{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "816420ea",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Data-set:-Quotes-on-Deribit-option-on-BTC-USD-on-April-26,-2023,-between-20:05-and-20:06\" data-toc-modified-id=\"Data-set:-Quotes-on-Deribit-option-on-BTC-USD-on-April-26,-2023,-between-20:05-and-20:06-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Data set: Quotes on Deribit option on BTC-USD on April 26, 2023, between 20:05 and 20:06</a></span><ul class=\"toc-item\"><li><span><a href=\"#Getting-data\" data-toc-modified-id=\"Getting-data-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Getting data</a></span></li><li><span><a href=\"#Putting-colors-on-Bid,-Ask,-Call,-Put\" data-toc-modified-id=\"Putting-colors-on-Bid,-Ask,-Call,-Put-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Putting colors on Bid, Ask, Call, Put</a></span></li></ul></li><li><span><a href=\"#What-is-the-forward?\" data-toc-modified-id=\"What-is-the-forward?-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>What is the forward?</a></span><ul class=\"toc-item\"><li><span><a href=\"#We-gather-data-by-strikes\" data-toc-modified-id=\"We-gather-data-by-strikes-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>We gather data by strikes</a></span></li><li><span><a href=\"#We-compute-the-best-bid-and-the-best-ask\" data-toc-modified-id=\"We-compute-the-best-bid-and-the-best-ask-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>We compute the best bid and the best ask</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fe1ffba7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "869b3946",
   "metadata": {},
   "source": [
    "# Data set: Quotes on Deribit option on BTC-USD on April 26, 2023, between 20:05 and 20:06"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea7c7ee6",
   "metadata": {},
   "source": [
    "## Getting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b9230542",
   "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>id</th>\n",
       "      <th>date</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>amount</th>\n",
       "      <th>strike</th>\n",
       "      <th>expiry</th>\n",
       "      <th>option_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16306</td>\n",
       "      <td>2023-04-26 20:05:00.110</td>\n",
       "      <td>a</td>\n",
       "      <td>0.0315</td>\n",
       "      <td>3.8</td>\n",
       "      <td>28500.0</td>\n",
       "      <td>12MAY23</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16304</td>\n",
       "      <td>2023-04-26 20:05:00.110</td>\n",
       "      <td>a</td>\n",
       "      <td>0.0305</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28500.0</td>\n",
       "      <td>12MAY23</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16302</td>\n",
       "      <td>2023-04-26 20:05:00.110</td>\n",
       "      <td>b</td>\n",
       "      <td>0.0275</td>\n",
       "      <td>25.8</td>\n",
       "      <td>28500.0</td>\n",
       "      <td>12MAY23</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16303</td>\n",
       "      <td>2023-04-26 20:05:00.110</td>\n",
       "      <td>b</td>\n",
       "      <td>0.0265</td>\n",
       "      <td>2.7</td>\n",
       "      <td>28500.0</td>\n",
       "      <td>12MAY23</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16305</td>\n",
       "      <td>2023-04-26 20:05:00.110</td>\n",
       "      <td>a</td>\n",
       "      <td>0.0310</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28500.0</td>\n",
       "      <td>12MAY23</td>\n",
       "      <td>C</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>18767</td>\n",
       "      <td>2023-04-26 20:05:52.953</td>\n",
       "      <td>b</td>\n",
       "      <td>0.0420</td>\n",
       "      <td>27.1</td>\n",
       "      <td>27500.0</td>\n",
       "      <td>12MAY23</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>18769</td>\n",
       "      <td>2023-04-26 20:05:52.953</td>\n",
