company_sentiment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as datetime\n",
    "from datetime import datetime\n",
    "import requests\n",
    "import os\n",
    "import json\n",
    "from dotenv import load_dotenv\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "\n",
    "import nltk\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "nltk.download('vader_lexicon')\n",
    "analyzer = SentimentIntensityAnalyzer()\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "%matplotlib inline"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     /Users/draganbogatic/nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "load_dotenv()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "FMP_API_KEY = os.getenv('FMP_API_KEY')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "company = 'TSM'"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Article Sentiment"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "# Pull company articles\n",
    "url = f'https://financialmodelingprep.com/api/v3/stock_news?tickers={company}&limit=100&apikey={FMP_API_KEY}'\n",
    "articles = requests.get(url).json()\n",
    "articles = pd.DataFrame(articles)\n",
    "articles['publishedDate'] = pd.to_datetime(articles['publishedDate'])\n",
    "articles.head(1)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>publishedDate</th>\n",
       "      <th>title</th>\n",
       "      <th>image</th>\n",
       "      <th>site</th>\n",
       "      <th>text</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TSM</td>\n",
       "      <td>2021-09-29 18:07:17</td>\n",
       "      <td>Why China (Probably) Won't Go To War Over Taiw...</td>\n",
       "      <td>https://cdn.snapi.dev/images/v1/g/p/why-china-...</td>\n",
       "      <td>Forbes</td>\n",
       "      <td>Is military action by China, aimed annexing Ta...</td>\n",
       "      <td>https://www.forbes.com/sites/georgecalhoun/202...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  symbol       publishedDate  \\\n",
       "0    TSM 2021-09-29 18:07:17   \n",
       "\n",
       "                                               title  \\\n",
       "0  Why China (Probably) Won't Go To War Over Taiw...   \n",
       "\n",
       "                                               image    site  \\\n",
       "0  https://cdn.snapi.dev/images/v1/g/p/why-china-...  Forbes   \n",
       "\n",
       "                                                text  \\\n",
       "0  Is military action by China, aimed annexing Ta...   \n",
       "\n",
       "                                                 url  \n",
       "0  https://www.forbes.com/sites/georgecalhoun/202...  "
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "# Preview articles\n",
    "pd.set_option('display.max_colwidth', None) \n",
    "articles['text'][:10]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0                                                                                                                                                                           Is military action by China, aimed annexing Taiwan and taking control of Taiwanese semiconductor assets, likely? Is it feasible?\n",
       "1                                                                                                                                                                                                 TSMC (TSM) closed the most recent trading day at $116.15, moving +0.44% from the previous trading session.\n",
       "2                                                                                                       Taiwan's TSMC said on Friday it is actively supporting and working with all stakeholders to overcome a global semiconductor shortage, after its participation at a White House meeting on the issue.\n",
       "3                                                                 It's rare for a stock to be able to achieve average annual returns above 20% for an entire decade. It's even more rare to do so while the company is consistently growing its top and bottom lines and maintaining a strong balance sheet.\n",
       "4    Samsung Electronics Co Ltd (OTC: SSNLF) won a Tesla Inc (NASDAQ: TSLA) contract beating Taiwan Semiconductor Manufacturing Co Ltd (NYSE: TSM), the Korea Economic Daily reports. Samsung will manufacture Tesla's next-generation hardware 4 (HW 4.0) chip for its fully autonomous driving technology.\n",
       "5                                                                                                                                                                                                      TSMC (TSM) closed at $114.96 in the latest trading session, marking a +0.23% move from the prior day.\n",
       "6                                                                                                                        Since our previous analysis, we analyzed the potential benefit to TSMC from the rising semiconductor demand of 13% in 2021 and long-term growth tailwinds from the rise of HPC, 5G.\n",
       "7                                                                                                                                                                                                                                                Donaldson: One Of The Best Dividend Aristocrats You Can Buy\n",
       "8                                                                                                                                                                                                                                  These three stocks already have established a track record of excellence.\n",
       "9                                                                                                                                                                                                                                                           VT: A Gold Standard ETF That Gives You The World\n",
       "Name: text, dtype: object"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "# Calculate sentiment scores for articles\n",
    "scores = []\n",
    "text = articles['text']\n",
    "\n",
    "for txt in text:\n",
    "\n",
    "    vs = analyzer.polarity_scores(txt)\n",
    "    scores.append(vs)\n",
    "\n",
    "text_df = pd.DataFrame(text, columns=['text'])\n",
