notebooks/1_gas_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "relative-volume",
   "metadata": {},
   "source": [
    "# EIP-1559, estimated transaction fees revenues for miners\n",
    "\n",
    "This notebook try to answer the following question:\n",
    "\n",
    "\"Can we estimate the economic impact of EIP-1559 on miners?\"\n",
    "\n",
    "There is already some works done on this subject on github. Most of them use a multi-agent simulation problem formulation like this repo: https://github.com/barnabemonnot/abm1559, or this one: https://github.com/gakonst/eip1559. \n",
    "\n",
    "In this notebook, we want to tackle this problem using ethereum historical transactions data. We hope to find some statistical properties from the gas price distriburion to get an estimate of the difference in miner's revenue caused by the upcoming eip-1559.\n",
    "\n",
    "Here is a small definition of the eip-1559 from the ethereum website:\n",
    "\n",
    "```\n",
    "EIP 1559 is a proposal to reform the Ethereum fee market, with the following key changes:\n",
    "\n",
    "    - The current gas limit of 12.5 million is replaced by two values: a “long-term average target” (12.5 million), and a “hard per-block cap” (25 million)\n",
    "\n",
    "    - There is a BASEFEE (which is burned) which transactions are required to pay, which gets adjusted on a block-by-block basis with the goal of targeting a value so that average block gas usage remains around 12.5 million.\n",
    "\n",
    "Essentially, instead of all of the short-term volatility in demand for transaction space within a block translating into volatility in transaction fees, some of the volatility instead translates into volatility in block size.\n",
    "```\n",
    "\n",
    "The BASEFEE in the eip-1559 will be the minimum gas price to pay in order to have a transaction mined into the next block. It's not trivial to \"translate\" this BASEFEE for the current legacy system. \n",
    "\n",
    "We cannot use the minimum gas price of the block to represent his BASEFEE, cause this is currently highly unpredictable (actually that's one of the thing eip-1559 will resolve). Instead, for a given block, to have an equivalent of what would have been an \"estimated\" BASEFEE on the current legacy ethereum blockchain, we can use the recommended gas price for that block. On this work we use the Geth recommendation algorithm (60th percentile of the last 100 blocks).\n",
    "\n",
    "Thus, the following strong assumption is made: estimated BASEFEE = Geth gas price recommendation\n",
    "\n",
    "The TIPS that will remain (FEES - BASEFEE) will be the part that will receive the miners from the transactions on the eip-1559 for a given transaction.\n",
    "\n",
    "The proposed approach use the following observation:\n",
    " - On the historical ethereum data, we have periods of \"normal\" low variance gas price distribution (& close to the recomended price) VS periods with high variance gas price distribution (& far from the recommended price).\n",
    "\n",
    "We will use this observation to filter the historical data and keep only periods that have a \"centered\" gas price distribution around the recommended gas price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "connected-quick",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import dask.dataframe as dd\n",
    "import dask.array as da\n",
    "from dask.distributed import Client, LocalCluster\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sized-armstrong",
   "metadata": {},
   "source": [
    "## Configurations\n",
    "\n",
    "We first select the dataset folder name generated by the first notebook (Big query ethereum dataset).\n",
    "The \"ROLLING_WINDOW\" parameter will be used as the length of the rolling average. \n",
    "\n",
    "This rolling average is used to smooth the statistical metrics (mean, median, ...).\n",
    "It will reduce the noise of the time series and allow for a better \"representation\" of the bididng behavior for a given timeperiod. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "built-vector",
   "metadata": {},
   "outputs": [],
   "source": [
    "configs = {\n",
    "    \"DATASET\": \"2019-01_to_2021-03_gas\",\n",
    "    \"ROLLING_WINDOW\": 20,\n",
    "    \"GROUP_1\": [\"2019-01\", \"2019-03\"],\n",
    "    \"GROUP_2\": [\"2021-01\", \"2021-03\"]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "respected-offer",
   "metadata": {},
   "source": [
    "## Dask client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "athletic-avenue",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://127.0.0.1:46363</li>\n",
       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:46365/status' target='_blank'>http://127.0.0.1:46365/status</a></li>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>5</li>\n",
       "  <li><b>Cores: </b>10</li>\n",
       "  <li><b>Memory: </b>40.00 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: 'tcp://192.168.12.6:46363' processes=0 threads=0, memory=0 B>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DEFAULT USAGE\n",
    "cluster = LocalCluster()\n",
    "client = Client(cluster)\n",
    "# USAGE WITH REMOTE CLUSTER\n",
    "#client = Client()\n",
    "#client.restart()\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "disciplinary-acrobat",
   "metadata": {},
   "source": [
    "## Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "hidden-adolescent",
   "metadata": {
    "scrolled": false
   },
   "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>block_timestamp</th>\n",
       "      <th>gas_price</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>block_number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6988615</th>\n",
       "      <td>2019-01-01 00:00:01</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6988615</th>\n",
       "      <td>2019-01-01 00:00:01</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6988615</th>\n",
       "      <td>2019-01-01 00:00:01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6988615</th>\n",
       "      <td>2019-01-01 00:00:01</td>\n",
       "      <td>2.4</td>\n",
       "      <td>21000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6988615</th>\n",
       "      <td>2019-01-01 00:00:01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 block_timestamp  gas_price      gas\n",
       "block_number                                        \n",
       "6988615      2019-01-01 00:00:01        3.0    27991\n",
       "6988615      2019-01-01 00:00:01        3.0    21000\n",
       "6988615      2019-01-01 00:00:01       10.0  1000000\n",
       "6988615      2019-01-01 00:00:01        2.4    21000\n",
       "6988615      2019-01-01 00:00:01       10.0   250000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddf = dd.read_parquet(f\"../notebooks/datasets/{configs['DATASET']}\", gather_statistics=True)\n",
    "ddf[\"block_timestamp\"] = dd.to_datetime(ddf[\"block_timestamp\"]).dt.tz_localize(None)\n",
    "ddf[\"gas_price\"] = ddf[\"gas_price\"] / 1000000000 # wei to gwei\n",
    "ddf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "linear-concept",
   "metadata": {},
   "source": [
    "## Dataset description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "canadian-hospital",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of transactions:  660668362\n"
     ]
    }
   ],
   "source": [
