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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y6GoBeftonJa"
      },
      "source": [
        "# Import Necessary Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SEjoC9tFl_vV",
        "collapsed": true,
        "outputId": "026c9e7c-1926-42a8-d3df-3c2c77e1942e"
      },
      "source": [
        "# Install all necessary packages for this notebook (API in brackets for convenience)\n",
        "\n",
        "!pip install yfinance # https://pypi.org/project/yfinance/\n",
        "!pip install praw  # https://pypi.org/project/praw/\n",
        "!pip install json # https://docs.python.org/3/library/json.html\n",
        "!pip install requests # https://pypi.org/project/requests/\n",
        "!pip install pygooglenews # https://pypi.org/project/pygooglenews/\n",
        "!pip install vaderSentiment # https://pypi.org/project/vaderSentiment/\n",
        "!pip install emoji # https://pypi.org/project/emoji/\n",
        "!pip install zeugma  # https://zeugma.readthedocs.io/\n",
        "!pip install regex # https://docs.python.org/3/library/re.html\n",
        "!pip install nltk  # https://www.nltk.org/\n",
        "!pip install matplotlib.pyplot # https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.html\n",
        "!pip intall keras  # https://keras.io/\n",
        "!pip install sklearn # https://scikit-learn.org/\n",
        "%matplotlib inline\n",
        "\n",
        "# Import necessary files\n",
        "import math\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import regex as re\n",
        "import string\n",
        "import keras\n",
        "import nltk\n",
        "import emoji\n",
        "import pytz\n",
        "import collections\n",
        "import json\n",
        "import gc\n",
        "import matplotlib.pyplot as plt\n",
        "import yfinance as yf\n",
        "import datetime as dt\n",
        "import os, logging' \n",
        "import warnings\n",
        "\n",
        "# Import Sklearn packages\n",
        "from sklearn import datasets, linear_model, svm\n",
        "from sklearn import preprocessing\n",
        "from sklearn.metrics import mean_squared_error, r2_score, f1_score\n",
        "from sklearn.linear_model import LinearRegression, LassoCV, LogisticRegression\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.naive_bayes import BernoulliNB,  MultinomialNB\n",
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "from sklearn.feature_selection import SelectFromModel\n",
        "from sklearn.model_selection import TimeSeriesSplit,  GridSearchCV, train_test_split\n",
        "\n",
        "# Import necessary files for time conversion and data collection\n",
        "from datetime import datetime, timedelta\n",
        "from pygooglenews import GoogleNews\n",
        "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
        "from keras.layers import Dense, Dropout, Activation\n",
        "from collections import Counter\n",
        "\n",
        "# Import additional files, including NLTK and WordNet corpus\n",
        "from datetime import datetime, timedelta\n",
        "from pygooglenews import GoogleNews\n",
        "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
        "from keras.layers import Dense, Dropout, Activation\n",
        "from collections import Counter\n",
        "from google.colab import files\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.corpus import wordnet\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "stop_words = set(stopwords.words('english')) - {'all'}\n",
        "\n",
        "# Remove Deprecated warnings\n",
        "logging.getLogger(\"tensorflow\").setLevel(logging.CRITICAL)\n",
        "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Connect to Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting praw\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/48/a8/a2e2d0750ee17c7e3d81e4695a0338ad0b3f231853b8c3fa339ff2d25c7c/praw-7.2.0-py3-none-any.whl (159kB)\n",
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            "\u001b[?25hCollecting prawcore<3,>=2\n",
            "  Downloading https://files.pythonhosted.org/packages/7d/df/4a9106bea0d26689c4b309da20c926a01440ddaf60c09a5ae22684ebd35f/prawcore-2.0.0-py3-none-any.whl\n",
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            "  Downloading https://files.pythonhosted.org/packages/0c/ba/8dd7fa5f0b1c6a8ac62f8f57f7e794160c1f86f31c6d0fb00f582372a3e4/update_checker-0.18.0-py3-none-any.whl\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f7/0c/d52a2a63512a613817846d430d16a8fbe5ea56dd889e89c68facf6b91cb6/websocket_client-0.59.0-py2.py3-none-any.whl (67kB)\n",
            "\u001b[K     |████████████████████████████████| 71kB 5.6MB/s \n",
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            "Installing collected packages: prawcore, update-checker, websocket-client, praw\n",
            "Successfully installed praw-7.2.0 prawcore-2.0.0 update-checker-0.18.0 websocket-client-0.59.0\n",
            "\u001b[31mERROR: Could not find a version that satisfies the requirement json (from versions: none)\u001b[0m\n",
            "\u001b[31mERROR: No matching distribution found for json\u001b[0m\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (2.23.0)\n",
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            "Collecting pygooglenews\n",
            "  Downloading https://files.pythonhosted.org/packages/59/cb/84f162888a07e501630f6be7c70997c5c8afd5ba4a40c3a079c321e97c29/pygooglenews-0.1.2-py3-none-any.whl\n",
            "Collecting dateparser<0.8.0,>=0.7.6\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c1/d5/5a2e51bc0058f66b54669735f739d27afc3eb453ab00520623c7ab168e22/dateparser-0.7.6-py2.py3-none-any.whl (362kB)\n",
            "\u001b[K     |████████████████████████████████| 368kB 6.0MB/s \n",
            "\u001b[?25hCollecting beautifulsoup4<5.0.0,>=4.9.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d1/41/e6495bd7d3781cee623ce23ea6ac73282a373088fcd0ddc809a047b18eae/beautifulsoup4-4.9.3-py3-none-any.whl (115kB)\n",
            "\u001b[K     |████████████████████████████████| 122kB 9.8MB/s \n",
            "\u001b[?25hCollecting requests<3.0.0,>=2.24.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/29/c1/24814557f1d22c56d50280771a17307e6bf87b70727d975fd6b2ce6b014a/requests-2.25.1-py2.py3-none-any.whl (61kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 5.1MB/s \n",
            "\u001b[?25hCollecting feedparser<6.0.0,>=5.2.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/91/d8/7d37fec71ff7c9dbcdd80d2b48bcdd86d6af502156fc93846fb0102cb2c4/feedparser-5.2.1.tar.bz2 (192kB)\n",
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            "\u001b[?25hRequirement already satisfied: tzlocal in /usr/local/lib/python3.7/dist-packages (from dateparser<0.8.0,>=0.7.6->pygooglenews) (1.5.1)\n",
            "Requirement already satisfied: regex!=2019.02.19 in /usr/local/lib/python3.7/dist-packages (from dateparser<0.8.0,>=0.7.6->pygooglenews) (2019.12.20)\n",
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            "Collecting soupsieve>1.2; python_version >= \"3.0\"\n",
            "  Downloading https://files.pythonhosted.org/packages/36/69/d82d04022f02733bf9a72bc3b96332d360c0c5307096d76f6bb7489f7e57/soupsieve-2.2.1-py3-none-any.whl\n",
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            "Building wheels for collected packages: feedparser\n",
            "  Building wheel for feedparser (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for feedparser: filename=feedparser-5.2.1-cp37-none-any.whl size=44940 sha256=51cc694c19b0199a96d4d148428698e0dae232236b9be5bbe2f8511877a3e7c4\n",
            "  Stored in directory: /root/.cache/pip/wheels/8c/69/b7/f52763c41c5471df57703a0ef718a32a5e81ee35dcf6d4f97f\n",
            "Successfully built feedparser\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement requests~=2.23.0, but you'll have requests 2.25.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Installing collected packages: dateparser, soupsieve, beautifulsoup4, requests, feedparser, pygooglenews\n",
            "  Found existing installation: beautifulsoup4 4.6.3\n",
            "    Uninstalling beautifulsoup4-4.6.3:\n",
            "      Successfully uninstalled beautifulsoup4-4.6.3\n",
            "  Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "Successfully installed beautifulsoup4-4.9.3 dateparser-0.7.6 feedparser-5.2.1 pygooglenews-0.1.2 requests-2.25.1 soupsieve-2.2.1\n",
            "Collecting vaderSentiment\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/76/fc/310e16254683c1ed35eeb97386986d6c00bc29df17ce280aed64d55537e9/vaderSentiment-3.3.2-py2.py3-none-any.whl (125kB)\n",
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            "Installing collected packages: vaderSentiment\n",
            "Successfully installed vaderSentiment-3.3.2\n",
            "Collecting emoji\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/24/fa/b3368f41b95a286f8d300e323449ab4e86b85334c2e0b477e94422b8ed0f/emoji-1.2.0-py3-none-any.whl (131kB)\n",
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            "\u001b[?25hInstalling collected packages: emoji\n",
            "Successfully installed emoji-1.2.0\n",
            "Collecting zeugma\n",
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            "Building wheels for collected packages: zeugma\n",
            "  Building wheel for zeugma (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for zeugma: filename=zeugma-0.48-cp37-none-any.whl size=8778 sha256=f376707fdf7a2151741a0df738f23dbc55cb999625e7d1b29003ead09d5ec9a5\n",
            "  Stored in directory: /root/.cache/pip/wheels/6a/b5/bc/5183ac478b0071d04d3ed0c0dd4a43db94c5c8ffb317b5eb53\n",
            "Successfully built zeugma\n",
            "Installing collected packages: zeugma\n",
            "Successfully installed zeugma-0.48\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (2019.12.20)\n",
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            "\u001b[31mERROR: Could not find a version that satisfies the requirement matplotlib.pyplot (from versions: none)\u001b[0m\n",
            "\u001b[31mERROR: No matching distribution found for matplotlib.pyplot\u001b[0m\n",
            "ERROR: unknown command \"intall\" - maybe you meant \"install\"\n",
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            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WtoZoHQdMIX9"
      },
      "source": [
        "# Data Set Collection & Preprocessing and Sentiment Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Gam1y3lCzEQ"
      },
      "source": [
        "## Google News"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LS_NqKN4aceN"
      },
      "source": [
        "### Data Collection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oOC27DBxtlUj",
        "outputId": "1fb9211d-f610-4a3c-d778-7df6e7140bd9"
      },
      "source": [
        "# Three parallel arrays with necessary stock information:\n",
        "\n",
        "# All companies search queries \n",
        "companies = [\"'intitle:Gamestop' OR 'intitle:GME'\", \"'intitle:AMC+Cinemas' OR 'intitle:AMC'\",\n",
        "             \"'intitle:Nokia' OR 'intitle:NOK'\", \"'intitle:Tesla' OR 'intitle:TSLA'\",\n",
        "             \"'intitle:BlackBerry' OR 'intitle:BB'\", \"'intitle:Koss' OR 'intitle:KOSS'\",\n",
        "             \"'intitle:Express+Inc' OR 'intitle:EXPR'\",\n",
        "             \"'intitle:Microsoft' OR 'intitle:MSFT'\",  \"'intitle:Facebook' OR 'intitle:FB'\", \n",
        "             \"'intitle:Amazon' OR 'intitle:AMZN'\", \n",
        "             \"'intitle:Google' OR 'intitle:GOOGL'\", \"'intitle:Apple' OR 'intitle:AAPL'\", \n",
        "             \"'intitle:Netflix' OR 'intitle:NFLX'\", \"'intitle:Alibaba' OR 'intitle:BABA'\",\n",
        "             \"'intitle:Royal+Caribbean' OR 'intitle:RCL'\", \"'intitle:Carnival' OR 'intitle:CCL'\",\n",
        "             \"intitle:Beyond+Meat\", \"'intitle:Zoom' OR 'intitle:ZM'\",\n",
        "             \"'intitle:American+Airlines' OR 'intitle:AAL'\", \"intitle:Boeing\",\n",
        "             \"intitle:Moderna\"]\n",
        "\n",
        "# Company stock tickers        \n",
        "compName = ['GME', 'AMC', 'NOK', 'TSLA', 'BB', 'KOSS', 'EXPR',\n",
        "            'MSFT', 'FB', 'AMZN', 'GOOGL', 'AAPL', 'NFLX', 'BABA',\n",
        "            'RCL', 'CCL', 'BY', 'ZM', 'AAL', 'BA', 'MRNA']\n",
        "\n",
        "# Company type\n",
        "compType = ['Hype', 'Hype', 'Hype', 'Hype', 'Hype', 'Hype', 'Hype',\n",
        "            'Lead', 'Lead', 'Lead', 'Lead', 'Lead', 'Lead', 'Lead',\n",
        "            'Volat', 'Volat', 'Volat', 'Volat', 'Volat', 'Volat', 'Volat']\n",
        "\n",
        "# Extract data from Google News API one company at a time            \n",
        "index = 0\n",
        "dfBig = pd.DataFrame([], columns = ['type', 'company', 'title', 'source', 'd', 'm', 'y'])\n",
        "for company in companies:\n",
        "  gn = GoogleNews(lang = 'en', country ='US')\n",
        "  start_date = datetime.date(2020, 10, 1)\n",
        "  end_date = datetime.date(2021, 4, 29)\n",
        "  delta = datetime.timedelta(days=1)\n",
        "\n",
        "# For every day in interval\n",
        "  while start_date <= end_date:\n",
        "    cur = start_date\n",
        "    start_date += delta\n",
        "    next = start_date\n",
        "    s = gn.search(company, from_=str(cur), to_=str(next))\n",
        "    for entry in s['entries']:\n",
        "      # For entry get date, source, and title\n",
        "      if entry.has_key('title'):\n",
        "        title = (entry['title'])\n",
        "        year = (entry['published_parsed'].tm_year)\n",
        "        month = (entry['published_parsed'].tm_mon)\n",
        "        day = (entry['published_parsed'].tm_mday)\n",
        "        source = (entry['source'].title)\n",
        "        dfCur = pd.DataFrame([[compType[index], compName[index], title, source, day, month, year]], columns = ['type', 'company', 'title', 'source', 'd', 'm', 'y'])\n",
        "        dfBig = dfBig.append(dfCur)\n",
        "  print(company)\n",
        "  index += 1\n",
        "dfBig = dfBig.reset_index()\n",
        "#dfBig = dfBig.drop('level_0', axis=1)    # If necessary only\n",
        "dfBig = dfBig.drop('index', axis=1)\n",
        "\n",
        "# Export extracted dat\n",
        "filename = r\"/content/drive/Shareddrives/COS424/NewsAll.csv\"\n",
        "dfBig.to_csv(filename, index=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "2020-11-21\n",
            "2020-11-22\n",
            "2020-11-23\n",
            "2020-11-24\n",
            "2020-11-25\n",
            "2020-11-26\n",
            "2020-11-27\n",
            "2020-11-28\n",
            "2020-11-29\n",
            "2020-11-30\n",
            "2020-12-01\n",
            "2020-12-02\n",
            "2020-12-03\n",
            "2020-12-04\n",
            "2020-12-05\n",
            "2020-12-06\n",
            "2020-12-07\n",
            "2020-12-08\n",
            "2020-12-09\n",
            "2020-12-10\n",
            "2020-12-11\n",
            "2020-12-12\n",
            "2020-12-13\n",
            "2020-12-14\n",
            "2020-12-15\n",
            "2020-12-16\n",
            "2020-12-17\n",
            "2020-12-18\n",
            "2020-12-19\n",
            "2020-12-20\n",
            "2020-12-21\n",
            "2020-12-22\n",
            "2020-12-23\n",
            "2020-12-24\n",
            "2020-12-25\n",
            "2020-12-26\n",
            "2020-12-27\n",
            "2020-12-28\n",
            "2020-12-29\n",
            "2020-12-30\n",
            "2020-12-31\n",
            "2021-01-01\n",
            "2021-01-02\n",
            "2021-01-03\n",
            "2021-01-04\n",
            "2021-01-05\n",
            "2021-01-06\n",
            "2021-01-07\n",
            "2021-01-08\n",
            "2021-01-09\n",
            "2021-01-10\n",
            "2021-01-11\n",
            "2021-01-12\n",
            "2021-01-13\n",
            "2021-01-14\n",
            "2021-01-15\n",
            "2021-01-16\n",
            "2021-01-17\n",
            "2021-01-18\n",
            "2021-01-19\n",
            "2021-01-20\n",
            "2021-01-21\n",
            "2021-01-22\n",
            "2021-01-23\n",
            "2021-01-24\n",
            "2021-01-25\n",
            "2021-01-26\n",
            "2021-01-27\n",
            "2021-01-28\n",
            "2021-01-29\n",
            "2021-01-30\n",
            "2021-01-31\n",
            "2021-02-01\n",
            "2021-02-02\n",
            "2021-02-03\n",
            "2021-02-04\n",
            "2021-02-05\n",
            "2021-02-06\n",
            "2021-02-07\n",
            "2021-02-08\n",
            "2021-02-09\n",
            "2021-02-10\n",
            "2021-02-11\n",
            "2021-02-12\n",
            "2021-02-13\n",
            "2021-02-14\n",
            "2021-02-15\n",
            "2021-02-16\n",
            "2021-02-17\n",
            "2021-02-18\n",
            "2021-02-19\n",
            "2021-02-20\n",
            "2021-02-21\n",
            "2021-02-22\n",
            "2021-02-23\n",
            "2021-02-24\n",
            "2021-02-25\n",
            "2021-02-26\n",
            "2021-02-27\n",
            "2021-02-28\n",
            "2021-03-01\n",
            "2021-03-02\n",
            "2021-03-03\n",
            "2021-03-04\n",
            "2021-03-05\n",
            "2021-03-06\n",
            "2021-03-07\n",
            "2021-03-08\n",
            "2021-03-09\n",
            "2021-03-10\n",
            "2021-03-11\n",
            "2021-03-12\n",
            "2021-03-13\n",
            "2021-03-14\n",
            "2021-03-15\n",
            "2021-03-16\n",
            "2021-03-17\n",
            "2021-03-18\n",
            "2021-03-19\n",
            "2021-03-20\n",
            "2021-03-21\n",
            "2021-03-22\n",
            "2021-03-23\n",
            "2021-03-24\n",
            "2021-03-25\n",
            "2021-03-26\n",
            "2021-03-27\n",
            "2021-03-28\n",
            "2021-03-29\n",
            "2021-03-30\n",
            "2021-03-31\n",
            "2021-04-01\n",
            "2021-04-02\n",
            "2021-04-03\n",
            "2021-04-04\n",
            "2021-04-05\n",
            "2021-04-06\n",
            "2021-04-07\n",
            "2021-04-08\n",
            "2021-04-09\n",
            "2021-04-10\n",
            "2021-04-11\n",
            "2021-04-12\n",
            "2021-04-13\n",
            "2021-04-14\n",
            "2021-04-15\n",
            "2021-04-16\n",
            "2021-04-17\n",
            "2021-04-18\n",
            "2021-04-19\n",
            "2021-04-20\n",
            "2021-04-21\n",
            "2021-04-22\n",
            "2021-04-23\n",
            "2021-04-24\n",
            "2021-04-25\n",
            "2021-04-26\n",
            "2021-04-27\n",
            "2021-04-28\n",
            "2021-04-29\n",
            "'intitle:Alibaba' OR 'intitle:BABA'\n",
            "2020-10-01\n",
            "2020-10-02\n",
            "2020-10-03\n",
            "2020-10-04\n",
            "2020-10-05\n",
            "2020-10-06\n",
            "2020-10-07\n",
            "2020-10-08\n",
            "2020-10-09\n",
            "2020-10-10\n",
            "2020-10-11\n",
            "2020-10-12\n",
            "2020-10-13\n",
            "2020-10-14\n",
            "2020-10-15\n",
            "2020-10-16\n",
            "2020-10-17\n",
            "2020-10-18\n",
            "2020-10-19\n",
            "2020-10-20\n",
            "2020-10-21\n",
            "2020-10-22\n",
            "2020-10-23\n",
            "2020-10-24\n",
            "2020-10-25\n",
            "2020-10-26\n",
            "2020-10-27\n",
            "2020-10-28\n",
            "2020-10-29\n",
            "2020-10-30\n",
            "2020-10-31\n",
            "2020-11-01\n",
            "2020-11-02\n",
            "2020-11-03\n",
            "2020-11-04\n",
            "2020-11-05\n",
            "2020-11-06\n",
            "2020-11-07\n",
            "2020-11-08\n",
            "2020-11-09\n",
            "2020-11-10\n",
            "2020-11-11\n",
            "2020-11-12\n",
            "2020-11-13\n",
            "2020-11-14\n",
            "2020-11-15\n",
            "2020-11-16\n",
            "2020-11-17\n",
            "2020-11-18\n",
            "2020-11-19\n",
            "2020-11-20\n",
            "2020-11-21\n",
            "2020-11-22\n",
            "2020-11-23\n",
            "2020-11-24\n",
            "2020-11-25\n",
            "2020-11-26\n",
            "2020-11-27\n",
            "2020-11-28\n",
            "2020-11-29\n",
            "2020-11-30\n",
            "2020-12-01\n",
            "2020-12-02\n",
            "2020-12-03\n",
            "2020-12-04\n",
            "2020-12-05\n",
            "2020-12-06\n",
            "2020-12-07\n",
            "2020-12-08\n",
            "2020-12-09\n",
            "2020-12-10\n",
            "2020-12-11\n",
            "2020-12-12\n",
            "2020-12-13\n",
            "2020-12-14\n",
            "2020-12-15\n",
            "2020-12-16\n",
            "2020-12-17\n",
            "2020-12-18\n",
            "2020-12-19\n",
            "2020-12-20\n",
            "2020-12-21\n",
            "2020-12-22\n",
            "2020-12-23\n",
            "2020-12-24\n",
            "2020-12-25\n",
            "2020-12-26\n",
            "2020-12-27\n",
            "2020-12-28\n",
            "2020-12-29\n",
            "2020-12-30\n",
            "2020-12-31\n",
            "2021-01-01\n",
            "2021-01-02\n",
            "2021-01-03\n",
            "2021-01-04\n",
            "2021-01-05\n",
            "2021-01-06\n",
            "2021-01-07\n",
            "2021-01-08\n",
            "2021-01-09\n",
            "2021-01-10\n",
            "2021-01-11\n",
            "2021-01-12\n",
            "2021-01-13\n",
            "2021-01-14\n",
            "2021-01-15\n",
            "2021-01-16\n",
            "2021-01-17\n",
            "2021-01-18\n",
            "2021-01-19\n",
            "2021-01-20\n",
            "2021-01-21\n",
            "2021-01-22\n",
            "2021-01-23\n",
            "2021-01-24\n",
            "2021-01-25\n",
            "2021-01-26\n",
            "2021-01-27\n",
            "2021-01-28\n",
            "2021-01-29\n",
            "2021-01-30\n",
            "2021-01-31\n",
            "2021-02-01\n",
            "2021-02-02\n",
            "2021-02-03\n",
            "2021-02-04\n",
            "2021-02-05\n",
            "2021-02-06\n",
            "2021-02-07\n",
            "2021-02-08\n",
            "2021-02-09\n",
            "2021-02-10\n",
            "2021-02-11\n",
            "2021-02-12\n",
            "2021-02-13\n",
            "2021-02-14\n",
            "2021-02-15\n",
            "2021-02-16\n",
            "2021-02-17\n",
            "2021-02-18\n",
            "2021-02-19\n",
            "2021-02-20\n",
            "2021-02-21\n",
            "2021-02-22\n",
            "2021-02-23\n",
            "2021-02-24\n",
            "2021-02-25\n",
            "2021-02-26\n",
            "2021-02-27\n",
            "2021-02-28\n",
            "2021-03-01\n",
            "2021-03-02\n",
            "2021-03-03\n",
            "2021-03-04\n",
            "2021-03-05\n",
            "2021-03-06\n",
            "2021-03-07\n",
            "2021-03-08\n",
            "2021-03-09\n",
            "2021-03-10\n",
            "2021-03-11\n",
            "2021-03-12\n",
            "2021-03-13\n",
            "2021-03-14\n",
            "2021-03-15\n",
            "2021-03-16\n",
            "2021-03-17\n",
            "2021-03-18\n",
            "2021-03-19\n",
            "2021-03-20\n",
            "2021-03-21\n",
            "2021-03-22\n",
            "2021-03-23\n",
            "2021-03-24\n",
            "2021-03-25\n",
            "2021-03-26\n",
            "2021-03-27\n",
            "2021-03-28\n",
            "2021-03-29\n",
            "2021-03-30\n",
            "2021-03-31\n",
            "2021-04-01\n",
            "2021-04-02\n",
            "2021-04-03\n",
            "2021-04-04\n",
            "2021-04-05\n",
            "2021-04-06\n",
            "2021-04-07\n",
            "2021-04-08\n",
            "2021-04-09\n",
            "2021-04-10\n",
            "2021-04-11\n",
            "2021-04-12\n",
            "2021-04-13\n",
            "2021-04-14\n",
            "2021-04-15\n",
            "2021-04-16\n",
            "2021-04-17\n",
            "2021-04-18\n",
            "2021-04-19\n",
            "2021-04-20\n",
            "2021-04-21\n",
            "2021-04-22\n",
            "2021-04-23\n",
            "2021-04-24\n",
            "2021-04-25\n",
            "2021-04-26\n",
            "2021-04-27\n",
            "2021-04-28\n",
            "2021-04-29\n",
            "'intitle:Royal+Caribbean' OR 'intitle:RCL'\n",
            "2020-10-01\n",
            "2020-10-02\n",
            "2020-10-03\n",
            "2020-10-04\n",
            "2020-10-05\n",
            "2020-10-06\n",
            "2020-10-07\n",
            "2020-10-08\n",
            "2020-10-09\n",
            "2020-10-10\n",
            "2020-10-11\n",
            "2020-10-12\n",
            "2020-10-13\n",
            "2020-10-14\n",
            "2020-10-15\n",
            "2020-10-16\n",
            "2020-10-17\n",
            "2020-10-18\n",
            "2020-10-19\n",
            "2020-10-20\n",
            "2020-10-21\n",
            "2020-10-22\n",
            "2020-10-23\n",
            "2020-10-24\n",
            "2020-10-25\n",
            "2020-10-26\n",
            "2020-10-27\n",
            "2020-10-28\n",
            "2020-10-29\n",
            "2020-10-30\n",
            "2020-10-31\n",
            "2020-11-01\n",
            "2020-11-02\n",
            "2020-11-03\n",
            "2020-11-04\n",
            "2020-11-05\n",
            "2020-11-06\n",
            "2020-11-07\n",
            "2020-11-08\n",
            "2020-11-09\n",
            "2020-11-10\n",
            "2020-11-11\n",
            "2020-11-12\n",
            "2020-11-13\n",
            "2020-11-14\n",
            "2020-11-15\n",
            "2020-11-16\n",
            "2020-11-17\n",
            "2020-11-18\n",
            "2020-11-19\n",
            "2020-11-20\n",
            "2020-11-21\n",
            "2020-11-22\n",
            "2020-11-23\n",
            "2020-11-24\n",
            "2020-11-25\n",
            "2020-11-26\n",
            "2020-11-27\n",
            "2020-11-28\n",
            "2020-11-29\n",
            "2020-11-30\n",
            "2020-12-01\n",
            "2020-12-02\n",
            "2020-12-03\n",
            "2020-12-04\n",
            "2020-12-05\n",
            "2020-12-06\n",
            "2020-12-07\n",
            "2020-12-08\n",
            "2020-12-09\n",
            "2020-12-10\n",
            "2020-12-11\n",
            "2020-12-12\n",
            "2020-12-13\n",
            "2020-12-14\n",
            "2020-12-15\n",
            "2020-12-16\n",
            "2020-12-17\n",
            "2020-12-18\n",
            "2020-12-19\n",
            "2020-12-20\n",
            "2020-12-21\n",
            "2020-12-22\n",
            "2020-12-23\n",
            "2020-12-24\n",
            "2020-12-25\n",
            "2020-12-26\n",
            "2020-12-27\n",
            "2020-12-28\n",
            "2020-12-29\n",
            "2020-12-30\n",
            "2020-12-31\n",
            "2021-01-01\n",
            "2021-01-02\n",
            "2021-01-03\n",
            "2021-01-04\n",
            "2021-01-05\n",
            "2021-01-06\n",
            "2021-01-07\n",
            "2021-01-08\n",
            "2021-01-09\n",
            "2021-01-10\n",
            "2021-01-11\n",
            "2021-01-12\n",
            "2021-01-13\n",
            "2021-01-14\n",
            "2021-01-15\n",
            "2021-01-16\n",
            "2021-01-17\n",
            "2021-01-18\n",
            "2021-01-19\n",
            "2021-01-20\n",
            "2021-01-21\n",
            "2021-01-22\n",
            "2021-01-23\n",
            "2021-01-24\n",
            "2021-01-25\n",
            "2021-01-26\n",
            "2021-01-27\n",
            "2021-01-28\n",
            "2021-01-29\n",
            "2021-01-30\n",
            "2021-01-31\n",
            "2021-02-01\n",
            "2021-02-02\n",
            "2021-02-03\n",
            "2021-02-04\n",
            "2021-02-05\n",
            "2021-02-06\n",
            "2021-02-07\n",
            "2021-02-08\n",
            "2021-02-09\n",
            "2021-02-10\n",
            "2021-02-11\n",
            "2021-02-12\n",
            "2021-02-13\n",
            "2021-02-14\n",
            "2021-02-15\n",
            "2021-02-16\n",
            "2021-02-17\n",
            "2021-02-18\n",
            "2021-02-19\n",
            "2021-02-20\n",
            "2021-02-21\n",
            "2021-02-22\n",
            "2021-02-23\n",
            "2021-02-24\n",
            "2021-02-25\n",
            "2021-02-26\n",
            "2021-02-27\n",
            "2021-02-28\n",
            "2021-03-01\n",
            "2021-03-02\n",
            "2021-03-03\n",
            "2021-03-04\n",
            "2021-03-05\n",
            "2021-03-06\n",
            "2021-03-07\n",
            "2021-03-08\n",
            "2021-03-09\n",
            "2021-03-10\n",
            "2021-03-11\n",
            "2021-03-12\n",
            "2021-03-13\n",
            "2021-03-14\n",
            "2021-03-15\n",
            "2021-03-16\n",
            "2021-03-17\n",
            "2021-03-18\n",
            "2021-03-19\n",
            "2021-03-20\n",
            "2021-03-21\n",
            "2021-03-22\n",
            "2021-03-23\n",
            "2021-03-24\n",
            "2021-03-25\n",
            "2021-03-26\n",
            "2021-03-27\n",
            "2021-03-28\n",
            "2021-03-29\n",
            "2021-03-30\n",
            "2021-03-31\n",
            "2021-04-01\n",
            "2021-04-02\n",
            "2021-04-03\n",
            "2021-04-04\n",
            "2021-04-05\n",
            "2021-04-06\n",
            "2021-04-07\n",
            "2021-04-08\n",
            "2021-04-09\n",
            "2021-04-10\n",
            "2021-04-11\n",
            "2021-04-12\n",
            "2021-04-13\n",
            "2021-04-14\n",
            "2021-04-15\n",
            "2021-04-16\n",
            "2021-04-17\n",
            "2021-04-18\n",
            "2021-04-19\n",
            "2021-04-20\n",
            "2021-04-21\n",
            "2021-04-22\n",
            "2021-04-23\n",
            "2021-04-24\n",
            "2021-04-25\n",
            "2021-04-26\n",
            "2021-04-27\n",
            "2021-04-28\n",
            "2021-04-29\n",
            "'intitle:Carnival' OR 'intitle:CCL'\n",
            "2020-10-01\n",
            "2020-10-02\n",
            "2020-10-03\n",
            "2020-10-04\n",
            "2020-10-05\n",
            "2020-10-06\n",
            "2020-10-07\n",
            "2020-10-08\n",
            "2020-10-09\n",
            "2020-10-10\n",
            "2020-10-11\n",
            "2020-10-12\n",
            "2020-10-13\n",
            "2020-10-14\n",
            "2020-10-15\n",
            "2020-10-16\n",
            "2020-10-17\n",
            "2020-10-18\n",
            "2020-10-19\n",
            "2020-10-20\n",
            "2020-10-21\n",
            "2020-10-22\n",
            "2020-10-23\n",
            "2020-10-24\n",
            "2020-10-25\n",
            "2020-10-26\n",
            "2020-10-27\n",
            "2020-10-28\n",
            "2020-10-29\n",
            "2020-10-30\n",
            "2020-10-31\n",
            "2020-11-01\n",
            "2020-11-02\n",
            "2020-11-03\n",
            "2020-11-04\n",
            "2020-11-05\n",
            "2020-11-06\n",
            "2020-11-07\n",
            "2020-11-08\n",
            "2020-11-09\n",
            "2020-11-10\n",
            "2020-11-11\n",
            "2020-11-12\n",
            "2020-11-13\n",
            "2020-11-14\n",
            "2020-11-15\n",
            "2020-11-16\n",
            "2020-11-17\n",
            "2020-11-18\n",
            "2020-11-19\n",
            "2020-11-20\n",
            "2020-11-21\n",
            "2020-11-22\n",
            "2020-11-23\n",
            "2020-11-24\n",
            "2020-11-25\n",
            "2020-11-26\n",
            "2020-11-27\n",
            "2020-11-28\n",
            "2020-11-29\n",
            "2020-11-30\n",
            "2020-12-01\n",
            "2020-12-02\n",
            "2020-12-03\n",
            "2020-12-04\n",
            "2020-12-05\n",
            "2020-12-06\n",
            "2020-12-07\n",
            "2020-12-08\n",
            "2020-12-09\n",
