notebooks/txpool.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tx pool in EIP 1559\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "sys.path.insert(1, os.path.realpath(os.path.pardir))\n",
    "# You may remove the two lines above if you have installed abm1559 from pypi\n",
    "\n",
    "from abm1559.utils import constants\n",
    "\n",
    "from abm1559.txpool import TxPool\n",
    "\n",
    "from abm1559.users import User1559\n",
    "\n",
    "from abm1559.userpool import UserPool\n",
    "\n",
    "from abm1559.chain import (\n",
    "    Chain,\n",
    "    Block1559,\n",
    ")\n",
    "\n",
    "from abm1559.simulator import (\n",
    "    spawn_poisson_heterogeneous_demand,\n",
    "    update_basefee,\n",
    ")\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class OptimisticUser(User1559):\n",
    "    def expected_time(self, params):\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class StrategicUser(User1559):\n",
    "    \"\"\"\n",
    "    A strategic affine user sending 1559 transactions.\n",
    "    \n",
    "    - Expects to be included in the next block\n",
    "    - Prefers not to participate if its expected payoff is negative\n",
    "    - Strategic gas_premium\n",
    "    \"\"\"\n",
    "\n",
    "    epsilon = 0.1 # how much the user overbids by\n",
    "\n",
    "    def expected_time(self, params):\n",
    "        return 1\n",
    "\n",
    "    def decide_parameters(self, params):\n",
    "        if params[\"min_premium\"] is None:\n",
    "            min_premium = 1 * (10 ** 9)\n",
    "        else:\n",
    "            min_premium = params[\"min_premium\"]\n",
    "\n",
    "        gas_premium = min_premium + self.epsilon * (10 ** 9)\n",
    "        max_fee = self.value\n",
    "\n",
    "        return {\n",
    "            \"max_fee\": max_fee, # in wei\n",
    "            \"gas_premium\": gas_premium, # in wei\n",
    "            \"start_block\": self.wakeup_block\n",
    "        }\n",
    "\n",
    "    def export(self):\n",
    "        return {\n",
    "            **super().export(),\n",
    "            \"user_type\": \"strategic_user_1559\",\n",
    "        }\n",
    "\n",
    "    def __str__(self):\n",
    "        return f\"1559 strategic affine user with value {self.value} and cost {self.cost_per_unit}\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (Bonus) Cancel culture\n",
    "\n",
    "We now allow users to cancel their transactions. We discussed in the previous notebook's bonus section the concept of _ex post individual rationality_. Suppose I estimate that should my transaction be included within 5 blocks, my payoff will be positive. In this case, I decide to join the pool and send my transaction. Fierce competition during transitionary periods however might prevent me from being included when I thought I would be. Without the ability to cancel my transaction, I must stick around in the transaction pool and could net a negative payoff once I am included.\n",
    "\n",
    "We add here the possibility for users to cancel their transaction. Cancelling is not completely trivial in the current, first-price auction paradigm (see [Etherscan's helpful guide for an overview](https://info.etherscan.com/how-to-cancel-ethereum-pending-transactions/)). To cancel transaction A, the trick is to send another transaction B with the same nonce (your address's \"transaction counter\") with a slightly higher fee. Miners with access to both A and B in their pool will strictly prefer B and _cannot_ include both.\n",
    "\n",
    "With EIP 1559, we'll assume users send a new transaction with a slightly higher premium. We also assumed that miners keep around an extensible list of pending transactions, even those rendered invalid from a high basefee, in case the basefee decreases. So we'll assume that as soon as a user sends a replacement transaction to cancel its original transaction, the original transaction disappears from the mempool. The costs to users vary according to the outcome:\n",
    "\n",
    "- In case the original and replacement transactions have a low gas price such that neither are ever included in a block, the user cancels \"for free\".\n",
    "- On the other hand, when the replacement transaction is included, the user incurs a cost. It is rational to cancel whenever the payoff from cancelling the transaction is greater than the payoff from inclusion. Suppose the replacement transaction posts a premium equal to $p'$ (gas price = $g'$), while the original transaction's is $p$ (gas price = $g$). Cancelling yields a payoff of $-g'$, while being included yields $v - w . c - g$, where $v$ is the value, $c$ is the cost for waiting per block and $w$ is the time waited. It is rational to cancel whenever:\n",
    "\n",
    "$$ v - w . c - g < -g' $$\n",
    "\n",
    "Of course, $g$ and $g'$ both depend on the current basefee, which complicates the matter. To simplify here, we assume that as soon as the current value $v - w . c$ of the user becomes negative, the user cancels their transaction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simulate(demand_scenario, shares_scenario):\n",
