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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Requirement already satisfied: tqdm in /home/anson/.local/lib/python3.8/site-packages (4.59.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests as r\n",
    "import pandas as pd\n",
    "from fuzzywuzzy import fuzz\n",
    "from functools import cache\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "    def stocks():\n",
    "\n",
    "        raw_symbols = r.get(\n",
    "            f\"https://cloud.iexapis.com/stable/ref-data/symbols?token=WOOOPS\"\n",
    "        ).json()\n",
    "        symbols = pd.DataFrame(data=raw_symbols)\n",
    "\n",
    "        symbols[\"description\"] = \"$\" + symbols[\"symbol\"] + \": \" + symbols[\"name\"]\n",
    "        symbols[\"id\"] = symbols[\"symbol\"]\n",
    "\n",
    "        symbols = symbols[[\"id\", \"symbol\", \"name\", \"description\"]]\n",
    "\n",
    "        return symbols\n",
    "\n",
    "\n",
    "\n",
    "    def coins():\n",
    "\n",
    "        raw_symbols = r.get(\"https://api.coingecko.com/api/v3/coins/list\").json()\n",
    "        symbols = pd.DataFrame(data=raw_symbols)\n",
    "\n",
    "        symbols[\"description\"] = \"$$\" + symbols[\"symbol\"] + \": \" + symbols[\"name\"]\n",
    "        symbols = symbols[[\"id\", \"symbol\", \"name\", \"description\"]]\n",
    "\n",
    "        return symbols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                  id symbol  \\\n",
       "0                  A      A   \n",
       "1                 AA     AA   \n",
       "2                AAA    AAA   \n",
       "3               AAAU   AAAU   \n",
       "4                AAC    AAC   \n",
       "...              ...    ...   \n",
       "6565            zyro   zyro   \n",
       "6566   zytara-dollar   zusd   \n",
       "6567             zyx    zyx   \n",
       "6568     zzz-finance    zzz   \n",
       "6569  zzz-finance-v2  zzzv2   \n",
       "\n",
       "                                                   name  \\\n",
       "0                             Agilent Technologies Inc.   \n",
       "1                                            Alcoa Corp   \n",
       "2     Listed Funds Trust - AAF First Priority CLO Bo...   \n",
       "3     Goldman Sachs Physical Gold ETF Shares - Goldm...   \n",
       "4                Ares Acquisition Corporation - Class A   \n",
       "...                                                 ...   \n",
       "6565                                               Zyro   \n",
       "6566                                      Zytara Dollar   \n",
       "6567                                                ZYX   \n",
       "6568                                        zzz.finance   \n",
       "6569                                     zzz.finance v2   \n",
       "\n",
       "                                            description  \n",
       "0                         $A: Agilent Technologies Inc.  \n",
       "1                                       $AA: Alcoa Corp  \n",
       "2     $AAA: Listed Funds Trust - AAF First Priority ...  \n",
       "3     $AAAU: Goldman Sachs Physical Gold ETF Shares ...  \n",
       "4          $AAC: Ares Acquisition Corporation - Class A  \n",
       "...                                                 ...  \n",
       "6565                                       $$zyro: Zyro  \n",
       "6566                              $$zusd: Zytara Dollar  \n",
       "6567                                         $$zyx: ZYX  \n",
       "6568                                 $$zzz: zzz.finance  \n",
       "6569                            $$zzzv2: zzz.finance v2  \n",
       "\n",
       "[16946 rows x 4 columns]"
      ],
      "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>symbol</th>\n      <th>name</th>\n      <th>description</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>A</td>\n      <td>A</td>\n      <td>Agilent Technologies Inc.</td>\n      <td>$A: Agilent Technologies Inc.</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>AA</td>\n      <td>AA</td>\n      <td>Alcoa Corp</td>\n      <td>$AA: Alcoa Corp</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>AAA</td>\n      <td>AAA</td>\n      <td>Listed Funds Trust - AAF First Priority CLO Bo...</td>\n      <td>$AAA: Listed Funds Trust - AAF First Priority ...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>AAAU</td>\n      <td>AAAU</td>\n      <td>Goldman Sachs Physical Gold ETF Shares - Goldm...</td>\n      <td>$AAAU: Goldman Sachs Physical Gold ETF Shares ...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>AAC</td>\n      <td>AAC</td>\n      <td>Ares Acquisition Corporation - Class A</td>\n      <td>$AAC: Ares Acquisition Corporation - Class A</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>6565</th>\n      <td>zyro</td>\n      <td>zyro</td>\n      <td>Zyro</td>\n      <td>$$zyro: Zyro</td>\n    </tr>\n    <tr>\n      <th>6566</th>\n      <td>zytara-dollar</td>\n      <td>zusd</td>\n      <td>Zytara Dollar</td>\n      <td>$$zusd: Zytara Dollar</td>\n    </tr>\n    <tr>\n      <th>6567</th>\n      <td>zyx</td>\n      <td>zyx</td>\n      <td>ZYX</td>\n      <td>$$zyx: ZYX</td>\n    </tr>\n    <tr>\n      <th>6568</th>\n      <td>zzz-finance</td>\n      <td>zzz</td>\n      <td>zzz.finance</td>\n      <td>$$zzz: zzz.finance</td>\n    </tr>\n    <tr>\n      <th>6569</th>\n      <td>zzz-finance-v2</td>\n      <td>zzzv2</td>\n      <td>zzz.finance v2</td>\n      <td>$$zzzv2: zzz.finance v2</td>\n    </tr>\n  </tbody>\n</table>\n<p>16946 rows × 4 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 51
    }
   ],
   "source": [
    "df = pd.concat([stocks(), coins()])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "    def search_symbols(search: str):\n",