            "2020-12-10\n",
            "2020-12-11\n",
            "2020-12-12\n",
            "2020-12-13\n",
            "2020-12-14\n",
            "2020-12-15\n",
            "2020-12-16\n",
            "2020-12-17\n",
            "2020-12-18\n",
            "2020-12-19\n",
            "2020-12-20\n",
            "2020-12-21\n",
            "2020-12-22\n",
            "2020-12-23\n",
            "2020-12-24\n",
            "2020-12-25\n",
            "2020-12-26\n",
            "2020-12-27\n",
            "2020-12-28\n",
            "2020-12-29\n",
            "2020-12-30\n",
            "2020-12-31\n",
            "2021-01-01\n",
            "2021-01-02\n",
            "2021-01-03\n",
            "2021-01-04\n",
            "2021-01-05\n",
            "2021-01-06\n",
            "2021-01-07\n",
            "2021-01-08\n",
            "2021-01-09\n",
            "2021-01-10\n",
            "2021-01-11\n",
            "2021-01-12\n",
            "2021-01-13\n",
            "2021-01-14\n",
            "2021-01-15\n",
            "2021-01-16\n",
            "2021-01-17\n",
            "2021-01-18\n",
            "2021-01-19\n",
            "2021-01-20\n",
            "2021-01-21\n",
            "2021-01-22\n",
            "2021-01-23\n",
            "2021-01-24\n",
            "2021-01-25\n",
            "2021-01-26\n",
            "2021-01-27\n",
            "2021-01-28\n",
            "2021-01-29\n",
            "2021-01-30\n",
            "2021-01-31\n",
            "2021-02-01\n",
            "2021-02-02\n",
            "2021-02-03\n",
            "2021-02-04\n",
            "2021-02-05\n",
            "2021-02-06\n",
            "2021-02-07\n",
            "2021-02-08\n",
            "2021-02-09\n",
            "2021-02-10\n",
            "2021-02-11\n",
            "2021-02-12\n",
            "2021-02-13\n",
            "2021-02-14\n",
            "2021-02-15\n",
            "2021-02-16\n",
            "2021-02-17\n",
            "2021-02-18\n",
            "2021-02-19\n",
            "2021-02-20\n",
            "2021-02-21\n",
            "2021-02-22\n",
            "2021-02-23\n",
            "2021-02-24\n",
            "2021-02-25\n",
            "2021-02-26\n",
            "2021-02-27\n",
            "2021-02-28\n",
            "2021-03-01\n",
            "2021-03-02\n",
            "2021-03-03\n",
            "2021-03-04\n",
            "2021-03-05\n",
            "2021-03-06\n",
            "2021-03-07\n",
            "2021-03-08\n",
            "2021-03-09\n",
            "2021-03-10\n",
            "2021-03-11\n",
            "2021-03-12\n",
            "2021-03-13\n",
            "2021-03-14\n",
            "2021-03-15\n",
            "2021-03-16\n",
            "2021-03-17\n",
            "2021-03-18\n",
            "2021-03-19\n",
            "2021-03-20\n",
            "2021-03-21\n",
            "2021-03-22\n",
            "2021-03-23\n",
            "2021-03-24\n",
            "2021-03-25\n",
            "2021-03-26\n",
            "2021-03-27\n",
            "2021-03-28\n",
            "2021-03-29\n",
            "2021-03-30\n",
            "2021-03-31\n",
            "2021-04-01\n",
            "2021-04-02\n",
            "2021-04-03\n",
            "2021-04-04\n",
            "2021-04-05\n",
            "2021-04-06\n",
            "2021-04-07\n",
            "2021-04-08\n",
            "2021-04-09\n",
            "2021-04-10\n",
            "2021-04-11\n",
            "2021-04-12\n",
            "2021-04-13\n",
            "2021-04-14\n",
            "2021-04-15\n",
            "2021-04-16\n",
            "2021-04-17\n",
            "2021-04-18\n",
            "2021-04-19\n",
            "2021-04-20\n",
            "2021-04-21\n",
            "2021-04-22\n",
            "2021-04-23\n",
            "2021-04-24\n",
            "2021-04-25\n",
            "2021-04-26\n",
            "2021-04-27\n",
            "2021-04-28\n",
            "2021-04-29\n",
            "intitle:Beyond+Meat\n",
            "2020-10-01\n",
            "2020-10-02\n",
            "2020-10-03\n",
            "2020-10-04\n",
            "2020-10-05\n",
            "2020-10-06\n",
            "2020-10-07\n",
            "2020-10-08\n",
            "2020-10-09\n",
            "2020-10-10\n",
            "2020-10-11\n",
            "2020-10-12\n",
            "2020-10-13\n",
            "2020-10-14\n",
            "2020-10-15\n",
            "2020-10-16\n",
            "2020-10-17\n",
            "2020-10-18\n",
            "2020-10-19\n",
            "2020-10-20\n",
            "2020-10-21\n",
            "2020-10-22\n",
            "2020-10-23\n",
            "2020-10-24\n",
            "2020-10-25\n",
            "2020-10-26\n",
            "2020-10-27\n",
            "2020-10-28\n",
            "2020-10-29\n",
            "2020-10-30\n",
            "2020-10-31\n",
            "2020-11-01\n",
            "2020-11-02\n",
            "2020-11-03\n",
            "2020-11-04\n",
            "2020-11-05\n",
            "2020-11-06\n",
            "2020-11-07\n",
            "2020-11-08\n",
            "2020-11-09\n",
            "2020-11-10\n",
            "2020-11-11\n",
            "2020-11-12\n",
            "2020-11-13\n",
            "2020-11-14\n",
            "2020-11-15\n",
            "2020-11-16\n",
            "2020-11-17\n",
            "2020-11-18\n",
            "2020-11-19\n",
            "2020-11-20\n",
            "2020-11-21\n",
            "2020-11-22\n",
            "2020-11-23\n",
            "2020-11-24\n",
            "2020-11-25\n",
            "2020-11-26\n",
            "2020-11-27\n",
            "2020-11-28\n",
            "2020-11-29\n",
            "2020-11-30\n",
            "2020-12-01\n",
            "2020-12-02\n",
            "2020-12-03\n",
            "2020-12-04\n",
            "2020-12-05\n",
            "2020-12-06\n",
            "2020-12-07\n",
            "2020-12-08\n",
            "2020-12-09\n",
            "2020-12-10\n",
            "2020-12-11\n",
            "2020-12-12\n",
            "2020-12-13\n",
            "2020-12-14\n",
            "2020-12-15\n",
            "2020-12-16\n",
            "2020-12-17\n",
            "2020-12-18\n",
            "2020-12-19\n",
            "2020-12-20\n",
            "2020-12-21\n",
            "2020-12-22\n",
            "2020-12-23\n",
            "2020-12-24\n",
            "2020-12-25\n",
            "2020-12-26\n",
            "2020-12-27\n",
            "2020-12-28\n",
            "2020-12-29\n",
            "2020-12-30\n",
            "2020-12-31\n",
            "2021-01-01\n",
            "2021-01-02\n",
            "2021-01-03\n",
            "2021-01-04\n",
            "2021-01-05\n",
            "2021-01-06\n",
            "2021-01-07\n",
            "2021-01-08\n",
            "2021-01-09\n",
            "2021-01-10\n",
            "2021-01-11\n",
            "2021-01-12\n",
            "2021-01-13\n",
            "2021-01-14\n",
            "2021-01-15\n",
            "2021-01-16\n",
            "2021-01-17\n",
            "2021-01-18\n",
            "2021-01-19\n",
            "2021-01-20\n",
            "2021-01-21\n",
            "2021-01-22\n",
            "2021-01-23\n",
            "2021-01-24\n",
            "2021-01-25\n",
            "2021-01-26\n",
            "2021-01-27\n",
            "2021-01-28\n",
            "2021-01-29\n",
            "2021-01-30\n",
            "2021-01-31\n",
            "2021-02-01\n",
            "2021-02-02\n",
            "2021-02-03\n",
            "2021-02-04\n",
            "2021-02-05\n",
            "2021-02-06\n",
            "2021-02-07\n",
            "2021-02-08\n",
            "2021-02-09\n",
            "2021-02-10\n",
            "2021-02-11\n",
            "2021-02-12\n",
            "2021-02-13\n",
            "2021-02-14\n",
            "2021-02-15\n",
            "2021-02-16\n",
            "2021-02-17\n",
            "2021-02-18\n",
            "2021-02-19\n",
            "2021-02-20\n",
            "2021-02-21\n",
            "2021-02-22\n",
            "2021-02-23\n",
            "2021-02-24\n",
            "2021-02-25\n",
            "2021-02-26\n",
            "2021-02-27\n",
            "2021-02-28\n",
            "2021-03-01\n",
            "2021-03-02\n",
            "2021-03-03\n",
            "2021-03-04\n",
            "2021-03-05\n",
            "2021-03-06\n",
            "2021-03-07\n",
            "2021-03-08\n",
            "2021-03-09\n",
            "2021-03-10\n",
            "2021-03-11\n",
            "2021-03-12\n",
            "2021-03-13\n",
            "2021-03-14\n",
            "2021-03-15\n",
            "2021-03-16\n",
            "2021-03-17\n",
            "2021-03-18\n",
            "2021-03-19\n",
            "2021-03-20\n",
            "2021-03-21\n",
            "2021-03-22\n",
            "2021-03-23\n",
            "2021-03-24\n",
            "2021-03-25\n",
            "2021-03-26\n",
            "2021-03-27\n",
            "2021-03-28\n",
            "2021-03-29\n",
            "2021-03-30\n",
            "2021-03-31\n",
            "2021-04-01\n",
            "2021-04-02\n",
            "2021-04-03\n",
            "2021-04-04\n",
            "2021-04-05\n",
            "2021-04-06\n",
            "2021-04-07\n",
            "2021-04-08\n",
            "2021-04-09\n",
            "2021-04-10\n",
            "2021-04-11\n",
            "2021-04-12\n",
            "2021-04-13\n",
            "2021-04-14\n",
            "2021-04-15\n",
            "2021-04-16\n",
            "2021-04-17\n",
            "2021-04-18\n",
            "2021-04-19\n",
            "2021-04-20\n",
            "2021-04-21\n",
            "2021-04-22\n",
            "2021-04-23\n",
            "2021-04-24\n",
            "2021-04-25\n",
            "2021-04-26\n",
            "2021-04-27\n",
            "2021-04-28\n",
            "2021-04-29\n",
            "'intitle:Zoom' OR 'intitle:ZM'\n",
            "2020-10-01\n",
            "2020-10-02\n",
            "2020-10-03\n",
            "2020-10-04\n",
            "2020-10-05\n",
            "2020-10-06\n",
            "2020-10-07\n",
            "2020-10-08\n",
            "2020-10-09\n",
            "2020-10-10\n",
            "2020-10-11\n",
            "2020-10-12\n",
            "2020-10-13\n",
            "2020-10-14\n",
            "2020-10-15\n",
            "2020-10-16\n",
            "2020-10-17\n",
            "2020-10-18\n",
            "2020-10-19\n",
            "2020-10-20\n",
            "2020-10-21\n",
            "2020-10-22\n",
            "2020-10-23\n",
            "2020-10-24\n",
            "2020-10-25\n",
            "2020-10-26\n",
            "2020-10-27\n",
            "2020-10-28\n",
            "2020-10-29\n",
            "2020-10-30\n",
            "2020-10-31\n",
            "2020-11-01\n",
            "2020-11-02\n",
            "2020-11-03\n",
            "2020-11-04\n",
            "2020-11-05\n",
            "2020-11-06\n",
            "2020-11-07\n",
            "2020-11-08\n",
            "2020-11-09\n",
            "2020-11-10\n",
            "2020-11-11\n",
            "2020-11-12\n",
            "2020-11-13\n",
            "2020-11-14\n",
            "2020-11-15\n",
            "2020-11-16\n",
            "2020-11-17\n",
            "2020-11-18\n",
            "2020-11-19\n",
            "2020-11-20\n",
            "2020-11-21\n",
            "2020-11-22\n",
            "2020-11-23\n",
            "2020-11-24\n",
            "2020-11-25\n",
            "2020-11-26\n",
            "2020-11-27\n",
            "2020-11-28\n",
            "2020-11-29\n",
            "2020-11-30\n",
            "2020-12-01\n",
            "2020-12-02\n",
            "2020-12-03\n",
            "2020-12-04\n",
            "2020-12-05\n",
            "2020-12-06\n",
            "2020-12-07\n",
            "2020-12-08\n",
            "2020-12-09\n",
            "2020-12-10\n",
            "2020-12-11\n",
            "2020-12-12\n",
            "2020-12-13\n",
            "2020-12-14\n",
            "2020-12-15\n",
            "2020-12-16\n",
            "2020-12-17\n",
            "2020-12-18\n",
            "2020-12-19\n",
            "2020-12-20\n",
            "2020-12-21\n",
            "2020-12-22\n",
            "2020-12-23\n",
            "2020-12-24\n",
            "2020-12-25\n",
            "2020-12-26\n",
            "2020-12-27\n",
            "2020-12-28\n",
            "2020-12-29\n",
            "2020-12-30\n",
            "2020-12-31\n",
            "2021-01-01\n",
            "2021-01-02\n",
            "2021-01-03\n",
            "2021-01-04\n",
            "2021-01-05\n",
            "2021-01-06\n",
            "2021-01-07\n",
            "2021-01-08\n",
            "2021-01-09\n",
            "2021-01-10\n",
            "2021-01-11\n",
            "2021-01-12\n",
            "2021-01-13\n",
            "2021-01-14\n",
            "2021-01-15\n",
            "2021-01-16\n",
            "2021-01-17\n",
            "2021-01-18\n",
            "2021-01-19\n",
            "2021-01-20\n",
            "2021-01-21\n",
            "2021-01-22\n",
            "2021-01-23\n",
            "2021-01-24\n",
            "2021-01-25\n",
            "2021-01-26\n",
            "2021-01-27\n",
            "2021-01-28\n",
            "2021-01-29\n",
            "2021-01-30\n",
            "2021-01-31\n",
            "2021-02-01\n",
            "2021-02-02\n",
            "2021-02-03\n",
            "2021-02-04\n",
            "2021-02-05\n",
            "2021-02-06\n",
            "2021-02-07\n",
            "2021-02-08\n",
            "2021-02-09\n",
            "2021-02-10\n",
            "2021-02-11\n",
            "2021-02-12\n",
            "2021-02-13\n",
            "2021-02-14\n",
            "2021-02-15\n",
            "2021-02-16\n",
            "2021-02-17\n",
            "2021-02-18\n",
            "2021-02-19\n",
            "2021-02-20\n",
            "2021-02-21\n",
            "2021-02-22\n",
            "2021-02-23\n",
            "2021-02-24\n",
            "2021-02-25\n",
            "2021-02-26\n",
            "2021-02-27\n",
            "2021-02-28\n",
            "2021-03-01\n",
            "2021-03-02\n",
            "2021-03-03\n",
            "2021-03-04\n",
            "2021-03-05\n",
            "2021-03-06\n",
            "2021-03-07\n",
            "2021-03-08\n",
            "2021-03-09\n",
            "2021-03-10\n",
            "2021-03-11\n",
            "2021-03-12\n",
            "2021-03-13\n",
            "2021-03-14\n",
            "2021-03-15\n",
            "2021-03-16\n",
            "2021-03-17\n",
            "2021-03-18\n",
            "2021-03-19\n",
            "2021-03-20\n",
            "2021-03-21\n",
            "2021-03-22\n",
            "2021-03-23\n",
            "2021-03-24\n",
            "2021-03-25\n",
            "2021-03-26\n",
            "2021-03-27\n",
            "2021-03-28\n",
            "2021-03-29\n",
            "2021-03-30\n",
            "2021-03-31\n",
            "2021-04-01\n",
            "2021-04-02\n",
            "2021-04-03\n",
            "2021-04-04\n",
            "2021-04-05\n",
            "2021-04-06\n",
            "2021-04-07\n",
            "2021-04-08\n",
            "2021-04-09\n",
            "2021-04-10\n",
            "2021-04-11\n",
            "2021-04-12\n",
            "2021-04-13\n",
            "2021-04-14\n",
            "2021-04-15\n",
            "2021-04-16\n",
            "2021-04-17\n",
            "2021-04-18\n",
            "2021-04-19\n",
            "2021-04-20\n",
            "2021-04-21\n",
            "2021-04-22\n",
            "2021-04-23\n",
            "2021-04-24\n",
            "2021-04-25\n",
            "2021-04-26\n",
            "2021-04-27\n",
            "2021-04-28\n",
            "2021-04-29\n",
            "'intitle:American+Airlines' OR 'intitle:AAL'\n",
            "2020-10-01\n",
            "2020-10-02\n",
            "2020-10-03\n",
            "2020-10-04\n",
            "2020-10-05\n",
            "2020-10-06\n",
            "2020-10-07\n",
            "2020-10-08\n",
            "2020-10-09\n",
            "2020-10-10\n",
            "2020-10-11\n",
            "2020-10-12\n",
            "2020-10-13\n",
            "2020-10-14\n",
            "2020-10-15\n",
            "2020-10-16\n",
            "2020-10-17\n",
            "2020-10-18\n",
            "2020-10-19\n",
            "2020-10-20\n",
            "2020-10-21\n",
            "2020-10-22\n",
            "2020-10-23\n",
            "2020-10-24\n",
            "2020-10-25\n",
            "2020-10-26\n",
            "2020-10-27\n",
            "2020-10-28\n",
            "2020-10-29\n",
            "2020-10-30\n",
            "2020-10-31\n",
            "2020-11-01\n",
            "2020-11-02\n",
            "2020-11-03\n",
            "2020-11-04\n",
            "2020-11-05\n",
            "2020-11-06\n",
            "2020-11-07\n",
            "2020-11-08\n",
            "2020-11-09\n",
            "2020-11-10\n",
            "2020-11-11\n",
            "2020-11-12\n",
            "2020-11-13\n",
            "2020-11-14\n",
            "2020-11-15\n",
            "2020-11-16\n",
            "2020-11-17\n",
            "2020-11-18\n",
            "2020-11-19\n",
            "2020-11-20\n",
            "2020-11-21\n",
            "2020-11-22\n",
            "2020-11-23\n",
            "2020-11-24\n",
            "2020-11-25\n",
            "2020-11-26\n",
            "2020-11-27\n",
            "2020-11-28\n",
            "2020-11-29\n",
            "2020-11-30\n",
            "2020-12-01\n",
            "2020-12-02\n",
            "2020-12-03\n",
            "2020-12-04\n",
            "2020-12-05\n",
            "2020-12-06\n",
            "2020-12-07\n",
            "2020-12-08\n",
            "2020-12-09\n",
            "2020-12-10\n",
            "2020-12-11\n",
            "2020-12-12\n",
            "2020-12-13\n",
            "2020-12-14\n",
            "2020-12-15\n",
            "2020-12-16\n",
            "2020-12-17\n",
            "2020-12-18\n",
            "2020-12-19\n",
            "2020-12-20\n",
            "2020-12-21\n",
            "2020-12-22\n",
            "2020-12-23\n",
            "2020-12-24\n",
            "2020-12-25\n",
            "2020-12-26\n",
            "2020-12-27\n",
            "2020-12-28\n",
            "2020-12-29\n",
            "2020-12-30\n",
            "2020-12-31\n",
            "2021-01-01\n",
            "2021-01-02\n",
            "2021-01-03\n",
            "2021-01-04\n",
            "2021-01-05\n",
            "2021-01-06\n",
            "2021-01-07\n",
            "2021-01-08\n",
            "2021-01-09\n",
            "2021-01-10\n",
            "2021-01-11\n",
            "2021-01-12\n",
            "2021-01-13\n",
            "2021-01-14\n",
            "2021-01-15\n",
            "2021-01-16\n",
            "2021-01-17\n",
            "2021-01-18\n",
            "2021-01-19\n",
            "2021-01-20\n",
            "2021-01-21\n",
            "2021-01-22\n",
            "2021-01-23\n",
            "2021-01-24\n",
            "2021-01-25\n",
            "2021-01-26\n",
            "2021-01-27\n",
            "2021-01-28\n",
            "2021-01-29\n",
            "2021-01-30\n",
            "2021-01-31\n",
            "2021-02-01\n",
            "2021-02-02\n",
            "2021-02-03\n",
            "2021-02-04\n",
            "2021-02-05\n",
            "2021-02-06\n",
            "2021-02-07\n",
            "2021-02-08\n",
            "2021-02-09\n",
            "2021-02-10\n",
            "2021-02-11\n",
            "2021-02-12\n",
            "2021-02-13\n",
            "2021-02-14\n",
            "2021-02-15\n",
            "2021-02-16\n",
            "2021-02-17\n",
            "2021-02-18\n",
            "2021-02-19\n",
            "2021-02-20\n",
            "2021-02-21\n",
            "2021-02-22\n",
            "2021-02-23\n",
            "2021-02-24\n",
            "2021-02-25\n",
            "2021-02-26\n",
            "2021-02-27\n",
            "2021-02-28\n",
            "2021-03-01\n",
            "2021-03-02\n",
            "2021-03-03\n",
            "2021-03-04\n",
            "2021-03-05\n",
            "2021-03-06\n",
            "2021-03-07\n",
            "2021-03-08\n",
            "2021-03-09\n",
            "2021-03-10\n",
            "2021-03-11\n",
            "2021-03-12\n",
            "2021-03-13\n",
            "2021-03-14\n",
            "2021-03-15\n",
            "2021-03-16\n",
            "2021-03-17\n",
            "2021-03-18\n",
            "2021-03-19\n",
            "2021-03-20\n",
            "2021-03-21\n",
            "2021-03-22\n",
            "2021-03-23\n",
            "2021-03-24\n",
            "2021-03-25\n",
            "2021-03-26\n",
            "2021-03-27\n",
            "2021-03-28\n",
            "2021-03-29\n",
            "2021-03-30\n",
            "2021-03-31\n",
            "2021-04-01\n",
            "2021-04-02\n",
            "2021-04-03\n",
            "2021-04-04\n",
            "2021-04-05\n",
            "2021-04-06\n",
            "2021-04-07\n",
            "2021-04-08\n",
            "2021-04-09\n",
            "2021-04-10\n",
            "2021-04-11\n",
            "2021-04-12\n",
            "2021-04-13\n",
            "2021-04-14\n",
            "2021-04-15\n",
            "2021-04-16\n",
            "2021-04-17\n",
            "2021-04-18\n",
            "2021-04-19\n",
            "2021-04-20\n",
            "2021-04-21\n",
            "2021-04-22\n",
            "2021-04-23\n",
            "2021-04-24\n",
            "2021-04-25\n",
            "2021-04-26\n",
            "2021-04-27\n",
            "2021-04-28\n",
            "2021-04-29\n",
            "intitle:Boeing\n",
            "2020-10-01\n",
            "2020-10-02\n",
            "2020-10-03\n",
            "2020-10-04\n",
            "2020-10-05\n",
            "2020-10-06\n",
            "2020-10-07\n",
            "2020-10-08\n",
            "2020-10-09\n",
            "2020-10-10\n",
            "2020-10-11\n",
            "2020-10-12\n",
            "2020-10-13\n",
            "2020-10-14\n",
            "2020-10-15\n",
            "2020-10-16\n",
            "2020-10-17\n",
            "2020-10-18\n",
            "2020-10-19\n",
            "2020-10-20\n",
            "2020-10-21\n",
            "2020-10-22\n",
            "2020-10-23\n",
            "2020-10-24\n",
            "2020-10-25\n",
            "2020-10-26\n",
            "2020-10-27\n",
            "2020-10-28\n",
            "2020-10-29\n",
            "2020-10-30\n",
            "2020-10-31\n",
            "2020-11-01\n",
            "2020-11-02\n",
            "2020-11-03\n",
            "2020-11-04\n",
            "2020-11-05\n",
            "2020-11-06\n",
            "2020-11-07\n",
            "2020-11-08\n",
            "2020-11-09\n",
            "2020-11-10\n",
            "2020-11-11\n",
            "2020-11-12\n",
            "2020-11-13\n",
            "2020-11-14\n",
            "2020-11-15\n",
            "2020-11-16\n",
            "2020-11-17\n",
            "2020-11-18\n",
            "2020-11-19\n",
            "2020-11-20\n",
            "2020-11-21\n",
            "2020-11-22\n",
            "2020-11-23\n",
            "2020-11-24\n",
            "2020-11-25\n",
            "2020-11-26\n",
            "2020-11-27\n",
            "2020-11-28\n",
            "2020-11-29\n",
            "2020-11-30\n",
            "2020-12-01\n",
            "2020-12-02\n",
            "2020-12-03\n",
            "2020-12-04\n",
            "2020-12-05\n",
            "2020-12-06\n",
            "2020-12-07\n",
            "2020-12-08\n",
            "2020-12-09\n",
            "2020-12-10\n",
            "2020-12-11\n",
            "2020-12-12\n",
            "2020-12-13\n",
            "2020-12-14\n",
            "2020-12-15\n",
            "2020-12-16\n",
            "2020-12-17\n",
            "2020-12-18\n",
            "2020-12-19\n",
            "2020-12-20\n",
            "2020-12-21\n",
            "2020-12-22\n",
            "2020-12-23\n",
            "2020-12-24\n",
            "2020-12-25\n",
            "2020-12-26\n",
            "2020-12-27\n",
            "2020-12-28\n",
            "2020-12-29\n",
            "2020-12-30\n",
            "2020-12-31\n",
            "2021-01-01\n",
            "2021-01-02\n",
            "2021-01-03\n",
            "2021-01-04\n",
            "2021-01-05\n",
            "2021-01-06\n",
            "2021-01-07\n",
            "2021-01-08\n",
            "2021-01-09\n",
            "2021-01-10\n",
            "2021-01-11\n",
            "2021-01-12\n",
            "2021-01-13\n",
            "2021-01-14\n",
            "2021-01-15\n",
            "2021-01-16\n",
            "2021-01-17\n",
            "2021-01-18\n",
            "2021-01-19\n",
            "2021-01-20\n",
            "2021-01-21\n",
            "2021-01-22\n",
            "2021-01-23\n",
            "2021-01-24\n",
            "2021-01-25\n",
            "2021-01-26\n",
            "2021-01-27\n",
            "2021-01-28\n",
            "2021-01-29\n",
            "2021-01-30\n",
            "2021-01-31\n",
            "2021-02-01\n",
            "2021-02-02\n",
            "2021-02-03\n",
            "2021-02-04\n",
            "2021-02-05\n",
            "2021-02-06\n",
            "2021-02-07\n",
            "2021-02-08\n",
            "2021-02-09\n",
            "2021-02-10\n",
            "2021-02-11\n",
            "2021-02-12\n",
            "2021-02-13\n",
            "2021-02-14\n",
            "2021-02-15\n",
            "2021-02-16\n",
            "2021-02-17\n",
            "2021-02-18\n",
            "2021-02-19\n",
            "2021-02-20\n",
            "2021-02-21\n",
            "2021-02-22\n",
            "2021-02-23\n",
            "2021-02-24\n",
            "2021-02-25\n",
            "2021-02-26\n",
            "2021-02-27\n",
            "2021-02-28\n",
            "2021-03-01\n",
            "2021-03-02\n",
            "2021-03-03\n",
            "2021-03-04\n",
            "2021-03-05\n",
            "2021-03-06\n",
            "2021-03-07\n",
            "2021-03-08\n",
            "2021-03-09\n",
            "2021-03-10\n",
            "2021-03-11\n",
            "2021-03-12\n",
            "2021-03-13\n",
            "2021-03-14\n",
            "2021-03-15\n",
            "2021-03-16\n",
            "2021-03-17\n",
            "2021-03-18\n",
            "2021-03-19\n",
            "2021-03-20\n",
            "2021-03-21\n",
            "2021-03-22\n",
            "2021-03-23\n",
            "2021-03-24\n",
            "2021-03-25\n",
            "2021-03-26\n",
            "2021-03-27\n",
            "2021-03-28\n",
            "2021-03-29\n",
            "2021-03-30\n",
            "2021-03-31\n",
            "2021-04-01\n",
            "2021-04-02\n",
            "2021-04-03\n",
            "2021-04-04\n",
            "2021-04-05\n",
            "2021-04-06\n",
            "2021-04-07\n",
            "2021-04-08\n",
            "2021-04-09\n",
            "2021-04-10\n",
            "2021-04-11\n",
            "2021-04-12\n",
            "2021-04-13\n",
            "2021-04-14\n",
            "2021-04-15\n",
            "2021-04-16\n",
            "2021-04-17\n",
            "2021-04-18\n",
            "2021-04-19\n",
            "2021-04-20\n",
            "2021-04-21\n",
            "2021-04-22\n",
            "2021-04-23\n",
            "2021-04-24\n",
            "2021-04-25\n",
            "2021-04-26\n",
            "2021-04-27\n",
            "2021-04-28\n",
            "2021-04-29\n",
            "intitle:Moderna\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BmrMNDYe5LWS"
      },
      "source": [
        "### Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u58jmL-B5O_c"
      },
      "source": [
        "# Gets the part of speech tag of word for lemmatization\n",
        "# This function is based on code from:\n",
        "#   https://www.machinelearningplus.com/nlp/lemmatization-examples-python/\n",
        "def get_wordnet_pos(word):\n",
        "    tag = nltk.pos_tag([word])[0][1][0].upper()\n",
        "    tag_dict = {\"J\": wordnet.ADJ,\n",
        "                \"N\": wordnet.NOUN,\n",
        "                \"V\": wordnet.VERB,\n",
        "                \"R\": wordnet.ADV}\n",
        "    return tag_dict.get(tag, wordnet.NOUN)\n",
        "\n",
        "# Preprocesses the tweets text\n",
        "# This function is based on code from:\n",
        "#   https://www.pluralsight.com/guides/building-a-twitter-sentiment-analysis-in-python\n",
        "\n",
        "# Preprocessing takes in a tweet and a boolean determining if the preprocessing\n",
        "# is for the GloVe embeddings or for other classifiers\n",
        "# default value is: False\n",
        "\n",
        "def preprocess_text(tweet):\n",
        "    # Changes emojis to words\n",
        "    tweet = emoji.demojize(tweet,  delimiters=(' ', ' '))\n",
        "    # Removes capitalization\n",
        "    tweet = tweet.lower()\n",
        "    # Removes single characters\n",
        "    tweet = re.sub(r'\\s+[a-zA-Z]\\s+', ' ', tweet)\n",
        "    # Removes all hashtags\n",
        "    tweet = re.sub(r'#[a-zA-Z0-9]+\\s+', ' ', tweet)\n",
        "    # Removes all special characters\n",
        "    tweet = re.sub(r'\\W', ' ', tweet)\n",
        "    #Removes Punctuation\n",
        "    tweet = re.sub(r'\\p{P}+', '', tweet)\n",
        "    #Removes multiple spaces with single space\n",
        "    tweet = re.sub(r'\\s+', ' ', tweet, flags=re.MULTILINE)\n",
        "    # Removes stopwords\n",
        "    tokens = [w for w in word_tokenize(tweet) if not w in stop_words]\n",
        "    # Perfoms lemmatization on tokens\n",
        "    lemmatizer = WordNetLemmatizer()\n",
        "    lemma_words = [lemmatizer.lemmatize(w, get_wordnet_pos(w)) for w in tokens]\n",
        "    return \" \".join(lemma_words)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-S8WSsdt5QAs"
      },
      "source": [
        "# Add cleaned title column and preprocess text\n",
        "dfBig['titleClean'] = ''\n",
        "index = 0\n",
        "for title in dfBig['title']:\n",
        "  dfBig['titleClean'][index] = preprocess_text(title)\n",
        "  index += 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UZnN3jkGufuC"
      },
      "source": [
        "# Export Cleaned News Data\n",
        "filename = r\"/content/drive/Shareddrives/COS424/NewsAllClean.csv\"\n",
        "dfBig.to_csv(filename, index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o7PFQBgjRfaj"
      },
      "source": [
        "### Sentiment Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c2yTAB8MRjLq",
        "outputId": "8ecd6f26-765c-432d-aee4-e80cb28779ea"
      },
      "source": [
        "# Start VADER sentiment analysis and store resulting scores for every post\n",
        "\n",
        "analyzer = SentimentIntensityAnalyzer()\n",
        "news = r\"/content/drive/Shareddrives/COS424/NewsAllClean.csv\" # Exported Cleaned News Data\n",
        "news_df = pd.read_csv(news)\n",
        "sentences = news_df['titleClean']\n",
        "index = 0\n",
        "\n",
        "news_df['comp'] = 0.0  # Compound\n",
        "news_df['neg'] = 0.0   # Positivity\n",
        "news_df['neu'] = 0.0   # Negativity\n",
        "news_df['pos'] = 0.0   # Positivity\n",
        "\n",
        "# For every headline, get score\n",
        "for sentence in sentences:\n",
        "  if type(sentence) != float:\n",
        "    vs = analyzer.polarity_scores(sentence)\n",
        "    news_df['comp'][index] = vs['compound']\n",
        "    news_df['neg'][index] = vs['neg']\n",
        "    news_df['neu'][index] = vs['neu']\n",
        "    news_df['pos'][index] = vs['pos']\n",
        "  index +=1\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  from ipykernel import kernelapp as app\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:16: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  app.launch_new_instance()\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XBdBP_buT3UX"
      },
      "source": [
        "# Add date column as index\n",
        "\n",
        "df = pd.DataFrame({'year': news_df['y'],\n",
        "                   'month': news_df['m'],\n",
        "                   'day': news_df['d']})\n",
        "dates = pd.to_datetime(df)\n",
        "news_df['date']=dates\n",
        "news_df = news_df.set_index('date')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dwdSrhy9UBSb"
      },
      "source": [
        "# Sum up sentiment scores of all headlines for every single day \n",
        "\n",
        "new_news = pd.DataFrame(columns=[\"date\",'sum', 'pos', 'neg', 'neu', 'num','company'])\n",
        "\n",
        "# List companies again for convenience\n",
        "compName = ['GME', 'AMC', 'NOK', 'TSLA', 'BB', 'KOSS', 'EXPR',\n",