    "scores_df = pd.DataFrame(scores)\n",
    "sentiment_df = pd.concat([text_df, scores_df], axis=1)\n",
    "\n",
    "combined_df = pd.concat([sentiment_df,articles],axis=1)\n",
    "combined_df = combined_df.T.drop_duplicates().T.set_index('publishedDate')\n",
    "combined_df.head(1)\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>pos</th>\n",
       "      <th>compound</th>\n",
       "      <th>symbol</th>\n",
       "      <th>title</th>\n",
       "      <th>image</th>\n",
       "      <th>site</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>publishedDate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-09-29 18:07:17</th>\n",
       "      <td>Is military action by China, aimed annexing Taiwan and taking control of Taiwanese semiconductor assets, likely? Is it feasible?</td>\n",
       "      <td>0</td>\n",
       "      <td>0.897</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.264</td>\n",
       "      <td>TSM</td>\n",
       "      <td>Why China (Probably) Won't Go To War Over Taiwan's Semiconductor Riches</td>\n",
       "      <td>https://cdn.snapi.dev/images/v1/g/p/why-china-probably-wont-go-to-war-over-taiwans-semiconductor-riches-1028406.jpg</td>\n",
       "      <td>Forbes</td>\n",
       "      <td>https://www.forbes.com/sites/georgecalhoun/2021/09/29/why-china-probably-wont-go-to-war-over-taiwans-semiconductor-riches/</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                                                                                                                                                 text  \\\n",
       "publishedDate                                                                                                                                           \n",
       "2021-09-29 18:07:17  Is military action by China, aimed annexing Taiwan and taking control of Taiwanese semiconductor assets, likely? Is it feasible?   \n",
       "\n",
       "                    neg    neu    pos compound symbol  \\\n",
       "publishedDate                                           \n",
       "2021-09-29 18:07:17   0  0.897  0.103    0.264    TSM   \n",
       "\n",
       "                                                                                       title  \\\n",
       "publishedDate                                                                                  \n",
       "2021-09-29 18:07:17  Why China (Probably) Won't Go To War Over Taiwan's Semiconductor Riches   \n",
       "\n",
       "                                                                                                                                   image  \\\n",
       "publishedDate                                                                                                                              \n",
       "2021-09-29 18:07:17  https://cdn.snapi.dev/images/v1/g/p/why-china-probably-wont-go-to-war-over-taiwans-semiconductor-riches-1028406.jpg   \n",
       "\n",
       "                       site  \\\n",
       "publishedDate                 \n",
       "2021-09-29 18:07:17  Forbes   \n",
       "\n",
       "                                                                                                                                            url  \n",
       "publishedDate                                                                                                                                    \n",
       "2021-09-29 18:07:17  https://www.forbes.com/sites/georgecalhoun/2021/09/29/why-china-probably-wont-go-to-war-over-taiwans-semiconductor-riches/  "
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "# Plot mean sentiment\n",
    "scores_df['symbol'] = company\n",
    "scores_df\n",
    "scores_mean = scores_df.groupby('symbol').mean()\n",
    "print(scores_mean)\n",
    "scores_mean.plot.bar(figsize=(12,6),title=f'{company} Sentiment Analysis',ylabel='sentiment', rot=0)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "            neg      neu      pos  compound\n",
      "symbol                                     \n",
      "TSM     0.03037  0.86129  0.10832  0.220939\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'TSM Sentiment Analysis'}, xlabel='symbol', ylabel='sentiment'>"
      ]
     },
     "metadata": {},
     "execution_count": 8
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Press Releases"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "# Pull company press releases\n",
    "url = f'https://financialmodelingprep.com/api/v3/press-releases/{company}?limit=100&apikey={FMP_API_KEY}'\n",
    "press = requests.get(url).json()\n",
    "press = pd.DataFrame(press)\n",
    "press['publishedDate'] = pd.to_datetime(articles['publishedDate'])\n",
    "press['text']"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0                                                                                                                                                                                                                                                                                                                                                                                                                                    HSINCHU, TAIWAN--(BUSINESS WIRE)--TSMC COMMITS TO REACH NET ZERO EMISSIONS BY 2050.\n",
       "1                                                                                                                                                                                                                                                                                                                                                                                                                    HSINCHU, TAIWAN--(BUSINESS WIRE)--TSMC UNVEILS INNOVATIONS AT THE 2021 ONLINE TECHNOLOGY SYMPOSIUM.\n",
       "2    HSINCHU, TAIWAN--(BUSINESS WIRE)--TSMC (TWSE: 2330, NYSE: TSM) TODAY FILED ITS 2020 ANNUAL REPORT ON FORM 20-F WITH THE U.S. SECURITIES AND EXCHANGE COMMISSION. THE REPORT IS AVAILABLE AT HTTPS://INVESTOR.TSMC.COM/ENGLISH/SEC-FILINGS. HARD COPIES OF THE REPORT ARE ALSO AVAILABLE, FREE OF CHARGE, UPON EMAIL REQUEST TO G_SEC@TSMC.COM. ABOUT TSMC TSMC PIONEERED THE PURE-PLAY FOUNDRY BUSINESS MODEL WHEN IT WAS FOUNDED IN 1987, AND HAS BEEN THE WORLD'S LEADING DEDICATED SEMICONDUCTOR FOUNDRY EVER SI\n",
