{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9da1250e-a1a4-4514-90f5-3c6b217348c0",
   "metadata": {},
   "source": [
    "<img src=\"https://5264302.fs1.hubspotusercontent-na1.net/hubfs/5264302/Demo%20Asset%20Resources/Demo%20Covers/CM-Demo-eth_staking_metrics-Cover.png\" width=1100 margin-left='auto' margin-right='auto'/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a91200e-f5a8-4453-853d-3799d3b3d454",
   "metadata": {},
   "source": [
    "## Resources\n",
    "This notebook demonstrates basic functionality offered by the Coin Metrics Python API Client and Network Data Pro.\n",
    "\n",
    "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.\n",
    "\n",
    "To understand the data that Coin Metrics offers, feel free to peruse the resources below.\n",
    "\n",
    "- The [Coin Metrics API v4](https://docs.coinmetrics.io/api/v4) website contains the full set of endpoints and data offered by Coin Metrics.\n",
    "- The [Coin Metrics Product Documentation](https://docs.coinmetrics.io/info) gives detailed, conceptual explanations of the data that Coin Metrics offers.\n",
    "- The [API Spec](https://docs.coinmetrics.io/python-api-client/reference) contains a full list of functions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b000177-66a6-4b4e-babc-dd0b2edf9d0b",
   "metadata": {},
   "source": [
    "### Notebook Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "505feb2a-c704-4cc1-8dfd-6c6e71a55127",
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import environ\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import logging\n",
    "from datetime import date, datetime, timedelta\n",
    "from coinmetrics.api_client import CoinMetricsClient\n",
    "import logging\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "e17ed9d7-ac38-40c3-8b7b-92715d12bd3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Chart themes\n",
    "sns.set_theme()\n",
    "warnings.filterwarnings('ignore')\n",
    "sns.set(rc={'figure.figsize':(14,9)})\n",
    "sns.set_style(\"whitegrid\",{'axes.grid' : True,'grid.linestyle': '--', 'grid.color': 'gray','axes.edgecolor': 'white',})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "bcb11025-a3bd-4ad4-b48f-80f9ae697426",
   "metadata": {},
   "outputs": [],
   "source": [
    "logging.basicConfig(\n",
    "    format='%(asctime)s %(levelname)-8s %(message)s',\n",
    "    level=logging.INFO,\n",
    "    datefmt='%Y-%m-%d %H:%M:%S'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "5bf4316c-3024-4fbd-8db5-97ac300447c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-09 15:07:36 INFO     Using API key found in environment\n"
     ]
    }
   ],
   "source": [
    "# We recommend privately storing your API key in your local environment.\n",
    "try:\n",
    "    api_key = environ[\"CM_API_KEY\"]\n",
    "    logging.info(\"Using API key found in environment\")\n",
    "except KeyError:\n",
    "    api_key = \"\"\n",
    "    logging.info(\"API key not found. Using community client\")\n",
    "client = CoinMetricsClient(api_key)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "043e503c-3780-4a16-b5e8-a88a2b714c69",
   "metadata": {},
   "source": [
    "# Calculate Validator Yield and Yield Inclusive of Priority Fees\n",
    "\n",
    "Using ETH Execution Layer and ETH Consensus Layer APR metrics, we can extract the total yield awarded to validators as well as break down how that yield is issued, whether it is from priority fees paid to include a transaction earlier in a block or from Ethereum's issuance formula. In combination with historical data on priority fees, 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.\n",
    "\n",
    "We assume to begin calculating metrics on the day Ethereum migrated from Proof-of-Work to Proof-of-Stake, colloquially known as \"The Merge\" on September 15, 2022, as this is when validators began to receive both native issuance as well as priority fees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "ae45a63a-f762-4e6c-b196-1f72ca800a87",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = '2022-09-15'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "4e8452fc-c00d-4b83-9e5f-61500a76b0e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "validator_yield = client.get_asset_metrics(\n",
    "    assets='eth_cl',\n",
    "    metrics=['ValidatorAPRNominal'],\n",
    "    start_time = start,\n",
    ").to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "592a0d6a-ec50-4ae7-afc7-084c2d59bebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asset</th>\n",