       "      <td>a</td>\n",
       "      <td>0.0455</td>\n",
       "      <td>7.5</td>\n",
       "      <td>27500.0</td>\n",
       "      <td>12MAY23</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>18768</td>\n",
       "      <td>2023-04-26 20:05:52.953</td>\n",
       "      <td>a</td>\n",
       "      <td>0.0450</td>\n",
       "      <td>29.5</td>\n",
       "      <td>27500.0</td>\n",
       "      <td>12MAY23</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>18766</td>\n",
       "      <td>2023-04-26 20:05:52.953</td>\n",
       "      <td>b</td>\n",
       "      <td>0.0425</td>\n",
       "      <td>16.9</td>\n",
       "      <td>27500.0</td>\n",
       "      <td>12MAY23</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>18770</td>\n",
       "      <td>2023-04-26 20:05:52.953</td>\n",
       "      <td>a</td>\n",
       "      <td>0.0460</td>\n",
       "      <td>4.0</td>\n",
       "      <td>27500.0</td>\n",
       "      <td>12MAY23</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                     date type   price  amount   strike   expiry  \\\n",
       "0    16306  2023-04-26 20:05:00.110    a  0.0315     3.8  28500.0  12MAY23   \n",
       "1    16304  2023-04-26 20:05:00.110    a  0.0305    23.0  28500.0  12MAY23   \n",
       "2    16302  2023-04-26 20:05:00.110    b  0.0275    25.8  28500.0  12MAY23   \n",
       "3    16303  2023-04-26 20:05:00.110    b  0.0265     2.7  28500.0  12MAY23   \n",
       "4    16305  2023-04-26 20:05:00.110    a  0.0310     4.0  28500.0  12MAY23   \n",
       "..     ...                      ...  ...     ...     ...      ...      ...   \n",
       "173  18767  2023-04-26 20:05:52.953    b  0.0420    27.1  27500.0  12MAY23   \n",
       "174  18769  2023-04-26 20:05:52.953    a  0.0455     7.5  27500.0  12MAY23   \n",
       "175  18768  2023-04-26 20:05:52.953    a  0.0450    29.5  27500.0  12MAY23   \n",
       "176  18766  2023-04-26 20:05:52.953    b  0.0425    16.9  27500.0  12MAY23   \n",
       "177  18770  2023-04-26 20:05:52.953    a  0.0460     4.0  27500.0  12MAY23   \n",
       "\n",
       "    option_type  \n",
       "0             C  \n",
       "1             C  \n",
       "2             C  \n",
       "3             C  \n",
       "4             C  \n",
       "..          ...  \n",
       "173           P  \n",
       "174           P  \n",
       "175           P  \n",
       "176           P  \n",
       "177           P  \n",
       "\n",
       "[178 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('DataCallPut-bidask-deribit-2604.csv') \n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b264c7c6",
   "metadata": {},
   "source": [
    "## Putting colors on Bid, Ask, Call, Put"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ae248efb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Call data= 92\n",
      "Number of Put data= 86\n"
     ]
    },
    {
     "data": {
      "image/png": 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JDDVERERtyTUN8Fuq/pmMNHihMBEREVnJ2WTg6HQANQDkwLBEoHsLXzukDeFMDRERUVtwTVMr0ED/fOxFztjUwlBDRETUFlzJwZ1Ac4uoBq600A0G2yCGGiIiorbAPRgmX9syBeDeQjcYbIMYaqhV5OfnQyaTGa4uTUREDeSq0q+hkd26waBMAQxdp28nAAw1bV5cXBxkMhlkMhkcHBzQrVs3zJkzx+xduy0ZOXIkEhISWq5IIiJqHt3jgSfygUdS9c9cJGyEZz9JwJgxY7BhwwZUVVXh8OHDmDp1Kq5evWq4oSUREUmIq4qzMxZwpkYCnJyc4OPjA39/f0yePBnPPvssdu3aBUA/kzNhwgSj/gkJCRg5cqTh/UOHDmHFihWGGZ/a9+Sq7R//+AcGDx4Md3d3+Pj4YPLkySguLja8f/nyZTz77LPw8vKCi4sLgoODsWHDBrPbqqmpwbRp09CjRw8UFBQ0dQiIiIg4U9Mirmn0q9Tdg62Spl1cXFBVVVWvvitWrMCZM2cQEhKChQsXAgC8vLzM9q2srMSiRYvQs2dPFBcXY9asWYiLi8O+ffsAAO+++y5+/vlnfPXVV+jUqRNyc3Nx/fp1s9uZPHkyzp49i++//x6dO3du5E9KRER0B0NNc7PyhZGOHTuGLVu24JFHHqlXf6VSCUdHR7i6usLHx+eufV944QXDn7t164aVK1di6NChKC8vR/v27VFYWIhBgwZh8ODBAICuXbuabKO8vByPPfYYrl+/joMHD0KpVNb/hyMiIroLHn5qTla6MNKePXvQvn17ODs7IywsDA8++CA+/fTTZt9PZmYmnnjiCajVari7uxsOYRUWFgIAXnrpJWzduhUDBw7Em2++ibS0NJNtTJo0CeXl5Thw4AADDRERNSuGmuZkpQsjPfzww8jKysKvv/6KGzduYOfOnYZDOnK5HEIIo