Comparing Volumes of Exchanges and Assets

Though decentralized finance has garnered significant attention in recent years, centralized exchanges remain a crucial cornerstone of the crypto asset industry. These trading venues serve as some of the deepest pools of liquidity for the top crypto assets, and facilitate the onboarding and offboarding of millions of users by sitting at the intersection of digital assets and fiat currencies. Coin Metrics collects a wide variety of data from a universe of 39+ top crypto exchanges, allowing our users to easily assess exchange dominance, asset trading volumes, and even the composition of trading patterns in specific 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

495KB

MDF_exchange_volume.ipynb

620KB

MDF_exchange_volume.pdf

pdf

Notebook Setup

from os import environ
import sys
import pandas as pd
import numpy as np
import logging
from datetime import date, datetime, timedelta
from coinmetrics.api_client import CoinMetricsClient
import json
import logging
import matplotlib.pyplot as plt
# import plotly.express as px
# import seaborn as sns
%matplotlib inline

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)

2024-10-07 12:48:33 INFO     Using API key found in environment

Daily Exchange Volumes

Coin Metrics creates aggregated metrics to allow users to easily compare the total trading volume of dominant centralized exchanges. By leveraging the timeseries/exchange-metrics endpoint, we can retrieve the daily reported spot trading volume for a single trading venue or a list of exchanges.

end = pd.to_datetime(datetime.now())
start = end - timedelta(days=28)

exchange_list = [
    e['exchange'] for e in client.get_snapshots_of_asset_metric_constituents(
        'volume_trusted_spot_usd_1d', at_time=end
    ).to_list()
]

volumes = client.get_exchange_metrics(
    exchanges=exchange_list,
    metrics='volume_reported_spot_usd_1d',
    frequency='1d',
    start_time=start,
    end_time=end
).to_dataframe()

top_volumes = volumes.groupby('exchange')['volume_reported_spot_usd_1d'].sum().sort_values(ascending=False)
volumes['exchange'] = np.where(volumes['exchange'].isin(top_volumes[:8].index), volumes['exchange'], 'other')

volumes = volumes.groupby(['exchange', 'time'])[['volume_reported_spot_usd_1d']].sum().reset_index()
exchange_list_truncated = list(volumes.exchange.unique())

pivot_volumes = volumes.pivot(
    index="time",
    columns="exchange",
    values="volume_reported_spot_usd_1d"
)

pivot_volumes.head()

exchange	binance	bullish	bybit	coinbase	crypto.com	gate.io	huobi	okex	other
time
2024-09-10 00:00:00+00:00	9714347717.47266	1264837837.1639	3667152874.20169	1323434212.37803	4694543620.14163	845127903.462162	1605960239.92127	1293157356.51002	1340992725.680607
2024-09-11 00:00:00+00:00	12274439317.785299	1102465276.62474	4522846470.92285	1706654989.73028	7265287964.56301	1116958520.42675	1758439622.21422	1589552806.98192	1766919607.712266
2024-09-12 00:00:00+00:00	11296654011.844999	1071014247.32579	4144560926.66379	1781063003.281	4845335792.93472	1035902360.91803	1640974256.92128	1418484926.0031	1631462282.025268
2024-09-13 00:00:00+00:00	11925481195.452999	1073344439.24967	4164462938.08954	1832416834.75893	4559652705.02926	1060397918.39275	1602703737.9729	1544920512.39772	1847874572.934688
2024-09-14 00:00:00+00:00	6374474955.75626	391985441.722983	2230639306.76154	1137971743.1438	1992728998.68231	587007576.028109	1267693924.07889	961073483.60885	870317615.504417

pivot_volumes[exchange_list_truncated]