        "          'MSFT', 'FB', 'AMZN', 'GOOGL', 'AAPL', 'NFLX', 'BABA',\n",
        "        'RCL', 'CCL', 'BY', 'ZM', 'AAL', 'BA', 'MRNA']\n",
        "\n",
        "compType = ['Hype', 'Hype', 'Hype', 'Hype', 'Hype', 'Hype', 'Hype',\n",
        "            'Lead', 'Lead', 'Lead', 'Lead', 'Lead', 'Lead', 'Lead',\n",
        "            'Volat', 'Volat', 'Volat', 'Volat', 'Volat', 'Volat', 'Volat']\n",
        "\n",
        "# For every company extract day's sentiment\n",
        "for company in compName:\n",
        "  df = news_df[news_df.company == company]\n",
        "  \n",
        "\n",
        "  for date in np.unique(df.index):\n",
        "    comp = df.loc[[date],['comp']]\n",
        "    pos = df.loc[[date],['pos']]\n",
        "    neg = df.loc[[date],['neg']]\n",
        "    neu = df.loc[[date],['neu']] \n",
        "    sum=0\n",
        "    sumP=0\n",
        "    sumNeg=0\n",
        "    sumNeu=0\n",
        "\n",
        "    # Sum compound score\n",
        "    for i in range(len(comp)):\n",
        "      val=comp.iloc[i, 0]\n",
        "      sum+=val\n",
        "    num = len(comp.index)\n",
        "\n",
        "    # Sum positivity score\n",
        "    for i in range(len(pos)):\n",
        "      val=pos.iloc[i, 0]\n",
        "      sumP+=val\n",
        "\n",
        "    # Sum negativity score\n",
        "    for i in range(len(neg)):\n",
        "      val=neg.iloc[i, 0]\n",
        "      sumNeg+=val\n",
        "    \n",
        "    # Sum neutrality score\n",
        "    for i in range(len(neu)):\n",
        "      val=neu.iloc[i, 0]\n",
        "      sumNeu+=val\n",
        "    \n",
        "    new_news.loc[len(new_news.index)] = [date, sum, sumP, sumNeg, sumNeu, num, company] "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YwCtzNyvUO_7"
      },
      "source": [
        "# Export data ready for Prediction\n",
        "new_news['avg']=new_news['sum']/new_news['num'] # Add average daily score column\n",
        "filename = r\"/content/drive/Shareddrives/COS424/news_compound_scores.csv\"\n",
        "new_news.to_csv(filename)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ey7eQqcbPBS4"
      },
      "source": [
        "## Reddit"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2b5JLJQyah5t"
      },
      "source": [
        "### Data Collection"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qQ9baxwrRdCI"
      },
      "source": [
        "Note: The below section requires Reddit API Access & Authentication. Account information has been redacted and the necessary portions have been commented out"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lAYgXvq5PEE3"
      },
      "source": [
        "# REDDIT API ACCESS & AUTHENTICATION\n",
        "# NOTE: username & password REDACTED // THIS SECTION IS NOT RUNABLE\n",
        "\n",
        "# note that CLIENT_ID refers to 'personal use script' and SECRET_TOKEN to 'token'\n",
        "# auth = requests.auth.HTTPBasicAuth('REDACTED', 'REDACTED')\n",
        "\n",
        "# here we pass our login method (password), username, and password\n",
        "data = {'grant_type': 'password',\n",
        "        'username': 'REDACTED',\n",
        "        'password': 'REDACTED'}\n",
        "\n",
        "# setup our header info, which gives reddit a brief description of our app\n",
        "headers = {'User-Agent': 'MyBot/0.0.1'}\n",
        "\n",
        "# send our request for an OAuth token\n",
        "# res = requests.post('https://www.reddit.com/api/v1/access_token',\n",
        "#                    auth=auth, data=data, headers=headers)\n",
        "\n",
        "# convert response to JSON and pull access_token value\n",
        "# TOKEN = res.json()['access_token']\n",
        "\n",
        "# add authorization to our headers dictionary\n",
        "# headers = {**headers, **{'Authorization': f\"bearer {TOKEN}\"}}\n",
        "\n",
        "# while the token is valid (~2 hours) we just add headers=headers to our requests\n",
        "# requests.get('https://oauth.reddit.com/api/v1/me', headers=headers)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8zVitrmetRc_",
        "outputId": "9fd3a70f-0aff-48a0-88b9-862da65a24b9"
      },
      "source": [
        "# Three parallel arrays with necessary stock information:\n",
        "\n",
        "companies = ['Gamestop|GME', 'AMC', 'Nokia|NOK', 'Tesla|TSLA', 'BlackBerry|BB', 'KOSS', 'Express|EXPR',\n",
        "            'Microsoft|MSFT', 'Facebook|FB', 'Amazon|AMZN', 'Google|GOOGL', 'Apple|AAPL', 'Netflix|NFLX', 'Alibaba|BABA',\n",
        "            '\"Royal Caribbea|RCL\"', 'Carnival|CCL', '\"Beyond Meat|BY\"', 'Zoom|ZM', '\"American Airlines\"|AAL', 'Boeing|BA', 'Moderna|MRNA']\n",
        "\n",
        "compName = ['GME', 'AMC', 'NOK', 'TSLA', 'BB', 'KOSS', 'EXPR',\n",
        "            'MSFT', 'FB', 'AMZN', 'GOOGL', 'AAPL', 'NFLX', 'BABA',\n",
        "            'RCL', 'CCL', 'BY', 'ZM', 'AAL', 'BA', 'MRNA']\n",
        "\n",
        "compType = ['Hype', 'Hype', 'Hype', 'Hype', 'Hype', 'Hype', 'Hype',\n",
        "            'Lead', 'Lead', 'Lead', 'Lead', 'Lead', 'Lead', 'Lead',\n",
        "            'Volat', 'Volat', 'Volat', 'Volat', 'Volat', 'Volat', 'Volat']\n",
        "\n",
        "\n",
        "arrM = ['10', '11', '12', '01', '02', '03', '04'] # Month we look at\n",
        "arrD = [31, 30, 31, 31, 28, 31, 29] # Days per month (note: we end April 29th)\n",
        "\n",
        "dfBig = pd.DataFrame([], columns = ['type', 'company', 'created_utc', 'title', 'score', 'upvote_ratio', 'selftext'])\n",
        "year = '2020-'\n",
        "counter = 0\n",
        "\n",
        "# For every company\n",
        "for company in companies:\n",
        "  # Compute date for extraction purposes:\n",
        "  year = '2020-'\n",
        "  index = 0\n",
        "  for month in arrM:\n",
        "    if index >= 3:\n",
        "      year = '2021-'\n",
        "    for day in range(1, arrD[index] + 1):\n",
        "      if day < 10:\n",
        "        dateFrom = year + str(arrM[index]) + \"-0\" + str(day)\n",
        "        if day + 1 < 10:\n",
        "          dateTo = year + str(arrM[index]) + \"-0\" + str(day + 1)\n",
        "        else:\n",
        "          dateTo = year + str(arrM[index]) + \"-\" + str(day + 1)\n",
        "    \n",
        "      elif day + 1 > arrD[index]:\n",
        "        dateFrom = year + str(arrM[index]) + \"-\" + str(day)\n",
        "        if index != 6:\n",
        "          dateTo = year + str(arrM[index + 1]) + \"-01\"\n",
        "        else:\n",
        "          dateTo = year + str(arrM[index]) + \"-\" + str(day + 1)\n",
        "\n",
        "      else:\n",
        "        dateFrom = year + str(arrM[index]) + \"-\" + str(day)\n",
        "        dateTo = year + str(arrM[index]) + \"-\" + str(day + 1)\n",
        "\n",
        "      # Get data from PushShift Reddit API\n",
        "      url = \"https://api.pushshift.io/reddit/search/submission/?subreddit=wallstreetbets&size=500&sort_type=score&sort=desc&fields=title,score,upvote_ratio,selftext,created_utc&title=\" + company + \"&after=\"\n",
        "      urlFull = url + dateFrom + \"&before=\" + dateTo\n",
        "      res = requests.get(urlFull)\n",
        "      #Attempt three times, just in case of connectivity issues\n",
        "      # If data retrieved: at to dfBig\n",
        "      try:\n",
        "        # print(res.json()['data']) # let's see what we get\n",
        "        df = pd.DataFrame.from_dict(res.json()['data'])\n",
        "        df.insert(0, 'company', compName[counter])\n",
        "        df.insert(0, 'type', compType[counter])\n",
        "        dfBig = dfBig.append(df)\n",
        "      except: \n",
        "        try: \n",
        "          res = requests.get(urlFull)\n",
        "          df = pd.DataFrame.from_dict(res.json()['data'])\n",
        "          df.insert(0, 'company', compName[counter])\n",
        "          df.insert(0, 'type', compType[counter])\n",
        "          dfBig = dfBig.append(df)\n",
        "        except:\n",
        "          try: \n",
        "            time.sleep(0.5)\n",
        "            res = requests.get(urlFull)\n",
        "            df = pd.DataFrame.from_dict(res.json()['data'])\n",
        "            df.insert(0, 'company', compName[counter])\n",
        "            df.insert(0, 'type', compType[counter])\n",
        "            dfBig = dfBig.append(df)\n",
        "          except:\n",
        "            try: \n",
        "              time.sleep(0.5)\n",
        "              res = requests.get(urlFull)\n",
        "              df = pd.DataFrame.from_dict(res.json()['data'])\n",
        "              df.insert(0, 'company', compName[counter])\n",
        "              df.insert(0, 'type', compType[counter])\n",
        "              dfBig = dfBig.append(df)\n",
        "            except:\n",
        "              print(dateFrom + \"= NONE\")\n",
        "    index += 1\n",
        "    \n",
        "  counter += 1\n",
        "  print(company)\n",
        "\n",
        "# Export all data for cleaning:\n",
        "filename = r\"/content/drive/Shareddrives/COS424/RedditAllBigNOCLEAN.csv\"\n",
        "dfBig.to_csv(filename, index=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10\n",
            "11\n",
            "12\n",
            "01\n",
            "02\n",
            "03\n",
            "04\n",
            "Gamestop|GME\n",
            "    type  ...                                           selftext\n",
            "0   Hype  ...  Hey guys, i am looking at the jan 2021 calls o...\n",
            "1   Hype  ...                                          [removed]\n",
            "2   Hype  ...    \\n\\nCredit for this information goes to Just...\n",
            "0   Hype  ...  Can we get an actual discussion regarding what...\n",
            "0   Hype  ...  **Never chase a stock as it rises.  So I ended...\n",
            "..   ...  ...                                                ...\n",
            "85  Hype  ...                                                   \n",
            "86  Hype  ...                                                   \n",
            "87  Hype  ...                                                   \n",
            "88  Hype  ...  The current short value of UNFI is 45%... the ...\n",
            "89  Hype  ...                                                   \n",
            "\n",
            "[11837 rows x 7 columns]\n",
            "10\n",
            "11\n",
            "12\n",
            "01\n",
            "02\n",
            "03\n",
            "04\n",
            "AMC\n",
            "    type  ...                                           selftext\n",
            "0   Hype  ...  Hey guys, i am looking at the jan 2021 calls o...\n",
            "1   Hype  ...                                          [removed]\n",
            "2   Hype  ...    \\n\\nCredit for this information goes to Just...\n",
            "0   Hype  ...  Can we get an actual discussion regarding what...\n",
            "0   Hype  ...  **Never chase a stock as it rises.  So I ended...\n",
            "..   ...  ...                                                ...\n",
            "33  Hype  ...                                                   \n",
            "34  Hype  ...  All of the odds against us, and yet staying st...\n",
            "35  Hype  ...  Here i'am,a young 25 years old fool,believe in...\n",
            "36  Hype  ...                                                   \n",
            "37  Hype  ...                                          [removed]\n",
            "\n",
            "[18418 rows x 7 columns]\n",
            "10\n",
            "11\n",
            "12\n",
            "01\n",
            "02\n",
            "03\n",
            "04\n",
            "Nokia|NOK\n",
            "    type  ...                                           selftext\n",
            "0   Hype  ...  Hey guys, i am looking at the jan 2021 calls o...\n",
            "1   Hype  ...                                          [removed]\n",
            "2   Hype  ...    \\n\\nCredit for this information goes to Just...\n",
            "0   Hype  ...  Can we get an actual discussion regarding what...\n",
            "0   Hype  ...  **Never chase a stock as it rises.  So I ended...\n",
            "..   ...  ...                                                ...\n",
            "95  Hype  ...                                                   \n",
            "96  Hype  ...                                                   \n",
            "97  Hype  ...                                          [removed]\n",
            "98  Hype  ...                                          [removed]\n",
            "99  Hype  ...                                          [removed]\n",
            "\n",
            "[21019 rows x 7 columns]\n",
            "10\n",
            "11\n",
            "12\n",
            "01\n",
            "02\n",
            "03\n",
            "04\n",
            "Tesla|TSLA\n",
            "    type  ...                                           selftext\n",
            "0   Hype  ...  Hey guys, i am looking at the jan 2021 calls o...\n",
            "1   Hype  ...                                          [removed]\n",
            "2   Hype  ...    \\n\\nCredit for this information goes to Just...\n",
            "0   Hype  ...  Can we get an actual discussion regarding what...\n",
            "0   Hype  ...  **Never chase a stock as it rises.  So I ended...\n",
            "..   ...  ...                                                ...\n",
            "13  Hype  ...                                                   \n",
            "14  Hype  ...                                                   \n",
            "15  Hype  ...                                                   \n",
            "16  Hype  ...  Despite being a climate change activists the g...\n",
            "17  Hype  ...                                                   \n",
            "\n",
            "[28064 rows x 7 columns]\n",
            "10\n",
            "11\n",
            "12\n",
            "01\n",
            "02\n",
            "03\n",
            "04\n",
            "BlackBerry|BB\n",
            "    type  ...                                           selftext\n",
            "0   Hype  ...  Hey guys, i am looking at the jan 2021 calls o...\n",
            "1   Hype  ...                                          [removed]\n",
            "2   Hype  ...    \\n\\nCredit for this information goes to Just...\n",
            "0   Hype  ...  Can we get an actual discussion regarding what...\n",
            "0   Hype  ...  **Never chase a stock as it rises.  So I ended...\n",
            "..   ...  ...                                                ...\n",
            "1   Hype  ...                                          [removed]\n",
            "2   Hype  ...                                                   \n",
            "3   Hype  ...                                                   \n",
            "4   Hype  ...                                                   \n",
            "5   Hype  ...  [https://www.investing.com/news/stock-market-n...\n",
            "\n",
            "[31918 rows x 7 columns]\n",
            "10\n",
            "11\n",
            "12\n",
            "01\n",
            "02\n",
            "03\n",
            "04\n",
            "KOSS\n",
            "    type  ...                                           selftext\n",
            "0   Hype  ...  Hey guys, i am looking at the jan 2021 calls o...\n",
            "1   Hype  ...                                          [removed]\n",
            "2   Hype  ...    \\n\\nCredit for this information goes to Just...\n",
            "0   Hype  ...  Can we get an actual discussion regarding what...\n",
            "0   Hype  ...  **Never chase a stock as it rises.  So I ended...\n",
            "..   ...  ...                                                ...\n",
            "0   Hype  ...                                    Let’s buy Koss!\n",
            "0   Hype  ...                                                   \n",
            "1   Hype  ...                                                   \n",
            "2   Hype  ...  [https://finance.yahoo.com/news/investor-alert...\n",
            "3   Hype  ...  [https://finance.yahoo.com/news/investor-alert...\n",
            "\n",
            "[32613 rows x 7 columns]\n",
            "10\n",
            "11\n",
            "12\n",
            "01\n",
            "02\n",
            "03\n",
            "04\n",
            "Express|EXPR\n",
            "    type  ...                                           selftext\n",
            "0   Hype  ...  Hey guys, i am looking at the jan 2021 calls o...\n",
            "1   Hype  ...                                          [removed]\n",
            "2   Hype  ...    \\n\\nCredit for this information goes to Just...\n",
            "0   Hype  ...  Can we get an actual discussion regarding what...\n",
            "0   Hype  ...  **Never chase a stock as it rises.  So I ended...\n",
            "..   ...  ...                                                ...\n",
            "0   Hype  ...                                                   \n",
            "1   Hype  ...                                          [removed]\n",
            "0   Hype  ...                                                   \n",
            "1   Hype  ...  [https://finance.yahoo.com/news/investor-alert...\n",
            "2   Hype  ...  [https://finance.yahoo.com/news/investor-alert...\n",
            "\n",
            "[33542 rows x 7 columns]\n",
            "10\n",
            "11\n",
            "12\n",
            "01\n",
            "02\n",
            "03\n",
            "04\n",
            "Microsoft|MSFT\n",
            "    type  ...                                           selftext\n",
            "0   Hype  ...  Hey guys, i am looking at the jan 2021 calls o...\n",
            "1   Hype  ...                                          [removed]\n",
            "2   Hype  ...    \\n\\nCredit for this information goes to Just...\n",
            "0   Hype  ...  Can we get an actual discussion regarding what...\n",
            "0   Hype  ...  **Never chase a stock as it rises.  So I ended...\n",
            "..   ...  ...                                                ...\n",
            "6   Lead  ...                                                   \n",
            "0   Lead  ...                                                   \n",
            "1   Lead  ...                                                   \n",
            "2   Lead  ...  Hi,\\n\\nI’ve seen a big drop in MSFT price... a...\n",
            "3   Lead  ...                                                   \n",
            "\n",
            "[33948 rows x 7 columns]\n",
            "10\n",
            "11\n",
            "12\n",
            "01\n",
            "02\n",
            "03\n",
            "04\n",
            "Facebook|FB\n",
            "    type  ...                                           selftext\n",
            "0   Hype  ...  Hey guys, i am looking at the jan 2021 calls o...\n",
            "1   Hype  ...                                          [removed]\n",
            "2   Hype  ...    \\n\\nCredit for this information goes to Just...\n",
            "0   Hype  ...  Can we get an actual discussion regarding what...\n",
            "0   Hype  ...  **Never chase a stock as it rises.  So I ended...\n",
            "..   ...  ...                                                ...\n",
            "2   Lead  ...                                                   \n",
            "3   Lead  ...                                                   \n",
            "4   Lead  ...                                                   \n",
            "5   Lead  ...                                          [removed]\n",
            "6   Lead  ...                                                   \n",
            "\n",
            "[34899 rows x 7 columns]\n",
            "10\n",
            "11\n",
            "12\n",
            "01\n",
            "02\n",
            "03\n",
            "04\n",
            "Amazon|AMZN\n",
            "    type  ...                                           selftext\n",
            "0   Hype  ...  Hey guys, i am looking at the jan 2021 calls o...\n",
            "1   Hype  ...                                          [removed]\n",
            "2   Hype  ...    \\n\\nCredit for this information goes to Just...\n",
            "0   Hype  ...  Can we get an actual discussion regarding what...\n",
            "0   Hype  ...  **Never chase a stock as it rises.  So I ended...\n",
            "..   ...  ...                                                ...\n",
            "7   Lead  ...  I had a dream last night and when I awoke I kn...\n",
            "8   Lead  ...                                                   \n",
            "9   Lead  ...                                          [removed]\n",
            "10  Lead  ...                                                   \n",
            "11  Lead  ...  Technically it looks like a 5-star set up on a...\n",
            "\n",
            "[36367 rows x 7 columns]\n",
            "10\n",
            "11\n",
            "12\n",
            "01\n",
            "02\n",
            "03\n",
            "04\n",
            "Google|GOOGL\n",
            "    type  ...                                           selftext\n",
            "0   Hype  ...  Hey guys, i am looking at the jan 2021 calls o...\n",
            "1   Hype  ...                                          [removed]\n",
            "2   Hype  ...    \\n\\nCredit for this information goes to Just...\n",
            "0   Hype  ...  Can we get an actual discussion regarding what...\n",
            "0   Hype  ...  **Never chase a stock as it rises.  So I ended...\n",
            "..   ...  ...                                                ...\n",
            "1   Lead  ...                                          [removed]\n",
            "2   Lead  ...                                                   \n",
            "3   Lead  ...                                                   \n",
            "0   Lead  ...                                                   \n",
            "1   Lead  ...  **ALL U.S. Data**  \\nBigger Companies at Top  ...\n",
            "\n",
            "[37426 rows x 7 columns]\n",
            "10\n",
            "11\n",
            "12\n",
            "01\n",
            "02\n",
            "03\n",
            "04\n",
            "Apple|AAPL\n",
            "    type  ...                                           selftext\n",
            "0   Hype  ...  Hey guys, i am looking at the jan 2021 calls o...\n",
            "1   Hype  ...                                          [removed]\n",
            "2   Hype  ...    \\n\\nCredit for this information goes to Just...\n",
            "0   Hype  ...  Can we get an actual discussion regarding what...\n",
            "0   Hype  ...  **Never chase a stock as it rises.  So I ended...\n",
            "..   ...  ...                                                ...\n",
            "2   Lead  ...                                                   \n",
            "3   Lead  ...                                                   \n",
            "4   Lead  ...                                                   \n",
            "5   Lead  ...                                          [removed]\n",
            "6   Lead  ...                                          [removed]\n",
            "\n",
            "[39406 rows x 7 columns]\n",
            "10\n",
            "11\n",
            "12\n",
            "01\n",
            "02\n",
            "03\n",
            "04\n",
            "Netflix|NFLX\n",
            "    type  ...                                           selftext\n",
            "0   Hype  ...  Hey guys, i am looking at the jan 2021 calls o...\n",
            "1   Hype  ...                                          [removed]\n",
            "2   Hype  ...    \\n\\nCredit for this information goes to Just...\n",
            "0   Hype  ...  Can we get an actual discussion regarding what...\n",
            "0   Hype  ...  **Never chase a stock as it rises.  So I ended...\n",
            "..   ...  ...                                                ...\n",
            "0   Lead  ...  Get on the CIDM train while it’s in low lows. ...\n",
            "1   Lead  ...                                          [removed]\n",
            "2   Lead  ...  **ALL U.S. Data**  \\nBigger Companies at Top  ...\n",
            "3   Lead  ...  Buy it now while it’s low. They will be an eas...\n",
            "4   Lead  ...                                                   \n",
            "\n",
            "[39904 rows x 7 columns]\n",
            "10\n",
            "11\n",
            "12\n",
            "01\n",
            "02\n",
            "03\n",
            "04\n",
            "Alibaba|BABA\n",
            "    type  ...                                           selftext\n",
            "0   Hype  ...  Hey guys, i am looking at the jan 2021 calls o...\n",
            "1   Hype  ...                                          [removed]\n",
            "2   Hype  ...    \\n\\nCredit for this information goes to Just...\n",
            "0   Hype  ...  Can we get an actual discussion regarding what...\n",
            "0   Hype  ...  **Never chase a stock as it rises.  So I ended...\n",
            "..   ...  ...                                                ...\n",
            "0   Lead  ...                                                   \n",
            "0   Lead  ...                                                   \n",
            "0   Lead  ...                                                   \n",
            "0   Lead  ...                                                   \n",
            "0   Lead  ...  Alibaba to join trillionaire club or is the cu...\n",
            "\n",
            "[40695 rows x 7 columns]\n",
            "10\n",
            "11\n",
            "12\n",
            "01\n",
            "02\n",
            "03\n",
            "04\n",
            "\"Royal Caribbea|RCL\"\n",
            "    type  ...                                           selftext\n",
            "0   Hype  ...  Hey guys, i am looking at the jan 2021 calls o...\n",
            "1   Hype  ...                                          [removed]\n",
            "2   Hype  ...    \\n\\nCredit for this information goes to Just...\n",
            "0   Hype  ...  Can we get an actual discussion regarding what...\n",
            "0   Hype  ...  **Never chase a stock as it rises.  So I ended...\n",
            "..   ...  ...                                                ...\n",
            "0   Lead  ...                                                   \n",
            "0   Lead  ...                                                   \n",
            "0   Lead  ...                                                   \n",
            "0   Lead  ...                                                   \n",
            "0   Lead  ...  Alibaba to join trillionaire club or is the cu...\n",
            "\n",
            "[40695 rows x 7 columns]\n",
            "10\n",
            "11\n",
            "12\n",
            "01\n",
            "02\n",
            "03\n",
            "04\n",
            "Carnival|CCL\n",
            "     type  ...                                           selftext\n",
            "0    Hype  ...  Hey guys, i am looking at the jan 2021 calls o...\n",
            "1    Hype  ...                                          [removed]\n",
            "2    Hype  ...    \\n\\nCredit for this information goes to Just...\n",
            "0    Hype  ...  Can we get an actual discussion regarding what...\n",
            "0    Hype  ...  **Never chase a stock as it rises.  So I ended...\n",
            "..    ...  ...                                                ...\n",
            "0   Volat  ...                                                   \n",
            "0   Volat  ...                                                   \n",
            "0   Volat  ...                                                   \n",
            "0   Volat  ...                                          [removed]\n",
            "1   Volat  ...                                                   \n",
            "\n",
            "[41170 rows x 7 columns]\n",
            "10\n",
            "11\n",
            "12\n",
            "01\n",
            "02\n",
            "03\n",
            "04\n",
            "\"Beyond Meat|BY\"\n",
            "     type  ...                                           selftext\n",
            "0    Hype  ...  Hey guys, i am looking at the jan 2021 calls o...\n",
            "1    Hype  ...                                          [removed]\n",
            "2    Hype  ...    \\n\\nCredit for this information goes to Just...\n",
            "0    Hype  ...  Can we get an actual discussion regarding what...\n",
            "0    Hype  ...  **Never chase a stock as it rises.  So I ended...\n",
            "..    ...  ...                                                ...\n",
            "0   Volat  ...                                                   \n",
            "0   Volat  ...                                                   \n",
            "0   Volat  ...                                                   \n",
            "0   Volat  ...                                          [removed]\n",
            "1   Volat  ...                                                   \n",
            "\n",
            "[41170 rows x 7 columns]\n",
            "10\n",
            "11\n",
            "12\n",
            "01\n",
            "02\n",
            "03\n",
            "04\n",
            "Zoom|ZM\n",
            "     type  ...                                           selftext\n",
            "0    Hype  ...  Hey guys, i am looking at the jan 2021 calls o...\n",
            "1    Hype  ...                                          [removed]\n",
            "2    Hype  ...    \\n\\nCredit for this information goes to Just...\n",
            "0    Hype  ...  Can we get an actual discussion regarding what...\n",
            "0    Hype  ...  **Never chase a stock as it rises.  So I ended...\n",
            "..    ...  ...                                                ...\n",
            "0   Volat  ...                                                   \n",
            "0   Volat  ...                                                   \n",
            "0   Volat  ...                                                   \n",
            "1   Volat  ...                                                   \n",
            "2   Volat  ...  Original post [here](https://www.reddit.com/r/...\n",
            "\n",
            "[41638 rows x 7 columns]\n",
            "10\n",
            "11\n",
            "12\n",
            "01\n",
            "02\n",
            "03\n",
            "04\n",
            "\"American Airlines\"|AAL\n",
            "     type  ...                                           selftext\n",
            "0    Hype  ...  Hey guys, i am looking at the jan 2021 calls o...\n",
            "1    Hype  ...                                          [removed]\n",
            "2    Hype  ...    \\n\\nCredit for this information goes to Just...\n",
            "0    Hype  ...  Can we get an actual discussion regarding what...\n",
            "0    Hype  ...  **Never chase a stock as it rises.  So I ended...\n",
            "..    ...  ...                                                ...\n",
            "0   Volat  ...                                          [removed]\n",
            "1   Volat  ...                                                   \n",
            "2   Volat  ...                                          [removed]\n",
            "0   Volat  ...  $AAL to the moon guys!! I just got 1,000 share...\n",
            "0   Volat  ...         That’s a stock getting hard hit by shorts.\n",
            "\n",
            "[42428 rows x 7 columns]\n",
            "10\n",
            "11\n",
            "12\n",
            "01\n",
            "02\n",
            "03\n",
            "04\n",
            "Boeing|BA\n",
            "     type  ...                                           selftext\n",
            "0    Hype  ...  Hey guys, i am looking at the jan 2021 calls o...\n",
            "1    Hype  ...                                          [removed]\n",
            "2    Hype  ...    \\n\\nCredit for this information goes to Just...\n",
            "0    Hype  ...  Can we get an actual discussion regarding what...\n",
