{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error reading the parquet file\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    markets = pd.read_parquet(\"../data/fpmms.parquet\")\n",
    "except Exception:\n",
    "    print(\"Error reading the parquet file\")\n",
    "\n",
    "# markets[\"currentAnswer\"] = markets[\"currentAnswer\"].apply(lambda x: x.lower())\n",
    "# # filter only markets with  yes, no answers\n",
    "# valid_answers = [\"yes\", \"no\"]\n",
    "# markets = markets.loc[markets[\"currentAnswer\"].isin(valid_answers)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    markets_df = pd.read_parquet(\"../data/fpmmTrades.parquet\")\n",
    "except Exception:\n",
    "    print(\"Error reading the parquet file\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 117525 entries, 0 to 117524\n",
      "Data columns (total 26 columns):\n",
      " #   Column                         Non-Null Count   Dtype              \n",
      "---  ------                         --------------   -----              \n",
      " 0   collateralAmount               117525 non-null  object             \n",
      " 1   collateralAmountUSD            117525 non-null  object             \n",
      " 2   collateralToken                117525 non-null  object             \n",
      " 3   creationTimestamp              117525 non-null  datetime64[ns, UTC]\n",
      " 4   trader_address                 117525 non-null  object             \n",
      " 5   feeAmount                      117525 non-null  object             \n",
      " 6   id                             117525 non-null  object             \n",
      " 7   oldOutcomeTokenMarginalPrice   117525 non-null  object             \n",
      " 8   outcomeIndex                   117525 non-null  object             \n",
      " 9   outcomeTokenMarginalPrice      117525 non-null  object             \n",
      " 10  outcomeTokensTraded            117525 non-null  object             \n",
      " 11  title                          117525 non-null  object             \n",
      " 12  transactionHash                117525 non-null  object             \n",
      " 13  type                           117525 non-null  object             \n",
      " 14  market_creator                 117525 non-null  object             \n",
      " 15  fpmm.answerFinalizedTimestamp  77324 non-null   object             \n",
      " 16  fpmm.arbitrationOccurred       117525 non-null  bool               \n",
      " 17  fpmm.currentAnswer             77324 non-null   object             \n",
      " 18  fpmm.id                        117525 non-null  object             \n",
      " 19  fpmm.isPendingArbitration      117525 non-null  bool               \n",
      " 20  fpmm.openingTimestamp          117525 non-null  object             \n",
      " 21  fpmm.outcomes                  117525 non-null  object             \n",
      " 22  fpmm.title                     117525 non-null  object             \n",
      " 23  fpmm.condition.id              117525 non-null  object             \n",
      " 24  creation_timestamp             117525 non-null  datetime64[ns, UTC]\n",
      " 25  creation_date                  117525 non-null  object             \n",
      "dtypes: bool(2), datetime64[ns, UTC](2), object(22)\n",
      "memory usage: 21.7+ MB\n"
     ]
    }
   ],
   "source": [
    "markets_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_creation_date(df):\n",
    "    df[\"creation_timestamp\"] = pd.to_datetime(df[\"creationTimestamp\"])\n",
    "    df[\"creation_date\"] = df[\"creation_timestamp\"].dt.date\n",
    "    df[\"creation_date\"] = pd.to_datetime(df[\"creation_date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "markets_df[\"creation_timestamp\"] = pd.to_datetime(markets_df[\"creationTimestamp\"])\n",
    "markets_df[\"creation_date\"] = markets_df[\"creation_timestamp\"].dt.date\n",
    "markets_df[\"creation_date\"] = pd.to_datetime(markets_df[\"creation_date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2024-12-28 00:00:00')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(markets_df.creation_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "creation_date\n",
       "2025-01-11    5275\n",
       "2024-12-12    5156\n",
       "2025-01-12    3919\n",
       "2024-12-21    3849\n",
       "2024-12-10    3799\n",
       "2024-12-11    3739\n",
       "2025-01-10    3711\n",
       "2024-12-24    3676\n",
       "2024-12-19    3486\n",
       "2024-12-20    3189\n",
       "2024-11-23    3124\n",
       "2024-11-30    3078\n",
       "2024-11-18    3069\n",
       "2024-12-09    3068\n",
       "2024-12-26    3066\n",
       "2024-12-23    2921\n",
       "2024-11-27    2905\n",
       "2024-11-28    2841\n",
       "2024-11-22    2840\n",
       "2024-12-02    2826\n",
       "2024-12-22    2663\n",
       "2024-11-24    2626\n",
       "2024-12-08    2621\n",
       "2024-12-04    2612\n",
       "2024-11-26    2549\n",
       "2024-11-25    2542\n",
       "2024-11-29    2541\n",
       "2024-12-01    2521\n",
       "2025-01-09    2517\n",
       "2024-11-20    2503\n",
       "2024-11-16    2502\n",
       "2024-11-14    2348\n",
       "2024-11-21    2315\n",
       "2024-12-07    2242\n",
       "2025-01-08    2231\n",
       "2024-11-15    2207\n",
       "2024-12-06    2203\n",
       "2024-11-19    2183\n",
       "2024-12-16    2168\n",
       "2024-12-14    2144\n",
       "2024-12-18    2088\n",
       "2024-12-13    2079\n",
       "2024-12-05    2060\n",
       "2024-12-27    1900\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "markets_df.creation_date.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Count', ylabel='creation_date'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HmKWlpRg7diyOHDmiM9nusmXL8MUXX4jfhw0bBgA4duxYo+NqoFQqcfToUajVagwfPhy9evXCsmXLEBISIvZZsmQJZDIZlixZgl9++QUODg6YPHmy3nca73U7IxF37qmEbS2+AE5ERNT5dKp54qht6TvPDBEREXUekpwnjoiIiIj0wySOiIiISIKYxBERERFJEJM4IiIiIgliEkdEREQkQUziiIiIiCSISRwRERGRBDGJIyIiIpIgJnFEREREEsQkjoiIiEiCmMQRERERSRCTOCIiIiIJYhJHREREJEFM4oiIiIgkiEkcERERkQQxiSMiIiKSICZxRERERBLEJI6IiIhIgpjEEREREUkQkzgiIiIiCWISR0RERCRBTOKIiIiIJIhJHBEREZEEMYkjIiIikiAmcUREREQSxCSOiIiISIKYxBERERFJkFGTuLi4OIwYMQLW1tZQqVSYNm0a8vLydPpUVVVBrVbjoYcegpWVFWbMmIHi4mJx+7lz5/DCCy/AxcUFCoUCgwYNwrp163TGSE1NhUwma/TRaDQtxicIApYtWwZnZ2coFAoEBATg8uXLOn1WrFiB0aNHo0ePHrC1tdX72M+fP49x48bB0tISLi4ueO+993S25+TkYMaMGXB3d4dMJsPatWv1HpuIiIi6PqMmcWlpaVCr1Th9+jSSkpJQW1uLwMBAVFRUiH3mz5+P/fv3Y8eOHUhLS0NhYSGmT58ubs/MzIRKpUJCQgJycnKwePFiREVFYf369Y32l5eXh6KiIvGjUqlajO+9997Dhx9+iE2bNuHMmTPo2bMngoKCUFVVJfapqanBn/70J7z22mt6H7dWq0VgYCDc3NyQmZmJ1atXIyYmBp988onYp7KyEv369cPKlSvh5OSk99hERETUTQidyK+//ioAENLS0gRBEITS0lLB3Nxc2LFjh9gnNzdXACBkZGQ0O87rr78uTJw4Ufx+7NgxAYBw8+ZNvWOpr68XnJychNWrV4ttpaWlglwuF7766qtG/ePj4wWlUqnX2Bs2bBDs7OyE6upqsW3hwoWCp6dnk/3d3NyENWvW6B17g7KyMgGAUFZW1urfEhERkXHoe/3uVO/ElZWVAQDs7e0B3L3LVltbi4CAALHPwIED4erqioyMjBbHaRjjXkOHDoWzszOefPJJnDx5ssVY8vPzodFodPatVCrh4+PT4r71kZGRgfHjx8PCwkJsCwoKQl5eHm7evGnwuNXV1dBqtTofIiIi6po6TRJXX1+PiIgIjBkzBt7e3gAAjUYDCwuLRu+aOTo6Nvs+26lTp7B9+3aEhISIbc7Ozti0aRO++eYbfPPNN3BxccGECRPwww8/NBtPw/iOjo5671tfGo2myXHv3a8h4uLioFQqxY+Li8sDxUlERESdV6dJ4tRqNbKzs7Ft2zaDx8jOzsbUqVMRHR2NwMBAsd3T0xOvvvoqhg8fjtGjR+Pzzz/H6NGjsWbNGgDA1q1bYWVlJX5OnDjxwMfTwMvLSxz36aefbrNxmxIVFYWysjLx8/PPP7fr/oiIiMh4zIwdAACEhYXhwIEDOH78OPr27Su2Ozk5oaamBqWlpTp344qLixu97H/x4kX4+/sjJCQES5Ysue8+R44cifT0dADAlClT4OPjI27r06cPioqKxH05Ozvr7Hvo0KF6H9uhQ4dQW1sLAFAoFOJx3Vth2zBuwzZDyeVyyOVyg39PRERE0mHUO3GCICAsLAy7d+9GSkoKPDw8dLYPHz4c5ubmSE5OFtvy8vJQUFAAX19fsS0nJwcTJ05EcHAwVqxYode+s7KyxOTM2toa/fv3Fz8KhQIeHh5wcnLS2bdWq8WZM2d09n0/bm5u4rh9+vQBAPj6+uL48eNicgcASUlJ8PT0hJ2dnd5jExERUfdl1DtxarUaiYmJ2Lt3L6ytrcX3wZRKJRQKBZRKJebNm4fIyEjY29vDxsYG4eHh8PX1xahRowDcfYTq5+eHoKAgREZGimOYmprCwcEBALB27Vp4eHjAy8sLVVVV2Lx5M1JSUnD06NFmY5PJZIiIiMC7776LAQMGwMPDA0uXLkXv3r0xbdo0sV9BQQFKSkpQUFCAuro6ZGVlAQD69+8PKyurJseeNWsWYmNjMW/ePCxcuBDZ2dlYt26d+HgXuDt1ycWLF8V///LLL8jKyoKVlRX69+9v2AknIiKirqNjimWbBqDJT3x8vNjn9u3bwuuvvy7Y2dkJPXr0EJ599lmhqKhI3B4dHd3kGG5ubmKfVatWCQ8//LBgaWkp2NvbCxMmTBBSUlLuG199fb2wdOlSwdHRUZDL5YK/v7+Ql5en0yc4OLjJ/R87dqzFsc+dOyeMHTtWkMvlQp8+fYSVK1fqbM/Pz29y3CeeeOK+cTfgFCNERETSo+/1WyYIgtCBOSN1IK1WC6VSibKyMtjY2Bg7HCIiItKDvtfvTlOdSkRERET6YxJHREREJEFM4oiIiIgkiEkcERERkQQxiSMiIiKSICZxRERERBLEJI6IiIhIgpjEEREREUkQkzgiIiIiCTLq2qnUMWaGRMDcwqLZ7b17KbHxg1UdGBERERE9KCZx3YDCdxbMFT2b3V6Y/kUHRkNERERtgY9TiYiIiCSISRwRERGRBDGJIyIiIpIgJnFEREREEiQTBEEwdhDUPrRaLZRKJZy9RsHEtPkaFrlJHbwfHdKBkRHpYoU0EdFvGq7fZWVlsLGxabYfq1O7Ad+/Lm+xOpXI2FghTUTUenycSkRERCRBTOKIiIiIJIhJHBEREZEEsbChC9O3sIHI2Fhc07mx8ISoY7GwgUQsbCCiB8HCE6LOiY9TiYiIiCSISRwRERGRBDGJIyIiIpIgvhPXDWR8upSFDV2U3MIM3oM8jR0GdXG9eymNHQIRNYFX9m6AhQ1d1+30L7D3y03GDoOIiIyAj1OJiIiIJIhJHBEREZEEMYkjIiIikiAmcUREREQSxGW3ujAuu9X1cbkqogfDJcWoM+KyWyRidSoRUdO4pBhJGR+nEhEREUkQkzgiIiIiCWISR0RERCRBfCeuG+CyW0TUgEu16eKSYiRlvLJ3AyxsIKIGXKqNqOvg41QiIiIiCWISR0RERCRBTOKIiIiIJIhJHBEREZEEsbChG2B1KpG0tGcFKasxiboOXtm7AVanEkkLK0iJSB9GfZwaFxeHESNGwNraGiqVCtOmTUNeXp5On6qqKqjVajz00EOwsrLCjBkzUFxcLG4/d+4cXnjhBbi4uEChUGDQoEFYt25ds/s8efIkzMzMMHTo0PvGt2vXLgQGBuKhhx6