       "3                                                                                                                                                                                                                                                                                                                                                                                                                               SAN FRANCISCO--(BUSINESS WIRE)--BRAINCHIP BEGINS VOLUME PRODUCTION OF AKIDA AI PROCESSOR\n",
       "4    HSINCHU, TAIWAN--(BUSINESS WIRE)--TSMC (TWSE: 2330, NYSE: TSM) TODAY ANNOUNCED THAT THE COMPANY HAS BEEN HONORED WITH THE 2021 IEEE CORPORATE INNOVATION AWARD FOR ITS LEADERSHIP IN 7-NANOMETER (7NM) SEMICONDUCTOR FOUNDRY TECHNOLOGY, WHICH HAS ENABLED CUSTOMERS' INNOVATIONS IN WIDESPREAD APPLICATIONS. THE IEEE IS THE WORLD'S LARGEST PROFESSIONAL ASSOCIATION DEDICATED TO ADVANCING TECHNOLOGY FOR HUMANITY. FIRST ESTABLISHED IN 1985, ITS PRESTIGIOUS ANNUAL CORPORATE INNOVATION AWARD HONORS A CORPOR\n",
       "Name: text, dtype: object"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Social Sentiment"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "# Absolute index field indicates how much people are talking about the stock, relative index also indicates it ​but relative to previous day. \n",
    "# Sentiment field indicates overall percentage of positive activity, while general perception indicates whether people are more positive or negative than usual.\n",
    "url = f'https://financialmodelingprep.com/api/v4/social-sentiment?symbol={company}&limit=100&apikey={FMP_API_KEY}'\n",
    "social_sentiment = requests.get(url).json()\n",
    "social_sentiment = pd.DataFrame(social_sentiment)\n",
    "social_sentiment['date'] = pd.to_datetime(social_sentiment['date'])\n",
    "social_sentiment = social_sentiment.set_index('date')\n",
    "social_sentiment.head()\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>symbol</th>\n",
       "      <th>absoluteIndex</th>\n",
       "      <th>relativeIndex</th>\n",
       "      <th>generalPerception</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>redditPostMentions</th>\n",
       "      <th>redditPostSentiment</th>\n",
       "      <th>redditCommentMentions</th>\n",
       "      <th>redditCommentSentiment</th>\n",
       "      <th>tweetMentions</th>\n",
       "      <th>tweetSentiment</th>\n",
       "      <th>stocktwitsPostMentions</th>\n",
       "      <th>stocktwitsPostSentiment</th>\n",
       "      <th>yahooFinanceCommentMentions</th>\n",
       "      <th>yahooFinanceCommentSentiment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-10-01 03:38:47</th>\n",
       "      <td>TSM</td>\n",
       "      <td>0.042105</td>\n",
       "      <td>0.968173</td>\n",
       "      <td>1.069836</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-01 02:38:47</th>\n",
       "      <td>TSM</td>\n",
       "      <td>0.042105</td>\n",
       "      <td>0.971552</td>\n",
       "      <td>1.068773</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-01 01:38:47</th>\n",
       "      <td>TSM</td>\n",
       "      <td>0.042105</td>\n",
       "      <td>0.999436</td>\n",
       "      <td>1.064877</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-01 00:38:47</th>\n",
       "      <td>TSM</td>\n",
       "      <td>0.052025</td>\n",
       "      <td>1.027472</td>\n",
       "      <td>1.060981</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30 23:38:46</th>\n",
       "      <td>TSM</td>\n",
       "      <td>0.052025</td>\n",
       "      <td>1.047254</td>\n",
       "      <td>1.055705</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    symbol  absoluteIndex  relativeIndex  generalPerception  \\\n",
       "date                                                                          \n",
       "2021-10-01 03:38:47    TSM       0.042105       0.968173           1.069836   \n",
       "2021-10-01 02:38:47    TSM       0.042105       0.971552           1.068773   \n",
       "2021-10-01 01:38:47    TSM       0.042105       0.999436           1.064877   \n",
       "2021-10-01 00:38:47    TSM       0.052025       1.027472           1.060981   \n",
       "2021-09-30 23:38:46    TSM       0.052025       1.047254           1.055705   \n",
       "\n",
       "                     sentiment  redditPostMentions redditPostSentiment  \\\n",
       "date                                                                     \n",
       "2021-10-01 03:38:47   0.900000                 0.0                None   \n",
       "2021-10-01 02:38:47   0.900000                 0.0                None   \n",
       "2021-10-01 01:38:47   0.900000                 0.0                None   \n",
       "2021-10-01 00:38:47   0.933333                 0.0                None   \n",
       "2021-09-30 23:38:46   0.933333                 0.0                None   \n",
       "\n",
       "                     redditCommentMentions  redditCommentSentiment  \\\n",
       "date                                                                 \n",
       "2021-10-01 03:38:47                    4.0                    0.75   \n",
       "2021-10-01 02:38:47                    4.0                    0.75   \n",
       "2021-10-01 01:38:47                    4.0                    0.75   \n",
       "2021-10-01 00:38:47                    4.0                    0.75   \n",
       "2021-09-30 23:38:46                    4.0                    0.75   \n",
       "\n",
       "                     tweetMentions  tweetSentiment  stocktwitsPostMentions  \\\n",
       "date                                                                         \n",
       "2021-10-01 03:38:47            7.0             1.0                     1.0   \n",
       "2021-10-01 02:38:47            7.0             1.0                     1.0   \n",
       "2021-10-01 01:38:47            7.0             1.0                     1.0   \n",
       "2021-10-01 00:38:47            2.0             1.0                     8.0   \n",
       "2021-09-30 23:38:46            2.0             1.0                     8.0   \n",
       "\n",
       "                     stocktwitsPostSentiment  yahooFinanceCommentMentions  \\\n",
       "date                                                                        \n",
       "2021-10-01 03:38:47                      NaN                          0.0   \n",
