# Comparing Stablecoin Prices Using Different Pricing Methods ![](https://5264302.fs1.hubspotusercontent-na1.net/hubfs/5264302/Demo%20Asset%20Resources/CM-Demo-mdf-stablecoins-Cover.png) 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](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="" %} ### Notebook Setup ```python 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 ``` ```python sns.set_theme() plt.rcParams.update({'font.size': 16, 'font.family': 'arial'}) sns.set(rc={'figure.figsize':(14,8)}) ``` ```python logging.basicConfig( format='%(asctime)s %(levelname)-8s %(message)s', level=logging.INFO, datefmt='%Y-%m-%d %H:%M:%S' ) ``` ```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) ``` ``` 2024-10-03 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. ```python ticker = 'usdt' stablecoin_markets = client.reference_data_markets( type='spot', asset=ticker, page_size=10000 ).to_dataframe()[['market','exchange','base','quote','symbol']] ``` ```python client.reference_data_markets( type='spot', asset=ticker, page_size=10000 ).to_dataframe().info() ``` ``` 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 ``` | | market | exchange | base | quote | symbol | | ----- | --------------------- | -------- | ----- | ----- | ----------- | | 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 ```python markets_by_exchange = pd.DataFrame(stablecoin_markets['exchange'].value_counts()).reset_index() markets_by_exchange['count'] = markets_by_exchange['count'].astype(int) ``` ```python markets_by_exchange.info() ``` ``` 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 ``` ```python 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. ```python market = 'coinbase-usdt-usd-spot' coinbase_trades = client.get_market_trades( markets = market, limit_per_market = 100, paging_from = 'end' ).to_dataframe() coinbase_trades ``` | | market | time | coin\_metrics\_id | amount | price | database\_time | side | | --- | ---------------------- | -------------------------------- | ----------------- | ------- | ------- | -------------------------------- | ---- | | 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. ```python market = 'coinbase-usdt-usd-spot' coinbase_candles = client.get_market_candles( markets = market, frequency = '1d' ).to_dataframe() ``` ```python coinbase_candles ``` | | market | time | price\_open | price\_close | price\_high | price\_low | vwap | volume | candle\_usd\_volume | candle\_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 ```python 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" ```python 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. ``` ```python pairs.loc[pairs['pair']=='usdt-usd'] ``` | | pair | frequency | min\_time | max\_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 | ```python pair_candles = client.get_pair_candles( pairs='usdt-usd', start_time = datetime.now() - timedelta(weeks=4), frequency='1d' ).to_dataframe() ``` ```python pair_candles.tail() ``` | | pair | time | price\_open | price\_close | price\_high | price\_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 | ```python prices = pair_candles[['price_open','price_close','price_high','price_low','time']].set_index('time') ``` ```python 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
#### Reference Rate ```python rr_catalog = client.catalog_asset_metrics_v2(assets='usdt', metrics='ReferenceRateUSD').to_dataframe() ``` ```python rr_catalog ``` | | asset | metrics | | - | ----- | -------------------------------------------------- | | 0 | usdt | \[{'metric': 'ReferenceRateUSD', 'frequencies':... | ```python asset_rr = client.get_asset_metrics( assets = 'usdt', metrics = 'ReferenceRateUSD', frequency = '1m', start_time = datetime.now() - timedelta(hours=24) ).to_dataframe() ``` ```python asset_rr ``` | | asset | time | ReferenceRateUSD | | ---- | ----- | ------------------------- | ---------------- | | 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 ```python 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. ```python 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() ``` ```python asset_pmp ``` | | asset | time | principal\_market\_price\_usd | principal\_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 ```python 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'] ``` ```python 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() ```