    "print(\"Total number of transactions: \", len(ddf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "personal-personal",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_histogram(ddf: dd.DataFrame, \n",
    "                   column: str=\"estimated_tips_median_rolling_mean\", \n",
    "                   max_range: int=100):\n",
    "    \n",
    "    x = ddf[column].values\n",
    "    print(f\"Histogram for feature: {column}:\")\n",
    "    h, bins = da.histogram(x, bins=max_range, range=[0, max_range])\n",
    "    h = h.compute()\n",
    "    print(f\"Mean : {x.mean().compute()} \")\n",
    "    print(f\"Bin with most elements : {np.argmax(h)}\")\n",
    "    plt.bar(bins[:-1], h)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "lightweight-sally",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gas price distribution: \n",
      "\n",
      "Histogram for feature: gas_price:\n",
      "Mean : 58.95510521522942 \n",
      "Bin with most elements : 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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GYQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Gas price distribution: \")\n",
    "print()\n",
    "x = ddf[\"gas_price\"].values\n",
    "\n",
    "bins = 350\n",
    "\n",
    "show_histogram(ddf, \"gas_price\", max_range = 350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "announced-shelf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between the gas and gas price: \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Correlation between the gas and gas price: \")\n",
    "\n",
    "plot = sns.heatmap(ddf.corr().compute(), vmin=-1, vmax=1, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "classical-arena",
   "metadata": {},
   "source": [
    "Note: As expected, there is no correlation between the gas price and the amount of gas set for a transaction. Because of this, we will be able to artificially construct a valid transaction history by randomly sampling transactions from a given period of interest, without introducing a bias"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "norman-conditioning",
   "metadata": {},
   "source": [
    "## Block-level analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "powerful-essence",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>gas_price_mean</th>\n",
       "      <th>gas_price_min</th>\n",
       "      <th>gas_price_max</th>\n",
       "      <th>txs_count</th>\n",
       "      <th>gas_price_median</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>block_number</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>6988615</th>\n",
       "      <td>7.722222</td>\n",
       "      <td>2.4</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2019-01-01 00:00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6988616</th>\n",
       "      <td>12.313851</td>\n",
       "      <td>2.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>81</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2019-01-01 00:00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6988617</th>\n",
       "      <td>17.752113</td>\n",
       "      <td>1.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>143</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2019-01-01 00:00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6988618</th>\n",
       "      <td>13.082658</td>\n",
       "      <td>3.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>120</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2019-01-01 00:01:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6988619</th>\n",
       "      <td>5.752326</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>86</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2019-01-01 00:01:13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              gas_price_mean  gas_price_min  gas_price_max  txs_count  \\\n",
       "block_number                                                            \n",
       "6988615             7.722222            2.4           20.0          9   \n",
       "6988616            12.313851            2.0           80.0         81   \n",
       "6988617            17.752113            1.0          350.0        143   \n",
       "6988618            13.082658            3.0           99.0        120   \n",
       "6988619             5.752326            3.0           23.0         86   \n",
       "\n",
       "              gas_price_median                date  \n",
       "block_number                                        \n",
       "6988615                    3.0 2019-01-01 00:00:01  \n",
       "6988616                    3.0 2019-01-01 00:00:08  \n",
       "6988617                    5.0 2019-01-01 00:00:30  \n",
       "6988618                    8.0 2019-01-01 00:01:09  \n",
       "6988619                    3.0 2019-01-01 00:01:13  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def group_by_block(df: dd.DataFrame) -> dd.DataFrame:\n",
    "    \n",
    "    df_block = df.map_partitions(lambda part: part.groupby(part.index).agg({'gas_price': ['mean', 'min', 'max', 'count']}))\n",
    "    df_median = df.map_partitions(lambda part: part.groupby(part.index)[[\"gas_price\"]].apply(lambda x: x.quantile(0.5)))\n",
    "    df_date = df.map_partitions(lambda part: part.groupby(part.index)[[\"block_timestamp\"]].last())\n",
    "    df_block.columns = [\"gas_price_mean\", \"gas_price_min\", \"gas_price_max\", \"txs_count\"]\n",
    "    df_median.columns = [\"gas_price_median\"]\n",
    "    df_date.columns = [\"date\"]\n",
    "    return dd.concat([df_block, df_median, df_date], axis=1)\n",
    "\n",
    "ddf_block = group_by_block(ddf)\n",
    "ddf_block = client.persist(ddf_block) # comment this line if you are short in RAM\n",
    "ddf_block_head = ddf_block.head()\n",
    "\n",
    "ddf_block_head"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "decent-hormone",
   "metadata": {},
   "source": [
    "## Geth gas price recommendation algorithm\n",
    "\n",
    "With the eip-1559, the users will have to set a \"tip\" on top of the base gas price (the base gas price will be burnt). Some variance in the gas price should be \"absorbed\" by the \"flexibilty\" of the block size. \n",
    "The eip-1559 should reduce by a lot the congestion periods (with high gas price overbidding). \n",
    "\n",
    "On top of that, it will become much easier to forecast the base gas price with high accuracy than on the current system. That's why we can actually assume in our analysis that the base gas price will be the \"forecasted minimum gas price\". Using the minimum gas price per block as the base gas price would be irrealistic because of the noisy/unpredictable nature of the minimum gas price per block (cf https://github.com/louisoutin/deep_gas_oracle). That's why I decided to use a more realistic forecast (defined here as the \"estimated base gas price\"): We define the forecast as being the 60th percentile of the minimum gas prices of the last 100 blocks (Geth algorithm) (cf: https://arxiv.org/abs/2003.03479)\n",
    "\n",
    "We make the following assumption:\n",
    "\n",
    "- Currently, for a user to make a transaction on Ethereum, for him the BASEFEE corresponds to the recommended gas price. For that reason, we can consider the tips as being the difference between the gas price choosen by the user and the recommended gas price.\n",
    "\n",
    "Once we got this \"estimated base gas price\", we can compute the \"estimated tips\" as:\n",