CTCZDVlaWzvaSkhKEh4fD09MTCoUCrq6ueOONN1BWVtbiuKmpqZg6dSqcnZ3Rs2dPDB06FFu3btXp8+mnn2LcuHGws7ODnZ0dAgIC8O233943ZiIiIuoejJrEpaWlQa1W4/Tp00hKSkJtbS0CAwNRUVEh9pk/fz7279+PHTt2IC0tDYWFhZg+fbq4PTMzEyqVCgkJCcjJycHixYsRFRWF9evXN9pfaWkpZs+eDX9/f73iq6iowNixY7Fq1aomtxcWFqKwsBDvv/8+srOzsWXLFhw5cgTz5s1rcdxTp05hyJAh+Oabb3D+/HnMnTsXs2fPxoEDB8Q+qampeOGFF3Ds2DFkZGTAxcUFgYGB+OWXX/SKnYiIiLo2mSAIgrGDaHD9+nWoVCqkpaVh/PjxKCsrg4ODAxITE/Hcc88BAC5duoRBgwYhIyMDo0aNanIctVqN3NxcpKSk6LTPnDkTAwYMgKmpKfbs2dPozlpzrl69Cg8PD5w9e/a+d/B27NiBv/zlL6ioqICZmf5PqydNmgRHR0d8/vnnTW6vq6uDnZ0d1q9fj9mzZzfZp7q6GtXV1eJ3rVYLFxcXTF+bxMepRBLCx6lE3ZtWq4VSqURZWRlsbGya7dep3olreAxpb28P4O5dttraWgQEBIh9Bg4cCFdX1xaTuLKyMnGMBvHx8bhy5QoSEhLw7rvvttMRQDzhrUngGn43aNCgZrdXVlaitra20XHdKy4uDrGxsY3aWdhAJC1ykzpMnR1q7DDoPnr3UmLjB00/qSHqCJ3myl5fX4+IiAiMGTMG3t7eAACNRgMLCwvY2trq9HV0dIRGo2lynFOnTmH79u04ePCg2Hb58mUsWrQIJ06caHVy1Ro3btzA8uXLERIS0qrfff311/juu+/wv//7v832WbhwIXr37q2T0P5eVFQUIiMjxe8Nd+JY2EBE1PYK078wdgjUzXWaeeLUajWys7Oxbds2g8fIzs7G1KlTER0djcDAQAB3H0POmjULsbGxeOSRR5r83datW2FlZSV+Tpw40ep9a7VaTJo0CYMHD0ZMTIzY7uXlJY779NNPN/rdsWPHMHfuXHz66afw8vJqcuyVK1di27Zt2L17NywtLZuNQS6Xw8bGRudDREREXVOnuBMXFhaGAwcO4Pjx4+jbt6/Y7uTkhJqaGpSWlurcjSsuLoaTk5POGBcvXoS/vz9CQkKwZMkSsb28vBzff/89zp49i7CwMAB37/oJggAzMzMcPXoUU6ZMgY+Pj/ibPn36tCr+8vJyPPXUU7C2tsbu3bthbm4ubjt06BBqa2sBAAqFQud3aWlpmDx5MtasWdPse27vv/8+Vq5ciX//+98YMmRIq+IiIiKirsvgJO6nn35CfHw8fvrpJ6xbtw4qlQqHDx+Gq6trs3eUfk8QBISHh2P37t1ITU2Fh4eHzvbhw4fD3NwcycnJmDFjBgAgLy8PBQUF8PX1Ffvl5OTAz88PwcHBWLFihc4YNjY2uHDhgk7bhg0bkJKSgp07d8LDwwM9e/aEtbW1IacBWq0WQUFBkMvl2LdvX6M7ZW5ubk3+LjU1FX/84x+xatWqZh+/vvfee1ixYgX+9a9/4fHHHzcoPiIiIuqaDEri0tLS8PTTT2PMmDE4fvw4VqxYAZVKhXPnzuGzzz7Dzp079RpHrVYjMTERe/fuhbW1tfiem1KphEKhgFKpxLx58xAZGQl7e3vY2NggPDwcvr6+YlFDdnY2/Pz8EBQUhMjISHEMU1NTODg4wMTERHzHroFKpYKlpWWj9t8rKSlBQUEBCgsLAUCcw87JyQlOTk7QarUIDAxEZWUlEhISoNVqodVqAQAODg4wNTVtctxjx47hj3/8I958803MmDFDjNnCwkIsXFi1ahWWLVuGxMREuLu7i30aHs0SERFR92bQFCO+vr7405/+hMjISFhbW+PcuXPo168fvv32W0yfPh3/+c9/9Nu5TNZke3x8PObMmQPg7mS/CxYswFdffYXq6moEBQVhw4YN4uPUmJiYJisy3dzccPXq1SbHj4mJ0WuKkS1btmDu3LmN2qOjoxETE4PU1FRMnDixyd/m5+fD3d29yW1z5szBF180fiH2iSeeQGpqKgDA3d0d165da3bf+mgoUXb2GsXqVCLqEO25ZFhnw+pUai/6TjFiUBJnZWWFCxcuwMPDQyeJu3r1KgYOHIiqqqoHCp7aRsMfAeeJI6KOwjnuiB6cvkmcQdWptra2KCoqatR+9uzZVhcFEBEREVHrGZTEzZw5EwsXLoRGo4FMJkN9fT1OnjyJv/3tb81WWRIRERFR2zEoifvHP/6BgQMHwsXFBbdu3cLgwYMxfvx4jB49Wmd6DyIiIiJqHw+0durPP/+MCxcu4NatWxg2bBgGDBjQlrHRA2JhAxF1NLlJHbwf7Zg5LVlYQF1Vu66d+ve//x1/+9vf4OLiAhcXF7H99u3bWL16NZYtW2bIsNROuOwWEXVFXPaKujuDHqfGxsbi1q1bjdorKyubnO6DiIiIiNqWQUmcIAhNzvF27tw5cbJaIiIiImo/rXqcamdnB5lMBplMhkceeUQnkaurq8OtW7cQGhra5kESERERka5WJXFr166FIAh4+eWXERsbC6VSKW6zsLCAu7u7zpqm1DlkfLqUhQ3U5XWnlQLort69lPfvRNSFterKHhwcDADw8PDA6NGjYW5u3i5BUdtiYQN1B1wpgIi6G4NuzzzxxBPiv6uqqlBTU6OzvaVyWCIiIiJ6cAYVNlRWViIsLAwqlQo9e/aEnZ2dzoeIiIiI2pdBSdxbb72FlJQUbNy4EXK5HJs3b0ZsbCx69+6NL7/8sq1jJCIiIqLfMehx6v79+/Hll19iwoQJmDt3LsaNG4f+/fvDzc0NW7duxYsvvtjWcRIRERHRPQxK4kpKStCvXz8Ad99/KykpAQCMHTsWr732WttFR22C1anSw0rL1mOlIhF1NwZd2fv164f8/Hy4urpi4MCB+PrrrzFy5Ejs378ftra2bRwiPShWp0oPKy2JiOh+DHonbu7cuTh37hwAYNGiRfj4449haWmJ+fPn46233mrTAImIiIioMYPuxM2fP1/8d0BAAC5duoTMzEz0798fQ4YMabPgiIiIiKhpbfKilJubG9zc3NpiKCIiIiLSg0wQBEGfjh9++KHeg77xxhsGB0RtR6vVQqlUwtlrFAsbJEZuUgfvR3lXu6307qXExg9WGTsMIiK9NFy/y8rKWlxAQe8r+5o1a3S+X79+HZWVlWIhQ2lpKXr06AGVSsUkrpNhYQN1d4XpXxg7BCKiNqd3YUN+fr74WbFiBYYOHYrc3FyUlJSgpKQEubm5eOyxx7B8+fL2jJeIiIiIYGB16tKlS/HRRx/B0/O3eaw8PT2xZs0aLFmypM2CIyIiIqKmGZTEFRUV4c6dO43a6+rqUFxc/MBBEREREVHLDEri/P398eqrr+KHH34Q2zIzM/Haa68hICCgzYIjIiIioqYZVLL4+eefIzg4GI8//jjMzc0BAHfu3EFQUBA2b97cpgHSg+OyW9SZdcQSY1ySi4i6Ir2nGGnK5cuXkZubCwAYOHAgHnnkkVb9Pi4uDrt27cKlS5egUCgwevRorFq1Suddu6qqKixYsADbtm1DdXU1goKCsGHDBjg6OgIAzp07h5UrVyI9PR03btyAu7s7QkND8eabbza5z5MnT+KJJ56At7c3srKyWoxv165d2LRpEzIzM1FSUoKzZ89i6NCh4vaSkhJER0fj6NGjKCgogIODA6ZNm4bly5dDqWz+olFVVYXQ0FBkZmYiNzcXf/zjH7Fnzx6dPqmpqZg4cWKj3xYVFcHJyanFuBs0lChPX5vE6lTqtLjEGBGRrjafYqQpAwYMwIABA5rdbmNjg6ysLPTr16/J7WlpaVCr1RgxYgTu3LmDd955B4GBgbh48SJ69rybdMyfPx8HDx7Ejh07oFQqERYWhunTp+PkyZMA7j7GValUSEhIgIuLC06dOoWQkBCYmpoiLCxMZ3+lpaWYPXs2/P399Xp3r6KiAmPHjsWf//xn/PWvf220vbCwEIWFhXj//fcxePBgXLt2DaGhoSgsLMTOnTubHbeurg4KhQJvvPEGvvnmmxZjyMvL0/kPqFKp7hs3ERERdX0PdCfufqytrXHu3Llmk7jfu379OlQqFdLS0jB+/HiUlZXBwcEBiYmJeO655wAAly5dwqBBg5CRkYFRo0Y1OY5arUZubi5SUlJ02mfOnIkBAwbA1NQUe/bsue+duAZXr16Fh4dHoztxTdmxYwf+8pe/oKKiAmZm98+R58yZg9LS0mbvxN28eVOci6+1eCeOpIB34oiIdOl7J86gwob2UlZWBgCwt7cHcPcuW21trU6xxMCBA+Hq6oqMjIwWx2kYo0F8fDyuXLmC6Ojodohcd982NjZ6JXD6GDp0KJydnfHkk0+Kdx+bU11dDa1Wq/MhIiKirqnTvO1eX1+PiIgIjBkzBt7e3gAAjUYDCwuLRneiHB0dodFomhzn1KlT2L59Ow4ePCi2Xb58GYsWLcKJEyfaLLlqyo0bN7B8+XKEhIQ88FjOzs7YtGkTHn/8cVRXV2Pz5s2YMGECzpw5g8cee6zJ38TFxSE2NrZROwsbqDOTm9Rh6uzQdhmby20RUVfWaa7sarUa2dnZSE9PN3iM7OxsTJ06FdHR0QgMDARw9/2zWbNmITY2ttnCi61bt+LVV18Vvx8+fBjjxo1r1b61Wi0mTZqEwYMHIyYmRmz38vLCtWvXAADjxo3D4cOH9RrP09NTp8Bj9OjR+Omnn7BmzRr885//bPI3UVFRiIyM1InJxcWFy25Rt8XltoioK2vXJE4mk+nVLywsDAcOHMDx48fRt29fsd3JyQk1NTUoLS3VuRtXXFzcqELz4sWL8Pf3R0hIiM6qEeXl5fj+++9x9uxZsdChvr4egiDAzMwMR48exZQpU+Dj4yP+pk+fPq06zvLycjz11FOwtrbG7t27xWlXAODQoUOora0FACgUilaN+3sjR45sMcmVy+WQy+UPtA8iIiKShnZN4u5XMyEIAsLDw7F7926kpqbCw8NDZ/vw4cNhbm6O5ORkzJgxA8Ddas2CggL4+vqK/XJycuDn54fg4GCsWLFCZwwbGxtcuHBBp23Dhg1ISUnBzp074eHhgZ49e8La2tqgY9RqtQgKCoJcLse+fftgaWmps93Nzc2gcZuSlZUFZ2fnNhuPiIiIpKtdk7jDhw+3eFdLrVYjMTERe/fuhbW1tfiem1KphEKhgFKpxLx58xAZGQl7e3vY2NggPDwcvr6+YmVqdnY2/Pz8EBQUhMjISHEMU1NTODg4wMTERHzHroFKpYKlpWWj9t8rKSlBQUEBCgsLAdxNIIG7dwidnJyg1WoRGBiIyspKJCQk6BQTODg4wNTUtNmxL168iJqaGpSUlKC8vFyslG2ofl27di08PDzg5eWFqqoqbN68GSkpKTh69GiLMRMREVH3YFASV1dXhy1btiA5ORm//vor6uvrdbY3TO0xduzYFsfZuHEjAGDChAk67fHx8ZgzZw4AYM2aNTAxMcGMGTN0JvttsHPnTly/fh0JCQlISEgQ293c3HD16lVDDk+0b98+zJ07V/w+c+ZMAEB0dDRiYmLwww8/4MyZMwCA/v376/w2Pz8f7u7uzY79zDPPiO/KAcCwYcMA/Hb3sqamBgsWLMAvv/yCHj16YMiQIfj3v//d5ATARERE1P0YNE9cWFgYtmzZgkmTJsHZ2bnRu29r1qxpswDJcA3zzDh7jWJ1KnVpzS3dxepUIpKidl2xYdu2bfj666/xzDPPGBwgdRxWp1JXxwmDiag7MmiyXwsLi0aPD4mIiIio4xiUxC1YsADr1q27b/UpEREREbUPgx6npqen49ixYzh8+DC8vLx05kUDgF27drVJcERERETUNIOSOFtbWzz77LNtHQu1Ey67RV1dey7dRUTdh9SKoQy6ssfHx7d1HNSOWNhARER0f1Jbqu+Bbs9cv35dnADX09MTDg4ObRIUEREREbXMoMKGiooKvPzyy3B2dsb48eMxfvx49O7dG/PmzUNlZWVbx0hEREREv2NQEhcZGYm0tDTs378fpaWlKC0txd69e5GWloYFCxa0dYxERERE9DsGrdjQq1cv7Ny5s9FyWceOHcOf//xnXL9+va3iowfAFRuIiFqvuRVAqOvrLIUN7bpiQ2VlJRwdHRu1q1QqPk7thFjYQESkP64AQlJh0ONUX19fREdHo6qqSmy7ffs2YmNj4evr22bBEREREVHTDLoTt27dOgQFBaFv3774wx/+AAA4d+4cLC0t8a9//atNAyQiIiKixgxK4ry9vXH58mVs3boVly5dAgC88MILePHFF6FQKNo0QCIiIiJqzOC33Xv06IG//vWvbRkLEREREelJ7+rUffv24emnn4a5uTn27dvXYt8pU6a0SXD0YFidSkTUenKTOng/OqRRe2epXKSur82rU6dNmwaNRgOVSoVp06Y1208mk6Gurq5VwVL7YnUqEdGDk9qSTNT16Z3E1dfXN/lvIiIiIup4Bk0x8uWXX6K6urpRe01NDb788ssHDoqIiIiIWmZQEjd37lyUlZU1ai8vL8fcuXMfOCgiIiIiaplBb7sLggCZTNao/T//+Q+USuUDB0VtK+PTpSxsICKDcRmqu3r34vWNOpdWXdmHDRsGmUwGmUwGf39/mJn99vO6ujrk5+fjqaeeavMg6cGwsIGIHgSXoSLqnFqVxDVUpWZlZSEoKAhWVlbiNgsLC7i7u2PGjBltGiARERERNdaqJC46OhoA4O7ujueffx6WlpbtEhQRERERtcygF6WCg4PbOg4iIiIiagWDkri6ujqsWbMGX3/9NQoKClBTU6OzvaSkpE2CIyIiIqKmGZTExcbGYvPmzViwYAGWLFmCxYsX4+rVq9izZw+WLVvW1jHSA2J1KlHn1tmrP1mVSdQ56b126r0efvhhfPjhh5g0aRKsra2RlZUltp0+fRqJiYl6jRMXF4ddu3bh0qVLUCgUGD16NFatWgVPz9/+Z1ZVVYUFCxZg27ZtqK6uRlBQEDZs2ABHR0cAwLlz57By5Uqkp6fjxo0bcHd3R2hoKN58880m93ny5Ek88cQT8Pb2RlZWVovx7dq1C5s2bUJmZiZKSkpw9uxZDB06VNxeUlKC6OhoHD16FAUFBXBwcMC0adOwfPny+061cv78eajVanz33XdwcHBAeHg43n77bZ0+a9euxcaNG1FQUIBevXrhueeeQ1xcnN7vIjasvTZ9bRKrU4k6MVZ/EtG99F071aDJfjUaDR599FEAgJWVlTjx7x//+EccPHhQ73HS0tKgVqtx+vRpJCUloba2FoGBgaioqBD7zJ8/H/v378eOHTuQlpaGwsJCTJ8+XdyemZkJlUqFhIQE5OTkYPHixYiKisL69esb7a+0tBSzZ8+Gv7+/XvFVVFRg7NixWLWq6QWPCwsLUVhYiPfffx/Z2dnYsmULjhw5gnnz5rU4rlarRWBgINzc3JCZmYnVq1cjJiYGn3zyidgnMTERixYtQnR0NHJzc/HZZ59h+/bteOedd/SKnYiIiLo2g56x9e3bF0VFRXB1dcXDDz+Mo0eP4rHHHsN3330HuVyu9zhHjhzR+b5lyxaoVCpkZmZi/PjxKCsrw2effYbExET4+fkBAOLj4zFo0CCcPn0ao0aNwssvv6wzRr9+/ZCRkYFdu3YhLCxMZ1toaChmzZoFU1NT7Nmz577xvfTSSwCAq1evNrnd29sb33zzjfj94YcfxooVK/CXv/wFd+7c0ZlH715bt25FTU0NPv/8c1hYWMDLywtZWVn44IMPEBISAgA4deoUxowZg1mzZgG4WxH8wgsv4MyZM/eNm4iIiLo+g+7EPfvss0hOTgYAhIeHY+nSpRgwYABmz57dKKlqjYY7evb29gDu3mWrra1FQECA2GfgwIFwdXVFRkZGi+M0jNEgPj4eV65cEadJaS8Ntz6bS+AAICMjA+PHj4eFhYXYFhQUhLy8PNy8eRMAMHr0aGRmZuLbb78FAFy5cgWHDh3CM8880+y41dXV0Gq1Oh8iIiLqmgy6E7dy5Urx388//7yYVA0YMACTJ082KJD6+npERERgzJgx8Pb2BnD3sa2FhQVsbW11+jo6OkKj0TQ5zqlTp7B9+3adx7qXL1/GokWLcOLEiRaTqwd148YNLF++XLyb1hyNRgMPDw+dtoZ3/DQaDezs7DBr1izcuHEDY8eOhSAIuHPnDkJDQ1t8nBoXF4fY2NhG7SxsIGpaZykoYOEAERmiTa7svr6+8PX1faAx1Go1srOzkZ6ebvAY2dnZmDp1KqKjoxEYGAjg7nQos2bNQmxsLB555JEmf7d161a8+uqr4vfDhw9j3Lhxrdq3VqvFpEmTMHjwYMTExIjtXl5euHbtGgBg3LhxOHz4sF7jpaam4h//+Ac2bNgAHx8f/Pjjj3jzzTexfPlyLF26tMnfREVFITIyUicmFxcXLrtF1AwWFBCRlBmcxP3zn//Epk2bkJ+fj4yMDLi5uWHt2rXw8PDA1KlTWzVWWFgYDhw4gOPHj6Nv375iu5OTE2pqalBaWqpzN664uBhOTk46Y1y8eBH+/v4ICQnBkiVLxPby8nJ8//33OHv2rPiOXH19PQRBgJmZGY4ePYopU6bAx8dH/E2fPn1aFX95eTmeeuopWFtbY/fu3TA3Nxe3HTp0CLW1tQAAhUIhHldxcbHOGA3fG45r6dKleOmll/DKK68AAB599FFUVFQgJCQEixcvholJ4yfhcrm8Ve8kEhERkXQZ9E7cxo0bERkZiWeeeQalpaWoq6sDANja2mLt2rV6jyMIAsLCwrB7926kpKQ0esQ4fPhwmJubi+/fAUBeXh4KCgp07vzl5ORg4sSJCA4OxooVK3TGsLGxwYULF5CVlSV+QkND4enpiaysLPj4+MDa2hr9+/cXPw3Jlj4aKk0tLCywb9++RtN/uLm5ieM2JIe+vr44fvy4mNwBQFJSEjw9PWFnZwcAqKysbJSomZqaiueNiIiIujeD7sR99NFH+PTTTzFt2jSd9+Mef/xx/O1vf9N7HLVajcTEROzduxfW1tbie25KpRIKhQJKpRLz5s1DZGQk7O3tYWNjg/DwcPj6+mLUqFEA7j5C9fPzQ1BQECIjI8UxTE1N4eDgABMTE/EduwYqlQqWlpaN2n+vpKQEBQUFKCwsBHA3gQTu3i1zcnISE7jKykokJCToFBM4ODiISdfvNTzenTdvHhYuXIjs7GysW7cOa9asEftMnjwZH3zwAYYNGyY+Tl26dCkmT57c7LhERETUfRiUxOXn52PYsGGN2uVyuc4cb/ezceNGAMCECRN02uPj4zFnzhwAwJo1a2BiYoIZM2boTPbbYOfOnbh+/ToSEhKQkJAgtru5uTU7NYi+9u3bh7lz54rfZ86cCQCIjo5GTEwMfvjhB3HKj/79++v8Nj8/H+7u7k2Oq1QqcfToUajVagwfPhy9evXCsmXLdAoilixZAplMhiVLluCXX36Bg4MDJk+e3OhOoz5Y2ED0m3uLGVhQQERSZtCKDYMHD0ZcXBymTp0Ka2trnDt3Dv369cNHH32E+Ph4/PDDD+0RK7USV2wgaozFDETU2em7YoNBt2ciIyOhVqtRVVUFQRDw7bff4quvvkJcXBw2b95scNBEREREpB+DkrhXXnkFCoUCS5YsQWVlJWbNmoXevXtj3bp14iNHIiIiImo/rU7i7ty5g8TERAQFBeHFF19EZWUlbt26BZVK1R7xEREREVETWj3FiJmZGUJDQ1FVVQUA6NGjBxM4IiIiog5m0OPUkSNH4uzZs3Bzc2vreKgdsDqVuipDls1iRSoRdRUGXdlff/11LFiwAP/5z38wfPhw9OypW/k4ZMiQNgmO2gaX3aKuipWmRNSdGZTENRQvvPHGG2KbTCaDIAiQyWTiCg5ERERE1D4MnuyXiIiIiIzHoLVTExMTkZycDDc3N51PcnIytm3b1tYxEhEREdHvGLRig7u7OxITEzF69Gid9jNnzmDmzJm8U9dJNMz47Ow1ioUN1CXJTerg/ai038Ht3UuJjR+sMnYYRNSJtOuKDRqNBs7Ozo3aHRwcUFRUZMiQ1I5Y2EDUeRWmf2HsEIhIogx6nOri4oKTJ082aj958iR69+79wEERERERUcsMuhP317/+FREREaitrYWfnx8AIDk5GW+//TYWLFjQpgESERERUWMGJXFvvfUW/vvf/+L1119HTU0NAMDS0hILFy5EVFRUmwZIRERERI0ZlMTJZDKsWrUKS5cuRW5uLhQKBQYMGAC5XN7W8RERERFREx6oZNHKygojRoxoq1ionXDZLeoIhiyBRVwGjIgMxyt7N8DqVOoIXAKLiKhjGVSdSkRERETGxSSOiIiISIKYxBERERFJkEHLbpE0cNkt6khdYQmsroDLeBFJX7suu0XSwsIGou6Dy3gRdR98nEpEREQkQUziiIiIiCSISRwRERGRBDGJIyIiIpIgFjZ0A1x2i8h4Ono5Mi7jRdR9GPXKHhcXh127duHSpUtQKBQYPXo0Vq1aBU/P3/6HV1VVhQULFmDbtm2orq5GUFAQNmzYAEdHRwDAuXPnsHLlSqSnp+PGjRtwd3dHaGgo3nzzzSb3efLkSTzxxBPw9vZGVlZWi/Ht2rULmzZtQmZmJkpKSnD27FkMHTpU3F5SUoLo6GgcPXoUBQUFcHBwwLRp07B8+XIolS3/j/T8+fNQq9X47rvv4ODggPDwcLz99tvi9pycHCxbtgyZmZm4du0a1qxZg4iIiJZPaDNYnUpkPFyOjIjai1Efp6alpUGtVuP06dNISkpCbW0tAgMDUVFRIfaZP38+9u/fjx07diAtLQ2FhYWYPn26uD0zMxMqlQoJCQnIycnB4sWLERUVhfXr1zfaX2lpKWbPng1/f3+94quoqMDYsWOxalXTcy4VFhaisLAQ77//PrKzs7FlyxYcOXIE8+bNa3FcrVaLwMBAuLm5ITMzE6tXr0ZMTAw++eQTsU9lZSX69euHlStXwsnJSa94iYiIqPvoVJP9Xr9+HSqVCmlpaRg/fjzKysrg4OCAxMREPPfccwCAS5cuYdCgQcjIyMCoUaOaHEetViM3NxcpKSk67TNnzsSAAQNgamqKPXv23PdOXIOrV6/Cw8Oj0Z24puzYsQN/+ctfUFFRATOzpm90bty4EYsXL4ZGo4GFhQUAYNGiRdizZw8uXbrUqL+7uzsiIiJafSeuYbLA6WuTeCeOyEh4J46IWkvfyX47VWFDWVkZAMDe3h7A3btstbW1CAgIEPsMHDgQrq6uyMjIaHGchjEaxMfH48qVK4iOjm6HyHX3bWNj02wCBwAZGRkYP368mMABQFBQEPLy8nDz5k2D911dXQ2tVqvzISIioq6p07ztXl9fj4iICIwZMwbe3t4AIN6psrW11enr6OgIjUbT5DinTp3C9u3bcfDgQbHt8uXLWLRoEU6cONFicvWgbty4geXLlyMkJKTFfhqNBh4eHjptDe/4aTQa2NnZGbT/uLg4xMbGNmpnYQOR8chN6jB1dqixw3hgXM6LqPPpNFd2tVqN7OxspKenGzxGdnY2pk6diujoaAQGBgIA6urqMGvWLMTGxuKRRx5p8ndbt27Fq6++Kn4/fPgwxo0b16p9a7VaTJo0CYMHD0ZMTIzY7uXlhWvXrgEAxo0bh8OHD7fyqPQXFRWFyMhInZhcXFxY2EBED4zLeRF1Pp0iiQsLC8OBAwdw/Phx9O3bV2x3cnJCTU0NSktLde7GFRcXN3rZ/+LFi/D390dISAiWLFkitpeXl+P777/H2bNnERYWBuDuXT9BEGBmZoajR49iypQp8PHxEX/Tp0+fVsVfXl6Op556CtbW1ti9ezfMzc3FbYcOHUJtbS0AQKFQiMdVXFysM0bD9wcpYpDL5ZDL5Qb/noiIiKTDqEmcIAgIDw/H7t27kZqa2ugR4/Dhw2Fubo7k5GTMmDEDAJCXl4eCggL4+vqK/XJycuDn54fg4GCsWLFCZwwbGxtcuHBBp23Dhg1ISUnBzp074eHhgZ49e8La2tqgY9BqtQgKCoJcLse+fftgaWmps93Nza3Rb3x9fbF48WLU1taKCV9SUhI8PT0NfpRKRERE3YtRkzi1Wo3ExETs3bsX1tbW4ntuSqUSCoUCSqUS8+bNQ2RkJOzt7WFjY4Pw8HD4+vqKlanZ2dnw8/NDUFAQIiMjxTFMTU3h4OAAExMT8R27BiqVCpaWlo3af6+kpAQFBQUoLCwEcDeBBO7eLXNychKnCqmsrERCQoJOMYGDgwNMTU2bHLfh8e68efOwcOFCZGdnY926dVizZo3Yp6amBhcvXhT//csvvyArKwtWVlbo379/q84zERERdT1GnWJEJpM12R4fH485c+YA+G2y36+++kpnst+Gx44xMTFNvszv5uaGq1evNjl+TEyMXlOMbNmyBXPnzm3UHh0djZiYGKSmpmLixIlN/jY/Px/u7u7Njn3vZL+9evVCeHg4Fi5cKG5vmNbk95544gmkpqa2GHeDhhJlZ69RLGwgMrKOXrmhrbGwgajj6DvFSKeaJ47aFueJI+o8OF8cEelLkvPEEREREZF+mMQRERERSRCTOCIiIiIJYhJHREREJEEsbOjCWJ1K1HnITerg/eiQDtkXK0mJpE3fwgZe2bsBLrtF1L1wiSyi7oGPU4mIiIgkiEkcERERkQQxiSMiIiKSIBY2dGEsbCDqnporomDBA5E0sLCBRCxsICKABQ9EXQ0fpxIRERFJEJM4IiIiIgliEkdEREQkQUziiIiIiCSIhQ3dQManS1mdStQJyC3M4D3I02j7791LabR9E1Hb45W9G2B1KlHncDv9C+z9cpOxwyCiLoKPU4mIiIgkiEkcERERkQQxiSMiIiKSIL4T1w2wsIHI+OQWZggcN9LYYRBRF8IrezfAwgYi47ud/gXXLSWiNsXHqUREREQSxCSOiIiISIKYxBERERFJkEwQBMHYQVD70Gq1UCqVcPYaxcIGIiOTm9TB+9EhzW7v3UvJd+aICMBv1++ysjLY2Ng0249X9m6AhQ1EnV9h+hfGDoGIJIaPU4mIiIgkiEkcERERkQQxiSMiIiKSICZxRERERBLE6tQujNWpRNJxv+rVzobVtETth9WpJGJ1KhG1NVbTEhkfH6cSERERSRCTOCIiIiIJYhJHREREJEF8J64byPh0KQsbiLoRuYUZvAd5tus+evdStuv4RHR/vLJ3AyxsIOpebqd/gb1fbjJ2GETUzoz6ODUuLg4jRoyAtbU1VCoVpk2bhry8PJ0+VVVVUKvVeOihh2BlZYUZM2aguLhY3H7u3Dm88MILcHFxgUKhwKBBg7Bu3bpm93ny5EmYmZlh6NCh941v165dCAwMxEMPPQSZTIasrKxGfT755BNMmDABNjY2kMlkKC0tve+4+sS8a9cuPPnkk3BwcICNjQ18fX3xr3/9675jExERUfdg1CQuLS0NarUap0+fRlJSEmpraxEYGIiKigqxz/z587F//37s2LEDaWlpKCwsxPTp08XtmZmZUKlUSEhIQE5ODhYvXoyoqCisX7++0f5KS0sxe/Zs+Pv76xVfRUUFxo4di1Wrmp8LqbKyEk899RTeeecdvY9bn5iPHz+OJ598EocOHUJmZiYmTpyIyZMn4+zZs3rvh4iIiLquTjXZ7/Xr16FSqZCWlobx48ejrKwMDg4OSExMxHPPPQcAuHTpEgYNGoSMjAyMGjWqyXHUajVyc3ORkpKi0z5z5kwMGDAApqam2LNnT5N31ppy9epVeHh44OzZs83ewUtNTcXEiRNx8+ZN2Nra6nvI9435Xl5eXnj++eexbNkyvcZsmCxw+tokPk4l6kb4OJVI2vSd7LdTVaeWlZUBAOzt7QHcvWNVW1uLgIAAsc/AgQPh6uqKjIyMFsdpGKNBfHw8rly5gujo6HaI/ME1FfO96uvrUV5e3mKf6upqaLVanQ8RERF1TZ2msKG+vh4REREYM2YMvL29AQAajQYWFhaN7mw5OjpCo9E0Oc6pU6ewfft2HDx4UGy7fPkyFi1ahBMnTsDMrNMcsqipmH/v/fffx61bt/DnP/+52T5xcXGIjY1t1M7qVKLW64gKz/bCylGi7qHTXNnVajWys7ORnp5u8BjZ2dmYOnUqoqOjERgYCACoq6vDrFmzEBsbi0ceeaTJ323duhWvvvqq+P3w4cMYN26cwXHc6+mnn8aJEycAAG5ubsjJyblvzL+XmJiI2NhY7N27FyqVqtl9RUVFITIyUvyu1Wrh4uLC6lQiA/CRJBF1dp0iiQsLC8OBAwdw/Phx9O3bV2x3cnJCTU0NSktLde7GFRcXw8nJSWeMixcvwt/fHyEhIViyZInYXl5eju+//x5nz55FWFgYgLt3/QRBgJmZGY4ePYopU6bAx8dH/E2fPn3a7Ng2b96M27dvAwDMzc31ivle27ZtwyuvvIIdO3boPFZuilwuh1wub5vAiYiIqFMzahInCALCw8Oxe/dupKamwsPDQ2f78OHDYW5ujuTkZMyYMQMAkJeXh4KCAvj6+or9cnJy4Ofnh+DgYKxYsUJnDBsbG1y4cEGnbcOGDUhJScHOnTvh4eGBnj17wtraul2OsbmEsKWYG3z11Vd4+eWXsW3bNkyaNKld4iMiIiJpMmoSp1arkZiYiL1798La2lp8z02pVEKhUECpVGLevHmIjIyEvb09bGxsEB4eDl9fX7EyNTs7G35+fggKCkJkZKQ4hqmpKRwcHGBiYiK+Y9dApVLB0tKyUfvvlZSUoKCgAIWFhQAgzmHn5OQk3gnUaDTQaDT48ccfAQAXLlyAtbU1XF1dmy1CuF/MwN1HqMHBwVi3bh18fHzEPg3nhYiIiLo3o04xIpPJmmyPj4/HnDlzANyd7HfBggX46quvUF1djaCgIGzYsEFMomJiYpp8md/NzQ1Xr15tcvyYmBi9phjZsmUL5s6d26g9OjoaMTExLe7/3mNoav/3i3nChAlIS0tr1Cc4OBhbtmxpMe4GDSXKzl6jWNhA1Epykzp4Pzrkgcbo3UuJjR80P88kEVFT9J1ipFPNE0dti/PEERkXiyOIyBCSnCeOiIiIiPTDJI6IiIhIgpjEEREREUkQkzgiIiIiCWLJYjfAZbeIjLOMFpe/IqL2xCt7N8Blt4hYKUpEXQ8fpxIRERFJEJM4IiIiIgliEkdEREQkQVyxoQvjsltEv2mLZbS6Ky4fRtSx9F2xgVf2boCFDUT0IArTvzB2CETUBD5OJSIiIpIgJnFEREREEsQkjoiIiEiC+E5cN8AVG8hQxljlgDofrjxB1Dnxyt4NsLCBDMVVDoiIOi8+TiUiIiKSICZxRERERBLEJI6IiIhIgpjEEREREUkQl93qwrjsFj0oLlX1YLhcFREZgstukYjVqUTGweWqiKg98XEqERERkQQxiSMiIiKSICZxRERERBLEd+K6AS67RfSbjlxKjMtVEVF74pW9G2BhA9FvuJQYEXUVRn2cGhcXhxEjRsDa2hoqlQrTpk1DXl6eTp+qqiqo1Wo89NBDsLKywowZM1BcXCxuP3fuHF544QW4uLhAoVBg0KBBWLduXbP7PHnyJMzMzDB06ND7xrdr1y4EBgbioYcegkwmQ1ZWVqM+n3zyCSZMmAAbGxvIZDKUlpbqdexvvPEGhg8fDrlc3mQsV69ehUwma/Q5ffq0XuMTERFR12bUJC4tLQ1qtRqnT59GUlISamtrERgYiIqKCrHP/PnzsX//fuzYsQNpaWkoLCzE9OnTxe2ZmZlQqVRISEhATk4OFi9ejKioKKxfv77R/kpLSzF79mz4+/vrFV9FRQXGjh2LVauan+epsrISTz31FN55551WHPldL7/8Mp5//vkW+/z73/9GUVGR+Bk+fHir90NERERdj1Efpx45ckTn+5YtW6BSqZCZmYnx48ejrKwMn332GRITE+Hn5wcAiI+Px6BBg3D69GmMGjUKL7/8ss4Y/fr1Q0ZGBnbt2oWwsDCdbaGhoZg1axZMTU2xZ8+e+8b30ksvAbh7V6w5ERERAIDU1NT7jnevDz/8EABw/fp1nD9/vtl+Dz30EJycnFo1NhEREXV9nao6taysDABgb28P4O5dttraWgQEBIh9Bg4cCFdXV2RkZLQ4TsMYDeLj43HlyhVER0e3Q+TtZ8qUKVCpVBg7diz27dvXYt/q6mpotVqdDxEREXVNnaawob6+HhERERgzZgy8vb0BABqNBhYWFrC1tdXp6+joCI1G0+Q4p06dwvbt23Hw4EGx7fLly1i0aBFOnDgBM7NOc8gtsrKywv/8z/9gzJgxMDExwTfffINp06Zhz549mDJlSpO/iYuLQ2xsbKN2VqcS/UZuUoeps0Nb9Rsun0VEnVGnubKr1WpkZ2cjPT3d4DGys7MxdepUREdHIzAwEABQV1eHWbNmITY2Fo888kiTv9u6dSteffVV8fvhw4cxbtw4g+O419NPP40TJ04AANzc3JCTk6PX73r16oXIyEjx+4gRI1BYWIjVq1c3m8RFRUXp/Ear1cLFxYXVqUQPiMtnEVFn1CmSuLCwMBw4cADHjx9H3759xXYnJyfU1NSgtLRU525ccXFxo/fELl68CH9/f4SEhGDJkiVie3l5Ob7//nucPXtWfEeuvr4egiDAzMwMR48exZQpU+Dj4yP+pk+fPm12bJs3b8bt27cBAObm5g80lo+PD5KSkprdLpfLIZfLH2gfREREJA1GTeIEQUB4eDh2796N1NRUeHh46GwfPnw4zM3NkZycjBkzZgAA8vLyUFBQAF9fX7FfTk4O/Pz8EBwcjBUrVuiMYWNjgwsXLui0bdiwASkpKdi5cyc8PDzQs2dPWFtbt8sxtmVCmJWVBWdn5zYbj4iIiKTLqEmcWq1GYmIi9u7dC2tra/E9N6VSCYVCAaVSiXnz5iEyMhL29vawsbFBeHg4fH19MWrUKAB3H6H6+fkhKCgIkZGR4himpqZwcHCAiYmJ+I5dA5VKBUtLy0btv1dSUoKCggIUFhYCgDiHnZOTk3gnUKPRQKPR4McffwQAXLhwAdbW1nB1dW1UXHGvH3/8Ebdu3YJGo8Ht27fFOegGDx4MCwsLfPHFF7CwsMCwYcMA3J2z7vPPP8fmzZv1Pr9ERETUdRk1idu4cSMAYMKECTrt8fHxmDNnDgBgzZo1MDExwYwZM1BdXY2goCBs2LBB7Ltz505cv34dCQkJSEhIENvd3NxanBpEH/v27cPcuXPF7zNnzgQAREdHIyYmBgCwadMmnWKC8ePHNzqGprzyyitIS0sTvzcka/n5+XB3dwcALF++HNeuXYOZmRkGDhyI7du347nnnmv1cbCwoevqyCWkujMun0VEnZFMEATB2EFQ+9BqtVAqlZi+NomFDV0Ul5AiIup6Gq7fZWVlsLGxabZfp5onjoiIiIj0wySOiIiISIKYxBERERFJEJM4IiIiIgliYUMX1vBipLPXKFandlFykzoEThzHJaGIiLoQfQsbeGXvBrjsVtfGJaGIiLonPk4lIiIikiAmcUREREQSxCSOiIiISIL4Tlw3cDsjEXcsLIwdBrUTLglFRNQ9MYnrBrZ9srbF6hYiIiKSHj5OJSIiIpIgJnFEREREEsQkjoiIiEiCmMQRERERSRCTOCIiIiIJYhJHREREJEFM4oiIiIgkiEkcERERkQQxiSMiIiKSICZxRERERBLEJI6IiIhIgpjEEREREUkQkzgiIiIiCWISR0RERCRBZsYOgNqPIAgAAK1Wa+RIiIiISF8N1+2G63hzmMR1Yf/9738BAC4uLkaOhIiIiFqrvLwcSqWy2e1M4rowe3t7AEBBQUGLfwRkOK1WCxcXF/z888+wsbExdjhdFs9z++M57hg8z+2vK5xjQRBQXl6O3r17t9iPSVwXZmJy95VHpVIp2T9kqbCxseE57gA8z+2P57hj8Dy3P6mfY31uvrCwgYiIiEiCmMQRERERSRCTuC5MLpcjOjoacrnc2KF0WTzHHYPnuf3xHHcMnuf2153OsUy4X/0qEREREXU6vBNHREREJEFM4oiIiIgkiEkcERERkQQxiSMiIiKSICZxXdTHH38Md3d3WFpawsfHB99++62xQ+q0jh8/jsmTJ6N3796QyWTYs2ePznZBELBs2TI4OztDoVAgICAAly9f1ulTUlKCF198ETY2NrC1tcW8efNw69YtnT7nz5/HuHHjYGlpCRcXF7z33nvtfWidRlxcHEaMGAFra2uoVCpMmzYNeXl5On2qqqqgVqvx0EMPwcrKCjNmzEBxcbFOn4KCAkyaNAk9evSASqXCW2+9hTt37uj0SU1NxWOPPQa5XI7+/ftjy5Yt7X14ncbGjRsxZMgQcZJTX19fHD58WNzOc9z2Vq5cCZlMhoiICLGN5/nBxcTEQCaT6XwGDhwobuc5/v8E6nK2bdsmWFhYCJ9//rmQk5Mj/PWvfxVsbW2F4uJiY4fWKR06dEhYvHixsGvXLgGAsHv3bp3tK1euFJRKpbBnzx7h3LlzwpQpUwQPDw/h9u3bYp+nnnpK+MMf/iCcPn1aOHHihNC/f3/hhRdeELeXlZUJjo6OwosvvihkZ2cLX331laBQKIT//d//7ajDNKqgoCAhPj5eyM7OFrKysoRnnnlGcHV1FW7duiX2CQ0NFVxcXITk5GTh+++/F0aNGiWMHj1a3H7nzh3B29tbCAgIEM6ePSscOnRI6NWrlxAVFSX2uXLlitCjRw8hMjJSuHjxovDRRx8JpqamwpEjRzr0eI1l3759wsGDB4X/+7//E/Ly8oR33nlHMDc3F7KzswVB4Dlua99++63g7u4uDBkyRHjzzTfFdp7nBxcdHS14eXkJRUVF4uf69evidp7ju5jEdUEjR44U1Gq1+L2urk7o3bu3EBcXZ8SopOH3SVx9fb3g5OQkrF69WmwrLS0V5HK58NVXXwmCIAgXL14UAAjfffed2Ofw4cOCTCYTfvnlF0EQBGHDhg2CnZ2dUF1dLfZZuHCh4Onp2c5H1Dn9+uuvAgAhLS1NEIS759Tc3FzYsWOH2Cc3N1cAIGRkZAiCcDfZNjExETQajdhn48aNgo2NjXhe3377bcHLy0tnX88//7wQFBTU3ofUadnZ2QmbN2/mOW5j5eXlwoABA4SkpCThiSeeEJM4nue2ER0dLfzhD39ochvP8W/4OLWLqampQWZmJgICAsQ2ExMTBAQEICMjw4iRSVN+fj40Go3O+VQqlfDx8RHPZ0ZGBmxtbfH444+LfQICAmBiYoIzZ86IfcaPHw8LCwuxT1BQEPLy8nDz5s0OOprOo6ysDABgb28PAMjMzERtba3OeR44cCBcXV11zvOjjz4KR0dHsU9QUBC0Wi1ycnLEPveO0dCnO/7t19XVYdu2baioqICvry/PcRtTq9WYNGlSo3PB89x2Ll++jN69e6Nfv3548cUXUVBQAIDn+F5M4rqYGzduoK6uTucPFwAcHR2h0WiMFJV0NZyzls6nRqOBSqXS2W5mZgZ7e3udPk2Nce8+uov6+npERERgzJgx8Pb2BnD3HFhYWMDW1lan7+/P8/3OYXN9tFotbt++3R6H0+lcuHABVlZWkMvlCA0Nxe7duzF48GCe4za0bds2/PDDD4iLi2u0jee5bfj4+GDLli04cuQINm7ciPz8fIwbNw7l5eU8x/cwM3YARNS9qNVqZGdnIz093dihdEmenp7IyspCWVkZdu7cieDgYKSlpRk7rC7j559/xptvvomkpCRYWloaO5wu6+mnnxb/PWTIEPj4+MDNzQ1ff/01FAqFESPrXHgnrovp1asXTE1NG1XpFBcXw8nJyUhRSVfDOWvpfDo5OeHXX3/V2X7nzh2UlJTo9GlqjHv30R2EhYXhwIEDOHbsGPr27Su2Ozk5oaamBqWlpTr9f3+e73cOm+tjY2PTbf7Hb2Fhgf79+2P48OGIi4vDH/7wB6xbt47nuI1kZmbi119/xWOPPQYzMzOYmZkhLS0NH374IczMzODo6Mjz3A5sbW3xyCOP4Mcff+Tf8j2YxHUxFhYWGD58OJKTk8W2+vp6JCcnw9fX14iRSZOHhwecnJx0zqdWq8WZM2fE8+nr64vS0lJkZmaKfVJSUlBfXw8fHx+xz/Hjx1FbWyv2SUpKgqenJ+zs7DroaIxHEASEhYVh9+7dSElJgYeHh8724cOHw9zcXOc85+XloaCgQOc8X7hwQSdhTkpKgo2NDQYPHiz2uXeMhj7d+W+/vr4e1dXVPMdtxN/fHxcuXEBWVpb4efzxx/Hiiy+K/+Z5bnu3bt3CTz/9BGdnZ/4t38vYlRXU9rZt2ybI5XJhy5YtwsWLF4WQkBDB1tZWp0qHflNeXi6cPXtWOHv2rABA+OCDD4SzZ88K165dEwTh7hQjtra2wt69e4Xz588LU6dObXKKkWHDhglnzpwR0tPThQEDBuhMMVJaWio4OjoKL730kpCdnS1s27ZN6NGjR7eZYuS1114TlEqlkJqaqjNlQGVlpdgnNDRUcHV1FVJSUoTvv/9e8PX1FXx9fcXtDVMGBAYGCllZWcKRI0cEBweHJqcMeOutt4Tc3Fzh448/ltyUAQ9i0aJFQlpampCfny+cP39eWLRokSCTyYSjR48KgsBz3F7urU4VBJ7ntrBgwQIhNTVVyM/PF06ePCkEBAQIvXr1En799VdBEHiOGzCJ66I++ugjwdXVVbCwsBBGjhwpnD592tghdVrHjh0TADT6BAcHC4Jwd5qRpUuXCo6OjoJcLhf8/f2FvLw8nTH++9//Ci+88IJgZWUl2NjYCHPnzhXKy8t1+pw7d04YO3asIJfLhT59+ggrV67sqEM0uqbOLwAhPj5e7HP79m3h9ddfF+zs7IQePXoIzz77rFBUVKQzztWrV4Wnn35aUCgUQq9evYQFCxYItbW1On2OHTsmDB06VLCwsBD69euns4+u7uWXXxbc3NwECwsLwcHBQfD39xcTOEHgOW4vv0/ieJ4f3PPPPy84OzsLFhYWQp8+fYTnn39e+PHHH8XtPMd3yQRBEIxzD5CIiIiIDMV34oiIiIgkiEkcERERkQQxiSMiIiKSICZxRERERBLEJI6IiIhIgpjEEREREUkQkzgiIiIiCWISR0RERCRBTOKIiIiIJIhJHBFRB9NoNAgPD0e/fv0gl8vh4uKCyZMnN1qMu73JZDLs2bOnQ/dJRG3HzNgBEBF1J1evXsWYMWNga2uL1atX49FHH0VtbS3+9a9/Qa1W49KlS8YOkYgkgmunEhF1oGeeeQbnz59HXl4eevbsqbOttLQUtra2KCgoQHh4OJKTk2FiYoKnnnoKH330ERwdHQEAc+bMQWlpqc5dtIiICGRlZSE1NRUAMGHCBAwZMgSWlpbYvHkzLCwsEBoaipiYGACAu7s7rl27Jv7ezc0NV69ebc9DJ6I2xsepREQdpKSkBEeOHIFarW6UwAGAra0t6uvrMXXqVJSUlCAtLQ1JSUm4cuUKnn/++Vbv74svvkDPnj1x5swZvPfee/j73/+OpKQkAMB3330HAIiPj0dRUZH4nYikg49TiYg6yI8//ghBEDBw4MBm+yQnJ+PChQvIz8+Hi4sLAODLL7+El5cXvvvuO4wYMULv/Q0ZMgTR0dEAgAEDBmD9+vVITk7Gk08+CQcHBwB3E0cnJ6cHOCoiMhbeiSMi6iD6vL2Sm5sLFxcXMYEDgMGDB8PW1ha5ubmt2t+QIUN0vjs7O+PXX39t1RhE1HkxiSMi6iADBgyATCZ74OIFExOTRglhbW1to37m5uY632UyGerr6x9o30TUeTCJIyLqIPb29ggKCsLHH3+MioqKRttLS0sxaNAg/Pzzz/j555/F9osXL6K0tBSDBw8GADg4OKCoqEjnt1lZWa2Ox9zcHHV1da3+HRF1DkziiIg60Mcff4y6ujqMHDkS33zzDS5fvozc3Fx8+OGH8PX1RUBAAB599FG8+OKL+OGHH/Dtt99i9uzZeOKJJ/D4448DAPz8/PD999/jyy+/xOXLlxEdHY3s7OxWx+Lu7o7k5GRoNBrcvHmzrQ+ViNoZkzgiog7Ur18//PDDD5g4cSIWLFgAb29vPPnkk0hOTsbGjRshk8mwd+9e2NnZYfz48QgICEC/fv2wfft2cYygoCAsXboUb7/9NkaMGIHy8nLMnj271bH8z//8D5KSkuDi4oJhw4a15WESUQfgPHFEREREEsQ7cUREREQSxCSOiIiISIKYxBERERFJEJM4IiIiIgliEkdEREQkQUziiIiIiCSISRwRERGRBDGJIyIiIpIgJnFEREREEsQkjoiIiEiCmMQRERERSdD/A8I6pUlDBEsoAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.histplot(markets_df, y=\"creation_date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "cutoff_date1 = \"2024-12-28\"\n",
    "cutoff_date2 = \"2025-01-08\"\n",
    "timestamp1 = pd.Timestamp(\n",
    "datetime.strptime(cutoff_date1, \"%Y-%m-%d\")\n",
    ").tz_localize(\"UTC\")\n",
    "timestamp2 = pd.Timestamp(\n",
    "datetime.strptime(cutoff_date2, \"%Y-%m-%d\")\n",
    ").tz_localize(\"UTC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_data = markets_df.loc[(markets_df[\"creation_timestamp\"]>=timestamp1) & (markets_df[\"creation_timestamp\"]<=timestamp2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "creation_date\n",
       "2025-01-07    194\n",
       "2024-12-28    106\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data.creation_date.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Count', ylabel='creation_date'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(missing_data, y=\"creation_date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "creation_date\n",
       "2025-01-11    5275\n",
       "2025-01-12    3919\n",
       "2025-01-10    3711\n",
       "2025-01-09    2517\n",
       "2025-01-08    2231\n",
       "2025-01-13    1124\n",
       "2025-01-07     194\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "markets_df.loc[markets_df[\"creation_timestamp\"]>timestamp2].creation_date.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_traders[\"month_year_week\"] =(\n",
    "        pd.to_datetime(filtered_traders[\"creation_timestamp\"]).dt.to_period(\"W\").dt.strftime(\"%b-%d-%Y\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['collateralAmount', 'collateralAmountUSD', 'collateralToken',\n",
       "       'creationTimestamp', 'trader_address', 'feeAmount', 'id',\n",
       "       'oldOutcomeTokenMarginalPrice', 'outcomeIndex',\n",
       "       'outcomeTokenMarginalPrice', 'outcomeTokensTraded', 'title',\n",
       "       'transactionHash', 'type', 'market_creator',\n",
       "       'fpmm.answerFinalizedTimestamp', 'fpmm.arbitrationOccurred',\n",
       "       'fpmm.currentAnswer', 'fpmm.id', 'fpmm.isPendingArbitration',\n",
       "       'fpmm.openingTimestamp', 'fpmm.outcomes', 'fpmm.title',\n",
       "       'fpmm.condition.id'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "markets_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(markets.id.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>currentAnswer</th>\n",
       "      <th>id</th>\n",
       "      <th>title</th>\n",
       "      <th>market_creator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>no</td>\n",
       "      <td>0x0017cd58d6a7ee1451388c7d5b1051b4c0a041f5</td>\n",
       "      <td>Will the first floating offshore wind research...</td>\n",
       "      <td>quickstart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>no</td>\n",
       "      <td>0x0020d13c89140b47e10db54cbd53852b90bc1391</td>\n",
       "      <td>Will the Francis Scott Key Bridge in Baltimore...</td>\n",
       "      <td>quickstart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>no</td>\n",
       "      <td>0x003ae5e007cc38b3f86b0ed7c82f938a1285ac07</td>\n",
       "      <td>Will FC Saarbrucken reach the final of the Ger...</td>\n",
       "      <td>quickstart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>yes</td>\n",
       "      <td>0x004c8d4c619dc6b9caa940f5ea7ef699ae85359c</td>\n",
       "      <td>Will the pro-life activists convicted for 'con...</td>\n",
       "      <td>quickstart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>yes</td>\n",
       "      <td>0x005e3f7a90585acbec807425a750fbba1d0c2b5c</td>\n",
       "      <td>Will Apple announce the release of a new M4 ch...</td>\n",
       "      <td>quickstart</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  currentAnswer                                          id  \\\n",
       "0            no  0x0017cd58d6a7ee1451388c7d5b1051b4c0a041f5   \n",
       "1            no  0x0020d13c89140b47e10db54cbd53852b90bc1391   \n",
       "2            no  0x003ae5e007cc38b3f86b0ed7c82f938a1285ac07   \n",
       "3           yes  0x004c8d4c619dc6b9caa940f5ea7ef699ae85359c   \n",
       "4           yes  0x005e3f7a90585acbec807425a750fbba1d0c2b5c   \n",
       "\n",
       "                                               title market_creator  \n",
       "0  Will the first floating offshore wind research...     quickstart  \n",
       "1  Will the Francis Scott Key Bridge in Baltimore...     quickstart  \n",
       "2  Will FC Saarbrucken reach the final of the Ger...     quickstart  \n",
       "3  Will the pro-life activists convicted for 'con...     quickstart  \n",