/71PTRV29WrVxEZGYn27dvjH//4B44fP44vv/wSgP5wEgBER0ejoKAACQkJuHjxIh555BHMmTPHaDtjx47Fjz/+iCNHjjTmRyUiIrKIoaY5WenCSG5ubggKCoJarYaDg4PRe15eXtBqtUZtda8V4+joiOrq6rvu45dffkFJSQmWLFmCiIgI9OrVy2iRcO39xcXF4R//+AeWL1+OxMREo/dfeuklLFmyBI8//jgOHTrUgJ+SiIjo7hhqmpMNXhhp1KhROHHiBDZv3oycnBzMnz8fp0+fNurTtWtXHD16FPn5+SgpKUFNTY3JdgICAuDo6IhPP/0U586dw+7du7Fo0SKjPu+99x7+7//+D7m5ufjpp5+wZ88e9O7d22Rbr776Kj744AOMGzcO33//ffP+wEREZLcYapqbjV0YKSoqCu+++y7efPNNDBkyBFeuXMHzzz9v1GfOnDlQKBTo06cPvLy8DGtkavPy8sLGjRvxz3/+E3369MGSJUuwdOlSoz6Ojo6YO3cu+vfvjwcffBAKhQJbt241W1dCQgLef/99jB071uzaGyIiooaSiboLLiRMp9NBqVSirKwMHh4eRu/duHEDeXl5CAwMhLOzs5UqJEv434eIyH7d7fu7Ns7UEBERkSQw1BAREZEkMNQQERGRJDDUEBERkSQw1BAREZEkMNQQERGRJDDUEBERkSQw1BAREZEkMNQQERGRJDDUUL117doVy5cvv2sfmUyGXbt2WXw/Pz8fMpnM5KaaRERETcVQ08bFxcVBJpNBJpPBwcEB3bp1w5w5c3D16tV6b2PkyJFISEholnq0Wi2io6ObZVtEREQN0c7aBVDTjRkzBhs2bEBVVRUOHz6MqVOn4urVq1izZk2r1+Lj49Pq+yQiIgI4UyMJTk5O8PHxgb+/PyZPnoxnn33WcAgoLi4OEyZMMOqfkJCAkSNHGt4/dOgQVqxYYZjxyc/Pt7ivK1euYPLkyWjfvj38/Pzw6aefGr1f9/DTsWPHMGjQIDg7O2Pw4MHIzMxshp+YiIjIFENNC9DoNEjNS4VGp7HK/l1cXFBVVVWvvitWrEBYWBimTZsGrVYLrVYLf39/i/0//vhj9O/fHydPnsTcuXMxa9YspKSkmO179epVjBs3Dj179kRGRgYWLFiAOXPmNOpnIiIiuhcefmpmySeTMX3PdNSIGshlciSOS0T8/fGttv9jx45hy5YteOSRR+rVX6lUwtHREa6urvU6dDRixAi89dZbAIAePXrghx9+wF//+lc8+uijJn0///xzVFdXY/369XB1dUXfvn2h0Wjw0ksvNeyHIiIiqgfO1DQjjU5jCDQAUCNq8OKeF1t8xmbPnj1o3749nJ2dERYWhgcffNDksFBzCQsLM3mdnZ1ttm92djYGDBgAV1dXi58nIiJqLpypaUY5pTmGQHNbtahG7u+5UHmoWmy/Dz/8MNasWQMHBwf4+fnBwcHB8J5cLocQwqh/fQ9N1ZdMJjPbXne/RERELalRMzWrV69GYGAgnJ2dERoaisOHD1vsq9VqMXnyZPTs2RNyudzsqcMbN240LFKt/bhx40aj92sNwR2DIZcZD6lCpkCQZ1CL7tfNzQ1BQUFQq9VGgQYAvLy8oNVqjdrqXiPG0dER1dXV9drXkSNHTF736tXLbN8+ffrgP//5D65fv27x80RERM2lwaFm27ZtSEhIwLx585CZmYmIiAhER0ejsLDQbP+Kigp4eXlh3rx5GDBggMXtenh4GBaq3n44Ozs3er/WoPJQIXFcIhQyBQB9oFk3bl2LztLcy6hRo3DixAls3rwZOTk5mD9/Pk6fPm3Up2vXrjh69Cjy8/NRUlKCmpoaC1sDfvjhB3z00Uc4c+YMVq1ahX/+8594/fXXzfadPHky5HI54uPj8fPPP2Pfvn1YunRps/58REREBqKBhg4dKmbMmGHU1qtXL/HWW2/d87MPPfSQeP31103aN2zYIJRKZYvt97aysjIBQJSVlZm8d/36dfHzzz+L69ev13t7lpwvOy9S81LF+bLzTd7WvcTGxoonnnjirn3ee+894e3tLZRKpZg1a5aYOXOmeOihhwzv//rrr2L48OHCxcVFABB5eXlmt6NWq8X7778vnn76aeHq6iq8vb3F8uXLjfoAEF9++aXhdXp6uhgwYIBwdHQUAwcOFDt27BAARGZmZoN+zub870NERG3L3b6/a2vQmprKykpkZGQYzn65LTIyEmlpaU0KV+Xl5VCr1aiursbAgQOxaNEiDBo0qEn7raioQEVFheG1TqdrUo31pfJQtdrszMaNG+/Z5/3338f7779v8f0ePXogPT39ntu52/VrbhN11tEMHz7c5HBX3T5ERETNoUGHn0pKSlBdXQ1vb2+jdm9vbxQVFTW6iF69emHjxo3YvXs3vvjiCzg7O2PEiBHIyclp0n4XL14MpVJpeNzt+itERETUtjVqoXDds12EEBbPgKmP4cOH47nnnsOAAQMQERGB7du3o0ePHmavVtuQ/c6dOxdlZWWGx/nz5xtdIxEREdm2Bh1+6tSpExQKhcnsSHFxscksSlPI5XIMGTLEMFPT2P06OTnBycmp2eoiIiIi29WgmRpHR0eEhoaaXBY/JSUF4eHhzVaUEAJZWVnw9fVt1f0SERFR29Xgi+/Nnj0bU6ZMweDBgxEWFobExEQUFhZixowZAPSHfC5cuIDNmzcbPnN7oWh5eTkuXbqErKwsODo6ok+fPgD0C1mHDx+O4OBg6HQ6rFy5EllZWVi1alW990tERET2rcGhJiYmBqWlpVi4cCG0Wi1CQkKwb98+qNVqAPqL7dW9dszts5gAICMjA1u2bIFarTacTfPHH39g+vTpKCoqglKpxKBBg/Ddd99h6NCh9d5vc+GZObaJ/12IiOheZMKOvi10Oh2USiXKysrg4eFh9F5VVRVyc3Ph5+cHpVJppQrJktLSUhQXF6NHjx5QKBTWLoeIiFrR3b6/a+O9n25p164dXF1dcenSJTg4OEAu570+bYEQAteuXUNxcTHuu+8+BhoiIrKIoeYWmUwGX19f5OXloaCgwNrlUB333XcffHx8rF0GERHZMIaaWhwdHREcHIzKykprl0K1ODg4cIaGiIjuiaGmDrlcbnQjTSIiImobuHCEiIiIJIGhhoiIiCSBoYaIiIgkgaGGiIiIJIGhhoiIiCSBoYaIiIgkgaGGiIiIJIGhhoiIiCSBoYaIiIgkgaGGiIiIJIGhhoiIiCSBoYaIiIgkgaGGiIiIJIGhhoiIiCSBoYaIiIgkgaGGiIiIJIGhhoiIiCSBoYaIiIgkgaGGiIiIJIGhhoiIiCSBoYaIiIgkgaGGiIiIJIGhhoiIiCSBoYaIiIgkgaGGiIiIJIGhhoiIiCSBoYaIiIgkgaGGiIiIJIGhhoiIiCSBoYaIiIgkgaGGiIiIJIGhhoiIiCShUaFm9erVCAwMhLOzM0JDQ3H48GGLfbVaLSZPnoyePXtCLpcjISHBpE9SUhIiIiLQoUMHdOjQAaNHj8axY8eM+ixYsAAymczo4ePj05jyiYiISIIaHGq2bduGhIQEzJs3D5mZmYiIiEB0dDQKCwvN9q+oqICXlxfmzZuHAQMGmO1z8OBBTJo0CampqUhPT0dAQAAiIyNx4cIFo359+/aFVqs1PE6dOtXQ8omIiEiiZEII0ZAPDBs2DPfffz/WrFljaOvduzcmTJiAxYsX3/WzI0eOxMCBA7F8+fK79quurkaHDh3wt7/9Dc8//zwA/UzNrl27kJWV1ZByjeh0OiiVSpSVlcHDw6PR2yEiIqLWU9/v7wbN1FRWViIjIwORkZFG7ZGRkUhLS2tcpWZcu3YNVVVV8PT0NGrPycmBn58fAgMD8cwzz+DcuXN33U5FRQV0Op3Rg4iIiKSpQaGmpKQE1dXV8Pb2Nmr39vZGUVFRsxX11ltvoUuXLhg9erShbdiwYdi8eTP279+PpKQkFBUVITw8HKWlpRa3s3jxYiiVSsPD39+/2WokIiIi29KohcIymczotRDCpK2xPvroI3zxxRfYuXMnnJ2dDe3R0dGYOHEi+vXrh9GjR2Pv3r0AgE2bNlnc1ty5c1FWVmZ4nD9/vllqJCIiItvTriGdO3XqBIVCYTIrU1xcbDJ70xhLly7FX/7yF3zzzTfo37//Xfu6ubmhX79+yMnJsdjHyckJTk5OTa6LiIiIbF+DZmocHR0RGhqKlJQUo/aUlBSEh4c3qZCPP/4YixYtwtdff43Bgwffs39FRQWys7Ph6+vbpP0SERGRNDRopgYAZs+ejSlTpmDw4MEICwtDYmIiCgsLMWPGDAD6Qz4XLlzA5s2bDZ+5fcZSeXk5Ll26hKysLDg6OqJPnz4A9Iec3n33XWzZsgVdu3Y1zAS1b98e7du3BwDMmTMH48ePR0BAAIqLi/HBBx9Ap9MhNja2SQNARERE0tDgUBMTE4PS0lIsXLgQWq0WISEh2LdvH9RqNQD9xfbqXrNm0KBBhj9nZGRgy5YtUKvVyM/PB6C/mF9lZSWeeuopo8/Nnz8fCxYsAABoNBpMmjQJJSUl8PLywvDhw3HkyBHDfomIiMi+Nfg6NW0Zr1NDRETU9rTIdWqIiIiIbBVDDREREUkCQw0RERFJAkMNERERSQJDDREREUkCQw0RERFJAkMNERERSQJDDREREUkCQw0RERFJAkMNERERSQJDDREREUkCQw0RERFJAkMNERERSQJDDREREUkCQw0RERFJAkMNERERSQJDDREREUkCQw0RERFJAkMNERERSQJDDREREUkCQw0RERFJAkMNERERSQJDDREREUkCQw0RERFJAkMNERERSQJDDREREUkCQw0RERFJAkMNERERSQJDDREREUkCQw0RERFJAkMNERERSQJDDREREUkCQw0RERFJAkMNERERSQJDDREREUkCQw0RERFJQqNCzerVqxEYGAhnZ2eEhobi8OHDFvtqtVpMnjwZPXv2hFwuR0JCgtl+O3bsQJ8+feDk5IQ+ffrgyy+/bNJ+iYiIyL40ONRs27YNCQkJmDdvHjIzMxEREYHo6GgUFhaa7V9RUQEvLy/MmzcPAwYMMNsnPT0dMTExmDJlCv7zn/9gypQpePrpp3H06NFG75eIiIjsi0wIIRrygWHDhuH+++/HmjVrDG29e/fGhAkTsHjx4rt+duTIkRg4cCCWL19u1B4TEwOdToevvvrK0DZmzBh06NABX3zxRZP3e5tOp4NSqURZWRk8PDzq9RkiIiKyrvp+fzdopqayshIZGRmIjIw0ao+MjERaWlrjKoV+pqbuNqOiogzbbKn9EhERkXS0a0jnkpISVFdXw9vb26jd29sbRUVFjS6iqKjortts7H4rKipQUVFheK3T6RpdIxEREdm2Ri0UlslkRq+FECZtLbHNhu538eLFUCqVhoe/v3+TaiQiIiLb1aBQ06lTJygUCpPZkeLiYpNZlIbw8fG56zYbu9+5c+eirKzM8Dh//nyjayQiIiLLNDoNUvNSodFprFZDg0KNo6MjQkNDkZKSYtSekpKC8PDwRhcRFhZmss0DBw4YttnY/To5OcHDw8PoQURERM0r+WQy1MvVGLV5FNTL1Ug+mWyVOhq0pgYAZs+ejSlTpmDw4MEICwtDYmIiCgsLMWPGDAD62ZELFy5g8+bNhs9kZWUBAMrLy3Hp0iVkZWXB0dERffr0AQC8/vrrePDBB/Hhhx/iiSeewP/93//hm2++wffff1/v/RIREVHr0+g0mL5nOmpEDQCgRtTgxT0vIiooCioPVavW0uBQExMTg9LSUixcuBBarRYhISHYt28f1Go1AP3F9upeO2bQoEGGP2dkZGDLli1Qq9XIz88HAISHh2Pr1q1455138O6776J79+7Ytm0bhg0bVu/9EhERUevLKc0xBJrbqkU1cn/PbfVQ0+Dr1LRlvE4NERFR89LoNFAvVxsFG4VMgfyE/GYLNS1ynRoiIiKi2lQeKiSOS4RCpgCgDzTrxq1r9VkagDM1RERE1Aw0Og1yf89FkGdQswea+n5/N3hNDRERUbMrPQ4UHwY6RwAdh1i7GmoElYfKKrMztTHUEBGRdaXHAXmb7rwOjAXCNlqrGmrDuKaGiIisp/S4caAB9K9Lj1unHmrTGGqIiMh6ig+bb7/0Q+vWQZLAUENERNbTOcJ8u9eI1q2DJIGhhoiIrKfjEP0amtoCY7lYmBqFC4WJiMi6wjYCPV7RH3LyGsFAQ43GUENERNbXcQjDDDUZDz8R2arjx4Fly/TPRER0T5ypIbJFcXHAplqnucbGAhs3WqsaIqI2gTM1RLbm+HHjQAPoX3PGhiRMo9MgNS8VGp3G2qVQG8ZQQ2RrDlu4bscPvG4HSVPyyWQE/DUAozaPQsBfA5B8MtnaJVEbxVBDZGsiLFy3YwSv20HSo9FpMO1f0yCgv7eygMC0f03jjA01CkMNka0ZMkS/hqa22Fh9O5HEpBWmGQLNbQIC6efTrVQRtWVcKExkizZuBF55RX/IacQIBhqSLpm1CyApYaghslVDhjDMkOSF+4dDBpnRbI1cJkeYf5gVq6K2ioefiIjIalQeKiSNT4JCpgAAKGQKJI5LhMpDZeXKqC2SCSHEvbtJg06ng1KpRFlZGTw8PKxdDhER3aLRaZD7ey6CPIMYaMhEfb+/efiJiIisTuWhYpihJuPhJyIiIpIEhhoiIiKSBIYaIiIikgSGGiIiIpIEhhoiIiKSBIYaIiKiNoR3NLeMp3QTERG1EcknkzF9z3TUiBrIZXIkjktE/P3x1i7LZnCmhoiIqA3Q6DSGQAMANaIGL+55kTM2tTDUEBERtQE5pTmGQHNbtahG7u+5VqrI9jDUEBERtQHBHYMhlxl/bStkCgR5BlmpItvDUENERNQGqDxUSByXaHTzz3Xj1vH2ErXwhpZERERtiD3e/JM3tCQiIpIg3vzTMh5+IiJqKaXHgexl+mcianGcqSEiagnpcUDepjuvA2OBsI3WqobILnCmhoiouZUeNw40gP41Z2yIWlSjQs3q1asRGBgIZ2dnhIaG4vDhw3ftf+jQIYSGhsLZ2RndunXD2rVrjd4fOXIkZDKZyeOxxx4z9FmwYIHJ+z4+Po0pn4ioZRVb+J146YfWrYPIzjQ41Gzbtg0JCQmYN28eMjMzERERgejoaBQWFprtn5eXh7FjxyIiIgKZmZl4++238dprr2HHjh2GPjt37oRWqzU8Tp8+DYVCgT/96U9G2+rbt69Rv1OnTjW0fCKiltc5wny714jWrYPIzjR4Tc2yZcsQHx+PqVOnAgCWL1+O/fv3Y82aNVi8eLFJ/7Vr1yIgIADLly8HAPTu3RsnTpzA0qVLMXHiRACAp6en0We2bt0KV1dXk1DTrl07zs4Qke3rOES/hqbumpqOQ6xXE5EdaNBMTWVlJTIyMhAZGWnUHhkZibS0NLOfSU9PN+kfFRWFEydOoKqqyuxnkpOT8cwzz8DNzc2oPScnB35+fggMDMQzzzyDc+fO3bXeiooK6HQ6owcRUasI2whEHQPu/6v+mYuEiVpcg0JNSUkJqqur4e3tbdTu7e2NoqIis58pKioy2//mzZsoKSkx6X/s2DGcPn3aMBN027Bhw7B582bs378fSUlJKCoqQnh4OEpLSy3Wu3jxYiiVSsPD39+/vj8qEVHTdRwC9ErgDA1RK2nUQmGZTGb0Wghh0nav/ubaAf0sTUhICIYOHWrUHh0djYkTJ6Jfv34YPXo09u7dCwDYtGmTyTZumzt3LsrKygyP8+fP3/0HIyIiojarQWtqOnXqBIVCYTIrU1xcbDIbc5uPj4/Z/u3atUPHjh2N2q9du4atW7di4cKF96zFzc0N/fr1Q05OjsU+Tk5OcHJyuue2iIiIqO1r0EyNo6MjQkNDkZKSYtSekpKC8PBws58JCwsz6X/gwAEMHjwYDg4ORu3bt29