Twitter-Crypto_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d448e4b8",
   "metadata": {},
   "source": [
    "# Cryptocurrencies Community Engagement Analysis\n",
    "\n",
    "The goal of this notebook is to analyse the twitter activity of the crypto ecosystem on selected cryptocurrencies to gain insights on the community engagement"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7ee35c2",
   "metadata": {},
   "source": [
    "# About the dataset\n",
    "\n",
    "Let's understand what is the data and what it looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "206d9c87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            tweetDate  \\\n",
      "0      Wed May 11 01:38:14 +0000 2022   \n",
      "1      Thu Apr 28 05:51:55 +0000 2022   \n",
      "2      Tue May 10 16:36:13 +0000 2022   \n",
      "3      Wed May 11 08:21:35 +0000 2022   \n",
      "4      Tue May 10 21:32:03 +0000 2022   \n",
      "...                               ...   \n",
      "11336  Sat May 14 14:19:57 +0000 2022   \n",
      "11337  Sat May 14 19:30:36 +0000 2022   \n",
      "11338  Sat May 14 19:51:22 +0000 2022   \n",
      "11339  Sun May 15 01:15:56 +0000 2022   \n",
      "11340  Sat May 14 19:29:46 +0000 2022   \n",
      "\n",
      "                                                 content  \\\n",
      "0      🎉 GIVEAWAY 🎉\\n\\nWe Partnered Up with @Fightbac...   \n",
      "1      Elon yapmış yapacağını yine. Favlayınca tesla ...   \n",
      "2      #BITCOIN ARE YOU READY FOR THE HUGE PUMP ????\\...   \n",
      "3      Same month last year\\n\\n#btc #cryptocrash #Bit...   \n",
      "4      Enjoy the dump boys \\nWhile am helping @crypto...   \n",
      "...                                                  ...   \n",
      "11336  Time for a #weekend #NFTGiveaway - I will be g...   \n",
      "11337  ✏️  Endless prospects, endless Business $SHIDO...   \n",
      "11338  🔥Staking pool is on fire🔥 only on @CronosBay \\...   \n",
      "11339  @RGVaerialphotos @RealShibaDoge @Shib_nobi @Sh...   \n",
      "11340  ✏️   Epitomize yourself everywhere by $SHIDO\\n...   \n",
      "\n",
      "                           twitterProfile  \\\n",
      "0          https://twitter.com/CitrusWGMI   \n",
      "1          https://twitter.com/CemErguler   \n",
      "2      https://twitter.com/AurelienOhayon   \n",
      "3       https://twitter.com/kennedybrand1   \n",
      "4            https://twitter.com/bull_bnb   \n",
      "...                                   ...   \n",
      "11336  https://twitter.com/cryptopeerani1   \n",
      "11337    https://twitter.com/venusha_MIMA   \n",
      "11338  https://twitter.com/Lil_Anubis_nft   \n",
      "11339   https://twitter.com/THExALPHAxADA   \n",
      "11340    https://twitter.com/venusha_MIMA   \n",
      "\n",
      "                                                tweetUrl  \\\n",
      "0      https://twitter.com/CitrusWGMI/status/15242021...   \n",
      "1      https://twitter.com/CemErguler/status/15195549...   \n",
      "2      https://twitter.com/AurelienOhayon/status/1524...   \n",
      "3      https://twitter.com/kennedybrand1/status/15243...   \n",
      "4      https://twitter.com/bull_bnb/status/1524140234...   \n",
      "...                                                  ...   \n",
      "11336  https://twitter.com/cryptopeerani1/status/1525...   \n",
      "11337  https://twitter.com/venusha_MIMA/status/152555...   \n",
      "11338  https://twitter.com/Lil_Anubis_nft/status/1525...   \n",
      "11339  https://twitter.com/THExALPHAxADA/status/15256...   \n",
      "11340  https://twitter.com/venusha_MIMA/status/152555...   \n",
      "\n",
      "                      timestamp    query  \n",
      "0      2022-05-11T12:20:21.508Z     #BTC  \n",
      "1      2022-05-11T12:20:21.508Z     #BTC  \n",
      "2      2022-05-11T12:20:21.508Z     #BTC  \n",
      "3      2022-05-11T12:20:21.508Z     #BTC  \n",
      "4      2022-05-11T12:20:21.508Z     #BTC  \n",
      "...                         ...      ...  \n",
      "11336  2022-05-15T02:09:57.474Z  #crypto  \n",
      "11337  2022-05-15T02:09:57.474Z  #crypto  \n",
      "11338  2022-05-15T02:09:57.474Z  #crypto  \n",
      "11339  2022-05-15T02:09:57.474Z  #crypto  \n",
      "11340  2022-05-15T02:09:57.474Z  #crypto  \n",
      "\n",