       "4  Will Apple announce the release of a new M4 ch...     quickstart  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "markets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "trades = pd.read_parquet(\"../data/fpmmTrades.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>collateralAmount</th>\n",
       "      <th>collateralAmountUSD</th>\n",
       "      <th>collateralToken</th>\n",
       "      <th>creationTimestamp</th>\n",
       "      <th>trader_address</th>\n",
       "      <th>feeAmount</th>\n",
       "      <th>id</th>\n",
       "      <th>oldOutcomeTokenMarginalPrice</th>\n",
       "      <th>outcomeIndex</th>\n",
       "      <th>outcomeTokenMarginalPrice</th>\n",
       "      <th>...</th>\n",
       "      <th>market_creator</th>\n",
       "      <th>fpmm.answerFinalizedTimestamp</th>\n",
       "      <th>fpmm.arbitrationOccurred</th>\n",
       "      <th>fpmm.currentAnswer</th>\n",
       "      <th>fpmm.id</th>\n",
       "      <th>fpmm.isPendingArbitration</th>\n",
       "      <th>fpmm.openingTimestamp</th>\n",
       "      <th>fpmm.outcomes</th>\n",
       "      <th>fpmm.title</th>\n",
       "      <th>fpmm.condition.id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>930596765045617408</td>\n",
       "      <td>0.9305977993411753386434828033666473</td>\n",
       "      <td>0xe91d153e0b41518a2ce8dd3d7944fa863463a97d</td>\n",
       "      <td>1728596605</td>\n",
       "      <td>0x01274796ce41aa8e8312e05a427ffb4b0d2148f6</td>\n",
       "      <td>9305967650456174</td>\n",
       "      <td>0x007068173910cf8719b6f2e66a18b6825c9dde820x01...</td>\n",
       "      <td>0.5581119797629801968338361802418564</td>\n",
       "      <td>0</td>\n",
       "      <td>0.611825749650855211231211687533889</td>\n",
       "      <td>...</td>\n",
       "      <td>quickstart</td>\n",
       "      <td>1728822710</td>\n",
       "      <td>False</td>\n",
       "      <td>0x00000000000000000000000000000000000000000000...</td>\n",
       "      <td>0x007068173910cf8719b6f2e66a18b6825c9dde82</td>\n",
       "      <td>False</td>\n",
       "      <td>1728691200</td>\n",
       "      <td>[Yes, No]</td>\n",
       "      <td>Will the emergency public warning tests planne...</td>\n",
       "      <td>0xa610166e379c42404bd27bf12a16119fdb5171990c3e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1033247234796193800</td>\n",
       "      <td>1.033250126003339493791032993674525</td>\n",
       "      <td>0xe91d153e0b41518a2ce8dd3d7944fa863463a97d</td>\n",
       "      <td>1728505575</td>\n",
       "      <td>0x034c4ad84f7ac6638bf19300d5bbe7d9b981e736</td>\n",
       "      <td>10332472347961938</td>\n",
       "      <td>0x007068173910cf8719b6f2e66a18b6825c9dde820x03...</td>\n",
       "      <td>0.6602089902983034451244461308090707</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7034159692833852946883644485233207</td>\n",
       "      <td>...</td>\n",
       "      <td>quickstart</td>\n",
       "      <td>1728822710</td>\n",
       "      <td>False</td>\n",
       "      <td>0x00000000000000000000000000000000000000000000...</td>\n",
       "      <td>0x007068173910cf8719b6f2e66a18b6825c9dde82</td>\n",
       "      <td>False</td>\n",
       "      <td>1728691200</td>\n",
       "      <td>[Yes, No]</td>\n",
       "      <td>Will the emergency public warning tests planne...</td>\n",
       "      <td>0xa610166e379c42404bd27bf12a16119fdb5171990c3e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1206692368842898300</td>\n",
       "      <td>1.206691596248187968367063717078884</td>\n",
       "      <td>0xe91d153e0b41518a2ce8dd3d7944fa863463a97d</td>\n",
       "      <td>1728562895</td>\n",
       "      <td>0x05e8bbdb89c84a14d05194bbbae81caf2340db72</td>\n",
       "      <td>12066923688428983</td>\n",
       "      <td>0x007068173910cf8719b6f2e66a18b6825c9dde820x05...</td>\n",
       "      <td>0.1931459183043721864309864210684546</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3033804066591317111055858533563476</td>\n",
       "      <td>...</td>\n",
       "      <td>quickstart</td>\n",
       "      <td>1728822710</td>\n",
       "      <td>False</td>\n",
       "      <td>0x00000000000000000000000000000000000000000000...</td>\n",
       "      <td>0x007068173910cf8719b6f2e66a18b6825c9dde82</td>\n",
       "      <td>False</td>\n",
       "      <td>1728691200</td>\n",
       "      <td>[Yes, No]</td>\n",
       "      <td>Will the emergency public warning tests planne...</td>\n",
       "      <td>0xa610166e379c42404bd27bf12a16119fdb5171990c3e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>930598203274544384</td>\n",
       "      <td>0.9305992375717008091217928729793422</td>\n",
       "      <td>0xe91d153e0b41518a2ce8dd3d7944fa863463a97d</td>\n",
       "      <td>1728596645</td>\n",
       "      <td>0x17c17ca981b7e244d0bad80b632a082dc1db36e5</td>\n",
       "      <td>9305982032745443</td>\n",
       "      <td>0x007068173910cf8719b6f2e66a18b6825c9dde820x17...</td>\n",
       "      <td>0.611825749650855211231211687533889</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6579972404391247884756597316198778</td>\n",
       "      <td>...</td>\n",
       "      <td>quickstart</td>\n",
       "      <td>1728822710</td>\n",
       "      <td>False</td>\n",
       "      <td>0x00000000000000000000000000000000000000000000...</td>\n",
       "      <td>0x007068173910cf8719b6f2e66a18b6825c9dde82</td>\n",
       "      <td>False</td>\n",
       "      <td>1728691200</td>\n",
       "      <td>[Yes, No]</td>\n",
       "      <td>Will the emergency public warning tests planne...</td>\n",
       "      <td>0xa610166e379c42404bd27bf12a16119fdb5171990c3e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1798695965102918400</td>\n",
       "      <td>1.798696795931342313936125782275225</td>\n",
       "      <td>0xe91d153e0b41518a2ce8dd3d7944fa863463a97d</td>\n",
       "      <td>1728337780</td>\n",
       "      <td>0x1d942103400c1f1657dcbffd5e08904787ea936b</td>\n",
       "      <td>17986959651029184</td>\n",
       "      <td>0x007068173910cf8719b6f2e66a18b6825c9dde820x1d...</td>\n",
       "      <td>0.7636157369419787681755577286755703</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8080447772492735383356100969932859</td>\n",
       "      <td>...</td>\n",
       "      <td>quickstart</td>\n",
       "      <td>1728822710</td>\n",
       "      <td>False</td>\n",
       "      <td>0x00000000000000000000000000000000000000000000...</td>\n",
       "      <td>0x007068173910cf8719b6f2e66a18b6825c9dde82</td>\n",
       "      <td>False</td>\n",
       "      <td>1728691200</td>\n",
       "      <td>[Yes, No]</td>\n",
       "      <td>Will the emergency public warning tests planne...</td>\n",
       "      <td>0xa610166e379c42404bd27bf12a16119fdb5171990c3e...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      collateralAmount                   collateralAmountUSD  \\\n",
       "0   930596765045617408  0.9305977993411753386434828033666473   \n",
       "1  1033247234796193800   1.033250126003339493791032993674525   \n",
       "2  1206692368842898300   1.206691596248187968367063717078884   \n",
       "3   930598203274544384  0.9305992375717008091217928729793422   \n",
       "4  1798695965102918400   1.798696795931342313936125782275225   \n",
       "\n",
       "                              collateralToken creationTimestamp  \\\n",
       "0  0xe91d153e0b41518a2ce8dd3d7944fa863463a97d        1728596605   \n",
       "1  0xe91d153e0b41518a2ce8dd3d7944fa863463a97d        1728505575   \n",
       "2  0xe91d153e0b41518a2ce8dd3d7944fa863463a97d        1728562895   \n",
       "3  0xe91d153e0b41518a2ce8dd3d7944fa863463a97d        1728596645   \n",
       "4  0xe91d153e0b41518a2ce8dd3d7944fa863463a97d        1728337780   \n",
       "\n",
       "                               trader_address          feeAmount  \\\n",
       "0  0x01274796ce41aa8e8312e05a427ffb4b0d2148f6   9305967650456174   \n",
       "1  0x034c4ad84f7ac6638bf19300d5bbe7d9b981e736  10332472347961938   \n",
       "2  0x05e8bbdb89c84a14d05194bbbae81caf2340db72  12066923688428983   \n",
       "3  0x17c17ca981b7e244d0bad80b632a082dc1db36e5   9305982032745443   \n",
       "4  0x1d942103400c1f1657dcbffd5e08904787ea936b  17986959651029184   \n",
       "\n",
       "                                                  id  \\\n",
       "0  0x007068173910cf8719b6f2e66a18b6825c9dde820x01...   \n",
       "1  0x007068173910cf8719b6f2e66a18b6825c9dde820x03...   \n",
       "2  0x007068173910cf8719b6f2e66a18b6825c9dde820x05...   \n",
       "3  0x007068173910cf8719b6f2e66a18b6825c9dde820x17...   \n",
       "4  0x007068173910cf8719b6f2e66a18b6825c9dde820x1d...   \n",
       "\n",
       "           oldOutcomeTokenMarginalPrice outcomeIndex  \\\n",
       "0  0.5581119797629801968338361802418564            0   \n",
       "1  0.6602089902983034451244461308090707            0   \n",
       "2  0.1931459183043721864309864210684546            1   \n",
       "3   0.611825749650855211231211687533889            0   \n",
       "4  0.7636157369419787681755577286755703            0   \n",