            "0    Hype  ...  **Never chase a stock as it rises.  So I ended...\n",
            "..    ...  ...                                                ...\n",
            "0   Volat  ...                                          [removed]\n",
            "1   Volat  ...  Thinking of buying, earrings are positive, wha...\n",
            "2   Volat  ...                                                   \n",
            "3   Volat  ...                                          [removed]\n",
            "4   Volat  ...                                                NaN\n",
            "\n",
            "[42756 rows x 7 columns]\n",
            "10\n",
            "11\n",
            "12\n",
            "01\n",
            "02\n",
            "03\n",
            "04\n",
            "Moderna|MRNA\n",
            "     type  ...                                           selftext\n",
            "0    Hype  ...  Hey guys, i am looking at the jan 2021 calls o...\n",
            "1    Hype  ...                                          [removed]\n",
            "2    Hype  ...    \\n\\nCredit for this information goes to Just...\n",
            "0    Hype  ...  Can we get an actual discussion regarding what...\n",
            "0    Hype  ...  **Never chase a stock as it rises.  So I ended...\n",
            "..    ...  ...                                                ...\n",
            "1   Volat  ...  **See part 1:**[https://www.reddit.com/r/walls...\n",
            "0   Volat  ...  Why does BioNTech have a market cap of  39B an...\n",
            "0   Volat  ...                                                   \n",
            "0   Volat  ...  MRNA is completely overvalued.    There are ma...\n",
            "0   Volat  ...                                                   \n",
            "\n",
            "[43185 rows x 7 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AT91a5-wamjn"
      },
      "source": [
        "### Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EdcN2Zx7Hl6D",
        "outputId": "0c49509a-8e82-4a37-fce9-aad141b08aca"
      },
      "source": [
        "# Change Unix Epoch time to EST time (for stock analysis)\n",
        "\n",
        "dfBig = dfBig.reset_index(drop=True)\n",
        "dfBig['minute'] = 0\n",
        "dfBig['hour'] = 0\n",
        "dfBig['day'] = 0\n",
        "dfBig['month'] = 0 \n",
        "dfBig['year'] = 0\n",
        "dfBig['est'] = 0\n",
        "\n",
        "# transform time to NYC time\n",
        "tz = pytz.timezone('America/New_York')\n",
        "index = 0\n",
        "print(dfBig)\n",
        "for row in dfBig.iterrows():\n",
        "  utc = row[1][2]\n",
        "  est = datetime.fromtimestamp(utc, tz)\n",
        "  strEst = est.strftime('%Y-%m-%d %H:%M:%S')\n",
        "  dfBig['year'][index] = est.strftime('%Y')\n",
        "  dfBig['month'][index] = est.strftime('%m')\n",
        "  dfBig['day'][index] = est.strftime('%d')\n",
        "  dfBig['hour'][index] = est.strftime('%H')\n",
        "  dfBig['minute'][index] = est.strftime('%M')\n",
        "  dfBig['est'][index] = strEst\n",
        "  index += 1\n",
        "\n",
        "print(dfBig) # Print resulting array with correct time"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "        type company created_utc  ... month year  est\n",
            "0       Hype     GME  1601541801  ...     0    0    0\n",
            "1       Hype     GME  1601593943  ...     0    0    0\n",
            "2       Hype     GME  1601522978  ...     0    0    0\n",
            "3       Hype     GME  1601669797  ...     0    0    0\n",
            "4       Hype     GME  1601705389  ...     0    0    0\n",
            "...      ...     ...         ...  ...   ...  ...  ...\n",
            "43180  Volat    MRNA  1619039554  ...     0    0    0\n",
            "43181  Volat    MRNA  1619085548  ...     0    0    0\n",
            "43182  Volat    MRNA  1619219619  ...     0    0    0\n",
            "43183  Volat    MRNA  1619458258  ...     0    0    0\n",
            "43184  Volat    MRNA  1619685317  ...     0    0    0\n",
            "\n",
            "[43185 rows x 13 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:22: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:23: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:24: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:25: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:26: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:27: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  iloc._setitem_with_indexer(indexer, value)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "        type company created_utc  ... month  year                  est\n",
            "0       Hype     GME  1601541801  ...    10  2020  2020-10-01 04:43:21\n",
            "1       Hype     GME  1601593943  ...    10  2020  2020-10-01 19:12:23\n",
            "2       Hype     GME  1601522978  ...     9  2020  2020-09-30 23:29:38\n",
            "3       Hype     GME  1601669797  ...    10  2020  2020-10-02 16:16:37\n",
            "4       Hype     GME  1601705389  ...    10  2020  2020-10-03 02:09:49\n",
            "...      ...     ...         ...  ...   ...   ...                  ...\n",
            "43180  Volat    MRNA  1619039554  ...     4  2021  2021-04-21 17:12:34\n",
            "43181  Volat    MRNA  1619085548  ...     4  2021  2021-04-22 05:59:08\n",
            "43182  Volat    MRNA  1619219619  ...     4  2021  2021-04-23 19:13:39\n",
            "43183  Volat    MRNA  1619458258  ...     4  2021  2021-04-26 13:30:58\n",
            "43184  Volat    MRNA  1619685317  ...     4  2021  2021-04-29 04:35:17\n",
            "\n",
            "[43185 rows x 13 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iekNhKVKuLmv"
      },
      "source": [
        "# Gets the part of speech tag of word for lemmatization\n",
        "# This function is based on code from:\n",
        "#   https://www.machinelearningplus.com/nlp/lemmatization-examples-python/\n",
        "def get_wordnet_pos(word):\n",
        "    tag = nltk.pos_tag([word])[0][1][0].upper()\n",
        "    tag_dict = {\"J\": wordnet.ADJ,\n",
        "                \"N\": wordnet.NOUN,\n",
        "                \"V\": wordnet.VERB,\n",
        "                \"R\": wordnet.ADV}\n",
        "    return tag_dict.get(tag, wordnet.NOUN)\n",
        "\n",
        "# Preprocesses the tweets text\n",
        "# This function is based on code from:\n",
        "#   https://www.pluralsight.com/guides/building-a-twitter-sentiment-analysis-in-python\n",
        "\n",
        "# Preprocessing takes in a tweet and a boolean determining if the preprocessing\n",
        "# is for the GloVe embeddings or for other classifiers\n",
        "# default value is: False\n",
        "\n",
        "def preprocess_text(tweet):\n",
        "    tweet = re.sub(r'🚀', ' winner ', tweet)\n",
        "    # Changes emojis to words\n",
        "    tweet = emoji.demojize(tweet,  delimiters=(' ', ' '))\n",
        "    # Removes capitalization\n",
        "    tweet = tweet.lower()\n",
        "    # Encode Subreddit Vernacular \n",
        "    tweet = re.sub(r'buy', 'good', tweet)\n",
        "    tweet = re.sub(r'sell', 'bad', tweet)\n",
        "    tweet = re.sub(r'to the moon', 'winner', tweet)\n",
        "    tweet = re.sub(r'short squeeze', 'winner', tweet)\n",
        "    \n",
        "    # Removes single characters\n",
        "    tweet = re.sub(r'\\s+[a-zA-Z]\\s+', ' ', tweet)\n",
        "    # Removes all hashtags\n",
        "    tweet = re.sub(r'#[a-zA-Z0-9]+\\s+', ' ', tweet)\n",
        "    # Removes all special characters\n",
        "    tweet = re.sub(r'\\W', ' ', tweet)\n",
        "    #Removes Punctuation\n",
        "    tweet = re.sub(r'[^\\w\\s]','',tweet)\n",
        "    #Removes multiple spaces with single space\n",
        "    tweet = re.sub(r'\\s+', ' ', tweet, flags=re.MULTILINE)\n",
        "    # Removes stopwords\n",
        "    tokens = [w for w in word_tokenize(tweet) if not w in stop_words]\n",
        "    # Perfoms lemmatization on tokens\n",
        "    lemmatizer = WordNetLemmatizer()\n",
        "    lemma_words = [lemmatizer.lemmatize(w, get_wordnet_pos(w)) for w in tokens]\n",
        "    return \" \".join(lemma_words)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vvS_plHJunyr",
        "outputId": "e87ab294-fe50-4161-976d-a9d7c03467db"
      },
      "source": [
        "# Clean both title and body text using above preprocessing code:\n",
        "dfBigCopy = dfBig.copy()\n",
        "dfBig['titleClean'] = ''\n",
        "dfBig['bodyClean'] = ''\n",
        "\n",
        "index = 0\n",
        "for title in dfBig['title']:\n",
        "  dfBig['titleClean'][index] = preprocess_text(title)\n",
        "  index += 1\n",
        "\n",
        "index = 0\n",
        "for body in dfBig['selftext']:\n",
        "  if type(body) == str:\n",
        "    dfBig['bodyClean'][index] = preprocess_text(body)\n",
        "  # If post removed or delated, remove text to avoid wrong sentiment, \n",
        "  # but keep for volume calculations\n",
        "  if dfBig['bodyClean'][index] == 'remove':\n",
        "    dfBig['bodyClean'][index] = ''\n",
        "  if dfBig['bodyClean'][index] == 'delete':\n",
        "    dfBig['bodyClean'][index] = ''\n",
        "  index += 1\n",
        "\n",
        "print(dfBig) # Print resulting array"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  import sys\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  del sys.path[0]\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  from ipykernel import kernelapp as app\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "        type  ...                                          bodyClean\n",
            "0       Hype  ...  hey guy look jan 2021 call gme even 22 price m...\n",
            "1       Hype  ...                                                   \n",
            "2       Hype  ...  credit information go justin dopierala founder...\n",
            "3       Hype  ...  get actual discussion regard fuck go gme somet...\n",
            "4       Hype  ...  never chase stock rise end catch good day day ...\n",
            "...      ...  ...                                                ...\n",
            "43180  Volat  ...  see part 1 http www reddit com r wallstreetbet...\n",
            "43181  Volat  ...  biontech market cap 39b moderna 67b one commer...\n",
            "43182  Volat  ...                                                   \n",
            "43183  Volat  ...  mrna completely overvalue many additional mrna...\n",
            "43184  Volat  ...                                                   \n",
            "\n",
            "[43185 rows x 15 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ARbLpwBlBrZe"
      },
      "source": [
        "# Export Cleaned Data\n",
        "filename = r\"/content/drive/Shareddrives/COS424/RedditAllBigClean.csv\"\n",
        "dfBig.to_csv(filename, index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oOohh8IloHn_"
      },
      "source": [
        "### Sentiment Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Y608X5eoupZ",
        "outputId": "e4b4a2fd-d542-4255-a6e3-e914d8c68dd4"
      },
      "source": [
        "# Start VADER sentiment analysis and store resulting scores for every post\n",
        "# For more thorough commenting go to \"Google News - Sentiment Analysis\" (similar code)\n",
        "\n",
        "analyzer = SentimentIntensityAnalyzer()\n",
        "reddit = r\"/content/drive/Shareddrives/COS424/RedditAllBigClean.csv\" # Cleaned Reddit Data\n",
        "reddit_df = pd.read_csv(reddit)\n",
        "print(reddit_df)\n",
        "sentences = reddit_df['titleClean']\n",
        "scores = []\n",
        "index = 0\n",
        "\n",
        "reddit_df['comp'] = 0.0\n",
        "reddit_df['neg'] = 0.0\n",
        "reddit_df['neu'] = 0.0\n",
        "reddit_df['pos'] = 0.0\n",
        "\n",
        "for sentence in sentences:\n",
        "  if type(sentence) != float:\n",
        "    vs = analyzer.polarity_scores(sentence)\n",
        "    reddit_df['comp'][index] = vs['compound']\n",
        "    reddit_df['neg'][index] = vs['neg']\n",
        "    reddit_df['neu'][index] = vs['neu']\n",
        "    reddit_df['pos'][index] = vs['pos']\n",
        "  index +=1\n",
        "\n",
        "# Repeat for body text\n",
        "sentences = reddit_df['bodyClean']\n",
        "scores = []\n",
        "index = 0\n",
        "\n",
        "reddit_df['compB'] = 0.0\n",
        "reddit_df['negB'] = 0.0\n",
        "reddit_df['neuB'] = 0.0\n",
        "reddit_df['posB'] = 0.0\n",
        "\n",
        "for sentence in sentences:\n",
        "  if type(sentence) != float:\n",
        "    vs = analyzer.polarity_scores(sentence)\n",
        "    reddit_df['compB'][index] = vs['compound']\n",
        "    reddit_df['negB'][index] = vs['neg']\n",
        "    reddit_df['neuB'][index] = vs['neu']\n",
        "    reddit_df['posB'][index] = vs['pos']\n",
        "  index +=1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "        type  ...                                          bodyClean\n",
            "0       Hype  ...  hey guy look jan 2021 call gme even 22 price m...\n",
            "1       Hype  ...                                                NaN\n",
            "2       Hype  ...  credit information go justin dopierala founder...\n",
            "3       Hype  ...  get actual discussion regard fuck go gme somet...\n",
            "4       Hype  ...  never chase stock rise end catch good day day ...\n",
            "...      ...  ...                                                ...\n",
            "43180  Volat  ...  see part 1 http www reddit com r wallstreetbet...\n",
            "43181  Volat  ...  biontech market cap 39b moderna 67b one commer...\n",
            "43182  Volat  ...                                                NaN\n",
            "43183  Volat  ...  mrna completely overvalue many additional mrna...\n",
            "43184  Volat  ...                                                NaN\n",
            "\n",
            "[43185 rows x 15 columns]\n",
            "{'neg': 0.0, 'neu': 0.87, 'pos': 0.13, 'compound': 0.7096}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iSRnSISR2CjL"
      },
      "source": [
        "# Add date column as index\n",
        "\n",
        "df = pd.DataFrame({'year': reddit_df['year'],\n",
        "                   'month': reddit_df['month'],\n",
        "                   'day': reddit_df['day']})\n",
        "dates = pd.to_datetime(df)\n",
        "reddit_df['date']=dates\n",
        "reddit_df = reddit_df.set_index('date')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hghMSehU1yhi"
      },
      "source": [
        "new_reddit = pd.DataFrame(columns=[\"date\",'comp','pos', 'neg', 'neu', 'sumB', 'posB', 'neuB', 'negB', 'num','company'])\n",
        "\n",
        "\n",
        "# For every company extract day's sentiment\n",
        "for company in compName:\n",
        "  df = reddit_df[reddit_df.company == company]\n",
        "  \n",
        " # Sum up all sentiment in one day\n",
        "  for date in np.unique(df.index):\n",
        "    comp = df.loc[[date],['comp']]\n",
        "    pos = df.loc[[date],['pos']]\n",
        "    neg = df.loc[[date],['neg']]\n",
        "    neu = df.loc[[date],['neu']]\n",
        "\n",
        "    compB = df.loc[[date],['compB']]\n",
        "    posB = df.loc[[date],['posB']]\n",
        "    negB = df.loc[[date],['negB']]\n",
        "    neuB = df.loc[[date],['neuB']]\n",
        "    \n",
        "    sum=0\n",
        "    sumP=0\n",
        "    sumNeg=0\n",
        "    sumNeu=0\n",
        "\n",
        "    for i in range(len(comp)):\n",
        "      val=comp.iloc[i, 0]\n",
        "      sum+=val\n",
        "    num = len(comp.index)\n",
        "\n",
        "    for i in range(len(pos)):\n",
        "      val=pos.iloc[i, 0]\n",
        "      sumP+=val\n",
        "\n",
        "    for i in range(len(neg)):\n",
        "      val= neg.iloc[i, 0]\n",
        "      sumNeg+=val\n",
        "    \n",
        "    for i in range(len(neu)):\n",
        "      val=neu.iloc[i, 0]\n",
        "      sumNeu+=val\n",
        "    \n",
        "    # Repeat for body text\n",
        "    bsum=0\n",
        "    bsumP=0\n",
        "    bsumNeg=0\n",
        "    bsumNeu=0\n",
        "\n",
        "    for i in range(len(compB)):\n",
        "      val=compB.iloc[i, 0]\n",
        "      bsum+=val\n",
        "\n",
        "    for i in range(len(posB)):\n",
        "      val=posB.iloc[i, 0]\n",
        "      bsumP+=val\n",
        "\n",
        "    for i in range(len(negB)):\n",
        "      val=negB.iloc[i, 0]\n",
        "      bsumNeg+=val\n",
        "    \n",
        "    for i in range(len(neuB)):\n",
        "      val=neuB.iloc[i, 0]\n",
        "      bsumNeu+=val\n",
        "\n",
        "    new_reddit.loc[len(new_reddit.index)] = [date, sum, sumP, sumNeg, sumNeu, bsum, bsumP, bsumNeg, bsumNeu, num, company] "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "icx2AP4yBJZ8"
      },
      "source": [
        "# Export Reddit Sentiment Scored Data Set\n",
        "new_reddit['avg']=new_reddit['comp']/new_reddit['num'] # Add average compound score (for feature selection)\n",
        "new_reddit['sum'] = new_reddit['comp']\n",
        "filename = r\"/content/drive/Shareddrives/COS424/reddit_compound_scores.csv\"\n",
        "new_reddit.to_csv(filename)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5y_uhGgG7DtQ"
      },
      "source": [
        "## Yahoo Finance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h7K0M8vtXh2S"
      },
      "source": [
        "### Data Collection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6NtQOvPfDSKb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c21fc5ea-28f4-4b6a-9064-26bd42d29cf2"
      },
      "source": [
        "# Get the data for all stocks\n",
        "companies = ['GME' , 'AMC', 'NOK', 'TSLA', 'BB', 'KOSS', 'EXPR', 'FB', 'MSFT', 'AMZN', 'NFLX', 'GOOGL', 'BABA', 'AAPL', 'AMZN', 'RCL', 'AAL', 'BA', 'ZM', 'MRNA', 'BYND', 'CCL']\n",
        "closing = pd.DataFrame([], columns = companies)\n",
        "trading = pd.DataFrame([], columns = companies)\n",
        "percentage = pd.DataFrame([], columns = companies)\n",
        "\n",
        "for company in companies:\n",
        "  data = yf.download(company,'2020-10-01','2021-04-29') # Download data in timeframe\n",
        "\n",
        "  # Save wanted data\n",
        "  closing[company] = data['Adj Close']\n",
        "  percentage[company] = (data['Adj Close'] - data['Open']) / data['Open'] * 100\n",
        "  trading[company] = data['Volume']\n",
        "\n",
        "  # Plot the close price of the company\n",
        "  trading[company].plot()\n",
        "  plt.show()\n",
        "\n",
        "print(closing)\n",
        "# Export Closing Prices\n",
        "filename = r\"/content/drive/Shareddrives/COS424/closing.csv\"\n",
        "closing.to_csv(filename, index=True)\n",
        "\n",
        "# Export Trading Volumes\n",
        "filename = r\"/content/drive/Shareddrives/COS424/trading.csv\"\n",
        "trading.to_csv(filename, index=True)\n",
        "\n",
        "# Export Percentage Price Changes\n",
        "filename = r\"/content/drive/Shareddrives/COS424/percent.csv\"\n",
        "percentage.to_csv(filename, index=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GME\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "                  Open        High  ...   Adj Close    Volume\n",
            "Date                                ...                      \n",
            "2020-10-01   10.090000   10.250000  ...    9.770000   4554100\n",
            "2020-10-02    9.380000    9.780000  ...    9.390000   4340500\n",
            "2020-10-05    9.440000    9.590000  ...    9.460000   2805000\n",
            "2020-10-06    9.560000    9.840000  ...    9.130000   4535400\n",
            "2020-10-07    9.230000    9.560000  ...    9.360000   3308600\n",
            "...                ...         ...  ...         ...       ...\n",
            "2021-04-22  159.100006  159.100006  ...  151.169998   4345800\n",
            "2021-04-23  149.000000  153.660004  ...  151.179993   4029100\n",
            "2021-04-26  150.979996  174.679993  ...  168.929993  10532100\n",
            "2021-04-27  183.550003  188.000000  ...  177.770004   8932700\n",
            "2021-04-28  172.100006  181.979996  ...  178.580002   5788900\n",
            "\n",
            "[144 rows x 6 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "AMC\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "             Open   High    Low  Close  Adj Close    Volume\n",
            "Date                                                       \n",
            "2020-10-01   4.78   4.80   4.63   4.65       4.65   3223800\n",
            "2020-10-02   4.48   4.66   4.42   4.65       4.65   3576000\n",
            "2020-10-05   4.30   4.36   4.05   4.13       4.13   9468800\n",
            "2020-10-06   4.26   4.27   4.05   4.06       4.06   8404200\n",
            "2020-10-07   4.08   4.11   3.94   4.04       4.04   6917700\n",
            "...           ...    ...    ...    ...        ...       ...\n",
            "2021-04-22   9.95  10.65   9.79   9.99       9.99  49923500\n",
            "2021-04-23  10.09  10.38   9.96  10.16      10.16  27465600\n",
            "2021-04-26  10.69  11.96  10.57  11.50      11.50  78592900\n",
            "2021-04-27  11.68  12.22  11.22  11.46      11.46  51629800\n",
            "2021-04-28  10.85  11.39  10.65  10.85      10.85  39720500\n",
            "\n",
            "[144 rows x 6 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "NOK\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "            Open  High   Low  Close  Adj Close    Volume\n",
            "Date                                                    \n",
            "2020-10-01  3.91  3.92  3.84   3.87       3.87  17028000\n",
            "2020-10-02  3.80  3.86  3.80   3.82       3.82  19307700\n",
            "2020-10-05  3.95  3.96  3.88   3.93       3.93  15811400\n",
            "2020-10-06  3.98  4.00  3.87   3.91       3.91  23824300\n",
            "2020-10-07  3.90  3.92  3.86   3.90       3.90  15216500\n",
            "...          ...   ...   ...    ...        ...       ...\n",
            "2021-04-22  4.20  4.23  4.16   4.16       4.16  30173600\n",
            "2021-04-23  4.16  4.20  4.12   4.20       4.20  24054100\n",
            "2021-04-26  4.21  4.23  4.17   4.20       4.20  33928800\n",
            "2021-04-27  4.20  4.23  4.17   4.22       4.22  29219000\n",
            "2021-04-28  4.26  4.30  4.19   4.20       4.20  30467300\n",
            "\n",
            "[144 rows x 6 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PHZBJRmrIZsaYpLJgN8YjsbNODzcrBiAcEsbmZ9lcdpNUFuzGeKSkqoHsjBT6pB35mvG2ZoxJNgt2YzxSXHnoBTbaM35QNrsq6qlpbPGhKtMbWLAb45GS6kOvddqe2EU3Nlur3SRJwsEuIrNF5GMR2SQid7bz+LdFZK2IrBKRN0RkZHJKNSZYSio7Fuy2ZoxJtoSCXUTCwH3AHGAScJWITGqz23JgmqqeADwL/DoZhRoTJNGoUlrdeMgFNtozIrcPaeGQLS1gkibRFvt0YJOqblHVJuBp4OL4HVT1LVWtc+8uAoZ1vkxjgmVvbRMtUe1Qiz0lHGJMfl822Vx2kySJBvtQYGfc/SJ32+HcALycaFHGBF1HzjqNN7bApjya5PFs8FRErgGmAb85zOM3i8hSEVlaVlbmVRnGdInWa512MNgnDspm5/469tc2eVmW6SUSDfZdwPC4+8PcbQcRkXOAHwAXqWq71/1S1YdUdZqqTsvPz0+wDGO6t5LqxFrsZ0/IRxXeXF/qZVmml0g02JcA40VktIikAVcCL8bvICInAX/CCXX7KzW9UkllAyGBvKxDL4nXnk8MzaGwXwavrS3xuDLTGyQU7KraAtwKvAqsA55R1TUi8jMRucjd7TdAFvA3EVkhIi8e5scZ02MVVzWQn51OSrhjL7FQSDh30iDe3lBGQ3PE4+pMT3fkc53boarzgHlttv047vY5SajLmEArPsoFNtpz3uRBPL5oO+9uLOecSYM8qsz0BnbmqTEeKK3q2MlJ8U4bPZCUkPDhjv0eVWV6Cwt2YzxQXNWxdWLipaWEGNI/k5376wHYWl5LNKpelGd6OAt2j/3Hsyt5YP7mri7D+KihOUJFXfMRl+s9nBG5fdi5r47dFfWc87u3eW75IZPOjDkqC3aPvbm+jLc32OSg3qS0dR32xFrsAMNzM9m5r461u6uIRJUlW/cluzzTCyQ8eGo6LhJV9tU2UlwZ7upSjI86cuWkwxme24e9tU2sLKoAaP3XmERYi91DFXVNRBX2VDagan2lvUVxgssJxBs+oA9w4ESlDSXV1DXZOu0mMRbsHiqvcU4Pb2yJUlHX3MXVGL+UdiLYR+Q6wb5mdxXpKSGiCqt3VSW1vqPZua+Of60p9vU5TXJZsHuovObAagp73LVDTM9XXNlAZmqYfhmJ93QOd4MdYPaUQgBW7vS3O+beNzdxyxMf2olSAWbB7qH4YC+uqu/CSoyfiqsaGNQvHRFJ+HsH9EklK915Q5gxZiBD+2eywud+9pVFFUSiatdhDTALdg+VVVuLvTcqPYazTmNEhGEDMgGYMCiL00bn8vraEhZt2ZvMEg+rtrGFDSXOBT/WF9uFP4LKgt1D5TVNpISEcEhal3E1PV9xVcMxzYiJifWzjyvI5gf/djwjcvtw/dwlLNvu/Rmpq3dVEjsn6uNif/v2TfJYsHuovKaRvKx0CrLTrcUeUD99aQ23Pvlhh/dX1WM66zTejDEDmTZyADmZqQzMSueJG0+jIDud6x79gFUed8vEplcO7Z9pLfYAs2D3UHlNI3nZaRTmZFiLPaDe3VjOgg1lHZ6uWlnfTFNLlIJOBPv1nxzNs7ec0Xq/oF8GT940g5w+qXzpESfcX1y5m7fWlyZ9Gu3KnZUMz83ktDG5fNwFwf7K6j1s31vr+/P2NBbsHoq12AfnZLCn0gZPg6YlEmX73jqqGlooq2n3ejGHaD05qRPB3p4h/TN56qYZ9EkLc9G9C7n9qeV8Ze4SLrz3XSrrkzOVNhJVlu/Yz4nD+jNxUDal1Y1HvKJTZV0zf35nC00t0aQ8f2NLhG88uZzfv74xKT+vN7Ng91B5dRN5WekU9su0k5QCqGh/PU0RJ7Q6eqHp1kvi5SS+TszRDM/tw5M3zeAL04Yx9yun8uvLT2DN7irufbPzQRiNKnc9t4rdlQ2cO2kQEwuzgSMPoP5t2U5+8c91PLl4e6efH2DH3joiUeX9zXvttdJJFuweUVX21h5osdc1RSip6lirz3QPW8oPhPmmso4Fe2ydmILs5LbYY0bn9eXXl5/IzIkFfGHacD5/yjDmvreNbeWd6774/RsbeWZpEbd/ZjwXTx3KpCH9EIHH3t9G5DArTMZm6vz3m5uoauj8p4bNZc7vUFzVwPa9dZ3+eb2ZBbtHqupbaI4oeVlpnD52IOkpIa5+eBE77A82MLa4QZOWEurwnO7OLCdwLO44byJp4RDff/6jwwbw0SzYUMYf39zIZScP41vnjAecN6a75hzHy6uL+e6zKw9ZPjgSVRZv3cdJI/qzr7aJb/91ZesZtx21dNs+fvnPtdQ3OSdCbY5783zfp+mdPZUFu0difbL52elMGZrDEzeexr66Ji59YGHrzIZNpdX8c9WeI/6c5kiUlkhy+jBNYjaX1TCgTyqTh/RjY0e7YqoaGNg3jbQUf15aBf0y+PGFk3hv817++ObGw/Z3l1a33xW4dncVtz21nAkF2fzic1MOOqnq5k+N5dvnTuC5D3fxgxdWH/T96/ZUUd3QwnVnjOLOOcexYEMZs/7rbR5esIXmo/y9ltc08p1nVnL5g+/z8DtbeW55EeC8kQ7ql05+drpv8/Z7Klvd8RhEo0oodOSzCmNnneZlOX2t00bl8vdbzuDLj37AFX9axN0XTuK3/9pAeU0jOZmn8cnxeajqQS+sD7bu4/anljO2oC+PXX8a4ZDwwdZ9vLBiF5mpYU4YlsOs4wrIzkj17pftxTaX1TImP4sxeX2Zv6HssPvtq22iX0YKKeEQJZUNnZoRcyy+MG04CzaW8/vXN3L//M1MGdKPqcMHMHVEf6YO689TS3bwwPzNzJ5cyK8u+wT9+zgX2P64uJprHllM37Qwf/7yNDLTDl2F9LZZ42hsiXDfW5tJTwlx94WTEBHe3+wE74wxAxnUL4PZkwv56Utr+OW8dfx16U5+fvEUTh87kGeW7OTRhVvJTAtz6cnDAPjNK+upb45wy8yxvLW+lP9dtIOrp49gc1kNY/OzGJiVzrsby9lSVsOY/Cz/DiSwqqiC/Ox0Budk+vq8yWbB3kG7Kup5ccVu/rFiF6XVjTx+w3QmD8k57P5vbygjJDC+4MAf5tj8LJ77+hncMHcpdz73EdkZKQzPzeR7f19FTmYqCjz/9TNIC4d44O3N/Ne/Pia3bzoLN+3lpy+tIarKE4t30DcthUhUeeTdCKlh4YyxeZw/uZDZUwrJ7Zvmw9EIpl0V9RTtq2N0ft/D9oG3RKKtF6DeUlbLrOPyGZufxd+WFbG+uIraxhaKKxvZU1lPSVUDH+2qZNGWfdz8qTF8/4LjKaluoPAYLrDRGSLC//3CVOZMKWTFjgpW7KzgicXbeXTh1tZ9zhqfxxvrS7jmkTr+8Y1PsqWshqsfXkRqWHjyphkHrVHT9mffcd5EGpqjPPLuVvqmh/nu+cexYGMZY/L6tnY5jcrry/98ZTpvrCvhpy+t5dpHF/PCN87knpfXkZOZSkpY+NELqwE4Y+xAfnbxFMYVZDG0fyY/fGE1y3dWsKWshoumDuHCE4Ywf30p5/9+ATeeNYbbZo2jT9rBUVVS1UBIhPzs5B3ryvpmPv/g+2Slp/CX66czZejhX9/dnXSH0edp06bp0qVLPX2OlkiUbz2zkhG5mdw2azwZqR1bIz0aVX732gbum78JVTh5RH/2VDbQ2BLlma+ezri44N5YUs3c97ZxwydH84U/LWLq8Bz+/OVTD/mZtY0t/O61DcyeUkgkqlz18CIK+2Wwp7KBa2aMYMe+ehZsKOOzJwzmnks/wd3/WMNzy3eRGhauOHU4d805nozUMMt37OfVNcW8uqaEHfvqyMtK49mvncGovL6AcyWf9JQQIkJjS4SSykZ2V9YTDgnTRg44prVMDuex97fx0srdXHHqCC48cTClVY08s3QnnxyXx2ljBibteY7VWx+X8vX//ZD65ggpIWHuV6bzyfF5rY/v2FvHgws28+yyIr4+cyxfmjGSU37xOnfOOY4Jg7K4fu6hf5/pKSFGDexLOCTs3FfHou9/hrN/M59zJxVwz6Un+PnrHaI5EuXj4mqW76wgPyud2VMKeWnlbm57ajk3nTWa55fvRgT+evOMDrWKVZW7nvuIp5fs5PJThvHssiLuOG8Ct84af8i+ZdWNfPq380kJCxV1zfz9ltM5ecQAFm7aS3MkysyJ+a1/ezWNLZz2y9c5fnA/lm7fz90XTuIrZ46mrLqRX728nr9/WMTgnAzuOG8idU0trCqqZENJNSuLKgkJnD0hn9lTCpl13KBOh/xTH+zgruc+YmDfNBpbojx87TROH9uxv91t5bV8sHUfuX3TmD4ml37tfIpWVZZt38+7m8r59MQCThzen4bmCOGQkBo+tq47EVmmqtMO2d5bgv2Jxdv5wfNOi2FEbh9+eckUzhqff8TvaWiO8O1nVjDvo2IuP2UYt88az4iBfdhWXsvn7l/IicP685frpxONKo8u3MqvX/2YppYo2RkpVDe08Odrp3XoavNby2sZ0j+DH7+whr8u3Uma+5H36ukjEBEamiO8s7Gc6aNyyenT/h/Mip0VXD93CVkZKZwxJo9VuypZt6eK/n1SCYuwt8185MtOHsYvPjel3Y/fidpUWsMFf3iHlLBQ1xQhLyuN+qYIte6g2KmjBvD1T49j5oT8g95MKuub2VJWw0kjBrRua2iOsGZ3FVX1zfTLTGVzaQ1jC7I4ZeSAQ573aBqaI7y1vpTnlu/izfWlTByUzR3nT+CeeevZX9fE0zfPIBKFB9/ezIsrdxMWYfygLNbsrmJ4bia79tfz91vOYPKQHJ5YvJ3M1DCDcjIYnJNBYb8McjJTERGWbtvH5Q++zy0zx/Lg25u5fdZ4vnXuhE4f12RTVa55ZDELN+0lLyuNp2+ewbiC7A5/f2NLhCv+tIgVOyuYPjqXJ288rfXTTVsPzN/Mf76ynumjc3nmq6cf8ef++Z0t/OKf6wD4y/XTOXvCgdflsu37+NELa1i7x1neIC8rnTF5fTl7Yj61jS28sHwXuysbEIGpw/szc0IBG0qq2V1Zz8PXTiMvK/2gT2FtvbepnHteXs+4giy2762lqqGFx2+YzrWPfMD2fXXce9VJnDe5kPqmCI+8u4Wt5XV8+rh8Zk4soCUS5f+t2sNzHxbx4Y4DZwQPycngy2eM4n8Xb+fUkblcd+YoXltbwgsrdrFz34HzWfKy0imvaeTZr53OtFG5HftPaKPHBntjS4QNxTXkZKYyuH9G6zvfE4u386e3t3DPpZ9gytAcZv12PmPzs/j3c8fzg+dXs7W8lktOGsqNZ42mtKqRreW1DOqXwZwphYRCQml1Azc9toxVRRV8f87x3HjW6INC6cG3N/Orl9fz+yum8uQHO/hg6z7OOX4Ql508lG8+vYIBfVNZ+L1Zh/2Dak9VQzO/f20jl5489Jg+Bi7fsZ9vPPEhzVFlTF5fThudy95a52IfQ3IyKMzJYHBOJh9s3csf39rEScP78+h1p7b2ucbbVVHPyx/tITUcYsXOCpbv2M/lpwzj9LF5VNQ1sb+umYq6Jirqmnl9XQl7Kht47dufYkNxDX95fxshge+efxzvbizjoQVb2F3ZwOQh/bj6tBFMGZLDlvIafvXyekqqGjl30iAG52SwYmcF6/ZU0Rw5+G8yLyuNd78365BPWS2RKJvKati1v56RA/swNj8LVVi6fT/PLy/in6v2UNXQQn52Op+bOoTbPzOe7IxUNpZUc9G9C6l3l6Xtkxbmi6eN4MazxjCgTxrXPLKYdbur+OPVJzFzYsFRj7uqcsF/v9v6Rvr0zTM4rrBfwv9/fthaXsv/mbeOO86b2DpXPRF7Kuu5/63N3Dpr3BFn/jS2RPj+c6v50ukjmTq8/xF/pqpyw1+W8ub6Ut67cxZD+h/cvx2JKgs3lTM8tw+j3U+j8d+7bk81r68r4Y11JawsqmRAn1TqmiJMHtKP9JQw72/ZS3ZGCgP7ppHbN43cvukM7JvGmj2VrN5V1RqwAP8xeyJfnzmO/bVNfGXuElYVVXDpycNYsKGM0upG+mWkUNXQQpr7um6KRJkwKIvLTh7GZ44fxO6Ker7//EcU7a9nfEEWW8traYkqIYEzx+VxyUlDOaIU6l8AABCTSURBVHNcHs8uK2JreS0jcvtwyUlDD9sVdjRJC3YRmQ38AQgDf1bVX7V5PB14DDgF2AtcoarbjvQzjzXY75m3jrnvbaPRnQkQEhick0leVhoriyrJTA0TUSUrPYX9dU384xtncsIw5+PP/fM388D8TYeEyMkj+jNnymDmvreNfbVN/P7KqZw/ufCQ565rauFTv36L8pomstNTuPuiyVx28lBEhA937CcswolH+YPuSq+sLub2p5YzpH8G3zp3Av/2icGkhEOs3V3FQws289KqPa3T57IzUjiuMJsl2w5dhCokkNs3nZ9fPJk5nxjc7nM1tUR5YfkuHnx7M1vi5luPL8ji/MmFPPzOFlJCwgnD+nPi8P5MHd6f/Ox0KuqaqG5o4d//uoJfXjKFL542kvqmCO9sLONfa50X8v64C5hkpacQEqhqaCEzNczsKYVcctJQzhg78JA32M1lNSzaspfG5iiXnDSUAXFjE82RKHVNEXIyOz4oPf/jUp5cvIMffXbSMb9Ie7PqhmY+3FFxUGv9WFTUNdEnLYVX1xRz21PL6ZeRwlXTR9DYEmVvbRN7axrZV9vE3tomCvtlcPkpw7ji1OHMfW8bcxdu44VvnNm6gFttYwu3PPEhCzeV86nxeXzt7LFMG5XLsu37eW1tMSLCRScOYfKQfod8Ev2oqJIzxg5kfXE1K4sqmHVcgSdTYJMS7CISBjYA5wJFwBLgKlVdG7fP14ETVPVrInIlcImqXnGkn3uswf63pTtZX1zNySMGUNvUQtG+Onbur6dofx0njxzATWeN4Vt/XUF6Sqj1PyXelrIaVuysYERuH0YO7MtbH5fyh9c3squinkH90nnky6ceseX8z1V7eGVNMXfOOY6h/YM3ir5oy15+9MJqNpbWMCK3DyNy+/DupnL6poW5cvoIrjtjFBmpYbIzUshIDbN6VyXlNY3075PGgD6p9O+TRnZ6ylFnCMWoKlvKa9lYUs2gfhlMHpJDWkqIhuYIqeEQ4XZ+jqryufvfo7y6kUlD+vHOxjIamqP0y0hh1nEFzJxYwPDcTDaW1LC+uJqmSJRTRw3gvEmF9E23uQG92dJt+xiV17d1ZtrRtJ2VBs4YW11zpHWN/O4mWcF+OvATVT3fvX8XgKreE7fPq+4+74tIClAM5OsRnsiPPvZE7KmsJycz9ZCR+J4oGlVeW1fC/fM3U1bVwBdnjOSa00a225ffVf61ppibH1/G4JwMzps0iPMmFzJ9dO4xDzgZ01McLtgTTa6hwM64+0XAaYfbR1VbRKQSGAiUJ/hcXSboc1gTEQoJ508ubLe7qbs4b3Ihi7//GQqyj+2qRMb0Nl3W5BGRm0VkqYgsLSs7/MkfxoBzir6FujEdk2iw7wKGx90f5m5rdx+3KyYHZxD1IKr6kKpOU9Vp+fmdGzAxxhhzQKLBvgQYLyKjRSQNuBJ4sc0+LwJfdm9fDrx5pP51Y4wxyZVQH7vbZ34r8CrOdMdHVXWNiPwMWKqqLwKPAI+LyCZgH074G2OM8UnC0z5UdR4wr822H8fdbgA+3/nSjDHGHAubL2aMMT2MBbsxxvQw3WKtGBEpA471wol5BGiOvCtoNQetXrCavRKEGtsKWs2J1DtSVQ+ZVtgtgr0zRGRpe2dedWdBqzlo9YLV7JUg1NhW0GpORr3WFWOMMT2MBbsxxvQwPSHYH+rqAo5B0GoOWr1gNXslCDW2FbSaO11v4PvYjTHGHKwntNiNMcbEsWA3xpgeJhDBLrZeq+fsGPsjCMc5CDW2FcSavRSIYMdZcAyw/0AP2XH1RxCOc+saUgF6vfWH1qXCuz0RmSginuVvtw52Efk3EXkd+J2IfAqguy8BLCKfE5Gfd3UdHSUiF4jIP4DfiMjMrq6nI4J2jCEYx1lEZruXtvytiFwCgXi95bg1vwLOCrRdXNIRici5IrIYuBEP87fbBruIjAJ+CfwRWAfcLCI3uo91u7pFJOTW91vgThE5q6trOhIRSRWR/wJ+AjwIVAJXiUjbSx12C+IIB+wYi4ikdefjHFfjb4EfAfcBHwOfF5HxXVtdh9QDFcAUEfk8gIiEj/wt/nKPcaq7vPn9wH+q6ndjb0JefCrqdgEZZyzwrqr+A/gf4M/AbSIyQFWj3e0joqpGgY3AScDXgW7dolTVZpwX8FWq+jLO8e0PRLq0sMNQRwTYRHCOsapqE7ABuLo7Hue4Gl8BznavqfAe0Axs7dLijsIN8AHAIuAKnEYgqhrpTvngHuNmIAo8q6rPAYjIWSLiyVXju808dhG5HNipqovd+xOB+cAoVW10tz0ANKjqt0REuvpjYjs1p7r/gYjIEuBBVX1EREJu8HepdupNA1qAFFVtEpF5wB9U9dWurDOeiNwOfAJYrKp/jv9/747HGA6q+QNVfTjuE2a3Oc5tj2vc9guAe4FSYAGwTFX/2k1eb7Ga3wf+R1VVRDKBl1T1HBH5F/Au8Jyqru7qmuPqXaKqD4lIIfArQIFpwDZgP/C2+zecvHpVtUu/gALgbWA38AIQinvsMeD3sdcxcCLwLDCoO9bs1hi7PQdYAwzorse4zbEeALwBFHZ1vXE1XYfTGpvt1n8XMDbu8W5zjI9S87judJzbqfH7sRqB6cCEuOP7Kk7jqrsd1+/jfKovAH7h7nM9TkNlqXs/tRvV+0P3//5zwBPAcW5eXAz8ExiRzOfv8q4YVS0F/oFzAPYAX417+KfAZ0VksjpHqwGoBmp8LzTOEWoWdbuJ1PnYHRsbyI71/3WFI9Qb3zoYCVSqarGIDBORWT6X2Z7P4PRHvgJ8B8gAvhh7sDsd4zjt1Xx13OOj6Prj3LbGNNzjqqofqOoGd791QBlOWHa1tjWn41yprR6Y47bWbwfe5MAS4F1Zd3v1flVVXwBuVtX1bqatwhkjaE7mk3dpsMd9RP0jsBb4F/BvIjIYQFU341xD9X4R+SRwDc47dJd95D5SzW6ohzhwXL8H3IPT917oe7EctV6Nmx42FAiLyG04LYguqRcOqnk58FkAVV2K8xF8qIicGbd7lx9j6FDNsYHeLjvOR6hxETCkzXEFp9XZB9jrV41tHeW4jgE+CbyG0+01VVXPA2aKyGg3OLtLvQuB0SJypqrWxn3Ll4FMnC6ZpPE12GOj1bGBDXX7RFW1WZ0R4veA9cA3Y9+jqvfghPsNwETgBlWt74Y13x57XJ3Bm7HAAzhdHyer6h+7ab2xVs25wIXAOOACVX3Sj3rj6m79W9QDfeULgZC4U12B1TifOIa43zMOZ5aBr8c4JoGad3MgwH09zgnWGDuu14rIamA0cIufrzf3+TtS8xqgCMgGfqyqP4z7ESNU1beB32P8271MRFbivDndos61opPGl2AXkTNF5C/AD0UkN/ZOKs70tfjR63LgRWCC+zG1wJ0F8xjOx5gvqGpxN615oltznoj0c7ffqqqXqurublzvIHf708B5qvpNVd3ldb1ubdPdAab4F0T8C2Ujzgv4ChEJq2oRMAinOwOcqYO+HeNO1FyI0x8M8HfgXC+PcydqHO0+vgqnu+DLqlriRY1JqHknTkiOVGdAOhzbt02LuLvUG/vbjR3jDcDXVPVaL46x58EuImNwWlVv4fTj/twdeUdVI253QLqIpLv3F+AckNU4gw557r5NXteapJrfwRncrYzrq+zO9c4XkfGqukhVX/ejXrfmfweex3kjmuNuC7s1x14o1TjHMx3npJlUnAGove5+Zaq6MSA1l7r7LVDVN7ppjeXufitU9T2vakxizf058LcQUZ9mRSXpGH+kqu97VqR6Pzp8JfC0ezsXuAmni2Kwu+1nwOO4I+/A13BeBP9JF41qB63moNXr1nAxznz0y3Cme7V9/KfA33BmDwwG5uL0Bf8JCFvNwa0x6DUHoV4vfukLgVuBGe79MTj9TSPc+5Nw5nJ+C2fg40kOng52Tvx9n/6jAlVz0Oo9TM1h9ysDmAfc7m4P4cz9fZKDpzaGgGyrOXg1Br3moNWrmsRgx3lnegnn48ePcKZKne8+9lvgO3EH5UvA3UBO3Pd3xTtvoGoOWr0dqDl2gtxngJVAXjvfH/Kr1iDVHIQag15z0Oo96LmTeBAuBP4j7v7XgL+7ty/G+Thymnt/FvBGVx+AoNUctHoPU/NXgefb7BPC+Zj6U/f+dPdfsZqDW2PQaw5avfFfnRo8dadFzRSRdJyz6R6Pe3gvzsgvwGKceZ2/E5EsYDKwXUT6wMGjyl4LWs1Bq7cDNe/Dafm0ziBwa/sF8D0RqQROFvH3dPAg1ByEGoNec9DqPZyE1y52p84V4vQjRYHNOIN131TVPXJgvZTBOKPAqDNF8Q8iMhJ4FGfmxrWqWpecX6Nn1Ry0ejtRc2wxt7E4C70tBP5dVT+ymoNTY9BrDlq9HZLgR5Ow++8E4H9j23DOanyuzT4vAee4twvcf1Pwf6AmUDUHrd5O1pwbqx34tNUcvBqDXnPQ6u3oV4da7O4czZ/jnAo9D+iHu+yoOmdZfhPYLSJnq+rb4qwaWAZsEJFf4qz3MlNV9+PM7/Rc0GoOWr1JrPnT6qxlU2o1B6fGoNcctHoTddQ+dhE5G1iG8xFkE87BaAY+LSLTobWf6Sc48zfBmQZ0HU4fVTbOu1xS10LoSTUHrd4k17zPag5WjUGvOWj1HpMOfFQ5C/hS3P37gVtwfsll7rYQTh/VM8AwnKU/HwOmdsXHkKDVHLR6rebeXWPQaw5avcf0O3bgIPTBOS021s/0ReAe9/YK4Db39jTcsx+7+itoNQetXqu5d9cY9JqDVu+xfB21K0ZV61S1UZ3LkoGzOl2Ze/srwPEi8v+Ap3A+3nhyDb9EBK3moNULVnNvrrGtoNUctHqPRYenO7qDDYqzQtmL7uZqnCuZTAG2qrtanbpvd10taDUHrV6wmntzjW0Freag1ZuIRE5QigKpOKuTneC+o/0IiKrqu+rTUq8JClrNQasXrGavBKHGtoJWc9Dq7bhE+m2AGTgH412cC150eV9ST6s5aPVazb27xqDXHLR6O/oVW8imQ0RkGM7iUr9T1caE30W6QNBqDlq9YDV7JQg1thW0moNWb0clFOzGGGO6vy69mLUxxpjks2A3xpgexoLdGGN6GAt2Y4zpYSzYjTGmh7FgN72OiEREZIWIrBGRlSLyHXGviHOE7xklIlf7VaMxnWHBbnqjelWdqqqTcdYJmYNz4e8jGQVYsJtAsHnsptcRkRpVzYq7PwZYAuThXFLwcaCv+/CtqvqeiCwCjge2An8B/hv4FTATZ6XA+1T1T779EsYcgQW76XXaBru7rQKYiLMIVFRVG0RkPPCUqk4TkZnAHar6WXf/m3EuR/gLcS58vBD4vKpu9fWXMaYdCV/M2pgeLhW4V0Sm4lwqbcJh9jsPZ+Goy937OcB4nBa9MV3Kgt30em5XTATn2pV3AyXAiThjUA2H+zacCzK86kuRxiTABk9NryYi+cCDwL3q9EvmAHvUuebll3CuWA9OF0123Le+CtwiIqnuz5kgIn0xphuwFrvpjTJFZAVOt0sLzmDp79zH7gf+LiLXAq8Ate72VUBERFYCc4E/4MyU+dC9uk4Z8Dm/fgFjjsQGT40xpoexrhhjjOlhLNiNMaaHsWA3xpgexoLdGGN6GAt2Y4zpYSzYjTGmh7FgN8aYHub/AxmNYepjFHquAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "TSLA\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "                  Open        High  ...   Adj Close    Volume\n",