       "      <th>time</th>\n",
       "      <th>ValidatorAPRNominal</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>4.266465</td>\n",
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       "      <th>1</th>\n",
       "      <td>eth_cl</td>\n",
       "      <td>2022-09-16 00:00:00+00:00</td>\n",
       "      <td>4.244164</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>eth_cl</td>\n",
       "      <td>2022-09-17 00:00:00+00:00</td>\n",
       "      <td>4.283294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>eth_cl</td>\n",
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       "      <td>4.309157</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>eth_cl</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>751</th>\n",
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       "      <td>2024-10-05 00:00:00+00:00</td>\n",
       "      <td>2.786805</td>\n",
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       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>eth_cl</td>\n",
       "      <td>2024-10-06 00:00:00+00:00</td>\n",
       "      <td>2.785373</td>\n",
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       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>eth_cl</td>\n",
       "      <td>2024-10-07 00:00:00+00:00</td>\n",
       "      <td>2.787284</td>\n",
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       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>eth_cl</td>\n",
       "      <td>2024-10-08 00:00:00+00:00</td>\n",
       "      <td>2.787763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>755 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      asset                      time  ValidatorAPRNominal\n",
       "0    eth_cl 2022-09-15 00:00:00+00:00             4.266465\n",
       "1    eth_cl 2022-09-16 00:00:00+00:00             4.244164\n",
       "2    eth_cl 2022-09-17 00:00:00+00:00             4.283294\n",
       "3    eth_cl 2022-09-18 00:00:00+00:00             4.309157\n",
       "4    eth_cl 2022-09-19 00:00:00+00:00             4.316741\n",
       "..      ...                       ...                  ...\n",
       "750  eth_cl 2024-10-04 00:00:00+00:00             2.786609\n",
       "751  eth_cl 2024-10-05 00:00:00+00:00             2.786805\n",
       "752  eth_cl 2024-10-06 00:00:00+00:00             2.785373\n",
       "753  eth_cl 2024-10-07 00:00:00+00:00             2.787284\n",
       "754  eth_cl 2024-10-08 00:00:00+00:00             2.787763\n",
       "\n",
       "[755 rows x 3 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validator_yield['time'] = pd.to_datetime(validator_yield['time'])\n",
    "validator_yield"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "2083fd82-51ed-4e77-9d01-ad23912fd2b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_stake_yield = client.get_asset_metrics(\n",
    "    assets='eth',\n",
    "    metrics=['ValidatorAPRNominal'],\n",
    "    start_time = start,\n",
    ").to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "2792ec03-4d87-45ea-83ed-b3feff84a8df",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "      <th>4</th>\n",
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       "      <td>eth</td>\n",
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       "      <td>3.182258</td>\n",
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       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>eth</td>\n",
       "      <td>2024-10-05 00:00:00+00:00</td>\n",
       "      <td>3.244051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>eth</td>\n",
       "      <td>2024-10-06 00:00:00+00:00</td>\n",
       "      <td>3.354408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>eth</td>\n",
       "      <td>2024-10-07 00:00:00+00:00</td>\n",
       "      <td>3.543015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>eth</td>\n",
       "      <td>2024-10-08 00:00:00+00:00</td>\n",
       "      <td>3.860227</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>755 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    asset                      time  ValidatorAPRNominal\n",
       "0     eth 2022-09-15 00:00:00+00:00            15.302587\n",
       "1     eth 2022-09-16 00:00:00+00:00             5.661437\n",
       "2     eth 2022-09-17 00:00:00+00:00             5.284938\n",
       "3     eth 2022-09-18 00:00:00+00:00             5.527239\n",