       "\n",
       "              outcomeTokenMarginalPrice  ... market_creator  \\\n",
       "0   0.611825749650855211231211687533889  ...     quickstart   \n",
       "1  0.7034159692833852946883644485233207  ...     quickstart   \n",
       "2  0.3033804066591317111055858533563476  ...     quickstart   \n",
       "3  0.6579972404391247884756597316198778  ...     quickstart   \n",
       "4  0.8080447772492735383356100969932859  ...     quickstart   \n",
       "\n",
       "  fpmm.answerFinalizedTimestamp fpmm.arbitrationOccurred  \\\n",
       "0                    1728822710                    False   \n",
       "1                    1728822710                    False   \n",
       "2                    1728822710                    False   \n",
       "3                    1728822710                    False   \n",
       "4                    1728822710                    False   \n",
       "\n",
       "                                  fpmm.currentAnswer  \\\n",
       "0  0x00000000000000000000000000000000000000000000...   \n",
       "1  0x00000000000000000000000000000000000000000000...   \n",
       "2  0x00000000000000000000000000000000000000000000...   \n",
       "3  0x00000000000000000000000000000000000000000000...   \n",
       "4  0x00000000000000000000000000000000000000000000...   \n",
       "\n",
       "                                      fpmm.id fpmm.isPendingArbitration  \\\n",
       "0  0x007068173910cf8719b6f2e66a18b6825c9dde82                     False   \n",
       "1  0x007068173910cf8719b6f2e66a18b6825c9dde82                     False   \n",
       "2  0x007068173910cf8719b6f2e66a18b6825c9dde82                     False   \n",
       "3  0x007068173910cf8719b6f2e66a18b6825c9dde82                     False   \n",
       "4  0x007068173910cf8719b6f2e66a18b6825c9dde82                     False   \n",
       "\n",
       "   fpmm.openingTimestamp fpmm.outcomes  \\\n",
       "0             1728691200     [Yes, No]   \n",
       "1             1728691200     [Yes, No]   \n",
       "2             1728691200     [Yes, No]   \n",
       "3             1728691200     [Yes, No]   \n",
       "4             1728691200     [Yes, No]   \n",
       "\n",
       "                                          fpmm.title  \\\n",
       "0  Will the emergency public warning tests planne...   \n",
       "1  Will the emergency public warning tests planne...   \n",
       "2  Will the emergency public warning tests planne...   \n",
       "3  Will the emergency public warning tests planne...   \n",
       "4  Will the emergency public warning tests planne...   \n",
       "\n",
       "                                   fpmm.condition.id  \n",
       "0  0xa610166e379c42404bd27bf12a16119fdb5171990c3e...  \n",
       "1  0xa610166e379c42404bd27bf12a16119fdb5171990c3e...  \n",
       "2  0xa610166e379c42404bd27bf12a16119fdb5171990c3e...  \n",
       "3  0xa610166e379c42404bd27bf12a16119fdb5171990c3e...  \n",
       "4  0xa610166e379c42404bd27bf12a16119fdb5171990c3e...  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trades.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 26835 entries, 0 to 26834\n",
      "Data columns (total 24 columns):\n",
      " #   Column                         Non-Null Count  Dtype \n",
      "---  ------                         --------------  ----- \n",
      " 0   collateralAmount               26835 non-null  object\n",
      " 1   collateralAmountUSD            26835 non-null  object\n",
      " 2   collateralToken                26835 non-null  object\n",
      " 3   creationTimestamp              26835 non-null  object\n",
      " 4   trader_address                 26835 non-null  object\n",
      " 5   feeAmount                      26835 non-null  object\n",
      " 6   id                             26835 non-null  object\n",
      " 7   oldOutcomeTokenMarginalPrice   26835 non-null  object\n",
      " 8   outcomeIndex                   26835 non-null  object\n",
      " 9   outcomeTokenMarginalPrice      26835 non-null  object\n",
      " 10  outcomeTokensTraded            26835 non-null  object\n",
      " 11  title                          26835 non-null  object\n",
      " 12  transactionHash                26835 non-null  object\n",
      " 13  type                           26835 non-null  object\n",
      " 14  market_creator                 26835 non-null  object\n",
      " 15  fpmm.answerFinalizedTimestamp  24829 non-null  object\n",
      " 16  fpmm.arbitrationOccurred       26835 non-null  bool  \n",
      " 17  fpmm.currentAnswer             24829 non-null  object\n",
      " 18  fpmm.id                        26835 non-null  object\n",
      " 19  fpmm.isPendingArbitration      26835 non-null  bool  \n",
      " 20  fpmm.openingTimestamp          26835 non-null  object\n",
      " 21  fpmm.outcomes                  26835 non-null  object\n",
      " 22  fpmm.title                     26835 non-null  object\n",
      " 23  fpmm.condition.id              26835 non-null  object\n",
      "dtypes: bool(2), object(22)\n",
      "memory usage: 4.6+ MB\n"
     ]
    }
   ],
   "source": [
    "trades.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['collateralAmount', 'collateralAmountUSD', 'collateralToken',\n",
       "       'creationTimestamp', 'trader_address', 'feeAmount', 'id',\n",
       "       'oldOutcomeTokenMarginalPrice', 'outcomeIndex',\n",
       "       'outcomeTokenMarginalPrice', 'outcomeTokensTraded', 'title',\n",
       "       'transactionHash', 'type', 'market_creator',\n",
       "       'fpmm.answerFinalizedTimestamp', 'fpmm.arbitrationOccurred',\n",
       "       'fpmm.currentAnswer', 'fpmm.id', 'fpmm.isPendingArbitration',\n",
       "       'fpmm.openingTimestamp', 'fpmm.outcomes', 'fpmm.title',\n",
       "       'fpmm.condition.id'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trades.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "markets = list(trades[\"fpmm.id\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "803"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(markets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gp/02mb1d514ng739czlxw1lhh00000gn/T/ipykernel_3094/2495807215.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  trade_markets.rename(\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "INVALID_ANSWER_HEX = (\n",
    "    \"0xffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff\"\n",
    ")\n",
    "columns_of_interest = [\n",
    "        \"fpmm.currentAnswer\",\n",
    "        \"fpmm.id\",\n",
    "        \"fpmm.openingTimestamp\",\n",
    "        \"market_creator\",\n",
    "    ]\n",
    "trade_markets = trades[columns_of_interest]\n",
    "trade_markets.rename(\n",
    "    columns={\n",
    "        \"fpmm.currentAnswer\": \"currentAnswer\",\n",
    "        \"fpmm.openingTimestamp\": \"openingTimestamp\",\n",
    "        \"fpmm.id\": \"id\",\n",
    "    },\n",
    "    inplace=True,\n",
    ")\n",
    "trade_markets = trade_markets.drop_duplicates(subset=['id'], keep='last')\n",
    "# remove invalid answers\n",
    "\n",
    "trade_markets = trade_markets.loc[trade_markets[\"currentAnswer\"]!= INVALID_ANSWER_HEX]\n",
    "trade_markets[\"currentAnswer\"] = trade_markets[\"currentAnswer\"].apply(\n",
    "    lambda x: convert_hex_to_int(x)\n",
    ")\n",
    "trade_markets[\"opening_datetime\"] = trade_markets[\"openingTimestamp\"].apply(\n",
    "    lambda x: datetime.fromtimestamp(int(x))\n",
    ")\n",
    "trade_markets = trade_markets.sort_values(by=\"opening_datetime\", ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "648"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(trade_markets.id.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "648"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(trade_markets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trade_markets.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "719"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(trade_markets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "trade_markets.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "648"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(trade_markets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>currentAnswer</th>\n",
       "      <th>id</th>\n",
       "      <th>openingTimestamp</th>\n",
       "      <th>market_creator</th>\n",
       "      <th>opening_datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20792</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0xcc9c26a86dd55aa04dcb0066c9b8fca2983f407d</td>\n",
       "      <td>1727136000</td>\n",
       "      <td>quickstart</td>\n",
       "      <td>2024-09-24 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21130</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0xd1bd18d7601d106639f922f1b5d2eda025c26be7</td>\n",
       "      <td>1727136000</td>\n",
       "      <td>quickstart</td>\n",
       "      <td>2024-09-24 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7494</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0x4eba0ec2464ec7c746e8872078165c8ad52d346f</td>\n",
