	Market Capitalization	USD	decimal	Product

list_capmrktcur_assets = [a['asset'] for a in client.catalog_asset_metrics_v2(metrics='CapMrktCurUSD')]

print(f"Number of assets with Market Cap: {len(list_capmrktcur_assets)}")

Number of assets with Market Cap: 153

capmrktcur = client.get_asset_metrics(
    assets=list_capmrktcur_assets,
    metrics='CapMrktCurUSD',
    start_time=start_time,
    end_time=end_time,
    page_size=1000
).to_dataframe()

capmrktcur.head()

	asset	time	CapMrktCurUSD
0	1inch	2024-06-18 00:00:00+00:00	601806926.332868
1	1inch	2024-06-19 00:00:00+00:00	669292018.418909
2	1inch	2024-06-20 00:00:00+00:00	638964090.844949
3	1inch	2024-06-21 00:00:00+00:00	602574091.306198
4	1inch	2024-06-22 00:00:00+00:00	614578628.290457

capmrktcur_pivot = capmrktcur.pivot(index='time',columns='asset',values='CapMrktCurUSD')

capmrktcur_pivot['Total Cap'] = capmrktcur_pivot.sum(axis=1)

capmrktcur_pivot

asset	1inch	aave	ada	ae_eth	algo	alpha	ant	avaxc	avaxp	avaxx	...	xem	xlm	xmr	xrp	xvg	yfi	zec	zil_eth	zrx	Total Cap
time
2024-06-18 00:00:00+00:00	601806926.332868	1306437999.295638	13424730961.241064	8832610.158148	1339360258.68476	80619082.899196	352010549.615121	2388322499.746325	12838611730.911514	713309094.675862	...	126980953.174033	9610695086.9286	3054203115.691232	49052418232.612778	6724446390.59112	212203308.017145	311270858.886043	2305684.889749	336195840.86146	2.229542e+12
2024-06-19 00:00:00+00:00	669292018.418909	1400637382.569757	13423644078.574947	9099450.288229	1382363720.84246	81094309.783218	356082846.868479	2414215130.39066	12984873845.978542	721319158.751801	...	126674840.822162	9820585062.035196	3020726381.652565	49268483622.64537	6829094885.807176	217383791.66592	303049562.236562	2343771.624651	353937035.765548	2.237127e+12
2024-06-20 00:00:00+00:00	638964090.844949	1358825403.114587	13453912813.410269	9227041.800181	1383256522.94337	84522774.743021	350618174.842791	2471190230.800348	13321331442.85092	739621942.386422	...	132373773.581515	9896582194.97669	3046146822.064857	48866370533.68499	6940480158.50313	216572442.484699	303519008.658101	2350164.716928	360417663.325525	2.231134e+12
2024-06-21 00:00:00+00:00	602574091.306198	1310711198.734895	13174182377.372046	9316984.240167	1391489499.4855	82641066.611264	353348338.081292	2456046637.217133	13261561451.744013	738741018.612855	...	129064560.654881	9697203679.229527	2863904454.917198	48889782361.155952	6827462838.701295	213908822.241556	304438515.678476	2329727.25247	359650229.244396	2.214003e+12
2024-06-22 00:00:00+00:00	614578628.290457	1303628383.546602	13475267480.649416	9080680.725316	1339705315.54913	81442254.171929	351060582.670069	2342337218.532271	12381600154.273886	639220357.617559	...	129465576.97741	9581669381.902111	2995259845.285223	48655540991.974068	7013277883.554922	214667247.899236	304854610.414748	2373452.370937	359377609.401009	2.213118e+12
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2024-09-11 00:00:00+00:00	370795675.998745	2394157493.994128	<NA>	7086282.571463	1272138738.08548	55696931.758843	236080888.610476	2381194755.211256	11102231898.578779	561574500.581236	...	150087591.429489	9790016731.104605	3120199983.171181	53527147783.98262	6095473842.098846	178156431.46414	459255827.96882	1810381.365383	277605159.385683	1.915254e+12
2024-09-12 00:00:00+00:00	384232522.92621	2327005794.360288	<NA>	7371122.985217	1299855257.21774	57785093.830921	236071341.779449	2452806790.290408	11431048630.015509	578191291.778559	...	154361726.733294	9997934147.338022	3105706133.057552	56215867452.010078	6267860632.323607	178663216.244342	454920192.594087	1848757.475975	287032283.247654	1.939759e+12
2024-09-13 00:00:00+00:00	400557336.250521	2311665536.23392	<NA>	7832592.091806	1330044901.28941	58858736.968168	240512223.869399	2545118411.801699	11866069991.509184	600182210.371027	...	157141907.013232	10153491708.845804	3064948299.210351	57221528978.257057	6405467690.943713	179478034.544076	473244206.395726	1885345.54056	297832199.512269	2.004984e+12
2024-09-14 00:00:00+00:00	393157001.05276	2244118799.251264	<NA>	7425554.562711	1312275710.52702	59357434.029832	241324187.737431	2585622436.498027	12055268780.843508	609742449.013718	...	163021038.424844	10239633535.133228	3044883873.886052	59683009523.175056	6404671209.191757	180551959.332886	474742175.553408	1876469.594495	293961123.7075	1.991043e+12
2024-09-15 00:00:00+00:00	368561790.184033	2297900163.986864	<NA>	7423784.032991	1265330435.59742	56307463.023177	237024036.656687	2436809618.506954	11348207028.418108	574093158.696821	...	160791667.882743	10027244938.128235	3063351773.476915	57120501674.456192	6073655509.257232	179087880.508707	453729407.723865	1816117.52146	286316863.361929	1.950918e+12