       "2021-10-01 02:38:47                      NaN                          0.0   \n",
       "2021-10-01 01:38:47                      NaN                          0.0   \n",
       "2021-10-01 00:38:47                      1.0                          0.0   \n",
       "2021-09-30 23:38:46                      1.0                          0.0   \n",
       "\n",
       "                     yahooFinanceCommentSentiment  \n",
       "date                                               \n",
       "2021-10-01 03:38:47                           NaN  \n",
       "2021-10-01 02:38:47                           NaN  \n",
       "2021-10-01 01:38:47                           NaN  \n",
       "2021-10-01 00:38:47                           NaN  \n",
       "2021-09-30 23:38:46                           NaN  "
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "# Average analyst sentiment for the last 100 datapoints\n",
    "social_sentiment.groupby('symbol').mean()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>absoluteIndex</th>\n",
       "      <th>relativeIndex</th>\n",
       "      <th>generalPerception</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>redditPostMentions</th>\n",
       "      <th>redditCommentMentions</th>\n",
       "      <th>redditCommentSentiment</th>\n",
       "      <th>tweetMentions</th>\n",
       "      <th>tweetSentiment</th>\n",
       "      <th>stocktwitsPostMentions</th>\n",
       "      <th>stocktwitsPostSentiment</th>\n",
       "      <th>yahooFinanceCommentMentions</th>\n",
       "      <th>yahooFinanceCommentSentiment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TSM</th>\n",
       "      <td>0.043906</td>\n",
       "      <td>1.072611</td>\n",
       "      <td>0.985559</td>\n",
       "      <td>0.842388</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.72</td>\n",
       "      <td>0.75919</td>\n",
       "      <td>3.88</td>\n",
       "      <td>0.891251</td>\n",
       "      <td>1.89</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        absoluteIndex  relativeIndex  generalPerception  sentiment  \\\n",
       "symbol                                                               \n",
       "TSM          0.043906       1.072611           0.985559   0.842388   \n",
       "\n",
       "        redditPostMentions  redditCommentMentions  redditCommentSentiment  \\\n",
       "symbol                                                                      \n",
       "TSM                    0.0                   6.72                 0.75919   \n",
       "\n",
       "        tweetMentions  tweetSentiment  stocktwitsPostMentions  \\\n",
       "symbol                                                          \n",
       "TSM              3.88        0.891251                    1.89   \n",
       "\n",
       "        stocktwitsPostSentiment  yahooFinanceCommentMentions  \\\n",
       "symbol                                                         \n",
       "TSM                    0.909091                         0.16   \n",
       "\n",
       "        yahooFinanceCommentSentiment  \n",
       "symbol                                \n",
       "TSM                              1.0  "
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Analyst Sentiment"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "# Grades given to companies by hedge funds, investment firms and analysts. It includes both the previous and new grade. \n",
    "url = f'https://financialmodelingprep.com/api/v3/grade/{company}?limit=500&apikey={FMP_API_KEY}'\n",
    "analyst_sentiment = requests.get(url).json()\n",
    "analyst_sentiment = pd.DataFrame(analyst_sentiment)\n",
    "analyst_sentiment['date'] = pd.to_datetime(analyst_sentiment['date'])\n",
    "analyst_sentiment = analyst_sentiment.set_index('date')\n",
    "analyst_sentiment"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>symbol</th>\n",
       "      <th>gradingCompany</th>\n",
       "      <th>previousGrade</th>\n",
       "      <th>newGrade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-07-13</th>\n",
       "      <td>TSM</td>\n",
       "      <td>Needham</td>\n",
       "      <td></td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>TSM</td>\n",
       "      <td>Susquehanna</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-22</th>\n",
       "      <td>TSM</td>\n",
       "      <td>Argus Research</td>\n",
       "      <td></td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-12</th>\n",
       "      <td>TSM</td>\n",
       "      <td>Atlantic Equities</td>\n",
       "      <td></td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-28</th>\n",
       "      <td>TSM</td>\n",
       "      <td>New Street</td>\n",
       "      <td></td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-24</th>\n",
       "      <td>TSM</td>\n",
       "      <td>Macquarie</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Outperform</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-17</th>\n",
       "      <td>TSM</td>\n",
       "      <td>China Renaissance</td>\n",
       "      <td>Hold</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>TSM</td>\n",
       "      <td>Goldman Sachs</td>\n",
       "      <td></td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-05</th>\n",
       "      <td>TSM</td>\n",
       "      <td>Morgan Stanley</td>\n",
       "      <td>Equal-Weight</td>\n",
       "      <td>Overweight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-19</th>\n",
       "      <td>TSM</td>\n",
       "      <td>CLSA</td>\n",
       "      <td>Underperform</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07</th>\n",
       "      <td>TSM</td>\n",
       "      <td>Goldman Sachs</td>\n",
       "      <td>Buy</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-16</th>\n",
       "      <td>TSM</td>\n",
       "      <td>Morgan Stanley</td>\n",
       "      <td>Underweight</td>\n",
       "      <td>Equal-Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-02</th>\n",
       "      <td>TSM</td>\n",
       "      <td>Susquehanna</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-22</th>\n",
       "      <td>TSM</td>\n",
       "      <td>Goldman Sachs</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-16</th>\n",
       "      <td>TSM</td>\n",
       "      <td>HSBC</td>\n",