HRUUFnnvuuXvWUlFRgezsbPj6+jbkRyAiIiKJavDhp9mzZ+Ozzz7D+vXrkZ2djVmzZqGwsBAzZswAoD/k8/zzzxv6z5gxAwUFBZg9ezays7Oxfv16JCcnY86cOSbbTk5OxoQJE0xmcABgzpw5OHToEPLy8nD06FE89dRT0Ol0iI2NbeiPQERERBLU4FO6Y2JiUFpaioULF0Kr1SIkJAT79u2DWq0GAGi1WqNr1gQGBmLfvn2YNWsWVq1aBT8/P6xcudJwOvdtZ86cwffff48DBw6Y3a9Go8GkSZNQUlICLy8vDB8+HEeOHDHsl4iIiOybTNxetWsH6nvrciIiIrId9f3+5r2fiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIWsjxC8exLH0Zjl84bu1SiOxCO2sXQEQkRXG74rDpP5sMr2MHxGLjhI3WK4jIDnCmhoik4ZoG+C1V/2xlxy8cNwo0ALDpP5s4Y0PUwhhqiKjtO5sM7FID347SP59Ntmo5hwsPm23/4fwPrVwJkX1hqCGitu2aBjg6HUDNrYYa4NiLVp2xiQiIMNs+wn9EK1dCZF8YaoiobbuSgzuB5hZRDVzJtUo5ADCkyxDEDog1aosdEIshXYZYqSIi+8CFwkTUtrkHQ//vs1rBRqYA3IOsVREAYOOEjXhlyCv44fwPGOE/goGGqBUw1BDZKo0GyMkBgoMBlcra1dguVxUwLLHWISg5MHSdvt3KhnQZwjBD1Ip4+InIFiUnA2o1MGqU/jnZugtfiYjaApkQQli7iNai0+mgVCpRVlYGDw8Pa5dDZJ5Gow8yNbUOpygUQH4+Z2zMuaYBdgUAqP2rTA5MKLCJ2Roiarr6fn9zpobI1uTkGAcaAKiuBnKtt/DVpl1Kg3GgAYAa4FK6NaohIitiqCGyNcHBgFwODbogFSOhQRf9TE2QdRe+EhHZOoYaIlujUiF5ykGoUYBRSIUaBUh+LpWHnizxCgcgq9MoA7zCrFENEVkRQw2RjdFogOl/j0ANFACAGijw4j8ioLH+1f9tk6sKGJaEO7/O5PrXXE9DZHd4SjeRjbnbkhpO1ljQPR7wjdJfcM89iIGGyE4x1BDZmFtLakxOfuKSmntwVTHMENk5Hn4isjEqFZCYqA82gP553TrO0hAR3UujQs3q1asRGBgIZ2dnhIaG4vBh83ekve3QoUMIDQ2Fs7MzunXrhrVr1xq9v3HjRshkMpPHjRs3mrRforbq8OE7MzU1NfrXRER0dw0ONdu2bUNCQgLmzZuHzMxMREREIDo6GoWFhWb75+XlYezYsYiIiEBmZibefvttvPbaa9ixY4dRPw8PD2i1WqOHs7Nzo/dL1FYdPw5s2mTctmmTvt0mXNMAv6Va9S7YRERmiQYaOnSomDFjhlFbr169xFtvvWW2/5tvvil69epl1Pbiiy+K4cOHG15v2LBBKJXKZt2vOWVlZQKAKCsrq/dniFrbJ58IAZg+/vpXa1cmhMj9TIjP5UJ8Dv1z7mfWroiI7EB9v78bNFNTWVmJjIwMREZGGrVHRkYiLS3N7GfS09NN+kdFReHEiROoqqoytJWXl0OtVkOlUmHcuHHIzMxs0n4BoKKiAjqdzuhBZOsiIsy3jxjRunWYuKapddNI6J+PvcgZGyKyGQ0KNSUlJaiuroa3t7dRu7e3N4qKisx+pqioyGz/mzdvoqSkBADQq1cvbNy4Ebt378YXX3wBZ2dnjBgxAjk5OY3eLwAsXrwYSqXS8PD392/Ij0tkFUOGAN27G7d1765vt6orObgTaG4R1frTqImIbECjFgrLZMZX7xRCmLTdq3/t9uHDh+O5557DgAEDEBERge3bt6NHjx749NNPm7TfuXPnoqyszPA4f/78vX84Iis7fhw4e9a47exZG1hT4x4MiDr/vwmZ/rowREQ2oEGhplOnTlAoFCazI8XFxSazKLf5+PiY7d+uXTt07NjRfFFyOYYMGWKYqWnMfgHAyckJHh4eRg8iW2fpTKcffmjdOkz8pAWSBFB963U19K9/0lqzKiIigwaFGkdHR4SGhiIlJcWoPSUlBeHh4WY/ExYWZtL/wIEDGDx4MBwcHMx+RgiBrKws+Pr6Nnq/RG2Vza6p+fxz4BCABQD+fuv5EIAvvrBiUUREdzT4isKzZ8/GlClTMHjwYISFhSExMRGFhYWYMWMGAP0hnwsXLmDz5s0AgBkzZuBvf/sbZs+ejWnTpiE9PR3Jycn4otYvwvfffx/Dhw9HcHAwdDodVq5ciaysLKxatare+yWSiiFDgNhY49O6Y2NtYE3Nb78BDwGYCv0/h2oAfAbgLuvaiIhaU4NDTUxMDEpLS7Fw4UJotVqEhIRg3759UKvVAACtVmt07ZjAwEDs27cPs2bNwqpVq+Dn54eVK1di4sSJhj5//PEHpk+fjqKiIiiVSgwaNAjfffcdhg4dWu/9EknJxo3AK6/oDzmNGGEDgQYAnK/dCTS49TwVwL+vWa8mIqJaZOL2ql07oNPpoFQqUVZWxvU1RA31/lgg+CvT9tyxwHt7W78eIrIb9f3+5r2fiKh+hnY13z6Es6VEZBsYaoioXn67WWmhvcpsOxFRa2OoIaJ6+flMewvt7q1cCRGReQw1RFQvKWeehRDA8evAssv6ZyGAb3MnWbs0IiIAjTj7iYjsU3H1EDzwY3ekud653HH4te7oc9MWTs0iIuJMDRHVU8d+x5HmYnz/hjSXs+jYz9r3byAi0mOoIaJ6KXT+F1D3VmsyoNCJp3MTkW1gqCGyVdc0wG+p+mcbUFbqbL79d6dWroSIyDyGGiJbdDYZ2KUGvh2lfz6bbO2K8EBf83fjfqCPjdylW6MBUlP1z0RklxhqiGzNNQ1wdBr0N1eC/vnodKvP2DzaKxyoqXP8qUaG0b3CrFNQbcnJgFoNjBqlf062fggkotbHUENkay6lAah795Ia4FK6NaoxKL+oAv6VBNTc+rVRIwf+lYSrWpVV64JGA0ybBtTcCoE1NfrXnLEhsjs8pZuI6iU4GJD/Jx41Z6MAz1zg9yAorqoQZO2jT//6l/6CObUJAezZA8yYYZ2aiMgqOFNDZGvaB1po79qqZdSlUgGJiYDiqgrIHwnFVRXWrdO3W9Wvv5pvP3OmdesgIqvjTA2RrblZbqH9auvWYUZ8PBAVBeTmAkFBNhBoAKBzZ/PtXl6tWwcRWR1DDZGtcQ+GfhK15k6bTAG4W/s4j55KZSNhhoioDh5+IrI1ripgWKI+yAD656Hr9O1kqrjYfPulS61bBxFZHWdqiGxR93jANwq4kqufoWGgsaxnTwCABl2Qg2AEIwcqXAB69LByYUTU2jhTQ2SjNDeB1GsCmpvWrsTGDR6MZLyAABRgFFIRgAIk4wUgNNTalRFRK2OoIbJBySeToV6uxqjNo6BerkbySV5MzhJNXhWmIQkC+sN1AgpMQyI0+UyDRPaGoYbIxmh0Gkz71zTUCP1C4RpRg+l7pkOj48XkzEkr7QlR51eZgALppTz8RGRvGGqIAJu6eWTa+TSIOlcUrhE1SD9v3SsK26yOHRvWTkSSxYXCRGeT9fdWQg0Auf7Mo+7xViun9Fppg9rtXaCFaxV27dqqZZin0QA5OfrLMfM8eKIWx5kasm82ePPIji7mZxg6unLmwZxyC9cqvGrtaxUmJwMBAfqbbAYE8CabRK2AoYbsmw3ePDI8IBwyGN8NWwYZwvxt4G7YNshSeLEUdlqFRgNMnXrnnlRC8CabRK2AoYbsW4WFQzqW2luBykOFpPFJkN/631MOOZLGJ0HlwcMX5li6xVNubuvWYeRf/zJtu32TTSJqMVxTQ2SD4u+PR1RQFHJ/z0WQZxADzV1YusaeVe8ezptsElkFQw3ZNycL61QstbcilYeKYaYe3NzMt7dv37p1GHFyMt/u6Ni6dRDZGR5+IvvWztVCu0vr1kGNFhwMyOv8JlMorDxTU1Jivr2UZ7ARtSSGGrJvJcfMt5eeaN06qNFUKiAxUR9kAP3zunVWPoNaJrt3HyJqdjz8RPbNxdd8u7NP69ZBTRIfD0RF6RcHBwXZwCVhunUDYOYmm7faiahlMNSQfes42EI7b4bY1qhUNhBmbgsKQjJewHQkogYKyFGNRExHvFWPiRFJHw8/kX0rzwMAaKqA1Gv6Z317vtVKorZPExiBaUhCza2bbNZAgelIhKbrA1aujEjaGGrIvlWUIrkMUOcDoy7on5PLYNXr1FDbl5bna3KTzRookJ5v4XAnETULhhqya5qbwLRio5skYHqxvp2osSyd5GQzJz8dPw4sW6Z/JpIQhhqya2k3zN4kAek3rFENSYVN3zg8Lg4YOhR44w39c1yctSsiajYMNWTXDl44ZbY99cKPrVwJSYmrhcsfuVj78kfHjwObNhm3bdrEGRuSjEaFmtWrVyMwMBDOzs4IDQ3F4cOH79r/0KFDCA0NhbOzM7p164a1a9cavZ+UlISIiAh06NABHTp0wOjRo3HsmPH1QxYsWACZTGb08PHhabfUNKcvnTbb/lPJT61cCUnJMQuXPzph7csfmbsnFQDs3du6dRC1kAaHmm3btiEhIQHz5s1DZmYmIiIiEB0djcLCQrP98/LyMHbsWERERCAzMxNvv/02XnvtNezYscPQ5+DBg5g0aRJSU1ORnp6OgIAAREZG4sKFC0bb6tu3L7RareFx6pT5f2WTDSs9DmQv0z/bgJDOIWbb+3r1beVKSEp8LawHtvq/w5ydzbdbuq0DURvT4OvULFu2DPHx8Zg6dSoAYPny5di/fz/WrFmDxYsXm/Rfu3YtAgICsHz5cgBA7969ceLECSxduhQTJ04EAHz++edGn0lKSsL//u//4ttvv8Xzzz9/p9h27Tg705alxwF5taa+A2OBsI3WqgYA0M+7n9n2/t79W7kSkpLx44GXXzZuk8mAceOsU4+Bpevk8Po5JBENmqmprKxERkYGIiMjjdojIyORlpZm9jPp6ekm/aOionDixAlUVVWZ/cy1a9dQVVUFT09Po/acnBz4+fkhMDAQzzzzDM6dO3fXeisqKqDT6YweZCWlx40DDaB/beUZm46u5lduWmonqg+VCvjsszv3pJLLgaQkG7g4YHg4IJNBgy5IxUho0EWftsLCrFwYUfNoUKgpKSlBdXU1vL29jdq9vb1RVFRk9jNFRUVm+9+8eRMlFm769tZbb6FLly4YPXq0oW3YsGHYvHkz9u/fj6SkJBQVFSE8PByldzlHcvHixVAqlYaHv79/fX9Uam7FFtZdXfqhdeuoI9w/HDIY36dHLpMjzJ+/5Klp4uOBggIgNVX/HB9v7YoAqFRIfv4Q1CjAKKRCjQIkP3/IBtIWUfNo1EJhWZ2btQkhTNru1d9cOwB89NFH+OKLL7Bz50441zr+Gx0djYkTJ6Jfv34YPXo09t5a2Lap7kr+WubOnYuysjLD4/z58/f+4ahlOHmab3e8r1XLqEvloULS+CQoZPorvypkCiSOS4TKg7/kqelUKmDkSNvJDBoNMG1zhPGVjv8eAY3GyoURNZMGranp1KkTFAqFyaxMcXGxyWzMbT4+Pmb7t2vXDh3rXLRh6dKl+Mtf/oJvvvkG/fvffU2Dm5sb+vXrh5ycHIt9nJyc4MQFcLbh9yzz7Zetf+p0/P3xiAqKQu7vuQjyDGKgIclKSwNEnQsz1dQA6enAn/5knZqImlODZmocHR0RGhqKlJQUo/aUlBSEh4eb/UxYWJhJ/wMHDmDw4MFwcHAwtH388cdYtGgRvv76awwebOEmg7VUVFQgOzsbvpZOMyDbct384UmL7a1M5aHCyK4jGWhI0mz6Sscajf5YHaeNqAkafPhp9uzZ+Oyzz7B+/XpkZ2dj1qxZKCwsxIwZMwDoD/nUPmNpxowZKCgowOzZs5GdnY3169cjOTkZc+bMMfT56KOP8M4772D9+vXo2rUrioqKUFRUhPLyckOfOXPm4NChQ8jLy8PRo0fx1FNPQafTITY2tik/P7UWFwtnrVlqJ6JmZ7NXOk5OBgICgFGj9M/JyVYuiNqqBoeamJgYLF++HAsXLsTAgQPx3XffYd++fVCr1QAArVZrdM2awMBA7Nu3DwcPHsTAgQOxaNEirFy50nA6N6C/mF9lZSWeeuop+Pr6Gh5Lly419NFoNJg0aRJ69uyJJ598Eo6Ojjhy5Ihhv2TjAp8FABy/Diy7rH8GAHSdZL2aiOxMYKD59q5dW7UMYxoNMG3aneNiQuhfc8aGGkEmRN0jrNKl0+mgVCpRVlYGDw8Pa5djd+ISg7BJe9bwOta3OzZOz7ViRUT2JTVVPxlirn3kyFYvR2/7diAmxnw7F/rQLfX9/ua9n6Tsmgb4LVX/bGXHLxw3CjQAsEl7Fscv2MaVhYnsQXDwnWvn3KZQ8Np7JB0MNVJ1NhnYFQB8O0r/fNa6x6g///Fzs+1fnPqilSshsl8qFZCYqA8ygP553Torn3Ju7oKAcjkvCEiNwlAjRdc0wNFpAG4fWRT619acsbF8GSOiZnH8wnEsS1/G2b97iI8H8vP1h5zy823gooDmLgg45aBtXdyHZ2W1GQw1UnQpDXcCzW0CuJRujWoAAM/2e9Zs+6R+XChMTRe3Kw5DPxuKNw68gaGfDUXcrjhrl2TTbOmigDZ9QUCeldXmMNRIUdFBC+2prVpGbUO6DEHsAOPT72MHxGJIlyFWqoik4viF49j0H+Mri2/6zybO2LQRd7sgoFXxrKw2iaFGin7PMN9++WTr1lHHxgkbcWzqMfw16q84NvUYNk7YaNV6SBoOF5q/r9gP5617XzGqH5u9IKC5tCWEDaQtupsG3SaB2ghHJQBAUwXkVAHBDoDKAYCD9U9jH9JlCGdnqFlFBESYbR/hP6KVK6HGsNkLAtps2qK74UyNFCn7ILkM8M8HRl3QPyeXAbgvxMqFETU/Htps226d/GREJrOBk59upSqjs7JqtZNtYqhpDjZ0PRgA0HiNxtRi47apxYCmk5mrbhFJAA9ttl0qFZCUdOf6OXK5/rXVFzGHhyMZ8Qi4dVZWAAqQLJtqA2kLPCPrLnj4qanOJtc6fVoGDEsCulv3HMnNBebvfP33glOYqx7XytUQtQ4e2my74uOBqCggN1d/IUCrBxoAGqgwDYkQt/7tL6DANKxDFOSwannJyXcWMMtk+gRo9fPybQdnaprCFq8HA+D7wu8b1E5EZG22dJo5cGudcJ2vSCHk1l0nzDOy7omhpils8HowAODr7mu23c/dr5UrISJqm2xynTDPyLonhpqmqLDwt9tSeyuZMXiG2fbpg6e3ciVERG2TTZ6VZZNJy7Yw1DSF063V8VVA6jX9c+12a+HZIERETWPurCyr35LK1s/IsoEFzAw1TeEVjuQyQJ2vP3VanQ8kl8kAL+uvjufZIEREjXf7rKzaN/9MTLT+zT9t9owsG7mlhEyIugfopEun00GpVKKsrAweHk2/EJ1Gp4H6rwGoqbWuRiGTIz+hACoPG1ntRkREjabR2M5ZWRoNEOBfY7SAWSarQWGh3Lq1aTT6IFM7TshkQGFhsw1afb+/OVPTBDmlOUaBBgCqRQ1yf8+1UkVERNScbOmsLJs8IwuwqQXMDDVN0N6xvdl2Nwe3Vq6EiIikzmbXCeda+Ie8pfYWxFDTBOWV5Wbbr1ZdbeVKiIhI6mzyjCwAKNZfwt5kAfOlS61eCkNNEwR3DIZcZjyECpkCQZ5BVqqIiIikymbvk9WzJ5LxAvxvLWD2RwGS8QLQo0erl8JQ0wQqDxUSxyVCIdMvj1fIFFg3bh0XCRMRUbOz1ftkaQZPwFQkAbh1qhgUmIokaEKfaPVaeO+nJoq/Px5RQVHI/T0XQZ5BDDRERNRibPE+WZ/+r7mr2Mvxtx2+WNLKl0djqGkGKg8VwwwREbUKlco2wsxtmZnm27OyWrUMADz8RERERE0webL59meead06AIYaIiIiaoK4OKB7d+O27t317a2NoYaIiIiaJDcX2LABmDBB/2yFS9QA4G0SiIiIyMbxNglERERkVxhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBLaWbuA1nT7Nlc6nc7KlRAREVF93f7evtftKu0q1Fy5cgUA4O/vb+VKiIiIqKGuXLkCpVJp8X27ukt3TU0NLl68CHd3d8hksmbbrk6ng7+/P86fP8+7f98Dx6r+OFYNw/GqP45V/XGs6q8lx0oIgStXrsDPzw9yueWVM3Y1UyOXy6FSqVps+x4eHvxLX08cq/rjWDUMx6v+OFb1x7Gqv5Yaq7vN0NzGhcJEREQkCQw1REREJAkMNc3AyckJ8+fPh5OTk7VLsXkcq/rjWDUMx6v+OFb1x7GqP1sYK7taKExERETSxZkaIiIikgSGGiIiIpIEhhoiIiKSBIYaIiIikgS7DDWLFy/GkCFD4O7ujs6dO2PChAn49ddfjfrExcVBJpMZPYYPH27Up6KiAq+++io6deoENzc3PP7449BoNEZ9Ll++jClTpkCpVEKpVGLKlCn4448/jPoUFhZi/PjxcHNzQ6dOnfDaa6+hsrKyRX72hqrPWAFAdnY2Hn/8cSiVSri7u2P48OEoLCw0vM+x0qv7d+r24+OPPzb04VjplZeXY+bMmVCpVHBxcUHv3r2xZs0aoz72MFZA/cbrt99+Q1xcHPz8/ODq6ooxY8YgJyfHqI89jNeaNWvQv39/wwXgwsLC8NVXXxneF0JgwYIF8PPzg4uLC0aOHImffvrJaBv2ME7Avcdq586diIqKQqdOnSCTyZCVlWWyDZsbK2GHoqKixIYNG8Tp06dFVlaWeOyxx0RAQIAoLy839ImNjRVjxowRWq3W8CgtLTXazowZM0SXLl1ESkqKOHnypHj44YfFgAEDxM2bNw19xowZI0JCQkRaWppIS0sTISEhYty4cYb3b968KUJCQsTDDz8sTp48KVJSUoSfn5+YOXNmyw9EPdRnrHJzc4Wnp6f485//LE6ePCnOnj0r9uzZI3777TdDH46VXu2/T1qtVqxfv17IZDJx9uxZQx+Old7UqVNF9+7dRWpqqsjLyxPr1q0TCoVC7Nq1y9DHHsZKiHuPV01NjRg+fLiIiIgQx44dE7/88ouYPn26yZjaw3jt3r1b7N27V/z666/i119/FW+//bZwcHAQp0+fFkIIsWTJEuHu7i527NghTp06JWJiYoSvr6/Q6XSGbdjDOAlx77HavHmzeP/990VSUpIAIDIzM022YWtjZZehpq7i4mIBQBw6dMjQFhsbK5544gmLn/njjz+Eg4OD2Lp1q6HtwoULQi6Xi6+//loIIcTPP/8sAIgjR44Y+qSnpwsA4pdffhFCCLFv3z4hl8vFhQsXDH2++OIL4eTkJMrKyprrR2w25sYqJiZGPPfccxY/w7E6ZLHPE088IUaNGmV4zbG6M1Z9+/YVCxcuNOp3//33i3feeUcIYb9jJYTpeP36668CgOHLSAj9F4Wnp6dISkoSQtj3eHXo0EF89tlnoqamRvj4+IglS5YY3rtx44ZQKpVi7dq1Qgj7Hich7oxVbXl5eWZDjS2OlV0efqqrrKwMAODp6WnUfvDgQXTu3Bk9evTAtGnTUFxcbHgvIyMDVVVViIyMNLT5+fkhJCQEaWlpAID09HQolUoMGzbM0Gf48OFQKpVGfUJCQuDn52foExUVhYqKCmRkZDT/D9tEdceqpqYGe/fuRY8ePRAVFYXOnTtj2LBh2LVrl+EzHCtPs+//9ttv2Lt3L+Lj4w1tHKs7Y/XAAw9g9+7duHDhAoQQSE1NxZkzZxAVFQXAfscKMB2viooKAICzs7Ohj0KhgKOjI77//nsA9jle1dXV2Lp1K65evYqwsDDk5eWhqKjIaAycnJzw0EMPGX4+exwnwHSs6sMWx8ruQ40QArNnz8YDDzyAkJAQQ3t0dDQ+//xz/Pvf/8Ynn3yC48ePY9SoUYZfHkVFRXB0dESHDh2Mtuft7Y2ioiJDn86dO5vss3PnzkZ9vL29jd7v0KEDHB0dDX1shbmxKi4uRnl5OZYsWYIxY8bgwIED+K//+i88+eSTOHToEACOVe2/V7Vt2rQJ7u7uePLJJw1tHKs7Y7Vy5Ur06dMHKpUKjo6OGDNmDFavXo0HHngAgH2OFWB+vHr16gW1Wo25c+fi8uXLqKysxJIlS1BUVAStVgvAvsbr1KlTaN++PZycnDBjxgx8+eWX6NOnj6G+uvXXHQN7GSfA8ljVhy2OlV3dpducmTNn4scffzT8a+a2mJgYw59DQkIwePBgqNVq7N271+hLqC4hBGQymeF17T83pY8tMDdWNTU1AIAnnngCs2bNAgAMHDgQaWlpWLt2LR566CGL27O3sapr/fr1ePbZZ43+dW2JPY7VypUrceTIEezevRtqtRrfffcdXn75Zfj6+mL06NEWtyflsQLMj5eDgwN27NiB+Ph4eHp6QqFQYPTo0YiOjr7n9qQ4Xj179kRWVhb++OMP7NixA7GxsYZ/ZAGm9dendimOE2B5rOobbMyx5ljZ9UzNq6++it27dyM1NRUqlequfX19faFWqw1nE/j4+KCyshKXL1826ldcXGxInD4+Pvjtt99MtnXp0iWjPnWT6OXLl1FVVWWSXK3J0lh16tQJ7dq1M/kfoHfv3oaznzhWpg4fPoxff/0VU6dONWrnWOldv34db7/9NpYtW4bx48ejf//+mDlzJmJiYrB06VIA9jdWwN3/boWGhhq+nLRaLb7++muUlpYiMDAQgH2Nl6OjI4KCgjB48GAsXrwYAwYMwIoVK+Dj4wMAJvXXHQN7GSfA8ljVh02OVYNW4EhETU2NeOWVV4Sfn584c+ZMvT5TUlIinJycxKZNm4QQdxZIbdu2zdDn4sWLZhdIHT161NDnyJEjZhdIXbx40dBn69atNrOYrD5jFRYWZrJQeMKECWLSpElCCI6VObGxsSI0NNSknWOlV1ZWJgCIffv2GbVPnz5dPProo0II+xkrIRr3O+vMmTNCLpeL/fv3CyHsa7zqGjVqlIiNjTUsFP7www8N71VUVJhdKGyP4yTEnbGq7V4LhW1prOwy1Lz00ktCqVSKgwcPGp1ee+3aNSGEEFeuXBFvvPGGSEtLE3l5eSI1NVWEhYWJLl26mJz2p1KpxDfffCNOnjwpRo0aZfZUtv79+4v09HSRnp4u+vXrZ/ZUtkceeUScPHlSfPPNN0KlUtnMaX/3GishhNi5c6dwcHAQiYmJIicnR3z66adCoVCIw4cPG/pwrO4oKysTrq6uYs2aNWa3w7HSe+ihh0Tfvn1FamqqOHfunNiwYYNwdnYWq1evNvSxh7ESon7jtX37dpGamirOnj0rdu3aJdRqtXjyySeNtmMP4zV37lzx3Xffiby8PPHjjz+Kt99+W8jlcnHgwAEhhP6UbqVSKXbu3ClOnTolJk2aZPaUbqmPkxD3HqvS0lKRmZkp9u7dKwCIrVu3iszMTKHVag3bsLWxsstQA8DsY8OGDUIIIa5duyYiIyOFl5eXcHBwEAEBASI2NlYUFhYabef69eti5syZwtPTU7i4uIhx48aZ9CktLRXPPvuscHd3F+7u7uLZZ58Vly9fNupTUFAgHnvsMeHi4iI8PT3FzJkzxY0bN1pyCOrtXmN1W3JysggKChLOzs5iwIABRtcSEYJjVdu6deuEi4uL+OOPP8xuh2Olp9VqRVxcnPDz8xPOzs6iZ8+e4pNPPhE1NTWGPvYwVkLUb7xWrFghVCqV4XfWO++8IyoqKoy2Yw/j9cILLwi1Wi0cHR2Fl5eXeOSRRwxf0kLoZ73mz58vfHx8hJOTk3jwwQfFqVOnjLZhD+MkxL3HasOGDWb/3s2fP9/Qx9bGSiaEEA07YEVERERke+x6oTARERFJB0MNERERSQJDDREREUkCQw0RERFJAkMNERERSQJDDREREUkCQw0RERFJAkMNERERSQJDDREREUkCQw0RERFJAkMNERERSQJDDREREUnC/wfJsPk8vJhYuAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "call_ask_strike=df[(df.type==\"a\") & (df.option_type==\"C\")].strike\n",
