Aggregating Orderbook Depth to Create Liquidity Metrics

A liquid order book (the collection of outstanding bids and asks for a market) is a necessary component of a mature financial asset. The ability to quickly enter and exit large positions with a small degree of price impact, or slippage, is a desirable feature for institutional market participants. Coin Metrics has been collecting order book snapshots for major crypto assets since 2019, and recently launched new liquidity metrics as part of our Market Data Feed offering to further enhance institutions’ view of the crypto markets. In this demo, we explore how these metrics can be used to quantify and aggregate order book depth across some of the most actively-traded BTC markets.

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
import matplotlib.ticker as ticker
from matplotlib.dates import MonthLocator, DateFormatter, YearLocator, AutoDateLocator
from matplotlib.ticker import NullFormatter
import matplotlib.pyplot as plt
import numpy as np
import plotly.express as px 
import matplotlib.ticker as mticker
import matplotlib as mpl
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import matplotlib.image as mpimg
%matplotlib inline

sns.set_theme()
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 17:14:45 INFO     Using API key found in environment

asset = 'btc'

asset_markets = client.reference_data_markets(base=asset,type='spot').to_dataframe()

asset_markets

398 rows × 37 columns

selected_markets = asset_markets.loc[(asset_markets['base']==asset)]

selected_markets

398 rows × 37 columns

selected_quote_assets = ['usd', 'usdc', 'usdt', 'busd']
selected_quote_markets = selected_markets.loc[selected_markets['quote'].isin(selected_quote_assets)]

selected_quote_markets

81 rows × 37 columns

selected_btc_usd_market_list = selected_quote_markets['market'].to_list()

market_metrics = client.catalog_market_metrics_v2(markets=selected_btc_usd_market_list).to_dataframe()

market_metrics

available_btc_markets = list(set(market_metrics['market'].to_list()))

available_btc_markets

['bittrex-btc-usdc-spot',
 'bullish-btc-usdc-spot',
 'bitfinex-btc-usd-spot',
 'binance.us-btc-usdc-spot',
 'ftx-btc-usd-spot',
 'kucoin-btc-usdt-spot',
 'bullish-btc-usd-spot',
 'binance.us-btc-usdt-spot',
 'cex.io-btc-usdt-spot',
 'mexc-btc-busd-spot',
 'coinbase-btc-usdc-spot',
 'hitbtc-btc-usdc-spot',
 'huobi-btc-usdt-spot',
 'okex-btc-usdc-spot',
 'binance-btc-busd-spot',
 'poloniex-btc-usdt-spot',
 'huobi-btc-usdc-spot',
 'kraken-btc-usdc-spot',
 'bitstamp-btc-usdt-spot',
 'binance-btc-usdc-spot',
 'coinbase-btc-usd-spot',
 'hitbtc-btc-usdt-spot',
 'kucoin-btc-usdc-spot',
 'coinbase-btc-usdt-spot',
 'mexc-btc-usdc-spot',
 'mexc-btc-usdt-spot',
 'kraken-btc-usdt-spot',
 'kraken-btc-usd-spot',
 'itbit-btc-usd-spot',
 'bittrex-btc-usd-spot',
 'crypto.com-btc-usd-spot',
 'lmax-btc-usd-spot',
 'bullish-btc-usdt-spot',
 'bitflyer-btc-usd-spot',
 'ftx-btc-usdt-spot',
 'binance.us-btc-busd-spot',
 'gemini-btc-usd-spot',
 'liquid-btc-usd-spot',
 'erisx-btc-usd-spot',
 'ftx.us-btc-usd-spot',
 'crypto.com-btc-usdt-spot',
 'bybit-btc-usdc-spot',
 'gate.io-btc-usdt-spot',
 'liquid-btc-usdt-spot',
 'okex-btc-usdt-spot',
 'poloniex-btc-usdc-spot',
 'binance.us-btc-usd-spot',
 'ftx.us-btc-usdt-spot',
 'binance-btc-usdt-spot',
 'bittrex-btc-usdt-spot',
 'bitfinex-btc-usdt-spot',
 'gate.io-btc-usdc-spot',
 'liquid-btc-usdc-spot',
 'bybit-btc-usdt-spot',
 'gemini-btc-usdt-spot',
 'bitstamp-btc-usd-spot',
 'gate.io-btc-usd-spot',
 'cex.io-btc-usd-spot',
 'bitstamp-btc-usdc-spot']

start = '2023-01-01'

metrics = ['liquidity_depth_1_percent_bid_volume_usd','liquidity_depth_1_percent_ask_volume_usd']

df = client.get_market_metrics(
    markets=available_btc_markets,
    metrics=metrics,
    start_time=start,
    end_time='2023-01-08',
    frequency='1h'
).to_dataframe()

for metric in metrics:
    df[metric] = pd.to_numeric(df[metric], errors='coerce')

df

6350 rows × 4 columns

df['liquidity_depth_1_percent_ask_volume_usd'] = df['liquidity_depth_1_percent_ask_volume_usd'] * -1
df = df.rename(columns={"liquidity_depth_1_percent_ask_volume_usd": "ASKS", "liquidity_depth_1_percent_bid_volume_usd": "BIDS"})