exchange	binance	bullish	bybit	coinbase	crypto.com	gate.io	huobi	okex	other
time
2024-09-10 00:00:00+00:00	9714347717.47266	1264837837.1639	3667152874.20169	1323434212.37803	4694543620.14163	845127903.462162	1605960239.92127	1293157356.51002	1340992725.680607
2024-09-11 00:00:00+00:00	12274439317.785299	1102465276.62474	4522846470.92285	1706654989.73028	7265287964.56301	1116958520.42675	1758439622.21422	1589552806.98192	1766919607.712266
2024-09-12 00:00:00+00:00	11296654011.844999	1071014247.32579	4144560926.66379	1781063003.281	4845335792.93472	1035902360.91803	1640974256.92128	1418484926.0031	1631462282.025268
2024-09-13 00:00:00+00:00	11925481195.452999	1073344439.24967	4164462938.08954	1832416834.75893	4559652705.02926	1060397918.39275	1602703737.9729	1544920512.39772	1847874572.934688
2024-09-14 00:00:00+00:00	6374474955.75626	391985441.722983	2230639306.76154	1137971743.1438	1992728998.68231	587007576.028109	1267693924.07889	961073483.60885	870317615.504417
2024-09-15 00:00:00+00:00	7361704730.28157	562957320.711511	2590884166.76392	832798774.766878	2931984212.55974	760804668.552838	1124162599.40243	997351542.148314	1032771425.499747
2024-09-16 00:00:00+00:00	12134299152.8419	1060106347.58518	4165725283.318	1361649870.77583	4722670462.41982	1191365703.76154	1583872653.47973	1593737943.29678	1535830455.197682
2024-09-17 00:00:00+00:00	12784370854.8181	1370902890.66798	4599028084.1107	1783827256.9648	4323879290.27791	1164978755.28512	1572931912.75403	1735321786.52275	1939514486.618056
2024-09-18 00:00:00+00:00	13949094048.295601	1509743657.16933	5191983189.97371	1812085945.13132	6524939071.87054	1588197115.85748	1677643847.70291	2025256403.67754	2131259660.134958
2024-09-19 00:00:00+00:00	15765392190.692499	1319652147.96947	5525980769.22584	2541879532.52425	5235187763.93785	1887336656.8817	1975000649.30877	2390932500.79671	2569561724.497508
2024-09-20 00:00:00+00:00	15637079381.6693	1265080412.99704	4627547221.99959	1976169614.44787	5064003012.65921	1629840500.88359	1700075845.88566	2139950015.81174	2114986008.292456
2024-09-21 00:00:00+00:00	7239200803.06984	459895539.058303	2482619303.48854	742642406.840901	2411519153.63723	833998109.245123	1087714374.93748	952109526.988626	991526560.843603
2024-09-22 00:00:00+00:00	9068630508.16427	802099932.54548	3151837904.3863	963747563.989523	3502445418.96481	1052501821.18361	1171972957.83752	1286837506.19408	1177308035.41588
2024-09-23 00:00:00+00:00	14119038812.017599	1329762723.44272	4669434414.70167	1776058068.84826	5796920448.02394	1604826350.38627	1572446737.44742	1883576140.48251	2059560504.183348
2024-09-24 00:00:00+00:00	13803897941.4459	1233617827.37224	4623955823.48951	1889015003.75608	4560270564.10611	1531245791.35488	1499667822.70557	2074103404.06031	2043368536.120572
2024-09-25 00:00:00+00:00	11243478001.8234	1056276031.71222	4504151719.14296	1543860513.03847	3791679743.58698	1510723332.35641	1492906823.75684	1894745371.91	1621159844.139992
2024-09-26 00:00:00+00:00	15599762219.3771	1610121770.7868	5623746968.56271	2281290095.10416	6042193867.25463	1884280762.68463	1716777384.93289	2384946966.64538	2319230690.451392
2024-09-27 00:00:00+00:00	14116726844.818899	1069480100.13979	5160096066.06245	2558606461.87492	5638677750.91947	1018072023.28832	1716844603.36935	2342997486.18092	2064558204.748724
2024-09-28 00:00:00+00:00	8159304277.82764	502782942.654992	2694065736.76692	964628082.625474	2081725893.72908	677192203.169288	1195274976.603	1451311955.47487	941584508.956109
2024-09-29 00:00:00+00:00	8921640553.12451	539803911.632983	2538588361.13329	1024493096.08131	2706231391.93362	900547717.914862	1067935423.38093	1460065096.9441	1018404267.377896
2024-09-30 00:00:00+00:00	15202552937.998301	1342394442.65625	4937717476.11833	2111602915.23605	6422163849.5578	1370536947.44636	1595747524.90984	2388331950.3912	2037584661.361227
2024-10-01 00:00:00+00:00	23240548408.584702	3184757131.55157	7759540621.79189	3415548316.38006	8959183206.512569	2137805079.14537	1908358048.62952	3470705407.36197	2871101695.353822
2024-10-02 00:00:00+00:00	17437229519.513699	1603025104.85705	5526619243.09343	2352353661.75056	8743651625.525089	1815221539.44225	1956203879.59561	2356068415.41375	2205618738.858672
2024-10-03 00:00:00+00:00	14999111682.6152	1605418668.37504	5120501662.45691	1930904616.17741	8193532164.49563	1538014044.673	1771828990.69422	2116511967.71281	1889111631.496463
2024-10-04 00:00:00+00:00	12876175910.416201	1332886726.35117	4279900711.68787	1918812943.46179	5759795028.96788	1280488009.44946	1437592080.67671	1697775201.17512	1770689490.345348
2024-10-05 00:00:00+00:00	6532364228.97254	392462635.006691	2126069409.03706	608167606.66407	1872894569.02638	594321770.03637	965511857.476451	815970303.962524	705386770.661552
2024-10-06 00:00:00+00:00	6930843434.83285	444045828.210464	2046183278.08736	679562882.140494	2229413627.75299	661460021.074824	882202743.119424	951842866.408241	823568622.1757