       "4     eth 2022-09-19 00:00:00+00:00             5.716192\n",
       "..    ...                       ...                  ...\n",
       "750   eth 2024-10-04 00:00:00+00:00             3.182258\n",
       "751   eth 2024-10-05 00:00:00+00:00             3.244051\n",
       "752   eth 2024-10-06 00:00:00+00:00             3.354408\n",
       "753   eth 2024-10-07 00:00:00+00:00             3.543015\n",
       "754   eth 2024-10-08 00:00:00+00:00             3.860227\n",
       "\n",
       "[755 rows x 3 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_stake_yield['time'] = pd.to_datetime(total_stake_yield['time'])\n",
    "total_stake_yield"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "138505a4-981e-4172-9d36-3a512f6a0b8b",
   "metadata": {},
   "source": [
    "# Combine Data and Solve for Priority Fees\n",
    "\n",
    "Subtracting ETH_CL's \"ValidatorAPRNominal\" from ETH \"ValidatorAPRNominal\" removes validator rewards to solve for Priority Fees. Separating Validator and Priority Fees for charting can better visually project from which source validators can expect rewards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "c9830662-277a-4740-9bb0-42a8f9ba8994",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
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       "      <th>4</th>\n",
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       "      <td>eth</td>\n",
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       "      <th>750</th>\n",
       "      <td>eth_cl</td>\n",
       "      <td>2024-10-04 00:00:00+00:00</td>\n",
       "      <td>2.786609</td>\n",
       "      <td>eth</td>\n",
       "      <td>3.182258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>eth_cl</td>\n",
       "      <td>2024-10-05 00:00:00+00:00</td>\n",
       "      <td>2.786805</td>\n",
       "      <td>eth</td>\n",
       "      <td>3.244051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>eth_cl</td>\n",
       "      <td>2024-10-06 00:00:00+00:00</td>\n",
       "      <td>2.785373</td>\n",
       "      <td>eth</td>\n",
       "      <td>3.354408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>eth_cl</td>\n",
       "      <td>2024-10-07 00:00:00+00:00</td>\n",
       "      <td>2.787284</td>\n",
       "      <td>eth</td>\n",
       "      <td>3.543015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>eth_cl</td>\n",
       "      <td>2024-10-08 00:00:00+00:00</td>\n",
       "      <td>2.787763</td>\n",
       "      <td>eth</td>\n",
       "      <td>3.860227</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>755 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    asset_validator                      time  ValidatorAPRNominal_validator  \\\n",
       "0            eth_cl 2022-09-15 00:00:00+00:00                       4.266465   \n",
       "1            eth_cl 2022-09-16 00:00:00+00:00                       4.244164   \n",
       "2            eth_cl 2022-09-17 00:00:00+00:00                       4.283294   \n",
       "3            eth_cl 2022-09-18 00:00:00+00:00                       4.309157   \n",
       "4            eth_cl 2022-09-19 00:00:00+00:00                       4.316741   \n",
       "..              ...                       ...                            ...   \n",
       "750          eth_cl 2024-10-04 00:00:00+00:00                       2.786609   \n",
       "751          eth_cl 2024-10-05 00:00:00+00:00                       2.786805   \n",
       "752          eth_cl 2024-10-06 00:00:00+00:00                       2.785373   \n",
       "753          eth_cl 2024-10-07 00:00:00+00:00                       2.787284   \n",
       "754          eth_cl 2024-10-08 00:00:00+00:00                       2.787763   \n",
       "\n",
       "    asset_total  ValidatorAPRNominal_total  \n",
       "0           eth                  15.302587  \n",
       "1           eth                   5.661437  \n",
       "2           eth                   5.284938  \n",
       "3           eth                   5.527239  \n",
       "4           eth                   5.716192  \n",
       "..          ...                        ...  \n",
       "750         eth                   3.182258  \n",
       "751         eth                   3.244051  \n",
       "752         eth                   3.354408  \n",
       "753         eth                   3.543015  \n",
       "754         eth                   3.860227  \n",
       "\n",
