Calculating Total Value Locked in Liquidity Pools using DEX Data

Automated Market Makers (AMMs) are an innovative new class of smart contracts introduced in decentralized exchange protocols like Uniswap, allowing users to permissionlessly provision liquidity for digital asset trading activity without needing a traditional central order book. However, an important aspect of providing users of these pools is understanding how the total USD value of the assets allocated to the contract can fluctuate over time, leading to impermanent loss for liquidity providers. In this notebook, we explore how Coin Metrics DEX market metadata can be combined with Reference Rates and ATLAS search engine capabilities to construct a timeseries representation pool TVL, allowing market participants to make more informed decisions about DEX market making and trading.

Resources

This notebook demonstrates basic functionality offered by the Coin Metrics Python API Client, ATLAS blockchain search engine, and DEX Market Data.

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

453KB

DEFI_liquidity_pool_tvl.ipynb

840KB

DEFI_liquidity_pool_tvl.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 plotly.graph_objs as go
import logging
from pytz import timezone as timezone_conv
from datetime import timezone as timezone_info
from dateutil.relativedelta import relativedelta
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
%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)

2024-10-09 14:36:29 INFO     Using API key found in environment

DEX Market Reference Data

The reference-data/markets endpoint returns a list of available markets meeting specified criteria. Users can pass in a list of markets, exchanges, or market types (spot, futures, options). For DEX markets, the endpoint also returns key liquidity pool metadata, such as fee tier and pool contract address.

uni_v3_markets = client.reference_data_markets(
    exchange = 'uniswap_v3_eth'
).to_dataframe()

uni_v3_markets

	market	exchange	base	quote	pair	symbol	type	size_asset	margin_asset	strike	...	order_price_increment	order_price_min	order_price_max	order_size_min	order_taker_fee	order_maker_fee	margin_trading_enabled	experimental	base_native	quote_native
0	uniswap_v3_eth-1-1inch-dai-spot	uniswap_v3_eth	1inch	dai	1inch-dai	<NA>	spot	<NA>	<NA>	<NA>	...	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	True	<NA>	<NA>
1	uniswap_v3_eth-1-1inch-usdc-spot	uniswap_v3_eth	1inch	usdc	1inch-usdc	<NA>	spot	<NA>	<NA>	<NA>	...	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	True	<NA>	<NA>
2	uniswap_v3_eth-1-1inch-weth-spot	uniswap_v3_eth	1inch	weth	1inch-weth	<NA>	spot	<NA>	<NA>	<NA>	...	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	True	<NA>	<NA>
3	uniswap_v3_eth-1-ageur_eth-eurcv_eth-spot	uniswap_v3_eth	ageur_eth	eurcv_eth	ageur_eth-eurcv_eth	<NA>	spot	<NA>	<NA>	<NA>	...	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	True	<NA>	<NA>
4	uniswap_v3_eth-1-ageur_eth-usdc-spot	uniswap_v3_eth	ageur_eth	usdc	ageur_eth-usdc	<NA>	spot	<NA>	<NA>	<NA>	...	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	True	<NA>	<NA>
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1638	uniswap_v3_eth-agg-yfi-cvx-spot	uniswap_v3_eth	yfi	cvx	yfi-cvx	<NA>	spot	<NA>	<NA>	<NA>	...	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	True	<NA>	<NA>
1639	uniswap_v3_eth-agg-yfi-link-spot	uniswap_v3_eth	yfi	link	yfi-link	<NA>	spot	<NA>	<NA>	<NA>	...	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	True	<NA>	<NA>
1640	uniswap_v3_eth-agg-yfi-usdc-spot	uniswap_v3_eth	yfi	usdc	yfi-usdc	<NA>	spot	<NA>	<NA>	<NA>	...	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	True	<NA>	<NA>
1641	uniswap_v3_eth-agg-yfi-wbtc-spot	uniswap_v3_eth	yfi	wbtc	yfi-wbtc	<NA>	spot	<NA>	<NA>	<NA>	...	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	True	<NA>	<NA>
1642	uniswap_v3_eth-agg-yfi-weth-spot	uniswap_v3_eth	yfi	weth	yfi-weth	<NA>	spot	<NA>	<NA>	<NA>	...	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	True	<NA>	<NA>

