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Comparing Stablecoin Prices Using Different Pricing Methods

Since Tether's launch in 2014, stablecoins have grown to become one of the most dominant sectors in the world of digital assets. Many considered them to be crypto's "killer app," providing global access to stable savings and payment rails beyond the rigid confines of the traditional banking system. In this walkthrough, we use Coin Metrics Market Data Feed and CM Prices to explore the various venues where these assets are traded, and examine various pricing methodologies that allow us to better understand how they perform in comparison to the underlying fiat currencies.

Resources

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

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.

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

Notebook Setup

from os import environ
import sys
import pandas as pd
import seaborn as sns
import logging
from datetime import date, datetime, timedelta
from coinmetrics.api_client import CoinMetricsClient
import json
import logging
from pytz import timezone as timezone_conv
from datetime import timezone as timezone_info
import matplotlib
import matplotlib.dates as mdates
from matplotlib.dates import MonthLocator, DateFormatter, YearLocator, AutoDateLocator
from matplotlib.ticker import NullFormatter
import matplotlib.pyplot as plt
import plotly.express as px 
import numpy as np
%matplotlib inline
sns.set_theme()
plt.rcParams.update({'font.size': 16, 'font.family': 'arial'})
sns.set(rc={'figure.figsize':(14,8)})
logging.basicConfig(
    format='%(asctime)s %(levelname)-8s %(message)s',
    level=logging.INFO,
    datefmt='%Y-%m-%d %H:%M:%S'
)
# 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)
2024-10-03 11:41:34 INFO     Using API key found in environment

Get Stablecoin Markets

The catalog/markets endpoint returns a list of available markets along with time ranges of available data. Users can pass in a list of markets, exchanges, or market types (spot, futures, options). We can retrieve our stablecoin markets by fetching a list of all 'spot' markets, then filtering for the markets where the 'base' or 'quote' parameter is equivalent to our stablecoin of interest.

ticker = 'usdt'

stablecoin_markets = client.reference_data_markets(
    type='spot',
    asset=ticker,
    page_size=10000
).to_dataframe()[['market','exchange','base','quote','symbol']]
client.reference_data_markets(
    type='spot',
    asset=ticker,
    page_size=10000
).to_dataframe().info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 19949 entries, 0 to 19948
Data columns (total 37 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 0   market                  19949 non-null  string 
 1   exchange                19949 non-null  string 
 2   base                    19949 non-null  string 
 3   quote                   19949 non-null  string 
 4   pair                    19949 non-null  string 
 5   symbol                  19808 non-null  string 
 6   type                    19949 non-null  string 
 7   size_asset              0 non-null      Int64  
 8   margin_asset            0 non-null      Int64  
 9   strike                  0 non-null      Int64  
 10  option_contract_type    0 non-null      Int64  
 11  is_european             0 non-null      Int64  
 12  contract_size           0 non-null      Int64  
 13  tick_size               0 non-null      Int64  
 14  multiplier_size         0 non-null      Int64  
 15  listing                 0 non-null      Int64  
 16  expiration              0 non-null      Int64  
 17  settlement_price        0 non-null      Int64  
 18  pool_config_id          0 non-null      Int64  
 19  contract_address        0 non-null      Int64  
 20  fee                     0 non-null      Int64  
 21  price_includes_fee      0 non-null      Int64  
 22  variable_fee            0 non-null      Int64  
 23  base_address            0 non-null      Int64  
 24  quote_address           0 non-null      Int64  
 25  status                  11787 non-null  string 
 26  order_amount_increment  11436 non-null  Float64
 27  order_amount_min        6300 non-null   Float64
 28  order_amount_max        2567 non-null   Float64
 29  order_price_increment   12022 non-null  Float64
 30  order_price_min         2012 non-null   Float64
 31  order_price_max         759 non-null    Float64
 32  order_size_min          5835 non-null   Float64
 33  order_taker_fee         8332 non-null   Float64
 34  order_maker_fee         8332 non-null   Float64
 35  margin_trading_enabled  6926 non-null   boolean
 36  experimental            0 non-null      Int64  
dtypes: Float64(9), Int64(19), boolean(1), string(8)
memory usage: 6.1 MB
marketexchangebasequotesymbol

0

bibox-1inch-usdt-spot

bibox

1inch

usdt

1INCH_USDT

1

bibox-aaa-usdt-spot

bibox

aaa

usdt

AAA_USDT

2

bibox-aave-usdt-spot

bibox

aave

usdt

AAVE_USDT

3

bibox-ac-usdt-spot

bibox

ac

usdt

AC_USDT

4

bibox-acmd-usdt-spot

bibox

acmd

usdt

ACMD_USDT

...