      "[11341 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#Read in the tweets dataset\n",
    "tweets = pd.read_csv(\"Dataset/Twitter_tophashtags_crypto.csv\")\n",
    "print(tweets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "87da488e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "💥BREAKING: El Salvador just bought $15.4m #Bitcoin \n",
      "Another 500 #BTC off the market!🙌\n"
     ]
    }
   ],
   "source": [
    "#Example of a tweet\n",
    "print(tweets[\"content\"].iloc[20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "90db208e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Just picked up these cuties! @SleepySachis NFTs #StaySleepy #SleepySols #Sol #Solana #SolanaNFT https://t.co/spp22gIy6M\n"
     ]
    }
   ],
   "source": [
    "#Example of a tweet related to a specific hashtag\n",
    "print(tweets[tweets[\"query\"] == \"#SOL\"]['content'].iloc[50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e07960a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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o5Yxj3GXXKdVuJa0GXmz7nvL8rd0PMyIiRtP1xDHBsusRETGgun5zfIJl10dzSMP+y4F1PtUIKaseEdEtvVqqGqvsep3Zxzdb3ONYh+3FwGKAqdvtvP7XVYmI6JGuJo5JlF2PiIgB1dWlqkmUXY+IiAHVi5vjEym7/tdOxpCy6hERnZOy6hER0VLKqkdEREckcURERC1JHBERUUsSR0RE1JLEERERtSRxRERELenHERGjSq+OaNazGYekBZJmS5ojaX4ZWyppuGGfIUk3lcfTJJ0l6UZJN0n6gaSdGgoc3iXpzobnm/TqWiIiNmSD0I+jnfcCv7G9O4CkXYC7SgkTJH0UuN/2p7oSbUREtDTI/Ti2A34x8sT2rV0LMiIixm0Q+nG08zXgA5KulvRxSTvXed3044iI6I5e3eNo14+jVaEsQ1VZF3gWsAjYEvixpGeP9wVtL7Y9bHt4yrTNJhh2REQ063c/jnuBLRoO2bLsB4Dt+4ELgAskPQK8GrilmzFHRMTo+t2PYylwuCSVQ44ErgCQtL+kLcrjTcrxvyAiIvqq3/04FlMtX62QZGAZcELZNgM4vSSVjYB/A86fSAzpxxER0TnpxxERES2lH0dERHREEkdERNSSxBEREbUkcURERC1JHBERUUsSR0RE1NKz6riSFgCXApsDM20vlLQvcBowtXx90/ZHy/5zqKrpbgI8BHzI9r+WbUuAi22Pp8pu+nFERPqKdFC/y6p/HXiT7RWSpgC7AEjaE/gU8DLbqyRNBy6TdIftlT2MOSIimvS7rPrTgF8D2F7LowUQjwM+YXtV2baqzFjmAUd0O+aIiGiv32XVPw3cKulCSe+QtGk57DnAdU2nWlbGxyVl1SMiuqOvZdVL8hgGvgu8GbikbBLrllxvNdZWyqpHRHRHX8uq237Q9u1UxQy/AvxW0lbAzVQJpfF+xnN5bC+PiIjog76WVZf0moaS6jsDa4HfU90YP0HSEED5/kHgn7oZb0REjK3fZdWPAD4t6QHgYeCwcpN8uaQPAN+R9ASqt+MeX7oCjviypM+Ux7+yvV+7GFJWPSKic1JWPSIiWkpZ9YiI6IgkjoiIqCWJIyIiakniiIiIWpI4IiKiliSOiIiopZfVcfsmZdUjohs21FLtHZ1xSFogabakOZLml7Elkt7QtN9SScMNz4ck3VQez5ZkSX/bsP1iSbMbnm8j6SFJ7+hk/BERMbZOL1XtA1wDHABcNYnzrAFOHGX7G4EfAXMn8RoRETEBHUkckhZJWklVNv1qqjLqp0v68ARPuQK4T9LL2myfC/wDsIOk7Sf4GhERMQEdSRxj9NyYqI8DJzUPStoR2Nb2tcC3gENaHZx+HBER3dHJpaqWPTfaaFUg6zFjtq8CkPSipv0OpUoYAOfQZrkq/TgiIrpj0u+qGqvnRpvD7gW2aHi+ZTm22SlU9zoebhibCzxd0mHl+TMk7Wz75xO8hIiIqGHSM46xem60OWwpcHhDL44jgStanPu7VAlmTwBJuwBPsr297SHbQ8ACqllIRET0QEc+xzFGzw1o6p1B9a6rmcAKSabqJ35Cm9OfAny7PJ4LXNi0/XyqJat/bBdf+nFERHRO+nFERERL6ccREREdkcQRERG1JHFEREQtSRwREVFLEkdERNSSxBEREbVM+HMckhYAlwKbAzNtL+xUUC1e64O2PzHR49OPIyL6aX3r2zGZGUftEuqSpkzwtT44weMiIqLDas84JC0CXgFMpyqhPgM4UNJ5wL8AXwK2AdZS9c3YEfgI8GtglqTzgXtsn1bOdwrwG2AlcDJVHatdgCuBdwGfAJ5Yal/dbPswSccCbyshnWH7M7WvPCIiJqR24rA9T9K5wBHAscBS2/sDSLoGWGj7QkmbUs1odgT2BnazvUrSEHABcJqkjajqTO0N7F6+7wr8ArgEeL3t+ZKOKfWwkPQ84O+oZjwCrpH077ZvmOgPISIixm+iS1XrlFCX9BRge9sXAtj+s+0Hyv7X2l5VxlcD90raC3g5cIPtexv2u8P2WuBs4IUtXvuFwIW2/2T7fqok1Fx6Pf04IiK6pNaMY4wS6geMcuifmp6fAbwV2Bb4WsN4c+GsVoW01GJsHbYXA4sBpm638/pfkCsiokdqzTjGKKF+H7BG0hwASVMlTWtzqguBV1J1C7y0YXxvSdPLEtYhwA/K+EOSnlAeXwnMkTRN0pOAg5lcf/OIiKhhIjfHRyuhfgRVCfWTgYeobo6vw/ZfJV0B/L4sS424GlhIdb/jSh4tob4YWCnp+nJzfAlwbdl2xlj3N1JWPSKic/pSVr3MKK4H3jjSuU/SbOA426/t9OulrHpERH0DU1Zd0q7AbcDlafcaEfH405EOgHWUpa1ntRhfStVSNiIiBlhqVUVERC1JHBERUUsSR0RE1JLEERERtfT85ng/pKx6RGyIulXOvdaMQ9ICSbMlzZE0v4wtkbRK0nJJP5X0kYb9zyhvv0XShEujS3qnpLdM9PiIiOicuktV7XpwzCulSGYBR0qaDmD7qIZPlk84cdj+ku0zJ3p8RER0zrgSh6RFklZS1Za6GjgKOF3Sh5t23bR8/1M5bqmkYUkLKT01JJ1Vtr1F0kpJKyR9o4ztJOnyMn65pGeW8Y9KOq7hnJ+UdK2kn0lapzJuRER0z7gSh+15VMliCVXyWGl7D9snl10WlQq5a4BzbN/ddPx84MFSDPEwSc8BTgReYntP4L1l188DZ9reAzgL+GybkDa2vTfwPqomUetIWfWIiO6os1S1Tg+OBiNLVdtSdQN8wRjneglwnu17AGz/rozvR9VFEOAbtO7HAVUPDoDrgKFWO9hebHvY9vCUaZuNEU5ERIzXmO+qGqMHx36N+9q+X9JSqj/4PxzttLTutdGs3T5/Kd/XsoG8MywiYlCMOeMYowfHg437StqY6gb67S1O1dhT43LgTZK2KsdtWcZ/SNVKFuAwHu3HERERA2Jc/1sfowcHVPc4TgI2oUoKF6xzknV7apwC/LuktcANVB0B3wN8TdI84LdUvcUnLf04IiI6py/9OHot/TgiIuobmH4cERHx+JbEERERtWwQS1WS/gjc2u84atia6h1sjxeJt7sSb3cl3vZ2sr1N8+CG8lbWW1ut0w0qScsSb/ck3u5KvN01CPFmqSoiImpJ4oiIiFo2lMSxuN8B1JR4uyvxdlfi7a6+x7tB3ByPiIjO2VBmHBER0SFJHBERUct6nTgkvVLSrZJuG2l122+SdpR0haRbJN0s6b1lfEtJl0n6efm+RcMxJ5RruFXSK/oU9xRJN0i6eNDjlbS5pPNKK+NbJO034PG+v/wu3CTpbEmbDlK8kr4m6W5JNzWM1Y5P0vMk3Vi2fVaSehjvovL7sFLShZI2H+R4G7YdJ8mSth6UeAGwvV5+AVOoqvQ+i6r44gpg1wGIazvgueXxU3i06vCpwPwyPh/4ZHm8a4l9KjC9XNOUPsR9LFWvlIvL84GNF/g6cFR5vAmw+aDGC2wPrAKeWJ5/i6rg58DEC/xX4LnATQ1jteMDrqVqxSDg/wCv6mG8L6dqAAfwyUGPt4zvCFwK/ALYelDitb1ezzj2Bm6zfYftvwLnAK/rc0zY/rXt68vjPwK3UP3xeB3VHzzK9znl8euouir+xfYq4Daqa+sZSTsArwHOaBgeyHglPZXqH+JXAWz/1fbvBzXeYmOq1sobU/W7+Q8GKF7bVwK/axquFZ+k7YCn2r7a1V+5MxuO6Xq8tr9r++Hy9EdU/YUGNt7i08DxPLYvUd/jhfV7qWp74FcNz9eUsYEhaYiqs+I1wNNt/xqq5AI8rew2CNfxGapf4EcaxgY13mdRleT/57K0doakJw1qvLbvBD4F/BL4NXCf7e8OarwN6sa3fXncPN4Pb6P6HzkMaLySDgLutL2iadNAxLs+J45W63sD895jSU8GzgfeZ/sPo+3aYqxn1yHptcDdtq8b7yEtxnr5c9+Yatp/uu29gD9RLaW00++f7xZU/4ucDjwDeJKkw0c7pMXYwPxe0z6+gYhb0onAw8BZI0MtdutrvJKmAScCH261ucVYz+NdnxPHGqo1whE7UC0B9J2qTojnA2fZHml69Zsy3aR8v7uM9/s69gcOkrSaarnvJZL+F4Mb7xpgje1ryvPzqBLJoMb7UmCV7d/afoiqCdoLBjjeEXXjW8Ojy0ON4z0j6UjgtcBhZTkHBjPeGVT/kVhR/t3tAFwvaVsGJN71OXH8GNhZ0nRJm1C1pL2ozzFR3unwVeAW2/+zYdNFwJHl8ZHAtxvGD5U0VdJ0YGeqm2A9YfsE2zvYHqL6GX7f9uEDHO9dwK8k7VKGDgR+MqjxUi1R7StpWvndOJDqvtegxjuiVnxlOeuPkvYt1/mWhmO6TtIrgQ8AB9l+oGHTwMVr+0bbT7M9VP7draF6Q81dAxNvt+66D8IX8Gqqdy3dDpzY73hKTC+kmkKuBJaXr1cDW1G13f15+b5lwzEnlmu4lS6+U2Icsc/m0XdVDWy8wCxgWfkZ/yuwxYDH+zHgp8BNwDeo3jEzMPECZ1Pdf3mI6o/Y2ycSHzBcrvF24POUyhU9ivc2qnsDI//mvjTI8TZtX015V9UgxGs7JUciIqKe9XmpKiIiuiCJIyIiakniiIiIWpI4IiKiliSOiIioJYkjIiJqSeKIiIha/hO8IumcBjRnEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What are the most commonly used hashtags in crypto from the pre-selected set?\n",
    "value_counts = tweets[\"query\"].value_counts().plot(kind='barh')\n",
    "print(value_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6edc833",
   "metadata": {},
   "source": [
    "# Data cleaning\n",
    "Before diving into the analysis, we should format the dates of the dataset, based on the tweetdate or timestamp columns, in order to do time series analysis.\n",
    "\n",
    "**Methods used**: Date format, outliers cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c13d8002",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  tweetDate  \\\n",
      "0 2022-05-11 01:38:14+00:00   \n",
      "1 2022-04-28 05:51:55+00:00   \n",
      "2 2022-05-10 16:36:13+00:00   \n",
      "3 2022-05-11 08:21:35+00:00   \n",
      "4 2022-05-10 21:32:03+00:00   \n",
      "5 2022-05-09 14:09:00+00:00   \n",
      "6 2022-05-10 20:23:56+00:00   \n",
      "7 2022-05-09 09:56:56+00:00   \n",
      "8 2022-05-10 00:33:04+00:00   \n",
      "9 2022-05-09 19:32:42+00:00   \n",
      "\n",
      "                                             content  \\\n",
      "0  🎉 GIVEAWAY 🎉\\n\\nWe Partnered Up with @Fightbac...   \n",
      "1  Elon yapmış yapacağını yine. Favlayınca tesla ...   \n",
      "2  #BITCOIN ARE YOU READY FOR THE HUGE PUMP ????\\...   \n",
      "3  Same month last year\\n\\n#btc #cryptocrash #Bit...   \n",
      "4  Enjoy the dump boys \\nWhile am helping @crypto...   \n",
      "5  Today's lunch.\\nThanks #BTC https://t.co/qf1S3...   \n",
      "6  #BITCOIN THE 4TH GREAT HISTORICAL CYCLE BEGINS...   \n",
      "7  Bunu kim çizdi bilmiyorum ama bayıldım😂\\n\\n#Bi...   \n",
      "8  You need to LEARN rapidly! #BTC\\n\\nIt's a Conf...   \n",
      "9  Que dice #Bukele ?? 😁😁 #bitcoin #BTC #cryptotr...   \n",
      "\n",
      "                       twitterProfile  \\\n",
      "0      https://twitter.com/CitrusWGMI   \n",
      "1      https://twitter.com/CemErguler   \n",
      "2  https://twitter.com/AurelienOhayon   \n",
      "3   https://twitter.com/kennedybrand1   \n",
      "4        https://twitter.com/bull_bnb   \n",
      "5        https://twitter.com/devchart   \n",
      "6  https://twitter.com/AurelienOhayon   \n",
      "7   https://twitter.com/EkinAlbayrak9   \n",
      "8      https://twitter.com/SC75351560   \n",
      "9      https://twitter.com/george28lw   \n",
      "\n",
      "                                            tweetUrl  \\\n",
      "0  https://twitter.com/CitrusWGMI/status/15242021...   \n",
      "1  https://twitter.com/CemErguler/status/15195549...   \n",
      "2  https://twitter.com/AurelienOhayon/status/1524...   \n",
      "3  https://twitter.com/kennedybrand1/status/15243...   \n",
      "4  https://twitter.com/bull_bnb/status/1524140234...   \n",
      "5  https://twitter.com/devchart/status/1523666347...   \n",
      "6  https://twitter.com/AurelienOhayon/status/1524...   \n",
      "7  https://twitter.com/EkinAlbayrak9/status/15236...   \n",