def format_ticks_usd(y, pos):
    if y >= 1_000_000_000_000:
        return '${:.1f}T'.format(y/1000000000000)
    elif y >= 1_000_000_000:
        return '${:.1f}B'.format(y/1000000000)
    elif y >= 1000000:
        return '${:.1f}M'.format(y/1000000)
    elif y >= 1000:
        return '${:.1f}K'.format(y/1000)
    else:
        return '${:.1f}'.format(y)

fig, ax = plt.subplots(figsize=(8,6))

ax.stackplot(
    pivot_volumes.index,
    [pivot_volumes[exchange].astype(float) for exchange in exchange_list_truncated],
    labels=exchange_list_truncated,
)
ax.set_title("Total Exchange Volume")
ax.yaxis.set_major_formatter(format_ticks_usd)
ax.yaxis.grid(visible=True, which='major', linestyle='--', alpha=0.5)
ax.legend(loc='lower right', bbox_to_anchor=(1.25, 0), title="Exchange")
ax.tick_params(axis='x', labelrotation=45)
ax.xaxis.set_major_locator(plt.MaxNLocator(4))
ax.set_xlim(volumes.time.min(), volumes.time.max())

pivot_volume_share = 100*pivot_volumes[exchange_list_truncated].div(pivot_volumes[exchange_list_truncated].sum(axis=1), axis=0)

import matplotlib.ticker as mtick


fig, ax = plt.subplots(figsize=(8,6))

ax.stackplot(
    pivot_volume_share.index,
    [pivot_volume_share[exchange].astype(float) for exchange in exchange_list_truncated],
    labels=exchange_list_truncated,
)
ax.set_title("Exchange Volume Share")
ax.yaxis.set_major_formatter(mtick.PercentFormatter())
ax.yaxis.grid(visible=True, which='major', linestyle='--', alpha=0.5)
ax.legend(loc='lower right', bbox_to_anchor=(1.25, 0), title="Exchange")
ax.tick_params(axis='x', labelrotation=45)
ax.xaxis.set_major_locator(plt.MaxNLocator(4))
ax.set_xlim(volumes.time.min(), volumes.time.max())
ax.set_ylim(0, 100)

Asset Share

In addition to retrieving aggregated trading volumes for all markets on an exchange, we can also utilize the timeseries/market-candles to obtain the USD trading volume of individual markets on a particular exchange.

# The wildcard parameter (*) enables users to retrieve all markets matching the designated format
exchange = 'coinbase'
markets=f"{exchange}-*-spot"

df = client.get_market_candles(
    markets=markets,
    start_time=start,
    end_time=end,
    frequency='1d',
    page_size=10000
).parallel(time_increment=timedelta(days=1)).to_dataframe()

Exporting to dataframe type:  79%|███████████████████████████████████████▎          | 22/28 [00:02<00:00, 10.38it/s]2024-10-07 12:48:38 INFO     no data to export
Exporting to dataframe type: 100%|██████████████████████████████████████████████████| 28/28 [00:02<00:00,  9.86it/s]