            "Date                                ...                      \n",
            "2020-10-01  440.760010  448.880005  ...  448.160004  50741500\n",
            "2020-10-02  421.390015  439.130005  ...  415.089996  71430000\n",
            "2020-10-05  423.350006  433.640015  ...  425.679993  44722800\n",
            "2020-10-06  423.790009  428.779999  ...  413.980011  49146300\n",
            "2020-10-07  419.869995  429.899994  ...  425.299988  43127700\n",
            "...                ...         ...  ...         ...       ...\n",
            "2021-04-22  741.500000  753.770020  ...  719.690002  35590300\n",
            "2021-04-23  719.799988  737.359985  ...  729.400024  28370000\n",
            "2021-04-26  741.000000  749.299988  ...  738.200012  31038500\n",
            "2021-04-27  717.960022  724.000000  ...  704.739990  29437000\n",
            "2021-04-28  696.409973  708.500000  ...  694.400024  22271000\n",
            "\n",
            "[144 rows x 6 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "BB\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "            Open  High   Low  Close  Adj Close    Volume\n",
            "Date                                                    \n",
            "2020-10-01  4.59  4.64  4.52   4.58       4.58   4971200\n",
            "2020-10-02  4.49  4.50  4.37   4.44       4.44   6539300\n",
            "2020-10-05  4.46  4.51  4.43   4.50       4.50   3106300\n",
            "2020-10-06  4.53  4.68  4.51   4.55       4.55   3966000\n",
            "2020-10-07  4.60  4.64  4.52   4.57       4.57   2166900\n",
            "...          ...   ...   ...    ...        ...       ...\n",
            "2021-04-22  8.69  8.97  8.58   8.64       8.64   8636300\n",
            "2021-04-23  8.66  8.88  8.59   8.85       8.85   5426100\n",
            "2021-04-26  8.86  9.19  8.84   9.09       9.09   8168100\n",
            "2021-04-27  9.16  9.62  8.87   9.29       9.29  21110700\n",
            "2021-04-28  9.23  9.67  9.12   9.30       9.30  12299900\n",
            "\n",
            "[144 rows x 6 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "KOSS\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "                 Open       High        Low      Close  Adj Close   Volume\n",
            "Date                                                                      \n",
            "2020-10-01   2.140000   2.180000   2.010000   2.170000   2.170000    10000\n",
            "2020-10-02   2.170000   2.170000   2.170000   2.170000   2.170000      200\n",
            "2020-10-05   2.140000   2.180000   2.130000   2.150000   2.150000     8500\n",
            "2020-10-06   2.170000   2.210000   2.150000   2.150000   2.150000     9300\n",
            "2020-10-07   2.160000   2.230000   2.160000   2.200000   2.200000     6900\n",
            "...               ...        ...        ...        ...        ...      ...\n",
            "2021-04-22  19.620001  19.620001  18.600000  19.170000  19.170000   439200\n",
            "2021-04-23  19.219999  19.270000  18.730000  18.799999  18.799999   261500\n",
            "2021-04-26  18.799999  21.490000  18.799999  20.240000  20.240000  1825700\n",
            "2021-04-27  20.570000  21.500000  19.580000  19.790001  19.790001   746300\n",
            "2021-04-28  19.870001  19.990000  18.879999  19.500000  19.500000   429600\n",
            "\n",
            "[144 rows x 6 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "EXPR\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "            Open  High   Low  Close  Adj Close    Volume\n",
            "Date                                                    \n",
            "2020-10-01  0.62  0.84  0.62   0.80       0.80  17630000\n",
            "2020-10-02  0.75  0.86  0.72   0.82       0.82   8230100\n",
            "2020-10-05  0.80  0.85  0.76   0.76       0.76   3495900\n",
            "2020-10-06  0.78  0.79  0.64   0.66       0.66   6800500\n",
            "2020-10-07  0.70  0.76  0.69   0.70       0.70   5911300\n",
            "...          ...   ...   ...    ...        ...       ...\n",
            "2021-04-22  3.21  3.34  3.09   3.15       3.15   3114600\n",
            "2021-04-23  3.17  3.20  3.09   3.15       3.15   1619000\n",
            "2021-04-26  3.19  3.40  3.18   3.21       3.21   3955700\n",
            "2021-04-27  3.26  3.47  3.16   3.26       3.26   3710600\n",
            "2021-04-28  3.25  3.30  3.13   3.28       3.28   2169200\n",
            "\n",
            "[144 rows x 6 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "FB\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "                  Open        High  ...   Adj Close    Volume\n",
            "Date                                ...                      \n",
            "2020-10-01  265.350006  268.329987  ...  266.630005  20009800\n",
            "2020-10-02  261.209991  265.149994  ...  259.940002  16367600\n",
            "2020-10-05  262.200012  264.869995  ...  264.649994  12822300\n",
            "2020-10-06  261.779999  265.690002  ...  258.660004  18696900\n",
            "2020-10-07  259.209991  260.179993  ...  258.119995  23133400\n",
            "...                ...         ...  ...         ...       ...\n",
            "2021-04-22  301.329987  302.920013  ...  296.519989  16375400\n",
            "2021-04-23  298.760010  302.589996  ...  301.130005  17536800\n",
            "2021-04-26  303.339996  305.799988  ...  303.040009  16172600\n",
            "2021-04-27  304.279999  305.339996  ...  303.570007  15309300\n",
            "2021-04-28  307.359985  310.920013  ...  307.100006  33907200\n",
            "\n",
            "[144 rows x 6 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "MSFT\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "                  Open        High  ...   Adj Close    Volume\n",
            "Date                                ...                      \n",
            "2020-10-01  213.490005  213.990005  ...  211.418289  27158400\n",
            "2020-10-02  208.000000  210.990005  ...  205.179031  33154800\n",
            "2020-10-05  207.220001  210.410004  ...  209.348495  21331600\n",
            "2020-10-06  208.820007  210.179993  ...  204.900406  28554300\n",
            "2020-10-07  207.059998  210.110001  ...  208.801178  25681100\n",
            "...                ...         ...  ...         ...       ...\n",
            "2021-04-22  260.209991  261.779999  ...  257.170013  25606200\n",
            "2021-04-23  257.880005  261.510010  ...  261.149994  21462600\n",
            "2021-04-26  261.660004  262.440002  ...  261.549988  19763300\n",
            "2021-04-27  261.579987  263.190002  ...  261.970001  31014200\n",
            "2021-04-28  256.079987  256.540009  ...  254.559998  46903100\n",
            "\n",
            "[144 rows x 6 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "AMZN\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "                   Open         High  ...    Adj Close   Volume\n",
            "Date                                  ...                      \n",
            "2020-10-01  3208.000000  3224.000000  ...  3221.260010  4971900\n",
            "2020-10-02  3153.629883  3195.800049  ...  3125.000000  5613100\n",
            "2020-10-05  3145.840088  3202.530029  ...  3199.199951  3775300\n",
            "2020-10-06  3165.000000  3182.000000  ...  3099.959961  5086900\n",
            "2020-10-07  3135.000000  3200.000000  ...  3195.689941  4309400\n",
            "...                 ...          ...  ...          ...      ...\n",
            "2021-04-22  3371.679932  3372.870117  ...  3309.040039  2580600\n",
            "2021-04-23  3319.100098  3375.000000  ...  3340.879883  3192800\n",
            "2021-04-26  3348.000000  3428.449951  ...  3409.000000  4880700\n",
            "2021-04-27  3443.469971  3460.000000  ...  3417.429932  3827100\n",
            "2021-04-28  3434.800049  3489.879883  ...  3458.500000  4631900\n",
            "\n",
            "[144 rows x 6 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MSm+BUSbKsVMh7FQunkoGZF8KuxHW4KsZAABTNKhIUNiZh7BNxM4UHRoLEAjLAS2otmxjBSU4Wg7IziFNXFQre3LJQjIkqLCYCKOI3e88CFki6ca+FHaT1hBoPGKPBDYpDOqAqm1SMaoBDX48iENvI+ypsAJfz0PNzQIAiqvSVmCUiSJ0psmIXbI99qWwW9RGoGUB8IhdSzBiT7EaAj3X+oSiQ0OAUERlZtgq7FlWQWCMQRFDOnyRtpGMJkykYpgIOkKZY5cMyL4U9hRsUF2kYlSjRdjXly+hsDqchqA0s8GMVmFnqgGdBaA+F2uL1pqfpxQ5VgG1xqFooq7Zk7fmo0yUioGM2CXbZN8N2mCUIs0cMIMLOxS9xUnv4ofeDEYUjL/jC9s6lu+5fLaq2U7YdegIQEXEnmLNwl6tFJElFCQ1DkWPGlZ6j9KTXL1E5Y5M58IeygYlyYDsO2F3nRosQgFDpGKEwDaS9VaQpq2pka1SKxcwBoBY+dYnVQMGCcCEWGc2CXulsIosACU1AVXjVTVUftFHmigVQ3RRsSUjdsmA7LtUTLXMx+IpJhd2KDp0EjaVEprMxixbhevU2u2i/2OVuCWA0lbYuc0A83iZo0V8eG49Iq8WuJWxnp2AakQRnIzYR5owEnbeYxEmOGRdMtrsO2F3qrzDU7F4eoSoPBr2G6Jhk7lQCMPS+We3dSy7wi8iWnq85Tki8uZw6h2nNXHRAQC7zBuS9OwkVD3qRJQ59lEmSsVEi+VMRuySAdmHws5rwbWUyHurPBvle/VoOM34gubGxe0JuyuGZhvp9qkYAIDXIOwNdeq+sOxN5aagipwrlXXNo40QdtWIqmJk6k0yGPtO2P0ab93XrEjYheGS8MKmYQgLYpLN8gvbOpYXCXumTcQujqt49Y5Tp1KP2P0KT+Okx6ag6Xxb+UUfbVgk7KaM2CXbY98JuyeEXRdRNBELk1HE7tgVKIQBANj6mW0dK6jxCDyVaxV2RRxXC+rCHkX4AEBtLuzZ8Rlo0a25jNhHmzhiFz5GUtglA7LvhN236/NOAYCIRcwoGq5V6mZcVuX8to4V2pGwT7Q8R2Jhry/QetV6Kiay7M3mxqEZUVWMFPZRhomJWpoleiyksEsGZN8JeyjscU2RHolSIoHHhd2t1SPocWd7A6SpWBjN5idbnouE3WiwEvDturArTgFlkgVRFOiiKgahTMWMNELIdTMSdvl+SwYjqQlK44SQTxJCniaEPEUIuTOJ4wxCJLbp7BgAgGhRxM6jYVekai6TWcyHi9ubO+mW4DMVppVueSrqJjVpDR7jC7hhrX63oHklVAgvydSEsDNZFTPaUB8BU6CJz6RMxUgGJamI/T8B+Bxj7AYALwbwVELH2Tpi3mkqy3PsUa478mzxRKpmKXUtLOJjbenCwIdS3BKqJAWitP6Zo27SFK2hQPi5NA7b0P0ibJULu2EKYZcR3EhDQh8BVCgav9DLVIxkUIYu7ISQMQCvBvBBAGCMeYyxQvdX7RzMK8NriKIVNao4EfMmhbA7kzcAAFbOPz3wsRS/ghrJtH0u6iZNo4aKkgdlpEnYraAMR+OCb0SpGFkVM9rQACHU+DMphV0yKElE7McBrAD4C0LII4SQ/y6GW+8JFK8SzzsF6hF75H3t2zzHbh58EQCgcuW5gY+l+RXYSmsaBqhH7BlWg6+YqMECaRD2dFiGr4u7ClWFz9SRzLHTMMSz7305HvnCX+/2qew6hPoIiApVF13JsvNUMiBJCLsG4KUA/itj7CUAqgDeuXkjQshbCSGnCCGnVlZWEjiN9ih+DTbqYqtE+cxoeo0YKj117EUImIJw7fTAxzKCClyRTtlMFLGrhCFQDNRICkpDs1KGVRAY9cYmHxrICAq769RwXfAs3NNf3+1T2X1YiAAatMhugsqIXTIYSQj7RQAXGWMPin9/Elzom2CMfYAxdjtj7PaZmZkETqM9alCFI8biAfXIOSolDB0u7JmxaSwrM9CL5/redxgE+MY9/zccUVljhlV4aodUjF4fvhEqBmwlE9e0c8veKqhVr3/3yWgKuyesiBVnY5fPZPchoc9TMSLHDinskgEZurAzxhYBXCCEXC8e+l4ATw77OIOihTW4DcIeOydGQw5EJ6iZyWHNWEDe7n/x9LlHvoJXPvMHePrrnwEAWLSKQG8fsWtGo7CbcJR0PB6vUi5AE5a9ET50EDp6wh4tWhueFHbCAgREiz+TMscuGZSkbHt/CcBHCSEGgNMAfj6h42wZI6jBU+upGFW06zuFy1i88DyYxxuG0pk8apnDOLT+lb737ZRWAQCBSOekWQ1hJ2HX63l+pujw1DQM0axUKawgB0BN1+vfA+ggI5hzDYT5muXtmfX1XUOhAUJo0ESOHXT03m/JzpCIsDPGHgVwexL73i4mrcE26oIZpURe+uhv4cwTfwUy/QrYzEBK08DGj2FivYRycR25sdYmo834wpGRetxELMtqoEYbAzBsithVE2BZ5Hz++mqBXyDUTP2YPtGhjGDEHk2FyoRyUDehAUKiQtWixVMZsUsGY991nprMRqDX897Rl0gjFNPBIohfhSOqZozZEwCApbP9leGHNe7IyDwbrlODQQLAajPvFM3CzhQDoZaJx+M54gJhZuvCHhINZAQjuGgq1BiTwk5YgJBocYOSzLFLBmXfCXuK2aAN6ZHJ+aM4rxzEM9r1mEAZmleMyyHHFq4DAJQu92nfW+N5YubbqBS5yLcdsgFAb1g8ZaoJqmeQBo/0PWHZa+Wn4m0CokMdwYg96h/Iowp/n890VagPSjSoarR4uo2uZ8m+Zt8Je4bZoHo9x57NT+DIe55E8Ya7AQB5+yI8IeyzR3mTkrvSX8kjcXiemPk27GhSUwdhb4rYVQPUyCLFHDBKEVT5BSIzVhf2cERTMWGDsVlxfXlLr334c3+Jy799XdPkqasZhfEcO1EU3rcwgndokp1hJIS9LKLjXniuw9MjRuuCpjGxAACYCy7HVTO5sUlsIA+lcLav/aueSCcEDhxhwau3mZ4EoG7sBYBpFoiRhU5CuK4NWosse6fjbQJFh8pG79Y8aBD2ysbSll7rXnocC2wJxbXFYZ/WrqCwAFQRvkFQZCpGMjBXvbCvXD4L8/89icf/6dM9t7XFhCJitgp7ZpILex5V+Gq9HHJZO4BMtT/7XkMIOwkcOGJQhp4ea7utLqpxAACaCYhzqpULsWVvJlt/LR3RVEzjgO7qxtYi9qhTt1LYuQa3JFFZCEpUAEAIFUSmYiQDcvUL+7mnYJAA1YuP99zWrnL3RMVsXdAcmz0c/+w3lEOW04cx6V3u61ysoC7sQY2LjpFpn4ohihK7OhLNjGewOtUyFLcYW/ZGhIoxkhE7bfC/cUtbE/aoU9curg71nHYLhQWgRLiNElVG7JKBueqF3Snw23BW6S0K0bxTNdUq7BMzC6CMAABCrS7sQf4o5uhKX3ncdCgGZQcOAqd5oEc7/KjaVLOgWdn4HDW3EFv2RlBFh8ZGL2JvHPfnl7YWeUcNXa6oIrraURtSMQHUkayCkuwMV72w+yWel1VrvUUhHmTdpgRRN0wUCH+cag2dqdPXQCUMSxd6m4FlmRD20AEV9sBdhZ3UI3YtxSN7r1qE4ZdQU5tfRxUD2ihG7A05dlrdWuQdCbtf7W+NZa+jsgCURDl2FYTt7VQMoxSri4PbWkuS46oXdlbmkbrp9BZ2X3i4RPNON1NQeN04bahzz8xfCwDYuPBM133TMESecaFRqQvq8Z+tDscCgEBE7IpuwRDbeXYZVliGqzW/jqkGNDZ6EVxk5QAAxN6aQEfTp+gICTtT6sK+18sdH//qfZj8r7dKZ849yFUv7MTmUV7G7/3l9m2eYzfapGIAoGKIKpQGYZ85wkse7eUXuu67XFyPh2CroQMmhD2V6RyxB+D5VEU3YYpcfGCXkArL8PRmYaeKDg2jF7FHw0MoI9CcrQm0KRq6mD0aPjMqQjARsVOiguzxOzSncBkKYTjx9V/D5TODzy2QDJ+rXtgNIez5sPeXOxR5byvTvlLFNbmwM6Mu7NPzR2AzA2z9TNd9Vwr1HL9GXcCrwWNqPP2oHT6JhN2CKapnAqeMLKsgNDedo2rAwChG7FzY18kYjC36xaSEsCvOaPjMKCwEVcTiKVQoe3zxNPJVspiDi3/3O7t8NpJGrnphT/lc0CdZoed80mjeqZVtL+xBZhYAoJh1YSeKgiV1Hma5u31vrcgX8EJGoFMXxK/F1gSdCEV0puoWUuKcqF3ilr2bhJ0p+kjm2JkQ9qI6ibS/tcg7DS4smru7wv78Y/ejuL79kksNDamYqyBij4T9vHYMuUr3wEeys1z1wp4N+O27RigKPRpVoiEa6WyHEsTcPABA2VTnvmEdwrhzqeu+I2fHNTIBnblQ/CocmF1fE4iIXTVSSOe4kLPyFaiENVn2AgDTzJGM2KOpUBVjGtmw1GPjOoxSZBgXFsPv/3VJMPu3P4mnPv5b296PihAQwk6vgjp2Jnx+NnLXYcbv/v2Q7CxXvbBP0AKWwRc9i6s96s29CkJGkOpQqaKPcWFXzebhGG72MObCRTBKO+7arfCIvaBNw6Au1NCG22/EbpgwrTRP3VT476CmJ5o3Vg1ohCIM9nYUt1Wi8W+uNYs861+gHbsKVaxpWMHuCTujFHlUkS706SfUBa1h8ZTn2Pe2sMO34TIdwcQJTKOAank0UmKjwFUt7Ha1jAxxsGhxF8ZKD2EnXoXPFlXa/9o5UQFjjs01PzFxDGniYm25c1QSCuOuqjEDAx7UwG4a6NH2NSJi1wy+nU0sZBx+19Fo2csf4J2qvjcavigRkbCH2XmkiQu7Wu7xCk61XE/bZGh/r0kCX3TOTrv9dSd3Q0MIRDl2okHZ46kYJajBIQbMWf69WTwrF1D3Cle1sG+scCGvTtwIAHAL3YVd8auwSWexve6ld+Hp1/8P3PTK1zY9ro8dAACU1zrvnwrLXi89B5N50EMbvtI9Yo+aUSLfGBtpTPi8Lr/RshcAiJiq442aA6JIxSh5/jcurvfnF2MLYS8gG/cP7Aauw9NB83QZjl3d1r40hE0R+14XdhLYcGFi7CAflla82J+9tSR5rmphr6xfAQDoC7cAAIJSd1FQ/eZ5p+244eXf1xLRW2N8UbVa6NzdSpwCqswCNbIw4UGndpM1QTtChYu1ZvLtHCWFacYvEKmx6aZticbz9b5rd93nVUfogTICQ6TBKn0KuyPsIVbVOeRRiycx7TTR+6EQhitnBp8AySiFTkJADLK+GlIxSujCIybmjonAavn5XT4jSUQiwk4IOUsIeZwQ8igh5FQSxwCAmhD2/MEb4DAdaLAVKK4t4YE/f3vTF14LqnC7ROydSE9wYXeLnYVddQookxygpaCTkM87VbsfK/IF0UVJpKuk4rxxusGyFwDIiKZiEAbwoSElLp52oT9hd4V7ZtkSd1OF3bEVaHw/ChcGF/YgatQSETsjKpQ9LuxqYMMjJrL5CaxiHGpBVsbsFZKM2L+HMXYbYyyxEXlekeej89MLWFcmoNXqwvvcA5/GnZf+EmeeeDB+TA/tpnmn/ZKb5Dn3oNy55V33i6iqORAxyzRHSwi1HsIu8qmGybdrPLfcePuIvdG/fBQg1IMPrX7x7NMvxhf2EG72EACgsktGYEGDsDuL3buTu+4nCkDEBZwSfe8LO3XidOOKvtC3C6okea7qVAwtcxEYn1lAWZ2E6dajNurw0kanWBcKk9bga80VL/0wJoSdVjtHhaZfhK3lQXQu0mOs0uQ50w6mNEfsgTg3n6lIb3KFJMLmNxiRoRIRJPTgEw35SZ6KCSr9CXQguogxfgQAUNsl697G1Ji23r07uet+hLATtZ5j3+tunnroIFB4wFHJHMWMJ0se9wpJCTsD8AVCyEOEkLe224AQ8lZCyClCyKmVlQG/lNVllFkKViqDmjGJbIOtQOTV4jZE2SatIRggYtcNEyVkoNQ6i05a2ABEwq4QBqZ3v4hEEbtp8XOKhL1MMi15fkUT4j9yEbuPECryEzMIGQG6XDwbiZrNzKmjAOpzYneaxog9Xz078H7CKGIXn4mrIRWjUReBKj6X48cwi/W+q5okyZKUsC7sCWcAACAASURBVL+KMfZSAK8D8AuEkFdv3oAx9gHG2O2MsdtnZmYGOghLTeJMii+ceqkZjNG6sEdeLWGDQZTFHIQ9xLYTJZKH5nbujMzQMnxjHIpRj9KZ3v0iwlQDISPQRDQemY9VSesgEFXMSA1GLMdOaIAAOhRVRZHkQOx+hZ1H7Pn5awAA/i4L+2Uyh/lgcKfDMI7YhbArKhTsbWE3mItQCLsxE5U8Dr7OIBkeiQg7Y+yS+P8ygL8F8IokjnPn//4HeNE7vwgAoOlZjLNyPVcp2p0bnf/SzO4ZRXeioo7D9NqbVDFKkWcVUGscilEXc2J2PxZTDXjCCAxAPGR7s2UvAChRjj0YLU92Qn0EolGrrIxBd/s0AnPL8JmK8Tmeigl2yeExFOWnG/ocxlDtaWvRiSBoFnZKdKh7PGI3qINQFAjkRcnjxsXtN2qNOk9/8x8Tr+IaurATQjKEcGNzQkgGwA8A+M6wj7MZJTcLhTAUVnilDPG5sEdWsIxSWPDA9K1XxQCAY4wjJSYkbcaulWGQACQ1ETcbAQAxugv7xCv+FR47+e/qD4gLwWbLXoB3pwJ1IRkVFOrFwl5Vx2D2aQSmeBXUiIWxSb7oyuzd6XoMPZ5j9zR+UfYGLEcNg9aIXd3rETs8UJEinDt2EwDAW+49t2A/c+G5x3DD3/8EHv/yxxM9jpbAPucA/C0hJNr/xxhjn0vgOE1ETUSFlYuYXjgKJWg2iPJ9DwZhfL7oAHjGBHK19lUPpY0VpAEo6QmoZj1iV3oI+/W3vwa4/TXxv4kYsu0ZrSZlqmhQosFoCTuhQWyt4BrjGLf7S2cofgU1pDGmG3zEoL879f2h8EsJxN2W57mw0q2ptF4EPi93JFqUY9f2fI7dYi6YKBDIj09hHXmQDVny2I2NS8/jMACvuLXB7Vtl6MLOGDsN4MXD3m8v0pNc2Kvrwmsl4F90XUSArlODAcSLm1slTE1ifKMERmnLwma1wBdV9ewkdKsu7Kq1tbSPKiY7hUZrxK6J7tRRK3dUqB9bK/jmBHLV/m7uNL8CRxF3ONBBgt0R9uhCG6XRBm0gi/ajiIgdezxi53fAbtMd8LJ2UJY89sDZ4JVDzE3W3+iqLndsJD/N65ldMQNVDfkXLEqfuKLdm2jd2/w7QdJTfGh2pTUdY4vaayM7Dd2sf9A1c2uRm2JxQafWeMtzmlg8ZSMWsavMjyP20JrEGCt3NVuL0MMqHFHh5BITSrg7i8rRaD9q8IvyoA1kkblbPRWj72lh9zyHN9M1CHs5cxjT7sVdPKu9T1gUtiROstVDIyPsE7MHAQBhiQu7LlIx0YBp3xVDGfTBhF3J8sqd4mqrNbAnKjJSY9PQGxZMtdTWhF0Xk502W/YCgCZq3ek2I/bv3P8ZPPPeO/oazr0TKDSIO3BJZho6CVEu9fZlN8IafJX/rT1igIS7c8GLrGth8vdu0D4DKszQFJFyY0TlpmA7BKM09r3pB8cWa1gNwh6MHcc8VuGIEZSSVpSKSMF4Utj7IpXJocJSIFXefapTHrHnhBWs5/B/E2OwVIyR58JebdPyHghnx8z4NIxUXdg7jeDrhC7shJXNlr2olztGo+QGpfzEF3B98DQ2VvZGM4nKfISijV7N8m7b0uqVnq+zaDVuNvOJCXWXIvboDiq62wr8zudx6rN/joc//5G2z1FRJaFoUSpG29GqmIc+++eo/d51fYu7Z3PxJg0lvfoMd1ldOjd4B+6oY9hcnxSvAkZpX3engzAywg4AG8oEdDEqz6D8C5ZjNYRBgCCO2AcTdmuMC7vdxggsrPEIMz8xGzcbAYC5xUW0iYUT8JiK/KGbWp6LHCC3m4oxyvxW2WmTUtoNVFaP2KOLZ6UPv5gUrSGM8trEgLpbEbt4P9SUEPYuqZj8Ix+A+dD72z4XCq8YRVgK7HRVjL/yPCZQRrWPuyUAcEVU3lgsUC953J6wXz7zNL71qT/d1j72KmmXp221oIJnvvVF+L89g+/c/5mhH2ekhL2iTcByhbAz/gVTCEO5sApflKWpxmCpmJxoede+8f/hud95WfPAC7sAl+mwUhmYDRG7mW4/qakT84evBX3Hedzw8u9reU4bkrBnbR6pu7XdnToUobIg7sCNjMAabSA6kWY2qKgiChQTKt2ltQdRpqilxMzaLsKuUw96h/Ok4k5MjSN2HSqSiebaQVyeGvD6jdgdvmbVKOxzx24GADjbLHk898X/hpc/+hsj2cU6FvC0rRZU4ZTXYJAgnnc8TEZK2G1zKh6VZzEHFcaj8/LGUhyxa8bWLQWAurDf7H0bJ8PnsXShblGquhsokSyIomyK2LeWigHQsVTONCNh314qZjrgawRetb+I/akHP49nH/7Kto7ZDY35sWdOdoJ78ng9jMDCIECGOGBiwTJQDGh0lxq3Ai7kmnivu/UZ6MztLOxRxC66kKFo0EmY2K36ZohYk/LdGlYvn8MT93+26/a+EPbGhryxyRkUkAVZP72tc1Fc/tlcXxqtChsahphi/I7IDKrwxdS19NhgnffdGClh960ZjFP+h7OYi1WV52yrhRWEIpJSzQEj9vwEPKYiYPxPtnruifg5zS2iqoiFT0XhFsIA0pmtC3snolQMtpFjt6tlTIF/aXy7v2jI+MI74H7+twc+Zi9U1CP2sWl+8aTV7kZgUWUSsSJht6DtUsTOQhcu06GJRflufQY686CzTsIucuzCBAyKCgAIw50xAlN9LtSeY+O5//k+XPeFN3WNmH2Xb69t6q5e1haQrnQf/N7zXDx+N1lcHtyiYS9SWFvknvsATFqN7U6yE1LYu0IzMxhDFY5dRZq4KBlcKNzyaizseo82/04QRcHD1/4iHrvjfQCA2pV6HtH0i6ip9bSLSwx4TIVuDNYM1Q5V0/hFZRvCvny+3u4duyP2YCZcgh4mVyPeOOczncnDZTpYD2G3K7w3QRGVKFQ1Ogpm0pDQgwctTvF16zMw4MHscJ7RiEC1YfEUaPBpTxg1jtirULwKdBLizOP3d9w+FMKub+rVKKWPYGqbLo+az/P39vreWOAfFhtL/EJVZimkaA3M3kDICHL51mKJ7TJSwq7muZAvnec5PjvNm5a80hqo8I7RB4zYAeCVb/qPeOlrfx5llgJZq6dirLAEV28QdpiwewyyHgQfGsg2cuyFK3VbWdpHHW2psIY8ajDo8IX9gQ+/C09+43PQEMSpGKIoKJIcVLu774sjhiZHC5ahakFnnS94jFK88O2vD+nMmyGhB48Y8RoI7VIVYzIPJtqfZ73ckQcDUT17uEPCbgTCNM91oIhmr9Jznf9mgagya6wCAwB/7Bjm6MqWSidbz4ULu1/oXR11NVFZ5cJ+RT+MFGwoTgElkoWiqkM/1kgJuzEubAUu8aG6NC9q26trcb1xYwPRIBBFwaJ+COlyvXU6E5YRNNgAeMSAgwSEnehQvNLANejOSj33GbkjdmPtEr94GQlEw7ed/gDK3/woF3ZRCQIAZXUs7hbuhC2mJ2li0YmqFowuwv7kA/+AE/e9Ds8/9jWce/phPPH/vBrV8nC8ZUjowYded+jsELEzSmHCg9UpYhcCrumbInZ/Z4Q9Kg8OvFrc7GUsPtxx+3qg1Czs2vQJqIRh6fzgZmBWyIWdlUdL2F1xB1LMHEOW2dDcDVTI8NK1jYyUsGcmFwAA9hKPTNWxBYSMgNXWQYWXiLHFNv92FNNHMe3W8395VkZo1puKfGLC7THIehBqSOMVG59F6XevHygiYutn4TCdp3S83k0kxUV+8Rq2sAe+hxTxoPll6CyIPcgBwNbGkPK7i240PSmqJmCa1THFAQDlc1yg7MIylp66Hzd7j+HK6Sc6br8VlNBFQPSeVUtB4EMlDAYJmiuqBPVUDL9ARAM3dsrN06TCDdWrxT0Bh6vf6bh4Gwm7mW7+PuXiksenBz6XFBX5+1rnUZRJcf7ZR1FcT2ZoS1jiF6pg/BoohCHjLLV1ch0GIyXsuWku7FjngqSaOZRIDoqzEUfshjVYVUwj/vg1mGO8w851akgTF6yhW9RXTHgJpGKqP/7X+ObkGzCNAtaubL1iwKhcwpI6hxpJQelD2N3VswAAC8MV9mqF3y3ofgUaQjC1LuyuMYFM0EPYxfqAkRHCrlow0TmyJWs8NRd6Tvw5cCrDsfnl7pR6fCfYafHUEZYW/OcKLp99BoWGLuZI2DWtXhXD9zfcxVOnVsEDf/WbLYGBJSL20LVjYZ/BBpYuta9wYcI91dzUXT13lPdg2IuDD7bOgO875ey8sBsf+wk8/bFfS2TfSmURG8jFXeyT/iKcNk6uQzlWInvdJSZmuLCnKlz0NCuDKslA9cpgoizNHIKw63PX8an0Z59CWRiAKenJ+PmaPomqMd3p5QNz4tZXwrr1xwAApdWte3LknUsoGgdQQwqKX+25PS3wuxKLDberM1r8NIMSrxJoSMUE5mTcLdyJaOHXyoqLqW51jIQBIFvm4hT6Npi4c/OHJOwq9eATA7roaO4UsTfWhzu1Cty/+kkE/+UOPPH1v+eviyJ2kdKJzMCCcLipmGce/Afcefo/44mv/o+mx1PgfxfqO9CpiyJ4JH7p8X9uux8mInZrU459fGoOJaRBBhwTyChFhvF9Z/2dH56SZyWMFQe/2+iGYS+joEzFZn+zWIffxsl1GIyUsJtWGgVkMe7yXJZmZeEoaWhBFfAdhIxA140ee+nN+KEbAfCp9NUN0UmWqQv7kZ//EA7//F9s+zjtyEzxi1dtfev5x5lwEXbmIBwlFZe3dcOo8L+jQUL4Q/SBd0W5YjYUkXlDxE7TUxhHpesgAiqEPZPjwh4Zu7lO+99pzuMXKOo58QXer/TXYQnwTsjl/3Ac5555tOU5hXoIFQNGjz4Dr+HcXLuKPC1gGgVc//mfwTc++h8B2izsUcQeDjnHHl0U3bPfjB9jlCItLt7Mt6FTB+dNnlLxOzUbBQ78NpVfRFGwpC7EwdVWqVaK3FwMwCTd2eEpjFKkiYsD/rlE+gcy3irKxnS8NgSgKYU7TEZK2AGgqExgPuQt6UYqC09NQw+rIIEDF0aL5e4gzB8XHXZLz8Mu8ajCyE3Fz0/OHsSkMCUbNmPTfL9+cWvCXiqsYQxVsLEjcJU0tLC3sGft+jEaUwnbxRE58jHKRYY0ROxKhv8di+udb8OZ6JJM5+oROwC4duu6Q3F9BdPgFxDqOyAiFUO3MJhj5ezjmMU6rnzzvpbnNOohJDqMaFG+Q8TeaOfrO1WkmY0HJ38Ej2e/C6987o9w8rkP8l9Fb86x0216A20mGvKeX6tfpFynBo1wIWO+DZ258IwxbCAPUm5fckh8Gw7aB0ml9GFMDujyWC1xMb+CGeRR3VFDMVe8R2OoYm15+KWW48EqHGsWRoOwU2v4pY7ACAp7RZuESXiUY6Sy8NU0jNAGCV24ZPvROgBk8xOwmQFSXYmHKFv54ade2jE+cwCUEdDy1oz6Vy7wyEufvgaemoYZ9BbqiWAZlBEAgDuE9u5vfPS38cgX/hpeje8rS4TYNUTsmjACq6x3/v2IW4bD9DhajPx/PLdV2K+c/nb8M/PtuFN0KxOXAmF4ZV1+sOU5bmJm9Owz8BtTMZUCUsQDzR7Abb/6GTxwzdswLtJP2mZhH3KOPao/P+4+E6euag2+Qcx3YFAXVLWwrk7DrLW6mQIACWy4pH2fhjdxEgfoEtYHEEe7zO+kViw+pHx9aedsgN2Gi8jiC613Z9shDAJMsgLCzBzMTF3YSRvDv2EwcsJum3WBNdM5BFoaFq2BBA78hvmi26VEclDdQtwWnEmgLbgdmm5gg+ShVLe2sFS6whez8gdOINAycRVEJzzXwTTbwGWFt/m7Tn+R0/OPfa1tGoVRipueez/w6Efjxc+IxojdjF00O/9+xK+gShonVQlhb1MpVLpQH67MAje2943a1vshFHcI19jfbsnja9RDKKp6fGggnYTdq0fsdpH/bsTMgCgK7vy538FTP/ARPHDs38YXq+hvMuzO02jIe5q4OPf0QwAAp/GiHTgw4YFqKZSNWeS89u+D2kXY5+/4CaiE4dn/9eEtn58jhL2W506RpeWdsxVoXFCuXhzuUO6N1SvQCIWSn4eVrQt7Ywp3mCQm7IQQlRDyCCHkfyZ1jHYE6brAWukcQi0Di9lQKG8kGRYVdQy6u5FoW3AnisoEDKd7d+Zm3FVeKTR96CQCLRNXQXRi5dILUAjDqnUMQN2mtRtLF1/ANff9MB77wl+1PLe2fAl5VGH6JYR2s6hGHuQAkBoXF5JSZ2FXvQrsNsLutxH2cPlZeEw0gAQOiIjYo7b1foiaufKo4exTp5qe0+CDqlzgfKJ39IUPGu4mfDEWLeqcBYBbvvsNuPMtv1f/nZIqd/Tqd2qrT/POUqfBN4gEDkzmgqkm3PQ8JsP2nzMldOF3EPbjN9+BF9RrMPVCa+qqF67oUSAz1wEAajvYfeo2fsZXhms9XBC+N/r4QaSy9by6np3q9JJtkWTE/nYATyW4/7awzGz8s5XJgRpZpBkv4er0QRwEW8vD8otxW3A2l8wiSDsqxhQy3taEHRvnUGUWxqfmQPUsUugesReunAUAOGM8cnL7EPbi8kUohMEvtaZRlk4/DgBIheWWrlfSIOz5Kd493M0ITAuqcJSGEYSihtxvs3hqFZ/HZXWBz0UNHChCeHV/C+6Wbn2/K9/5UtNTOvVAFX7+HvSOqZiwwfUxKAtP7i5+/fVUzHAXT4lfQ5VZKCALXOIXKa/B6ZOEDix4gJ4Gyx3EBEpt89xqaMPr0quxcs0bcTJ4Duee7tzk1A6/xoU9d4ivY3mFy1t6/XbwGtaRMuXBqno6UV3jC/iZqYPINFgImPmrSNgJIYcAvB7Af09i/93Q8jziCxmBaabAjCwyxIEaOgiU4UXsrjGBTFhMtC2447HNaeSD/qs6AMCsXsKyOg+iKOJi53Rd+bfXeW6TTHFhDzpUnDTilPjFhrXpaq1c4re2GVoGdbsI+yS/MHczAjPCKly1Udj5z+0sc6ecc1hPHRNzUR0owizM9PtfM2BeBR5TcQUzMC490PScDj/unA2gQ+kg7FFDDwCQGv/dNKuzsEcROxtyKkYJarCJhfPWjZgp8vmyjcKueSUohIHpFtRxvlC/evlsy3406sBXOgdKx+/6GQDAlYe7u0RuJqzxu4epw9fBZypYuX2OPwkiY7NVjGPO3Z6R2WZcMYt5bO4ITCvNAw0AmbFk1uaSitj/BMCvAZ0NpQkhbyWEnCKEnFpZGV6nlylsBWxYIIoCIuaOWkFpqBF7YE4gx0rQ3EJibcEdj52ewSTb2FJJ1rhzGUWL/21gZqGTMK4CaHsMMZsxs8BviYM2C5Ob8URaql1XK1vhLeY5VgHc5uebcuxWmk/CqnUudTPDKnyt3hijCU/wcNM5+p6LA+Ei3PETcIkJErrxQI4U7b/aQvEqsImFi2MvwdHKY01/dx0+mAgYfKJD6WAf3Bixa2IYjJ7q3JyiJJRjV/waHJJCdfYlOBqeR6W0gaDhDsrwhHOmnkZq+jAAoLjUKnI6dRF2idin5/niJ7O3NtAl2j47NoU1MgFti2tJ2yEKXi6lrscMNlDc2OJdcReirtOpOf43jdaIsuOzHV+zHYYu7ISQHwawzBh7qNt2jLEPMMZuZ4zdPjMzvPx0ZpKLlyNEnIhhDNmwgHCIETtNTSLPqjC99cTagjtBsnMwSIBSob8GDkYpZsNFOBk+8DvK7da6+aWUF1FlFnLCpiHsY/E0FE0/7bpa0yXeJJQiHlSnWbRVvXlRu6TkoTldhJ3WEGj1xhjdjCL25gvV5TNPQSchtNnr4UOHErjxQI4M7T9iV4IabKTAjnwXplDEhefrlTY6C8BEHX3QRdhpw7mZLn/fzEwXYdeSScWooQ1XsZA5fgcUwnD28a8hEBbOJaRhBaIEVU9hbI6Ls73WuoBpUAeB2tl3SdU0VJkFssXZnswtIWAK0pk8StokrB3sPg3FXZU9dQsA4Mrzw6uMUapLWEc+Xhy3Cf/b5cavnlTMdwP4EULIWQD3AngNIeSvEzhOW8ZmuXg5oqU/6vIap0WE6vAidpKehEIYJr3LibUFd0Ib43noQp9+1cX1ZWSIA0zwL6oi/iZRB2g79Ooi1pVJ6KJlPPR6R+xURNmq3yrs025dHKzalbiMEqg7GkZU1DEYXYzA0qw+Fg+oR+yRH1DE+jmeahg7fDN8xYBCXahCeLOs/7p81a/CUVKYv/V7AACL367n2U149VRMF2GPGqMAIOOLYQtdBrGQOBUzXGHXwxp8JYWjL/oXAIDy898AE3dQJTKGjOgtUM00pheuAQD4G60lhwZzEGrdbTOqJA3F3dqkLsUtoUpSIIqCqjGzo92nkWOleeSlAIDyheH4CQGAaS9jQ62LuK2kUUI6Lm8dNkMXdsbYrzPGDjHGjgG4G8CXGGM/O+zjdGJimht/RV4tmrjdTRMXYZec4FaJ6q3n6UpibcGdsCb4XUl5rb+KgZULPA1iTh8HAGhi0a7b3NO0u4KSPh1PdGJ9CDtxhOviphp5p1bBPF3GOYXfho55i1gj9cXmzcJu6+NIdfGLyTSMxQMAwxLt/JsiducKX7s/cOJW+MSAEtYnGFnE77vpSgtr8JQUDl/7IqxhDMp5bmcb+B7vkhTnHxIdCm0vxKzhopOn/HdLZTovuKuitp/S4aZi9NCGp6YwNjWHC2QB1tLD8ZpHRR1HTtzJKEYKqUwOBWShlFsXME3mgmrd7TlsJQMt2FqDkeqVUSX8bsxLzWCC7pywR+sg09e8GC7TES4Nr/Yj466gotfz6a6SQTnBFO7I1bGrmoYCycMTi2taQ1REhxixGzn+JimEJdYW3IncFF/UcjauoLC6iG996r/g4T/8YXzjz97advuS8GHPH7gWQP1iV1k9j+cebe8Fkg9WYVuzdWF3e4ugKoRd3yTsl08/AYUwLOVvBQDMhCsoaPUPeTxcQuDreaTD9pGe5zq8Aa2hVDBy7NwcsWvrz2MZk8iNTSIgfC6q1mDvWyn2Jxp6WIOnpkEUBeeyL8bB0qPiXPjx4ppzRYfaUdiFlQFTMcH4BTWV6xwQRCWgw148NaiDUKRQlnI343DtScCtIGAKXD2HnGgaU4Ud77oyDaNNk1KKOWBadwvs2M5jC2hBBbYi3s/sPMZRievLH3j/L+CRP3z9lva3FSJhT2XHcEk9iFRpeJUx4+Ea3FQ9n15NH8SaeWho+9+MltieATDGvgLgK0keox0FdSoW9sZBscMU9lTDokdSbcGdGJ/hwn7k1O8i961fw8sJg89U0IqCMPgzqFrz2+qv8Rr2mcMnASBuaZ7659/CNF1DeMti02sYpZii67iQnoMlUjHM7z1sQxMlhOYmu4KN8zwlgsN3AIW/R5q4cNQcar6JNHGh6s3vC9VSHf3Va+UCDACkQdijAeKbzzFfPYNl8whmUZ+LqjOPe5yQENXCKqbnj/T8vcywhqLOL97ewTtx4Jl/wpVzzyCdbbY0CBUjzlFHrC1d5D4xgYOAKagRC2Pgf59Ul1SMmlCO3WR1YQ8XXobp0j/iQuF51EiqaTE0mg1cNmeRc5vLVxmlcUlkN1wtC3OLEbsRVOCq/DOn5vmd6frSBRw4ej2y609ixk1uXB5rsPbeyFyDA5XhpGKirtMXMvPxY7f+nx9KdOzhyEXsAOD9wO8h9dr/AAAw0vX8N+uRE9wKmQZhT6otuBP5iRmcUY6hquTxzcM/j+d+9DN45EXvgUl8LJ5vNW0ihXMoIoO8WKixREvzIbYIi/gobTRXJZUKa7CID+Tmoagqt0/we6diLJ9HotamrlZv8RlQRjBz43fHj/laJq4MaCfsZger4GjBV7Hq72vk2MkaphcxSnEguIBqjueJQ8WERl0YzMMG4b+/Xe7PZMpkNkKRdpi55TUAgEuPfakesUepGMWAypqF+PyH34ryR34WJHDhQYcLvm2VWV1LZONyxw53AINiMRuhzi+Ek9fz9+NE7RHYsEAbqpN0cbF0UvOY2NSk5Do1URLZPWLnjXBbmxtghhV4ouLJnOAL99HsUzOsIMuS846JrIhT6Sy8iZOYp8td5772y8byJaiEgeTrwm6ls7GJXRKMpLDfeMcP4uRtfHEo1dC+y9ThCXtUbw0k1xbcCaIoOP6ex3D8PY/hzv/jj3HyJa9G/hD3wV49+3jL9qnqJayo9Q+VmW1OAZTWmg3FNkR5mz7Ov1gOMUGC3hF7SqRPLNa8rb7xPBaVGUzO1aPjUMugJm65Nws701IdJw1FC756wwVbN0zu09KwQMk7XWtgU/wuJRQRuwEvTgO5m4S9Wi7ggb94R8vFMcXseLH22I23o4QM6Nn74YtJVpGwM0WHtknYD9aewniwGnureKJaq0a6i2KUnhp2KsZibpxCOXrTK+AyHXnU4Chp0IbvRzTLlOYWMIViU7t91LBEjO5DawItGw/N6JcUrcLX+Z1MdpqnKqLZp6mwgiyxuzp/bguf31XphgnzwI1QCMOlhgqoQdkQtgiG+D7tBCMp7I00Cnt0yzwMMtmxuMlAz+5sxN6O+RMvAgDYV1oXfMa8KyhZdbfJxpZmAKhuNN9ql1d4hJSa4l8sF2Y8B7MbWbHwFvlpR4zXzmLVPIrs2FRcDUONLByFi6W2qdwRegoGCdt+gT3RwKJtqgF3YcR2AUC90zVzkFssR3NRDebHXvl+td7ktXL5LK78yWtw57n/hnOf/p2mfaeYAyaiXFXTcCZ1C+YLD8MXtemKuDBRxYDeIOwbK1cwi3WMsTKUkEfskbA7PYQ9k59CwBRkvvPRodVT0zDkQ2GEIBumhTM6X3dxlVTTHW00kEYd55+B1cv1WnanFi2wdk/FUDOPNOv9uWkkw6qgQtjHZvliu7cheipEtF7us8x3q5DAhiPuqCaP8fWgwrnWQGmrVMXshIzoC9gJA/B3PAAAIABJREFURl7YM43CPsRUTDR4GQDM3M44O3ZjfHoeG8hDWWuONhmlmAuX4GXrwp7JclEsiWEKTrFZ2B0RIcVfLKW3sDNKkRdfvBTxYlGmYYiF4CJq+WugahoqIv1CjSxcccu9OWIn4ha/XdWKJ3xNGq1PAcAlzcIedbrOHOdfUKqaMJiLFPHgWfz9Cmtc2F94/BugH3gNDgSX8Lx6AtetfTk+/zAIkCJeLIYAYB+4A0foJZQWeW1+5C5JVaMpYr/0NPc8N4kPzSvCIyZ8kcd2le7CPj49j0df/vu41nsKV/7sDUPxB7eFIDdG2oVJHhB4ahq04fthWvy9SU3xu6zi0tn4OVe8L0qPMZPMyCFL7I4DUDbjey7GUAVN8TvgyZkFBEwBLS+CUYqsCBgqxeE1DjVCAid2gF245hYETIG/tP2hG+4G/z6Nz/VezxkWIy/siqqixkSzkja8xVMAqChcXNIJtQVvlUX9MLINQ7YBYG3xAizig0weix/TdANPGC/CE8d+DgDgl5u/KFHX6ZToHvSIFY9L64RjV2GQACvgdy/VMhfg5UunkSZubOpUIaJM0cjG3aPaZmEXkWA7jxJfdCZam+46PBixDwzAO11rzMTsAi/xZKqBtBAGmuG2E9Qu4LEvfRzzn/xRAMDiT3wapVf8CiZQwlMP/AP/PURJaNTBDADjN/1L/txTXwRQvzAxRYfeMKKvcr7e4JL1VuATA4EouW20ROjE7T/8Vjxyy7twg/8kvvO1T/fcvhdRvrhR2LWjLwcA+Gq6KfCJcux50aRUW633IXiioUnrkYohol+i2qWstpH1ZR7ZKmLRVFFVrJNxqNVluE4NBuEXCLu0hie/8Tk8/eAX+tpvvygNjpWGaeGyegBmYfARfxG0xPs2kprR0I6RF3agns8kPRZ7trxf0ZiUHd85Z8dulLPHMec3Vw2sXuIRvDVzTdPjN//GP+Old78HAEArzYunSnkRRWTiUkdfsaCH3SP20gbvEFzXuGhGufCVM/xWNnuQrwFUFf5lV6wcQoP/rBvNd1LRLb7XJmIPha9JKtec/uJ16vWLT7p0Gpe1Q/ECJdNScSkfsfKwmYGj5+7DLV99K65oB6G89Us4cesrcdO/eCOqzIL96CcAAE5VdGI21M1fc+t3o8ZMzKzwevZY2FWTD+cWqMt169fxYBWBYiAQeWxf7W+o+m1v+LdYxiTI/X/S1/bdcMXfTmm4SC3c/GoA4IvDDcKeEu/91AEu7EFDk5IvDOG0HhG7YvHAp1buz9coXiSdqOeii9oULGcZlYb0i1Nag/bF34T5+X/f1377RQ3tJtuRNesYpuwzXV7R536rS1gnY4k1I7VjXwh7lM9UhphjBwDX4FFjfgcte7tBp67FFIoortVTK5VFXos7fuBEy/amlUaZpUBqzTlL3V7GhlLvkvNVCxrtHrFXxezXSop/KSNhr17mOf+5a3hKxNGEsJs5UINfGHWj+QOviEjQa2NjEE1PSm1aAPYUsylin3bPoZA+Vn9dw90a0S0sq3OYoBs4Nf2jOPDLX8LMAt/WSmfxVP67cXL9qwiDILa0Va2GhijTwmnrRpwIxdB0cWFiqgmjIWKfrDwb3y1OsgJ8xYy7n8MezT0RppXG6WvfjFvcR/Hsw1/p6zWdiAZJqA2CfODISVwk8/Bzh+LAp3GEZDY/wWeYlurNcJGnSi9hV1NR9VF/wl7PRdfruyvGDLLeKqql+mfUq65jLFjD0fAcysXhjc9TQzdeAwEAZ/xaLIRXtj0W0rSXUVCTsQ7oxP4QdmHxqhjDFXbfmkEB2R29EnfDOsAXCq+cri/4BGt80Wv2yHVtX9POlyXjrqDc0CUXqikYtPuH2xZ5Ty8rFlyFsJPVZ1FCBlPiNtTXuZirqTzI+GFUWKo+Vk6giU7SdhF7JOyZTcIeECP2gbGrZd4RPHFt407jH4luYezffRH+rzyNO37pw61lZze8DhMo4blHvwpXROybF2vLc3fUdy384KEZMEgARil8z8Xh4DyeT/EctkoYQsWs15Dr/UXsAHDLj7wdJWRQ+V/v6/s17YhTKA0RO1EU5N/+dbzs534fRAQ+DsymEZJrykxTk1IgLAiMLrbDQL1yye1iXdF0fsKid3y2vsjIu0/Xm0pTw8o6JlgBCmE49+32DXaDoIVOnCoDAH3+RugkxOXT26tnz3iriQy378a+EPaoWUntsYq/VY792Ltw5XUfGuo+t8Pk4RsAAOVL9SEBSvEcVjGOVKb9l7CijsP0moV9LFiDY9XLOUPVgtGjusGrcGFXJvgCUWQFmy2fwRXtcCwUvujS1VN5vOSNv4Lqv7m/5cIYdT2281cnbhkVlmqpAQ8UE5qI2KNOV2P++vrrGiJ2xUhhfHo+ruvfzMk7fwwBU7DxyGfiMX5aKtu0Tf76u+KftXjqEf+/5zm4+NyjMEiA2sKd8XahYoKKUkOqN++vG9n8BJ449NO4rfI1nH92cGMqXwi7vqkxKj8+BcO04kXgzSMky8Yssg2TlCIXTTPd/XcwxDqIV+0vx85KiwgZwcRMPRVDM3OYQAn2Rv3CwtbPwCAhP7fnv97XvvtBoy78hpLPsSPcDGzt7PZKHjd3ne4E+0LY/VjYhxuxzx06gRvv+MGh7nM7TIuFwqBYv21O1y5iTeS922HrE0gH9S8eDUNMsnUEmfprqJaC2aGuPMKv8Ntta+YY/7cQ9lnvPErZ4/V9mTzSNrNjMEwLc4daU0S6EPagjY2B4lfa1oAHigldnGNBdLpOiC8m0Ly+ovZYaxmbnMGzxk2YXfxqPMbP2BSxn3jJXfFkJi36XAkbAM91sPYCHzAxdev3x6+hqhmXFLIeC4+bue4NvwoPGhb/4Q+29LpGohSKbrUX5GhtI2qiinBSc5gI6uswVLwvZof9REReONHwjF4o1UVsbMpFR92n7mI9WEkV6wua6eVH+tp3P+jUafKTOngtv9ty25QQ90vge5hkRYQNXac7wb4Q9kDkMyMXwFElnR1DCRko5XrD0ZR7CeV05/pZz5xELqx/8QprizBIGFcmAADV0j2FPXJ2jHL51CmhVFjDDDYQTJ6MtyMW/7Kb6c6OmIaoyAjbCLvmV2Arre9jqFrQhLOit8Q7XReuubl+3Ib1FdXovYhePPwanAhPw1nk5W6NA4gBnos/bfA7gkjYo7sC37URXH4cLtNx/JZXwmF6fI5Rc1BjlU0/TM0dwmMzb8Bt65/D8qXBFvQi6+VOkXYU+HibzPLC3AImWRGeaMiKOjTNDneBEZZIcYV2fw6PprPSkos2xEKqus6LAHymYkYMwbhI5nHEfnKgUtBTn3k/Hvr7DwIAvvmffgYPfPg3oDMvTpUB/Pt0BTPQ15/DxsqVgY6zvnwJ/397Zx4f11ne++8z+4ykGUmWZFne99hxEtsxjpOQm5XcJASSFMKWJqFbIC0k5aaFlrUQuJReLp9SILRQbsvSspQQlhZIC2VN4hgDWewszmbHie1YlrWONItmnvvHe0YaSaPRyJrtSO/389FHmnPOzPnNq5nnvOd5n8UjiidqDXvZyTi1u30lfKHdTk9e0aaR+CBLmORrnkQm3EqzDox9aHuPmS9NIC8yQf1hwiSLfrB1pJeUesf8o5nEAMccX38ozyUSaF9DSn3E2qbPwsvN2Cc3zgBTOTJZwLBnveMz9kDv0xzzjBcwg4kL56UY9s6zrwag+ZAJqQsVMGK97TscvZHcCwOQTiVo6H2cw74V+PwBBpx8B/UGxpLk8mvdlMryV74LD8qz3/3YrJ6XGInz4KduJn3iWee9FC4+lnNVTm5I440tNf1vj5rPRq7SZ3gGV0xD1MSjZwt01CpEYwFfdC77NDpktHd72ujMmruHFzoupoVBnn9qdq6SF599nDP3vpvobz4LwIre3URf/DlBTUypJ9UdWsXpA7+g5TOn8at7Pjmr88B4r9NgS/VCHWGBGPZciVdfcP4b9sFAO40p88E/8owxrIHFp017vEQWEZBRhpwaLENOZEIu6xQAfxifZEkXSeX2jvQwIE00OBULNTlE//Nm0al15bhLZOvlN9F3y16a26afweRmlNnUVL9+YDQ+tmaSj0lAMvqahw/SHZyYDOLJM+a+ElxyK0/bQT8NrE+Z2/DJ2boA666+g93r7xhbGM5loI6mEnQln+Fkk7mg5UI81RdGnMJZ3lnO2AG6Vp/GQ7FLOPPYt2aVjXrw0fs5p+fbrDx8DzB9HXi/8/2Y3PIu3DYpSSk1TEp9MwYNRBqiJtO4RMMey5yc4otubjcThaXp50mpj37fIjyiALSddyMAR/d+t6TXz/HS3X9OQEYJZ5wm5TpILN1NgNSUipXxjm34yNBPA41PfBOg5IQrgPgJp9dpW+UqORZiQRh2dQx7Lk16PpPvD+09nDOsm6c93tNoQjVz9WLGsuQ6xg1jLqFlpEDCUI7QyHF6vW34/AFTNCw5yGj3AdLqpWv1pvHzeb10LF097esABMYqSk6dsQezwxPa4uVQb4ggybFM15HYRN99/vqKf4YwvZzOg+HTTb11IFJgxt7etYpdN7x/bGFYHMPee/RZFtFPtsO4gnL5DuoLjvn6J0fZlIp/6+uJSJIjB4o2KJtAot98Hrr0+LTvBcDnfD9GJ7W8i3aYWPa4k6TkGR0e61BWDI/XS5wQlNBFadwXPXE9qKVjqWkWLyMMSgNJZywT6mftGedy0LOCpkM/mvH1c+y/7z/YHv8FQxomqoMkE8NEJElbtsepozPxvZ99w50M3/4kj624gdNS+/ntf36F4TuX8cAX/qwk10wu0qelo3pZp7BADHvOn+mf5z52gEyj8YemU0nSLx0gq8KSNVumPT4YM4Z96KSJfc/0GwPfunjcL5+bZSaHp/+CNqW6GQqa2VZcIkhqkGDfMxz1do61AyuVsVv8AjP2cHaY0UKGPdRMow5z+KmHCUsKaZsY3pm/YOoPlraIPrz4bMAYkVJCWj2OURh6xpQSaFq5zbwNv+P68IXGFih9RUr2FiPX1T6/AfVMpAfHFz6TRd6L37mrGZ1ULK81tyjvJClJenispspMxKWhYKvEyYz7opdM2O71+TjpNGUZlgbSTu7DSU8L4vFwtPNiNiYfpf/kzH2TM6OjhP77PRylnX1Lfoco8bGcj5CkCUkanVSKOBAMEWttp+vcN+AR5fT7biNIinMPf57dn3/7jOfMDhw1kT5VzDqFBWLYo2t38ZRvPbFF00eHzBe8sS48ovQce55A79Mc9XQQCk8/Qw03G5fISL+ZzXnixzhJlECe8fM6F8TkyPRf0JbsCVLObfSIRPCm47SOHKQntHLW78HvDzCqnoIz9gjDE7on5Qit3olXlCO/+CIATcsm3qXkr6+UeoFvWm/K2s5UiTFHLgM18JIJSVy2aSfAWIct8YfGkoOCkVPrupXzj48O9/Pk3v/m2Q+dRbxY71ogMzTuthkpMtMeq+g4ybA3xVoZ0jA40VbeTIKklHZxTHgi+NIzz9jHsk4LVEDs9xpf/Yi3kYwzloPOtpbt1+CTLE/df8+M59h7zydZm3mOIzvfA1FznhOHn5x40DQRUys3nc1BzwoCkuGxCz7D3uhlbDvy9Rn7Dnvjx+mV2JQeCZWmEs2sQyKyR0QeFpH9IvLBcp9jtmw+90rWv3fvWN3u+UxwkZlp9710yFRVnMGwNrYYw54aMIZ9cm9GAH+jeTzYPbX3JZiaLs0MkW1yQtM8YQLpfroyR0jEpl+4nQ7xeEySzKTCY5rNEpnUFi/HyjNNXPnaF42/NZfpmsOX978v1SW3+swLGFXPjJUYc+QMe1f8MY7RRqzV3A1lndh98YfZeP61PLD6baw547ySXnMyuRo5mcQgvU/tZk32IN0vzFDPJC+zOMH07yUQNuOSKVDeusc7vijvzYyQ8pRq2Bvwl9Bso1DWaY6hgBnHhK8JdaKqhgPmM7lh20X0EEOe/MGU5/3m3i/T85J53f7eE2zY/7c85