      "8  https://twitter.com/SC75351560/status/15238234...   \n",
      "9  https://twitter.com/george28lw/status/15237478...   \n",
      "\n",
      "                  timestamp query  \n",
      "0  2022-05-11T12:20:21.508Z  #BTC  \n",
      "1  2022-05-11T12:20:21.508Z  #BTC  \n",
      "2  2022-05-11T12:20:21.508Z  #BTC  \n",
      "3  2022-05-11T12:20:21.508Z  #BTC  \n",
      "4  2022-05-11T12:20:21.508Z  #BTC  \n",
      "5  2022-05-11T12:20:21.508Z  #BTC  \n",
      "6  2022-05-11T12:20:21.508Z  #BTC  \n",
      "7  2022-05-11T12:20:21.508Z  #BTC  \n",
      "8  2022-05-11T12:20:21.508Z  #BTC  \n",
      "9  2022-05-11T12:20:21.508Z  #BTC  \n"
     ]
    }
   ],
   "source": [
    "#Format date and see the result on first 10 tweets in the dataset\n",
    "tweets[\"tweetDate\"] = pd.to_datetime(tweets[\"tweetDate\"])\n",
    "print(tweets.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "247ada16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-11-25 01:39:01+00:00    1\n",
      "2021-03-21 00:51:57+00:00    1\n",
      "2021-11-19 15:57:35+00:00    1\n",
      "2022-01-05 01:17:00+00:00    1\n",
      "2022-01-24 00:02:37+00:00    1\n",
      "                            ..\n",
      "2022-05-15 01:38:57+00:00    1\n",
      "2022-05-15 01:41:58+00:00    1\n",
      "2022-05-15 01:53:15+00:00    1\n",
      "2022-05-15 02:04:07+00:00    1\n",
      "2022-05-15 02:09:48+00:00    1\n",
      "Name: tweetDate, Length: 11120, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Sorting tweets per chronological order\n",
    "data_hour = tweets[\"tweetDate\"].value_counts()\n",
    "data_hour = data_hour.sort_index()\n",
    "print(data_hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4df1eaa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#Plotting the data in a graph\n",
    "data_hour.plot(kind=\"line\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dc49199",
   "metadata": {},
   "source": [
    "By sorting the data by dates in a chronological order, we can see there are clear **outliers** with the first tweet that dates from Nov-2018.\n",
    "\n",
    "This is what highlights the chart above, most data is centered around the last few weeks, it is therefore irrelevant to keep the outliers from the past. \n",
    "We should remove them and focus the analysis on the last few days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a140345f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Narrowing down the period and filtering outliers\n",
    "data_filter = data_hour.iloc[1000:]\n",
    "data_filter.plot(kind=\"line\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d021f7fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-05-06     144\n",
      "2022-05-07     130\n",
      "2022-05-08     213\n",
      "2022-05-09     399\n",
      "2022-05-10     780\n",
      "2022-05-11    3207\n",
      "2022-05-12    2697\n",
      "2022-05-13    2169\n",
      "2022-05-14    1158\n",
      "2022-05-15      13\n",
      "Name: tweetDate, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#count the number of tweets per date\n",
    "data_hour = tweets[\"tweetDate\"].dt.date.value_counts()\n",
    "data_hour = data_hour.sort_index().iloc[30:]\n",
    "print(data_hour)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2caacc20",
   "metadata": {},
   "source": [
    "# Deep Dive Analysis\n",
    "The objective will be to assess the tweet intensity and community engagement behind each crypto asset and derive potential investment signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9aa841dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             Date  Hashtag\n",
      "0      2022-05-11     #BTC\n",
      "1      2022-04-28     #BTC\n",
      "2      2022-05-10     #BTC\n",
      "3      2022-05-11     #BTC\n",
      "4      2022-05-10     #BTC\n",
      "...           ...      ...\n",
      "11336  2022-05-14  #crypto\n",
      "11337  2022-05-14  #crypto\n",
      "11338  2022-05-14  #crypto\n",
      "11339  2022-05-15  #crypto\n",
      "11340  2022-05-14  #crypto\n",
      "\n",
      "[11341 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "#Regroup the data around dates and hashtags\n",
    "date = tweets[\"tweetDate\"].dt.date\n",
    "hashtag = tweets['query']\n",
    "weekly_data = pd.concat([date, hashtag], axis=1)\n",
    "weekly_data.columns = [\"Date\",\"Hashtag\"]\n",