df

	market	time	price_open	price_close	price_high	price_low	vwap	volume	candle_usd_volume	candle_trades_count
0	coinbase-00-usd-spot	2024-09-10 00:00:00+00:00	0.0369	0.0361	0.0393	0.036	0.037349	4538849.98	169520.073709	1586
1	coinbase-1inch-btc-spot	2024-09-10 00:00:00+00:00	0.000005	0.000005	0.000005	0.000005	0.000005	14287.41	3720.280159	21
2	coinbase-1inch-eur-spot	2024-09-10 00:00:00+00:00	0.238	0.237	0.239	0.232	0.236182	616552.97	160541.151569	271
3	coinbase-1inch-gbp-spot	2024-09-10 00:00:00+00:00	0.201	0.199	0.201	0.196	0.199207	358978.94	93500.859211	194
4	coinbase-1inch-usd-spot	2024-09-10 00:00:00+00:00	0.263	0.26	0.264	0.256	0.259755	3483735.74	904916.71491	2772
...	...	...	...	...	...	...	...	...	...	...
11027	coinbase-zen-usd-spot	2024-10-06 00:00:00+00:00	7.49	8.01	8.04	7.49	7.726637	3059.792	23641.90321	243
11028	coinbase-zeta-usd-spot	2024-10-06 00:00:00+00:00	0.5964	0.6084	0.6121	0.5815	0.59916	666852.1	399550.77625	3258
11029	coinbase-zk-usd-spot	2024-10-06 00:00:00+00:00	0.12432	0.12904	0.13047	0.12409	0.126848	562305.1	71327.114648	462
11030	coinbase-zro-usd-spot	2024-10-06 00:00:00+00:00	4.109	4.236	4.253	4.067	4.162722	119387.31	496976.15616	1341
11031	coinbase-zrx-usd-spot	2024-10-06 00:00:00+00:00	0.309497	0.323902	0.323902	0.307255	0.317242	599437.77086	190166.885533	1235

11032 rows × 10 columns

df["candle_usd_volume"] = df.candle_usd_volume.astype(float)
df["time"] = pd.to_datetime(df.time)

df.sort_values(["market","time"],inplace=True)
    
# Create Addt. Cols
df['exchange'] = df.market.apply(lambda x: x.split("-")[0])
df['exchange-base'] = df.market.apply(lambda x: x.split("-")[0]+"-"+x.split("-")[1])
df['market_type'] = df.market.apply(lambda x: x.split("-")[-1])
df['base'] = df.market.apply(lambda x: x.split("-")[1])
df['quote'] = df.market.apply(lambda x: x.split("-")[2])

# Get volume by base asset by day

# Get top 10 assets by volume
total_vol_by_base = df.groupby('base',as_index=False).candle_usd_volume.sum()
total_vol_by_base.sort_values(by="candle_usd_volume",inplace=True)
base_top_list = total_vol_by_base.tail(10).base.tolist()
df["base2"] = np.where(df.base.isin(base_top_list),df.base,f"{len(total_vol_by_base)-10} others")

# Get sum by base asset by day
df_vol_by_base = df.groupby(["time","base2"],as_index=False).candle_usd_volume.sum()
df_vol_by_base['total_vol'] = df_vol_by_base.groupby("time").candle_usd_volume.transform(sum)
df_vol_by_base.columns=["time","base_asset","vol","total_vol"]
df_vol_by_base["vol_pct"]=(df_vol_by_base.vol/df_vol_by_base.total_vol)*100
df_vol_by_base.sort_values(["base_asset","time"],inplace=True)

df_vol_by_base

	time	base_asset	vol	total_vol	vol_pct
0	2024-09-10 00:00:00+00:00	248 others	2.285934e+08	1.325089e+09	17.251175
11	2024-09-11 00:00:00+00:00	248 others	2.546721e+08	1.708684e+09	14.904575
22	2024-09-12 00:00:00+00:00	248 others	2.478359e+08	1.783317e+09	13.897469
33	2024-09-13 00:00:00+00:00	248 others	2.823741e+08	1.834706e+09	15.390699
44	2024-09-14 00:00:00+00:00	248 others	1.710470e+08	1.139076e+09	15.016301
...	...	...	...	...	...
252	2024-10-02 00:00:00+00:00	xrp	1.025636e+08	2.355545e+09	4.354134
263	2024-10-03 00:00:00+00:00	xrp	8.681132e+07	1.934456e+09	4.487634
274	2024-10-04 00:00:00+00:00	xrp	4.813218e+07	1.924832e+09	2.500591
285	2024-10-05 00:00:00+00:00	xrp	1.525881e+07	6.100742e+08	2.501140
296	2024-10-06 00:00:00+00:00	xrp	1.258580e+07	6.807883e+08	1.848710