t/C9ituxud8poeOHpjwnGJlR/rOezcPrP4Ttl72RqIX3UZI0jzxoy8WfV+BZM/YhamaVGLGngQuUdWzgK3AFSKyqwLnsRSgqd348uLHDxpfc3RN0eOjzl1M1rldn9ybEWDFGaa2ff+Bwll+PU4kjc8p8Zr0NrA8cYCAZPB2FM54nYlCNeATI3F8koXgVP90S/sSDksXHZyknwZa2yfO/Px5M/ZSDXtDUzMHfatJlmrYHT9+Jyc4Fh6/oGnEGBGPP0xDUzPn3vyRU57BRZwaOdnEAJow+QcjAzPMGhMnGcC56ypikHN3MoUa0vQHOmh0Oin5MiNjVSpnIuVrnNJRqxBJxxcd65gampsKG8M+6mtEws0Ttnm8Xp5pPp/1g7snpP4PD/Wz9f6388xXTT2Zx7/2XmI6SODqv0E8nrHJSubExPZ3niIRU1sveyPn3vy/AVi/9QIOeZbT9OS/FX1fkVQPcf88MOxqyF2i/c6Plvs8lsIsWrIKgPShPQV9zZNpaIyRVP/YrK5QllxL+xIOeZYTObqn4Gv0OYY91OqU+fU10sIAx2hnzTmvPqX3kZKppYLjTs0RT6iwf/pY7CwAjvpWTEiJh4k5DLO5c0tc/Ff0n/uuko7NLz880jq+YOx1OmxJGWoV5UopaGIQcQz75IYhk/Ene+n2dnJYuorOtEPhnGGfatwSocU0jxqXjj+TKNmwp30NhEpotlGsAmIuuWc0EMUbMYY92zD+GfVvfiVRhnlyz71j2/p7juERZXPvT3jm0d2cfewb7G29inVnGfdabq0i0H9wwrlmqjGfQzwejqy6jk3pxzj89PQ126OjJ0kGq1/9tSI+dhHxishDwHHgv1T1wUqcxzKVaEs7CfWz7rj5kMdWby16vHg89EkUb+KkE5nQR6ZxyZTjjrVsZ83IvoKhXiM9xj8adWZb/vNu5YE1txH9s1/T1jV7HztASgJ4RycWHssVk/JOY9izS00J2vxM1xwBp/5MrkNOqWx5+avZ+oo3lXSsN89wB5aOu4J8jWbGVqrRKHoOn49hDSKpQTxOtEl6qLhhD6f7GPY189K22+g9/eZpjwsEQqaSZOvU8cs0ddGmvaRTSfzZxJQF1ukYDTbTnO0jm8kUPa5YBcRcck82EMXvdCvzNI2vl20871Uk1M/QI98b2xbvM3egjTJC7O7XkyTAmtePx/83OKW2YyPmszui5ryz+R+tvfT3yajwwk8LlxXRbJZW7WM0Ut1yAlAhw66qGVXdCiwDdorIlLAMEblFRPaKyN7u7plXtC2lIR4PJzxttNHHI6EdbNx+8YzPGfQ2E0iepLf7CN4CkQkA3pXn0SQjPLd/6jV6tM8sqrU6dwtbXv5qzr3pTiKNp7ZACKZUsHdSqeDEWEGuwq/b4ZSgzbZO9esHnIXBFP4p+8pFfnx8+7qzx/4ONrU7+8uTRxGXCJ7U0Fjz8FzDkOloyPSTDLSw49W3svO66SM5xOMh9hf7edlr/teUfbkkpZ5jzxPQRMnVKWXZ2USJc3iGGjfFKiCOLaiGmgk5vYbzE34ijTGeiJzNiu6fjYUgjjghnhkV2uhj39o/oq1z3M3T1GIuDJ2jR8iocMRn3IizyU7vWLqa/eHtrH7hewUvXAN9PQRkFGmsfvXXikbFqGof8BPgigL7PqeqO1R1R3t7fZS9nS/0+9tJqp/W135yikuiEMP+ZsLp3qJZcsu2XgpA5nvv4LGPnM8LT+8b3zl4lLiGaIqVz5eY9oTwTaoomYzn+p0WNuyrTjubPWfdycYr/3jKvmCocIGrcpKrKz+iAZbmhZhueNll7F57Oxt2XVmW84x4InjTQ/jTxuOpI8WjYqI6QCZUWvvGYChSsMn22KL8sYOENDElcmY6us4wzb+P7fvJlH2HnvgN+z56If29J5wKiIUNe25BVcIxNmy/mL3b/5otF75mwjHJNZfTpS9x6ElToyfpNI/Z23YNT/g3s+11fznh+KZmM2NvlBEGpJHBgLlgzLbsSGrLG+ikm8d2f3/Kvj4n0scXq245AahMVEy7iAk8FZEw8Apg7v2lLCUjl7yHxy/4FMvWTR+/nk8q0EJjpr9oZELnivUc8ixjZfpZlqWfJfiVqzn0uEmSCQwfo6fM9aZHPSH8k2rAp50qgZObcecQj4ed191GS/vUO45caeAUFTTsTojoYf+qCYuj/kCQXTd+qGjY6WwwkSZxghmnqXRiesOeTiWJEicbntv/J7p4FWCSlIKaJOsvzQAuXbOZEzTjPfzAlH3HfvRptiQf4vlH7zNrO6HCLosla8/gKe86Wjeci8frZcerb53isllzvjH0R/d8C4CMY9jXXv9hTnvPA1PWVbw+31hryCFpIhkxM/hSktfy2XLJmxjUMCN7vjJl32CPuZMNtUz9PFaaSgRXLgG+KCJezIXjG6r67xU4j2UaNu+acoNUlNFQK9H+AQ47WafTZck1v+2npL1eTh55loavv5bo16/l6eu+SkPiOAP+8t51jXpD+PNKxYIJ8YOpBblKweP1klQ/aU/lZ+x90Y0zHDk3Ut4IgUycsGPYfcnpDXv/yeO0AZ6GuRn21iXG754+edjUDSqwwFoI8Xh4vvEslg1MdMWMplOsO2EyRoePPk6L9o+1LJxMY7SF9e8rnmnb3rWKA74NtB7+MTBelC7WOr1/e1AaiWqcYW8T2cYl0AP+WYZEhyKNPNJ6KVtO/hfxwb4Jtf0TvSbZr2lRdZOToDJRMY+o6jZVPVNVt6jqh8p9Dkt50cgiGmWEbO/BollysdZ2mmKtrNq0g+Tv/jtJgnTccz1d6UOMTDPbOlWy3hABnThjz1UJLFS3pRSS4p9S4KqcNERbTBLLpqsqdg4wbfWCmWHCTg9Xf3r6LNShXidEcY4N16OxVrNo23fIhJzOYpExtXQXnZzg6KHxZKAndv+ARTjdqV7Yg1cUKbC2Mxt6ll7C+vSTnDh2GM+ICfEstlAe95oJQsIXJdh5Gmn10tgy+yTGpl03EZEkj/144qx9dMCMfXP7PDDsFvfhaTCz7YbeJ+iRlpJirJet2wK//wP6pZkmGWG0oby3m5kCNeBzVQIbo6X5iyeTIlDhGXuQVe9/tOQomlNl1N9IKBunUZ266KPTZ3bGnVIRwejc7qjE4+GEt42GQVNlUUp0xQC0bTbJYy8++rOxbcO/+QZxDfGCLGHFoKmpHmie22eoY8d1eER59r678Sb7GJTiZRsSTt2ZdCDG1stvovvN909YYC2V0172Cl6QTiKPfWPCdh08Tkq9NWmdaQ27hUDUzOa6Ek/T7yv9lr1zxXrCb7mX30bOI3ZmeWepWqi5R2qIUfWccgZxGj+jFVw8rRZZfyMxHSAkpr9qrkphIZJORnGkee7lNPr9i+lKOoZ9Fo1Clm/cxqh6SB8zlTJTyQQbe3/C47EL6A6voQPjNmlom71RzWfN6TvpIQaHHySQ6h2bkU9HroZPJtiMx+ula/X0VVCLIR4Ph5dfw+mphyd0uPINH+ektJQUwFBurGG3EIyZL30bfWPp26XS1rmCbe/8waz9+jOhvhAhJpYJ9iQHiUv4lL8oKU+QUW/lXDHVIhtsIiLjF72G7PSGPe0sIja1zt2wD6++nFbMXZN3FtEjwVCEo55Ogr2mWcbj932bGHH8W19LMrpq7LhYx9xK24rHw3H/MhqGDxNO9zPiK27YM06ph2yJEUPFWH/V24lriJ7vvGdsWzB5gkHf3F/7VLCG3UJDy3g4VqrKvRmnxR/GL5kJaeKe9BDDnHqSz5CvhVSg+undZSevpMJxWmnS6TM7s3Fj2KNFFhFLZdMVtzCs5sLomWU9+RPhVSwaOQhA+qFv0k8Dm86/FllkSl6YrNO51ywfiiynPfUiDdkB0oHiazHZsJMRHJm78W3rXM4jq3+PbfFf8tjuHwLQkO4h7i9vtFipWMNuIbZo3LBnG6sfc1sIcRbnEiPjRsuXHiLhOfWQwc4/+Brr3/zZOWurNflG9aS/k6CkSUxTK19GTjKk4bIUwIs2L2Jfq+nhOtt470TzOroyRxjsP8mm/l/wZMvFBIIhGjpN28Reic66vHMhRptX0cFJWrO9jM4wExfHsPsaynOx33r9e+ijkfgD/wRALNNLKlT9cgJgDbsFU4YgowKMNw+uNWM14PNKBfszcZIFuieVyqLFy8YqLroZb16TjqGwycoc7JvaUWmw/ySLT+yh11M+d0DH5e/gkGcZHWvOnNXzfB2n4ZcM++75GxokQWT76wBYtML4tafLOp0t/nbTYCUkaTRc3GB7HYOeKwg2V8INTbwYWE3T8POkkomiIZyVxhp2Cx6vl34ngiDYWv3QrELkanYkh8dn7IHMMClveZJ83Ex+SYV01Knm2T/RsI/EBzn8mVezPPM8Pee/v2znXrVpByvfv5/OFetnPjiP2AqTLLf54Jc4QTObzn0lAB1L15JSL0NlcllEu8ZzCDyR4oY90r7KPGfxqdUzKkQ8spy29BGOPvcYXlG87bMvW10OrGG3ADDgMcaicY6RCeUitziXSozP2EPZOGmfNez5JRW8LcYo5ZfuTSUTHPjUdZyW3MdDL/sYWy97Y9U1TqZrnZnhx4jzTNulYyG1Xp+P/Q3nMLxkZ1nOs3jleGTLTLH7m3ddwXOv+xGrTz+nLOcGyDavpo0+ug+Ymkqx5dO3pawk1W3rYalb4r5mSB2mZXG9GHZjwNOJ8Rl7KDtCxm8Ne6Bh3BUT6TAZobnSvaPpFPv+7nq2J37FnjM+wM6rb6mJxsk0Rls4RhudnCD2sjdM2LftnVObZJwqsUWL6aeBGHGCMxh28XhYvfllZTs3gL9jLRwEnjZZtZ2TGr5UCztjtwCQDLSQUi/Ni+pj8dSXM+x5i6cNOkw2cGq9QucTuS5KWRViS0xUSXroJNlMht9++ka2x3/O7vV3sPO1U6s01pKXwms5Sjsbdlxa0fMc95myweaKAAAQIUlEQVR1h3Cs+usp0S7T/2DdwG5O0Ey0uTZRMXbGbgEgtWQHT4wOc2aByn61wB/OzdhNjHZmdJQGSaDWsI+VVIgTItpiwhgzwyf51Wf/kHP6f8gDK27h3BvK51cvF4vfdBfp5EjB6pHlZCC8DAafoqGl+qG7HStNg5VmhtgfOIPaxMRYw25x2HXDB2otYQKLlppFp8RRUxg0PtRPFJBpmmwsJHIlFeLSSHvMzAjXH/g8i+hn9+I3suvNHyv29JrRubw6C4mp5rWMDvysJnefsZY2+mikmSGGmoq3pawk1hVjqUsWLV7GMdrxv2RStEeGTAVDT9Aa9mAoQlq9jHga8Pp8nKCZBh1m96o/4Zy33FWTFPZ6YvO17+SJy/6ZUGR2SVTlIucK0gINX6qFnbFb6pYjDZtYPGTqiyQGjWHPj+FeqIjHw7CEGPEawzX0+rsZbWxmV5VmxPVObNFiYhdcU7PzD4SXw+ABwktOrfZMObCG3VK3JBdvZdmzP6e/5yVGnO5Jvmm6Jy00homQ8hnDvmrTjhqrseSTjq2EQWhbVZuIGLCG3VLHNK3eAc/C8/vuAzGZsYGInbEDHOq8fKzOiqW+WHbhm3ngp8o5KzfUTIM17Ja6ZfmW8+HHMPTcHoKd5rY22HBqTTbmG7veeletJVimYfn6s1i+/m9rqqESPU+Xi8hPROQxEdkvIreX+xyWhUGspY3D0kXo+COMOt2TQqfYPcliWUhUYvl8FLhDVTcDu4A/EZHa5NVaXE9PeCXR5BGyjmGPTNPI2mKxjFOJnqdHVfU3zt+DwONAfVSWsriOVHARsUwvmjSJSpEmO2O3WGaiogGvIrIK2AY8WGDfLSKyV0T2dnd3V1KGxcVkIu006wCS6CehfgLBUK0lWSx1T8UMu4g0AncDf6qqU9qoq+rnVHWHqu5ob3d/jWxLZZDGdnySJRB/kbjMvVmExbIQqIhhFxE/xqj/i6p+qxLnsCwM/FHTqCA2cpgRCddYjcXiDioRFSPAF4DHVfUT5X59y8Ii2GwM+5LRI3Nqi2exLCQqMWM/H7gRuEREHnJ+rqrAeSwLgKZWU3cjIsk5tcWzWBYSZU9QUtVfAlLu17UsTGLt4wFVadsWz2IpiYVdBs5S90Rb2hlV8zEd9demWp/F4jasYbfUNR6vl14xSUkZa9gtlpKwht1S9wx4TWOJbMAadoulFKxht9Q9cb8x7FjDbrGUhDXslronGTTt3yRkS/ZaLKVgDbul7smEjGH3WMNusZSENeyW+qfRdJv3hW2/U4ulFKxht9Q93iaTfeq3bfEslpKwht1S98RWbCGjQvMS2wrOYikF2xrPUvds2H4h/aueYnmrrQJqsZSCnbFbXEHMGnWLpWSsYbdYLJZ5hjXsFovFMs+wht1isVjmGdawWywWyzzDGnaLxWKZZ1jDbrFYLPMMUdVaa0BEuoFDp/j0NuBEGeVUA7dpdptesJorhRs0TsZtmmejd6WqTokFrgvDPhdEZK+q7qi1jtngNs1u0wtWc6Vwg8bJuE1zOfRaV4zFYrHMM6xht1gslnnGfDDsn6u1gFPAbZrdphes5krhBo2TcZvmOet1vY/dYrFYLBOZDzN2i8ViseRhDbvFYrHMM1xh2EVEaq1hvmPHuDq4YZzdoHEybtRcSVxh2AFv7g/7D6wYdlyrgxvGeawBj4u+b80AIuKK5kEislFEKmZ/69qwi8grReRHwCdE5H8AaJ2v9orItSJyZ611lIqIXCUi3wH+j4hcVGs9peC2MQZ3jLOIXCEi9wIfF5HrwBXft5ij+YcAqjpaY0lFEZFXiMiDwB9SQftbt4ZdRFYBHwE+BTwO3CIif+jsqzvdIuJx9H0c+AsRuaDWmoohIn4R+b/AXwF/D/QDbxSRc2oqbBrE4HXZGIuIBOp5nPM0fhx4H/AZ4EngehFZX1t1JTEC9AFbROR6ABHxFn9KdXHG2C8iHwLuAj6mqn+euwhV4q6o7gxkHmuBX6rqd4B/Av4ReLuItKhqtt5uEVU1CzwFbAP+GKjrGaWqpjFf4Deq6g8w49sMZGoqbBrUkAGexj1jrKqaAg4Ab6rHcc7T+EPgQlX9LnA/kAaeq6m4GXAMeAuwG3g9ZhKIqmbqyT44Y5wGssA3VfVbACJygYj4K3HOuoljF5HXAodV9UHn8Ubgp8AqVU062z4LJFT1HSIitb5NLKDZ7/wDEZFfAX+vql8QEY9j+GtKAb0BYBTwqWpKRL4PfFJV762lznxE5DbgDOBBVf3H/P97PY4xTNC8R1U/n3eHWTfjPHlc87ZfBXwaOA78HPi1qn69Tr5vOc0PAP+kqioiYeB7qnqZiPwn8EvgW6q6r9aa8/T+SlU/JyKdwF8DCuwADgK9wM+cz3D59KpqTX+ADuBnwBHg24Anb9+XgL/NfY+Bs4BvAovrUbOjMff3lcB+oKVex3jSWLcAPwY6a603T9ObMbOxKxz9fwmszdtfN2M8g+Z19TTOBTS+O6cR2AlsyBvfezGTq3ob13dj7uo7gA87x/w+ZqKy13nsryO973X+99cC/wKc5tiLa4D/AFaU8/w1d8Wo6nHgO5gBOAq8JW/3B4GrReR0NaOVAAaBoaoLzaOIZlHHTaTmtju3NtCU8//VgiJ682cHK4F+VT0mIstE5JIqyyzEpRh/5A+BO4AQcENuZz2NcR6FNL8pb/8qaj/OkzUGcMZVVfeo6gHnuMeBboyxrDWTNQeB6zE+9iud2fptwH8zXgK8lroL6X2Lqn4buEVVn3Bs2iOYNYJ0OU9eU8Oed4v6KeAx4D+BV4rIEgBVfQb4AnCXiLwc+F3MFbpmt9zFNDtG3cP4uL4L+CjG995ZdbHMqFfzwsOWAl4ReTtmBlETvTBB82+BqwFUdS/mFnypiJyfd3jNxxhK0pxb6K3ZOBfRuBvomjSuYGadEaCnWhonM8O4rgFeDvwXxu21VVUvBy4SkdWO4awXvfcBq0XkfFWN5z3lZiCMccmUjaoa9txqdW5hQx2fqKqm1awQ3w88Adyee46qfhRj3P8A2Aj8gaqO1KHm23L71SzerAU+i3F9bFfVT9Wp3tys5hXAq4B1wFWq+q/V0June+yzqOO+8vsAjzihrsA+zB1Hl/OcdZgog6qOcY5ZaD7CuAGv6jjPUmNuXG8SkX3AauDWan7fnPOXonk/8ALQBLxfVd+b9xIrVLVqC7+n+Nl9jYg8jLk43aqqiXJqqophF5HzReSLwHtFpDV3JRUTvpa/en0C+C6wwblN7XCiYL6EuY15naoeq1PNGx3NbSISdba/TVV/R1WP1LHexc72rwGXq+rtqvpipfU62nY6C0z5X4j8L8pTmC/w60XEq6ovAIsx7gwwoYNVG+M5aO7E+IMB7gZeUclxnoPG1c7+RzDugptV9aVKaCyD5sMYI7lSzYK0N3fspBlxvejNfXZzY3wAeKuq3lSJMa64YReRNZhZ1U8wftw7nZV3VDXjuAOCIhJ0Hv8cMyD7MIsObc6xqUprLZPmX2AWd/vzfJX1rPenIrJeVXer6o+qodfR/KfAPZgL0ZXONq+jOfdFGcSMZxCTNOPHLED1OMd1q+pTLtF83Dnu56r64zrVeMI57iFVvb9SGsuouZnxz0JGqxQVVaYxflRVH6iYSK386vAbgK85f7cCf4RxUSxxtn0I+DLOyjvwVsyX4GPUaFXbbZrdptfRcA0mHv01mHCvyfs/CPwbJnpgCfDPGF/wPwBeq9m9Gt2u2Q16K/GmXwW8DdjlPF6D8TetcB5vxsRyvgOz8PGvTAwHuyz/cZX+Ua7S7Da902j2Oj8h4PvAbc52Dyb291+ZGNroAZqsZvdpdLtmt+lVLaNhx1yZvoe5/XgfJlTqfzr7Pg7ckTcoNwIfAGJ5z6/FlddVmt2mtwTNuQS5S4GHgbYCz/dUS6ubNLtBo9s1u03vhHOXcRBeBbwz7/Fbgbudv6/B3I6c4zy+BPhxrQfAbZrdpncazW8B7pl0jAdzm/pB5/FO57dYze7V6HbNbtOb/zOnxVMnLOoiEQlisum+nLe7B7PyC/AgJq7zEyLSCJwOHBKRCExcVa40btPsNr0laD6JmfmMRRA42j4MvEtE+oHtItVNB3eDZjdodLtmt+mdjlnXLnZC5zoxfqQs8Axmse52VT0q4/VSlmBWgVETovhJEVkJ/D9M5MZNqjpcnrcxvzS7Te8cNOeKua3FFHq7D/hTVX3UanaPRrdrdpvekpjlrYnX+b0B+EpuGyar8VuTjvkecJnzd4fz20f1F2pcpdlteueouTWnHbjYanafRrdrdpveUn9KmrE7MZp3YlKhvw9EccqOqsmyvB04IiIXqurPxFQN7AYOiMhHMPVeLlLVXkx8Z8Vxm2a36S2j5ovV1LI5bjW7R6PbNbtN72yZ0ccuIhcCv8bcgjyNGYw0cLGI7IQxP9NfYeI3wYQBvRnjo2rCXOXKWgthPml2m94yaz5pNbtLo9s1u03vKVHCrcoFwI15j+8CbsW8yV872zwYH9U3gGWY0p9fArbW4jbEbZrdptdqXtga3a7ZbXpP6T2WMAgRTFpszs90A/BR5++HgLc7f+/AyX6s9Y/bNLtNr9W8sDW6XbPb9J7Kz4yuGFUdVtWkmrZkYKrTdTt//x6wSUT+Hfgq5vamIj38ZoPbNLtNL1jNC1njZNym2W16T4WSwx2dxQbFVCj7rrN5ENPJZAvwnDrV6tS53NUat2l2m16wmheyxsm4TbPb9M6G2SQoZQE/pjrZmc4V7X1AVlV/qVUq9TpL3KbZbXrBaq4UbtA4Gbdpdpve0pmN3wbYhRmMX2IaXtTclzTfNLtNr9W8sDW6XbPb9Jb6kytkUxIisgxTXOoTqpqc9VWkBrhNs9v0gtVcKdygcTJu0+w2vaUyK8NusVgslvqnps2sLRaLxVJ+rGG3WCyWeYY17BaLxTLPsIbdYrFY5hnWsFssFss8wxp2y4JDRDIi8pCI7BeRh0XkDnE64hR5zioReVO1NFosc8EadstCZERVt6rq6Zg6IVdiGn8XYxVgDbvFFdg4dsuCQ0SGVLUx7/Ea4FdAG6al4JeBBmf321T1fhHZDWwCngO+CPwd8NfARZhKgZ9R1X+o2puwWIpgDbtlwTHZsDvb+oCNmCJQWVVNiMh64KuqukNELgL+TFWvdo6/BdOO8MNiGh/fB1yvqs9V9c1YLAWYdTNri2We4wc+LSJbMa3SNkxz3OWYwlGvdR7HgPWYGb3FUlOsYbcseBxXTAbTu/IDwEvAWZg1qMR0T8M0ZLi3KiItlllgF08tCxoRaQf+Hvi0Gr9kDDiqpufljZiO9WBcNE15T70XuFVE/M7rbBCRBiyWOsDO2C0LkbCIPIRxu4xiFks/4ey7C7hbRG4CfgjEne2PABkReRj4Z+CTmEiZ3zjddbqBa6v1BiyWYtjFU4vFYplnWFeMxWKxzDOsYbdYLJZ5hjXsFovFMs+wht1isVjmGdawWywWyzzDGnaLxWKZZ1jDbrFYLPOM/w+YYPnOAwrDQwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "NFLX\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "                  Open        High         Low       Close   Adj Close   Volume\n",
            "Date                                                                           \n",
            "2020-10-01  506.029999  529.549988  503.600006  527.510010  527.510010  8153700\n",
            "2020-10-02  516.429993  526.369995  502.700012  503.059998  503.059998  6071200\n",
            "2020-10-05  506.799988  520.989990  501.700012  520.650024  520.650024  4088100\n",
            "2020-10-06  518.719971  521.820007  504.160004  505.869995  505.869995  4199000\n",
            "2020-10-07  518.000000  536.489990  515.820007  534.659973  534.659973  7988700\n",
            "...                ...         ...         ...         ...         ...      ...\n",
            "2021-04-22  513.820007  513.960022  500.549988  508.779999  508.779999  9061100\n",
            "2021-04-23  509.010010  509.700012  500.700012  505.549988  505.549988  7307700\n",
            "2021-04-26  506.760010  510.480011  503.000000  510.299988  510.299988  4388800\n",
            "2021-04-27  512.619995  512.989990  504.579987  505.549988  505.549988  3761300\n",
            "2021-04-28  505.200012  508.399994  503.339996  506.519989  506.519989  3193000\n",
            "\n",
            "[144 rows x 6 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GO++ujEyC4Qh+j0Tr2DVidwGtwQgZPq+VY1djH9HsqmwEYHqxZex5GX5AjX2kE52gZEfsOkHJBVgRu5U/620q5o/L9/PilopBVqYMNTsrbGOPi9jrA8GkaVKST1vYTsXo4Kl7CATDpPusVExv1zy99/U9PPrugUFWpgw1u6oayc/0U5STBliT1kCNfaQTCIbJtNO1oKkYVxAIRkj3e0j3eWgL9a67Y3NbiONNbYOsTBlqdtkVMfYSv+0Re4umYkYyVuWcDp66Cqfc0e8Vgr2M2BtbQ9Q0q7EPN3ZXNkbz6xCbY9eIfaQSDEcIRQyZfm97KsYNxi4iD4hIpYhs6mK7iMgvRWSXiGwQkYUDIzM1aA1FEqqKCYUjBIKRfkXs97+5l7+tO9zn5ysDT01TG9VNbdH8OrQbe70Ono5YnF5SGX5vtComWeWviUbsDwIXd7P9EmCG/XMjcHffZKUmgWCYDHvmaShielzjsqnN+kc3BEJ9zrXd98YeHlmhOfpUYldVx4FTgJxouaNG7COVllhjd1MTMGPM68DxbnZZCvzBWCwHCkRkXH8EphLtqRjrsPU0+7SptT1660s6pi0U4Wh9gMM1LQk/Vxk8oqWOMcbu9Qg56T7NsY9gWu1eUrEeMVwW2igFDsbcP2Q/dgIicqOIrBKRVVVVVQMsY+CJRAwNgRC5GT7Sejni3cHYmxKP5MrrWjDGmgyjMxpTh12VjWT6vZQWZHZ4PC/Dp1UxI5j2iN0zcnvFGGPuNcYsMsYsKi4uTpaMXnOsqZVQxFCSn0Gaz47Ye6hlb4wx9r7k2Q/ZkXo4YqhoaE34+crgsKuykanF2XjsL69DboZfUzEjGCfHnun34ndy7G4YPO0Fh4EJMffL7MdSnvUHa3ljZ9dXDpX1lrGOzcuIqVHtIcfe2l4S2ZdUzKGa5uhtTcekDrtimn/FkpepqZiRTEtbZzn24ZGKeRq41q6OWQLUGWPKB/g9BoVfvbKT257Z0uX2o3UBwDH23i171dTWvxz7oRgzjzV5JXk0t4U4XNvSodTRITfDT0OrRuwjlUCoPcee7Dp2XyI7i8ijwPlAkYgcAr4D+AGMMfcAzwGXAruAZuDTAyl2MKlpDlLf0vWX8mi9ZewleRnsr24CrPLH7uiYY++bsRflpHGssU0j9hRhT5X1v+80Ys/wsatSI/aRSnvE7kl6E7CEjN0Yc00P2w3wn/1SlCTqWoLdNnCqrA/gESjKSevT4OnxPgyeHqppZlpxDtDI4Vo19lRgZ2UD0Lmxa459ZNMaas+xO8slJqu9t848talrCdISDHdp1kfrAxTlpOPzeqKDpz0Ze6OdYy/KSetzKqasMIvSwiw19hRhV2UjXo8waXT2CdvyMn3UB0IYXXBjRBI7QQlgXH5G0r63auw2dXYaprGLqL2ivpWS/AyA9jr2XqRiPALjCzITropxatjLCjMpK8jUVEyKsKuykUmjs6In91jyMvyEIyZa9hbPr17eyW9f2z3YEpUk4aRiMm1jn1KUzd5jTUnRosaOdaZ1TLqrdExFfYCxeXHG3mPEHiI7zceo7MQjdqeGvawwk9LCTA7VtvQ401UZfHbF9YiJJddpK9BFZcyzG8t5dqMragmUPhA7eArtxp6MKzjXG/tAmF3soGlXE0yO1gcYm5cOEJOK6ancMUR2uo9RWWkJR+xORUxZYRalBZm0hSIca9Ja9mQSDEfYX93caX4drFQMdN1WoK4lSJXORxi2OBF7uu0Pk0dn0xAIUZ2E7q6uNvZvPbWJi372Wr9fpy7G2DuL2APBMLXNQUrsiD2tt6mYthDZ6V4KstISropxyhvLCjOjVwpOLb2SHPZXNxGKGGaM7SFi78LYa5stY9crr+FJIBQm3eeJTlybUmyNwyQjHeNqY89O93HweHO3X5QbHlrJr1/d1e3rdDT2E7+UsZOTAPy+Xtaxt4bJSfcxKttPU1s4OrjSGw7VtOD1COPyMyjOta4UqhrV2JNJ+3J4uZ1uz4uuotR5cNASDBOKGGq7KatV3EugLRxNwwBMLVJj7xNlhZkEw4bKbi5vV+6rYdW+7vqWdTT2zr6UTg372LiIvTfljtnpPgqzrVV2ErkkO1TTQkleBj6vh+Icy9iP6WV8UnGMfdqYEytiIDbHfqJxxz5W2RAYBHVKsgkEI9GBU4DSgkx8HlFjT5TSQqsJU1ezMiMRQ30gSEUPKYyeIvbyOivfHV8V09MEpUbb2GeNtSK8TYfrut0/lkM1zZTZf19RrnViONaoC3Ykk12VjZQWZJKV1vn0DyfHvrvqxAGz2Chd8+zDkxZ79SQHn9fDxNFZ7FNjT4wJtvF1VSvaEAhhDN1G9NB9jj0SMTz49j4Ks/xMHJUF0Os69qa2EDnpPuaV5pPm9bBmf033f1AMTg07QFaaj6w0rxpCktlV1ci0LgZOAQqz0igrzOSXL+/kkl+8wWOrD0UnrdQ2q7EPd5y23rFMGZ2ckkdXG3tpgWV8h7qo8XYMu7qptdtmPM5+aT7PCRH7Iyv2s/ZALd+6bE70nxZNxfRYxx4mK81Lht/LvNI8VvfS2GNr2B2KctI5pjn2pBGJGHZXNnVZ6gjWldxL//Ue/u+KU4gYw3//dT3n/OhVnll/hNqYcteeAg3FnbR0YuxTiy1jH+oZqK429sw0L6Oz07pMxdS2WF8mY7pPY9S1BMlN95Gf6e8QsZfXtfCj57dzzvQi/v3U9rbyfl/v69hz0q3L89MmFbLhcF00guuO2Bp2h+JcNfZkcqSuhZZguMtSR4cMv5ePnD6BF754Hg9fv5j8TD8/fmG7pmJGAK1xOXaARZNH0RqKsO5g7ZBqcbWxg2V+PUXs0P2AVV1LkLxMP7kZvg7G/p2/bSYUifD9f58XXY0erDpVka5nqYKVpmkLRciOMfa2UITNR+p7/Jtia9gdrGZgagjJYmcnqyZ1h4hw7oxi3jdnLOV1LdFy16KcNI3YhynxOXaAJVNH4xF4c9exIdUyDIw9q8vp9rF5ze5qwOujxu6P1iA/v+ko/9xSwRcvmnlCXxC/18PY3AwO13Z9snAagDnGvnBSIUCv8uyxNewOVipGB0+Txe4Ejd1hfIFVubWzshGfR5hSlE2VVsUMSzrLsedn+jm5rIC31NgTo7Qwk8O1LZ1O242N2Ct6iNjzM33k2RF7fSDId57exOxxeVx/zpROn1NWmMnh2q57pDsLWeekW//oMbkZTByV1as8e2wNu0NRTjo1zW1JawM60tlV2cio7DRG2aWrvaW0wPofbjlST0GWnzG5GRqxD1NaguETUjEA50wfzbqDtUPa+dP1xl5WmElrKNLp5J0OqZhuInbL2J1UTJCfvbiDyoZWfvjhk6OljfGUdpMCghMjdrDSMav21/TYOyK2ht2hKDcdY/q2xJ7Sf7rrEdMd4+11UXdWNpCf6ac4N11z7MOUQDBCeifGfvb0IsIRw7t7u59PM5C43tidBYU7M9m6liDpPg+js7vPa9a3hMjP9JOXYQ2ePr/pKJfMK2H+hIIun1NWmEl5XcdFpg/XtnDtA+9y8HhzdL3TWGNfOKmQqobWbk8I1t/S3CENA0QnKakpDD3GmB5LHbvC+XwGw4aCrDSKc9NpCIQSmoWsuINAFxH7womFZPg9Q5pnd72xO7Xl2482nLCtrjloXf7mZVBZ31MqxorYjzW2Ul4X4LRJo7p937LCLMIRE52VCtZg6+s7qli9vyYasefERuwTrTx7T+mY2Bp2h+LoJCU19sHmtR1V3P2v9va61U1t1DYHmdEHY8/NsD5XAAV2xA56gh6OBDoZPAWrUur0yaOGNM/uemOfPiaHqUXZPLHm0AnbalvayM/0MyY3vcuIvS0UoSUYto3dj9N2Zn5ZfrfvW1bY8UrhxS0VvLS1ArBa/Dopk8Isf/Q5s0pyyUn3dWvsndWwg5VjB3fPPn137/HotPxU5tEVB/jJP7dHB9LbWwkkbuzQHrXnZ/kZn2/dPqhr2A4rguEIoYjpNGIHKx2zo6Kx2wBzIHG9sYsIHz19Aiv31ZxgGnUtQQoy02xj7/yAOgbsROwAXo8wd3xPxt4+Oaq5LcR3n97MrLG5ZKV5qai3on6Akvx2g/Z6hFMnFnRr7J3VsEOssbsz0guGI9zw0Ep+8sL2ZEvpkSN1LYQjhnd2VwPtTZymFXfeI6YnnDx7QWZatM+Ms3aq0nsOVDez+Ujv23IMJfGrJ8VzzvQiAN7aPTRRe8LGLiIXi8h2EdklIl/rZPunRKRKRNbZPzcMjNSu+fDCMnwe4a+rDnZ4vK4lRF6mnzF51oBVOK4L5MHjzXz6wZWIwJzx+dEmTjPH5pKZ1vk/yGG8Xe1wqKaZX768i8O1Ldzx7/MoycugoiHA0boAuem+DqkYsPJt247WR3PwDoFgmJqmtk5r2MHK1Wf6vRyt698Zv7qxlafXH+nXa/SFlfuOUx8IcaRucFeCWr2/hua27heU7mnw+ojdosK5dN57rIk0nycabSeK81kpyPJTkpdBVprXFVcu3dHUOvTjBHc8u4WbH1kzpO/ZWwJBZ5GNzi11zrg8CrL8vLWrekj0JGTsIuIFfg1cAswBrhGROZ3s+mdjzAL7574B0NktxbnpXDh7DI+vOdRh6m5ds5WKKSvMImJg29H2yUFv7Kzig3e9yeGaZn7/qdM5bVJhNGLvKQ0DkO7zMjYvndd3VHHfG3v4yKIyTp88yjqJ1LdytC4QbRoWy2mTCokYWHegfSbawePNXPzz1/nove90WsPucMbUUTy2+lC0KVmiGGO4ddk6bnl0bZ9fo6+8vLUSIHolMxhUN7Zyxd1vc/W9y7usHlqxp5p533mhyyu4QDAcTXe9udMy9j1VjUwZnR3ts50oTuuLgiw/IsK04hx2V7nb2G94aBVX3P12dHGJoeDA8Wb2VzefEBSlAj1F7B6PcPa0It7adWxIVlRKNGJfDOwyxuwxxrQBy4ClAy8rca4+fSLHGtt4ZVtF9LG6Fmvw9OK5JaT7PPxx+QGMMdzz2m6ue+BdSvIyeOYL53D+rDEA7cbeTTVMLGWFWaw5UEtOho+vXTIbsOrVKxoClNd3buwLJhYg0j6A2tIW5qp73mFfdTM7KhpZvb/mhBp2h9s/NI9QJMI3n9yU2MGxWbbyYHRkft+x3uV4d1Q0sKefJmSM4WV7/OFYY+ug1eI7hrzhUB1X3vN2p83h1h+qpakt3OUMYOfEM3d8HnuONXG4toU9x5qY2sc0DLRH7PmZ1hXhtOLsHlMxdc1BFt3xIm8P8cSW3rKzspHNR+q5ddlafv3qroQ6l/YV50pqZ8WJhRLJpidjByvPXl4XYM8QNAVL1NhLgdh8xyH7sXiuEJENIvKYiEzos7oEOG9mMSV5GSxbackLhiM0tVmDooXZaVy+oJQn1x7i5kfW8MN/bOOSk8fxxM1ndZhVekpZAVeeVsb754zt1Xs6g2LfuGR2dOLK2Lx0KuoDlNe2dGrOeRl+Zo3NZfUBy9i3lNdxtD7ATedNBeAfm46eUMPuMHF0FjedN42Xt1UmnGs/XNvC95/dGm0hfOB47z5cX/rzOr75VN9OJA67q5rYV93M3PF5veq22VecdWVvvXAGVfWtXHn32+yq7GgCR+zZwru7SIU4s5g/err1sX1lawUHqpuZUtR3Y58xxjrmE+wKrmnFORyubek2ZbSjsoFjjW1sHALDTJTWUJhjja2UFmTyzy0V/PiF7XzmwZWDOseisTUUXSth+9EG/rLqIE+tPTxo75cozgLmXQ2eQkyefQhO1oMxePoMMNkYcwrwIvBQZzuJyI0iskpEVlVVVfX7Tb0e4apFZby2o4ojtS3RyUkFdlXKdWdNJhCM8MLmo3z9kpO465pTT+irnZPu4ydXzWe0PVDZE0sXjOcTSyZy5Wll0cfG5mUQCEaobGiNLqUXz2mTClm7v4ZIxLC13DKeqxdPJN3noSEQ6jQN43ByqZUm6qkWPhZjDF97fAMRY/jtJ0/D5xH2V/ccsYfCEXZWNPZq3+5wqoU+fsYkAI4OUhrI6aD4/rlj+fNNZxIMG666550ODZicKH53FxGzExWeP3MMpQWZPPTOfkIR0y9jnzM+jzf+5wIW2uWuTnVNd1H7wePWMU/FsrF8g7gAACAASURBVEhnst+tF85g+dcv5Imbz6K2OchXH9/QaZohEjEse/dAv2ZelsdcfW0pr+f7z27lrh5WRhtK2nPsXRv7xNFZlBVmRlN8g0mixn4YiI3Ay+zHohhjqo0xzqfxPuC0zl7IGHOvMWaRMWZRcXFxgjI65yOLJmAMPLb6UNTYncvfOePz+N7l83jkhiXc9J5pHZp69ZULZ4/ljstP7pB7HRNj5iVdDLadNqmQhtYQOysb2X60gdx0H5NHZzG/zEoBxQ+cxlI2qvvFRTrjzysP8sbOY3z9kpOYXJTNhFFZvTLrfdXNtIUjlNe19Ct98vLWCuaMy2PhJOvvi8+z765qHJC8aY3dG6gwK4054/N4/HNnkpPh42O/W84bO63gwTHurtJLh2tbELEWVblo9pjoIOfUPsw6jcWJ1sGK2IFu8+wHj1s6U3E5ROcYjivIoCQ/g4UTC/mfi2fx4paK6BVzLCv2HudrT2zkwbf29f097c9MmtfDk2sPU9cSZO+xpl51Sx0KohF7WveWes70It7ZU31CIcdAk6ixrwRmiMgUEUkDrgaejt1BRMbF3P0QsLV/EnvPhFFZnDO9iD+vPBjtppeX2V5H/sklkzhz2uhB1TAmtz3a7ywVA5axg1Upsu1oPbNKchGRaKOw7iL27mbadsbh2hbueHYrS6aOikbME0dlsb8XqZgddi4zYqC8m4ZnraFwlx/UmqY2Vu+v4aLZYxiXZ2mPrexpCAS55BdvcNkv3+h0klkiOKmYwiwrLTZpdDaPf/YsJo7K4jMPrmTvsaboSaW7iH1sbgZpPg8Xzm5PyU3tR8Qez6TRWXikaw3QXueeiuWtzqS82M/3Z86ewjnTi7j9mS0nnDSdk+pT6w73auBwzYEafv7Sjg6fKedksnjKqGgH1nDEDFh1USRi+lVx5uTY033dV9OdPb2IhkCIDYcGt41vQsZujAkBnwdewDLsvxhjNovI7SLyIXu3W0Rks4isB24BPjWQgnviI6dP4HBtC4+sOABYs/2GkrEdIvbOjX3iqCwmjsri+U1H2Xa0gVklVg72tF4Ye26Gn4Isf68j9v99disRY/jxlfOjVxaTRlsRe09fsh0xg1RdTaj527rDLLjtRU757gtcc+9yfvzCNupiumq+ur2SiIGL5owlL/PEks0tR+ppC0U4Uhfg8l+/xTP9KMWsaw6S5vN0KDkbk5fBbz6+kGDY8Oq2So43tVGY5edYY2sHnQ6Ha1uig51nTB1FdpqXgix/dN3agSDD72XGmFz+uuogB7q4chrqVEwwHIl+pp5ef4TrHni3y6u0zuZoeDzCTz8yn3S/h1uXretQnfbGzmN4PcLuqia2lHfftnr70Qaue+Bdfv7STn71ys7o40dqW/AInDvDylM7J9r+BgMOL2w+yjk/eiV6AkkU5wQTX94cz7kzikjzegZ9fCDhHLsx5jljzExjzDRjzPftx75tjHnavv11Y8xcY8x8Y8wFxphtAy26O94/ZywFWX6eXHuYMbnpJ7TcHWx6E7GLCEsXjOfNXcdoCIQ4aVweYF2mXXfmJC44aUy379FZD/rWUDh6lRLL6v01XDyvpEMqYOKoLBoCoWjqoit2VjRGB4PiTyThiOEHz23l1mXrmFeax4cXltHYGuLXr+7mr6vbL8df2lrBmNx05o3PR8Sq9imPmX3nDA4+efNZzB2fxxceXcsdf9/S7YpX8TyyYj97jzVR02yZdnyabUpRNrkZPl7cYuX6z7IHsXYfOzHaO1LbEp1QlO7z8uGFZZw7Y2BShbHc+dH5tATDXPO75VETj8X5/3Zn7PWBIP/cfHRA9Pzhnf2896evUdXQyl9XHeS1HVU8tvrE2dxg5btzM06cozE2L4MffvgUNh6u42cv7QCsEtRNR+q49sxJ+DzCk2u6NjRjDDc9vIpMv5f3zxnLL17eGR1oPFIbYGxeRnSM6YZzp5Lm9fRo7JGI6VVJ5vaKBkIRw4ZDiQ9Wbz/awC9e3skFs4qZNLrrNCpAQVYal55cwhNrDkfbjgwGrp95Gk+G38td1yzk5x9dwBtfvSDhNqv9JTvdR266jwy/J5rf74ylC9qLiWbbEXtmmpfbls6LzjLtirKCrA7GHgiGufre5Zz3f6+y9kD7rNbWUJiKhkC0n47DZPtkt7+6+3TM9ooGzpw2Gq9HojlfsAYpP/X7d/nt63u49sxJPHLDEr53+Tye+cI5lBZkstau0W8LRXh9xzEunD0merUwNi+jQ8S++Ug9JXkZzB2fz5/+YwnXnTmJ+97cy5f/ur5bbQ4V9QH+35ObePTdA9Q0B6NpmFhEhHnj83l3n9Vd7zw76osfvIxEDEfqAtF0F8D3Lp/Hr645tVdaEmHu+Hz+eP0ZNLaGuPrednNvbA3RFrLGNfxeoaY52GnkHAiGuf7Bldz48OoBKf97Y2cVbaEIL22tYKV9nH758s5OJyGV1wW6DFounlfCRxdN4Lev7WZPVSNv7a7GGPjQ/PG8f+5Y7ntzL996alOnA6mHa1vYV93MF947nZ9fvYBpxTncumwtlfWB6Al3ydTR/OqaU7lqURnTxuSwrQdj/+bfNrHwey/y85d2dGvwTjXU1h6uKOIJBMPc8uha8jL8/Piq+b0au/vEkkk0tIYGdaLgsDN2gHNmFHH5qaU95rsGizF56ZTkZXT7T54+JodT7IlQM21j7y1WxG6lUowxfPOpTaw9UEtmmpdP3v8ud/x9C1uO1HO4xmpPMCFuMNaJKjYcqqOii94VbaEI+441MXtcLiV5GdFUzKGaZpb++i2W76nmhx8+mduXzosu7g1WB8s19sllxd5qGltDXBSTqx6Xn8GR2hZ++fJOVu47zsbDdcwrta5Y0nwebls6jysWlvHKtspeHQtnPsDhmhZqm9uiVVDxzCvNi+Zsz5gyGr9XTsjP/vb1PbSFIswYm9j/o6/MK83nkRvOoCEQ5JrfLee+N/aw8PYX+c7Tm4kYom0tqjvpD/SNJzeycp/1t/d32bVQOMIq+7V+/eouAsEInzprMuV1Ab779OYTxk8sY+86XfjfH5hFms/DT1/cwYNv7aUgy88pZQX8+Mr5fPrsyfxxxX7e/7PXO8w5AdhoR8unlBWQlebjNx9fSFNrmFuWreVgTTPjCzLxeIQPzh+P3+thdklutxH72gM1/GnFAUryM/j5Szu54Cf/4ok1h4h0Mh7kzIiOncQYz89e3ME19y7vkML84T+2sb2igZ9cdUqPAZnDaZMKOakklz+8s3/QJisNS2NPNrPH5TFnfF6P+33popl8+uzJ5GUkNg5QVphJIBihuqmNB9/ex2OrD3HrhTP42+fPZuGkQv7wzn7+6y/rOGhHIRPiIvYJo7IQge88vZnzf/wv9nUyYWLlvuOEIoaZY3OZMKo99fPQ2/s4UtvCshvP5OrFE0943sKJBZTXBSiva+GlLRVk+D2cbac+wBp3KK8LcOeLO/jisnXsrmo8oS/PSSW5NARCnebA43EM6VBtCzXNVm+gzphnX8KLWL3055Xm89qO9jLbR989wI+e38YH54/vsL7tYGOZ+xLqW4Lc8exWDIZH37XGh5zyyPgB1MbWEM+sP8Inl0wiO83b71r3reUNNLaG7PWDraqgL100k5vPn8aylQe5ZdnaDjnz7iJ2sGaCX3vmZJ7dUM6aA7XcvnQeXo+Qne7jOx+cy+OfO4vcDB+feXAVtzy6NpqS2HC4Dr9XOGmcdWKdOTaX710+j+V7jnOopoXxce85qySXo/UBXth81O6x1G6Sxhi++/RmxuSm88wXzuEvN53JmLx0/usv67n8N2+xct9x9h5r4tF3DxCOmJiIvesTxdPrj/DOnmqW77GuaF7dVsmDb+/j02dPjk5y7A0iwmfOnsLW8nre3j04LQbU2AeBn310Ab+4uufL9wtOGsN3Pjg34dd3yiH/vPIgdzy7lffPGcutF85gXH4mf/jMYm48byo7KxujEemEUR2jqwy/lx9++GS+evFJ+DzC157oWH+8fE81Nz28mtKCTM6dUcyEwiwOHreuEF7cUsGZ04qiA73xnDrRWQKwlpe2VnLO9KIOtb1O/vqc6UX2ylfttfkOjt7edEB0JnodqW2htjlIYXZXEbv1HmNzM/B7PVy+oJSt5fVsO1rP3zcc4RtPbuT8WcX89Kr5ePvYOqCvnFyWz7Ibz+Trl5zE3R9vrw52ykPj8+zv7K4mGDZcMq+EeaX5fcoLx7Jir2Uun3/vdMCadZuf5ed/Lj6Jb1x6Es9uKOf6h1bS1BqKTk7qLmIHuPG8qUwclcXXLzmJD80f32HbwomF/P0L5/Kli2by7MZyPnn/Cupagmw6XMesktwOV9pXnlbGVfY8kfiTyelTRuH1CDc9vJozf/AK82/7Jx/57Tu8vfsY+6qbWX+ojpvPn0ZOuo/FU0bx1M1nc+dH5lNZ38pV97zDe3/6L77+xEbe2FnFkdoAaV4PB4533rLgaF0g2gzuwbf3UtXQylceW89JJbl89eKTEjzisPTU8RTlpHPv63sSfm5v6H4IV+kTXa26NFA4tew/fmE7M8fmcOdHF3SopZ8z3ko7vLKtgjR7fdZ4Pnq6FW0XZPn5+hMbWbbyINcsnsgr2yr43B/XUFaYyR9vOINR2WmUFWZR2dDK5iP17Ktu5vpzp3apbc64PNJ9Hn71yk4O17ZEzcLhQwvGU5Dl59J547j63uW8u+84c0s7Xt04J66Dx5ujhtwZLW1hNh+uI93noaqhFa9HKOgkxw4wZXQ22WneaMXLZaeM43t/38JtT29h1f7jnDaxkLs/flqHtNJQMme8dZVnjGFacTYHjjdHT3jxxv6v7ZVkpXk5bXIhp5Tl89A7+2kLRfqk3Riri+Xk0VlccVoZP/zHtg6DxTeeN42CrDS+9vgGPnH/Cr75b1ZrqO4idrC6kb72lfO7TEem+TzcetEMZpXk8IVH13LrsrVsOFTHpSeXnLDv7UvnUZSbzgfmddy2cGIha771PrYfbWD70Xq2Hm3guY3l/Pa1PfzbyVbV9Tkz2q8WPR7hwwvLuHheCX94Zz8NgSC/fnU3/9peRVs4wnkzi3l9RxXbj9afsB7D8j3Wye89M4t5cUsFaw/U0hAI8eh/LOl2UlJXpPu8fPrsyfz4he1sO1rPSSU9X+Enghq7C4n2987087trF51QnTDXTgMt33OciaOyum1edfXpE3h63RH+99mttLSF+d/ntjJ7XB4Pfvr06AzciaOt97vj2S0AXDS768vONJ+Hk0vzWbW/hgtPGsPlCzqmNfIy/Fx2ihXB/fCKk3ltR9UJ0Z+TOuopYt9wqJZQxPCBuSU8u7GccMR06H8fi8cjXHlaWXQC2eicdN4zs5iXt1Uye1we93/q9B47eg4FIsJtH5rH+kO10dLZ2ElKxhhe21HFWdOKSPd5ObmsgLbQXnZUNHR7EoylLRRhxd5qaw2BLRUcqQtw7ZmTyMvw89yt555g2h9ZNIG8DD+3PLqWK+5+G2i/8urpb+mJi+eN46sXW3MtAE4uPbFPU2aat8uoOD/Tz+Ipo1g8xTLiDJ+XR1bsJzfDR2GWPzoZLJasNB+ffc80AJ5ZXx6tlrpo9hhe31HFcxuPUlHfSl1LkLqWICV5Gbyzu5q8DB8/+PDJ3PDQKiaNzuLjZ0zq13jMx8+YyI6KhkEJBNXYXUhuhp8vXTSTs6eP7rScc0JhFjnpPhpbu29PANaX74dXnMwHfv46t/99C4unjOL+6xZFWxgDvG9OCYsmHWD5nuOcXJrf42X41y+dzb5jTXx4YWm3X+6pxTmdzui0lin0caCTMsBY/rHpKCJW9P3sxnKALiN2gNuWzutw/+YLpuPxCN//93ndVjANNefMKIpGmrnpvg4R++s7j3GopoWbbGNyOpFag9A9G/vaAzV86vcrqWsJkuH3cO6MYr74vpnRdElnRghWtcsTN5/FO7uryU73sWRq9yuMJcKnzprM42sOs7W8PlpQ0FfeM6uYB97ay3Mby7lw9tgeTy5zx+fxj01WyeiiSaMoyknj/jf3cv+bezvs5xFrpvn4gkyeu/Xcfml0KMhK61XKti+osbuUWy+a0eU2j0eYPS6XlftqThg47YxJo7P50RWnsGLvcb592ZwTLi1z0n386T+WcN+be6JtD7rjtEmFXebge8vE0VkdSizj2VnRwB+X7+fq0yd2MLREJqSdNqmQ3127qF86B5ui3HSqGlvZUdHA/z2/nZe2VjAuP4MPzLUqjSaOymJUdhqvbKvkmrjB7PK6Fv688iA56T5uOHcqgWCYL/9lPdlpXn561XzOmVGUUBphXml+r68KEsHn9XDnR+bzpxUHOCnBCrF4zpgyinSfh9ZQhMWTez75zCvNjxp7aWEmf/3sWVTWB8jPstZAzsv0c+/re/jlyzt5z8yBn88wWKixD1PmjMuzjL2bvjOxLF1Q2qG2Pp40n4ebz5/e5faBZkJhFtvtlsGGjpGkMYbvPrOZ7HQfX/nALHIzfHjEan0wkDNEU4HinHT+ta2Sf2wsJzvN+ns/c/aUaNpIRPjEkkn88uWd7KhoYMaYHN7eXc3D7+znxa0VhCMGEWugftm7B9hzrImHr188KJOu+sPscVYvp/6S4feyZOpoXttRxelTejZ2J22Zm+4jP9NPfqb/hIZv//W+mVyxsLTX36VUQI19mOKUW8ZXxLiFCaOyeHlbJZ+4bwWjc6ySNYfnNx3lrV3V3L50bky7ZKuMsqscu1uZNiabdQdr+czZU/jPC6Z3euL69FmTue+NPXzlsQ00BILsqWqiMMvPDedO4QNzS7j63uV89bENrD5QwzWLJ6acqQ80Vy0qo7qpNWra3eGU2vY0ZjDUM9j7ixr7MOXcGcUsmFDAokkDlwsdSiYUZkZ7yFQ2tNorwHtpaQtzx7NbOakkl4/FpB7GF2RSXhfoNsfuRr592Vy+evFJ3f5dhdlpXHfWZO7+125OnVjAnR+Zz6Unj4umWa46rYxHVhygtCCTb1yaeGme27jslPHRAfqeKM5NZ1x+hmsDoK5QYx+mjC/I5Kn/PDvZMvpMmT02kJ/pp64lyJbyehZOLOTu13ZzuLaFZTcu6bAYSWlBJqv316TUIOhAkJnm7VW1zpffN5OPLZ7Y6ZjKZ98zjY2H6/j6JbM7DIorFnd97NSEJwmmOmrsSkqyoKyAxVNG8bnzp/Hp369k46E6inPSuee13Xxw/niWTO3YfnnxlFHsr24a9DkEqYrP6+lyoHzCqCye/vw5nW5TOKFmfTigxq6kJIXZafzlpjMxxlCcm876Q7W8tesYXpFO0wmfWDKJTyyZlASlipJ6qLErKY2IcEppPi9sOkpTW5ivfGBWj3X0ijLSGZnXrYqrOKWsgKa2MJNHZ3HDuVOSLUdRUh41diXlWTJ1lNWN8oNzk9aKWVHchKZilJTnjKmjWfX/Lor2rlEUpXs0YldcgZq6ovQeNXZFUZRhRsLGLiIXi8h2EdklIl/rZHu6iPzZ3r5CRCYPhFBFURSldyRk7CLiBX4NXALMAa4RkTlxu10P1BhjpgM/A340EEIVRVGU3pFoxL4Y2GWM2WOMaQOWAUvj9lkKPGTffgy4UHrTcV9RFEUZEBI19lLgYMz9Q/Zjne5jjAkBdcDouH0QkRtFZJWIrKqqqorfrCiKovSRpA2eGmPuNcYsMsYsKi4e3m1EFUVRhpJE69gPAxNi7pfZj3W2zyER8QH5QHV3L7p69epjIrI/QS0ORcCxPj43WbhNs9v0gmoeLNygMR63aU5Eb6cNkhI19pXADBGZgmXgVwMfi9vnaeA64B3gSuAVY4zp7kWNMX0O2UVklTEmtdc3i8Ntmt2mF1TzYOEGjfG4TfNA6E3I2I0xIRH5PPAC4AUeMMZsFpHbgVXGmKeB+4GHRWQXcBzL/BVFUZQhIuGWAsaY54Dn4h77dsztAHBV/6UpiqIofWE4zDy9N9kC+oDbNLtNL6jmwcINGuNxm+Z+65Ue0t+KoiiKyxgOEbuiKIoSgxq7oijKMMMVxq4tCQYfPcZDgxuOsxs0xuNGzYOJK4wdq7QS0H/gIKLHdWhww3GOVsu56PtWAGBPikx5RGSWiAya/6a0sYvIv4nIS8CdInIeQE+TnZKNiFwuIt9Lto7eIiKXisjfgB+LyPnJ1tMb3HaMwR3H2W7J/QLwExH5d3DF9y3f1vw8RPtTpSwi8j4RWQHcwCD6b8oau93H/fvAr4CtwI0icoO9LeV0i4jH1vcT4Gsicm6yNXWHiPhF5KfAd4F7sJq1XSMiZyRVWBeIhddlx1hEJC2Vj3OMxp8A38Jqy70duEpEZiRXXa9oAWqBeSJyFUTbi6cM9jH22xM5fwP8yBjzFeckNBhXRSlnkDFMA940xvwN+D1wH/AFESk0xkRS7RLRGBMBdgKnAjcDKR1RGmOCWF/ga4wx/8A6vgVAOKnCusBYhIFduOcYG7u99Q7gY6l4nGM0Pg+8x549/jYQBPYmVVwP2AZeCCwHPooVBGKMCaeSP9jHOAhEgMeMMU8AiMi5IuIfjPdMmTp2EbkSOGiMWWHfnwX8C5hsjGm1H7sbCBhjviQikuzLxE40++1/ICKyErjHGHO/iHhs408qnehNA0KAzxjTJiLPAb8wxryQTJ2xiMgtwMnACmPMfbH/91Q8xtBB87vGmN/FXGGmzHGOP64xj18K3AVUAq8Dq40xf06R75uj+R3g98YYIyKZwDPGmItE5J/Am8ATxphNydYco3elMeZeESkBfggYYBGwD6gBXrM/wwOn1xiT1B9gDPAacAR4CvDEbPsD8HPnewzMx1q8Y2wqarY1OrcvATYDhal6jOOOdSHwMlCSbL0xmj6FFY1dbOv/OjAtZnvKHOMeNE9PpePcicZvOBqxFtOZGXN8X8AKrlLtuH4D66p+DHCHvc9nsAKVVfZ9fwrp/ab9v78ceAQ4yfaLpcCzwMSBfP+kp2KMMZXA37AOQDlwU8zm24DLRGSusY5WAGgAGodcaAzdaBZjp4mMddntjA3kOvm/ZNCN3tjoYBJQZ4w5KiJlIvLeIZbZGRdi5SOfB74MZAAfdzam0jGOoTPNsR1QJ5P84xyvMQ37uBpj3jXG7LD32wpUYZllsonXnI7Vk6oFuMSO1m8BXgGcFuDJ1N2Z3puMMU8BNxpjttmetgFrjCA4kG+eVGOPuUT9FbAF+CfwbyIyDsAYsxurW+RvROQc4BNYZ+ikXXJ3p9k2dQ/tx/WrwA+wcu8lQy6WHvWamPKwUsArIl/AiiCSohc6aF4LXAZgjFmFdQleKiJnx+ye9GMMvdLsDPQm7Th3o3E5MD7uuIIVdWbRw3oKg0kPx3UqcA7wIlbaa4Ex5v3A+SIyxTbOVNH7FjBFRM42xjTFPOU6IBMrJTNgDKmxO6PVzsCGsXOixpigsUaI3wa2Abc6zzHG/ADL3K8HZgHXG2NaUlDzLc52Yw3eTAPuxkp9LDTG/CpF9TpRzfuADwLTgUuNMX8aCr0xuqOfRdOeK38L8Ihd6gpswrriGG8/ZzpWlcGQHmOHBDQfod3Ah/Q4J6jROa7XisgmYArwuaH8vtnv3xvNm7GW5swFvm2M+WbMS0w0xgzZwG8fP7tXiMh6rJPT54zVFXfAGBJjF5GzReQh4JsiMso5k4pVvhY7en0Ma6GOmfZl6hi7CuYPWJcxHzHGHE1RzbNszUUikmc//nljzIeNMUdSWO9Y+/FlwPuNMbcaY+JXxRoszYvtAabYL0TsF2Un1hf4oyLiNcYcAsZipTPAKh0csmPcD80lWPlggMeB9w3mce6Hxin29g1Y6YLrjDEVg6FxADQfxDLJScYakPY6+8ZFxKmi1/nsOsd4B/BZY8y1g3GMB93YRWQqVlT1KlYe93v2yDvGmLCdDkgXkXT7/utYB2QT1qBDkb1v22BrHSDNb2AN7tbF5CpTWe+/RGSGMWa5MealodBra/4i8CTWiegS+zGvrdn5ojRgHc90rEkzfqwBqGp7vypjzE6XaK6093vdGPNyimo8Zu+3zhjz9mBpHEDNBbR/FsJmiKqiBugYbzTGvDNoIs3gjw5fDSyzb48C/gMrRTHOfux24GHskXfgs1hfgh+RpFFtt2l2m15bw1KsevQrsMq94rffBvwVq3pgHPAgVi74t4BXNbtXo9s1u0HvYPzRHwQ+Dyyx70/FyjdNtO/Pwarl/BLWwMef6FgOdlHs/SH6R7lKs9v0dqHZa/9kYK3IdYv9uAer9vdPdCxt9AC5qtl9Gt2u2W16jRlAY8c6Mz2DdfnxLaxSqQ/Y234CfDnmoHwS+A6QH/P8ZJx5XaXZbXp7odmZIHchsB4o6uT5nqHS6ibNbtDods1u09vhvQfwIHwQ+J+Y+58FHrdvL8W6HDnDvv9e4OVkHwC3aXab3i403wQ8GbePB+sy9Tb7/mL7t6hm92p0u2a36Y396dfgqV0Wdb6IpGPNpns4ZnM11sgvwAqsus47RSQHmAvsF5Es6DiqPNi4TbPb9PZC83GsyCdaQWBruwP4qojUAQtFhnY6uBs0u0Gj2zW7TW9XJNy72C6dK8HKI0WA3ViDdbcaY8qlvV/KOKxRYIxVovgLEZkEPIBVuXGtMaZ5YP6M4aXZbXr7odlp5jYNq9HbW8AXjTEbVbN7NLpds9v09ooEL0289u+ZwB+dx7BmNT4Rt88zwEX27TH2bx9DP1DjKs1u09tPzaMc7cAFqtl9Gt2u2W16e/vTq4jdrtH8HtZU6OeAPOy2o8aaZXkrcERE3mOMeU2sroFVwA4R+T5Wv5fzjTE1WPWdg47bNLtN7wBqvsBYvWwqVbN7NLpds9v0JkqPOXYReQ+wGusSZBfWwQgCF4jIYojmmb6LVb8JVhnQp7ByVLlYZ7kB7YUwnDS7Te8Aaz6umt2l0e2a3aa3T/TiUuVc4JMx938DfA7rj1xtP+bBylH9BSjDav35KYzVpwAAAuJJREFUB2BBMi5D3KbZbXpV88jW6HbNbtPbp7+xFwchC2tarJNn+jjwA/v2OuAL9u1F2LMfk/3jNs1u06uaR7ZGt2t2m96+/PSYijHGNBtjWo21LBlY3emq7NufBmaLyN+BR7EubwZlDb9EcJtmt+kF1TySNcbjNs1u09sXel3uaA82GKwOZU/bDzdgrWQyD9hr7G51xj7dJRu3aXabXlDNI1ljPG7T7Da9iZDIBKUI4MfqTnaKfUb7FhAxxrxphqjVa4K4TbPb9IJqHizcoDEet2l2m97ek0jeBliCdTDexFrwIum5pOGm2W16VfPI1uh2zW7T29sfp5FNrxCRMqzmUncaY1oTPoskAbdpdpteUM2DhRs0xuM2zW7T21sSMnZFURQl9UnqYtaKoijKwKPGriiKMsxQY1cURRlmqLEriqIMM9TYFUVRhhlq7MqIQ0TCIrJORDaLyHoR+bLYK+J085zJIvKxodKoKP1BjV0ZibQYYxYYY+Zi9Qm5BGvh7+6YDKixK65A69iVEYeINBpjcmLuTwVWAkVYSwo+DGTbmz9vjHlbRJYDs4G9wEPAL4EfAudjdQr8tTHmt0P2RyhKN6ixKyOOeGO3H6sFZmE1gYoYYwIiMgN41BizSETOB/7bGHOZvf+NWMsR3iHWwsdvAVcZY/YO6R+jKJ2Q8GLWijLM8QN3icgCrKXSZnax3/uxGkddad/PB2ZgRfSKklTU2JURj52KCWOtXfkdoAKYjzUGFejqaVgLMrwwJCIVJQF08FQZ0YhIMXAPcJex8pL5QLmx1rz8JNaK9WClaHJjnvoC8DkR8duvM1NEslGUFEAjdmUkkiki67DSLiGswdI77W2/AR4XkWuB54Em+/ENQFhE1gMPAr/AqpRZY6+uUwVcPlR/gKJ0hw6eKoqiDDM0FaMoijLMUGNXFEUZZqixK4qiDDPU2BVFUYYZauyKoijDDDV2RVGUYYYau6IoyjDj/wPQVfi0/8dDjgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "GOOGL\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "                   Open         High  ...    Adj Close   Volume\n",
            "Date                                  ...                      \n",
            "2020-10-01  1483.660034  1495.069946  ...  1487.900024  1644200\n",
            "2020-10-02  1459.630005  1479.030029  ...  1455.599976  1561100\n",
            "2020-10-05  1462.650024  1484.699951  ...  1482.829956  1080100\n",
            "2020-10-06  1475.500000  1484.520020  ...  1451.020020  1304800\n",
            "2020-10-07  1459.310059  1468.969971  ...  1459.140015  1805400\n",
            "...                 ...          ...  ...          ...      ...\n",
            "2021-04-22  2275.399902  2288.879883  ...  2252.520020  1207300\n",
            "2021-04-23  2267.000000  2306.120117  ...  2299.929932  1453300\n",
            "2021-04-26  2304.520020  2324.530029  ...  2309.929932  1601900\n",
            "2021-04-27  2317.629883  2318.449951  ...  2290.979980  2219300\n",
            "2021-04-28  2392.500000  2431.379883  ...  2359.040039  4055300\n",
            "\n",
            "[144 rows x 6 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "BABA\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "                  Open        High  ...   Adj Close    Volume\n",
            "Date                                ...                      \n",
            "2020-10-01  295.260010  295.589996  ...  290.049988  16304000\n",
            "2020-10-02  286.619995  291.979004  ...  288.170013  11482800\n",
            "2020-10-05  289.059998  290.000000  ...  288.269989  17876400\n",
            "2020-10-06  287.410004  296.839996  ...  292.390015  15860400\n",
            "2020-10-07  297.989990  299.510010  ...  296.500000  16669300\n",
            "...                ...         ...  ...         ...       ...\n",
            "2021-04-22  228.764999  234.000000  ...  229.350006  11986200\n",
            "2021-04-23  230.649994  233.000000  ...  232.080002   8967000\n",
            "2021-04-26  231.500000  233.490005  ...  232.699997   7792900\n",
            "2021-04-27  233.440002  236.929993  ...  235.919998  11646300\n",
            "2021-04-28  237.570007  239.220001  ...  236.720001   9187700\n",
            "\n",
            "[144 rows x 6 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "AAPL\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "                  Open        High  ...   Adj Close     Volume\n",
            "Date                                ...                       \n",
            "2020-10-01  117.639999  117.720001  ...  116.217491  116120400\n",
            "2020-10-02  112.889999  115.370003  ...  112.465973  144712000\n",
            "2020-10-05  113.910004  116.650002  ...  115.928909  106243800\n",
            "2020-10-06  115.699997  116.120003  ...  112.605286  161498200\n",
            "2020-10-07  114.620003  115.550003  ...  114.515877   96849000\n",
            "...                ...         ...  ...         ...        ...\n",