    "\n",
    "`estimated tips = gas price - estimated base gas price`\n",
    "\n",
    "Note: For a given block, if a transaction inside this block is below the \"estimated base gas price\", we set the \"estimated tips\" to 1 symbolic Gwei for this transaction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "framed-footwear",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>gas_price_mean</th>\n",
       "      <th>gas_price_min</th>\n",
       "      <th>gas_price_max</th>\n",
       "      <th>txs_count</th>\n",
       "      <th>gas_price_median</th>\n",
       "      <th>date</th>\n",
       "      <th>gas_price_recommendation</th>\n",
       "      <th>estimated_tips_median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>block_number</th>\n",
       "      <th></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>6988715</th>\n",
       "      <td>25.790394</td>\n",
       "      <td>3.3</td>\n",
       "      <td>350.0</td>\n",
       "      <td>108</td>\n",
       "      <td>11.115385</td>\n",
       "      <td>2019-01-01 00:24:14</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.115385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6988716</th>\n",
       "      <td>9.220141</td>\n",
       "      <td>3.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>65</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2019-01-01 00:24:27</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6988717</th>\n",
       "      <td>2.875018</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>86</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>2019-01-01 00:24:29</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6988718</th>\n",
       "      <td>17.795414</td>\n",
       "      <td>2.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>151</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2019-01-01 00:25:21</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6988719</th>\n",
       "      <td>19.009101</td>\n",
       "      <td>3.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>94</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2019-01-01 00:25:26</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              gas_price_mean  gas_price_min  gas_price_max  txs_count  \\\n",
       "block_number                                                            \n",
       "6988715            25.790394            3.3          350.0        108   \n",
       "6988716             9.220141            3.0          134.0         65   \n",
       "6988717             2.875018            2.0           10.0         86   \n",
       "6988718            17.795414            2.0          350.0        151   \n",
       "6988719            19.009101            3.0          350.0         94   \n",
       "\n",
       "              gas_price_median                date  gas_price_recommendation  \\\n",
       "block_number                                                                   \n",
       "6988715              11.115385 2019-01-01 00:24:14                       3.0   \n",
       "6988716               4.000000 2019-01-01 00:24:27                       3.0   \n",
       "6988717               2.400000 2019-01-01 00:24:29                       3.0   \n",
       "6988718              10.000000 2019-01-01 00:25:21                       3.0   \n",
       "6988719              10.000000 2019-01-01 00:25:26                       3.0   \n",
       "\n",
       "              estimated_tips_median  \n",
       "block_number                         \n",
       "6988715                    8.115385  \n",
       "6988716                    1.000000  \n",
       "6988717                    1.000000  \n",
       "6988718                    7.000000  \n",
       "6988719                    7.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def geth_recommend(ddf_block: dd.DataFrame, \n",
    "                   lookback: int = 100, \n",
    "                   percentile: int = 0.6) -> dd.DataFrame:\n",
    "    \"\"\"\n",
    "    The Ethereum client implementation in go, namely Geth, accounts\n",
    "    for over 79% of all Ethereum clients. To recommend a gas price, Geth uses\n",
    "    the minimum gas price of the previous blocks. It looks back at the 100 blocks\n",
    "    preceding the current one and then uses the value of the 60th percentile of the\n",
    "    minimum gas prices as the price recommendation.\n",
    "    \"\"\"\n",
    "    # todo: can be optimized\n",
    "    reco = ddf_block[[\"gas_price_min\"]].rolling(lookback).quantile(percentile)\n",
    "    start_index = ddf_block.head().index[0]\n",
    "    reco.columns = [\"gas_price_recommendation\"]\n",
    "    reco = reco.loc[start_index+lookback:]\n",
    "    return reco\n",
    "\n",
    "def geth_and_estimated_tips(ddf_block: dd.DataFrame, \n",
    "                            ddf_geth_recommend: dd.DataFrame)-> dd.DataFrame:\n",
    "    \n",
    "    start_index = ddf_geth_recommend.head().index[0]\n",
    "    ddf_block = ddf_block.loc[start_index:].copy()\n",
    "    ddf_geth_recommend = ddf_geth_recommend.loc[start_index:].copy()\n",
    "    estimated_tips = (ddf_block[\"gas_price_median\"] - ddf_geth_recommend[\"gas_price_recommendation\"])\n",
    "    estimated_tips = estimated_tips.apply(lambda x: x if x > 0 else 1, meta=\"float\")\n",
    "    estimated_tips.name = \"estimated_tips_median\"\n",
    "    return dd.concat([ddf_block, ddf_geth_recommend, estimated_tips], axis=1)\n",
    "\n",
    "    \n",
    "ddf_geth_recommend = geth_recommend(ddf_block)\n",
    "ddf_block = geth_and_estimated_tips(ddf_block,ddf_geth_recommend )\n",
    "start_index = ddf_geth_recommend.head().index[0]\n",
    "ddf = ddf.loc[start_index:].copy()\n",
    "ddf_block.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "catholic-philippines",
   "metadata": {},
   "source": [
    "## Blocks rolling average\n",
    "\n",
    "To analyse the gas price statistical \"behaviors\", we average the metrics over some blocks, to reduce noise and have a good picture of when people tends to overpay fees or when the fees are in general close to the recommended gas price (or \"estimated BASEFEE\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "third-passport",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>gas_price_mean_rolling_mean</th>\n",
       "      <th>gas_price_min_rolling_mean</th>\n",
       "      <th>gas_price_max_rolling_mean</th>\n",
       "      <th>txs_count_rolling_mean</th>\n",
       "      <th>gas_price_median_rolling_mean</th>\n",
       "      <th>gas_price_recommendation_rolling_mean</th>\n",
       "      <th>estimated_tips_median_rolling_mean</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>block_number</th>\n",
       "      <th></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>6988735</th>\n",
       "      <td>18.304848</td>\n",
       "      <td>7.70</td>\n",
       "      <td>182.545</td>\n",
       "      <td>86.25</td>\n",
       "      <td>11.01689</td>\n",
       "      <td>2.577</td>\n",
       "      <td>8.87189</td>\n",
       "      <td>2019-01-01 00:31:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6988736</th>\n",
       "      <td>18.585705</td>\n",
       "      <td>7.70</td>\n",
       "      <td>193.345</td>\n",
       "      <td>85.80</td>\n",
       "      <td>10.96689</td>\n",
       "      <td>2.529</td>\n",
       "      <td>8.86989</td>\n",