df

6350 rows × 4 columns

sum_bids = df.groupby('time')['BIDS'].sum()
sum_asks = df.groupby('time')['ASKS'].sum()

sum_asks

time
2023-01-01 00:00:00+00:00     -51711674.3155
2023-01-01 01:00:00+00:00   -45829606.793167
2023-01-01 02:00:00+00:00   -45264924.078603
2023-01-01 03:00:00+00:00   -49672194.152268
2023-01-01 04:00:00+00:00   -45360291.558192
                                  ...       
2023-01-08 19:00:00+00:00   -48736064.951989
2023-01-08 20:00:00+00:00   -42514146.912984
2023-01-08 21:00:00+00:00   -48193752.138421
2023-01-08 22:00:00+00:00   -46457643.168556
2023-01-08 23:00:00+00:00   -43813017.641122
Name: ASKS, Length: 192, dtype: Float64

average_df = pd.DataFrame(sum_bids).resample('24H').mean()
average_df

# Create the figure and axes
plt.rcParams['font.family'] = 'arial'

fig, ax = plt.subplots(figsize=(15, 9))
ax.set_facecolor('white')

# # Add logo image
# logo_path = './cm-logo.png'
# logo_img = mpimg.imread(logo_path)
# logo_offset = OffsetImage(logo_img, zoom=0.1)
# logo_annotation = AnnotationBbox(logo_offset, xy=(0.1, 1.05), xycoords='axes fraction', 
#                                  bboxprops={'facecolor': 'white', 'edgecolor': 'none'})
# ax.add_artist(logo_annotation)

# Plot the area chart for BIDS (above x-axis) in green
ax.fill_between(sum_bids.index, sum_bids.astype(float), color='green', alpha=0.5)

# Plot the area chart for ASKS (below x-axis) in red
ax.fill_between(sum_asks.index, sum_asks.astype(float), color='red', alpha=0.5)

ax.set_xlabel('')
ax.set_ylabel('ASKS                                                   BIDS\n',fontweight='bold',fontsize=16)

ax.set_title(str(asset).upper() + ' Market Depth (\u00B11%)\n', fontsize=24)
plt.suptitle('       USD & Stablecoin Pairs (USDC, USDT, BUSD)', fontsize=13.5)

formatter = mticker.FuncFormatter(lambda x, pos: f'${x/1e6:.1f}M' if x >= 0 else f'-$ {abs(x)/1e6:.1f}M')

ax.yaxis.set_major_formatter(formatter)
ax.yaxis.grid(color='gray', linestyle='dashed', alpha=0.2)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))
ax.xaxis.set_major_locator(mdates.MonthLocator(interval=1))

plt.xticks(rotation=0)
ax.set_xlim(df['time'].min(), df['time'].max())
plt.subplots_adjust(top=0.95, bottom=0.1)

plt.savefig('./' + str(asset).upper() + '-Market-Depth-1-Percent.png', dpi=300, bbox_inches='tight',pad_inches=0.5,facecolor='white')

plt.show()

PreviousExamining Orderbook Depth NextComparing Stablecoin Prices Using Different Pricing Methods

Last updated 25 days ago

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 import matplotlib.ticker as ticker from matplotlib.dates import MonthLocator, DateFormatter, YearLocator, AutoDateLocator from matplotlib.ticker import NullFormatter import matplotlib.pyplot as plt import numpy as np import plotly.express as px import matplotlib.ticker as mticker import matplotlib as mpl from matplotlib.offsetbox import OffsetImage, AnnotationBbox import matplotlib.image as mpimg %matplotlib inline

# 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)

market

exchange

base

quote

pair

symbol

type

size_asset

margin_asset

strike

...

order_amount_min

order_amount_max

order_price_increment

order_price_min

order_price_max

order_size_min

order_taker_fee

order_maker_fee

margin_trading_enabled

experimental

market

exchange

base

quote

pair

symbol

type

size_asset

margin_asset

strike

...

order_amount_min

order_amount_max

order_price_increment

order_price_min

order_price_max

order_size_min

order_taker_fee

order_maker_fee

margin_trading_enabled

experimental

market

exchange

base

quote

pair

symbol

type

size_asset

margin_asset

strike

...