    "        \"\"\"Performs a fuzzy search to find stock symbols closest to a search term.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        search : str\n",
    "            String used to search, could be a company name or something close to the companies stock ticker.\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        List[tuple[str, str]]\n",
    "            A list tuples of every stock sorted in order of how well they match. Each tuple contains: (Symbol, Issue Name).\n",
    "        \"\"\"\n",
    "\n",
    "        try:\n",
    "            if search_index[search]: return search_index[search]\n",
    "        except KeyError:\n",
    "            pass\n",
    "\n",
    "\n",
    "\n",
    "        search = search.lower()\n",
    "\n",
    "        df[\"Match\"] = df.apply(\n",
    "            lambda x: fuzz.ratio(search, f\"{x['symbol']}\".lower()),\n",
    "            axis=1,\n",
    "        )\n",
    "\n",
    "        df.sort_values(by=\"Match\", ascending=False, inplace=True)\n",
    "        if df[\"Match\"].head().sum() < 300:\n",
    "            df[\"Match\"] = df.apply(\n",
    "                lambda x: fuzz.partial_ratio(search, x[\"name\"].lower()),\n",
    "                axis=1,\n",
    "            )\n",
    "\n",
    "            df.sort_values(by=\"Match\", ascending=False, inplace=True)\n",
    "\n",
    "        symbols = df.head(20)\n",
    "        symbol_list = list(zip(list(symbols[\"symbol\"]), list(symbols[\"description\"])))\n",
    "        \n",
    "        return symbol_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "search_list = df['id'].to_list() + df['description'].to_list()\n",
    "search_index = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "  5%|▍         | 1545/33892 [06:51<2:23:40,  3.75it/s]\n"
     ]
    },
    {
     "output_type": "error",
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-92-559f57361529>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msearch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0msearch_index\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msearch_symbols\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-79-2cfb15c9428a>\u001b[0m in \u001b[0;36msearch_symbols\u001b[0;34m(search)\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0msearch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msearch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m     df[\"Match\"] = df.apply(\n\u001b[0m\u001b[1;32m     25\u001b[0m         \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfuzz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mratio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf\"{x['symbol']}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, axis, raw, result_type, args, **kwds)\u001b[0m\n\u001b[1;32m   7763\u001b[0m             \u001b[0mkwds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7764\u001b[0m         )\n\u001b[0;32m-> 7765\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   7766\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7767\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapplymap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_action\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.9/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    183\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_raw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_empty_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.9/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    275\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 276\u001b[0;31m         \u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_series_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m         \u001b[0;31m# wrap results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.9/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    287\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0moption_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"mode.chained_assignment\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 288\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries_gen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    289\u001b[0m                 \u001b[0;31m# ignore SettingWithCopy here in case the user mutates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    290\u001b[0m                 \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.9/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mseries_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    408\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m                 \u001b[0;31m# GH#35462 re-pin mgr in case setitem changed it\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 410\u001b[0;31m                 \u001b[0mser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmgr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    411\u001b[0m                 \u001b[0mblk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    412\u001b[0m                 \u001b[0mser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__setattr__\u001b[0;34m(self, name, value)\u001b[0m\n\u001b[1;32m   5473\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5474\u001b[0m             \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5475\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5476\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5477\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "for i in tqdm(search_list):\n",
    "    search_index[i] = search_symbols(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "\n",
    "\n",
    "with open('search_index.pickle', 'wb') as handle:\n",
    "    pickle.dump(search_index, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "# with open('filename.pickle', 'rb') as handle:\n",
    "#     b = pickle.load(handle)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}