       "      <td>2019-01-01 00:31:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6988737</th>\n",
       "      <td>20.172723</td>\n",
       "      <td>8.60</td>\n",
       "      <td>196.395</td>\n",
       "      <td>82.15</td>\n",
       "      <td>12.34689</td>\n",
       "      <td>2.502</td>\n",
       "      <td>10.19689</td>\n",
       "      <td>2019-01-01 00:32:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6988738</th>\n",
       "      <td>21.782953</td>\n",
       "      <td>11.00</td>\n",
       "      <td>181.395</td>\n",
       "      <td>74.65</td>\n",
       "      <td>14.34689</td>\n",
       "      <td>2.502</td>\n",
       "      <td>12.19689</td>\n",
       "      <td>2019-01-01 00:32:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6988739</th>\n",
       "      <td>22.471371</td>\n",
       "      <td>11.35</td>\n",
       "      <td>181.395</td>\n",
       "      <td>72.10</td>\n",
       "      <td>14.44689</td>\n",
       "      <td>2.502</td>\n",
       "      <td>12.29689</td>\n",
       "      <td>2019-01-01 00:32:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              gas_price_mean_rolling_mean  gas_price_min_rolling_mean  \\\n",
       "block_number                                                            \n",
       "6988735                         18.304848                        7.70   \n",
       "6988736                         18.585705                        7.70   \n",
       "6988737                         20.172723                        8.60   \n",
       "6988738                         21.782953                       11.00   \n",
       "6988739                         22.471371                       11.35   \n",
       "\n",
       "              gas_price_max_rolling_mean  txs_count_rolling_mean  \\\n",
       "block_number                                                       \n",
       "6988735                          182.545                   86.25   \n",
       "6988736                          193.345                   85.80   \n",
       "6988737                          196.395                   82.15   \n",
       "6988738                          181.395                   74.65   \n",
       "6988739                          181.395                   72.10   \n",
       "\n",
       "              gas_price_median_rolling_mean  \\\n",
       "block_number                                  \n",
       "6988735                            11.01689   \n",
       "6988736                            10.96689   \n",
       "6988737                            12.34689   \n",
       "6988738                            14.34689   \n",
       "6988739                            14.44689   \n",
       "\n",
       "              gas_price_recommendation_rolling_mean  \\\n",
       "block_number                                          \n",
       "6988735                                       2.577   \n",
       "6988736                                       2.529   \n",
       "6988737                                       2.502   \n",
       "6988738                                       2.502   \n",
       "6988739                                       2.502   \n",
       "\n",
       "              estimated_tips_median_rolling_mean                date  \n",
       "block_number                                                          \n",
       "6988735                                  8.87189 2019-01-01 00:31:35  \n",
       "6988736                                  8.86989 2019-01-01 00:31:44  \n",
       "6988737                                 10.19689 2019-01-01 00:32:01  \n",
       "6988738                                 12.19689 2019-01-01 00:32:03  \n",
       "6988739                                 12.29689 2019-01-01 00:32:05  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NB_BLOCKS_TO_GROUP = configs[\"ROLLING_WINDOW\"]\n",
    "\n",
    "ddf_block_roll = ddf_block.rolling(NB_BLOCKS_TO_GROUP).mean()\n",
    "ddf_block_roll = dd.concat([ddf_block_roll, ddf_block[[\"date\"]]], axis=1)\n",
    "start_index = ddf_block.head().index[0]+NB_BLOCKS_TO_GROUP\n",
    "ddf_block_roll = ddf_block_roll.loc[start_index:].copy()\n",
    "ddf_block_roll.columns = [c+\"_rolling_mean\" if c != \"date\" else \"date\" for c in ddf_block_roll.columns]\n",
    "ddf_block_roll.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "approved-captain",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.heatmap(ddf_block_roll.corr().compute(), vmin=-1, vmax=1, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dependent-sphere",
   "metadata": {},
   "source": [
    "We notice that the average estimated tips are slightly correlated with the average gas price recommendation (0.58). \n",
    "\n",
    "In order to compute an estimation of what would have been the tips reward won by the miners under eip-1559, we will sample transactions that come from blocks from a period where most of the people were giving a relatively small tips (close to the geth recommendation). Still we want to include thoses ponctual outliers with very high gas price cause they likely will remain (flash loans, transactions that represent a high amount of eth, human error in the gas value typing ...). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "iraqi-minute",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Histogram for feature: estimated_tips_median_rolling_mean:\n",
      "Mean : 8.687488681667471 \n",
      "Bin with most elements : 4\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_histogram(ddf_block_roll)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "architectural-silly",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Histogram for feature: txs_count_rolling_mean:\n",
      "Mean : 137.08578161309015 \n",
      "Bin with most elements : 122\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Histogram for feature: gas_price_median_rolling_mean:\n",
      "Mean : 38.61996390822418 \n",
      "Bin with most elements : 7\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Histogram for feature: gas_price_recommendation_rolling_mean:\n",
      "Mean : 31.140895113097926 \n",
      "Bin with most elements : 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_histogram(ddf_block_roll, \"txs_count_rolling_mean\", max_range=250)\n",
    "show_histogram(ddf_block_roll, \"gas_price_median_rolling_mean\")\n",
    "show_histogram(ddf_block_roll, \"gas_price_recommendation_rolling_mean\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "rubber-composite",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From 2019-01 to 2019-03 :\n",
      "\n",
      "Histogram for feature: estimated_tips_median_rolling_mean:\n",
      "Mean : 6.893330396617452 \n",
      "Bin with most elements : 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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7fizJt5O8cRDPc5JNSfYleayrNuG5TccN7fgfSXLmkb7fnAuDOfLVFweBz1TVSmA1cEU7xmFgR1WtAHa05UFzFfBE1/KXgOur6h3AAWB9X0Y1c74K/LCq3gW8h86xD+x5TrIE+FtgVVWdRmfCyaUM5nm+GVgzrjbZub0AWNFeG4Abj/TN5lwYMAe++qKq9lbVT1r7V3R+QSyhc5ybW7fNwMV9GeAMSbIUuAj4RlsOcA5we+syUMec5G3AXwA3AVTVy1X1AgN+nunMgjwpyXzgTcBeBvA8V9WPgf3jypOd27XALdVxL7AgyalH8n5zMQyWAM92Le9utYGUZAg4A7gPWFxVe9uq54DF/RrXDPkK8FngD2357cALVXWwLQ/auV4OjAH/3G6NfSPJmxng81xVe4B/Av6HTgi8CDzIYJ/nbpOd26P+vTYXw2DOSPIW4LvAp6vqpe511ZlTPDDzipN8CNhXVQ/2eyyzaD5wJnBjVZ0B/B/jbgkN4HleSOev4OXAnwJv5rW3UuaE6T63czEM5sRXXyR5A50g+FZVfa+Vnz906dh+7uvX+GbA+4EPJ3mazq2/c+jcT1/QbifA4J3r3cDuqrqvLd9OJxwG+Tz/FfDzqhqrqt8B36Nz7gf5PHeb7Nwe9e+1uRgGA//VF+1e+U3AE1X15a5VW4B1rb0OuGO2xzZTqurqqlpaVUN0zundVfUx4B7gktZt0I75OeDZJO9spXOBxxng80zn9tDqJG9q/84PHfPAnudxJju3W4DL26yi1cCLXbeTelNVc+4FXAj8N/Az4B/7PZ4ZOL4P0Ll8fAR4uL0upHMPfQewC/gP4OR+j3WGjv+DwJ2t/efA/cAo8K/Aif0e3zQf6+nASDvX/wYsHPTzDHwO+CnwGPBN4MRBPM/At+k8F/kdnavA9ZOdWyB0Zkn+DHiUzmyrI3o/v45CkjQnbxNJksYxDCRJhoEkyTCQJGEYSJIwDCRJGAaSJOD/AfK6K71yqi/8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From 2021-01 to 2021-03 :\n",