1643 rows × 39 columns

weth_usdc_markets = uni_v3_markets.loc[(uni_v3_markets['base']=='usdc') & (uni_v3_markets['quote']=='weth')]

weth_usdc_pools = weth_usdc_markets.dropna(subset=['contract_address'])
weth_usdc_pools

	market	exchange	base	quote	pair	symbol	type	size_asset	margin_asset	strike	...	order_price_increment	order_price_min	order_price_max	order_size_min	order_taker_fee	order_maker_fee	margin_trading_enabled	experimental	base_native	quote_native
72	uniswap_v3_eth-1-usdc-weth-spot	uniswap_v3_eth	usdc	weth	usdc-weth	<NA>	spot	<NA>	<NA>	<NA>	...	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	True	<NA>	<NA>
281	uniswap_v3_eth-2-usdc-weth-spot	uniswap_v3_eth	usdc	weth	usdc-weth	<NA>	spot	<NA>	<NA>	<NA>	...	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	True	<NA>	<NA>
650	uniswap_v3_eth-3-usdc-weth-spot	uniswap_v3_eth	usdc	weth	usdc-weth	<NA>	spot	<NA>	<NA>	<NA>	...	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	True	<NA>	<NA>
1045	uniswap_v3_eth-4-usdc-weth-spot	uniswap_v3_eth	usdc	weth	usdc-weth	<NA>	spot	<NA>	<NA>	<NA>	...	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	True	<NA>	<NA>

4 rows × 39 columns

Fetch contract balances over time with ATLAS

Now that we have a list of target liquidity pool contracts, we can use ATLAS blockchain search engine to query for balance updates in the pool for each asset.

assets = ['usdc','weth']
pools = weth_usdc_pools['contract_address'].to_list()
pools_tvl = pd.DataFrame()
start = datetime.now() - timedelta(days=30)

for asset in assets:
    tvl = client.get_list_of_balance_updates_v2(
        asset=asset,
        accounts=pools,
        start_time = start
    ).parallel(max_workers=10,time_increment=relativedelta(days=1)).to_dataframe()
    
    # Add the asset name to a new 'asset' column
    tvl['asset'] = asset
    pools_tvl = pd.concat([pools_tvl, tvl], axis=0)

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Exporting to dataframe type: 100%|█████████████████████████████████████████████████████████████| 31/31 [00:41<00:00,  1.34s/it]

# Create a mapping from contract_address to market
contract_to_market = weth_usdc_pools.set_index('contract_address')['market']
contract_to_market

contract_address
e0554a476a092703abdb3ef35c80e0d76d32939f    uniswap_v3_eth-1-usdc-weth-spot
88e6a0c2ddd26feeb64f039a2c41296fcb3f5640    uniswap_v3_eth-2-usdc-weth-spot
8ad599c3a0ff1de082011efddc58f1908eb6e6d8    uniswap_v3_eth-3-usdc-weth-spot
7bea39867e4169dbe237d55c8242a8f2fcdcc387    uniswap_v3_eth-4-usdc-weth-spot
Name: market, dtype: string

pools_tvl['market'] = pools_tvl['account'].map(contract_to_market)