    "print(weekly_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "134954fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-05-06     144\n",
      "2022-05-07     130\n",
      "2022-05-08     213\n",
      "2022-05-09     399\n",
      "2022-05-10     780\n",
      "2022-05-11    3207\n",
      "2022-05-12    2697\n",
      "2022-05-13    2169\n",
      "2022-05-14    1158\n",
      "2022-05-15      13\n",
      "Name: Date, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# count the number of tweets per date\n",
    "hashtag_count = weekly_data[\"Date\"].value_counts()\n",
    "hashtag_count = hashtag_count.sort_index().iloc[30:]\n",
    "print(hashtag_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8c4c34ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Hashtag        Date  #of Occurences\n",
      "0    #APE  2022-03-26               1\n",
      "1    #APE  2022-04-30               2\n",
      "2    #APE  2022-05-01               2\n",
      "3    #APE  2022-05-02               3\n",
      "4    #APE  2022-05-03               4\n",
      "5    #APE  2022-05-04               8\n",
      "6    #APE  2022-05-05               5\n",
      "7    #APE  2022-05-06              13\n",
      "8    #APE  2022-05-07              22\n",
      "9    #APE  2022-05-08              26\n"
     ]
    }
   ],
   "source": [
    "#Reformat the data to count the #of tweets per hashtag per date\n",
    "#Display only first 10 results\n",
    "hashtag_date = weekly_data.groupby(['Hashtag', 'Date']).size()\n",
    "hashtag_date = hashtag_date.reset_index()\n",
    "hashtag_date = hashtag_date.rename(columns={0: \"#of Occurences\"})\n",
    "print(hashtag_date.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6b02d7a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hashtag  Date      \n",
      "#APE     2022-03-26     1\n",
      "         2022-04-30     2\n",
      "         2022-05-01     2\n",
      "         2022-05-02     3\n",
      "         2022-05-03     4\n",
      "         2022-05-04     8\n",
      "         2022-05-05     5\n",
      "         2022-05-06    13\n",
      "         2022-05-07    22\n",
      "         2022-05-08    26\n",
      "Name: #of Occurences, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "hashtag_date = hashtag_date.groupby([\"Hashtag\", \"Date\"]).mean()[\"#of Occurences\"]\n",
    "print(hashtag_date.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e46748e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hashtag      #APE  #AVAX   #BTC  #Bitcoin  #DOT  #EGLD   #ETH  #LINK  #LUNA  \\\n",
      "Date                                                                          \n",
      "2022-05-06   13.0   35.0    NaN       NaN  13.0   25.0    NaN   11.0    3.0   \n",
      "2022-05-07   22.0   18.0    NaN       NaN  10.0   25.0    NaN   10.0    NaN   \n",
      "2022-05-08   26.0   21.0    3.0       1.0  17.0   37.0    1.0   27.0    1.0   \n",
      "2022-05-09   38.0   40.0    9.0       7.0  17.0   64.0    3.0   28.0    5.0   \n",
      "2022-05-10   69.0   34.0   40.0      35.0  36.0   79.0   24.0   51.0   24.0   \n",
      "2022-05-11   98.0   90.0  216.0     514.0  81.0  141.0  291.0  108.0  461.0   \n",
      "2022-05-12   96.0   85.0  149.0     422.0  82.0  121.0  252.0   48.0  353.0   \n",
      "2022-05-13  119.0   77.0  114.0     337.0  75.0  122.0  225.0   60.0  257.0   \n",
      "2022-05-14   38.0   29.0   48.0     131.0  49.0   45.0  117.0   25.0  164.0   \n",
      "2022-05-15    NaN    NaN    NaN       NaN   NaN    NaN    NaN    NaN    NaN   \n",
      "\n",
      "Hashtag     #MATIC   #SOL   #UST   #XTZ  #crypto  \n",
      "Date                                              \n",
      "2022-05-06     8.0    6.0    2.0   28.0      NaN  \n",
      "2022-05-07    14.0   10.0    2.0   19.0      NaN  \n",
      "2022-05-08    21.0   26.0    4.0   28.0      NaN  \n",
      "2022-05-09    35.0   70.0   28.0   51.0      4.0  \n",
      "2022-05-10    51.0  158.0   92.0   75.0     12.0  \n",
      "2022-05-11   100.0  253.0  381.0  113.0    359.0  \n",
      "2022-05-12    74.0  263.0  224.0  123.0    405.0  \n",
      "2022-05-13    65.0  230.0  145.0   75.0    268.0  \n",
      "2022-05-14    43.0  120.0   65.0   34.0    250.0  \n",
      "2022-05-15     NaN    NaN    NaN    NaN     13.0  \n"
     ]
    }
   ],
   "source": [
