Using Staking Metrics to Get Yield and Staked Supply

Resources

This notebook demonstrates basic functionality offered by the Coin Metrics Python API Client and Network Data Pro.

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

198KB

NDP - ETH Staking Metrics.ipynb

198KB

NDP - ETH Staking Metrics.ipynb

Notebook Setup

from os import environ
import pandas as pd
import numpy as np
import seaborn as sns
import logging
from datetime import date, datetime, timedelta
from coinmetrics.api_client import CoinMetricsClient
import logging
import matplotlib.pyplot as plt
import warnings
%matplotlib inline

# Chart themes
sns.set_theme()
warnings.filterwarnings('ignore')
fig = plt.style.use('seaborn')
sns.set(rc={'figure.figsize':(8,6)})
sns.set_style("whitegrid",{'axes.grid' : True,'grid.linestyle': '--', 'grid.color': 'gray','axes.edgecolor': 'white','font.family': ['arial']})

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)

2024-09-27 14:45:08 INFO     Using API key found in environment

Calculate Estimated Validator Yield

Using ETH_CL validator metrics it is possible to estimate the yield from the protocol. In combination with historical data on priority tips, we can estimate what a validator should expect to earn. Note that maximal extractable value (MEV) is another source of revenue for validators but is currently not considered as part of this analysis.

A validator’s expected annual percentage return (APR) from staking rewards accumulated on the Consensus Layer, assuming perfect performance and uptime, can be estimated with the formula below (where ValidatorActOngCnt = number of active validators):

2940.21 ÷ sqrt(ValidatorActOngCnt) = Staking Yield

start_time = '2022-01-01'
end_time = '2022-12-31'

Retrieve Consensus Layer Metrics

consensus_metrics = client.get_asset_metrics(
    assets='eth_cl',
    metrics=['ValidatorActOngCnt'],
    start_time = start_time,
    end_time = end_time
).to_dataframe()

consensus_metrics['time'] = pd.to_datetime(consensus_metrics['time'])

consensus_metrics

	asset	time	ValidatorActOngCnt
0	eth_cl	2022-01-01 00:00:00+00:00	275880
1	eth_cl	2022-01-02 00:00:00+00:00	276301
2	eth_cl	2022-01-03 00:00:00+00:00	276784
3	eth_cl	2022-01-04 00:00:00+00:00	277530
4	eth_cl	2022-01-05 00:00:00+00:00	278349
...	...	...	...
360	eth_cl	2022-12-27 00:00:00+00:00	491923
361	eth_cl	2022-12-28 00:00:00+00:00	492863
362	eth_cl	2022-12-29 00:00:00+00:00	493116
363	eth_cl	2022-12-30 00:00:00+00:00	493662
364	eth_cl	2022-12-31 00:00:00+00:00	493896

365 rows × 3 columns

Rewards from staking are only one part of a validator’s yield. Post-Merge, validators now also receive user transaction priority fees, or tips, that used to go to miners on the Execution Layer. Considering the historical record of fees, we can estimate the magnitude of this additional source of yield. For our analysis, we show how to estimate both staking revenues and priority tips as yields on staked ETH.

To do this, we use the results we found above and divide gross annual emission by the total number of validators to produce average validator revenue, which for this purpose only consider revenues that originate from the protocol and not from fees.

940.87 × sqrt(ValidatorActOngCnt) ÷ ValidatorActOngCnt = Avg. Validator Revenue

The expected annual number of blocks proposed in turn allows us to estimate the priority tip that is earned by each block proposal. Using a 14-day moving average to smooth priority tips, we then estimate what a proposer should expect to earn in tips.

1 ÷ ValidatorActOngCnt × 2,629,800 = Annual Num. of Proposals per Validator

sma(FeePrioTotNtv ÷ BlkCnt, 14) × Ann. Num. of Proposals per Validator = Average Priority Tip per Block

Retrieve Execution Layer Metrics

execution_metrics = client.get_asset_metrics(
    assets='eth',
    metrics=['FeePrioTotNtv', 'BlkCnt'],
    start_time = start_time,
    end_time = end_time
).to_dataframe()

execution_metrics['time'] = pd.to_datetime(execution_metrics['time'])

execution_metrics

	asset	time	BlkCnt	FeePrioTotNtv
0	eth	2022-01-01 00:00:00+00:00	6506	747.204977
1	eth	2022-01-02 00:00:00+00:00	6495	905.938725
2	eth	2022-01-03 00:00:00+00:00	6461	898.990928
3	eth	2022-01-04 00:00:00+00:00	6494	1245.74813
4	eth	2022-01-05 00:00:00+00:00	6460	1485.351677
...	...	...	...	...
360	eth	2022-12-27 00:00:00+00:00	7156	343.663114
361	eth	2022-12-28 00:00:00+00:00	7173	353.437599
362	eth	2022-12-29 00:00:00+00:00	7161	324.786107
363	eth	2022-12-30 00:00:00+00:00	7166	319.41694
364	eth	2022-12-31 00:00:00+00:00	7166	290.772518