            "2021-04-22  133.039993  134.149994  ...  131.716278   84566500\n",
            "2021-04-23  132.160004  135.119995  ...  134.092239   78657500\n",
            "2021-04-26  134.830002  135.059998  ...  134.491562   66905100\n",
            "2021-04-27  135.009995  135.410004  ...  134.162109   66015800\n",
            "2021-04-28  134.309998  135.020004  ...  133.353485  107760100\n",
            "\n",
            "[144 rows x 6 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UZEoLt8gBi+vW5bCh1eeU3adFUseO3bwOWktmOspUFaPT2Nv96TpyNVOqiP3hvSP4/G/2YVCNBBMKg1MdmxzwpaSJepFiHtw9jMf2j6Y9Nh/jjr2aIvZgJAHGUjdyEakzhow8lSCWSGrVTFatQsX16nbYEPA5q1aqqnbq17FnifhEJFiuiD0cV+C082342n1cipks4CI4NhHCSAU2ZBA3QKs7T8XnEVV/v5JkcKhbFLb5nJl17BZ/Jkcn5vGB72/Tfn+pOTkVzggu5qMJVYpxWl4qWCwiEIrEuK0jMxHNaQ9nkWNiiSS6mtwgStWyh2IJHBwN5qx/z4U4P1x2G9p8LjkIrEgclTagVERExG7U2ONZNHabTXvOSkKx1CTJNlWKKaTE7bYfbMfyDh/uePdZltuWi1JF7CICF0vuuJLUdrAK+Fw4PhkCkHLsVksxLxyZwkN7RnBsMoQ1Pc2W/m4jsUQSI3MRrZRTMB9V0NXsRsDnAmPAbDiu5V8qhfg8wnEFSpJhZC6Ky9Z145G9I9rqyuxn3E4bWr1OfPfpI/jeM0c06aTF48COz10JEjvM5ElML8V4nTgxVdwNotEpKGInom8T0SgR7c7y/CVENENEL6v//sEaMwtHi9iVdMeUSp6m/+ml1NhFBU6T2wGnnfKeF5NMMhwaC+KY6uzKScqxW6uxiyoYcQHziJ1f/PqIPZU8tdaxi8i0HFsU8umUmQn8oKqxt4tkehXIDeJGGokrmAhGoSQZzlwWAJCqkDESU5JwO2x49znLsWV5G649vQ+fuGot3rK5H7ORRFHNbWJ143KoEXsVHJtapNCI/R4AtwP4Xo7XbGWMXVu0RRaRLXkqLmxjuWOpZsWE44pWOUBEXFfNM2IfC0YRTSQrI8VoydNSR+wMDpuQYlyYCsXBGNMks3jC2putuOGXYnVmRESbxnNwPsalmIBPXcFVQYJQH7ELff3U7mb4XfaslTGxRBIuuw1/86a1aY//9IXjuH/HIKbmY2hyF+Zi9FJMQJWqkkkGm62wyL/RKShiZ4w9CaC0c1wtIppNiomZV8U47GS5ExHvp5d9Cuk+FZH6eDBW9g0ZQrHSRLYpx56KyEXEHvC5EEsk+abfJUqe6h1YqTmpOnbjZyeqYtp8ovy18lGpOC4RnWPva/Wgt9WTU2N3OTJdSKt6wyqmucgoxSQZMBep/r6PaqMUydPziGgHET1IRBuyvYiIbiWibUS0bWxsLNvLikacqBl17FmqYrxOe0ku9nA8fZJkIZUQx3USjLHRqtQIhx6KK5Z2yoqltriAE0mmaextughWJFfjFstjqSRhGSJ2NYGYZLxVXrx/XGFaHTtQfVKMWCH2tnrQH/Bmj9gVc8cu/q5iShX15Y7a75G17AVjtWN/EcByxthmAF8D8MtsL2SM3ckY28IY29LV1WWxGXqNPb+qGJ/bUZLdgsKx9NnvxUTsQO4OQKthjCEUV9QNvlnGzXExiGmN4ncmlKQmxQR0EawWsVucPI2VMWI/MZX6/MTfO69OdvS77AioyfRqqNVORexJDM9G4LAR2n0u9LaYR+xKkkFJMrjs9oznhMRUjEM2ljsC1SFV1RqWOnbG2CxjLKh+/QAAJxF1Wvke+bJQVYzbEGn4XXbtorMSvcYO8MqYfE/UY5MhiKKCbMvhUhBTklCSDB1qcs9KOcYoxRiTpwB3dOIGbH3ytPxSjP59xWRHn9uBZnd6t20lESukcFxBMJJAs8cBm43Q1+rB6FxEW3EI9JKJkYC3+BtWSmO3azd6mUAtHEsdOxH1klrfRERnq79/wsr3MOPYRChDLshex863xTOWYflcDq3UyypePDaFAyNB9LV6tcfaVSkmn/c5PhnCut4WAOV17MKRd6hleiELnWDcIMXw5Knq2HVVIsLxxiweAlbOqhh9qZ74e0VnZ5PboXbbuqoiIhXD8cIxRWugAoDeVi+SjCfy9eRy7C2aYy8iYtf93sAitPpGp9Byxx8DeAbAWiI6QUTvJaLbiOg29SVvB7CbiHYA+CqAd7ISjzIcnA7jki8+jkf2jqQ9ni15qi8/1ON388esiuSm5mO47fvb0dvqwcevXKM93u53pe0UlItjkyFs6G+B12kvqxQjEqedTSJit06iiiUMUkwyqTUoaUv4UCzVeWqRFCNOQ3HDL3WDUkLhkkZ/K2+5F+ejiNiF42yrku5KEbFHEgpCUQV+F7evT7XfqLNH1TJiM8fucdrhddqLjNhTv7eaksu1RkG1SIyxmxZ4/nbwcsiyMTwbQZIB+4bmcOWGXu1xrdzROFIgZu7YfeqJHFJL0RbLtqNTGJ2L4ofvO0dbUgKpqHRyPqbVMZvBk1hRLGv35axMKAUi1yCkGCslKiGtCAeb0EXsAa+I2OO6ztPFO/b9w7O4/van8NBfXlQ2KWZkjteCr+xqwuBMRAsw5rSIXW1a81XHjH5hX0SN2H2qfb2qYzeefynJxLwMsc3nLKprVF/H7lFvGrL7tHBqfqTArPqhH52cT3s8Wx17OK7A4zJz7PyxkEVOTDimnpb0rsN8m1LEMn5Zu48nsCoSsbvTvrcCTYpRdFUxqnNwOWxocjswEYymGpkskGJeODKFaCKJ41MhnRRT2hk0J9TE98ouPwB+Pk7Ox/DvD+6Hx2nD0nYfAKjzUCrvuLSqmEQSodjCEXsuKQYAWn2uRWrsNjjsNjR7HFVxfGqN2nfsao3rsYn07sxcUoyx1BFIRezzFskOES1Jm/5ebb5UxJ4LUeq4tCIRe7rGHo6XQIrRzYIRVTEAd3R6J2JM2hXDodEgAL5aK1dVjJiVsrJTOHYFf/+LXTg8Po+7//T12rzxahkEFtVr7NGEFui0ep3wOG0YMsx+ETdes6oYgCdQZ4qpilHtcGoJddl9Wgy179i1iD3dsYuqiqhJuaOx1BFIaexWRafiQnEbRhdoEfsCjl2UOgopZmQ2gmSZhkWlkqfc1lJE7OnljqnlfJvPlebYrWhQOigce1xJa8QpJaIi5pSuJgD87z06EcKFqztxwepUoRjfbCNe8V219MdFnzwlIvS1ejE0W1jEXuxKJKrWxovihkCRkk6jU/OOXXSljc1F0zYJFhGhmRSTS2Oft2h3I20OuzNLxK5GIdGEgnd86xm8eGwq7XXHJkPwOu3obOK1xIkkw3iZ5rgbk6eldewpKQbIjNit2BpPOPaIzrGXuirmxFQYnU1uNHv4eRVNJHni3mU8H5yIKcmSb2iyEJpEFefJU32JrlkteyxH8hRA0bPmY4kk3Hb9Cq46qoZqjZp37LO6TXT1DT1ZNfaYkjEADNBVxVgcsWd0uLp4xYCI2IdnInj+8CSeMMzsPjYZwtJ2L4hIG59ariRbKnmqauwWbuWX0aCkq4oB+I1vXC2tc9lti47Y5yJxLT8RjilpDqyUnJwOY0mbV+uXiMa58/ZlOPbKd58qSabdQEXEri8g6DORAqM6LdyMVq8LM+HC9/c1jikIeJ2YkVJMwdS+Y9ct046qOjtjLKtjjySUjCgagJYsKmYinRmRuAKilFaoh3efcrvFiuO18fTk7/HJEJapCbbWRTR8FINwemKDaSvr2GMZs2KYQYpxal83exyLduyHxlLHNRxPlq3c8cRUCANtXi3HwqPyRMZqUeuurOB2ifpZNvNRBZF4UlvBAtCkQH3vxUJSTJvPibjCCl6JZDj2KtplqpaofcceSWi69TG1MkZf4pgxUiBruaPQ2K2JTqOJJDwOu+k8at59GlPt5yftYZ1jZ4zh+GRIq5wQjr1cjRriYmzzOUFUms7TmF6KsaUvvQXNHocW4ReLSJwC6VJMKR17MskwOB0xROyKmt9JL6Vtq4LRveJmB6RWhWIFC/CIPZFkmNA1KYnPz9jBLUiNFSjsnDXOnwn4XJiNVMdmJLVE7Tv2cBxL27wI+Jw4okbs+tkmpuWOOTV26yJ2M8kHSK9dFhH74fF5bdk6OR/DfEzJiNhnyhS5CMfucdjhc9pLq7EnjclTfcTuzLgxF8rBsSAcNoLLbkMkXp6qmPFgFDEliSWBlGMPxxXElKSJFCPmoVTOsUd0EfuEel6mR+y8c1qf+9DXm5vR6i1uHEA0nkyTdwJeJxjjkpokf2resc9F4mjxOrG83aeVPOqjMbOt8cyqYviYAesi9khcySh1FHToBoEFI6m556NzPCLSV8QAKHtrdViVDGw2snw4WjxjCFh68lS/k5AVUszB0SBWdPrhd9vVqpjSa+zH1YoYvRQjZDTjanExkxCtIj1i5+egMWIHDI59AY094CtOPjRG7NquY1KOKYiad+yz6sCiZR1+rUlJf6LqIz7GWNaInYjgdzksjNiT2SN2f2qzDX0k8pqqBx/T1bADfK6I3UZlG1+qT/L5XKWJ2GM6jd1pL60Us7qrCR6nXU2eiqqY0jUoiRr2JW0+zUlpjt0QVIjVWEWlGPWY+Fx2CMVDH7H3ad2nmbNvclXFAEU49ozkqRwEVgy179jDcbR4eMQ+OB1BXEmmzXTWR+xapUoWh+tz2S1rxolmSdICfBDYXDSBWCKZtomA0NlF1+nSNu7YiQit3vJ1KIZjqbI8b6mlGCUJexYppsXjRCLJiq7xjiWSODoZwuruJm3efjk0dlHDPhDw6hy7kDiMG7zY0FLh7kpxLMRURoBPOxW0+11w2W1ptewLSTHFVvuIXZkErUXeIBqd2nfsQorp8EFJMpycCmvL7RaPIy1iFyewWfIU4IOZrIzY3VneR8gN06EY5qIJuBw2uB02HB7nib5jEyF0NbvTortWr9MSKSYYTeAPB8ZzvsYYsVubPE0fxhVPGqSYtIjdmfYzhXJkYh5KkmF1N4/Yg9GEloQLW7yBiJ4TUyEEfE741ZWWw0ZaEtHs3GsrYEZ/KRA3u1bdsffpyh2JCL2tHgxNm0gxCzj2Qv+uqFGKkZttFEVNO/ZYIolIPIkWjwPLO3jr9pGJee1EbfY40yL28AKOncsOVmrs5odXVPFMhmI8R+Bx4pROvxaxH9OVOgqscuz/t+043n33cxiby97sFIorWvWG3+2wbMwCkJrWKG64iqHcMWAodwSK7z4VjUmru5vgdaWmDbZ4HGm121YjatgFbodNi9jN8juBCo8VEIFQqzflzJvc6XYax1osVMfOpzM6C96vN5ZIpl03i5nt3sjUtGMX+nSzh0fsAHeKIhps9jjSHXuW/U4FPgs324gkklmlGH00MxdJoMXjwCmdfq2WvZSOfVDVf48ZRjDoCccS8DlTUoyVEbu+jp0xpjr21GnYpG4+AVjn2Fd2+eF12rWeB6HjlyqBemIqjIGAzrE77bkj9goPAhPjN4SeDaRr7ADX2YdmTTT2LI4dAHpaPFpBQL7EEkpaxN7i5SW3MnlaGDXt2MUAsBavA93NbnicNhydCGnlWy2GcrnU7knZHHvuCpBH9o5kDBvLRjSuaGNHjaTmxcQxF0mgSXXsxyZCCMcUDM2EtcSpwKopgOJC02/bZqSUydNEMiXFiK/1TVxi8wmX3aZJWdki68HpMH743NGsksqhsSAGAl74XA54dM5VrApKobMzxuXAJW2pz89lt2mlqkaHCaQ2X6kUImIXFShAqmFP0NvqwchMVJtXFFNn/Nhs5mN7AaC7xYPRQiN2JV1jt9sILR7ZfVoote3Yw2Jp7QQRYXm7H0cn0iN2sTcjkH2/U4Hfbc/aeXpoLIhbv78N33n6cF62RXNF7OoFJKSYZtWxJ5IMzx2eQJIBS3VLecC6iF1siq3f3cdIWvLU5bDMses/i5iS1Cpe7Lb007DN54TbadNmfWeL2P9v2wn8/S924/FXRk2fPzgaxKruJvXvsGvHT1SilGJezFQojnBcMUTstlTEnkWKqYZyxxZd8tRoZ1+LBzElqc04MlavmNHd7MZIgZuwm/3eNtl9WjC17dhVKUackMs6fDg6Ma9FIGIpHzNUQmTX2B1ZL/b/efI1MAbMhvPTm3Np7EKKmQhGMRdJoNnt1OZ2P/kqT2wapZiA14nZSHzREx5H53gElcuxZyZPrdHY9Q46Gk8inkwf0Spo87ngddo1iSZbyeOI+rd86eFXM6L2ZJLh0BgvdQQAr9Om3VQ0x16CiF2shAYMGrt4bzPH3uZzIqhWScNlq2AAACAASURBVFUCoZcLKcZlt2U4V9GkJHT2fBx7T4sbY8FoQees2e9t9bnkhMcCqW3HrjpZ4cCXt/twbDKkOXBRVaF1G4qOyizljn6X3TRRODIbwc9fPAkg/+mPuSJ2p92GrmY3hmci3LF7HDilkzug37/Ko89lHemOvUXrwFuck81PiklokoHfZUfIogoS4dg9ThuiCQWKFrEbHLvfCa/LDqd6gWfrPh2djcBGwJ7B2YytEU9OhxGJJ7FaROy6z0JIMaVw7KLUUZ881Tsqn8k5EfBXtlZbK3dUj4vPnWmjsUkpbpBMzOhp8UBJMq2bNR94uaNhno4cBFYwte3YIykpBgCWd/j4TjmT/OLSRqYqqf0cgRwRu9uRsYPSTDiOW7+/HQwMfa2evCtEco0UAID+Vg9OTocRjHKNvc3nRKvXiUNj83DZbehRN2IQBCwo+4rEFe3GkFOKiadLMYylZKzFILTyJrcTSZb6PBwGB3HrRSvxyavWwWnLLcWMzEZxwepOuOw2vHhsOu25g2OpihgAabtmiYg9YqEUc9+OQVz/9ae0sRZLAqkbsz6nky1iByqXIExF7NwOo74OZDYp5SvFAMi7MmZsLor5mIL+QPq5L6WYwqlpxz6XIcVwOePVkTkA0EaPZkbsWRy7046YktRePxOK4+a7n8PewRl8/U9eh9XdTdou87lgjOUcKQAA/QEvTk5xx96s5ghWqLvtLGn3ZiSlrBgEJkocO/wunJwKmy6R40oScYVpkaWVw9HEbkiilE5UIDkNf+tZy9txzaY+rSM1qxQzG0F/qxetJhtCHxo1OHbdZyEkBysj9mdfm8CO49P41csn0ex2oEVXOigkOSLzoVntFR7dmyp3VB27ScTe0eSGw0ZaxG6sNzeju4U76FyltXr2Ds0CADb0t6Y9HihyF6UnXhmtiv1kK0FNO/bZcAI2SnXJrVDlC+HYhcM3auxZHbt6IwjHFMyE4nj33c9h39As7njXWbhyQ686cmBhBxdXGJIsu+QDcMcudn1qUVcWYhu1pW2+jNcX26KtR+jrZy5rQ0xJYiyYecGFDCWhXs2xL94JCklF7M4jbhZGKUYgGpfMpJiEksR4MIqeFrdpueDB0SDa/S6tAimt2asEUozQnvcPz2GgzZs21VM4QJ/TfNqnthqrmBTDZRVx/ptV7ththB7dhhvGDlEzelTHnm/EvneQO/b1fS1pjwd8TsxGEgVtkxiJK/jze17A/75wLO+fqSdq27FH4lq0C3BnabcRjk6GQKSL2BXh2BeoilEfPzkdxrvufhavDM/hWzefhcvX9/Dn8+xMFRFQthsIwJe2IqEmJKNTVMduTJwC1kTsoiLmdcsDAMx1drGqSWnswgkv3gmmpBj+O8Xqx5mtyUWL2DMv6In5GJKMR4UBrytDojo4mkqcAgaNXUgxFshLgkHdnqD6ihggFaUbR/YKKj3oKprgiX4RiJhF7AAveRzUSTHZigMEXU1CiskvYt8zOIMlbV7txisQn9dsAfmluUgCSQtyUrVKTTv2JrcDa3pSF6/TbsNAwAvG+MUkHINxVGu2+nIRsXzgB9vw6kgQ37rlLFy2rkf3fva8pBjhMLKNFADSL36R5M3l2LUOvMU4dnVJ/LplbQDMdXYRReurYvSP5yKaUPDRH7+UNlteT1yTYtSbhXqTzBaxi+SpWR27iAJ7WjyqFJM6LowxHBwLYlW3X3ssPXlqvRQzPBvRVl5LDKWqLodY/Vjbfm8V0UQSbqdNO0ZmETvAA6fB6fyrYlwOG9r9Lm2lmI1P/GwHvvn7Q9g7NJsRrQPFrWjEdVrqLRCrlZp27J+4ah3+77bz0x4THahuh1078fSO3WmnjGSdQETsxyfD+PwfbcSla7vTn3dzKWahCpGI1giV/fD26Ry7cHTr+1tABKzra854fYs2k734i390LgK7jbBpgGuYx026T0NaxJ4uxeRzgRyfDOG+HYN4+pD5LBrxOQgpRiSizXaZAqB1oJolT0UUaCbFTM7HMB2KY5UuYtcnT7UGJYsu+nBMwXQojreftRRuhw1retM/P7cmxZg7TI/TDo/TVtGqGLfDrq0w/VlWtEvbvBicDkNJsozxutnIp5b9kb0j+PLDr+Lw+HyGvg7odpkqYEUjxmE3qmM3P9NqmOUdPmw9oEbsBsceyTKyVyAilXNXtuPGs5ZkPO93O5BIspyljNGEkpcUo8/8CylmVVcTnvrkZVoFgh5x8S9WiulscsHvdsDvspteKCGDFOMrQGMPqhF4tjyE6DRt8qTLOw6buYMQEo25Y09F7MZZKwcNiVMgPWK3uo5dyBOblrTgyYsvRYdupjygl2Kynw9tFdy0WUTs4nzVDwDTs6TNh0SSYWQ2gkhc0VYouehu8eSM2JUkw3Q4DhErre/PHrHPFFARNhflx7LUe9tWKzUdsZuxvJ0vv93OlGOPKinHnq3UEeBJmyvX9+ALbzvdNMll1IaNHB6fx8bPPYTnD08ByC75AECn361JRUKKAfhy1+y9AaC72YPBmcJatPWMzkXRrZZRZttAQzzmdaUvy/Mp8xRRUjBLHsIoxYgbgD1LxJ5y7JkrJFHD3uF3IeBzIppIaislY6kjkO7YfS47nHbK+6Ifmgnj2q9txdEJc4lJJBT7Wr3oafFkrAjFeZjr3GsrsvLDCqLxpBqxqxp7lhuQkJiOT4ZwbCKUMfbCjN4Wd1r+wcis6tQ7m1ywEbTVpB5RDjpZwL6wIhdm5TiMWqLuHPsyvRRj1Nh1rfJmtPqcuPOWLZrWbcRvcEhGth2ZRFxh2HWS11Tn0thtNj4KFUBekQ8AnNbXrFUOFAN37Dyh5c8y8CxskGJ8BUgxQTVKCmZJWInJjinHLsods0Xs3OEnkuZSTGeTGw67TbcZA3//g6NBeJ129Lem5C69vi1kh3yX6btPzmL3yVn8dNtx0+eF4zJbaYn3AzJnsevh++BWLnnqcfKc1JqeJqzrzYyagZRj335sCnPRBE7V3Tizsa63BePBWNaZMWJEwd9dfRru+/CF2jWhRwQjhUyKFOdiqTctr1bqzrELjd3jtGlLYL3G7slRW74Qov46W8S+f5iXWYqkZK6IHUjJMfqIPRcb+1txeHw+rwSuGWNzEXS3cMeebeCZUWMvRIoRFQjZbnyFljs6DTdmPSNzEa2cLqXBcifBZ8T403oBhMxAxG8YLR5nzkhSj9gu7tc7h0zzKyJiF/YYEeehJ4djr+ToXh6x20BEePivLsYNJjIkwFeTAPD4ft4dvbo7MxdkZKMage8enDF9Xuwk1t3s1l5rxOuyo9Vb2Ajg1JaTsiqmLhAVJWbJ00g8mfPiWgi/IdI0sk9tsNAce46IHQD6W3l5Zq56dz0bBlrS3qcQEkoSE/MxdKnRT1OW0s2QmKdjTJ7mEfkIh57txpMqd1QblBZInuaSYkZmU6sPY43/IUOpI5CSQVx27sCu2tiLx18ZxbhJLb8R0RJ/dCKEPSYrpsGZCDr8rqyft76OPRttPqfm5MoNL3dc+LrwOO3obnZj+1EuNZ7as3DEvkEtCNh1wvycFZVA7Ya8hJG+Vk/anqsLMSeqYiwsabWafUOzeOedz2D3SfOb3mKoO8fuc/ERvmnJUyUVsXvzdKJmGLVhPYwxzeGezNOxX7mhF287cyCrpm5EVAwUcyKMB2NgLNXm7XObbyoS1sod+d/qsttgt1FekU9wAcee6jzljliUO2arUsolxYzORrTORv2+mPPRBAZnImn6OpC6QYno+aazlyKuMNy7/cSCf9dEMAaX3QaHjXD/zsGM54dnwugLmEfr+vfMKcX4XJgJL37IWzHwYoD8roslbV4kGXfEnWqdei78bgdWdvqxK8s5K1YpAV/uVWtPi6eoiL1QKeZTP9+JX7y08DlhBWNzUTz72mRJErx159gB4JK1Xdg00JqhsS9UFbMQuZKnY3NRTSMVN5KFGjiu2tiL/7xxc97v393sRmeT2zRqXAhRmZDS2B2mI4q1zlNNuqC8NyARUdLCUky6pOXI2nlqLsXEEnz10dNiiNjDcRxSE6erDBG7kOBE3mN1dzPOXtGOHz1/bMGLf3I+hq5mNy48tRO/MZFjhmYi6G3xZvnp/KWYJEvNPyonC42/0CPmzBtvnLnYONCKPVmkGJEQXShi720pLGIX51YhUgxjDD/bfgK3P3awZNsm6lloR7fFUJeO/T/evhmfuuY003LHxRzEXMnTfaq+rt+MeTE3ETOICBv6W4qK2EXXqYhyfS47QiZ/RzjGuxD1une++56mqmJySzHNxnLHLFKM1nlqiGLFKAShaWv7YobipqWOQGbEDvBhY0cnQrjl28/nLCOdmI+ho8mFa0/vx4mpMF4+nj5wbHA6nDVxyt9TzVdkqWMHgPYKdp9G8+giFSxt5zewfBKngk0DrRiaiZjKXtOhGNwO24LXZW+rB+PBaN67aRVTxz4ViiOuMBwam9fm1pQSY6GClRTk2Ino20Q0SkS7szxPRPRVIjpIRDuJ6HXWmFkc5lLM4h27meMSMswFqzu1x/Jd3hbChv4WHBwNFjy7WzhDLWJ3O0z/Dv0sdoHP5dC091wsJMXEFfMGpWwRu5Bi4oa/NVXDzv8Wj1raOh2K4dBYEHYbaXvgCoTj0juwy9f34Ks3nYmXjk3hj7/1TNqennom56No97twxfoeuOw2/GbnkPbcbCSO2Ugio9s07b2dC0sxgQoNApuaj2F0Lpo18WtEROyFOHYhIZrJMZPzMbT7XQvKkb2tHjCGvLfa0zpPC5A59FLP/TuGcrzSGow9I1ZSqOe5B8BVOZ6/GsCp6r9bAdxRnFnWICK+qFbumMxZgrgQor53NhzHdbf/AQ/vGdae2z80i/5WT3q3YwmWWEvbeZNIPkk/PSJiF7qo2PLOuOTkjj39ROP7ni68pF04eao6dlf6yidbg5I9S+epKJ0TZXBEhIDXqUXsyzt8GV2RRASv054hOVy3uR/fec/ZOD4Zwg13PK1F/HomgzF0+N1o9Tpx0ZpO/GbXkKaFi+7dXDXd4jzMJcW0VWgQ2CP7RqAkGa7c0LPwi5FaCWWrYDFDJFmPmoyamArFtJtaLnq1scH5yTHiHIwrLO8oX9w0OptcuH/HYMnlGK1npNJSDGPsSQCTOV5yPYDvMc6zAAJE1LcYAxcDEcFlt2nRbXSREbvDzgcl7Ruew84TM1p1AMBLHdf1taCzOZVQWmj6XTEUO1dkdC6Cdr9Lc3iii9Y4OTEcT2TU+vvdeWrsunJHs4vCOARsPppbihGfX9wgxaTGCaSizDYfHwRmHP6lx+uya9GzngtP7cRPPnAeogkFb//m0zigTgcFuO4qpBgAuPb0fgzNRPDiMf7Zi9n/ZhM5BVrEvkBVDFBYE44V/Hb3MAYCXtPGIDO2LG/Dgx97A7asaM/7PTrU886suY5H7AuX+/a2FOfYgfyjdhEwvPvc5Tg5HdY+41IRNkxStRKrPc8AAH0Xxwn1sQyI6FYi2kZE28bGxiw2I4XLwR17NKFgLprQ2smLpcnt0DRuMWc6lkji4GgQ63qb0aU6AJfDlnOj32IRDqaQXWmA9OYkQFefbnDYZlKMt0ApJq4wbZWkJ7WDEu/8DMVyR+wAd/pmUozdRmmt+60+J0bnojg6Ecqa2OMRu/l7bRxoxb0fPB/z0QTuVXfLAvjxiCaSWnLv8vU9cDls+LUqx4iI3WxwmyCfBqVKjO6djcSx9cAYrt7Ym3dlFhHhNJNBXQv9zIC6/4CRqVBcC1ZyoW30oTrfw+PzOJJl2ByQ3iSX70wgEbG/65zlcDtsJZdjQnEFDhvlNXOnUCqWPGWM3ckY28IY29LV1VWy93E5bIgpiuaEe1sXLtHKhd/t0LLzQrc+NBZEIsl4xK5KHQs1JxWLcDCiaSZfRuei6NI5dn+WUQGhWOaqxpenFKOPkswSzMJBO+wEt8O+YMQO8Fp241Ja1LDrb5xtPideOjaNRJLhnJUdpr+LN61ld67LO/xY19uCnSdSyVFjnXWT24FL13bhgV1DUJIMx6dCaPY4MkbN6hERmT/LDBaAdx/bbVQSjT2uJPGVRw9kJN0f2zeKuMJw9aZey9/TSH+A7xhmZCoUW7AiBuDzfdwOm6aD/9VPXsb7vrctq1wyF01oN/F8xwqMqhM6u5rduGxdN369c6igGfCFslAn/GKw2vucBLBU9/0S9bGKIaQYcUJ055kkyoZ+27DxIL8IReJ0fV+z5tgXo+XnQkSpE8HCHMDYbETTpIHUvpbGkz5smjy153VxzOsuJjPpRjhoh41w5rKAJgNlS54CPIFqlGJG5yIZn6OoZX/z6X24eI15oLCyqynruAjB5qWt2HliRtPQxcpIvzq49vR+jM5F8cKRSRyfDOWUYQDgjCUBfOnGzTh/lfkNB+BRbam2gNtxfBr/9eiruO72P+Cura9pjz+4ewg9LW6cubTN8vc0MhDwZnT6JpQkZsL5RexEpDUpJZMM+4dncXA0iJ0nzCvEgpGE1mWdtxQzF9XOq+s292M8yOvMS4XZtWYVVjv2+wDcolbHnAtghjFW+vRyDgI+JybnY5ou27tIx96ki7rEKmD/8BxcDhtWdPg1jb0UFTEA39+10MiOMYaxYFQ70YHMBKZAv5G1wJunY5+LJLQkl5iupyemMK3z8/1vWKk9nq1BCVAjdhMppqc5feV1ak8Tels8+OfrNmT9Xf9zyxb8Y47nAWDzkgCC0QReG+dJVLEy0keVbzytGx4nr445NhnKKcMAfC7QDWctyfl3AsVvAbcQ4twfaPPiO08dAcA/9ydeGcNVG3pLIhka6Q94MToX1SafAnzTGMbSS4Rz0dPiwchMBMenQtqeB/e+mNlMlFCSCMcVLcjKN2IfmY1ocuWl67rR5Hbgvh2FxaWFJFxD8cxCBasotNzxxwCeAbCWiE4Q0XuJ6DYiuk19yQMAXgNwEMD/APgLS60tgqXtPhybDC04zyNf9LvLTM5HoSR5x+maniY47Db4XXZ4nfZFzaTJhc1GaPO5CkqeivpcvcaemteSGbFnJk/N58ro4XmMpHbjNIvYE0pSK2F8w6mdmlabO2K3ZdSxm5Xnve8NK7H1k5eiI49uyFxsXsp3l9pxnEeCYmXU4dfnJxx447oePLh7CCemwlpt92LhYwWsj9jFavWaTX04OR3G1HwMT7wyhmgiias3lae2QcyZ0Sc/RXDSlocUA/CNaF4ZmcO+oTnt+/t2DGaU/orGO7GDU77dp/rzyuO048r1Pfjt7uG0m1HWn52N4EM/fBEbP/cQvv74wbzeLxxLlKRyDii8KuYmxlgfY8zJGFvCGLubMfZNxtg31ecZY+xDjLFVjLFNjLFtJbG6AJapjn1kNgKXw5Z3dJCNJnVgV4efdwpOhWK8IkadiEdE6GzOPjfECjr8roKkmFTXqU6KcYkNpQ0Re9wkeeq0IxJP5mx3F79HROxBk4g9riS1qJWI8Nk3n4brz+jP2RzjsFNa5U4kzje16GnJdODZttgrhFVdTfC77Nih6uyaFNOU7nyuPb0P48EYoolkXuNr86FUg8BG5iJw2W24YBXvsdg7NIsHdw+hs8mF1xdQ3bIYxI5hep09365TwbkrOzATjuNXL/Mo+m+uXIvpUByP7B1Je53I9YgVaj4RO2Mso8DgLWf0YzaSwJOvmm8co+cf79+DR/eNgAF5NzeZFSpYRV12nupZ1u5DJJ7E7sEZ9LS4887+Z0MMsBIXxCvDcxibi6ZVCnQ3e0pSmypo92dG7IyxrMvAVNfpwhF7yCRi10b35oh8xMUkInazmewxhaU53/NXd+Ir7zwz52fiMkgxQv5abK4kG3YbYeNAK146xh375DzvjDRegJeu69Yes8qxc43desc+OstlOFF7/uLRKTy2fxRXrO/NOlnTaoRjF1vrKUmG+3fwuTv5zJwBgPPUHMVDe4axpM2Lqzb2YiDgzdiwWlTEdDXxcyQfjX0mHEcskUwrMLhwdSfafE785IVjpruNCZ4/PIkHdg3jLy5ZjTU9zZjNczOcsEkQZRUN4dgB4KVj0+hpXrwzENr0lhU84fTkAV6qeZpuO7RPX7MOn7x67aLfKxvtTZmO/R9+tQfv+NYzpq8XZVxdTXqNPX3CIsAvtlgimdH6ns/oXuHYezQpxqQqRknClaMCxgyHndKkGP3OSaXi0nXd2HVyBtuPTmIiGEOHSWekx2nH5afxpp6Fkqf50ubnuyhZ3RgzMstHHLf7Xehr9eC7zxxBKKbgmjJUwwjESm5wOoyZcBzv/e4L+P6zR/Huc5dhXe/C438B/pmv7PQjyYC1Pc2w2wh//Pql2HpgHMcmUo5XrBZFIJNPuaO4RvTnldNuw1s29+PRfaN4w388bjqPP5lk+Jdf70Vfqwe3XrQSAZ8z713OwiYVaFZR945d6J+hmIKeHPM88iXgc4IoFbH/4QBfpq3VnZxnLW/HWctLt8Tt8LvS6tj3Ds7iB88dxY4TM6ZOQZNidBG7z2TujXEja+21rvT56WYEjVKMyWYbcSWpbVCdL8ZyR/1ep6XilvOWo7PJjc/+cg9+/+qo1kZv5NaLVuK6zf3aHgCLpc3nQiyRxB2/P4R3fOsZyxw8d+z8eG3o5xtftHqdODdLWWgp8Djt6Gxy47nDE3jr15/CHw6M41/fuhGf/6NNBa2iz1Wj9lN7+PX2ji1LYSOkRe1zWsQupJiFS3W1Va0hKf+pq0/D9/78bGzob8EdTxxCOKbgB88e1c73X7x0ErtOzuATV63V5sZP51nZJKWYRaC/KK2I2G86exnu+bOzsUItm9szOIvuZveik3aF0O7nI16Fw/vCg/vAGE9gmkULo7NRNLkdaRl4ESnok5zZOuHEHqW5IhHhyIUDMRsrkDBIMfmQ6djViN2CzzIbPpcDH3vjauwdmoXLbsP/e9tG09dtHGjFV2860xJtH0hVh3z7D0fw/OFJLUm4WEZnU1sirlfntly5vscyu/NloM2Lpw5OYCYcx4/efy7edc7ygn/HeerNaG0vb0LrbfXgsnU9+Om2E9p5Is49IauYzWT/xM924LO/TI28OqZKLcYdnLwuOy5a04UPXLwKh8fn8bY7nsZnfrkbd219DaFYAv/x0H5sXtKK6zfzPsyAN/+I3Uz2tIq6d+wep11zNottTgKAjiY3Ll7ThRaPQ+sYW1dgJ96ibfCnBkb9/tUxbD0wji3LuTQ0bDKzeiwYzYhE7DY+O0UfzRh3TxKI+TcHRjLnqADAC0cmtfdt8Tjhc9kxOR/Dt35/KO0kjynJnBUwZrgdtrQLUyQCF5rfvVjeefYy/Mv1G/Dzv7ggr52CrEB0n4o5QA/vHc718ryYjyYwF01oEsMZS7ljv+b08k/6uGRNF845pR33feRCnH1KcSvaK9b34EOXrtJkMAD4k3OWYjwYxaNqElUEGaL02Nhc99KxKfx02wn8emdqHszv9o1gIODNWrp69cZe9LZ4sG9oFq1eJ376wnHc8cQhjMxG8dlr12slo61eJ2Yj+c3VD8cS8OaY+LkY6t6xAymd3Updloi0pd5pfeW58AXtaund+FwMX3hgH5a1+/DXV64BYD5LY2w2vetU4Hfb02ayZ3PsK9ShWq+MZEaQTx8ax43ffAb/+dArAHh0z+t/B/GFB/fjM7qoKK4kC26f5js9pS5MkQhcbBJ8IZx2G24+b4XpHpylQt+o0+ZzZlR7FENKO1brs9d242e3nYdLsjRxlZK/umINfvKB87REajF4nHb87ZvWpW0nefGabvS3evCj57kcIyL2Zo+DD7AzJE+/+DA/V6dCcZyYCqujFcZxzabsoxWcdhu+8LZN+Ns3rcW/vW0TBmciuP3xg3jz6X1pc3NavE4wlpKDssEYk8nTxbK0BI4d4FPgAOC0LJv/lgpRHnbX1tewf3gOn7hqrZbAE1qhHrNOTUAdx5s2LEmdNmdomnDYbTi1u0nb01UQiSv4zC+44xbJXL+bO3YRqd+/YxC/VncdiivJgpf/RscuEoH1iJBimj0OvP+ildgzOGvahl8IxmQzEWHLivaS3xjLCU+iLsPWA+M4PhnCvqE5NLsd8LscGV3TTx8ax1MHJ/C2M7l08vLxaTy2bxQxZeGa/kvXdeNDl67G5et70NnkhtNuw99dtS7tNWLVNROO40fPHTOdFgrwibNJVpoBYECjOPa2Ujl2HgWtK3vEzk+en790EmcsDeDNm/q0xKiZFGOszxX4XOkRu9DbzaKItb3NeGU4vT73G08cwmvj8/jAxakuUr/LoZVSvmPLEmxe0orP/nI3RuciiCss6/6m2WjyONISsfpEYL0hGnXOW9mBN23gFStPvDK6qN9pnF1fr7zj9UtgI+DbTx3Gb3cP4epNvep+wqmInTGGLz70CvpaPfjnP9oIl8OGnSem8eudQ+hr9eCMJYG83stpt+E/3r4J//WOMzJKXcWQwZG5CD79i1346u8OmP6OUm6yATSIY3/Thl5cf0Z/zs0QiqG7xQ2nnbCyM/9NB6xA39Dx928+DUR8qFa735Xh2IPRBEIxxdSxGztKjdvi6VnX24yR2ajW8n5wNIg7njiI68/oxyfftA5L273wu+yw20gbu3D1pj586R1nIBRT8Kl7dyGWKC5iD8ZSY4D1icB6o83nwlnL23DjlqVY2elHm8+JHYbdmgrFuHNWvdLX6sVl67pxz9NHMB9T8NYzlwBI3/3r8VdG8eKxaXzkslPR5HZgfV8LHtg1jN/tH8FbzxwoaLTCZet68GaTPIXI/YjV7ROvjJrOgxfTUqVjXwTr+1vwlXdaV70geO+FK/G1m84sydjNXLT5nHA5bLhyfU9a56CYpaFH25TCJGLzux3pVTFx83JHAFiryk37h+fAGMOnf7ELPpdDSxz9zZVr8bbX8YupyeOA32XH+as6sLq7CZ+4ah1+t38Uu0/OFOXYGeM3HWMisN6wJzijQQAAFidJREFU2wj3fvB8XLG+B0SE05cEsg65ypeR2Qi8Tjuac0yWrBduOnsZGOPNUOeoyVmvi0fsySTDFx96Fcs7fLhxCz9Pz1gawMnpMJrdDtx60cpcvzpvRMQuBgPORhJp+zYIREK3VB3q9f9pl5DV3U0FbeprFQ67DT+59VysMrx3b4sbI3MGxy46NU2iXL/LjuGZlIYrInaz8bKiieSV4Tkcmwzh+cOT+Le3bdLkqOvPGMD1Z3Dd8raLV+KG1w1oI3L/7PwVeHjPMJ47PFmwFKPfZ1bIRvUuKwg2L2nF7Y+PmQ5my5cDo0H0Bzx1paln45K13Th9SSuu29yvRd8eJ9fYH9w9jL1Ds/ivP96sBRenL+EVQh+8ZHVeuzjlg3Ds+3VjBX63bySjZyAc41F8qYaAScdeo5y5LHPUak+LB7tOpuvgKcduprE78qpjFz/f5nPiu08fwWQohtevaMM7tizNeB2AjOYsm43wxRs346r/fhJN7sLKFMXG13PRhDZOoF4jdiOnLwkgyXivRDEzXabmY3jq4Djee+EpJbCu+rDbCPd9+MK0x3wuO0Zno/jSI69gTU8Trtuc2vfn6o19mAnHcdPZyyyzQTj2V1Qp5uwV7Xh03yg+fc1paTfXbM2AVtEQUkyj0NPiwcR8+k7uxv1B9fjdWerYTZaHRIR/v+F02G2EaDyJ//fWTQVpkkvbffjVhy/EJ68qbNSCGOEQjCQaJhEoOF2tOTfT2eejiQWnDj64exiJJMNbNveXxL5awOu0Y+/QLF4bm8dfX7E2bTaO12XHn11wiqVyiEfdpWs+pqDN58Tbz1qCw+PzeOa1ibTXCY29VFUxMmKvI8RO7v/6m33wu+34myvXYmwuCpfDhhZv5kctInbGGIgIoZgCl92WdW74lRt6ccX6HrX+tvBTpxjZSnS9zkcTDZMIFHQ3e9Df6sEOg87OGMMNdzyN5R0+fOvmLVl//v4dg1jZ6ceG/vKW41YTwnGevqQVb8pzw+7FEvA5MTIbRV+rF9ed0Y//eGg/7tp6GOer0zUBWRUjKQARyd7z9BF8/fFDuOfpI1qpo5nG2tnkQkxJjSEIxzI3sjZCRCXTBc0QFTZz0URDJQIFW1a04+E9w3hgV2q/mldHgtg/PIeH9oxoSTojo7MRPHt4Am/Z3N8Q+no2RIXXx69cW7bjIOSY/oAHHqcdN5+7Ao/tH8XB0VQfSK4KNCuQjr2OGAjwmtrLT+vGFet78Pnf7MOje0dM9XX+el7+eULdZLiUQ4mKpUmXPB2Zi1oyermW+Oy167GhvwV/8cMX8ZVHD4Axht/uHgYRj/bueOKQ6c/9eucQGAPesrn8owOqiTef3ocPXboKF53aufCLLUJs0yi6lt997jK4HTbctfWw9pqwlGIk+bKmpwl3/+kWnL+qE4lkEv/96AE8/sooLjzVvH18oE3MyA5j40ArQvHSDSUqFiHFBNWIvVFkGEFXsxs/vvVcfOrnu/Bfj76KV0fncGBkDluWt+HMZW24a+tr+Oy16zNGRty/cxCn9bWUbc5NtXL+qs40CaQctKgRe18rv746mty44awl+Nn2E/j4lWvR1ezWyh2rYms8SXVDRHjjaT3wuuxo9jjx2WvX47GPX4K/vmKN6ev7AynHDpR2c91iERF7MJrAaB2PE8iF22HHl27cjL+7eh0e2DWEV0eCeNOGXly5vgdJlplcPT4ZwkvHpnFdAydNK4leihG898JTEFeS+P6zRwFIKUZSQjr8LrgdNgyqTU2hWCJjk41K43bY4LCRWhUTzdjEulEgItx28SrcefMWXLC6A9ed0Y/1/S2wEbDzZHpy9X51Ns+1FZjgKEl1n4qIHeATUt+4rgc/ePYoInEF4ZgCt8NWsh2spGNvYIgIAwEvTk6lIvZqk2KICH63A8MzEYTjSkNG7HquWN+DH77vXHQ3e+BzObC6uwm7DY79vpcH8bplAcu27JMUhhaxt6aPMHn/G07B5HwM9754ouT5LOnYG5z+gFebIFiNyVOAyzGHxviUPLPRCI3MxoFW7NTtnHVgZA77h+cauna90py3qgOXrO1CXyA9CDn7lHacvqQVd289jPlooqT7IkvH3uD0Bzyaxl7KHV0WA3fs8wAap+s0X04faMV4MKptGXj/ziHYCHjzAiNoJaXj9Svacc+fnZ0xF4mI8P43rMRr4/N4aM9wSa816dgbnP6AF6NzUUQTSkkH/y+GJo8jY7NsCWeTOu9k54lpMMZw/45BnLuyo+Gqh2qFazb14cOXrgZDugZvNdWVKZOUHVHLPjwTWdSwqVLSpGtIylaT36is72uFjfiGEf0BLw6Pz1s2qVBiPXYb4W/etBbvv2glStmOUX1XsaSs6JuUIvFkSXW/YhGOvcntMJ082ch4XXact6oDv3zpJKIJvqfs1Rt7K22WZAFEgrVUSCmmwRG17CI5WZVSjOrMZeLUnFvOW4HBmQi++/QRXLSmy7IRtJLaRTr2Bkdk7g+MVK9jF1F6T53unLRYLj+tBwMBrzrJUSZNJdKxNzxuhx1dzW5t013jRtbVgBgr0CjjegvFbiPcdvFKdDa5cMV6KcNIpMYuAZdjDla1FMNtkhUx2Xn3ucvxrnOWFzQjX1K/yIhdgoGAR9udqDrr2HmiSZbwZYeIpFOXaEjHLtEqYwDz3ZMqjV+L2KUUI5Hkg3TsEq0yBijdGNHFIDbMXtImZ59IJPlQsGMnoquI6BUiOkhEf2fy/HuIaIyIXlb/vc8aUyWlQu/Yq1GKOW9lB378/nOxWe2ylEgkuSkoPCMiO4CvA7gCwAkALxDRfYyxvYaX/oQx9mGLbJSUmDQppgodu81GOG9VR6XNkEhqhkIj9rMBHGSMvcYYiwH4XwDXW2+WpJxUu2OXSCSFUahjHwBwXPf9CfUxIzcQ0U4i+hkRLS3aOklZCPic2iiBapRiJBJJYZQieXo/gBWMsdMBPALgu2YvIqJbiWgbEW0bGxsrgRmSfCEi9Ac8sNsILrvMp0sktU6hV/FJAPoIfIn6mAZjbIIxFlW/vQvAWWa/iDF2J2NsC2NsS1eX+WbLkvIx0OaDz2kHlXLknEQiKQuFOvYXAJxKRKcQkQvAOwHcp38BEemHVVwHYN/iTJSUg/V9LRhoK918aIlEUj4KqophjCWI6MMAHgJgB/BtxtgeIvpnANsYY/cB+CgRXQcgAWASwHsstllSAv76ijX4yGWrK22GRCKxABJ7JVaSLVu2sG3btlXaDIlEIqkpiGg7Y2yL8XGZKZNIJJI6Qzp2iUQiqTOkY5dIJJI6Qzp2iUQiqTOkY5dIJJI6Qzp2iUQiqTOqotyRiMYAHC3yxzsBjFtoTjmoNZtrzV5A2lwqasFGI7VmcyH2LmeMZbTuV4VjXwxEtM2sjrOaqTWba81eQNpcKmrBRiO1ZrMV9kopRiKRSOoM6dglEomkzqgHx35npQ0oglqzudbsBaTNpaIWbDRSazYv2t6a19glEolEkk49ROwSiUQi0SEdu0QikdQZNeHYSW7rU3LkMS4PtXCca8FGI7VocympCccOvqkHAPkBlhB5XMtDLRxnbQOeGrreAgBARAVtHlQpiGgtEZXM/1a1YyeiNxPRowC+TEQXAQCr8mwvEf0REf1Lpe3IFyK6hoh+BeA/ieiSStuTD7V2jIHaOM5EdBURPQTgi0T0VqAmrrdW1ebfAnyXtwqblBMiuoKIngPwPpTQ/1atYyeiFQD+FcDXwPdNvZWI3qc+V3V2E5FNte+LAP6OiN5QaZtyQUROIvoSgH8E8E0AMwBuIqJzKmpYFohjr7FjTETkqubjrLPxiwA+C+DrAF4BcCMRnVpZ6/IiDGAawEYiuhEAiMie+0fKi3qMneoWot8A8O+Msb8VN6FSrIqqzkHqWAXgD4yxXwH4DoC7AHyEiNoYY8lqWyIyxpIADgA4E8BfAKjqiJIxFge/gG9ijD0IfnwDAJSKGpYFxlEAHETtHGPGGIsBeBXAn1TjcdbZ+FsAF6v7Fj8NIA7gcEWNWwDVgbcBeBbAH4MHgWCMKdXkH9RjHAeQBPAzxtjPAYCI3kBEzlK8Z9XUsRPR2wEcZ4w9p36/FsATAFYwxqLqY3cAiDDG/oqIqNLLRBObneoHCCJ6AcA3GWN3E5FNdfwVxcReF/im4w7GWIyIHgDwFcbYQ5W0Uw8RfRTAJgDPMcbu0n/u1XiMgTSbn2eM/Y9uhVk1x9l4XHWPXwPgdgCjAJ4EsJ0x9pMqud6Ezc8A+A5jjBGRF8D9jLHLiehhAH8A8HPG2O5K26yz9wXG2J1E1Avg3wAwAFsAHAEwBeD36jlsnb2MsYr+A9AN4PcABgH8EoBN99z3APy3uI4BbAbwMwA91WizaqP4+moAewC0VesxNhzrNgC/A9BbaXt1Nr0HPBq7SrX/UwBW6Z6vmmO8gM2rq+k4m9j4aWEjgLMBrNEd34fAg6tqO66fBl/VdwP4vPqaPwcPVLap3zuryN7PqJ/9HwH4IYB1qr+4HsBvACyz8v0rLsUwxkYB/Ar8AAwB+IDu6X8CcC0RbWD8aEUAzAEIlt1QHTlsJqbKRIwvu0VuoFnof5Ugh7366GA5gBnG2DARLSGiy8psphlvBNcjfwvg4wA8AN4lnqymY6zDzOY/0T2/ApU/zkYbXVCPK2PsecbYq+rr9gEYA3eWlcZosxvAjeAa+9VqtP5RAI8hNQK8knab2fsBxtgvAdzKGNuv+rSd4DmCuJVvXlHHrluifg3AXgAPA3gzEfUBAGPsEIC7AXyDiC4E8G7wO3TFlty5bFadug2p4/pJAF8A1957y24sFrSX6crDBgDYiegj4BFERewF0mx+CcC1AMAY2wa+BB8gogt0L6/4MQbyslkkeit2nHPY+CyAfsNxBXjU6QMwUS4bjSxwXFcCuBDAI+Cy1xmMsSsBXEJEp6iOs1rsfQrAKUR0AWNsXvcjfwrACy7JWEZZHbvIVovEBlM1UcZYnPEM8dMA9gP4mPgZxtgXwJ37ewGsBfBexli4Cm3+qHie8eTNKgB3gEsfr2OMfa1K7RVRzRUA3gJgNYBrGGM/Koe9Oru1c5GltPKnANhILXUFsBt8xdGv/sxq8CqDsh5jQQE2DyLlwMt6nAu0URzXW4hoN4BTAHywnNeb+v752LwHwAkAzQD+gTH2Gd2vWMYYK1vit8hz9wYi2gF+c/ogYyxipU1lcexEdAERfRfAZ4ioXdxJiZev6bPX4wDuA7BGXaZ2q1Uw3wNfxryDMTZcpTavVW3uJKIW9fEPM8bexhgbrGJ7e9TH/xfAlYyxjzHGTpbaXtW2s9UEk/6C0F8oB8Av4D8mIjtj7ASAHnA5A+Clg2U7xouwuRdcDwaAewFcUcrjvAgbT1Gf3wkuF/wpY2ykFDZaYPNxcCe5nPGEtF281hARV4u94twVx/hVALcxxm4pxTEuuWMnopXgUdXj4Druv6iZdzDGFFUOcBORW/3+SfADshs86dCpvjZWalstsnkreHJ3RqdVVrO9TxDRqYyxZxljj5bDXtXmvwTwC/Ab0dXqY3bVZnGhzIEfTzd404wTPAE1ob5ujDF2oEZsHlVf9yRj7HdVauO4+rqXGWNPl8pGC20OIHUuKKxMVVEWHeNdjLFnSmYkK312+J0A/lf9uh3A+8Elij71sX8G8H2omXcAt4FfBP+OCmW1a83mWrNXteF68Hr0G8DLvYzP/xOA/wOvHugDcA+4FvwtAHZpc+3aWOs214K9pfij3wLgwwDOVb9fCa43LVO/Xw9ey/lX4ImPHyG9HOxy/fdl+qBqyuZaszeLzXb1nwfAAwA+qj5uA6/9/RHSSxttAJqlzbVnY63bXGv2MmahYwe/M90Pvvz4LHip1JvU574I4OO6g3IzgM8BaNX9fCXuvDVlc63Zm4fNokHujQB2AOg0+XlbuWytJZtrwcZat7nW7E17bwsPwlsAfEL3/W0A7lW/vh58OXKO+v1lAH5X6QNQazbXmr1ZbP4AgF8YXmMDX6b+k/r92er/JG2uXRtr3eZas1f/b1HJU7Us6hIicoN3031f9/QEeOYXAJ4Dr+v8MhE1AdgA4CgR+YD0rHKpqTWba83ePGyeBI98tAoC1bbPA/gkEc0AeB1RedvBa8HmWrCx1m2uNXuzUfDsYrV0rhdcR0oCOASerPsYY2yIUvNS+sCzwGC8RPErRLQcwLfBKzduYYyFrPkz6svmWrN3ETaLYW6rwAe9PQXgLxlju6TNtWNjrdtca/bmRYFLE7v6/xoAPxCPgXc1/tzwmvsBXK5+3a3+70D5EzU1ZXOt2btIm9uF7QAulTbXno21bnOt2Zvvv7widrVG81/AW6EfANACdewo412WHwMwSEQXM8Z+T3xq4BiAV4noX8HnvVzCGJsCr+8sObVmc63Za6HNlzI+y2ZU2lw7Nta6zbVmb6EsqLET0cUAtoMvQQ6CH4w4gEuJ6GxA05n+Ebx+E+BlQO8B16iawe9yls5CqCeba81ei22elDbXlo21bnOt2VsUeSxV3gDgZt333wDwQfA/crv6mA1co/opgCXgoz+/B+CMSixDas3mWrNX2tzYNta6zbVmb1F/Yx4HwQfeFit0pncB+IL69csAPqJ+vQVq92Ol/9WazbVmr7S5sW38/+3dPWgUURTF8f8pUkiUNNpZBCGRoGgKC8tUgmBhoY2gYCMIdtqKjYVVQIminWKRykYsTB9FECQRrFOKpBH8SsDstXhPCItZd6LZzXtzfrCQnd0JZ1Lc2X0zubf0zKXl3c7jr0sxEfE9ItYjjSWD1J1uNf98GZiS9AKYJ3292ZEZfk2Ulrm0vODMbc7YrbTMpeXdjr5vd8wXG4LUoex53vyFNMnkKLASuVtd5NPdsJWWubS84MxtztittMyl5W2iyT8odYARUneyY/mMdhPoRMRiDKjVa0OlZS4tLzjzTikhY7fSMpeWt39N1m2Ak6Q/xiJp4MXQ15Jqy1xaXmdud8bSM5eWt9/H70Y2fZF0kNRcajYi1hufRYagtMyl5QVn3iklZOxWWubS8varUWE3M7Pdb6jDrM3M7P9zYTczq4wLu5lZZVzYzcwq48JuZlYZF3ZrHUkbkpYkfZC0LOm68kScHvuMS7owqIxm/8KF3droR0RMR8QRUp+Q06TB372MAy7sVgTfx26tI+lrROzd9PwQ8BbYTxop+BQYzS9fi4jXkt4AU8AK8AS4B9wBZkidAu9HxKOBHYRZDy7s1jrdhT1v+wwcJjWB6kTEmqQJYD4iTkiaAW5ExJn8/iukcYS3lQYfvwLOR8TKQA/G7A8aD7M2q9wIMCdpmjQqbXKL950iNY46l5+PAROkT/RmQ+XCbq2Xl2I2SLMrbwGfgOOka1BrW+1GGsiwMJCQZg344qm1mqQDwENgLtK65BjwMdLMy4ukifWQlmj2bdp1AbgqaST/nklJo5jtAv7Ebm20R9ISadnlJ+li6Wx+7QHwTNIl4CXwLW9/D2xIWgYeA3dJd8q8y9N1VoGzgzoAs1588dTMrDJeijEzq4wLu5lZZVzYzcwq48JuZlYZF3Yzs8q4sJuZVcaF3cysMr8AHYRc5uo7bxoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "AMZN\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "                   Open         High  ...    Adj Close   Volume\n",
            "Date                                  ...                      \n",
            "2020-10-01  3208.000000  3224.000000  ...  3221.260010  4971900\n",
            "2020-10-02  3153.629883  3195.800049  ...  3125.000000  5613100\n",
            "2020-10-05  3145.840088  3202.530029  ...  3199.199951  3775300\n",
            "2020-10-06  3165.000000  3182.000000  ...  3099.959961  5086900\n",
            "2020-10-07  3135.000000  3200.000000  ...  3195.689941  4309400\n",
            "...                 ...          ...  ...          ...      ...\n",
            "2021-04-22  3371.679932  3372.870117  ...  3309.040039  2580600\n",
            "2021-04-23  3319.100098  3375.000000  ...  3340.879883  3192800\n",
            "2021-04-26  3348.000000  3428.449951  ...  3409.000000  4880700\n",
            "2021-04-27  3443.469971  3460.000000  ...  3417.429932  3827100\n",
            "2021-04-28  3434.800049  3489.879883  ...  3458.500000  4631900\n",
            "\n",
            "[144 rows x 6 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "RCL\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "                 Open       High        Low      Close  Adj Close   Volume\n",
            "Date                                                                      \n",
            "2020-10-01  65.150002  65.919998  63.570000  65.550003  65.550003  5432000\n",
            "2020-10-02  62.500000  65.760002  62.299999  65.260002  65.260002  4949000\n",
            "2020-10-05  65.000000  66.099998  64.500000  65.250000  65.250000  3575800\n",
            "2020-10-06  66.000000  69.910004  65.769997  66.180000  66.180000  7355400\n",
            "2020-10-07  67.470001  68.639999  66.099998  68.019997  68.019997  4551400\n",
            "...               ...        ...        ...        ...        ...      ...\n",
            "2021-04-22  84.400002  86.550003  83.610001  84.180000  84.180000  2982200\n",
            "2021-04-23  84.650002  85.959999  83.279999  85.809998  85.809998  2512100\n",
            "2021-04-26  87.400002  88.839996  86.669998  86.940002  86.940002  2229500\n",
            "2021-04-27  87.010002  88.220001  85.820000  87.629997  87.629997  2112200\n",
            "2021-04-28  87.260002  88.430000  86.589996  87.470001  87.470001  1943400\n",
            "\n",
            "[144 rows x 6 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "AAL\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "                 Open       High        Low      Close  Adj Close     Volume\n",
            "Date                                                                        \n",
            "2020-10-01  12.450000  12.770000  12.350000  12.580000  12.580000   48842000\n",
            "2020-10-02  12.050000  13.350000  12.000000  13.000000  13.000000  111883300\n",
            "2020-10-05  13.090000  13.200000  12.750000  13.120000  13.120000   53465500\n",
            "2020-10-06  13.280000  13.440000  12.460000  12.530000  12.530000   85252400\n",
            "2020-10-07  12.980000  13.180000  12.810000  13.070000  13.070000   61274200\n",
            "...               ...        ...        ...        ...        ...        ...\n",
            "2021-04-22  21.580000  21.610001  20.040001  20.070000  20.070000   72635100\n",
            "2021-04-23  20.270000  21.200001  20.180000  21.110001  21.110001   47995300\n",
            "2021-04-26  21.520000  22.049999  21.480000  22.040001  22.040001   44349200\n",
            "2021-04-27  22.010000  22.070000  21.600000  21.760000  21.760000   26767100\n",
            "2021-04-28  21.639999  21.889999  21.480000  21.700001  21.700001   22572100\n",
            "\n",
            "[144 rows x 6 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "BA\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "                  Open        High  ...   Adj Close    Volume\n",
            "Date                                ...                      \n",
            "2020-10-01  170.149994  172.759995  ...  167.860001  30677100\n",
            "2020-10-02  161.860001  169.779999  ...  168.080002  31773800\n",
            "2020-10-05  169.490005  172.080002  ...  171.199997  19082100\n",
            "2020-10-06  173.039993  175.559998  ...  159.539993  47132500\n",
            "2020-10-07  163.710007  166.339996  ...  164.610001  26019100\n",
            "...                ...         ...  ...         ...       ...\n",
            "2021-04-22  235.960007  239.649994  ...  234.330002  13854400\n",
            "2021-04-23  233.889999  238.580002  ...  238.380005  11533100\n",
            "2021-04-26  240.240005  244.649994  ...  241.440002  10599600\n",
            "2021-04-27  241.880005  243.490005  ...  242.470001  10583300\n",
            "2021-04-28  239.000000  239.649994  ...  235.460007  19054100\n",
            "\n",
            "[144 rows x 6 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "ZM\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "                  Open        High  ...   Adj Close    Volume\n",
            "Date                                ...                      \n",
            "2020-10-01  477.000000  483.730011  ...  482.989990   7859300\n",
            "2020-10-02  485.005005  507.489990  ...  482.230011  12791700\n",
            "2020-10-05  493.970001  496.899994  ...  485.369995   7534700\n",
            "2020-10-06  480.510010  487.899994  ...  478.250000   6097700\n",
            "2020-10-07  482.500000  487.500000  ...  480.609985   4449500\n",
            "...                ...         ...  ...         ...       ...\n",
            "2021-04-22  322.665009  338.049988  ...  328.320007   3328300\n",
            "2021-04-23  331.489990  338.549988  ...  336.880005   2454300\n",
            "2021-04-26  337.510010  338.000000  ...  336.420013   2286700\n",
            "2021-04-27  336.390015  341.679993  ...  333.489990   2208300\n",
            "2021-04-28  330.239990  334.700989  ...  331.250000   1437600\n",
            "\n",
            "[144 rows x 6 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "MRNA\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "                  Open        High  ...   Adj Close    Volume\n",
            "Date                                ...                      \n",
            "2020-10-01   69.570000   70.199997  ...   70.029999   6839000\n",
            "2020-10-02   67.900002   72.769997  ...   68.809998   8004900\n",
            "2020-10-05   69.639999   72.019997  ...   71.949997   6518100\n",
            "2020-10-06   72.000000   73.919998  ...   71.040001   7285500\n",
            "2020-10-07   71.230003   73.239998  ...   72.370003   4190600\n",
            "...                ...         ...  ...         ...       ...\n",
            "2021-04-22  168.259995  179.000000  ...  168.100006  15922400\n",
            "2021-04-23  170.304993  175.509995  ...  173.630005   8791000\n",
            "2021-04-26  171.789993  179.600006  ...  177.610001  12201700\n",
            "2021-04-27  178.360001  185.529999  ...  183.410004  12925700\n",
            "2021-04-28  182.369995  182.889999  ...  178.679993   8375400\n",
            "\n",
            "[144 rows x 6 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "BYND\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "                  Open        High         Low       Close   Adj Close   Volume\n",
            "Date                                                                           \n",
            "2020-10-01  169.240005  172.690002  165.880005  170.600006  170.600006  4729200\n",
            "2020-10-02  165.589996  171.750000  164.809998  166.240005  166.240005  2949000\n",
            "2020-10-05  168.649994  175.889999  166.759003  175.490005  175.490005  4163200\n",
