HomeCharts

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.

File Download

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

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
metricfull_namedescriptionproductcategorysubcategoryunitdata_typetype

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()
assettimeCapMrktCurUSD

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
asset1inchaaveadaae_ethalgoalphaantavaxcavaxpavaxx...xemxlmxmrxrpxvgyfizeczil_ethzrxTotal 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
metricfull_namedescriptionproductcategorysubcategoryunitdata_typetype

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
metricfull_namedescriptionproductcategorysubcategoryunitdata_typetype

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
assettimeCapMrktCurUSDCapMrktFFUSD

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()
PreviousCreating Custom Network Data Metrics Using ATLASNextComparing the Dominance of Mining Pools Using ATLAS

Last updated