365 rows × 4 columns

eth_metrics = consensus_metrics.merge(execution_metrics, on='time', how='inner')

eth_metrics = eth_metrics[['time','ValidatorActOngCnt','BlkCnt','FeePrioTotNtv']]

Calculate theoretical validator yield based on Active Validator count

eth_metrics['Validator Yield'] = 100 * (
    (32 + ((940.87 * (eth_metrics['ValidatorActOngCnt'] ** (1/2))) / eth_metrics['ValidatorActOngCnt']))/32 - 1
)
eth_metrics

	time	ValidatorActOngCnt	BlkCnt	FeePrioTotNtv	Validator Yield
0	2022-01-01 00:00:00+00:00	275880	6506	747.204977	5.597828
1	2022-01-02 00:00:00+00:00	276301	6495	905.938725	5.593561
2	2022-01-03 00:00:00+00:00	276784	6461	898.990928	5.588679
3	2022-01-04 00:00:00+00:00	277530	6494	1245.74813	5.581163
4	2022-01-05 00:00:00+00:00	278349	6460	1485.351677	5.572946
...	...	...	...	...	...
360	2022-12-27 00:00:00+00:00	491923	7156	343.663114	4.192095
361	2022-12-28 00:00:00+00:00	492863	7173	353.437599	4.188095
362	2022-12-29 00:00:00+00:00	493116	7161	324.786107	4.187021
363	2022-12-30 00:00:00+00:00	493662	7166	319.41694	4.184704
364	2022-12-31 00:00:00+00:00	493896	7166	290.772518	4.183713

365 rows × 5 columns

Calculate estimated blocks proposals per year based on Active Validator count

eth_metrics['est_block_proposals_per_yr'] = ((1/eth_metrics['ValidatorActOngCnt']) * (2629800))

Estimate tips per block

eth_metrics['avg_per_block_tip_2w'] = (eth_metrics['FeePrioTotNtv'] / eth_metrics['BlkCnt']).rolling(window=14).mean()

eth_metrics

	time	ValidatorActOngCnt	BlkCnt	FeePrioTotNtv	Validator Yield	est_block_proposals_per_yr	avg_per_block_tip_2w
0	2022-01-01 00:00:00+00:00	275880	6506	747.204977	5.597828	9.532405	NaN
1	2022-01-02 00:00:00+00:00	276301	6495	905.938725	5.593561	9.517881	NaN
2	2022-01-03 00:00:00+00:00	276784	6461	898.990928	5.588679	9.501272	NaN
3	2022-01-04 00:00:00+00:00	277530	6494	1245.74813	5.581163	9.475732	NaN
4	2022-01-05 00:00:00+00:00	278349	6460	1485.351677	5.572946	9.447851	NaN
...	...	...	...	...	...	...	...
360	2022-12-27 00:00:00+00:00	491923	7156	343.663114	4.192095	5.345959	0.048594
361	2022-12-28 00:00:00+00:00	492863	7173	353.437599	4.188095	5.335763	0.048059
362	2022-12-29 00:00:00+00:00	493116	7161	324.786107	4.187021	5.333025	0.047630
363	2022-12-30 00:00:00+00:00	493662	7166	319.41694	4.184704	5.327127	0.045280
364	2022-12-31 00:00:00+00:00	493896	7166	290.772518	4.183713	5.324603	0.044691

365 rows × 7 columns

Calculate priority tip yield

eth_metrics['Priority Tip Yield'] = (100 *
    ((32 + eth_metrics['avg_per_block_tip_2w'] * eth_metrics['est_block_proposals_per_yr'])/32+(-1)))

eth_metrics = eth_metrics.dropna().set_index('time')

ax = eth_metrics[['Validator Yield', 'Priority Tip Yield']].plot.area(stacked=True, figsize=(8, 6),color=['orange', 'red'])
ax.set_ylabel('Yield (%)')
ax.set_xlabel('')
ax.set_title('\nETH Staking \nEstimated Yield',fontsize=14)
plt.legend(loc='upper right', bbox_to_anchor=(1.00, 1.0))
plt.savefig("NDP_ETH_staking_metrics_validator_priority_tip_yield.png")
plt.show()

Calculate ETH Supply: Staked vs. Unstaked

One of the many advantages of a blockchain-based ledger is auditability, but increasingly complex consensus architectures and supply mechanics can make it difficult to understand the full picture of asset supply. Ethereum's shift to proof-of-stake introduced a number of novel considerations in obtaining network-wide supply figures. In the following example, we combine various Supply metrics from ETH's Consensus and Execution Layers to ascertain the total amount of staked vs. unstaked supply.