      "\n",
      "Histogram for feature: estimated_tips_median_rolling_mean:\n",
      "Mean : 25.843553294922735 \n",
      "Bin with most elements : 11\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "GROUP_1 = configs[\"GROUP_1\"]\n",
    "GROUP_2 = configs[\"GROUP_2\"]\n",
    "\n",
    "print(f\"From {GROUP_1[0]} to {GROUP_1[1]} :\\n\")\n",
    "\n",
    "group_1 = ddf_block_roll.loc[(ddf_block_roll[\"date\"] >= GROUP_1[0]) & (ddf_block_roll[\"date\"] < GROUP_1[1])].copy()\n",
    "show_histogram(group_1)\n",
    "\n",
    "print(f\"From {GROUP_2[0]} to {GROUP_2[1]} :\\n\")\n",
    "\n",
    "group_2 = ddf_block_roll.loc[(ddf_block_roll[\"date\"] >= GROUP_2[0]) & (ddf_block_roll[\"date\"] < GROUP_2[1])].copy()\n",
    "show_histogram(group_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "smooth-warrant",
   "metadata": {},
   "source": [
    "From January to March 2019, the averaged \"estimated tips\" are lower than the ones from January to March 2021 . But what is very interesting is:\n",
    "- The distribution looks like a normal distribution on 2019-01 -> 2019-03 (with a very thin right tail) whereas the distriution on 2021-01 -> 2021-03 is highly skewed to the right (because of the \"bidding war\" on the later period)\n",
    "\n",
    "For that reason, we will clip the historical data to only keep periods close to the most given averaged \"estimated tips\": **4**. (we thus clip every rows with a value higher than 10 to get some variance and obtain sort of a \"normal distribution\" centered on 4)\n",
    "\n",
    "This clipped dataset will be our reference to sample transactions from. It simulate the fact that eip-1559 will remove this variance in the gas price (cf \"all of the short-term volatility in demand for transaction space within a block translating into volatility in transaction fees [for current system]\")\n",
    "\n",
    "Note: We clip the blocks on the low end when their average \"estimated_tips\"  is lower than 0.5 Gwei (this is to discard the period where there is only blocks mined by miners or this can of strange behaviors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "least-packet",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Histogram for feature: estimated_tips_median_rolling_mean:\n",
      "Mean : 5.3939755662918385 \n",
      "Bin with most elements : 4\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MIN_CLIP = 0.5\n",
    "MAX_CLIP = 10\n",
    "\n",
    "ddf_low_tips = ddf_block_roll[(ddf_block_roll[\"estimated_tips_median_rolling_mean\"] > MIN_CLIP) & (ddf_block_roll[\"estimated_tips_median_rolling_mean\"] <= MAX_CLIP)]\n",
    "show_histogram(ddf_low_tips, max_range=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cutting-telling",
   "metadata": {},
   "source": [
    "By clipping the value above 10, the distribution now almost follow a gaussian distribution. We could expect such distribution on eip-1559 because of his ability to keep low variance on the gas price distribution per blocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "useful-rescue",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRANSACTIONS COUNT AVERAGED DISTRIBUTION :\n",
      "Histogram for feature: txs_count_rolling_mean:\n",
      "Mean : 127.45414395456834 \n",
      "Bin with most elements : 119\n"
     ]
    },
    {
     "data": {
      "image/png": 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bc4DrgS3tsQe4FzrhAdwOfBi4Grj9fIBo+fywS1qKfpwm2gUcaNMHgBu76g9Wx+PARUmuAK4DjlTVuap6DTgC7OxDuyTpH3nA9KtWGgYF/HWSp5PsabXLq+p0m/4JcHmb3gC80rXuyVabqy5JWiMrDYOPVNVVdE4B7U3y0e6ZVVV0AmNVJNmTZDrJ9NmzZ1frZSVNKEcH71hRGFTVqfbzDPA9Ouf8X22nf2g/z7TFTwGbulbf2Gpz1Xu9331Vtb2qtk9NTa2k6ZKkLssOgyS/luR956eBa4HngEPA+TuCdgMPtelDwC3trqIdwOvtdNIjwLVJLm4Xjq9tNS2TRzvS0vidgfUrWPdy4HtJzr/Of6uqv0ryFPCtJLcCfw/8m7b8YeAGYAb4P8C/A6iqc0m+CDzVlvtCVZ1bQbskSUu07DCoqhPA7/So/xT4WI96AXvneK39wP7ltkWSVmrzvod5+a5PDLoZA+O/QJYkGQbjxnOf0vJN8vfHMJAkGQbjZJKPaqTVMqnfI8NAknqYtFAwDCRJhoEkzWWSRgeGgSTJMBgHk3T0Iq21zfsenojvmGEgSTIMRt0kHLFIw2Dcv2uGgSQtwbiGgmEgSTIMRtm4HqFIw24cv3uGgSQtw7jdZWQYjKhx+hBKGjzDQJJWwagfoBkGkrRKRjkQDIMRM8ofNmkSjOp31DAYIaP6IZMmzfnv6ih9Zw0DSeqjUbnryDAYEaPwYZI0t+5QGMbv8/pBN0ALG8YPjqSV6f5ev3zXJwbYkg7DYMgZBNJkGHQ4DM1poiQ7k7yYZCbJvkG3Z9AMAWlyzT6ltBb7g6EYGSRZB9wDfBw4CTyV5FBVPT/Ylq09Q0BSL+f3Df0aNQxFGABXAzNVdQIgyUFgFzC2YTB7p//yXZ8wCCQNTKpq0G0gyaeAnVX179vzTwMfrqrbZi23B9jTnv4m8OIy3/Iy4B+Wue6oss+TwT5PhpX0+V9U1dTs4rCMDBalqu4D7lvp6ySZrqrtq9CkkWGfJ4N9ngz96POwXEA+BWzqer6x1SRJa2BYwuApYEuSK5NcANwEHBpwmyRpYgzFaaKqeivJbcAjwDpgf1Ud6+NbrvhU0wiyz5PBPk+GVe/zUFxAliQN1rCcJpIkDZBhIEmavDCYlD97keTlJM8meSbJdKtdkuRIkuPt58WDbudKJNmf5EyS57pqPfuYjrvbdj+a5KrBtXz55ujzHUlOtW39TJIbuuZ9vvX5xSTXDabVy5dkU5LHkjyf5FiSz7T62G7nefrc3+1cVRPzoHNx+iXgN4ALgB8BWwfdrj719WXgslm1/wzsa9P7gC8Nup0r7ONHgauA5xbqI3AD8H0gwA7giUG3fxX7fAfwH3osu7V9xi8Ermyf/XWD7sMS+3sFcFWbfh/wv1q/xnY7z9Pnvm7nSRsZ/OOfvaiqXwDn/+zFpNgFHGjTB4AbB9eUlauqHwDnZpXn6uMu4MHqeBy4KMkVa9LQVTRHn+eyCzhYVW9W1Y+BGTrfgZFRVaer6odt+ufAC8AGxng7z9PnuazKdp60MNgAvNL1/CTz/5JHWQF/neTp9mc8AC6vqtNt+ifA5YNpWl/N1cdx3/a3tdMi+7tO/41Vn5NsBj4EPMGEbOdZfYY+budJC4NJ8pGqugq4Htib5KPdM6szvhzr+4onoY/NvcD7gW3AaeDLA21NHyR5L/Ad4LNV9Ub3vHHdzj363NftPGlhMDF/9qKqTrWfZ4Dv0Rk2vnp+yNx+nhlcC/tmrj6O7bavqler6u2q+iVwP++cIhiLPid5D52d4ter6rutPNbbuVef+72dJy0MJuLPXiT5tSTvOz8NXAs8R6evu9tiu4GHBtPCvpqrj4eAW9rdJjuA17tOM4y0WefEP0lnW0OnzzcluTDJlcAW4Mm1bt9KJAnwAPBCVX2la9bYbue5+tz37TzoK+cDuFJ/A52r8y8Bfzbo9vSpj79B5+6CHwHHzvcTuBR4FDgO/A/gkkG3dYX9/Aad4fL/o3Oe9Na5+kjn7pJ72nZ/Ftg+6PavYp//a+vT0bZjuKJr+T9rfX4RuH7Q7V9Gfz9C5xTQUeCZ9rhhnLfzPH3u63b2z1FIkibuNJEkqQfDQJJkGEiSDANJEoaBJAnDQJKEYSBJAv4/Bb1SFGEQRXEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GAS PRICE MEDIAN AVERAGED DISTRIBUTION :\n",
      "Histogram for feature: gas_price_median_rolling_mean:\n",
      "Mean : 20.401178634468067 \n",
      "Bin with most elements : 7\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GAS PRICE RECOMMENDATION AVERAGED DISTRIBUTION :\n",
      "Histogram for feature: gas_price_recommendation_rolling_mean:\n",
      "Mean : 15.751077277940094 \n",
      "Bin with most elements : 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"TRANSACTIONS COUNT AVERAGED DISTRIBUTION :\")\n",
    "show_histogram(ddf_low_tips, \"txs_count_rolling_mean\", max_range=250)\n",
    "print(\"GAS PRICE MEDIAN AVERAGED DISTRIBUTION :\")\n",
    "show_histogram(ddf_low_tips, \"gas_price_median_rolling_mean\")\n",
    "print(\"GAS PRICE RECOMMENDATION AVERAGED DISTRIBUTION :\")\n",
    "show_histogram(ddf_low_tips, \"gas_price_recommendation_rolling_mean\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "proof-howard",
   "metadata": {},
   "source": [
    "We can see that despite removing all blocks with high fees overpayments (estimated_tips - gas price recommendation), the properties of the sampled dataset reamins the same (same distribution in the others variables). It is nice cause we are not introducing any bias in the selection.\n",
    "\n",