       "      <td>Buy</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-27</th>\n",
       "      <td>TSM</td>\n",
       "      <td>Morgan Stanley</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>Equal-Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-14</th>\n",
       "      <td>TSM</td>\n",
       "      <td>Pacific Crest</td>\n",
       "      <td>Underweight</td>\n",
       "      <td>Sector Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-04</th>\n",
       "      <td>TSM</td>\n",
       "      <td>Susquehanna</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-12</th>\n",
       "      <td>TSM</td>\n",
       "      <td>JP Morgan</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-12</th>\n",
       "      <td>TSM</td>\n",
       "      <td>Nomura</td>\n",
       "      <td>Buy</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-18</th>\n",
       "      <td>TSM</td>\n",
       "      <td>Morgan Stanley</td>\n",
       "      <td>Equal-Weight</td>\n",
       "      <td>Overweight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>TSM</td>\n",
       "      <td>HSBC</td>\n",
       "      <td></td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-26</th>\n",
       "      <td>TSM</td>\n",
       "      <td>Citigroup</td>\n",
       "      <td></td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-09</th>\n",
       "      <td>TSM</td>\n",
       "      <td>Pacific Crest</td>\n",
       "      <td>Sector Perform</td>\n",
       "      <td>Underperform</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>TSM</td>\n",
       "      <td>Credit Suisse</td>\n",
       "      <td>Buy</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-08</th>\n",
       "      <td>TSM</td>\n",
       "      <td>Bank of America</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-14</th>\n",
       "      <td>TSM</td>\n",
       "      <td>Morgan Stanley</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>Equal-Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-21</th>\n",
       "      <td>TSM</td>\n",
       "      <td>Bank of America</td>\n",
       "      <td>Underperform</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-06</th>\n",
       "      <td>TSM</td>\n",
       "      <td>Credit Suisse</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Outperform</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-08</th>\n",
       "      <td>TSM</td>\n",
       "      <td>Credit Suisse</td>\n",
       "      <td>Outperform</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol     gradingCompany   previousGrade       newGrade\n",
       "date                                                               \n",
       "2021-07-13    TSM            Needham                            Buy\n",
       "2021-06-30    TSM        Susquehanna        Negative        Neutral\n",
       "2021-06-22    TSM     Argus Research                            Buy\n",
       "2021-04-12    TSM  Atlantic Equities                        Neutral\n",
       "2020-08-28    TSM         New Street                            Buy\n",
       "2020-07-24    TSM          Macquarie         Neutral     Outperform\n",
       "2019-12-17    TSM  China Renaissance            Hold            Buy\n",
       "2019-09-30    TSM      Goldman Sachs                            Buy\n",
       "2019-09-05    TSM     Morgan Stanley    Equal-Weight     Overweight\n",
       "2019-07-19    TSM               CLSA    Underperform            Buy\n",
       "2019-05-07    TSM      Goldman Sachs             Buy            Buy\n",
       "2018-11-16    TSM     Morgan Stanley     Underweight   Equal-Weight\n",
       "2018-07-02    TSM        Susquehanna         Neutral       Positive\n",
       "2018-01-22    TSM      Goldman Sachs         Neutral            Buy\n",
       "2018-01-16    TSM               HSBC             Buy            Buy\n",
       "2017-11-27    TSM     Morgan Stanley      Overweight   Equal-Weight\n",
       "2016-10-14    TSM      Pacific Crest     Underweight  Sector Weight\n",
       "2016-10-04    TSM        Susquehanna        Negative        Neutral\n",
       "2016-07-12    TSM          JP Morgan      Overweight        Neutral\n",
       "2016-07-12    TSM             Nomura             Buy        Neutral\n",
       "2016-02-18    TSM     Morgan Stanley    Equal-Weight     Overweight\n",
       "2015-12-01    TSM               HSBC                            Buy\n",
       "2015-03-26    TSM          Citigroup                        Neutral\n",
       "2015-03-09    TSM      Pacific Crest  Sector Perform   Underperform\n",
       "2015-01-08    TSM      Credit Suisse             Buy        Neutral\n",
       "2014-12-08    TSM    Bank of America         Neutral            Buy\n",
       "2013-04-14    TSM     Morgan Stanley      Overweight   Equal-Weight\n",
       "2013-02-21    TSM    Bank of America    Underperform            Buy\n",
       "2012-12-06    TSM      Credit Suisse         Neutral     Outperform\n",
       "2012-10-08    TSM      Credit Suisse      Outperform        Neutral"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "# Count recent analyst sentiment \n",
    "analyst_sentiment.loc['2021-07-13':'2020-07-23'].groupby('newGrade').count()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>gradingCompany</th>\n",
       "      <th>previousGrade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>newGrade</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Buy</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neutral</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outperform</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            symbol  gradingCompany  previousGrade\n",
       "newGrade                                         \n",
       "Buy              3               3              3\n",
       "Neutral          2               2              2\n",
       "Outperform       1               1              1"
      ]
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Analyst Estimates"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "# Ratings and recommendations from FMP analysts who use CAGR formula to predict and analyse stocks\n",
    "url = f'https://financialmodelingprep.com/api/v3/analyst-estimates/{company}?apikey={FMP_API_KEY}'\n",
    "analyst_estimates = requests.get(url).json()\n",
    "analyst_estimates = pd.DataFrame(analyst_estimates)\n",
    "analyst_estimates['date'] = pd.to_datetime(analyst_estimates['date'])\n",
    "analyst_estimates = analyst_estimates.set_index('date')\n",
    "analyst_estimates.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>estimatedRevenueLow</th>\n",
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       "      <th>numberAnalystEstimatedRevenue</th>\n",
       "      <th>numberAnalystsEstimatedEps</th>\n",
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       "      <th>date</th>\n",
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       "      <th>2023-12-31</th>\n",
       "      <td>TSM</td>\n",
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       "      <td>5.06</td>\n",
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       "      <td>11</td>\n",
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       "      <td>1558500415208</td>\n",
       "      <td>847811228147</td>\n",
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       "      <td>524520681583</td>\n",
       "      <td>786781022377</td>\n",
       "      <td>655650851980</td>\n",
       "      <td>...</td>\n",
       "      <td>481513341025</td>\n",
       "      <td>601891676283</td>\n",
       "      <td>32017407381</td>\n",
       "      <td>48026111072</td>\n",
       "      <td>40021759227</td>\n",
       "      <td>3.83</td>\n",
       "      <td>4.60</td>\n",
       "      <td>3.06</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>TSM</td>\n",
       "      <td>1133454847424</td>\n",
       "      <td>1700182271139</td>\n",
       "      <td>1416818559282</td>\n",
       "      <td>770737480136</td>\n",
       "      <td>1156106220205</td>\n",
       "      <td>963421850171</td>\n",
       "      <td>476836983260</td>\n",
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       "      <td>3.48</td>\n",
       "      <td>4.18</td>\n",
       "      <td>2.78</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
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       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>TSM</td>\n",
       "      <td>1014068777907</td>\n",
       "      <td>1521103166862</td>\n",
       "      <td>1267585972385</td>\n",
       "      <td>675038213027</td>\n",
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       "      <td>426335654673</td>\n",
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       "      <td>532919568342</td>\n",
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       "      <td>486923328310</td>\n",
       "      <td>26047527680</td>\n",
       "      <td>39071291520</td>\n",
       "      <td>32559409600</td>\n",
       "      <td>3.04</td>\n",
       "      <td>3.65</td>\n",
       "      <td>2.43</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>TSM</td>\n",
       "      <td>855138876852</td>\n",
       "      <td>1282708315278</td>\n",
       "      <td>1068923596065</td>\n",
       "      <td>526900555943</td>\n",
       "      <td>790350833916</td>\n",
       "      <td>658625694930</td>\n",
       "      <td>298178122852</td>\n",
       "      <td>447267184280</td>\n",
       "      <td>372722653566</td>\n",
       "      <td>...</td>\n",
       "      <td>276201794596</td>\n",
       "      <td>345252243246</td>\n",
       "      <td>22841709022</td>\n",
       "      <td>34262563533</td>\n",
       "      <td>28552136278</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2.58</td>\n",
       "      <td>1.72</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
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      ],
      "text/plain": [
       "           symbol  estimatedRevenueLow  estimatedRevenueHigh  \\\n",
       "date                                                           \n",
       "2023-12-31    TSM        1371480365380         2057220548075   \n",
       "2022-12-31    TSM        1246800332165         1870200498251   \n",
       "2021-12-31    TSM        1133454847424         1700182271139   \n",
       "2020-12-31    TSM        1014068777907         1521103166862   \n",
       "2019-12-31    TSM         855138876852         1282708315278   \n",
       "\n",
       "            estimatedRevenueAvg  estimatedEbitdaLow  estimatedEbitdaHigh  \\\n",
       "date                                                                       \n",
       "2023-12-31        1714350456728        932592350960        1398888526445   \n",
       "2022-12-31        1558500415208        847811228147        1271716842224   \n",
       "2021-12-31        1416818559282        770737480136        1156106220205   \n",
       "2020-12-31        1267585972385        675038213027        1012557319543   \n",
       "2019-12-31        1068923596065        526900555943         790350833916   \n",
       "\n",
       "            estimatedEbitdaAvg  estimatedEbitLow  estimatedEbitHigh  \\\n",
       "date                                                                  \n",
       "2023-12-31       1165740438703      576972749740       865459124613   \n",
       "2022-12-31       1059764035186      524520681583       786781022377   \n",
       "2021-12-31        963421850171      476836983260       715255474890   \n",
       "2020-12-31        843797766285      426335654673       639503482010   \n",
       "2019-12-31        658625694930      298178122852       447267184280   \n",
       "\n",
       "            estimatedEbitAvg  ...  estimatedNetIncomeHigh  \\\n",
       "date                          ...                           \n",
       "2023-12-31      721215937177  ...            529664675125   \n",
       "2022-12-31      655650851980  ...            481513341025   \n",
       "2021-12-31      596046229075  ...            437739400934   \n",
       "2020-12-31      532919568342  ...            389538662647   \n",
       "2019-12-31      372722653566  ...            276201794596   \n",
       "\n",
       "            estimatedNetIncomeAvg  estimatedSgaExpenseLow  \\\n",
       "date                                                        \n",
       "2023-12-31           662080843909             35219148118   \n",
       "2022-12-31           601891676283             32017407381   \n",
       "2021-12-31           547174251169             29106733984   \n",
       "2020-12-31           486923328310             26047527680   \n",
       "2019-12-31           345252243246             22841709022   \n",
