Comparing Stablecoin Prices Using Different Pricing Methods

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

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

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'});

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

coinbase_candles

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');

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()

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

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');

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

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()