    "Note: In this analysis, we don't make any estimate on what would have been the BASEFEE. Indeed, it's not really feasible taking into account only the historical data. What we do instead is only estimating the tips that miners will receive. We are interested to estimate their possible loss in terms of revenue from the fees (not taking into consideration the block reward)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "million-symbol",
   "metadata": {},
   "source": [
    "## Distincts periods comparison\n",
    "\n",
    "To have a visual idea of how different can be the variance in the gas price distribution, we can plot a boxplot of the gas prices for each blocks over multiple timestamps.\n",
    "\n",
    "Here, we compare a period of 20 consective blocks choosen randomly on the ethereum historical data where we had clear gas price overpayments VERSUS another series where the gas price distribution was much lower/normal, with small variance\n",
    "\n",
    "Note: We sample the 2 examples to have a similar gas price recommendation (\"estimated base\") for an easier comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "tamil-sleeve",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_multiple_blocks_gas_price_distr(ddf_txs: dd.DataFrame,\n",
    "                                         ddf_recommendation: dd.DataFrame,\n",
    "                                         start_block_number: int,\n",
    "                                         length: int = 20,\n",
    "                                         clip_max: float = None):\n",
    "    end_block_number = start_block_number + length\n",
    "    ddf_txs = ddf_txs.reset_index()\n",
    "    ddf_recommendation = ddf_recommendation[[\"gas_price_recommendation\"]]\n",
    "    ddf_recommendation = ddf_recommendation.reset_index()\n",
    "    ddf_recommendation.columns = [\"block_number\", \"gas_price_recommendation\"]\n",
    "    ddf_txs = ddf_txs[(ddf_txs[\"block_number\"] >= start_block_number) &\n",
    "                      (ddf_txs[\"block_number\"] <= end_block_number)].compute()\n",
    "    ddf_recommendation = ddf_recommendation[(ddf_recommendation[\"block_number\"] >= start_block_number) &\n",
    "                                            (ddf_recommendation[\"block_number\"] <= end_block_number)].compute()\n",
    "    if clip_max is not None:\n",
    "        ddf_txs[\"gas_price\"] = ddf_txs[\"gas_price\"].clip(0, clip_max)\n",
    "    ddf_txs[[\"block_number\", \"gas_price\"]].boxplot(by=\"block_number\", figsize=(30,10))\n",
    "    plt.plot(range(1,len(ddf_recommendation)+1), ddf_recommendation[[\"gas_price_recommendation\"]].values, 'k-', lw=2)\n",
    "    #plt.plot(range(1,len(df_recommendation)+1), df_txs.groupby(\"block_number\")[[\"gas_price\"]].mean(), 'k-', lw=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "static-carroll",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "recommended price for plot : 50\n"
     ]
    }
   ],
   "source": [
    "PREFERED_BASE_PRICE = 50\n",
    "\n",
    "high_fees_blocks = ddf_block_roll.nlargest(10000, 'estimated_tips_median_rolling_mean').index.values.compute()\n",
    "low_fees_blocks = ddf_low_tips.index.values.compute()\n",
    "\n",
    "low_reco_prices = (ddf_low_tips[\"gas_price_recommendation_rolling_mean\"]).astype(int).unique().compute()\n",
    "ddf_high_tips = ddf_block_roll.nlargest(500000, 'estimated_tips_median_rolling_mean')\n",
    "high_reco_prices = (ddf_high_tips[\"gas_price_recommendation_rolling_mean\"]).astype(int).unique().compute()\n",
    "reco_prices_intersec = list(set(low_reco_prices).intersection(set(high_reco_prices)))\n",
    "\n",
    "if PREFERED_BASE_PRICE in reco_prices_intersec:\n",
    "    reco_price = PREFERED_BASE_PRICE\n",
    "else:\n",
    "    reco_price = np.random.choice(reco_prices_intersec, 1)[0]\n",
    "print(\"recommended price for plot :\", reco_price)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "weird-reset",
   "metadata": {},
   "source": [
    "### Period with high variance in the gas_price\n",
    "\n",
    "We randomly sample 20 consecutive blocks from the sorted \"averaged blocks\" by their `estimated_tips` value (we take the 10 000 highest ones). This is a typical example of a series of block that would not be taken into consideration for the computation of the estimated revenues for miners under eip-1559.\n",
    "\n",
    "Note: There is 5 consecutive timesteps with missing boxplot, because we clip\n",
    "values above 200 on the plot. It means that all gas price were higher than 200 in those blocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "reliable-saturn",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From 11243349 to block 11243369 :\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idxs_reco_price = ddf_block_roll.loc[(ddf_block_roll[\"gas_price_recommendation_rolling_mean\"] < reco_price+1) &\n",
    "                                     (ddf_block_roll[\"gas_price_recommendation_rolling_mean\"] > reco_price-1) &\n",
    "                                     (ddf_block_roll.index.isin(high_fees_blocks))].index\n",
    "block_high = np.random.choice(idxs_reco_price, 1)[0]\n",
    "\n",
    "print(f\"From {block_high-20} to block {block_high} :\")\n",
    "plot_multiple_blocks_gas_price_distr(ddf, ddf_block[[\"gas_price_recommendation\"]], block_high-20, clip_max=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "imposed-margin",
   "metadata": {},
   "source": [
    "### Period with low variance in the gas_price\n",
    "\n",
    "We randomly sample 20 consecutive blocks from the sampled transactions where the variance in the gas price inside a block is small (median close to the gas price recommendation).\n",
    "This is only this kind of series of block that would be taken into consideration for the computation of the estimated revenues for miners under eip-1559."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "toxic-manor",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From 10562742 to block 10562762 :\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idxs_reco_price = ddf_low_tips.loc[(ddf_low_tips[\"gas_price_recommendation_rolling_mean\"] < reco_price+1) &\n",
    "                                (ddf_low_tips[\"gas_price_recommendation_rolling_mean\"] > reco_price-1)].index\n",
    "block_low = np.random.choice(idxs_reco_price, 1)[0]\n",
    "print(f\"From {block_low-20} to block {block_low} :\")\n",
    "plot_multiple_blocks_gas_price_distr(ddf, ddf_block[[\"gas_price_recommendation\"]], block_low-20, clip_max=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bright-occupation",
   "metadata": {},
   "source": [
    "We can see that the first plot of the gas price distribution over consecutive blocks have some very high \"overpaying gas prices\". In comparison, the second plot that is sampled from low overpayment consecutive blocks, have a much smaller and consistent variance.\n",
    "\n",
    "Note: In both case, we can see some extreme gas price (outliers). We can believe that thoses outliers will remain even after the adoption of eip-1559 (transactions with high eth value, human error, flash loans, bots, etc)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sweet-vanilla",
   "metadata": {},
   "source": [
    "## Estimation results\n",
    "\n",
    "On this section, we will compare 3 cases:\n",
    "\n",
    "1. Actual miners profit made (legacy blockchain)\n",
    "2. Estimation of miners profit made with eip-1559 (without remove periods with fees overpayments)\n",