       "[755 rows x 5 columns]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the 'ValidatorAPRNominal' column to float (if necessary)\n",
    "validator_yield['ValidatorAPRNominal'] = validator_yield['ValidatorAPRNominal'].astype(float)\n",
    "total_stake_yield['ValidatorAPRNominal'] = total_stake_yield['ValidatorAPRNominal'].astype(float)\n",
    "\n",
    "# Merge the DataFrame to create a simplified table to work from\n",
    "priority_fee_yield = validator_yield.merge(total_stake_yield, how='left', on='time', sort=True, suffixes=('_validator', '_total'))\n",
    "priority_fee_yield"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "f042e189-af1d-4c39-b948-c04e0796c2e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    asset_validator                      time  ValidatorAPRNominal_validator  \\\n",
      "0            eth_cl 2022-09-15 00:00:00+00:00                       4.266465   \n",
      "1            eth_cl 2022-09-16 00:00:00+00:00                       4.244164   \n",
      "2            eth_cl 2022-09-17 00:00:00+00:00                       4.283294   \n",
      "3            eth_cl 2022-09-18 00:00:00+00:00                       4.309157   \n",
      "4            eth_cl 2022-09-19 00:00:00+00:00                       4.316741   \n",
      "..              ...                       ...                            ...   \n",
      "750          eth_cl 2024-10-04 00:00:00+00:00                       2.786609   \n",
      "751          eth_cl 2024-10-05 00:00:00+00:00                       2.786805   \n",
      "752          eth_cl 2024-10-06 00:00:00+00:00                       2.785373   \n",
      "753          eth_cl 2024-10-07 00:00:00+00:00                       2.787284   \n",
      "754          eth_cl 2024-10-08 00:00:00+00:00                       2.787763   \n",
      "\n",
      "    asset_total  ValidatorAPRNominal_total  Priority Fee Yield  \n",
      "0           eth                  15.302587           11.036122  \n",
      "1           eth                   5.661437            1.417272  \n",
      "2           eth                   5.284938            1.001644  \n",
      "3           eth                   5.527239            1.218082  \n",
      "4           eth                   5.716192            1.399451  \n",
      "..          ...                        ...                 ...  \n",
      "750         eth                   3.182258            0.395649  \n",
      "751         eth                   3.244051            0.457246  \n",
      "752         eth                   3.354408            0.569035  \n",
      "753         eth                   3.543015            0.755730  \n",
      "754         eth                   3.860227            1.072464  \n",
      "\n",
      "[755 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "# Using the updated DataFrame, subtract one column from the other to get the \"Priority Fee Yield\"\n",
    "priority_fee_yield['Priority Fee Yield'] = priority_fee_yield['ValidatorAPRNominal_total']-priority_fee_yield['ValidatorAPRNominal_validator']\n",
    "# Output the DataFrame\n",
    "print(priority_fee_yield)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "c0720050-4264-4f95-86e9-cb8cc8f6e9fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>ValidatorAPRNominal</th>\n",
       "      <th>Priority Fee Yield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-09-15 00:00:00+00:00</td>\n",
       "      <td>4.266465</td>\n",
       "      <td>11.036122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-09-16 00:00:00+00:00</td>\n",
       "      <td>4.244164</td>\n",
       "      <td>1.417272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-09-17 00:00:00+00:00</td>\n",
       "      <td>4.283294</td>\n",
       "      <td>1.001644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-09-18 00:00:00+00:00</td>\n",
       "      <td>4.309157</td>\n",
       "      <td>1.218082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-09-19 00:00:00+00:00</td>\n",
       "      <td>4.316741</td>\n",
       "      <td>1.399451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>2024-10-04 00:00:00+00:00</td>\n",
       "      <td>2.786609</td>\n",
       "      <td>0.395649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>2024-10-05 00:00:00+00:00</td>\n",
       "      <td>2.786805</td>\n",
       "      <td>0.457246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>2024-10-06 00:00:00+00:00</td>\n",
       "      <td>2.785373</td>\n",