    "#Display all tweets per hashtag on the refined period in a table\n",
    "hashtag_date = hashtag_date.unstack(level=0).iloc[30:]\n",
    "print(hashtag_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "242ef45e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "#Plot a heatmap of the data to see the tweet intensity by date and hashtag\n",
    "sns.heatmap(hashtag_date, cmap=\"YlGnBu\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa0ed4ae",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "This chart gives a first sense of the tweet intensity over the period. \n",
    "\n",
    "First it appears that there has been a lot happening between the 11th and the 12th in the crypto ecosystem.\n",
    "\n",
    "The following hashtags are the most used: #Bitcoin, #ETH, #LUNA, #SOL, #UST, #Crypto.\n",
    "\n",
    "This gives potential indication about the popularity of projects, but also of the news intensity related to the ecosystem and projects"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f001e9ca",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "To refine the analysis and get an understanding of the **community engagement** behind each cryptocurrency from the set, we will divid the number of tweets by the average Market capitalization of each crypto over the period.\n",
    "\n",
    "\n",
    "Link to the Market Cap data: https://bit.ly/crypto-MC\n",
    "\n",
    "This data is from: https://coinmarketcap.com/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a6eba0c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hashtag          #APE     #AVAX      #BTC      #DOT      #EGLD      #ETH  \\\n",
      "Date                                                                       \n",
      "2022-05-06   4.421769  3.025065       NaN  1.087866  10.869565       NaN   \n",
      "2022-05-07   7.482993  1.555748       NaN  0.836820  10.869565       NaN   \n",
      "2022-05-08   8.843537  1.815039  0.004819  1.422594  16.086957  0.003508   \n",
      "2022-05-09  12.925170  3.457217  0.014458  1.422594  27.826087  0.010523   \n",
      "2022-05-10  23.469388  2.938634  0.064259  3.012552  34.347826  0.084181   \n",
      "2022-05-11  33.333333  7.778738  0.346999  6.778243  61.304348  1.020694   \n",
      "2022-05-12  32.653061  7.346586  0.239365  6.861925  52.608696  0.883900   \n",
      "2022-05-13  40.476190  6.655143  0.183138  6.276151  53.043478  0.789197   \n",
      "2022-05-14  12.925170  2.506482  0.077111  4.100418  19.565217  0.410382   \n",
      "2022-05-15        NaN       NaN       NaN       NaN        NaN       NaN   \n",
      "\n",
      "Hashtag         #LINK      #LUNA     #MATIC       #SOL       #UST       #XTZ  \n",
      "Date                                                                          \n",
      "2022-05-06   2.644231   0.234926   1.178203   0.270270   0.165426  14.507772  \n",
      "2022-05-07   2.403846        NaN   2.061856   0.450450   0.165426   9.844560  \n",
      "2022-05-08   6.490385   0.078309   3.092784   1.171171   0.330852  14.507772  \n",
      "2022-05-09   6.730769   0.391543   5.154639   3.153153   2.315964  26.424870  \n",
      "2022-05-10  12.259615   1.879405   7.511046   7.117117   7.609595  38.860104  \n",
      "2022-05-11  25.961538  36.100235  14.727541  11.396396  31.513648  58.549223  \n",
      "2022-05-12  11.538462  27.642913  10.898380  11.846847  18.527709  63.730570  \n",
      "2022-05-13  14.423077  20.125294   9.572901  10.360360  11.993383  38.860104  \n",
      "2022-05-14   6.009615  12.842600   6.332842   5.405405   5.376344  17.616580  \n",
      "2022-05-15        NaN        NaN        NaN        NaN        NaN        NaN  \n"
     ]
    }
   ],
   "source": [
    "#Let's divide the #of tweets by the related cryptocurrency market cap to normalize results\n",
    "#Market caps are expressed in Billion$\n",
    "#We get rid of generic hashtags namely Bitcoin and Crypto that would bias our analysis otherwise\n",
    "hashtag_date['#BTC'] = hashtag_date['#BTC'].div(622.48)\n",
    "hashtag_date['#ETH'] = hashtag_date['#ETH'].div(285.1)\n",
    "hashtag_date['#SOL'] = hashtag_date['#SOL'].div(22.2)\n",
    "hashtag_date['#LUNA'] = hashtag_date['#LUNA'].div(12.77)\n",
    "hashtag_date['#UST'] = hashtag_date['#UST'].div(12.09)\n",
    "hashtag_date['#DOT'] = hashtag_date['#DOT'].div(11.95)\n",