       "\n",
       "            estimatedSgaExpenseHigh  estimatedSgaExpenseAvg  estimatedEpsAvg  \\\n",
       "date                                                                           \n",
       "2023-12-31              52828722178             44023935148             4.21   \n",
       "2022-12-31              48026111072             40021759227             3.83   \n",
       "2021-12-31              43660100976             36383417480             3.48   \n",
       "2020-12-31              39071291520             32559409600             3.04   \n",
       "2019-12-31              34262563533             28552136278             2.15   \n",
       "\n",
       "            estimatedEpsHigh  estimatedEpsLow  numberAnalystEstimatedRevenue  \\\n",
       "date                                                                           \n",
       "2023-12-31              5.06             3.36                             11   \n",
       "2022-12-31              4.60             3.06                             10   \n",
       "2021-12-31              4.18             2.78                             16   \n",
       "2020-12-31              3.65             2.43                             13   \n",
       "2019-12-31              2.58             1.72                              7   \n",
       "\n",
       "            numberAnalystsEstimatedEps  \n",
       "date                                    \n",
       "2023-12-31                          11  \n",
       "2022-12-31                          10  \n",
       "2021-12-31                          16  \n",
       "2020-12-31                          13  \n",
       "2019-12-31                           7  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "# Future analyst estimates\n",
    "analyst_estimates.loc['2023-12-31':'2021-12-31']"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "      <th>2023-12-31</th>\n",
       "      <td>TSM</td>\n",
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       "      <td>576972749740</td>\n",
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       "      <td>11</td>\n",
       "      <td>11</td>\n",
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       "      <th>2022-12-31</th>\n",
       "      <td>TSM</td>\n",
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       "      <td>601891676283</td>\n",
       "      <td>32017407381</td>\n",
       "      <td>48026111072</td>\n",
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       "      <td>3.83</td>\n",
       "      <td>4.60</td>\n",
       "      <td>3.06</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
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       "      <th>2021-12-31</th>\n",
       "      <td>TSM</td>\n",
       "      <td>1133454847424</td>\n",
       "      <td>1700182271139</td>\n",
       "      <td>1416818559282</td>\n",
       "      <td>770737480136</td>\n",
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       "      <td>4.18</td>\n",
       "      <td>2.78</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
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      ],
      "text/plain": [
       "           symbol  estimatedRevenueLow  estimatedRevenueHigh  \\\n",
       "date                                                           \n",
       "2023-12-31    TSM        1371480365380         2057220548075   \n",
       "2022-12-31    TSM        1246800332165         1870200498251   \n",
       "2021-12-31    TSM        1133454847424         1700182271139   \n",
       "\n",
       "            estimatedRevenueAvg  estimatedEbitdaLow  estimatedEbitdaHigh  \\\n",
       "date                                                                       \n",
       "2023-12-31        1714350456728        932592350960        1398888526445   \n",
       "2022-12-31        1558500415208        847811228147        1271716842224   \n",
       "2021-12-31        1416818559282        770737480136        1156106220205   \n",
       "\n",
       "            estimatedEbitdaAvg  estimatedEbitLow  estimatedEbitHigh  \\\n",
       "date                                                                  \n",
       "2023-12-31       1165740438703      576972749740       865459124613   \n",
       "2022-12-31       1059764035186      524520681583       786781022377   \n",
       "2021-12-31        963421850171      476836983260       715255474890   \n",
       "\n",
       "            estimatedEbitAvg  ...  estimatedNetIncomeHigh  \\\n",
       "date                          ...                           \n",
       "2023-12-31      721215937177  ...            529664675125   \n",
       "2022-12-31      655650851980  ...            481513341025   \n",
       "2021-12-31      596046229075  ...            437739400934   \n",
       "\n",
       "            estimatedNetIncomeAvg  estimatedSgaExpenseLow  \\\n",
       "date                                                        \n",
       "2023-12-31           662080843909             35219148118   \n",
       "2022-12-31           601891676283             32017407381   \n",
       "2021-12-31           547174251169             29106733984   \n",
       "\n",
       "            estimatedSgaExpenseHigh  estimatedSgaExpenseAvg  estimatedEpsAvg  \\\n",
       "date                                                                           \n",
       "2023-12-31              52828722178             44023935148             4.21   \n",
       "2022-12-31              48026111072             40021759227             3.83   \n",
       "2021-12-31              43660100976             36383417480             3.48   \n",
       "\n",
       "            estimatedEpsHigh  estimatedEpsLow  numberAnalystEstimatedRevenue  \\\n",
       "date                                                                           \n",
       "2023-12-31              5.06             3.36                             11   \n",
       "2022-12-31              4.60             3.06                             10   \n",
       "2021-12-31              4.18             2.78                             16   \n",
       "\n",
       "            numberAnalystsEstimatedEps  \n",
       "date                                    \n",
       "2023-12-31                          11  \n",
       "2022-12-31                          10  \n",
