# Comparing Volumes of Exchanges and Assets ![](https://5264302.fs1.hubspotusercontent-na1.net/hubfs/5264302/Demo%20Asset%20Resources/CM-Demo-mdf-exchange-volume-Cover.png) 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](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 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 ``` ```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) ``` ``` 2024-10-07 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. ```python end = pd.to_datetime(datetime.now()) start = end - timedelta(days=28) ``` ```python exchange_list = [ e['exchange'] for e in client.get_snapshots_of_asset_metric_constituents( 'volume_trusted_spot_usd_1d', at_time=end ).to_list() ] ``` ```python volumes = client.get_exchange_metrics( exchanges=exchange_list, metrics='volume_reported_spot_usd_1d', frequency='1d', start_time=start, end_time=end ).to_dataframe() ``` ```python 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') ``` ```python volumes = volumes.groupby(['exchange', 'time'])[['volume_reported_spot_usd_1d']].sum().reset_index() exchange_list_truncated = list(volumes.exchange.unique()) ``` ```python pivot_volumes = volumes.pivot( index="time", columns="exchange", values="volume_reported_spot_usd_1d" ) ``` ```python 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 | ```python 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 | ```python 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()) ```
```python pivot_volume_share = 100*pivot_volumes[exchange_list_truncated].div(pivot_volumes[exchange_list_truncated].sum(axis=1), axis=0) ``` ```python 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. ```python # The wildcard parameter (*) enables users to retrieve all markets matching the designated format exchange = 'coinbase' markets=f"{exchange}-*-spot" ``` ```python 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] ``` ```python 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 ```python df["candle_usd_volume"] = df.candle_usd_volume.astype(float) df["time"] = pd.to_datetime(df.time) ``` ```python 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) ``` ```python 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 ```python 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") ``` ```python 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 | ```python 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. ```python 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 ``` ```python start = datetime.today() - timedelta(hours=24) end = datetime.today() ``` ```python 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 ``` ```python 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 | ```python 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 | ```python 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 | ```python 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) ```