90 rows × 137 columns

current_market_cap_last = capmrktcur_pivot['Total Cap'][-1]

formatted_market_cap = '${:,.2f}'.format(current_market_cap_last)

print('Current Market Cap based on verified on-chain supply: ' + formatted_market_cap)

Current Market Cap based on verified on-chain supply: $1,950,918,193,666.17

plt.figure(figsize=(8, 6))
capmrktcur_pivot['Total Cap'].plot(kind='area', stacked=True)

# Set the title and labels
plt.title('Total Crypto Market Cap \n(CapMrktCurUSD)\n',fontsize=16)
plt.xlabel('') 
plt.ylabel('Market Cap (USD)',fontsize=14)
plt.grid(True, alpha=0.3, linestyle='--')
formatter = mticker.FuncFormatter(lambda x, pos: '${:,.2f}T'.format(x/1000000000000))
plt.gca().yaxis.set_major_formatter(formatter)

plt.savefig("ndp_capmrktcurusd.png");

Estimated Market Cap

Due to the complexity of running blockchain nodes, some assets pose additional challenges in directly verifying supply data. Coin Metrics partners with CoinGecko to offer a "estimated supply" metric, sourced from a variety of third-party sources like token projects or blockchain explorers.

The CapMrktEstUSD metric offers slightly wider asset coverage, with the trade-off of leveraging more "trusted" sources for supply.

df_reference_capmrktest = client.reference_data_asset_metrics(metrics='CapMrktEstUSD').to_dataframe()

df_reference_capmrktest

	metric	full_name	description	product	category	subcategory	unit	data_type	type
0	CapMrktEstUSD	Capitalization, market, estimated supply, USD	The sum USD value of the estimated supply in c...	Network Data	Market	Market Capitalization	USD	decimal	Product

list_capmrktest_assets = [a['asset'] for a in client.catalog_asset_metrics_v2(metrics='CapMrktEstUSD')]

capmrktest = client.get_asset_metrics(
    assets=list_capmrktest_assets,
    metrics='CapMrktEstUSD',
    start_time=start_time,
    end_time=end_time,
    page_size=10000
).to_dataframe()

capmrktest_pivot = capmrktest.pivot(index='time',columns='asset',values='CapMrktEstUSD')

capmrktest_pivot['Total Cap'] = capmrktest_pivot.sum(axis=1)

est_market_cap_last = capmrktest_pivot['Total Cap'][-1]

formatted_est_market_cap = '${:,.2f}'.format(est_market_cap_last)

print('Estimated Market Cap based on estimated supply (3rd-party sources): ' + formatted_est_market_cap)

Estimated Market Cap based on estimated supply (3rd-party sources): $2,084,782,376,216.38

plt.figure(figsize=(8, 6))

capmrktest_pivot['Total Cap'].plot(kind='area', stacked=True, color='green')
plt.title('Total Crypto Market Cap \n(CapMrktEstUSD)\n',fontsize=16)
plt.xlabel('') 
plt.ylabel('Market Cap (USD)',fontsize=14)
plt.grid(True, alpha=0.3, linestyle='--')

formatter = mticker.FuncFormatter(lambda x, pos: '${:,.2f}T'.format(x/1000000000000))
plt.gca().yaxis.set_major_formatter(formatter)

plt.show()

Free Float Market Cap

While traditional market capitalization metrics rely on circulating supply to quantify the aggregate value of an asset, circulating supply may not necessarily be the appropriate metric for measuring the liquid, readily-available count of units available on the markert.