297 rows × 5 columns

num_assets = len(total_vol_by_base)
top_assets  = df_vol_by_base[~df_vol_by_base.base_asset.isin(["btc","eth",f"{num_assets-10} others"])].groupby("base_asset").vol.sum().sort_values(ascending=False).index.tolist()
top_assets  = ["btc","eth",f"{num_assets-10} others"] + top_assets

# Pivot back to assets in columns
df_vol_pivot = df_vol_by_base.pivot(index='time',
                             columns="base_asset",
                             values="vol_pct")

df_vol_pivot = df_vol_pivot[top_assets]
df_vol_pivot

base_asset	btc	eth	248 others	usdt	sol	sui	xrp	bonk	doge	shib	fet
time
2024-09-10 00:00:00+00:00	30.972149	13.067446	17.251175	24.945950	6.146731	1.292479	1.117807	1.095419	1.176307	0.505209	2.429329
2024-09-11 00:00:00+00:00	42.533098	14.601279	14.904575	16.508054	5.409347	0.986693	0.859503	0.918263	1.086848	0.510467	1.681872
2024-09-12 00:00:00+00:00	36.055903	11.246405	13.897469	24.472115	5.432146	2.323582	2.768770	0.686577	0.897056	0.317287	1.902690
2024-09-13 00:00:00+00:00	41.600482	12.469632	15.390699	17.687048	5.396664	1.283156	1.744660	0.639577	2.029411	0.438210	1.320460
2024-09-14 00:00:00+00:00	18.104549	7.169948	15.016301	46.855937	4.118700	1.842687	3.210786	0.853040	1.347843	0.469507	1.010702
2024-09-15 00:00:00+00:00	31.125051	16.348491	22.996837	10.391716	7.648190	3.747606	2.454636	1.244448	1.527748	0.543500	1.971777
2024-09-16 00:00:00+00:00	37.193874	17.206186	14.866560	17.164195	5.416218	2.069775	2.074828	0.735451	1.292425	0.482445	1.498044
2024-09-17 00:00:00+00:00	43.462805	13.122193	14.039608	16.877017	4.806221	2.639338	1.338655	0.617046	1.527054	0.304794	1.265270
2024-09-18 00:00:00+00:00	42.578945	14.389160	16.363534	10.759089	6.367782	4.161422	1.541983	0.689565	1.025603	0.504158	1.618760
2024-09-19 00:00:00+00:00	40.554355	11.913760	16.697873	13.990523	7.922874	3.051438	1.419195	1.071619	1.016350	0.619502	1.742510
2024-09-20 00:00:00+00:00	37.449647	13.966163	17.361613	13.270193	8.267577	3.294778	1.693500	1.156477	1.183191	0.508040	1.848821
2024-09-21 00:00:00+00:00	18.083126	17.647521	28.822949	11.717905	7.503045	4.002590	3.399164	1.988337	2.762844	1.646701	2.425818
2024-09-22 00:00:00+00:00	21.307804	12.658318	26.451636	17.494754	7.398947	7.118906	2.268850	1.122223	1.443169	1.173459	1.561934
2024-09-23 00:00:00+00:00	28.062013	17.667165	23.868440	13.861710	6.594981	4.093083	1.568748	0.654969	1.279564	0.547387	1.801941
2024-09-24 00:00:00+00:00	33.094029	16.229860	21.218012	10.361613	8.680876	4.290091	1.515116	0.933201	1.290790	0.589167	1.797243
2024-09-25 00:00:00+00:00	27.404906	14.144505	24.530905	14.985292	6.672537	4.867293	1.458899	1.815678	1.218182	1.596375	1.305427
2024-09-26 00:00:00+00:00	39.177160	9.940609	19.496714	11.109452	6.368470	3.448674	1.442450	1.690203	2.378929	4.152833	0.794507
2024-09-27 00:00:00+00:00	26.766935	11.660842	17.983374	23.777946	5.385791	2.343162	1.394474	2.964885	2.609585	4.088519	1.024488
2024-09-28 00:00:00+00:00	20.230763	8.003782	28.134525	9.949252	5.924345	2.526298	5.794979	5.232725	5.858864	6.783351	1.561114
2024-09-29 00:00:00+00:00	18.938614	8.169610	25.586889	12.172558	8.480627	6.501959	7.499683	4.049526	3.323746	4.029056	1.247732
2024-09-30 00:00:00+00:00	35.243463	10.555167	17.243217	15.949195	5.059958	3.853798	2.801514	3.926700	2.007723	2.283844	1.075420
2024-10-01 00:00:00+00:00	31.632666	12.956976	15.578165	19.152425	6.907327	4.017289	2.204173	3.274045	1.561403	1.602777	1.112754
2024-10-02 00:00:00+00:00	25.281508	11.474000	16.978840	20.736372	7.466111	5.570698	4.354134	3.818578	1.535713	1.664415	1.119630
2024-10-03 00:00:00+00:00	27.779234	10.982394	19.681539	13.232334	7.575048	8.686570	4.487634	3.914550	1.509132	1.266215	0.885351
2024-10-04 00:00:00+00:00	27.744248	9.435388	18.426154	22.715653	6.293791	5.623055	2.500591	2.882626	1.619108	1.761227	0.998159
2024-10-05 00:00:00+00:00	19.659108	8.427409	29.770955	14.614917	6.718545	6.955554	2.501140	3.304475	2.228612	3.536127	2.283157
2024-10-06 00:00:00+00:00	20.993449	7.617764	28.486867	14.267944	6.844585	8.255616	1.848710	3.945631	2.145486	4.223452	1.370494