       "      <td>0.569035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>2024-10-07 00:00:00+00:00</td>\n",
       "      <td>2.787284</td>\n",
       "      <td>0.755730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>2024-10-08 00:00:00+00:00</td>\n",
       "      <td>2.787763</td>\n",
       "      <td>1.072464</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>755 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         time  ValidatorAPRNominal  Priority Fee Yield\n",
       "0   2022-09-15 00:00:00+00:00             4.266465           11.036122\n",
       "1   2022-09-16 00:00:00+00:00             4.244164            1.417272\n",
       "2   2022-09-17 00:00:00+00:00             4.283294            1.001644\n",
       "3   2022-09-18 00:00:00+00:00             4.309157            1.218082\n",
       "4   2022-09-19 00:00:00+00:00             4.316741            1.399451\n",
       "..                        ...                  ...                 ...\n",
       "750 2024-10-04 00:00:00+00:00             2.786609            0.395649\n",
       "751 2024-10-05 00:00:00+00:00             2.786805            0.457246\n",
       "752 2024-10-06 00:00:00+00:00             2.785373            0.569035\n",
       "753 2024-10-07 00:00:00+00:00             2.787284            0.755730\n",
       "754 2024-10-08 00:00:00+00:00             2.787763            1.072464\n",
       "\n",
       "[755 rows x 3 columns]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the above DataFrame, set the DataFrame to the requested columns to prepare to chart\n",
    "priority_fee_yield = priority_fee_yield[[\"time\",\"ValidatorAPRNominal_validator\",\"Priority Fee Yield\"]]\n",
    "priority_fee_yield = priority_fee_yield.rename({\"ValidatorAPRNominal_validator\": \"ValidatorAPRNominal\"}, axis=1)\n",
    "priority_fee_yield"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "6897a821-cf84-43d0-b3c4-52492129eec4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = priority_fee_yield.plot(x=\"time\", y=[\"ValidatorAPRNominal\",\"Priority Fee Yield\"], kind=\"area\", color=[\"orange\", \"red\"], figsize=(14, 8))\n",
    "ax.set_ylabel('Yield (%)')\n",
    "ax.set_xlabel('')\n",
    "ax.set_title('\\nETH Staking \\nYield After the Merge', fontsize=18)\n",
    "ax.legend(loc='upper right', bbox_to_anchor=(1, 1.11))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6cda25e-1999-4af7-a4c4-a143fdd4cbdd",
   "metadata": {},
   "source": [
    "# Calculate ETH Supply: Staked vs. Unstaked"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31a5132c-0517-41d2-ba6c-c831ef51a9fc",
   "metadata": {},
   "source": [
    "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "79a731b3-e9cc-4f24-9f5d-84016e535345",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = '2022-01-01'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d84b3e65-c12e-403c-9fd4-de9a27fd0112",
   "metadata": {},
   "source": [
    "### Consensus Layer Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "2f6ca81e-ed39-4ea3-ad3c-5f8b5e6e7a4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "cl_supply = client.get_asset_metrics(\n",
    "    assets='eth_cl',\n",
    "    metrics=['SplyCur','SplyStkedNtv'],\n",
    "    start_time = start,\n",
    "    frequency = '1d'\n",
    ").to_dataframe()\n",
    "\n",
    "cl_supply = cl_supply.rename(columns={\"SplyCur\": \"SplyCur_CL\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b23a3a42-360f-435d-aa25-1606bfa7e4ab",
   "metadata": {},
   "source": [
    "### Execution Layer Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "62266a7e-cbfd-443e-b230-57f95d66d2c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "el_supply = client.get_asset_metrics(\n",
    "    assets='eth',\n",
    "    metrics=['SplyCur','SplyCLCont'],\n",
    "    start_time = start,\n",
    "    frequency = '1d'\n",
    ").to_dataframe()\n",
    "\n",
    "el_supply = el_supply.rename(columns={\"SplyCur\": \"SplyCur_EL\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "faed732b-1c44-425a-a03d-032982161737",
   "metadata": {},
   "outputs": [],
   "source": [
    "adjusted_supply = cl_supply.merge(el_supply, on='time', how='inner')\n",
    "adjusted_supply = adjusted_supply.set_index('time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "55e4bca5-81b5-4ea6-af27-d44c70a29ade",
   "metadata": {},
   "outputs": [],
   "source": [
    "adjusted_supply = adjusted_supply[['SplyCur_EL','SplyCur_CL','SplyCLCont','SplyStkedNtv']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3319741c-9c9b-448e-8988-8bd86d26d38a",