       "2021-12-31                          16  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Earnings Surprises"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "# Pull actual vs estimated earnings \n",
    "url = f'https://financialmodelingprep.com/api/v3/earnings-surprises/{company}?apikey={FMP_API_KEY}'\n",
    "earnings_surprises = requests.get(url).json()\n",
    "earnings_surprises = pd.DataFrame(earnings_surprises)\n",
    "earnings_surprises['date'] = pd.to_datetime(earnings_surprises['date'])\n",
    "earnings_surprises= earnings_surprises.set_index('date')\n",
    "earnings_surprises.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>symbol</th>\n",
       "      <th>actualEarningResult</th>\n",
       "      <th>estimatedEarning</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-07-15</th>\n",
       "      <td>TSM</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-15</th>\n",
       "      <td>TSM</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-14</th>\n",
       "      <td>TSM</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-15</th>\n",
       "      <td>TSM</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-16</th>\n",
       "      <td>TSM</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol  actualEarningResult  estimatedEarning\n",
       "date                                                    \n",
       "2021-07-15    TSM                 0.93              0.93\n",
       "2021-04-15    TSM                 0.96              0.94\n",
       "2021-01-14    TSM                 0.97              0.95\n",
       "2020-10-15    TSM                 0.90              0.77\n",
       "2020-07-16    TSM                 0.78              0.77"
      ]
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "source": [
    "# Earnings surprises\n",
    "earnings_surprises.loc[earnings_surprises['actualEarningResult'] > earnings_surprises['estimatedEarning']]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>symbol</th>\n",
       "      <th>actualEarningResult</th>\n",
       "      <th>estimatedEarning</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-04-15</th>\n",
       "      <td>TSM</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-14</th>\n",
       "      <td>TSM</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-15</th>\n",
       "      <td>TSM</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-16</th>\n",
       "      <td>TSM</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-16</th>\n",
       "      <td>TSM</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-19</th>\n",
       "      <td>TSM</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol  actualEarningResult  estimatedEarning\n",
       "date                                                    \n",
       "2021-04-15    TSM                 0.96              0.94\n",
       "2021-01-14    TSM                 0.97              0.95\n",
       "2020-10-15    TSM                 0.90              0.77\n",
       "2020-07-16    TSM                 0.78              0.77\n",
       "2020-04-16    TSM                 0.75              0.73\n",
       "2019-04-19    TSM                 0.39              0.38"
      ]
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# FMP Rating \n",
    "## [FMP Rating Calculation](https://financialmodelingprep.com/developer/docs/recommendations-formula)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "source": [
    "# FMP Stock Rating based on financial statement, Discounted cash flow analysis, financial ratios and its intrinsic value.\n",
    "url = f'https://financialmodelingprep.com/api/v3/rating/{company}?apikey={FMP_API_KEY}'\n",
    "stock_rating = requests.get(url).json()\n",
    "stock_rating = pd.DataFrame(stock_rating).set_index('date')\n",
    "stock_rating.T"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>date</th>\n",
       "      <th>2021-09-30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <td>TSM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating</th>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratingScore</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratingRecommendation</th>\n",
       "      <td>Strong Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratingDetailsDCFScore</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratingDetailsDCFRecommendation</th>\n",
       "      <td>Strong Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratingDetailsROEScore</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratingDetailsROERecommendation</th>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratingDetailsROAScore</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratingDetailsROARecommendation</th>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratingDetailsDEScore</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratingDetailsDERecommendation</th>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratingDetailsPEScore</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratingDetailsPERecommendation</th>\n",
       "      <td>Strong Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratingDetailsPBScore</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratingDetailsPBRecommendation</th>\n",
       "      <td>Strong Buy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "date                            2021-09-30\n",
       "symbol                                 TSM\n",
       "rating                                  A+\n",
       "ratingScore                              5\n",
       "ratingRecommendation            Strong Buy\n",
       "ratingDetailsDCFScore                    5\n",
       "ratingDetailsDCFRecommendation  Strong Buy\n",
       "ratingDetailsROEScore                    3\n",
       "ratingDetailsROERecommendation     Neutral\n",
       "ratingDetailsROAScore                    3\n",
       "ratingDetailsROARecommendation     Neutral\n",
       "ratingDetailsDEScore                     4\n",
       "ratingDetailsDERecommendation          Buy\n",
       "ratingDetailsPEScore                     5\n",
       "ratingDetailsPERecommendation   Strong Buy\n",
       "ratingDetailsPBScore                     5\n",
       "ratingDetailsPBRecommendation   Strong Buy"
      ]
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "metadata": {}
  }
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