fig, ax = plt.subplots(figsize=(8,6))

ax.stackplot(
    df_vol_pivot.index,
    [df_vol_pivot[asset].astype(float) for asset in df_vol_by_base.base_asset.unique()],
    labels=df_vol_by_base.base_asset.unique(),
)
ax.set_title("Asset Volume Share")
ax.yaxis.set_major_formatter(mtick.PercentFormatter())
ax.yaxis.grid(visible=True, which='major', linestyle='--', alpha=0.5)
ax.legend(loc='lower right', bbox_to_anchor=(1.25, 0), title="Asset")
ax.tick_params(axis='x', labelrotation=45)
ax.xaxis.set_major_locator(plt.MaxNLocator(4))
ax.set_xlim(volumes.time.min(), volumes.time.max())
ax.set_ylim(0, 100)

Retrieve Trade Sizes

Trade size is a useful metric for understanding the composition of an exchange's trading patterns. Is the platform more popular for retail traders, or large institutions? Which assets are driven by whale trades vs. small buys? These patterns are especially relevant for venues like Coinbase, where fee revenue is determined by trade size.

def get_trade_size_stats(start,end,market):
    """ 
    For a given date and market, get stats on number of trades and dist of trade sizes 
        Returns a df with:
             Number of trades
             Volume (USD/Native)
             Number of trades by size groupings
             Volume derived from trades of various size groupings
    """
    
    #Call api
    df_trades = client.get_market_trades(
        markets=market,
        start_time=start,
        end_time=end,
        page_size=10000
    ).parallel(time_increment=timedelta(hours=1)).to_dataframe()
    #Prep data
    df_trades["amount_usd"] = df_trades.amount*df_trades.price
    df_trades["amount_usd_groups"] = pd.cut(df_trades["amount_usd"],bins=[0,1e3,1e4,1e5,1e6,1e7,1e100])
    print(df_trades.time.min())
    print(df_trades.time.max())

    #Get stats by group
    sum_count_by_size = df_trades.groupby("amount_usd_groups").agg({"amount_usd":['count',sum]})
    
    #Collect into a df
    df_day = pd.DataFrame()

    df_day.loc[start,"NumTrades"] = len(df_trades)
    df_day.loc[start,"VolUSD"] = df_trades.amount_usd.sum()
    df_day.loc[start,"VolNTV"] = df_trades.amount.sum()

    df_day.loc[start,"AvgSizeUSD"] = df_trades.amount_usd.mean()
    df_day.loc[start,"MedSizeUSD"] = df_trades.amount_usd.median()
    df_day.loc[start,"MaxSizeUSD"] = df_trades.amount_usd.max()

    df_day.loc[start,"NumTrades_0-1K"]      = sum_count_by_size.iloc[0,0]
    df_day.loc[start,"NumTrades_1K-10K"]    = sum_count_by_size.iloc[1,0]
    df_day.loc[start,"NumTrades_10K-100K"]  = sum_count_by_size.iloc[2,0]
    df_day.loc[start,"NumTrades_100K-1M"]   = sum_count_by_size.iloc[3,0]
    df_day.loc[start,"NumTrades_1M-10M"]    = sum_count_by_size.iloc[4,0]
    df_day.loc[start,"NumTrades_10M-Over"]  = sum_count_by_size.iloc[5,0]