   "metadata": {},
   "source": [
    "### Calculate the total 'adjusted' ETH supply "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "e9c249c2-f5d6-44a1-9131-bb76c430ec2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "adjusted_supply['Total ETH Supply'] = adjusted_supply['SplyCur_EL'] + (adjusted_supply['SplyCur_CL'] - adjusted_supply['SplyCLCont'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "6a556e62-a3ae-4214-b662-0a50f5626e90",
   "metadata": {},
   "outputs": [],
   "source": [
    "adjusted_supply['Staked Supply'] = adjusted_supply['SplyStkedNtv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "8f943b26-ba22-4fc1-a90c-229629a7e85c",
   "metadata": {},
   "outputs": [],
   "source": [
    "adjusted_supply['Unstaked Supply'] = adjusted_supply['Total ETH Supply'] - adjusted_supply['Staked Supply'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "477d0900-a9dc-4ccf-a186-708f526c4556",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SplyCur_EL</th>\n",
       "      <th>SplyCur_CL</th>\n",
       "      <th>SplyCLCont</th>\n",
       "      <th>SplyStkedNtv</th>\n",
       "      <th>Total ETH Supply</th>\n",
       "      <th>Staked Supply</th>\n",
       "      <th>Unstaked Supply</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-01 00:00:00+00:00</th>\n",
       "      <td>118049177.834937</td>\n",
       "      <td>9227820.793013</td>\n",
       "      <td>8852770.0</td>\n",
       "      <td>8828515</td>\n",
       "      <td>118424228.62795</td>\n",
       "      <td>8828515</td>\n",
       "      <td>109595713.62795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-02 00:00:00+00:00</th>\n",
       "      <td>118054804.893592</td>\n",
       "      <td>9242509.802432</td>\n",
       "      <td>8866898.0</td>\n",
       "      <td>8841891</td>\n",
       "      <td>118430416.696023</td>\n",
       "      <td>8841891</td>\n",
       "      <td>109588525.696023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-03 00:00:00+00:00</th>\n",
       "      <td>118060102.632049</td>\n",
       "      <td>9264680.84189</td>\n",
       "      <td>8887122.0</td>\n",
       "      <td>8862723</td>\n",
       "      <td>118437661.47394</td>\n",
       "      <td>8862723</td>\n",
       "      <td>109574938.47394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-04 00:00:00+00:00</th>\n",
       "      <td>118063057.343795</td>\n",
       "      <td>9286929.282994</td>\n",
       "      <td>8915506.0</td>\n",
       "      <td>8883635</td>\n",
       "      <td>118434480.626789</td>\n",
       "      <td>8883635</td>\n",
       "      <td>109550845.626789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05 00:00:00+00:00</th>\n",
       "      <td>118064691.965118</td>\n",
       "      <td>9311811.478199</td>\n",
       "      <td>8931074.0</td>\n",
       "      <td>8907187</td>\n",
       "      <td>118445429.443318</td>\n",
       "      <td>8907187</td>\n",
       "      <td>109538242.443318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-04 00:00:00+00:00</th>\n",
       "      <td>120374548.840799</td>\n",
       "      <td>34825174.317181</td>\n",
       "      <td>51368824.455364</td>\n",
       "      <td>34517583</td>\n",
       "      <td>103830898.702616</td>\n",
       "      <td>34517583</td>\n",
       "      <td>69313315.702616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-05 00:00:00+00:00</th>\n",
       "      <td>120376085.966294</td>\n",
       "      <td>34873055.876068</td>\n",
       "      <td>51414791.455364</td>\n",
       "      <td>34501136</td>\n",
       "      <td>103834350.386997</td>\n",
       "      <td>34501136</td>\n",
       "      <td>69333214.386997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-06 00:00:00+00:00</th>\n",
       "      <td>120378389.065311</td>\n",
       "      <td>34850415.642479</td>\n",
       "      <td>51434953.455364</td>\n",
       "      <td>34493548</td>\n",
       "      <td>103793851.252426</td>\n",
       "      <td>34493548</td>\n",
       "      <td>69300303.252426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-07 00:00:00+00:00</th>\n",
       "      <td>120378752.16686</td>\n",
       "      <td>34831981.173273</td>\n",
       "      <td>51491217.455364</td>\n",
       "      <td>34453370</td>\n",
       "      <td>103719515.884769</td>\n",
       "      <td>34453370</td>\n",
       "      <td>69266145.884769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-08 00:00:00+00:00</th>\n",