Consensus Layer Metrics

cl_supply = client.get_asset_metrics(
    assets='eth_cl',
    metrics=['SplyCur','SplyStkedNtv'],
    start_time = start_time,
    end_time = end_time,
    frequency = '1d'
).to_dataframe()

cl_supply = cl_supply.rename(columns={"SplyCur": "SplyCur_CL"})

Execution Layer Metrics

el_supply = client.get_asset_metrics(
    assets='eth',
    metrics=['SplyCur','SplyCLCont'],
    start_time = start_time,
    end_time = end_time,
    frequency = '1d'
).to_dataframe()

el_supply = el_supply.rename(columns={"SplyCur": "SplyCur_EL"})

adjusted_supply = cl_supply.merge(el_supply, on='time', how='inner')
adjusted_supply = adjusted_supply.set_index('time')

adjusted_supply = adjusted_supply[['SplyCur_EL','SplyCur_CL','SplyCLCont','SplyStkedNtv']]

Calculate the total 'adjusted' ETH supply

adjusted_supply['Total ETH Supply'] = adjusted_supply['SplyCur_EL'] + (adjusted_supply['SplyCur_CL'] - adjusted_supply['SplyCLCont'])

adjusted_supply['Staked Supply'] = adjusted_supply['SplyStkedNtv']

adjusted_supply['Unstaked Supply'] = adjusted_supply['Total ETH Supply'] - adjusted_supply['Staked Supply'] 

adjusted_supply

	SplyCur_EL	SplyCur_CL	SplyCLCont	SplyStkedNtv	Total ETH Supply	Staked Supply	Unstaked Supply
time
2022-01-01 00:00:00+00:00	118049177.834937	9227820.793013	8852770.0	8828515	118424228.62795	8828515	109595713.62795
2022-01-02 00:00:00+00:00	118054804.893592	9242509.802432	8866898.0	8841891	118430416.696023	8841891	109588525.696023
2022-01-03 00:00:00+00:00	118060102.632049	9264680.84189	8887122.0	8862723	118437661.47394	8862723	109574938.47394
2022-01-04 00:00:00+00:00	118063057.343795	9286929.282994	8915506.0	8883635	118434480.626789	8883635	109550845.626789
2022-01-05 00:00:00+00:00	118064691.965118	9311811.478199	8931074.0	8907187	118445429.443318	8907187	109538242.443318
...	...	...	...	...	...	...	...
2022-12-27 00:00:00+00:00	120528662.458689	16755428.289539	15813639.05565	15767369	121470451.692578	15767369	105703082.692578
2022-12-28 00:00:00+00:00	120528732.408086	16761680.161693	15819655.05565	15771865	121470757.51413	15771865	105698892.51413
2022-12-29 00:00:00+00:00	120528750.981834	16772281.136252	15834119.05565	15780713	121466913.062437	15780713	105686200.062437
2022-12-30 00:00:00+00:00	120528698.245699	16790630.62361	15846119.05565	15797305	121473209.81366	15797305	105675904.81366
2022-12-31 00:00:00+00:00	120528769.150157	16800132.616449	15854791.05565	15805049	121474110.710956	15805049	105669061.710956

365 rows × 7 columns

Plot staked vs. unstaked supply

ax = adjusted_supply[['Staked Supply', 'Unstaked Supply']].plot.area(stacked=True, figsize=(8, 4),color=['pink', 'purple'])
ax.set_ylabel('')
ax.set_xlabel('')
ax.set_title('\nETH Staked vs. Unstaked Supply\n',fontsize=14)
ax.yaxis.set_major_formatter(lambda x, _: f'{x*1e-6}M')
plt.legend(loc='upper right', bbox_to_anchor=(1.05, 1.15), fontsize=10)
plt.savefig("NDP_ETH_Staking_Metrics_staked_vs_unstaked_supply.png")
plt.show()