...

...

...

...

...

19944

zb.com-yfii-usdt-spot

zb.com

yfii

usdt

yfii_usdt

19945

zb.com-ygg-usdt-spot

zb.com

ygg

usdt

ygg_usdt

19946

zb.com-zb-usdt-spot

zb.com

zb

usdt

zb_usdt

19947

zb.com-zkn-usdt-spot

zb.com

zkn

usdt

zkn_usdt

19948

zb.com-zrx-usdt-spot

zb.com

zrx

usdt

zrx_usdt

19949 rows × 5 columns

markets_by_exchange = pd.DataFrame(stablecoin_markets['exchange'].value_counts()).reset_index()
markets_by_exchange['count'] = markets_by_exchange['count'].astype(int)
markets_by_exchange.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 30 entries, 0 to 29
Data columns (total 2 columns):
 #   Column    Non-Null Count  Dtype 
---  ------    --------------  ----- 
 0   exchange  30 non-null     string
 1   count     30 non-null     int64 
dtypes: int64(1), string(1)
memory usage: 608.0 bytes
fig, ax1 = plt.subplots()
ax1.bar(x=markets_by_exchange['exchange'], height=markets_by_exchange['count'], width=0.8)
plt.setp(ax1.get_xticklabels(), rotation=45);
ax1.set_facecolor("white")
plt.grid(color = 'black', linestyle = '--', linewidth = 0.2)
plt.title('\nCount of '+ str(ticker).upper() + ' Markets \nby Exchange\n',fontdict={'fontsize':20,'font':'arial'});

Get stablecoin prices

Single market trades

Trades are one of the foundational data types we collect from exchanges. From raw trades data, we can construct additional aggregated metrics.

market = 'coinbase-usdt-usd-spot'
coinbase_trades = client.get_market_trades(
    markets = market,
    limit_per_market = 100,
    paging_from = 'end'
).to_dataframe()
coinbase_trades
markettimecoin_metrics_idamountpricedatabase_timeside

0

coinbase-usdt-usd-spot

2024-10-03 16:37:50.356121+00:00

109485916

3000.0

0.99985

2024-10-03 16:37:51.099860+00:00

buy

1

coinbase-usdt-usd-spot

2024-10-03 16:37:52.857847+00:00

109485917

3000.0

0.99985

2024-10-03 16:37:53.278230+00:00

buy

2

coinbase-usdt-usd-spot

2024-10-03 16:37:54.748770+00:00

109485918

1555.08

0.99985

2024-10-03 16:37:55.566500+00:00

buy

3

coinbase-usdt-usd-spot

2024-10-03 16:37:55.358549+00:00

109485919

3000.0

0.99985

2024-10-03 16:37:55.652974+00:00

buy

4

coinbase-usdt-usd-spot

2024-10-03 16:37:59.787414+00:00

109485920

2069.41

0.99985

2024-10-03 16:38:00.856793+00:00

buy

...

...

...

...

...

...

...

...

95

coinbase-usdt-usd-spot

2024-10-03 16:41:24.050200+00:00

109486011

19.56

0.99985

2024-10-03 16:41:24.700790+00:00

buy

96

coinbase-usdt-usd-spot

2024-10-03 16:41:24.746299+00:00

109486012

1170.67

0.99985

2024-10-03 16:41:25.211754+00:00

buy

97

coinbase-usdt-usd-spot

2024-10-03 16:41:26.109516+00:00

109486013

7.18

0.99985

2024-10-03 16:41:26.660344+00:00

buy

98

coinbase-usdt-usd-spot

2024-10-03 16:41:26.855646+00:00

109486014

3000.0

0.99984

2024-10-03 16:41:27.752326+00:00

sell

99

coinbase-usdt-usd-spot

2024-10-03 16:41:34.937992+00:00

109486015

23.49

0.99984

2024-10-03 16:41:35.141694+00:00

sell

100 rows × 7 columns

Single market candles

From raw trades data, we construct OHLC candles for each market. Candles include the following data types:

  • price_open: The opening price of the candle.