pools_tvl

	chain_sequence_number	account	account_creation_height	change	previous_balance	new_balance	transaction_sequence_number	n_debits	n_credits	block_hash	...	consensus_time	credit	total_received	total_sent	previous_debit_height	previous_credit_height	previous_chain_sequence_number	txid	asset	market
0	88964646899286016	88e6a0c2ddd26feeb64f039a2c41296fcb3f5640	12376729	-60426.466497	72674047.982769	72613621.516272	0	4019999	3619510	96131a3e3702feb38e1ce25c71773962cdf6ed8beb5ee2...	...	2024-09-09 14:37:23+00:00	False	738732040663.426392	738659427041.910034	20713695	20713694	88964642604318722	a8f5e44bb9ae9f93a3d1b11b5fce58fe2bcad8ba73561f...	usdc	uniswap_v3_eth-2-usdc-weth-spot
1	88964646899286022	88e6a0c2ddd26feeb64f039a2c41296fcb3f5640	12376729	-551.745218	72613621.516272	72613069.771054	0	4020000	3619510	96131a3e3702feb38e1ce25c71773962cdf6ed8beb5ee2...	...	2024-09-09 14:37:23+00:00	False	738732040663.426392	738659427593.655396	20713696	20713694	88964646899286016	3072cd48442d1e61b7232590812737dabdd52d60ed2181...	usdc	uniswap_v3_eth-2-usdc-weth-spot
2	88964651194253318	88e6a0c2ddd26feeb64f039a2c41296fcb3f5640	12376729	-1189.382003	72613069.771054	72611880.389051	0	4020001	3619510	d1b93582ac0242d6c4d78a2e15fa7a14cb8f23cdd315a7...	...	2024-09-09 14:37:35+00:00	False	738732040663.426392	738659428783.037231	20713696	20713694	88964646899286022	785845bb01a0c4c95a4fce1a5b684b607eff59db5e35a0...	usdc	uniswap_v3_eth-2-usdc-weth-spot
3	88964651194253320	e0554a476a092703abdb3ef35c80e0d76d32939f	13616454	-1185.605465	601863.20449	600677.599025	1	231967	225267	d1b93582ac0242d6c4d78a2e15fa7a14cb8f23cdd315a7...	...	2024-09-09 14:37:35+00:00	False	5991391248.046802	5990790570.447777	20713695	20713695	88964642604318733	785845bb01a0c4c95a4fce1a5b684b607eff59db5e35a0...	usdc	uniswap_v3_eth-1-usdc-weth-spot
4	88964651194253338	88e6a0c2ddd26feeb64f039a2c41296fcb3f5640	12376729	-1178.099514	72611880.389051	72610702.289537	0	4020002	3619510	d1b93582ac0242d6c4d78a2e15fa7a14cb8f23cdd315a7...	...	2024-09-09 14:37:35+00:00	False	738732040663.426392	738659429961.136841	20713697	20713694	88964651194253318	e8c9bf688ee4d41f22d95f34936a425edb4f61244318b7...	usdc	uniswap_v3_eth-2-usdc-weth-spot
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
273787	89893038440054825	e0554a476a092703abdb3ef35c80e0d76d32939f	13616454	11.45984	2397.232798	2408.692638	1	292522	301912	de4360d0ae6f824e4ca7525da2268497de62906fec744c...	...	2024-10-09 18:33:11+00:00	True	4128265.590843	4125856.898204	20929853	20929850	89893034145087672	aefc38b1fa84766bb2e50b8565d7e6b23031d98a28ccd8...	weth	uniswap_v3_eth-1-usdc-weth-spot
273788	89893038440054892	e0554a476a092703abdb3ef35c80e0d76d32939f	13616454	-0.015523	2408.692638	2408.677115	0	292523	301912	de4360d0ae6f824e4ca7525da2268497de62906fec744c...	...	2024-10-09 18:33:11+00:00	False	4128265.590843	4125856.913727	20929853	20929854	89893038440054825	9b83a0daab76cd6db6aedbafc5f1e61adc0f5d31e72e47...	weth	uniswap_v3_eth-1-usdc-weth-spot
273789	89893038440054968	88e6a0c2ddd26feeb64f039a2c41296fcb3f5640	12376729	-2.123209	27357.193086	27355.069877	0	3685872	4078043	de4360d0ae6f824e4ca7525da2268497de62906fec744c...	...	2024-10-09 18:33:11+00:00	False	378530260.402821	378502905.332944	20929852	20929854	89893038440054823	e79fe9981166fccb9fb15b872da4ee5f8ea4f581a86e0d...	weth	uniswap_v3_eth-2-usdc-weth-spot
273790	89893051324956808	88e6a0c2ddd26feeb64f039a2c41296fcb3f5640	12376729	-0.119392	27355.069877	27354.950485	0	3685873	4078043	2823efc897ab6e43f4382e7670f1cfa4aa621571e79d9d...	...	2024-10-09 18:33:47+00:00	False	378530260.402821	378502905.452336	20929854	20929854	89893038440054968	a46cab246dea451a42d4b26c79994c5fc06ce1a10a53a7...	weth	uniswap_v3_eth-2-usdc-weth-spot
273791	89893051324956894	88e6a0c2ddd26feeb64f039a2c41296fcb3f5640	12376729	-0.825174	27354.950485	27354.125311	0	3685874	4078043	2823efc897ab6e43f4382e7670f1cfa4aa621571e79d9d...	...	2024-10-09 18:33:47+00:00	False	378530260.402821	378502906.27751	20929857	20929854	89893051324956808	3004b50008190b1b36f5cdf602b63b9f9792267d89f2fe...	weth	uniswap_v3_eth-2-usdc-weth-spot

547648 rows × 21 columns

# Sort by 'consensus_time', 'market', and 'chain_sequence_number'
pools_tvl_sorted = pools_tvl.sort_values(by=['consensus_time', 'market', 'chain_sequence_number'], ascending=[True, True, False])