    "    # Instantiate a couple of things\n",
    "    txpool = TxPool()\n",
    "    basefee = constants[\"INITIAL_BASEFEE\"]\n",
    "    chain = Chain()\n",
    "    metrics = []\n",
    "    user_pool = UserPool()\n",
    "    min_premium = 1 * (10 ** 9)\n",
    "\n",
    "    for t in range(len(demand_scenario)):\n",
    "        \n",
    "        # `params` are the \"environment\" of the simulation\n",
    "        params = {\n",
    "            \"basefee\": basefee,\n",
    "            \"current_block\": t,\n",
    "            \"min_premium\": min_premium,\n",
    "            \"cancel_cost\": 2 * (10 ** 9), # in wei/gas\n",
    "        }\n",
    "        \n",
    "        #########\n",
    "        ## ADDED\n",
    "        ##\n",
    "        # We ask whether current users want to cancel their transactions waiting in the pool\n",
    "        cancelled_txs = user_pool.cancel_transactions(txpool, params)        \n",
    "        ##\n",
    "        # Cancel transactions in the pool, adds new empty transactions with higher fee\n",
    "        txpool.cancel_txs(cancelled_txs, params[\"cancel_cost\"])\n",
    "        ##\n",
    "        #########\n",
    "        \n",
    "        # We return some demand which on expectation yields 2000 new users per round\n",
    "        users = spawn_poisson_heterogeneous_demand(t, demand_scenario[t], shares_scenario[t])\n",
    "        \n",
    "        # Add new users to the pool\n",
    "        # We query each new user with the current basefee value\n",
    "        # Users either return a transaction or None if they prefer to balk\n",
    "        decided_txs = user_pool.decide_transactions(users, params)\n",
    "\n",
    "        # New transactions are added to the transaction pool\n",
    "        txpool.add_txs(decided_txs)\n",
    "\n",
    "        # The best valid transactions are taken out of the pool for inclusion\n",
    "        selected_txs = txpool.select_transactions(params)\n",
    "        txpool.remove_txs([tx.tx_hash for tx in selected_txs])\n",
    "\n",
    "        # We create a block with these transactions\n",
    "        block = Block1559(txs = selected_txs, parent_hash = chain.current_head, height = t, basefee = basefee)\n",
    "\n",
    "        # Record the min premium in the block\n",
    "        min_premium = block.min_premium()\n",
    "        \n",
    "        # The block is added to the chain\n",
    "        chain.add_block(block)\n",
    "\n",
    "        # A couple of metrics we will use to monitor the simulation\n",
    "        row_metrics = {\n",
    "            \"block\": t,\n",
    "            \"basefee\": basefee / (10 ** 9),\n",
    "            \"users\": len(users),\n",
    "            \"strategic\": len([user for user in users if type(user) is StrategicUser]),\n",
    "            \"nonstategic\": len([user for user in users if type(user) is OptimisticUser]),\n",
    "            \"decided_txs\": len(decided_txs),\n",
    "            \"included_txs\": len(selected_txs),\n",
    "            \"cancelled_txs\": len(cancelled_txs),\n",
    "            \"blk_min_premium\": block.min_premium() / (10 ** 9), # to Gwei\n",
    "            \"pool_length\": txpool.pool_length,\n",
    "        }\n",
    "        metrics.append(row_metrics)\n",
    "\n",
    "        # Finally, basefee is updated and a new round starts\n",
    "        basefee = update_basefee(block, basefee)\n",
    "\n",
    "    return (pd.DataFrame(metrics), user_pool, chain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "blocks = 200\n",
    "demand_scenario = [2500 for i in range(blocks)]\n",
    "\n",
    "strategic_share = 0.5\n",
    "shares_scenario = [{\n",
    "    StrategicUser: strategic_share,\n",
    "    OptimisticUser: 1 - strategic_share,\n",
    "} for i in range(blocks)]\n",
    "\n",
    "(df, user_pool, chain) = simulate(demand_scenario, shares_scenario)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>block</th>\n",
       "      <th>basefee</th>\n",
       "      <th>users</th>\n",
       "      <th>strategic</th>\n",
       "      <th>nonstategic</th>\n",
       "      <th>decided_txs</th>\n",
       "      <th>included_txs</th>\n",
       "      <th>cancelled_txs</th>\n",
       "      <th>blk_min_premium</th>\n",
       "      <th>pool_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2498</td>\n",
       "      <td>1249</td>\n",
       "      <td>1249</td>\n",
       "      <td>2170</td>\n",
       "      <td>952</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.124900</td>\n",
       "      <td>2595</td>\n",
       "      <td>1297</td>\n",
       "      <td>1298</td>\n",
       "      <td>2242</td>\n",
       "      <td>952</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.265400</td>\n",
       "      <td>2409</td>\n",
       "      <td>1204</td>\n",
       "      <td>1205</td>\n",
       "      <td>2086</td>\n",
       "      <td>952</td>\n",
