logos-blockchain-simulations/transaction_fee_models/eip-vs-stablefee.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transaction Fees on Nomos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import plotly.express as px\n",
    "import pandas as pd\n",
    "pd.options.plotting.backend = \"plotly\"\n",
    "from simulation import run_simulation\n",
    "from simulation_parameters import SimulationParameters\n",
    "from tx_fees_models import PROTOCOL_CONSTANTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "days = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = SimulationParameters(\n",
    "    num_blocks=int(days * 24 * 60 * 60 / 12),  # number of blocks in \"days\",\n",
    "    demand_sizes=[0, 50, 1000, 2000],  # very low, normal, very high demand\n",
    "    demand_probabilities=[0.02, 0.06, 0.86, 0.06],  # they must sum to 1\n",
    "    fee_cap_range=(0.05, 0.1),  # in percentage, mean and std deviation\n",
    "    max_tip_pct=0.1,\n",
    "    scale_block_size_limits=(0.05, 0.1),\n",
    "    probability_stop_below_gas_limit=0.5,\n",
    "    purge_after=2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stats_merged, df_chain_stats_merged = run_simulation(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stats_merged[[\"price_EIP\", \"price_StableFee\"]].plot(\n",
    "    title=\"EIP-1559 vs StableFee price\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stats_merged[[\"num_tx_EIP\", \"num_tx_StableFee\"]].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stats_merged[[\"tot_paid_EIP\", \"tot_paid_StableFee\"]].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stats_merged[[\"demand_size_EIP\"]].plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(df_stats_merged[\"num_tx_EIP\"].sub(df_stats_merged[\"num_tx_StableFee\"]) > 0).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_chain_stats_merged[[\"tot_gas_used_EIP\", \"tot_gas_used_StableFee\"]].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = df_chain_stats_merged[\"tot_gas_used_EIP\"].plot.bar()\n",
    "fig.add_shape(\n",
    "    type=\"line\",\n",
    "    x0=0,\n",
    "    y0=PROTOCOL_CONSTANTS[\"TARGET_GAS_USED\"],\n",
    "    x1=len(df_chain_stats_merged),\n",
    "    y1=PROTOCOL_CONSTANTS[\"TARGET_GAS_USED\"],\n",
    "    line=dict(color=\"red\", width=2)\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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