    "hashtag_date['#AVAX'] = hashtag_date['#AVAX'].div(11.57)\n",
    "hashtag_date['#MATIC'] = hashtag_date['#MATIC'].div(6.79)\n",
    "hashtag_date['#LINK'] = hashtag_date['#LINK'].div(4.16)\n",
    "hashtag_date['#APE'] = hashtag_date['#APE'].div(2.94)\n",
    "hashtag_date['#EGLD'] = hashtag_date['#EGLD'].div(2.3)\n",
    "hashtag_date['#XTZ'] = hashtag_date['#XTZ'].div(1.93)\n",
    "hashtag_date = hashtag_date.drop(['#crypto'], axis = 1)\n",
    "hashtag_date = hashtag_date.drop(['#Bitcoin'], axis = 1)\n",
    "print(hashtag_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9b9b3543",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot a heatmap of the Crypto community engagement\n",
    "sns.heatmap(hashtag_date, cmap=\"YlGnBu\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a45ffba",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "Results are very interesting as they highlight a very strong community behind notably **#EGLD (Elrond)** and **#XTZ (Tezos)** over the period.\n",
    "\n",
    "This is relevant to have such insights because as cryptocurrencies are still at a very early stage of their development, the community supporting a project is crucial and can drive returns.\n",
    "\n",
    "Indeed, this analysis over a longer period could be insightful. It would enable us to see how the interest about a crypto evolves and if fades over time or reinforce.\n",
    "\n",
    "\n",
    "**Limitation**: This analysis is slighlty biased, as the market cap of top crypto assets such as #BTC (Bitcoin) or #ETH (Ethereum) is too large compared to the #of tweets. The analysis is therefore more relevant for projects with market caps ranging from **1 to 15 Billion dollars**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "75a8e83c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's see if there is a common momentum in terms of tweets over the period\n",
    "hashtag_date.plot(figsize=(15,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fd95da3",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "The chart enhances the trend previously observed. There is an overall increase in tweets intensity between 22/05/10 and 22/05/13.\n",
    "All crypto-currencies are likely to react to market events either up or down.\n",
    "\n",
    "Let's see how we can apply it to an investment strategy based on basic signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9a4e57eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hashtag          #APE     #AVAX      #BTC      #DOT      #EGLD      #ETH  \\\n",
      "Date                                                                       \n",
      "2022-05-06        NaN       NaN       NaN       NaN        NaN       NaN   \n",
      "2022-05-07   3.061224 -1.469317       NaN -0.251046   0.000000       NaN   \n",
      "2022-05-08   1.360544  0.259291       NaN  0.585774   5.217391       NaN   \n",
      "2022-05-09   4.081633  1.642178  0.009639  0.000000  11.739130  0.007015   \n",
      "2022-05-10  10.544218 -0.518583  0.049801  1.589958   6.521739  0.073658   \n",
      "2022-05-11   9.863946  4.840104  0.282740  3.765690  26.956522  0.936514   \n",
      "2022-05-12  -0.680272 -0.432152 -0.107634  0.083682  -8.695652 -0.136794   \n",
      "2022-05-13   7.823129 -0.691443 -0.056227 -0.585774   0.434783 -0.094704   \n",
      "2022-05-14 -27.551020 -4.148660 -0.106028 -2.175732 -33.478261 -0.378814   \n",
      "2022-05-15        NaN       NaN       NaN       NaN        NaN       NaN   \n",
      "\n",
      "Hashtag         #LINK      #LUNA    #MATIC      #SOL       #UST       #XTZ  \n",
      "Date                                                                        \n",
      "2022-05-06        NaN        NaN       NaN       NaN        NaN        NaN  \n",
      "2022-05-07  -0.240385        NaN  0.883652  0.180180   0.000000  -4.663212  \n",
      "2022-05-08   4.086538        NaN  1.030928  0.720721   0.165426   4.663212  \n",
      "2022-05-09   0.240385   0.313234  2.061856  1.981982   1.985112  11.917098  \n",
      "2022-05-10   5.528846   1.487862  2.356406  3.963964   5.293631  12.435233  \n",
      "2022-05-11  13.701923  34.220830  7.216495  4.279279  23.904053  19.689119  \n",
      "2022-05-12 -14.423077  -8.457322 -3.829161  0.450450 -12.985939   5.181347  \n",
      "2022-05-13   2.884615  -7.517619 -1.325479 -1.486486  -6.534326 -24.870466  \n",
