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.

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

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

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

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

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']

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

pair_candles.tail()

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

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

asset_rr

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

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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Last updated 26 days ago