       "      <td>1727136000</td>\n",
       "      <td>quickstart</td>\n",
       "      <td>2024-09-24 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9911</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0x61065f131e2ec851c40765bb0b078a318a36f53e</td>\n",
       "      <td>1727136000</td>\n",
       "      <td>quickstart</td>\n",
       "      <td>2024-09-24 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26182</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0x7e191324f0efb8aa20b8c702d95e812e55b4179c</td>\n",
       "      <td>1727136000</td>\n",
       "      <td>pearl</td>\n",
       "      <td>2024-09-24 02:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       currentAnswer                                          id  \\\n",
       "20792            1.0  0xcc9c26a86dd55aa04dcb0066c9b8fca2983f407d   \n",
       "21130            1.0  0xd1bd18d7601d106639f922f1b5d2eda025c26be7   \n",
       "7494             0.0  0x4eba0ec2464ec7c746e8872078165c8ad52d346f   \n",
       "9911             1.0  0x61065f131e2ec851c40765bb0b078a318a36f53e   \n",
       "26182            0.0  0x7e191324f0efb8aa20b8c702d95e812e55b4179c   \n",
       "\n",
       "      openingTimestamp market_creator    opening_datetime  \n",
       "20792       1727136000     quickstart 2024-09-24 02:00:00  \n",
       "21130       1727136000     quickstart 2024-09-24 02:00:00  \n",
       "7494        1727136000     quickstart 2024-09-24 02:00:00  \n",
       "9911        1727136000     quickstart 2024-09-24 02:00:00  \n",
       "26182       1727136000          pearl 2024-09-24 02:00:00  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade_markets.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "def market_KL_divergence(market_row: pd.DataFrame) -> float:\n",
    "    \"\"\"Function to compute the divergence based on the formula\n",
    "    Formula in https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence\"\"\"\n",
    "    current_answer = market_row.currentAnswer  # \"yes\", \"no\"\n",
    "    target_prob = 1  # = 100%\n",
    "    if current_answer == \"yes\":\n",
    "        candidate_prob = market_row.first_outcome_prob\n",
    "    else:  # \"no\"\n",
    "        candidate_prob = market_row.second_outcome_prob\n",
    "\n",
    "    # we have only one sample, the final probability based on tokens\n",
    "    kl_divergence = candidate_prob * round(math.log(candidate_prob / target_prob), 4)\n",
    "    return kl_divergence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "719"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(trade_markets.id.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trade_markets.currentAnswer.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "INVALID_ANSWER_HEX = (\n",
    "    \"0xffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def convert_hex_to_int(x):\n",
    "    \"\"\"Convert hex to int\"\"\"\n",
    "    if isinstance(x, float):\n",
    "        return np.nan\n",
    "    if isinstance(x, str):\n",
    "        if x == INVALID_ANSWER_HEX:\n",
    "            return -1\n",
    "        answer = int(x, 16)\n",
    "        return answer\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "market_ids = list(markets.id.unique())\n",
    "for i in range(len(trade_markets)):\n",
    "    market = trade_markets.iloc[i]\n",
    "    if market.id in market_ids:\n",
    "        current_answer = convert_hex_to_int(market.currentAnswer)\n",
    "        market_answer = markets.loc[markets[\"id\"]==market.id].currentAnswer.values[0]\n",
    "        print(f\"current answer = {current_answer} and market answer {market_answer}\")\n",
    "        trade_markets.at[i, \"currentAnswer\"] = market_answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "markets[\"currentAnswer\"] = markets[\"currentAnswer\"].apply(lambda x: convert_hex_to_int(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "currentAnswer\n",
       " 1.0    407\n",
       " 0.0    241\n",
       "-1.0     84\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "markets.currentAnswer.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0769610411361284"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "candidate_prob = 9/25\n",
    "target_prob = 1/3\n",
    "math.log(candidate_prob/target_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KL divergence: 6.296890976997244\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def kl_divergence(p, q):\n",
    "    \"\"\"\n",
    "    Compute KL divergence for a single sample with two probabilities.\n",
    "    \n",
    "    :param p: First probability (true distribution)\n",
    "    :param q: Second probability (approximating distribution)\n",
    "    :return: KL divergence value\n",
    "    \"\"\"\n",
    "    # Ensure probabilities sum to 1\n",
    "    p = np.array([p, 1-p])\n",
    "    q = np.array([q, 1-q])\n",
    "    \n",
    "    # Avoid division by zero\n",
    "    epsilon = 1e-10\n",
    "    q = np.clip(q, epsilon, 1-epsilon)\n",
    "    \n",
    "    # Compute KL divergence\n",
    "    kl_div = np.sum(p * np.log(p / q))\n",
    "    \n",
    "    return kl_div\n",
    "\n",
    "# Example usage\n",
    "p = 0.7  # probability from true distribution\n",
    "q = 1.0  # probability from approximating distribution\n",
    "\n",
    "result = kl_divergence(p, q)\n",
    "print(f\"KL divergence: {result}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KL divergence: inf\n"
     ]
    }
   ],
   "source": [
    "from scipy.special import kl_div\n",
    "\n",
    "# For multiple probabilities\n",
    "p = np.array([0.3, 0.7])\n",
    "q = np.array([0.0, 1.0])\n",
    "\n",
    "kl = np.sum(kl_div(p, q))\n",
    "print(f\"KL divergence: {kl}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This library is not useful if we have extreme values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>currentAnswer</th>\n",
       "      <th>id</th>\n",
       "      <th>openingTimestamp</th>\n",
       "      <th>market_creator</th>\n",
       "      <th>opening_datetime</th>\n",
       "      <th>first_outcome_prob</th>\n",
       "      <th>second_outcome_prob</th>\n",
       "      <th>kl_divergence</th>\n",
       "      <th>off_by_perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [currentAnswer, id, openingTimestamp, market_creator, opening_datetime, first_outcome_prob, second_outcome_prob, kl_divergence, off_by_perc]\n",
       "Index: []"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "markets_div = pd.read_parquet(\"../data/closed_markets_div.parquet\")\n",
    "markets_div.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>currentAnswer</th>\n",
       "      <th>id</th>\n",
       "      <th>openingTimestamp</th>\n",
       "      <th>market_creator</th>\n",
       "      <th>opening_datetime</th>\n",
       "      <th>first_outcome_prob</th>\n",
       "      <th>second_outcome_prob</th>\n",
       "      <th>kl_divergence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>yes</td>\n",
       "      <td>0x4eba0ec2464ec7c746e8872078165c8ad52d346f</td>\n",
       "      <td>1727136000</td>\n",
       "      <td>quickstart</td>\n",
       "      <td>2024-09-24 02:00:00</td>\n",
       "      <td>0.5392</td>\n",
       "      <td>0.4608</td>\n",
       "      <td>9.920241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>no</td>\n",
       "      <td>0x3535b4cea3ea7b1862fbe1af5a458702cc1c0dad</td>\n",
       "      <td>1727136000</td>\n",
       "      <td>quickstart</td>\n",
       "      <td>2024-09-24 02:00:00</td>\n",
       "      <td>0.2812</td>\n",
       "      <td>0.7188</td>\n",
       "      <td>5.880786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>yes</td>\n",
       "      <td>0x7e191324f0efb8aa20b8c702d95e812e55b4179c</td>\n",
       "      <td>1727136000</td>\n",
       "      <td>pearl</td>\n",
       "      <td>2024-09-24 02:00:00</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>10.819778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>no</td>\n",
       "      <td>0xd1bd18d7601d106639f922f1b5d2eda025c26be7</td>\n",
       "      <td>1727136000</td>\n",
       "      <td>quickstart</td>\n",
       "      <td>2024-09-24 02:00:00</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>10.819778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>no</td>\n",
       "      <td>0x61065f131e2ec851c40765bb0b078a318a36f53e</td>\n",
       "      <td>1727136000</td>\n",
       "      <td>quickstart</td>\n",
       "      <td>2024-09-24 02:00:00</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>10.819778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    currentAnswer                                          id  \\\n",
       "642           yes  0x4eba0ec2464ec7c746e8872078165c8ad52d346f   \n",
       "643            no  0x3535b4cea3ea7b1862fbe1af5a458702cc1c0dad   \n",
       "644           yes  0x7e191324f0efb8aa20b8c702d95e812e55b4179c   \n",