# Drop duplicates, keeping only the first occurrence (in this case, the highest 'chain_sequence_number')
pools_tvl_deduplicated = pools_tvl_sorted.drop_duplicates(subset=['consensus_time', 'market','asset'], keep='first')

pools_tvl_clean = pools_tvl_deduplicated[['market','consensus_time','asset','new_balance']].copy()
pools_tvl_clean['new_balance'] = pools_tvl_clean.apply(
    lambda row: -row['new_balance'] if row['asset'] == 'weth' else row['new_balance'],
    axis=1
)
pools_tvl_clean

	market	consensus_time	asset	new_balance
1	uniswap_v3_eth-2-usdc-weth-spot	2024-09-09 14:37:23+00:00	weth	-3.126659e+04
1	uniswap_v3_eth-2-usdc-weth-spot	2024-09-09 14:37:23+00:00	usdc	7.261307e+07
3	uniswap_v3_eth-1-usdc-weth-spot	2024-09-09 14:37:35+00:00	weth	-4.664425e+02
3	uniswap_v3_eth-1-usdc-weth-spot	2024-09-09 14:37:35+00:00	usdc	6.006776e+05
4	uniswap_v3_eth-2-usdc-weth-spot	2024-09-09 14:37:35+00:00	weth	-3.126763e+04
...	...	...	...	...
273850	uniswap_v3_eth-2-usdc-weth-spot	2024-10-09 18:36:11+00:00	usdc	8.006068e+07
273853	uniswap_v3_eth-1-usdc-weth-spot	2024-10-09 18:36:23+00:00	usdc	2.503766e+06
273851	uniswap_v3_eth-2-usdc-weth-spot	2024-10-09 18:36:23+00:00	usdc	8.005964e+07
273854	uniswap_v3_eth-2-usdc-weth-spot	2024-10-09 18:36:47+00:00	usdc	8.005916e+07
273855	uniswap_v3_eth-1-usdc-weth-spot	2024-10-09 18:36:59+00:00	usdc	2.493272e+06

382679 rows × 4 columns

df = pd.DataFrame(pools_tvl_clean)
df['consensus_time'] = pd.to_datetime(df['consensus_time'])
# Split the DataFrame by market and store in a dictionary
market_dfs = {market: group.pivot(index='consensus_time', columns='asset', values='new_balance').ffill()
              for market, group in df.groupby('market')}

display(market_dfs)

{'uniswap_v3_eth-1-usdc-weth-spot': asset                              usdc         weth
 consensus_time                                      
 2024-09-09 14:37:35+00:00  6.006776e+05  -466.442498
 2024-09-09 14:37:47+00:00  5.927582e+05  -469.913266
 2024-09-09 14:38:11+00:00  5.834235e+05  -474.007791
 2024-09-09 14:38:23+00:00  5.852796e+05  -473.193522
 2024-09-09 14:38:35+00:00  5.783144e+05  -476.250599
 ...                                 ...          ...
 2024-10-09 18:35:11+00:00  2.504156e+06 -2408.677115
 2024-10-09 18:35:35+00:00  2.507156e+06 -2408.677115
 2024-10-09 18:36:11+00:00  2.503081e+06 -2408.677115
 2024-10-09 18:36:23+00:00  2.503766e+06 -2408.677115
 2024-10-09 18:36:59+00:00  2.493272e+06 -2408.677115
 
 [93275 rows x 2 columns],
 'uniswap_v3_eth-2-usdc-weth-spot': asset                              usdc          weth
 consensus_time                                       
 2024-09-09 14:37:23+00:00  7.261307e+07 -31266.590416
 2024-09-09 14:37:35+00:00  7.261070e+07 -31267.627526
 2024-09-09 14:37:47+00:00  7.244846e+07 -31338.733700
 2024-09-09 14:37:59+00:00  7.240161e+07 -31359.275300
 2024-09-09 14:38:11+00:00  7.227468e+07 -31414.964479
 ...                                 ...           ...
 2024-10-09 18:34:47+00:00  7.997092e+07 -27354.125311
 2024-10-09 18:35:35+00:00  7.998951e+07 -27354.125311
 2024-10-09 18:36:11+00:00  8.006068e+07 -27354.125311
 2024-10-09 18:36:23+00:00  8.005964e+07 -27354.125311
 2024-10-09 18:36:47+00:00  8.005916e+07 -27354.125311
 