    df_day.loc[start,"VolUSD_Trades_0-1K"]     = sum_count_by_size.iloc[0,1]
    df_day.loc[start,"VolUSD_Trades_1K-10K"]   = sum_count_by_size.iloc[1,1]
    df_day.loc[start,"VolUSD_Trades_10K-100K"] = sum_count_by_size.iloc[2,1]
    df_day.loc[start,"VolUSD_Trades_100K-1M"]  = sum_count_by_size.iloc[3,1]
    df_day.loc[start,"VolUSD_Trades_1M-10M"]   = sum_count_by_size.iloc[4,1]
    df_day.loc[start,"VolUSD_Trades_10M-Over"] = sum_count_by_size.iloc[5,1]
    
    return df_day

start = datetime.today() - timedelta(hours=24)
end = datetime.today()

df = get_trade_size_stats(start, end,'coinbase-btc-usd-spot')

Exporting to dataframe type:  44%|██████████████████████                            | 11/25 [00:02<00:02,  6.99it/s]2024-10-07 12:48:43 INFO     no data to export
Exporting to dataframe type: 100%|██████████████████████████████████████████████████| 25/25 [00:04<00:00,  5.16it/s]

2024-10-06 12:48:39.766637+00:00
2024-10-07 12:48:31.860605+00:00

df.head()

	NumTrades	VolUSD	VolNTV	AvgSizeUSD	MedSizeUSD	MaxSizeUSD	NumTrades_0-1K	NumTrades_1K-10K	NumTrades_10K-100K	NumTrades_100K-1M	NumTrades_1M-10M	NumTrades_10M-Over	VolUSD_Trades_0-1K	VolUSD_Trades_1K-10K	VolUSD_Trades_10K-100K	VolUSD_Trades_100K-1M	VolUSD_Trades_1M-10M	VolUSD_Trades_10M-Over
2024-10-06 12:48:39.638626	233725.0	2.681840e+08	4249.99384	1147.434052	48.436912	475567.911314	187168.0	41234.0	5278.0	45.0	0.0	0.0	1.983836e+07	1.350884e+08	1.056560e+08	7.601298e+06	0.0	0.0

df.transpose()#.rename_axis('value')

	2024-10-06 12:48:39.638626
NumTrades	2.337250e+05
VolUSD	2.681840e+08
VolNTV	4.249994e+03
AvgSizeUSD	1.147434e+03
MedSizeUSD	4.843691e+01
MaxSizeUSD	4.755679e+05
NumTrades_0-1K	1.871680e+05
NumTrades_1K-10K	4.123400e+04
NumTrades_10K-100K	5.278000e+03
NumTrades_100K-1M	4.500000e+01
NumTrades_1M-10M	0.000000e+00
NumTrades_10M-Over	0.000000e+00
VolUSD_Trades_0-1K	1.983836e+07
VolUSD_Trades_1K-10K	1.350884e+08
VolUSD_Trades_10K-100K	1.056560e+08
VolUSD_Trades_100K-1M	7.601298e+06
VolUSD_Trades_1M-10M	0.000000e+00
VolUSD_Trades_10M-Over	0.000000e+00

trade_sizes = df[['VolUSD_Trades_0-1K',
                  'VolUSD_Trades_1K-10K',
                  'VolUSD_Trades_10K-100K',
                  'VolUSD_Trades_100K-1M',
                  'VolUSD_Trades_1M-10M',
                  'VolUSD_Trades_10M-Over']]

trade_sizes = trade_sizes.rename(columns=lambda x: x.split('_')[2])
trade_sizes

	0-1K	1K-10K	10K-100K	100K-1M	1M-10M	10M-Over
2024-10-06 12:48:39.638626	1.983836e+07	1.350884e+08	1.056560e+08	7.601298e+06	0.0	0.0

fig, ax = plt.subplots()
ax.bar(
    x=trade_sizes.columns,
    height=np.hstack([trade_sizes[c].astype(float) for c in trade_sizes.columns])
)

ax.set_title(f"Coinbase Volume by Trade Size \n{start.isoformat()[:16]} to {end.isoformat()[:16]}")
ax.yaxis.set_major_formatter(format_ticks_usd)
ax.yaxis.grid(visible=True, which='major', linestyle='--', alpha=0.5)