       "      <td>120379288.433149</td>\n",
       "      <td>34807272.492576</td>\n",
       "      <td>51518064.455364</td>\n",
       "      <td>34436652</td>\n",
       "      <td>103668496.470361</td>\n",
       "      <td>34436652</td>\n",
       "      <td>69231844.470361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1012 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 SplyCur_EL       SplyCur_CL       SplyCLCont  \\\n",
       "time                                                                            \n",
       "2022-01-01 00:00:00+00:00  118049177.834937   9227820.793013        8852770.0   \n",
       "2022-01-02 00:00:00+00:00  118054804.893592   9242509.802432        8866898.0   \n",
       "2022-01-03 00:00:00+00:00  118060102.632049    9264680.84189        8887122.0   \n",
       "2022-01-04 00:00:00+00:00  118063057.343795   9286929.282994        8915506.0   \n",
       "2022-01-05 00:00:00+00:00  118064691.965118   9311811.478199        8931074.0   \n",
       "...                                     ...              ...              ...   \n",
       "2024-10-04 00:00:00+00:00  120374548.840799  34825174.317181  51368824.455364   \n",
       "2024-10-05 00:00:00+00:00  120376085.966294  34873055.876068  51414791.455364   \n",
       "2024-10-06 00:00:00+00:00  120378389.065311  34850415.642479  51434953.455364   \n",
       "2024-10-07 00:00:00+00:00   120378752.16686  34831981.173273  51491217.455364   \n",
       "2024-10-08 00:00:00+00:00  120379288.433149  34807272.492576  51518064.455364   \n",
       "\n",
       "                           SplyStkedNtv  Total ETH Supply  Staked Supply  \\\n",
       "time                                                                       \n",
       "2022-01-01 00:00:00+00:00       8828515   118424228.62795        8828515   \n",
       "2022-01-02 00:00:00+00:00       8841891  118430416.696023        8841891   \n",
       "2022-01-03 00:00:00+00:00       8862723   118437661.47394        8862723   \n",
       "2022-01-04 00:00:00+00:00       8883635  118434480.626789        8883635   \n",
       "2022-01-05 00:00:00+00:00       8907187  118445429.443318        8907187   \n",
       "...                                 ...               ...            ...   \n",
       "2024-10-04 00:00:00+00:00      34517583  103830898.702616       34517583   \n",
       "2024-10-05 00:00:00+00:00      34501136  103834350.386997       34501136   \n",
       "2024-10-06 00:00:00+00:00      34493548  103793851.252426       34493548   \n",
       "2024-10-07 00:00:00+00:00      34453370  103719515.884769       34453370   \n",
       "2024-10-08 00:00:00+00:00      34436652  103668496.470361       34436652   \n",
       "\n",
       "                            Unstaked Supply  \n",
       "time                                         \n",
       "2022-01-01 00:00:00+00:00   109595713.62795  \n",
       "2022-01-02 00:00:00+00:00  109588525.696023  \n",
       "2022-01-03 00:00:00+00:00   109574938.47394  \n",
       "2022-01-04 00:00:00+00:00  109550845.626789  \n",
       "2022-01-05 00:00:00+00:00  109538242.443318  \n",
       "...                                     ...  \n",
       "2024-10-04 00:00:00+00:00   69313315.702616  \n",
       "2024-10-05 00:00:00+00:00   69333214.386997  \n",
       "2024-10-06 00:00:00+00:00   69300303.252426  \n",
       "2024-10-07 00:00:00+00:00   69266145.884769  \n",
       "2024-10-08 00:00:00+00:00   69231844.470361  \n",
       "\n",
       "[1012 rows x 7 columns]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjusted_supply"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1052776c-06f1-408d-8835-e36c3081025b",
   "metadata": {},
   "source": [
    "### Plot staked vs. unstaked supply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "128489ec-11c4-4a9a-bb67-eaabc9765b0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = adjusted_supply[['Staked Supply', 'Unstaked Supply']].plot.area(stacked=True, figsize=(14, 8),color=['pink', 'purple'])\n",
    "ax.set_ylabel('')\n",
    "ax.set_xlabel('')\n",
    "ax.set_title('\\nETH Staked vs. Unstaked Supply\\n',fontsize=18)\n",
    "ax.yaxis.set_major_formatter(lambda x, _: f'{x*1e-6}M')\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1, 1.11))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93b5ae03-0cf9-40ce-be81-3a40fdfce401",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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