    "call_ask_price=df[(df.type==\"a\") & (df.option_type==\"C\")].price\n",
    "plt.scatter(call_ask_strike, call_ask_price, color=\"red\", marker=\".\", label=\"Call ask\")\n",
    "\n",
    "call_bid_strike=df[(df.type==\"b\") & (df.option_type==\"C\")].strike\n",
    "call_bid_price=df[(df.type==\"b\") & (df.option_type==\"C\")].price\n",
    "plt.scatter(call_bid_strike, call_bid_price, color=\"blue\", marker=\".\", label=\"Call bid\")\n",
    "\n",
    "put_ask_strike=df[(df.type==\"a\") & (df.option_type==\"P\")].strike\n",
    "put_ask_price=df[(df.type==\"a\") & (df.option_type==\"P\")].price\n",
    "plt.scatter(put_ask_strike, put_ask_price, color=\"orange\", marker=\".\", label=\"Put ask\")\n",
    "\n",
    "put_bid_strike=df[(df.type==\"b\") & (df.option_type==\"P\")].strike\n",
    "put_bid_price=df[(df.type==\"b\") & (df.option_type==\"P\")].price\n",
    "plt.scatter(put_bid_strike, put_bid_price, color=\"green\", marker=\".\", label=\"Put bid\")\n",
    "\n",
    "plt.legend()\n",
    "print (\"Number of Call data=\",np.size(call_ask_price)+np.size(call_bid_price))\n",
    "print (\"Number of Put data=\",np.size(put_ask_price)+np.size(put_bid_price))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "926707c0",
   "metadata": {},
   "source": [
    "# What is the forward?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dead38f",
   "metadata": {},
   "source": [
    "\n",
    "## We gather data by strikes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dcdd668c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[25000. 25500. 26000. 26500. 27000. 27500. 28000. 28500. 29000. 29500.\n",
      " 30000. 30500. 31000.]\n"
     ]
    }
   ],
   "source": [
    "list_of_strikes=[]\n",
    "for strikes in df.strike:\n",
    "    if strikes not in list_of_strikes:\n",
    "        list_of_strikes.append(strikes)\n",
    "list_of_strikes=np.sort(list_of_strikes) \n",
    "print(list_of_strikes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa96a138",
   "metadata": {},
   "source": [
    "## We compute the best bid and the best ask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "68b8c85e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1279c2690>"
      ]
     },
     "execution_count": 5,
     "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": [
    "best_bid_call=[]\n",
    "best_ask_call=[]\n",
    "best_bid_put=[]\n",
    "best_ask_put=[]\n",
    "for strikes in list_of_strikes:\n",
    "    best_bid_call=np.append(best_bid_call, np.max(df[(df.type==\"b\") & (df.option_type==\"C\") & (df.strike==strikes)].price))\n",
    "    best_ask_call=np.append(best_ask_call, np.min(df[(df.type==\"a\") & (df.option_type==\"C\") & (df.strike==strikes)].price))\n",
    "    best_bid_put=np.append(best_bid_put, np.max(df[(df.type==\"b\") & (df.option_type==\"P\") & (df.strike==strikes)].price))\n",
    "    best_ask_put=np.append(best_ask_put, np.min(df[(df.type==\"a\") & (df.option_type==\"P\") & (df.strike==strikes)].price))\n",
    "\n",
    "plt.plot(list_of_strikes, best_bid_call, label=\"best bid call\")\n",
    "plt.plot(list_of_strikes, best_ask_call, label=\"best ask call\")\n",
    "plt.plot(list_of_strikes, best_bid_put, label=\"best bid put\")\n",
    "plt.plot(list_of_strikes, best_ask_put, label=\"best ask put\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90aa7206-3579-44c2-b78b-2446f66ecb0d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e959e412-d1c9-40c8-b39e-38a84917ef1c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": false,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {
    "height": "491px",
    "left": "10px",
    "top": "150px",
    "width": "165px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}