       "      <td>37</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1.423448</td>\n",
       "      <td>2534</td>\n",
       "      <td>1267</td>\n",
       "      <td>1267</td>\n",
       "      <td>2122</td>\n",
       "      <td>952</td>\n",
       "      <td>57</td>\n",
       "      <td>1.4</td>\n",
       "      <td>4718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.601237</td>\n",
       "      <td>2618</td>\n",
       "      <td>1309</td>\n",
       "      <td>1309</td>\n",
       "      <td>2190</td>\n",
       "      <td>952</td>\n",
       "      <td>84</td>\n",
       "      <td>1.5</td>\n",
       "      <td>5872</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>195</td>\n",
       "      <td>14.561289</td>\n",
       "      <td>2520</td>\n",
       "      <td>1260</td>\n",
       "      <td>1260</td>\n",
       "      <td>536</td>\n",
       "      <td>536</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>196</td>\n",
       "      <td>14.789901</td>\n",
       "      <td>2461</td>\n",
       "      <td>1230</td>\n",
       "      <td>1231</td>\n",
       "      <td>429</td>\n",
       "      <td>429</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>197</td>\n",
       "      <td>14.606691</td>\n",
       "      <td>2483</td>\n",
       "      <td>1241</td>\n",
       "      <td>1242</td>\n",
       "      <td>490</td>\n",
       "      <td>490</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>198</td>\n",
       "      <td>14.659640</td>\n",
       "      <td>2468</td>\n",
       "      <td>1234</td>\n",
       "      <td>1234</td>\n",
       "      <td>458</td>\n",
       "      <td>458</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>199</td>\n",
       "      <td>14.589641</td>\n",
       "      <td>2570</td>\n",
       "      <td>1285</td>\n",
       "      <td>1285</td>\n",
       "      <td>499</td>\n",
       "      <td>499</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     block    basefee  users  strategic  nonstategic  decided_txs  \\\n",
       "0        0   1.000000   2498       1249         1249         2170   \n",
       "1        1   1.124900   2595       1297         1298         2242   \n",
       "2        2   1.265400   2409       1204         1205         2086   \n",
       "3        3   1.423448   2534       1267         1267         2122   \n",
       "4        4   1.601237   2618       1309         1309         2190   \n",
       "..     ...        ...    ...        ...          ...          ...   \n",
       "195    195  14.561289   2520       1260         1260          536   \n",
       "196    196  14.789901   2461       1230         1231          429   \n",
       "197    197  14.606691   2483       1241         1242          490   \n",
       "198    198  14.659640   2468       1234         1234          458   \n",
       "199    199  14.589641   2570       1285         1285          499   \n",
       "\n",
       "     included_txs  cancelled_txs  blk_min_premium  pool_length  \n",
       "0             952              0              1.1         1218  \n",
       "1             952              0              1.2         2508  \n",
       "2             952             37              1.3         3605  \n",
       "3             952             57              1.4         4718  \n",
       "4             952             84              1.5         5872  \n",
       "..            ...            ...              ...          ...  \n",
       "195           536              2              1.0          456  \n",
       "196           429              0              1.0          456  \n",
       "197           490              3              1.0          453  \n",
       "198           458              3              1.0          450  \n",
       "199           499              4              1.0          446  \n",
       "\n",
       "[200 rows x 10 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe a similar plot of basefee."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='block'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df.plot(\"block\", [\"basefee\", \"blk_min_premium\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But we can now see the txpool slowly emptying, as users cancel their transactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='block'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df.plot(\"block\", [\"pool_length\", \"users\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cancelled transactions are at their peak early in the simulation. During the basefee's transitionary period, many hopeful users find it profitable to send transactions in, believing they will be included quickly. But as basefee rises, they are priced out, and remain in the pool. Those with higher time-sensitivity see their current value decrease faster, and cancel early."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='block'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df.plot(\"block\", [\"users\", \"decided_txs\", \"included_txs\", \"cancelled_txs\"])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}