      "2022-05-14  -8.413462  -7.282694 -3.240059 -4.954955  -6.617039 -21.243523  \n",
      "2022-05-15        NaN        NaN       NaN       NaN        NaN        NaN  \n"
     ]
    }
   ],
   "source": [
    "#We do the difference of tweets intensity day after day\n",
    "hashtag_diff = hashtag_date.diff(periods=1,axis=0)\n",
    "print(hashtag_diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c917f03c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hashtag     #APE  #AVAX  #BTC  #DOT  #EGLD  #ETH  #LINK  #LUNA  #MATIC  #SOL  \\\n",
      "Date                                                                           \n",
      "2022-05-06   0.0    0.0   0.0   0.0    0.0   0.0    0.0    0.0     0.0   0.0   \n",
      "2022-05-07   3.0   -1.0   0.0  -0.0    0.0   0.0   -0.0    0.0     1.0   0.0   \n",
      "2022-05-08   1.0    0.0   0.0   1.0    5.0   0.0    4.0    0.0     1.0   1.0   \n",
      "2022-05-09   4.0    2.0   0.0   0.0   12.0   0.0    0.0    0.0     2.0   2.0   \n",
      "2022-05-10  11.0   -1.0   0.0   2.0    7.0   0.0    6.0    1.0     2.0   4.0   \n",
      "2022-05-11  10.0    5.0   0.0   4.0   27.0   1.0   14.0   34.0     7.0   4.0   \n",
      "2022-05-12  -1.0   -0.0  -0.0   0.0   -9.0  -0.0  -14.0   -8.0    -4.0   0.0   \n",
      "2022-05-13   8.0   -1.0  -0.0  -1.0    0.0  -0.0    3.0   -8.0    -1.0  -1.0   \n",
      "2022-05-14 -28.0   -4.0  -0.0  -2.0  -33.0  -0.0   -8.0   -7.0    -3.0  -5.0   \n",
      "2022-05-15   0.0    0.0   0.0   0.0    0.0   0.0    0.0    0.0     0.0   0.0   \n",
      "\n",
      "Hashtag     #UST  #XTZ  \n",
      "Date                    \n",
      "2022-05-06   0.0   0.0  \n",
      "2022-05-07   0.0  -5.0  \n",
      "2022-05-08   0.0   5.0  \n",
      "2022-05-09   2.0  12.0  \n",
      "2022-05-10   5.0  12.0  \n",
      "2022-05-11  24.0  20.0  \n",
      "2022-05-12 -13.0   5.0  \n",
      "2022-05-13  -7.0 -25.0  \n",
      "2022-05-14  -7.0 -21.0  \n",
      "2022-05-15   0.0   0.0  \n"
     ]
    }
   ],
   "source": [
    "#We normalize the data to replace NaN values to 0 and round it\n",
    "watchlist = hashtag_diff.fillna(0)\n",
    "watchlist = watchlist.apply(pd.to_numeric)\n",
    "print(round(watchlist))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b0281d5",
   "metadata": {},
   "source": [
    "***\n",
    "Based on this output we can create a four scale signal that will enable to take trading decisions and benefit from potential arbitrage decisions between cryptocurrencies scoring very high in tweets intensity evolution and those scoring neutral.\n",
    "\n",
    "**The rationale behind this basic but efficient strategy** *(supposing having enough capital to perform many trades)* **is that crypto assets with high scores will experience larger swings in their prices (either up or down) while crypto assets scoring low scores will not have such significant changes in their prices.**\n",
    "\n",
    "\n",
    "Therefore, it leaves potential **arbitrage opportunities** between cryptocurrencies.\n",
    "\n",
    "The scale would be the following: \n",
    "- If score >= 3 == **'Very High'**\n",
    "- If score >= 2 and <3 == **'High'**\n",
    "- If score >= 1 and <2 == **'Moderate'**\n",
    "- If score <1 == **'Neutral'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "506c1149",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date\n",
      "2022-05-06      Neutral\n",
      "2022-05-07    Very High\n",
      "2022-05-08     Moderate\n",
      "2022-05-09    Very High\n",
      "2022-05-10    Very High\n",
      "2022-05-11    Very High\n",
      "2022-05-12      Neutral\n",
      "2022-05-13    Very High\n",
      "2022-05-14      Neutral\n",
      "2022-05-15      Neutral\n",
      "Name: #APE, dtype: object\n"
     ]
    }
   ],
   "source": [
    "watchlist_index = watchlist['#APE'].apply(lambda x: 'Very High' if x>=3 else 'High' if x>=2 and x<3 else 'Moderate' if x>=1 and x<2 else 'Neutral')\n",
    "print(watchlist_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cca1cbc2",
   "metadata": {},
   "source": [
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed153657",
   "metadata": {},
   "source": [
    "\n",
    "*Cheers, Lucas*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4a67c9e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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