       "645            no  0xd1bd18d7601d106639f922f1b5d2eda025c26be7   \n",
       "646            no  0x61065f131e2ec851c40765bb0b078a318a36f53e   \n",
       "\n",
       "    openingTimestamp market_creator    opening_datetime  first_outcome_prob  \\\n",
       "642       1727136000     quickstart 2024-09-24 02:00:00              0.5392   \n",
       "643       1727136000     quickstart 2024-09-24 02:00:00              0.2812   \n",
       "644       1727136000          pearl 2024-09-24 02:00:00              0.5000   \n",
       "645       1727136000     quickstart 2024-09-24 02:00:00              0.5000   \n",
       "646       1727136000     quickstart 2024-09-24 02:00:00              0.5000   \n",
       "\n",
       "     second_outcome_prob  kl_divergence  \n",
       "642               0.4608       9.920241  \n",
       "643               0.7188       5.880786  \n",
       "644               0.5000      10.819778  \n",
       "645               0.5000      10.819778  \n",
       "646               0.5000      10.819778  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "markets_div.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "647"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(markets_div)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kl_divergence(p, q):\n",
    "    \"\"\"\n",
    "    Compute KL divergence for a single sample with two probabilities.\n",
    "\n",
    "    :param p: First probability (true distribution)\n",
    "    :param q: Second probability (approximating distribution)\n",
    "    :return: KL divergence value\n",
    "    \"\"\"\n",
    "    # Ensure probabilities sum to 1\n",
    "    p = np.array([p, 1 - p])\n",
    "    q = np.array([q, 1 - q])\n",
    "\n",
    "    # Avoid division by zero\n",
    "    epsilon = 1e-10\n",
    "    q = np.clip(q, epsilon, 1 - epsilon)\n",
    "    print(q)\n",
    "\n",
    "    # Compute KL divergence\n",
    "    kl_div = np.sum(p * np.log(p / q))\n",
    "\n",
    "    return kl_div"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-22.82067008,   1.6847004 ])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p= 0\n",
    "q = 0.8145\n",
    "p = np.array([p, 1 - p])\n",
    "q = np.array([q, 1 - q])\n",
    "epsilon = 1e-10\n",
    "p = np.clip(p, epsilon, 1 - epsilon)\n",
    "np.log(p/q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.28206701e-09,  1.68470040e+00])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p*np.log(p/q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6847003943841101"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(p * np.log(p / q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.791663620863367"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = 0.8145\n",
    "q = 1.0\n",
    "kl_divergence(p,q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16397451204513597"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = 0.99\n",
    "q = 0.8145\n",
    "kl_divergence(p, q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KL divergence: 0.16397451204513597\n"
     ]
    }
   ],
   "source": [
    "from scipy.special import kl_div\n",
    "\n",
    "# For multiple probabilities\n",
    "p = np.array([0.99, 0.01])\n",
    "q = np.array([0.8145, 0.1855])\n",
    "\n",
    "kl = np.sum(kl_div(p, q))\n",
    "print(f\"KL divergence: {kl}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2051808486854041"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p= 1\n",
    "q = 0.8145\n",
    "kl_divergence(p, q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KL divergence: 0.20518085094003724\n"
     ]
    }
   ],
   "source": [
    "# For multiple probabilities\n",
    "p = np.array([1.0, 0.0])\n",
    "q = np.array([0.8145, 0.1855])\n",
    "\n",
    "kl = np.sum(kl_div(p, q))\n",
    "print(f\"KL divergence: {kl}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.e+00 1.e-10]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gp/02mb1d514ng739czlxw1lhh00000gn/T/ipykernel_28964/3714966623.py:19: RuntimeWarning: divide by zero encountered in log\n",
      "  kl_div = np.sum(p * np.log(p / q))\n",
      "/var/folders/gp/02mb1d514ng739czlxw1lhh00000gn/T/ipykernel_28964/3714966623.py:19: RuntimeWarning: invalid value encountered in multiply\n",
      "  kl_div = np.sum(p * np.log(p / q))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = 0\n",
    "q = 1\n",
    "kl_divergence(p, q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KL divergence: 0.010050335853501449\n"
     ]
    }
   ],
   "source": [
    "p = np.array([0.0, 1.0])\n",
    "q = np.array([0.01, 0.99])\n",
    "\n",
    "kl = np.sum(kl_div(p, q))\n",
    "print(f\"KL divergence: {kl}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.17425697504355725"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = 0.01\n",
    "q = 0\n",
    "kl_divergence(p, q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KL divergence: inf\n"
     ]
    }
   ],
   "source": [
    "p = np.array([0.01, 0.99])\n",
    "q = np.array([0.0, 1.0])\n",
    "\n",
    "kl = np.sum(kl_div(p, q))\n",
    "print(f\"KL divergence: {kl}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.01, 0.99])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epsilon = 1e-10\n",
    "q = 0\n",
    "q = np.clip(p, epsilon, 1 - epsilon)\n",
    "q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# New function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kl_divergence(P, Q):\n",
    "    \"\"\"\n",
    "    Compute KL divergence for a single sample with two prob distributions.\n",
    "\n",
    "    :param P: True distribution)\n",
    "    :param Q: Approximating distribution)\n",
    "    :return: KL divergence value\n",
    "    \"\"\"\n",
    "    # Review edge cases\n",
    "    if P[0] == Q[0]:\n",
    "        return 0.0\n",
    "    # If P is complete opposite of Q, divergence is some max value.\n",
    "    # Here set to 20--allows for Q [\\mu, 1-\\mu] or Q[1-\\mu, \\mu] where \\mu = 10^-8\n",
    "    if P[0] == Q[1]:\n",
    "        return 20\n",
    "\n",
    "    nonzero = P > 0.0\n",
    "    # Compute KL divergence\n",
    "    kl_div = np.sum(P[nonzero] * np.log(P[nonzero] / Q[nonzero]))\n",
    "\n",
    "    return kl_div"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "P = np.array([0.0, 1.0])\n",
    "Q = np.array([0.0, 1.0])\n",
    "print(kl_divergence(P,Q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    }
   ],
   "source": [
    "P = np.array([0.0, 1.0])\n",
    "Q = np.array([1.0, 0.0])\n",
    "print(kl_divergence(P,Q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    }
   ],
   "source": [
    "P = np.array([1.0, 0.0])\n",
    "Q = np.array([0.0, 1.0])\n",
    "print(kl_divergence(P,Q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.010050335853501506\n"
     ]
    }
   ],
   "source": [
    "P = np.array([0.0, 1.0])\n",
    "Q = np.array([0.01, 0.99])\n",
    "print(kl_divergence(P,Q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.010050335853501506"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 * np.log(1 / 0.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5108256237659907\n"
     ]
    }
   ],
   "source": [
    "P = np.array([1.0, 0.0])\n",
    "Q = np.array([0.60, 0.05])\n",
    "print(kl_divergence(P,Q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.815510557964274\n"
     ]
    }
   ],
   "source": [
    "Q = np.array([1e-6, 0.999999])#or [0.99, 0.01]\n",
    "P = np.array([1.0, 0.0])\n",
    "print(kl_divergence(P,Q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.605170185988092"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(1/0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Assuming you have your dataframe as 'df'\n",
    "# If not, you can create a sample dataframe like this:\n",
    "df = pd.DataFrame({\n",
    "    'week': [1, 1, 1, 2, 2, 2, 3, 3, 3],\n",
    "    'kl_divergence': [0.1, 0.2, 0.15, 0.3, 0.25, 0.35, 0.4, 0.45, 0.5],\n",
    "    'off_by_perc': [5, 10, 7, 15, 12, 18, 20, 22, 25]\n",
    "})\n",
    "\n",
    "# Create the main figure and axis\n",
    "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "# Create the boxplot using seaborn\n",
    "sns.boxplot(x='week', y='kl_divergence', data=df, ax=ax1)\n",
    "\n",
    "# Set labels and title for the main axis\n",
    "ax1.set_xlabel('Week')\n",
    "ax1.set_ylabel('KL Divergence')\n",
    "ax1.set_title('KL Divergence Boxplot with Off-by Percentage')\n",
    "\n",
    "# Create a secondary y-axis\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "# Plot the off_by_perc values on the secondary y-axis\n",
    "for i, week in enumerate(df['week'].unique()):\n",
    "    off_by_perc = df[df['week'] == week]['off_by_perc']\n",
    "    ax2.scatter([i] * len(off_by_perc), off_by_perc, color='red', alpha=0.01)\n",
    "\n",
    "# Set label for the secondary y-axis\n",
    "ax2.set_ylabel('Off-by Percentage')\n",
    "\n",
    "# Adjust the layout and display the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "hf_dashboards",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}