    "3. Estimation of miners profit made with eip-1559 (with removing periods with fees overpayments)\n",
    "\n",
    "Computation 2) can be considered as a minimum loss in revenues for miners while computation 3) can be considered as a more realistic estimation (given the assumptions of this notebook of course)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "humanitarian-walter",
   "metadata": {},
   "source": [
    "Note: we make the computation for the miners revenue under the eip-1559 following the implementation that is presented below. \n",
    "\n",
    "        \n",
    "```\n",
    "# ensure that the user was willing to at least pay the base fee\n",
    "assert transaction.max_fee_per_gas >= block.base_fee_per_gas\n",
    "# inclusion fee is capped because the base fee is filled first\n",
    "inclusion_fee_per_gas = min(transaction.max_inclusion_fee_per_gas, transaction.max_fee_per_gas - block.base_fee_per_gas)\n",
    "# signer pays both the inclusion fee and the base fee\n",
    "effective_gas_price = inclusion_fee_per_gas + block.base_fee_per_gas\n",
    "signer.balance -= transaction.gas_limit * effective_gas_price\n",
    "assert signer.balance >= 0, 'invalid transaction: signer does not have enough ETH to cover gas'\n",
    "gas_used = self.execute_transaction(transaction, effective_gas_price)\n",
    "gas_refund = transaction.gas_limit - gas_used\n",
    "cumulative_transaction_gas_used += gas_used\n",
    "# signer gets refunded for unused gas\n",
    "signer.balance += gas_refund * effective_gas_price\n",
    "# miner only receives the inclusion fee; note that the base fee is not given to anyone (it is burned)\n",
    "self.account(block.author).balance += gas_used * inclusion_fee_per_gas\n",
    "```\n",
    "(source: https://github.com/ethereum/EIPs/blob/master/EIPS/eip-1559.md)\n",
    "\n",
    "We see that the miner receive `gas_used * inclusion_fee_per_gas`. So we will use `gas_used * estimated_tips` to compute the fees \"won\" by the miner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "valued-enlargement",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_actual_miners_profit(ddf: dd.DataFrame, \n",
    "                                 from_date:str = \"2019-01\", \n",
    "                                 to_date:str = \"2021-02\") -> float:\n",
    "    \"\"\"\n",
    "    Compute the actual miners profit on the legacy blockchain for a given time range\n",
    "    \"\"\"\n",
    "    \n",
    "    ddf = ddf.loc[((ddf[\"block_timestamp\"] >= from_date) & (ddf[\"block_timestamp\"] <= to_date))]\n",
    "    return (ddf[\"gas_price\"] * ddf[\"gas\"]).sum().compute() / 1000000000 # to eth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "least-hawaii",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_eip1559_miners_profit(ddf: dd.DataFrame, \n",
    "                                  df_recommendation: dd.DataFrame, \n",
    "                                  from_date:str = \"2019-01\", \n",
    "                                  to_date:str = \"2021-02\") -> float:\n",
    "    \"\"\"\n",
    "    Compute the miners profit with only using the difference between the gas price and the gas price recommendation\n",
    "    for a given time range.\n",
    "    \"\"\"\n",
    "    \n",
    "    ddf = ddf.loc[((ddf[\"block_timestamp\"] >= from_date) & (ddf[\"block_timestamp\"] <= to_date))]\n",
    "    ddf = ddf.merge(ddf_block[[\"gas_price_recommendation\"]], left_index=True, right_index=True, how='left')\n",
    "    ddf[\"gas_price\"] = ddf[\"gas_price\"] - ddf[\"gas_price_recommendation\"]\n",
    "    ddf[\"gas_price\"] = ddf[\"gas_price\"].apply(lambda x: x if x > 0 else 1, meta=\"float\")\n",
    "    return (ddf[\"gas_price\"] * ddf[\"gas\"]).sum().compute() / 1000000000 # to eth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "collectible-shock",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_refined_eip1559_miners_profit(low_tips_idxs: np.array, \n",
    "                                          ddf: dd.DataFrame,\n",
    "                                          ddf_recommendation: dd.DataFrame,\n",
    "                                          from_date:str = \"2019-01\",\n",
    "                                          to_date:str = \"2021-02\") -> float:\n",
    "    \"\"\"\n",
    "    Compute the miners profit with only using the difference between the gas price and the gas price recommendation\n",
    "    for a given time range.\n",
    "    In addition, the blocks used will only be sampled from periods with low variance in the gas price.\n",
    "    \"\"\"\n",
    "    ddf = ddf.loc[((ddf[\"block_timestamp\"] >= from_date) & (ddf[\"block_timestamp\"] <= to_date))]\n",
    "    df_subset_txs = ddf.loc[ddf.index.isin(low_tips_idxs)].copy()\n",
    "    len_multiplier = len(ddf) / len(df_subset_txs)\n",
    "    return compute_eip1559_miners_profit(df_subset_txs, ddf_recommendation, from_date, to_date) * len_multiplier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "temporal-roman",
   "metadata": {},
   "outputs": [],
   "source": [
    "def estimate_profit(ddf: dd.DataFrame,\n",
    "                    ddf_recommendation: dd.DataFrame,\n",
    "                    low_fees_blocks: np.array,\n",
    "                    from_date:str = \"2019-01\", \n",
    "                    to_date:str = \"2021-02\") -> tuple:\n",
    "    \n",
    "    print(f\"Range: {from_date} to {to_date}\\n\")\n",
    "    # Actual profit: the current fees system\n",
    "    actual_profit = compute_actual_miners_profit(ddf, from_date=from_date, to_date=to_date)\n",
    "    print(f\"Current miners profit from fees was {actual_profit:.2f} ETH\\n\")\n",
    "    # Estimated profit upper bound: substract the gas recommendation (\"estimated base gas price\") only\n",
    "    estimated_profit = compute_eip1559_miners_profit(ddf, ddf_recommendation,\n",
    "                                                     from_date=from_date, to_date=to_date)\n",
    "    print(f\"Estimated miners minimal profit from fees with eip 1559 is {estimated_profit:.2f} ETH\")\n",
    "    loss = 1 - (estimated_profit/actual_profit)\n",
    "    print(f\"-> minimum estimated loss : {loss*100:.2f}% \\n\")\n",
    "    # Estimated profit: same as above + only samples from period with \"normal/low\" fees\n",
    "    refined_estimated_profit = compute_refined_eip1559_miners_profit(low_fees_blocks, ddf, \n",
    "                                                                     ddf_recommendation,\n",
    "                                                                     from_date=from_date, to_date=to_date)\n",
    "    print(f\"Estimated miners profit from fees with the eip 1559 is {refined_estimated_profit:.2f} ETH\")\n",
    "    loss = 1 - (refined_estimated_profit/actual_profit)\n",
    "    print(f\"-> estimated loss: {loss*100:.2f}% \\n\")\n",
    "    \n",
    "    return actual_profit, estimated_profit, refined_estimated_profit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "planned-valuation",
   "metadata": {},
   "source": [
    "## Evaluation from January 2019 to February 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "spanish-chosen",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Range: 2019-01 to 2021-02\n",
      "\n",
      "Current miners profit from fees was 5808504.00 ETH\n",
      "\n",
      "Estimated miners minimal profit from fees with eip 1559 is 2016833.67 ETH\n",