Free Float Market Capitalization, or CapMrktFFUSD, is a measure of the market value of an asset’s supply that is issued and available to market participants. This excludes supply that is held by insiders (i.e. protocol treasuries), controlling investors, and long term strategic holders (units with 5+ years of inactivity).

df_reference_capmrktffusd = client.reference_data_asset_metrics(metrics='CapMrktFFUSD').to_dataframe()

df_reference_capmrktffusd

	metric	full_name	description	product	category	subcategory	unit	data_type	type
0	CapMrktFFUSD	Capitalization, market, free float, USD	The sum USD value of the current free float su...	Network Data	Market	Market Capitalization	USD	decimal	Product

catalog_ff = client.catalog_asset_metrics_v2(metrics='CapMrktFFUSD').to_list()

catalog_ff[:5]

[{'asset': '1inch',
  'metrics': [{'metric': 'CapMrktFFUSD',
    'frequencies': [{'frequency': '1d',
      'min_time': '2020-12-26T00:00:00.000000000Z',
      'max_time': '2024-09-15T00:00:00.000000000Z',
      'community': True}]}]},
 {'asset': 'aave',
  'metrics': [{'metric': 'CapMrktFFUSD',
    'frequencies': [{'frequency': '1d',
      'min_time': '2020-10-10T00:00:00.000000000Z',
      'max_time': '2024-09-15T00:00:00.000000000Z',
      'community': True}]}]},
 {'asset': 'ada',
  'metrics': [{'metric': 'CapMrktFFUSD',
    'frequencies': [{'frequency': '1d',
      'min_time': '2017-12-01T00:00:00.000000000Z',
      'max_time': '2024-09-08T00:00:00.000000000Z',
      'community': True}]}]},
 {'asset': 'aion_eth',
  'metrics': [{'metric': 'CapMrktFFUSD',
    'frequencies': [{'frequency': '1d',
      'min_time': '2017-12-22T00:00:00.000000000Z',
      'max_time': '2023-03-03T00:00:00.000000000Z',
      'community': True}]}]},
 {'asset': 'algo',
  'metrics': [{'metric': 'CapMrktFFUSD',
    'frequencies': [{'frequency': '1d',
      'min_time': '2019-06-22T00:00:00.000000000Z',
      'max_time': '2024-09-15T00:00:00.000000000Z',
      'community': True}]}]}]

Comparing BTC market capitalization with BTC free float market capitalization

btc_ff_and_cur = client.get_asset_metrics(
    assets='btc',
    metrics=['CapMrktFFUSD','CapMrktCurUSD'],
    start_time=start_time,
    end_time=end_time
).to_dataframe()

btc_ff_and_cur

	asset	time	CapMrktCurUSD	CapMrktFFUSD
0	btc	2024-06-18 00:00:00+00:00	1283620750458.029053	892930547450.634399
1	btc	2024-06-19 00:00:00+00:00	1279047103315.121094	889718923157.247559
2	btc	2024-06-20 00:00:00+00:00	1279448394407.814453	889934057173.784424
3	btc	2024-06-21 00:00:00+00:00	1263474830674.393311	878772804275.674194
4	btc	2024-06-22 00:00:00+00:00	1266898089628.073975	881035407234.292114
...	...	...	...	...
85	btc	2024-09-11 00:00:00+00:00	1133986205007.474609	785339554938.315308
86	btc	2024-09-12 00:00:00+00:00	1148145814023.138916	795139187847.963867
87	btc	2024-09-13 00:00:00+00:00	1195818457804.406494	828180803304.224121
88	btc	2024-09-14 00:00:00+00:00	1185380845498.997314	820893975766.963257
89	btc	2024-09-15 00:00:00+00:00	1168079552742.053711	808927520410.375

90 rows × 4 columns

plt.figure(figsize=(8, 6))

plt.plot(btc_ff_and_cur['time'], btc_ff_and_cur['CapMrktCurUSD'] / 1e9, label='Market Cap', color='blue')
plt.plot(btc_ff_and_cur['time'], btc_ff_and_cur['CapMrktFFUSD'] / 1e9, label='Free Float Market Cap', color='green')

plt.title('Bitcoin Market Cap Over Time\n(Circulating vs. Free Float Supply)\n', fontsize=16)
plt.xlabel('')
plt.ylabel('Market Cap (USD)', fontsize=14)
plt.grid(True, alpha=0.3, linestyle='--')

formatter = mticker.FuncFormatter(lambda x, pos: '${:,.0f}B'.format(x))
plt.gca().yaxis.set_major_formatter(formatter)
plt.gca().xaxis.set_major_locator(mdates.AutoDateLocator())
plt.gca().xaxis.set_major_formatter(mdates.ConciseDateFormatter(mdates.AutoDateLocator()))

plt.legend()

plt.show()