  • price_high: The high price of the candle.

  • price_low: The low price of the candle.

  • price_close: The close price of the candle.

  • vwap: The volume-weighted average price of the candle.

  • volume: The volume of the candle in units of the base asset.

  • candle_usd_volume: The volume of the candle in units of U.S. dollars.

  • candle_trades_count: The number of trades in the candle interval.

market = 'coinbase-usdt-usd-spot'
coinbase_candles = client.get_market_candles(
    markets = market,
    frequency = '1d'
).to_dataframe()
coinbase_candles
markettimeprice_openprice_closeprice_highprice_lowvwapvolumecandle_usd_volumecandle_trades_count

0

coinbase-usdt-usd-spot

2021-05-04 00:00:00+00:00

1.002

1.0006

1.003

0.999

1.000696

24564061.73

24581147.409593

30527

1

coinbase-usdt-usd-spot

2021-05-05 00:00:00+00:00

1.0006

1.0013

1.002

0.9997

1.000816

40170830.16

40203590.541009

43688

2

coinbase-usdt-usd-spot

2021-05-06 00:00:00+00:00

1.0013

1.0009

1.002

1.0004

1.001023

51129166.79

51181449.603235

51177

3

coinbase-usdt-usd-spot

2021-05-07 00:00:00+00:00

1.0008

1.0011

1.0018

1.0

1.001045

44247619.59

44293836.840656

48729

4

coinbase-usdt-usd-spot

2021-05-08 00:00:00+00:00

1.001

1.0016

1.0022

1.0009

1.00147

26972741.16

27012382.918589

50140

...

...

...

...

...

...

...

...

...

...

...

1243

coinbase-usdt-usd-spot

2024-09-28 00:00:00+00:00

1.00012

1.00011

1.00021

1.0

1.000127

69876132.62

69884972.64875

33216

1244

coinbase-usdt-usd-spot

2024-09-29 00:00:00+00:00

1.00011

1.00013

1.00021

1.00001

1.000107

85000340.62

85009396.570012

34237

1245

coinbase-usdt-usd-spot

2024-09-30 00:00:00+00:00

1.00014

0.99971

1.00014

0.99958

0.999863

250585092.98

250550678.3691

81461

1246

coinbase-usdt-usd-spot

2024-10-01 00:00:00+00:00

0.9997

0.99975

0.99991

0.99923

0.999677

460273457.69

460124819.483157

169777

1247

coinbase-usdt-usd-spot

2024-10-02 00:00:00+00:00

0.99975

0.99977

1.0

0.99958

0.999796

345349657.46

345279261.97222

144072

1248 rows × 10 columns

ax1 = plt.subplot()
coinbase_price = sns.lineplot(data=coinbase_candles,y=coinbase_candles.vwap,x=coinbase_candles.time)
plt.setp(ax1.get_xticklabels(), rotation=45);
ax1.set_facecolor("white")
plt.grid(color = 'black', linestyle = '--', linewidth = 0.2)
coinbase_price.set_xlabel("", fontsize = 15)
coinbase_price.set_ylabel("Price", fontsize = 15)
coinbase_price.set_title('\nCoinbase USDT-USD\n', fontsize = 18, font = 'arial');

Reference Rate Candles

We offer reference rates quoted in USD, Euro, Bitcoin, and Ethereum. We now support these quote currencies for our entire reference rates coverage universe of over 500 assets and for all of our frequencies, including 1s, 1m, 1h, 1d-ny-close and 1d.