      "-> minimum estimated loss : 65.28% \n",
      "\n",
      "Estimated miners profit from fees with the eip 1559 is 1375667.37 ETH\n",
      "-> estimated loss: 76.32% \n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_recommendation = ddf_block[[\"gas_price_recommendation\"]]\n",
    "estimation = estimate_profit(ddf, df_recommendation, low_fees_blocks)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beautiful-accommodation",
   "metadata": {},
   "source": [
    "## Evaluation from January to March 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "under-health",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Range: 2019-01 to 2019-03\n",
      "\n",
      "Current miners profit from fees was 100871.54 ETH\n",
      "\n",
      "Estimated miners minimal profit from fees with eip 1559 is 74796.40 ETH\n",
      "-> minimum estimated loss : 25.85% \n",
      "\n",
      "Estimated miners profit from fees with the eip 1559 is 67478.60 ETH\n",
      "-> estimated loss: 33.10% \n",
      "\n"
     ]
    }
   ],
   "source": [
    "estimation_2019 = estimate_profit(ddf, df_recommendation, low_fees_blocks, from_date=\"2019-01\", to_date=\"2019-03\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "infrared-ticket",
   "metadata": {},
   "source": [
    "## Evaluation from January to March 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "changing-economics",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Range: 2021-01 to 2021-03\n",
      "\n",
      "Current miners profit from fees was 1963751.95 ETH\n",
      "\n",
      "Estimated miners minimal profit from fees with eip 1559 is 589969.03 ETH\n",
      "-> minimum estimated loss : 69.96% \n",
      "\n",
      "Estimated miners profit from fees with the eip 1559 is 263080.37 ETH\n",
      "-> estimated loss: 86.60% \n",
      "\n"
     ]
    }
   ],
   "source": [
    "estimation_2021 = estimate_profit(ddf, df_recommendation, low_fees_blocks, from_date=\"2021-01\", to_date=\"2021-03\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "former-hygiene",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>2019-01 to 2021-03</th>\n",
       "      <th>2019-01 to 2019-03</th>\n",
       "      <th>2021-01 to 2021-03</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>actual revenue</th>\n",
       "      <td>5.808504e+06</td>\n",
       "      <td>100871.544314</td>\n",
       "      <td>1.963752e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>estimated minimal revenue with eip 1559</th>\n",
       "      <td>2.016834e+06</td>\n",
       "      <td>74796.395873</td>\n",
       "      <td>5.899690e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>estimated revenue with eip 1559</th>\n",
       "      <td>1.375667e+06</td>\n",
       "      <td>67478.602310</td>\n",
       "      <td>2.630804e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         2019-01 to 2021-03  \\\n",
       "actual revenue                                 5.808504e+06   \n",
       "estimated minimal revenue with eip 1559        2.016834e+06   \n",
       "estimated revenue with eip 1559                1.375667e+06   \n",
       "\n",
       "                                         2019-01 to 2019-03  \\\n",
       "actual revenue                                100871.544314   \n",
       "estimated minimal revenue with eip 1559        74796.395873   \n",
       "estimated revenue with eip 1559                67478.602310   \n",
       "\n",
       "                                         2021-01 to 2021-03  \n",
       "actual revenue                                 1.963752e+06  \n",
       "estimated minimal revenue with eip 1559        5.899690e+05  \n",
       "estimated revenue with eip 1559                2.630804e+05  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_res = pd.DataFrame({'2019-01 to 2021-03': estimation, '2019-01 to 2019-03': estimation_2019, '2021-01 to 2021-03': estimation_2021})\n",
    "df_res.index = [\"actual revenue\", \"estimated minimal revenue with eip 1559\", \"estimated revenue with eip 1559\"]\n",
    "df_res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "through-focus",
   "metadata": {},
   "source": [
    "## Outliers analysis\n",
    "\n",
    "In this last section, let's just see what are the proportion of miner's revenues coming from the \"outliers\" transactions (the ones with high gas fees compared to the rest of the block)\n",
    "\n",
    "Here we define a block outlier as: \n",
    "\n",
    "    - greater than 75th percentile + (1.5 * InterQuartileRange)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "unauthorized-inside",
   "metadata": {},
   "outputs": [],
   "source": [
    "def above_percentile(x):\n",
    "    q1 = np.percentile(x[\"gas_price\"], 25)\n",
    "    q3 = np.percentile(x[\"gas_price\"], 75)\n",
    "    iqr = q3 - q1\n",
    "    thr = q3 + 1.5 * iqr\n",
    "    outliers = x[x[\"gas_price\"] >= thr]\n",
    "    return (outliers[\"gas_price\"] * outliers[\"gas\"]).sum()\n",
    "\n",
    "def outliers_fees(ddf: dd.DataFrame, \n",
    "                  from_date: str = \"2019-01\",\n",
    "                  to_date: str = \"2019-03\") -> float:\n",
    "    ddf_for_outliers = ddf.loc[((ddf[\"block_timestamp\"] >= from_date) & (ddf[\"block_timestamp\"] <= to_date))]\n",
    "    return ddf_for_outliers.map_partitions(lambda part: part.groupby(part.index)[[\"gas_price\", \"gas\"]].apply(above_percentile)).sum().compute() / 1000000000 # to eth\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "portuguese-brown",
   "metadata": {},
   "source": [
    "## Outliers from 2019-01 to 2019-03"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "divine-gabriel",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Range: 2019-01 to 2019-03\n",
      "\n",
      "The fees coming from the detected outliers transactions correspond to : 34059.21 ETH\n",
      "This amount represent 33.76% of the total fees on this period\n"
     ]
    }
   ],
   "source": [
    "outliers_2019 = outliers_fees(ddf, from_date=\"2019-01\", to_date=\"2019-03\")\n",
    "ratio_outliers_2019 = outliers_2019 / estimation_2019[0]\n",
    "print(f\"Range: 2019-01 to 2019-03\\n\")\n",
    "print(f\"The fees coming from the detected outliers transactions correspond to : {outliers_2019:.2f} ETH\")\n",
    "print(f\"This amount represent {ratio_outliers_2019*100:.2f}% of the total fees on this period\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "genetic-resistance",
   "metadata": {},
   "source": [
    "## Outliers from 2021-01 to 2021-03"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "spectacular-simple",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Range: 2021-01 to 2021-03\n",
      "\n",
      "The fees coming from the detected outliers transactions correspond to : 375088.22 ETH\n",
      "This amount represent 19.10% of the total fees on this period\n"
     ]
    }
   ],
   "source": [
    "outliers_2021 = outliers_fees(ddf, from_date=\"2021-01\", to_date=\"2021-03\")\n",
    "ratio_outliers_2021 = outliers_2021 / estimation_2021[0]\n",
    "print(f\"Range: 2021-01 to 2021-03\\n\")\n",
    "print(f\"The fees coming from the detected outliers transactions correspond to : {outliers_2021:.2f} ETH\")\n",
    "print(f\"This amount represent {ratio_outliers_2021*100:.2f}% of the total fees on this period\")"
   ]
  }
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