# Applying Different Types of Marketcap Metrics ![](https://5264302.fs1.hubspotusercontent-na1.net/hubfs/5264302/Demo%20Asset%20Resources/Demo%20Covers/CM-Demo-market_cap_metrics-Cover.png) 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](https://docs.coinmetrics.io/api/v4) website contains the full set of endpoints and data offered by Coin Metrics. * The [Coin Metrics Product Documentation](https://docs.coinmetrics.io/info) gives detailed, conceptual explanations of the data that Coin Metrics offers. * The [API Spec](https://coinmetrics.github.io/api-client-python/site/api_client.html) 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 {% file src="" %} {% file src="" %} ### Setup ```python 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 ``` ```python logging.basicConfig( format='%(asctime)s %(levelname)-8s %(message)s', level=logging.INFO, datefmt='%Y-%m-%d %H:%M:%S' ) ``` ```python end_time = datetime.today().date() start_time = end_time - timedelta(days=90) ``` ```python ``` ```python # 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. ```python df_reference_capmkrtcur = client.reference_data_asset_metrics(metrics='CapMrktCurUSD').to_dataframe() ``` ```python 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 | ```python list_capmrktcur_assets = [a['asset'] for a in client.catalog_asset_metrics_v2(metrics='CapMrktCurUSD')] ``` ```python print(f"Number of assets with Market Cap: {len(list_capmrktcur_assets)}") ``` ``` Number of assets with Market Cap: 153 ``` ```python capmrktcur = client.get_asset_metrics( assets=list_capmrktcur_assets, metrics='CapMrktCurUSD', start_time=start_time, end_time=end_time, page_size=1000 ).to_dataframe() ``` ```python 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 | ```python capmrktcur_pivot = capmrktcur.pivot(index='time',columns='asset',values='CapMrktCurUSD') ``` ```python capmrktcur_pivot['Total Cap'] = capmrktcur_pivot.sum(axis=1) ``` ```python 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 | \ | 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 | \ | 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 | \ | 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 | \ | 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 | \ | 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 ```python 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: $1,950,918,193,666.17 ``` ```python plt.figure(figsize=(8, 6)) capmrktcur_pivot['Total Cap'].plot(kind='area', stacked=True) # Set the title and labels plt.title('Total Crypto Market Cap \n(CapMrktCurUSD)\n',fontsize=16) plt.xlabel('') plt.ylabel('Market Cap (USD)',fontsize=14) plt.grid(True, alpha=0.3, linestyle='--') formatter = mticker.FuncFormatter(lambda x, pos: '${:,.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. ```python df_reference_capmrktest = client.reference_data_asset_metrics(metrics='CapMrktEstUSD').to_dataframe() ``` ```python 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 | ```python list_capmrktest_assets = [a['asset'] for a in client.catalog_asset_metrics_v2(metrics='CapMrktEstUSD')] ``` ```python capmrktest = client.get_asset_metrics( assets=list_capmrktest_assets, metrics='CapMrktEstUSD', start_time=start_time, end_time=end_time, page_size=10000 ).to_dataframe() ``` ```python capmrktest_pivot = capmrktest.pivot(index='time',columns='asset',values='CapMrktEstUSD') ``` ```python capmrktest_pivot['Total Cap'] = capmrktest_pivot.sum(axis=1) ``` ```python 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): $2,084,782,376,216.38 ``` ```python plt.figure(figsize=(8, 6)) capmrktest_pivot['Total Cap'].plot(kind='area', stacked=True, color='green') plt.title('Total Crypto Market Cap \n(CapMrktEstUSD)\n',fontsize=16) plt.xlabel('') plt.ylabel('Market Cap (USD)',fontsize=14) plt.grid(True, alpha=0.3, linestyle='--') formatter = mticker.FuncFormatter(lambda x, pos: '${:,.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). ```python df_reference_capmrktffusd = client.reference_data_asset_metrics(metrics='CapMrktFFUSD').to_dataframe() ``` ```python 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 | ```python catalog_ff = client.catalog_asset_metrics_v2(metrics='CapMrktFFUSD').to_list() ``` ```python 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 ```python btc_ff_and_cur = client.get_asset_metrics( assets='btc', metrics=['CapMrktFFUSD','CapMrktCurUSD'], start_time=start_time, end_time=end_time ).to_dataframe() ``` ```python 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 ```python 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() ```
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