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Using Staking Metrics to Get Yield and Staked Supply

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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.

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 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
assettimeValidatorActOngCnt

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
assettimeBlkCntFeePrioTotNtv

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
timeValidatorActOngCntBlkCntFeePrioTotNtvValidator 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
timeValidatorActOngCntBlkCntFeePrioTotNtvValidator Yieldest_block_proposals_per_yravg_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_ELSplyCur_CLSplyCLContSplyStkedNtvTotal ETH SupplyStaked SupplyUnstaked 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()
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