order_amount_min

order_amount_max

order_price_increment

order_price_min

order_price_max

order_size_min

order_taker_fee

order_maker_fee

margin_trading_enabled

experimental

market

metrics

['bittrex-btc-usdc-spot', 'bullish-btc-usdc-spot', 'bitfinex-btc-usd-spot', 'binance.us-btc-usdc-spot', 'ftx-btc-usd-spot', 'kucoin-btc-usdt-spot', 'bullish-btc-usd-spot', 'binance.us-btc-usdt-spot', 'cex.io-btc-usdt-spot', 'mexc-btc-busd-spot', 'coinbase-btc-usdc-spot', 'hitbtc-btc-usdc-spot', 'huobi-btc-usdt-spot', 'okex-btc-usdc-spot', 'binance-btc-busd-spot', 'poloniex-btc-usdt-spot', 'huobi-btc-usdc-spot', 'kraken-btc-usdc-spot', 'bitstamp-btc-usdt-spot', 'binance-btc-usdc-spot', 'coinbase-btc-usd-spot', 'hitbtc-btc-usdt-spot', 'kucoin-btc-usdc-spot', 'coinbase-btc-usdt-spot', 'mexc-btc-usdc-spot', 'mexc-btc-usdt-spot', 'kraken-btc-usdt-spot', 'kraken-btc-usd-spot', 'itbit-btc-usd-spot', 'bittrex-btc-usd-spot', 'crypto.com-btc-usd-spot', 'lmax-btc-usd-spot', 'bullish-btc-usdt-spot', 'bitflyer-btc-usd-spot', 'ftx-btc-usdt-spot', 'binance.us-btc-busd-spot', 'gemini-btc-usd-spot', 'liquid-btc-usd-spot', 'erisx-btc-usd-spot', 'ftx.us-btc-usd-spot', 'crypto.com-btc-usdt-spot', 'bybit-btc-usdc-spot', 'gate.io-btc-usdt-spot', 'liquid-btc-usdt-spot', 'okex-btc-usdt-spot', 'poloniex-btc-usdc-spot', 'binance.us-btc-usd-spot', 'ftx.us-btc-usdt-spot', 'binance-btc-usdt-spot', 'bittrex-btc-usdt-spot', 'bitfinex-btc-usdt-spot', 'gate.io-btc-usdc-spot', 'liquid-btc-usdc-spot', 'bybit-btc-usdt-spot', 'gemini-btc-usdt-spot', 'bitstamp-btc-usd-spot', 'gate.io-btc-usd-spot', 'cex.io-btc-usd-spot', 'bitstamp-btc-usdc-spot']

market

time

liquidity_depth_1_percent_ask_volume_usd

liquidity_depth_1_percent_bid_volume_usd

df['liquidity_depth_1_percent_ask_volume_usd'] = df['liquidity_depth_1_percent_ask_volume_usd'] * -1 df = df.rename(columns={"liquidity_depth_1_percent_ask_volume_usd": "ASKS", "liquidity_depth_1_percent_bid_volume_usd": "BIDS"})

market

time

ASKS

BIDS

time 2023-01-01 00:00:00+00:00 -51711674.3155 2023-01-01 01:00:00+00:00 -45829606.793167 2023-01-01 02:00:00+00:00 -45264924.078603 2023-01-01 03:00:00+00:00 -49672194.152268 2023-01-01 04:00:00+00:00 -45360291.558192 ... 2023-01-08 19:00:00+00:00 -48736064.951989 2023-01-08 20:00:00+00:00 -42514146.912984 2023-01-08 21:00:00+00:00 -48193752.138421 2023-01-08 22:00:00+00:00 -46457643.168556 2023-01-08 23:00:00+00:00 -43813017.641122 Name: ASKS, Length: 192, dtype: Float64

BIDS

# Create the figure and axes plt.rcParams['font.family'] = 'arial' fig, ax = plt.subplots(figsize=(15, 9)) ax.set_facecolor('white') # # Add logo image # logo_path = './cm-logo.png' # logo_img = mpimg.imread(logo_path) # logo_offset = OffsetImage(logo_img, zoom=0.1) # logo_annotation = AnnotationBbox(logo_offset, xy=(0.1, 1.05), xycoords='axes fraction', # bboxprops={'facecolor': 'white', 'edgecolor': 'none'}) # ax.add_artist(logo_annotation) # Plot the area chart for BIDS (above x-axis) in green ax.fill_between(sum_bids.index, sum_bids.astype(float), color='green', alpha=0.5) # Plot the area chart for ASKS (below x-axis) in red ax.fill_between(sum_asks.index, sum_asks.astype(float), color='red', alpha=0.5) ax.set_xlabel('') ax.set_ylabel('ASKS BIDS\n',fontweight='bold',fontsize=16) ax.set_title(str(asset).upper() + ' Market Depth (\u00B11%)\n', fontsize=24) plt.suptitle(' USD & Stablecoin Pairs (USDC, USDT, BUSD)', fontsize=13.5) formatter = mticker.FuncFormatter(lambda x, pos: f'${x/1e6:.1f}M' if x >= 0 else f'-$ {abs(x)/1e6:.1f}M') ax.yaxis.set_major_formatter(formatter) ax.yaxis.grid(color='gray', linestyle='dashed', alpha=0.2) ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y')) ax.xaxis.set_major_locator(mdates.MonthLocator(interval=1)) plt.xticks(rotation=0) ax.set_xlim(df['time'].min(), df['time'].max()) plt.subplots_adjust(top=0.95, bottom=0.1) plt.savefig('./' + str(asset).upper() + '-Market-Depth-1-Percent.png', dpi=300, bbox_inches='tight',pad_inches=0.5,facecolor='white') plt.show()