Current composition of markets for USDT-USD Reference Rate pair (as of May 17, 2023):

  • "coinbase-usdt-usd-spot",

  • "coinbase-eth-usdt-spot",

  • "coinbase-btc-usdt-spot",

  • "kraken-usdt-usd-spot",

  • "binance-btc-usdt-spot",

  • "binance-eth-usdt-spot",

  • "crypto.com-usdt-usd-spot"

pairs = client.catalog_asset_pair_candles().to_dataframe()
2024-10-03 11:41:41 WARNING  /catalog/ endpoints will be deprecated in the future. Consider using /catalog-v2/ and /reference-data/ endpoints instead.
pairs.loc[pairs['pair']=='usdt-usd']
pairfrequencymin_timemax_time

8640

usdt-usd

1m

2013-12-28 00:00:00+00:00

2024-10-03 16:39:00+00:00

8641

usdt-usd

5m

2013-12-28 00:00:00+00:00

2024-10-03 16:35:00+00:00

8642

usdt-usd

10m

2013-12-28 00:00:00+00:00

2024-10-03 16:30:00+00:00

8643

usdt-usd

15m

2013-12-28 00:00:00+00:00

2024-10-03 16:15:00+00:00

8644

usdt-usd

30m

2013-12-28 00:00:00+00:00

2024-10-03 16:00:00+00:00

8645

usdt-usd

1h

2013-12-28 00:00:00+00:00

2024-10-03 15:00:00+00:00

8646

usdt-usd

4h

2013-12-28 00:00:00+00:00

2024-10-03 12:00:00+00:00

8647

usdt-usd

1d

2013-12-28 00:00:00+00:00

2024-10-02 00:00:00+00:00

pair_candles = client.get_pair_candles(
    pairs='usdt-usd',
    start_time =  datetime.now() - timedelta(weeks=4),
    frequency='1d'
).to_dataframe()
pair_candles.tail()
pairtimeprice_openprice_closeprice_highprice_low

22

usdt-usd

2024-09-28 00:00:00+00:00

1.00016

1.000153

1.00053

0.999861

23

usdt-usd

2024-09-29 00:00:00+00:00

1.000116

1.00013

1.000492

0.999772

24

usdt-usd

2024-09-30 00:00:00+00:00

1.00013

0.999649

1.00053

0.99937

25

usdt-usd

2024-10-01 00:00:00+00:00

0.999639

0.9998

1.000897

0.998931

26

usdt-usd

2024-10-02 00:00:00+00:00

0.9998

0.99982

1.000601

0.999219

prices = pair_candles[['price_open','price_close','price_high','price_low','time']].set_index('time')
fig, ax = plt.subplots()
ax.ticklabel_format(useOffset=False)
width = 0.8
width2 = .1
ax.set_facecolor("white")
plt.grid(color = 'black', linestyle = '--', linewidth = 0.2)
plt.title('\n'+ str(ticker).upper() + ' Reference Rate Candles (1D)',fontdict={'fontsize':20,'font':'arial'});

up = prices[prices.price_close>=prices.price_open]
down = prices[prices.price_close<prices.price_open]
col1 = 'green'
col2 = 'red'

#plot prices
ax.bar(up.index,up.price_close-up.price_open,width,bottom=up.price_open,color=col1)
ax.bar(up.index,up.price_high-up.price_close,width2,bottom=up.price_close,color=col1)
ax.bar(up.index,up.price_low-up.price_open,width2,bottom=up.price_open,color=col1)
ax.bar(down.index,down.price_close-down.price_open,width,bottom=down.price_open,color=col2)
ax.bar(down.index,down.price_high-down.price_open,width2,bottom=down.price_open,color=col2)
ax.bar(down.index,down.price_low-down.price_close,width2,bottom=down.price_close,color=col2)

#rotate x-axis tick labels
plt.xticks(rotation=45, ha='right')
ax.xaxis.set_minor_locator(MonthLocator(bymonthday=30))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))

plt.show()

Reference Rate

rr_catalog = client.catalog_asset_metrics_v2(assets='usdt', metrics='ReferenceRateUSD').to_dataframe()
rr_catalog
assetmetrics

0

usdt

[{'metric': 'ReferenceRateUSD', 'frequencies':...

asset_rr = client.get_asset_metrics(
    assets = 'usdt',
    metrics = 'ReferenceRateUSD',
    frequency = '1m',
    start_time =  datetime.now() - timedelta(hours=24)
).to_dataframe()
asset_rr
assettimeReferenceRateUSD

0

usdt

2024-10-02 11:42:00+00:00

0.99984

1

usdt

2024-10-02 11:43:00+00:00

0.99979

2

usdt

2024-10-02 11:44:00+00:00

0.99983

3

usdt

2024-10-02 11:45:00+00:00

0.999845

4

usdt

2024-10-02 11:46:00+00:00

0.99982

...