 [89932 rows x 2 columns],
 'uniswap_v3_eth-3-usdc-weth-spot': asset                              usdc          weth
 consensus_time                                       
 2024-09-09 14:40:11+00:00  9.802691e+06 -22517.703981
 2024-09-09 15:06:59+00:00  9.815312e+06 -22512.198607
 2024-09-09 15:07:11+00:00  9.836937e+06 -22502.771507
 2024-09-09 15:13:23+00:00  9.817752e+06 -22511.184829
 2024-09-09 15:13:47+00:00  9.797839e+06 -22519.924545
 ...                                 ...           ...
 2024-10-09 18:15:47+00:00  1.306390e+07 -19482.217019
 2024-10-09 18:16:59+00:00  1.293324e+07 -19536.165795
 2024-10-09 18:17:11+00:00  1.276007e+07 -19607.732168
 2024-10-09 18:18:23+00:00  1.255446e+07 -19692.807793
 2024-10-09 18:23:47+00:00  1.255437e+07 -19692.792301
 
 [7850 rows x 2 columns],
 'uniswap_v3_eth-4-usdc-weth-spot': asset                               usdc        weth
 consensus_time                                      
 2024-09-09 15:17:35+00:00  288875.833924 -166.205531
 2024-09-09 16:12:35+00:00  291361.212465 -165.137952
 2024-09-09 16:56:47+00:00  293088.964462 -164.400079
 2024-09-09 17:10:23+00:00  293188.964462 -164.357478
 2024-09-09 19:54:35+00:00  293683.964462 -164.146777
 ...                                  ...         ...
 2024-10-08 21:45:59+00:00  311219.129512 -157.116899
 2024-10-08 21:56:11+00:00  311402.896623 -157.042040
 2024-10-09 01:33:23+00:00  312960.239993 -156.409115
 2024-10-09 01:38:11+00:00  314224.468721 -155.897241
 2024-10-09 18:30:11+00:00  313919.457241 -156.023085
 
 [435 rows x 2 columns]}

Retrieve Reference Rates to calculate the equivalent USD value for TVL

To normalize pool TVL into USD-denominated terms, we'll leverage the Coin Metrics Reference Rate, which represents a volume-weighted median price across a subset of the asset's most highly-liquid markets.

ref_rate = client.get_asset_metrics(
    assets=['usdc','weth'],
    metrics='ReferenceRateUSD',
    start_time=start,
    frequency='1m'
).parallel(max_workers=10,time_increment=relativedelta(days=1)).to_dataframe()

Exporting to dataframe type: 100%|█████████████████████████████████████████████████████████████| 62/62 [00:06<00:00,  8.93it/s]

ref_rate = ref_rate.pivot(index='time', columns='asset', values='ReferenceRateUSD')
ref_rate

asset	usdc	weth
time
2024-09-09 14:38:00+00:00	1.000228	2281.628477
2024-09-09 14:39:00+00:00	1.000066	2278.047686
2024-09-09 14:40:00+00:00	0.99975	2279.877181
2024-09-09 14:41:00+00:00	0.999917	2278.211943
2024-09-09 14:42:00+00:00	0.999867	2279.118291
...	...	...
2024-10-09 18:34:00+00:00	0.999862	2422.199211
2024-10-09 18:35:00+00:00	0.999822	2418.294121
2024-10-09 18:36:00+00:00	0.999827	2420.816853
2024-10-09 18:37:00+00:00	0.999789	2420.182517
2024-10-09 18:38:00+00:00	0.999873	2421.764058

43441 rows × 2 columns

# Iterate over each market DataFrame
for market, df in market_dfs.items():
    # Resample the DataFrame to 1-minute intervals
    df_resampled = df.resample('min').last().dropna()

    # Reindex the market DataFrame to the ref_rate DataFrame's index
    aligned_df = df_resampled.reindex(ref_rate.index, method='nearest')

    # Multiply the 'usdc' and 'weth' columns by the corresponding rate
    aligned_df['usdc'] = aligned_df['usdc'] * ref_rate['usdc']
    aligned_df['weth'] = aligned_df['weth'] * ref_rate['weth']