...

...

...

1735

usdt

2024-10-03 16:37:00+00:00

0.999861

1736

usdt

2024-10-03 16:38:00+00:00

0.99985

1737

usdt

2024-10-03 16:39:00+00:00

0.99985

1738

usdt

2024-10-03 16:40:00+00:00

0.99984

1739

usdt

2024-10-03 16:41:00+00:00

0.999835

1740 rows × 3 columns

ax1 = plt.subplot()
ax1.ticklabel_format(useOffset=False)
asset_rr_chart = sns.lineplot(data=asset_rr,y=asset_rr.ReferenceRateUSD,x=asset_rr.time)
plt.setp(ax1.get_xticklabels());
ax1.set_facecolor("white")
plt.grid(color = 'black', linestyle = '--', linewidth = 0.2)
asset_rr_chart.set_xlabel("", fontsize = 15)
asset_rr_chart.set_ylabel("Price", fontsize = 15)
asset_rr_chart.set_title('\nUSDT Reference Rate (1 min)\n', fontsize = 18, font = 'arial');

Principal Market Price

The Principal Market Prices identify a principal market for each asset and utilize the most recent price from this market. Common use cases are for fair value measurement, preparing financial statements, and calculating closing prices for indexes or financial benchmarks.

asset_pmp = client.get_asset_metrics(
    assets = 'usdt',
    metrics = ['principal_market_price_usd','principal_market_usd'],
    frequency = '1m',
    start_time =  datetime.now() - timedelta(hours=24)
).to_dataframe()
asset_pmp
assettimeprincipal_market_price_usdprincipal_market_usd

0

usdt

2024-10-02 11:42:00+00:00

0.999835

binance-btc-usdt-spot

1

usdt

2024-10-02 11:43:00+00:00

0.999921

binance-btc-usdt-spot

2

usdt

2024-10-02 11:44:00+00:00

0.999892

binance-btc-usdt-spot

3

usdt

2024-10-02 11:45:00+00:00

0.999845

binance-btc-usdt-spot

4

usdt

2024-10-02 11:46:00+00:00

0.999763

binance-btc-usdt-spot

...

...

...

...

...

1735

usdt

2024-10-03 16:37:00+00:00

0.999861

crypto.com-btc-usdt-spot

1736

usdt

2024-10-03 16:38:00+00:00

0.999839

crypto.com-btc-usdt-spot

1737

usdt

2024-10-03 16:39:00+00:00

0.999777

crypto.com-btc-usdt-spot

1738

usdt

2024-10-03 16:40:00+00:00

0.999679

crypto.com-btc-usdt-spot

1739

usdt

2024-10-03 16:41:00+00:00

0.999798

crypto.com-btc-usdt-spot

1740 rows × 4 columns

market_list = list(set(asset_pmp['principal_market_usd'].to_list()))
market_list
['crypto.com-eth-usdt-spot',
 'binance-eth-usdt-spot',
 'binance-btc-usdt-spot',
 'crypto.com-btc-usdt-spot']
unique_markets = asset_pmp['principal_market_usd'].unique()
colors = plt.cm.jet(np.linspace(0,1,len(unique_markets)))
color_map = dict(zip(unique_markets, colors))

asset_pmp = asset_pmp.sort_values('time')
fig, ax = plt.subplots()
ax.set_facecolor("white")
ax.grid(color='lightgray', linestyle='--')

for i in range(1, len(asset_pmp)):
    ax.plot(asset_pmp['time'].iloc[i-1:i+1], 
            asset_pmp['principal_market_price_usd'].iloc[i-1:i+1], 
            color=color_map[asset_pmp['principal_market_usd'].iloc[i]], 
            label=asset_pmp['principal_market_usd'].iloc[i])

handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
import matplotlib.ticker as ticker

ax.legend(by_label.values(), by_label.keys(), loc='upper right', frameon=False, bbox_to_anchor=(1, 1.135))
ax.get_yaxis().set_major_formatter(ticker.FuncFormatter(lambda x, p: format(float(x), '.4f')))

plt.title('USDT Principal Market Price\n', fontsize=22)
plt.xlabel('')
plt.ylabel('Principal Market Price (USD)\n',fontsize=16)
plt.show()
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