    # Replace the original DataFrame in the dictionary with the updated one
    market_dfs[market] = aligned_df

first_pool_key = list(market_dfs.keys())[0]
first_pool = market_dfs[first_pool_key]  
first_pool

asset	usdc	weth
time
2024-09-09 14:38:00+00:00	580007.558512	-1085063.725878
2024-09-09 14:39:00+00:00	591547.333378	-1071736.155646
2024-09-09 14:40:00+00:00	591664.869298	-1072292.759274
2024-09-09 14:41:00+00:00	584444.264977	-1078824.970611
2024-09-09 14:42:00+00:00	604433.557236	-1059255.13815
...	...	...
2024-10-09 18:34:00+00:00	2480138.279715	-5834295.808101
2024-10-09 18:35:00+00:00	2506710.799699	-5824889.708489
2024-10-09 18:36:00+00:00	2492840.090089	-5830966.153299
2024-10-09 18:37:00+00:00	2492746.887517	-5829438.242635
2024-10-09 18:38:00+00:00	2492954.27474	-5833247.665283

43441 rows × 2 columns

Plot USD-denominated TVL for target liquidity pools

def generate_area_figure(df, layout, columns, diverging_colors=False):
    traces = []
    for series in columns:
        traces.append(
            go.Scatter(
                x=df.index,
                y=df[series],
                name=series,
                fill='tozeroy'  # Ensures filling to the zero line on the y-axis
            ))
    return go.Figure(data=traces, layout=layout)

# Plotting for each market using Matplotlib
market_to_contract = contract_to_market.reset_index().set_index('market')
for market, data in market_dfs.items():
    address = market_to_contract.loc[market, 'contract_address']
    print(f'{market}')
    print(f'{address}')
    print(f'USDC: https://atlas.coinmetrics.io/address-details?asset=usdc&address={address}')
    print(f'WETH: https://atlas.coinmetrics.io/address-details?asset=weth&address={address}')

    # Create the figure and axis
    fig, ax = plt.subplots(figsize=(10, 6))

    # Plot the area chart for each series in the data
    for column in data.columns:
        ax.fill_between(data.index, data[column], label=column)

    # Set chart title and labels
    ax.set_title(f'{market.upper()}\nLiquidity Pool TVL', fontsize=16, color='grey')
    ax.set_xlabel('', color='grey')
    ax.set_ylabel('TVL Value (USD)', color='grey')

    # Set grid and background colors
    ax.set_facecolor('#49494a')
    ax.grid(True, color='white', linestyle='--', linewidth=0.5)

    # Customize tick parameters (color and size)
    ax.tick_params(axis='x', colors='grey', rotation=45)
    ax.tick_params(axis='y', colors='grey')
    ax.xaxis.set_major_locator(mdates.DayLocator(interval=4))
    ax.set_xlim([data.index.min(), data.index.max()])

    # Set legend and its appearance
    ax.legend(loc='upper right', fontsize=10)

    # Adjust the plot layout
    plt.tight_layout()

    # Show the plot
    plt.show()

uniswap_v3_eth-1-usdc-weth-spot
e0554a476a092703abdb3ef35c80e0d76d32939f
USDC: https://atlas.coinmetrics.io/address-details?asset=usdc&address=e0554a476a092703abdb3ef35c80e0d76d32939f
WETH: https://atlas.coinmetrics.io/address-details?asset=weth&address=e0554a476a092703abdb3ef35c80e0d76d32939f

uniswap_v3_eth-2-usdc-weth-spot
88e6a0c2ddd26feeb64f039a2c41296fcb3f5640
USDC: https://atlas.coinmetrics.io/address-details?asset=usdc&address=88e6a0c2ddd26feeb64f039a2c41296fcb3f5640
WETH: https://atlas.coinmetrics.io/address-details?asset=weth&address=88e6a0c2ddd26feeb64f039a2c41296fcb3f5640

uniswap_v3_eth-3-usdc-weth-spot
8ad599c3a0ff1de082011efddc58f1908eb6e6d8
USDC: https://atlas.coinmetrics.io/address-details?asset=usdc&address=8ad599c3a0ff1de082011efddc58f1908eb6e6d8
WETH: https://atlas.coinmetrics.io/address-details?asset=weth&address=8ad599c3a0ff1de082011efddc58f1908eb6e6d8

uniswap_v3_eth-4-usdc-weth-spot
7bea39867e4169dbe237d55c8242a8f2fcdcc387
USDC: https://atlas.coinmetrics.io/address-details?asset=usdc&address=7bea39867e4169dbe237d55c8242a8f2fcdcc387
WETH: https://atlas.coinmetrics.io/address-details?asset=weth&address=7bea39867e4169dbe237d55c8242a8f2fcdcc387