            "2020-10-06  176.229996  186.699997  175.000000  181.860001  181.860001  8478800\n",
            "2020-10-07  184.649994  191.600006  184.199997  189.139999  189.139999  5167900\n",
            "...                ...         ...         ...         ...         ...      ...\n",
            "2021-04-22  133.729996  136.619995  132.160004  132.830002  132.830002  1558400\n",
            "2021-04-23  133.039993  134.880005  130.899994  131.419998  131.419998  1812700\n",
            "2021-04-26  132.119995  132.899994  129.509995  132.059998  132.059998  1962000\n",
            "2021-04-27  131.843994  134.199997  131.550003  133.500000  133.500000  2208900\n",
            "2021-04-28  133.970001  135.800003  132.669998  134.220001  134.220001  1446100\n",
            "\n",
            "[144 rows x 6 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "CCL\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "                 Open       High        Low      Close  Adj Close    Volume\n",
            "Date                                                                       \n",
            "2020-10-01  15.430000  15.430000  14.700000  15.030000  15.030000  28158400\n",
            "2020-10-02  14.260000  15.220000  14.230000  15.160000  15.160000  30936100\n",
            "2020-10-05  15.110000  15.230000  14.800000  14.960000  14.960000  22169300\n",
            "2020-10-06  15.260000  15.970000  15.120000  15.190000  15.190000  43724300\n",
            "2020-10-07  15.480000  16.049999  15.290000  15.990000  15.990000  30265000\n",
            "...               ...        ...        ...        ...        ...       ...\n",
            "2021-04-22  26.969999  27.900000  26.770000  26.910000  26.910000  29698400\n",
            "2021-04-23  27.000000  27.410000  26.570000  27.270000  27.270000  19297600\n",
            "2021-04-26  27.820000  28.500000  27.309999  27.549999  27.549999  20033500\n",
            "2021-04-27  27.590000  27.879999  27.139999  27.809999  27.809999  18145200\n",
            "2021-04-28  27.700001  28.270000  27.570000  27.799999  27.799999  17273000\n",
            "\n",
            "[144 rows x 6 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "                   GME    AMC   NOK  ...        MRNA        BYND        CCL\n",
            "Date                                 ...                                   \n",
            "2020-10-01    9.770000   4.65  3.87  ...   70.029999  170.600006  15.030000\n",
            "2020-10-02    9.390000   4.65  3.82  ...   68.809998  166.240005  15.160000\n",
            "2020-10-05    9.460000   4.13  3.93  ...   71.949997  175.490005  14.960000\n",
            "2020-10-06    9.130000   4.06  3.91  ...   71.040001  181.860001  15.190000\n",
            "2020-10-07    9.360000   4.04  3.90  ...   72.370003  189.139999  15.990000\n",
            "...                ...    ...   ...  ...         ...         ...        ...\n",
            "2021-04-22  151.169998   9.99  4.16  ...  168.100006  132.830002  26.910000\n",
            "2021-04-23  151.179993  10.16  4.20  ...  173.630005  131.419998  27.270000\n",
            "2021-04-26  168.929993  11.50  4.20  ...  177.610001  132.059998  27.549999\n",
            "2021-04-27  177.770004  11.46  4.22  ...  183.410004  133.500000  27.809999\n",
            "2021-04-28  178.580002  10.85  4.20  ...  178.679993  134.220001  27.799999\n",
            "\n",
            "[144 rows x 22 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oM7Qga5sTAqn"
      },
      "source": [
        "# Bag of Words"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tRW4d80yTD0Y"
      },
      "source": [
        "# Read in Cleaned Reddit or Google News Data for Bag of Words Creation\n",
        "\n",
        "reddit = r\"/content/drive/Shareddrives/COS424/RedditAllBigClean.csv\" # alternative: Google News\n",
        "reddit_df = pd.read_csv(reddit)\n",
        "reddit_df['date']=pd.to_datetime(reddit_df['est'])\n",
        "reddit_df['date']=pd.DatetimeIndex(reddit_df.date).normalize()\n",
        "reddit_df=reddit_df.set_index(reddit_df['date'])\n",
        "reddit_df=reddit_df.fillna('The')\n",
        "\n",
        "compName = ['GME', 'AMC', 'NOK', 'TSLA', 'BB', 'KOSS', 'EXPR',\n",
        "          'MSFT', 'FB', 'AMZN', 'GOOGL', 'AAPL', 'NFLX', 'BABA',\n",
        "        'RCL', 'CCL', 'BY', 'ZM', 'AAL', 'BA', 'MRNA']\n",
        "\n",
        "compType = ['Hype', 'Hype', 'Hype', 'Hype', 'Hype', 'Hype', 'Hype',\n",
        "            'Lead', 'Lead', 'Lead', 'Lead', 'Lead', 'Lead', 'Lead',\n",
        "            'Volat', 'Volat', 'Volat', 'Volat', 'Volat', 'Volat', 'Volat']\n",
        "\n",
        "# Create bag df\n",
        "reddit_bag = pd.DataFrame(columns=['Title','Body','Company','Date'])\n",
        "\n",
        "# For every company and every day create individual bag of words df\n",
        "for company in compName:\n",
        "  df = reddit_df[reddit_df.company == company]\n",
        "\n",
        "  bag = pd.DataFrame(columns=['Title','Body','Company','Date'])\n",
        "  dates = pd.date_range(start=\"2020-10-01\",end=\"2021-04-29\")\n",
        "  bag['Date']=dates\n",
        "  bag=bag.set_index(bag['Date'])\n",
        "  \n",
        "  # for every day, find all posts or headlines for that day and append them to each other\n",
        "  for date in dates:\n",
        "    bag.at[date,'Company']=company\n",
        "\n",
        "    if date in df.index:\n",
        "\n",
        "\n",
        "      title = df.loc[[date],['titleClean']]\n",
        "      stringTitle=\"\"\n",
        "      for i in range(len(title)):\n",
        "        string=title.iloc[i, 0]\n",
        "        stringTitle += string\n",
        "        \n",
        "      body = df.loc[[date],['bodyClean']]\n",
        "      stringBody=\"\"\n",
        "      for i in range(len(body)):\n",
        "        string=body.iloc[i, 0]\n",
        "        stringBody += string\n",
        "      bag.at[date,'Title']=stringTitle\n",
        "      bag.at[date,'Body']=stringBody\n",
        "      \n",
        "    else: \n",
        "      #if no posts or headlines for that day, fill with filler word \"the\"\n",
        "      bag.at[date,'Title']='the'\n",
        "      bag.at[date,'Body']='the'\n",
        "    \n",
        "  new = new.append(bag) # new includes all bag of words, ready for vectorization"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fM1zKDUiTIv7"
      },
      "source": [
        "#https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html \n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "# Uses a TfidfVectorizer to construct bag-of-words matrix\n",
        "vectorizer = TfidfVectorizer(ngram_range=(1,2),max_features=1000) # for SVC\n",
        "\n",
        "# create separate bag-of-word representations for Reddit post title and bodies\n",
        "r_body = vectorizer.fit_transform(new['Body'])\n",
        "r_title = vectorizer.fit_transform(new['Title'])\n",
        "\n",
        "# create bag of word representation of Google news headline (if applicable)\n",
        "r_news= vectorizer.fit_transform(new['Body'])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1uQGdlWeAjLN"
      },
      "source": [
        "# Prediction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LXFXjeF3zD5V"
      },
      "source": [
        "## Neural"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rfTauI06zBmL"
      },
      "source": [
        "To run the neural network, please first run this section before going to \"Main Predictor\" and running with the wanted settings."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RqpKa0m2A4yT"
      },
      "source": [
        "# The code in this section is adapted from T. Davidson's neural network: https://github.com/t-davidson/fragile-families-challenge/blob/master/preprocess/clean_files.py\n",
        "def make_model(activation_function, num_hidden_layers, hidden_layer_size): \n",
        "  model = Sequential()\n",
        "\n",
        "    # Single layer model\n",
        "  if num_hidden_layers == 0: # then just specify a single layer, 1 is size of output\n",
        "        model.add(Dense(1, \n",
        "                        input_dim=X_train.shape[1], \n",
        "                        activation=activation_function,\n",
        "                        use_bias=True))\n",
        "        model.add(Dropout(0.5))\n",
        "    \n",
        "    # Specify initial layer with a hidden layer\n",
        "  if num_hidden_layers >= 1: \n",
        "        model.add(Dense(hidden_layer_size, \n",
        "                        input_dim=X_train.shape[1], \n",
        "                        activation=activation_function,\n",
        "                        use_bias=True))\n",
        "        model.add(Dropout(0.5))\n",
        "    \n",
        "    # Now add additional hidden layers\n",
        "  for i in range(0,num_hidden_layers-1):\n",
        "        model.add(Dense(hidden_layer_size, \n",
        "                        activation=activation_function, \n",
        "                        use_bias=True))\n",
        "        model.add(Dropout(0.5))\n",
        "    \n",
        "  if num_hidden_layers > 0:       \n",
        "        model.add(Dense(1)) # Final output layer, don't add if no hidden layers\n",
        "\n",
        "  model.compile(loss='mean_squared_error',\n",
        "                  optimizer='adam')\n",
        "  return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "q3lo4SE4ZmcU",
        "outputId": "80169cdf-584d-4391-96e4-d7c09dd3e262"
      },
      "source": [
        "# Set to 100 epochs and set parameters\n",
        "from sklearn import datasets, linear_model\n",
        "from sklearn.model_selection import cross_val_score, KFold\n",
        "from keras.models import Sequential\n",
        "from sklearn.metrics import accuracy_score\n",
        "from keras.layers import Dense\n",
        "from keras.wrappers.scikit_learn import KerasRegressor\n",
        "model = KerasRegressor(make_model, batch_size=32, epochs=100)\n",
        "\n",
        "# Choose wanted parameters for CrossValidation (might take several hours to run)\n",
        "params = [{'num_hidden_layers': [0],\n",
        "          'hidden_layer_size': [0],\n",
        "          'activation_function': ['sigmoid', 'tanh']},\n",
        "          {'num_hidden_layers': [1, 2, 3],\n",
        "          'hidden_layer_size': [64, 128, 256],\n",
        "          'activation_function': ['linear', 'sigmoid', 'tanh']}]\n",
        "\n",
        "grid = GridSearchCV(model,\n",
        "                        param_grid=params,\n",
        "                        scoring='neg_mean_squared_error', #sklearn optimizing by maximizing negative MSE\n",
        "                        n_jobs=1,\n",
        "                        verbose=0,\n",
        "                        cv=5 # Number of folds for CV\n",
        "                  )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"   \\nparams = [{'num_hidden_layers': [0],\\n          'hidden_layer_size': [0],\\n          'activation_function': ['sigmoid', 'tanh']},\\n          {'num_hidden_layers': [1],\\n          'hidden_layer_size': [64],\\n          'activation_function': ['linear']}]\\n\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kw1HvLZR9w7K"
      },
      "source": [
        "## Lasso Feature Selection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "IiwvsGVG9zri",
        "outputId": "ef613b72-fc03-4307-8d8d-1598a148828e"
      },
      "source": [
        "# Perform Feature Selection (note: first perform the train-test split in \"Main Predictor\")\n",
        "lasso = LassoCV().fit(X_train, y_train)\n",
        "\n",
        "# Print feature importances\n",
        "importance = np.abs(lasso.coef_)\n",
        "feature_names = np.array(X_train.columns)\n",
        "plt.bar(height=importance, x=feature_names)\n",
        "plt.title(\"Feature importances via coefficients\")\n",
        "plt.show()\n",
        "\n",
        "# Detailed feature importances\n",
        "for i in range(0, len(importance)):\n",
        "    print(str(importance[i]) + ' ' + feature_names[i])\n",
        "    \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "0.0 index\n",
            "0.0 s5\n",
            "0.0 s4\n",
            "0.0 s3\n",
            "0.0 s2\n",
            "0.0981190765757166 s1\n",
            "0.0 p5\n",
            "0.005357651139734147 p4\n",
            "0.023743481109756503 p3\n",
            "0.0019060096320370264 p2\n",
            "0.0013698972960457258 p1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8qFe3bfly9Do"
      },
      "source": [
        "## Main Predictor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tGlKrfwcRzpJ",
        "outputId": "c6f3d4a1-08d8-4f7f-9122-314c171a1eb1"
      },
      "source": [
        "# CHOOSE OPTIONS BELOW BEFORE RUNNING:\n",
        "\n",
        "gn = False # For Google News: True // For Reddit: False\n",
        "all = False # For full dataset: True // For 2021 data only: False\n",
        "numb = 80 # Days in training set: 80 (rest automatically test)\n",
        "toModel = 'NN' # NN: Neural Network // LINREG: Logistic Regression\n",
        "binary = False # Categorical or Continuous Prediction\n",
        "\n",
        "# Read in wanted data\n",
        "reddit = r\"/content/drive/Shareddrives/COS424/reddit_compound_scores.csv\"\n",
        "df = pd.read_csv(reddit)\n",
        "\n",
        "if not all:\n",
        "  numb = 40\n",
        "\n",
        "if gn:\n",
        "  news = r\"/content/drive/Shareddrives/COS424/news_compound_scores.csv\"\n",
        "  df = pd.read_csv(news)\n",
        "\n",
        "stock = r\"/content/drive/Shareddrives/COS424/percent.csv\"\n",
        "stocks = pd.read_csv(stock)\n",
        "stocks['Date']= pd.to_datetime(stocks['Date'])\n",
        "stocks['Date'] = [time.date() for time in stocks['Date']]\n",
        "\n",
        "compName = ['GME', 'AMC', 'NOK', 'TSLA', 'BB', 'KOSS', 'EXPR',\n",
        "            'MSFT', 'FB', 'AMZN', 'GOOGL', 'AAPL', 'NFLX', 'BABA',\n",
        "            'RCL', 'CCL', 'BY', 'ZM', 'AAL', 'BA', 'MRNA']\n",
        "\n",
        "\n",
        "compType = ['Hype', 'Hype', 'Hype', 'Hype', 'Hype', 'Hype', 'Hype',\n",
        "            'Lead', 'Lead', 'Lead', 'Lead', 'Lead', 'Lead', 'Lead',\n",
        "            'Volat', 'Volat', 'Volat', 'Volat', 'Volat', 'Volat', 'Volat']\n",
        "\n",
        "# Fit model and predict prices for every single company in wanted interval\n",
        "for company in compName:\n",
        "  x = df.loc[df['company'] == company]\n",
        "\n",
        "  # Adjust comapny name error\n",
        "  if company == 'BY':\n",
        "    company = 'BYND'\n",
        "  x['date']= pd.to_datetime(x['date'])\n",
        "  x['date'] = [time.date() for time in x['date']]\n",
        "  x = x.set_index('date') # make dates column index\n",
        "\n",
        "  # Remove dates not in wanted interval\n",
        "  if all:\n",
        "    prevDates = pd.date_range(start=\"2020-09-30\", end='2020-09-30')\n",
        "    dates = pd.date_range(start=\"2020-10-02\",end=\"2021-04-29\")\n",
        "    date = datetime.strptime('01102020', \"%d%m%Y\").date()\n",
        "  else:\n",
        "    prevDates = pd.date_range(start=\"2020-09-30\", end='2020-12-30')\n",
        "    dates = pd.date_range(start=\"2021-01-01\",end=\"2021-04-29\")\n",
        "    date = datetime.strptime('31122020', \"%d%m%Y\").date()\n",
        "\n",
        "  # Fill out non-existent dates [CLEAN DATA]\n",
        "  if date not in list(x.index.values):\n",
        "    if gn:\n",
        "      x.loc[date] = [0,0,0,0,0,0, company, 0]\n",
        "    else:\n",
        "      x.loc[date] = [0,0,0,0,0,0,0,0,0,0, company,0, 0]\n",
        "  arr = []\n",
        "  for date in prevDates:\n",
        "     if date in list(x.index.values):\n",
        "        x = x.drop(date)\n",
        "\n",
        "  for date in prevDates:\n",
        "     if date in list(stocks['Date'].values):\n",
        "       stocks.drop( stocks[ stocks['Date'] == date ].index , inplace=True)\n",
        "  \n",
        "  for date in dates:\n",
        "      date = date.date()\n",
        "      if date not in list(x.index.values):\n",
        "        if gn:\n",
        "          x.loc[date] = [0,0,0,0,0,0, company, 0]\n",
        "        else:\n",
        "          x.loc[date] = [0,0,0,0,0,0,0,0,0,0, company, 0, 0]\n",
        "  \n",
        "  # ADJUST NO-TRADING DAYS, by taking weighted average\n",
        "  for date in dates:\n",
        "    date = date.date()\n",
        "    if date not in stocks['Date'].values:\n",
        "      arr.append(date - timedelta(days=1))\n",
        "      # print(date)\n",
        "    else:\n",
        "      if len(arr) > 0:\n",
        "        arr.append(date - timedelta(days=1))\n",
        "        sum = 0\n",
        "        num = 0\n",
        "        neu = 0\n",
        "        neg = 0\n",
        "        pos = 0\n",
        "\n",
        "        sumB = 0\n",
        "        neuB = 0\n",
        "        negB = 0\n",
        "        posB = 0\n",
        "\n",
        "        for days in arr:\n",
        "          sum += x['sum'][days]\n",
        "          num += x['num'][days]\n",
        "          neu += x['neu'][days]\n",
        "          neg += x['neg'][days]\n",
        "\n",
        "          if not gn:\n",
        "            sumB += x['sumB'][days]\n",
        "            neuB += x['neuB'][days]\n",
        "            negB += x['negB'][days]\n",
        "            posB += x['posB'][days]\n",
        "        sum = sum/len(arr)\n",
        "        num = num/len(arr)\n",
        "        neu = neu/len(arr)\n",
        "        neg = neg/len(arr)\n",
        "        sumB = sumB/len(arr)\n",
        "        neuB = neuB/len(arr)\n",
        "        negB = negB/len(arr)\n",
        "        posB = posB/len(arr)\n",
        "        arr.remove(date - timedelta(days=1))\n",
        "        x = x.drop(labels=arr, axis=0)\n",
        "        x['sum'][date - timedelta(days=1)] = sum\n",
        "        x['num'][date - timedelta(days=1)] = num\n",
        "        x['pos'][date - timedelta(days=1)] = pos\n",
        "        x['neu'][date - timedelta(days=1)] = neu\n",
        "        x['neg'][date - timedelta(days=1)] = neg\n",
        "        if not gn:\n",
        "          x['sumB'][date - timedelta(days=1)] = sumB\n",
        "          x['posB'][date - timedelta(days=1)] = posB\n",
        "          x['neuB'][date - timedelta(days=1)] = neuB\n",
        "          x['negB'][date - timedelta(days=1)] = negB\n",
        "      arr = []\n",
        "  print(company)\n",
        "\n",
        "  # PREPARE DF FOR FITTING AND PREDICTION\n",
        "  X = x.copy()\n",
        "  if company == 'BY':\n",
        "    Y = stocks['BYND']\n",
        "  else:\n",
        "    Y = stocks[company]\n",
        "  \n",
        "  \n",
        "\n",
        "  X = X.reset_index()\n",
        "  X = X.drop(df.index[0])\n",
        "  X = X.drop('Unnamed: 0', axis=1)\n",
        "  X = X.reset_index()\n",
        "  X = X.drop('date', axis=1)\n",
        "  X = X.drop('company', axis=1)\n",
        "  Y = Y.reset_index()\n",
        "\n",
        "  # Choose wante features (tuned from feature selection)\n",
        "  columns = ['s5', 's4', 's3', 's2', 's1', 'p5', 'p4', 'p3', 'p2', 'p1', 'p']\n",
        "  newDf = pd.DataFrame([], columns = columns)\n",
        "\n",
        "  v = 'pos' # Predict on positivity score\n",
        "  # v = 'neu'\n",
        "  # v = 'neg'\n",
        "  \n",
        "  # Create dataframe with last 5 days of data(optional: add more features)\n",
        "  for i in range(5, (len(Y) - 1)):\n",
        "    act = [[  X[v][i-5], X[v][i-4], X[v][i-3], X[v][i-2], X[v][i-1],\n",
        "         #  X[v2][i-5], X[v2][i-4], X[v2][i-3], X[v2][i-2], X[v2][i-1], \n",
        "           #  X[v3][i-5], X[v3][i-4], X[v3][i-3], X[v3][i-2], X[v3][i-1], \n",
        "            Y[company][i-5], Y[company][i-4],Y[company][i-3], Y[company][i-2], Y[company][i-1], \n",
        "            Y[company][i]]]\n",
        "    row = pd.DataFrame(act, columns = columns)\n",
        "    newDf = newDf.append(row)\n",
        "  \n",
        "  newDf = newDf.reset_index()\n",
        "  # print(newDf)\n",
        "  \n",
        "  if binary:\n",
        "    newDf.loc[(newDf.p >= 0),'p'] = 1\n",
        "    newDf.loc[(newDf.p < 0), 'p'] = 0\n",
        "  \n",
        "  X_train = newDf.drop('p', axis=1).iloc[0:numb]\n",
        "  y_train = newDf['p'].iloc[0:numb]\n",
        "  X_test = newDf.drop('p', axis=1).iloc[numb:]\n",
        "  y_test = newDf['p'].iloc[numb:]\n",
        "\n",
        "  # FIT MODEL BASED ON CHOICE ('toModel')\n",
        "\n",
        "  # Neural Network\n",
        "  if toModel == 'NN':\n",
        "    model = KerasRegressor(make_model, batch_size=32, epochs=100)\n",
        "    params = [{'num_hidden_layers': [1, 2],\n",
        "              'hidden_layer_size': [64, 128],\n",
        "              'activation_function': ['linear', 'sigmoid', 'tanh']}]\n",
        "    grid = GridSearchCV(model,\n",
        "                        param_grid=params,\n",
        "                        scoring='neg_mean_squared_error', #sklearn optimizing by maximizing negative MSE\n",
        "                        n_jobs=1,\n",
        "                        verbose=0,\n",
        "                        cv=5 )\n",
        "  \n",
        "\n",
        "  # Logistic Regression\n",
        "  elif toModel == 'LOGREG':\n",
        "    parameters = {'solver':('newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga')}\n",
        "    grid = GridSearchCV(LogisticRegression(), parameters)\n",
        "\n",
        "  # Linear Regression\n",
        "  elif toModel == 'LINREG':\n",
        "    linreg = LinearRegression()\n",
        "    lin_params = [{'fit_intercept': [True, False], 'normalize': [True, False]}]\n",
        "\n",
        "    # Perform Grid Search\n",
        "    grid = GridSearchCV(linreg,\n",
        "                            param_grid=lin_params,\n",
        "                            scoring='neg_mean_squared_error', #sklearn optimizing by maximizing negative MSE\n",
        "                            n_jobs=1,\n",
        "                            verbose=0,\n",
        "                            cv=5)\n",
        "  \n",
        "  # SVC\n",
        "  elif toModel == 'SVC':\n",
        "    parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}\n",
        "    svc = svm.SVC()\n",
        "    grid = GridSearchCV(svc, parameters)\n",
        "\n",
        "\n",
        "  # Fit and Run chosen Model\n",
        "  grid.fit(np.array(X_train), np.array(y_train))\n",
        "  best_model = grid.best_estimator_ #scikit-wrapped best model\n",
        "  # print(best_model) # optional\n",
        "  prediction = best_model.predict(X_test)\n",
        "  \n",
        "  # Turn predictions into binary\n",
        "  b = (prediction > 0).astype(int)\n",
        "  # print(b)\n",
        "  c = (y_test > 0).astype(int)\n",
        "  # print(c)\n",
        "  direction = ((b-c) == 0)\n",
        "\n",
        "  # PRINT OUT PERCENT ACCURACY FOR GIVEN STOCK\n",
        "  if ((b-c) == 0).unique()[0] == True:\n",
        "    print(list(Counter(direction).values())[0]/len(direction))\n",
        "  else:\n",
        "    print(list(Counter(direction).values())[1]/len(direction))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GME\n",
            "0.4\n",
            "AMC\n",
            "0.3142857142857143\n",
            "NOK\n",
            "0.37142857142857144\n",
            "TSLA\n",
            "0.5714285714285714\n",
            "BB\n",
            "0.45714285714285713\n",
            "KOSS\n",
            "0.3142857142857143\n",
            "EXPR\n",
            "0.45714285714285713\n",
            "MSFT\n",
            "0.5428571428571428\n",
            "FB\n",
            "0.5428571428571428\n",
            "AMZN\n",
            "0.6285714285714286\n",
            "GOOGL\n",
            "0.5428571428571428\n",
            "AAPL\n",
            "0.4857142857142857\n",
            "NFLX\n",
            "0.6\n",
            "BABA\n",
            "0.6\n",
            "RCL\n",
            "0.45714285714285713\n",
            "CCL\n",
            "0.42857142857142855\n",
            "BYND\n",
            "0.4\n",
            "ZM\n",
            "0.5428571428571428\n",
            "AAL\n",
            "0.4\n",
            "BA\n",
            "0.4857142857142857\n",
            "MRNA\n",
            "0.6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2SFCAFa-nqd6"
      },
      "source": [
        "## Ensemble"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D04aC2YTyrsy"
      },
      "source": [
        "Note this section was not finished in time, but can be used as a base to construct an ensemble model as suggestion in the \"Discussion Section\" of our paper."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hqbLbL0cnp9-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cbfd6435-de35-4297-82c8-72d56776a541"
      },
      "source": [
        "\n",
        "# CHOOSE OPTIONS BELOW BEFORE RUNNING\n",
        "gn = False # For Google News: True // For Reddit: False\n",
        "all = False # For full dataset: True // For 2021 data only: False\n",
        "numb = 80 # Days in training set: 80 (rest automatically test)\n",
        "toModel = 'LINREG' # NN: Neural Network // LINREG: Logistic Regression\n",
        "binary = False\n",
        "reddit = r\"/content/drive/Shareddrives/COS424/reddit_compound_scores.csv\"\n",
        "df = pd.read_csv(reddit)\n",
        "\n",
        "if not all:\n",
        "  numb = 40\n",
        "\n",
        "if gn:\n",
        "  news = r\"/content/drive/Shareddrives/COS424/news_compound_scores.csv\"\n",
        "  df = pd.read_csv(news)\n",
        "\n",
        "stock = r\"/content/drive/Shareddrives/COS424/trading.csv\"\n",
        "stocks = pd.read_csv(stock)\n",
        "stocks['Date']= pd.to_datetime(stocks['Date'])\n",
        "stocks['Date'] = [time.date() for time in stocks['Date']]\n",
        "# print(stocks['Date'])\n",
        "\n",
        "compName = ['GME', 'AMC', 'NOK', 'TSLA', 'BB', 'KOSS', 'EXPR',\n",
        "            'MSFT', 'FB', 'AMZN', 'GOOGL', 'AAPL', 'NFLX', 'BABA',\n",
        "            'RCL', 'CCL', 'BY', 'ZM', 'AAL', 'BA', 'MRNA']\n",
        "\n",
        "# compName = ['MSFT']\n",
        "\n",
        "compType = ['Hype', 'Hype', 'Hype', 'Hype', 'Hype', 'Hype', 'Hype',\n",
        "            'Lead', 'Lead', 'Lead', 'Lead', 'Lead', 'Lead', 'Lead',\n",
        "            'Volat', 'Volat', 'Volat', 'Volat', 'Volat', 'Volat', 'Volat']\n",
        "\n",
        "for company in compName:\n",
        "  x = df.loc[df['company'] == company]\n",
        "  if company == 'BY':\n",
        "    company = 'BYND'\n",
        "  x['date']= pd.to_datetime(x['date'])\n",
        "  x['date'] = [time.date() for time in x['date']]\n",
        "  x = x.set_index('date') # make dates column index\n",
        "\n",
        "  if all:\n",
        "    prevDates = pd.date_range(start=\"2020-09-30\", end='2020-09-30')\n",
        "    dates = pd.date_range(start=\"2020-10-02\",end=\"2021-04-29\")\n",
        "    date = datetime.strptime('01102020', \"%d%m%Y\").date()\n",
        "  else:\n",
        "    prevDates = pd.date_range(start=\"2020-09-30\", end='2020-12-30')\n",
        "    dates = pd.date_range(start=\"2021-01-01\",end=\"2021-04-29\")\n",
        "    date = datetime.strptime('31122020', \"%d%m%Y\").date()\n",
        "\n",
        "  if date not in list(x.index.values):\n",
        "    if gn:\n",
        "      x.loc[date] = [0,0,0,0,0,0, company, 0]\n",
        "    else:\n",
        "      x.loc[date] = [0,0,0,0,0,0,0,0,0,0, company,0, 0]\n",
        "  arr = []\n",
        "  for date in prevDates:\n",
        "     if date in list(x.index.values):\n",
        "        x = x.drop(date)\n",
        "\n",
        "  for date in prevDates:\n",
        "     if date in list(stocks['Date'].values):\n",
        "       stocks.drop( stocks[ stocks['Date'] == date ].index , inplace=True)\n",
        "  \n",
        "  for date in dates:\n",
        "      date = date.date()\n",
        "      if date not in list(x.index.values):\n",
        "        if gn:\n",
        "          x.loc[date] = [0,0,0,0,0,0, company, 0]\n",
        "        else:\n",
        "          x.loc[date] = [0,0,0,0,0,0,0,0,0,0, company, 0, 0]\n",
        "  \n",
        "  # print(stocks['Date'].values)\n",
        "  for date in dates:\n",
        "    date = date.date()\n",
        "    if date not in stocks['Date'].values:\n",
        "      arr.append(date - timedelta(days=1))\n",
        "      # print(date)\n",
        "    else:\n",
        "      if len(arr) > 0:\n",
        "        arr.append(date - timedelta(days=1))\n",
        "        sum = 0\n",
        "        num = 0\n",
        "        neu = 0\n",
        "        neg = 0\n",
        "        pos = 0\n",
        "\n",
        "        sumB = 0\n",
        "        neuB = 0\n",
        "        negB = 0\n",
        "        posB = 0\n",
        "\n",
        "        for days in arr:\n",
        "          sum += x['sum'][days]\n",
        "          num += x['num'][days]\n",
        "          neu += x['neu'][days]\n",
        "          neg += x['neg'][days]\n",
        "\n",
        "          if not gn:\n",
        "            sumB += x['sumB'][days]\n",
        "            neuB += x['neuB'][days]\n",
        "            negB += x['negB'][days]\n",
        "            posB += x['posB'][days]\n",
        "        sum = sum/len(arr)\n",
        "        num = num/len(arr)\n",
        "        neu = neu/len(arr)\n",
        "        neg = neg/len(arr)\n",
        "        sumB = sumB/len(arr)\n",
        "        neuB = neuB/len(arr)\n",
        "        negB = negB/len(arr)\n",
        "        posB = posB/len(arr)\n",
        "        arr.remove(date - timedelta(days=1))\n",
        "        x = x.drop(labels=arr, axis=0)\n",
        "        x['sum'][date - timedelta(days=1)] = sum\n",
        "        x['num'][date - timedelta(days=1)] = num\n",
        "        x['pos'][date - timedelta(days=1)] = pos\n",
        "        x['neu'][date - timedelta(days=1)] = neu\n",
        "        x['neg'][date - timedelta(days=1)] = neg\n",
        "        if not gn:\n",
        "          x['sumB'][date - timedelta(days=1)] = sumB\n",
        "          x['posB'][date - timedelta(days=1)] = posB\n",
        "          x['neuB'][date - timedelta(days=1)] = neuB\n",
        "          x['negB'][date - timedelta(days=1)] = negB\n",
        "      arr = []\n",
        "  print(company)\n",
        "\n",
        "  X = x.copy()\n",
        "  if company == 'BY':\n",
        "    Y = stocks['BYND']\n",
        "  else:\n",
        "    Y = stocks[company]\n",
        "  \n",
        "  \n",
        "\n",
        "  X = X.reset_index()\n",
        "  X = X.drop(df.index[0])\n",
        "  X = X.drop('Unnamed: 0', axis=1)\n",
        "  X = X.reset_index()\n",
        "  X = X.drop('date', axis=1)\n",
        "  X = X.drop('company', axis=1)\n",
        "  Y = Y.reset_index()\n",
        "\n",
        "  columns = ['s5', 's4', 's3', 's2', 's1', 'p5', 'p4', 'p3', 'p2', 'p1', 'p']\n",
        "  columns = ['s5', 's4', 's3', 's2', 't1','t5', 't4', 't3', 't2', 't1', 'p5', 'p4', 'p3', 'p2', 'p1', 'p']\n",
        "\n",
        "  # columns = ['s5', 's4', 's3', 's2', 's1', 'p5', 'p4', 'p3', 'p2', 'p1', 'p']\n",
        "  v = 'avg'\n",
        "  v2 = 'pos'\n",
        "  v3 = 'neg'\n",
        " \n",
        "  newDf = pd.DataFrame([], columns = columns)\n",
        "  # newDf = pd.DataFrame([], columns = ['s1', 'p1', 'p'])\n",
        "\n",
        "  for i in range(5, (len(Y) - 1)):\n",
        "    act = [[X[v][i-5], X[v][i-4], X[v][i-3], X[v][i-2], X[v][i-1],\n",
        "           X[v2][i-5], X[v2][i-4], X[v2][i-3], X[v2][i-2], X[v2][i-1], \n",
        "          #   X[v3][i-5], X[v3][i-4], X[v3][i-3], X[v3][i-2], X[v3][i-1], \n",
        "            Y[company][i-5], Y[company][i-4], Y[company][i-3], Y[company][i-2], Y[company][i-1], \n",
        "            Y[company][i]]]\n",
        "    row = pd.DataFrame(act, columns = columns)\n",
        "    newDf = newDf.append(row)\n",
        "  \n",
        "  newDf = newDf.reset_index()\n",
        "  # print(newDf)\n",
        "  \n",
        "  if binary:\n",
        "    newDf.loc[(newDf.p >= 0),'p'] = 1\n",
        "    newDf.loc[(newDf.p < 0), 'p'] = 0\n",
        "  \n",
        "  X_train = newDf.drop('p', axis=1).iloc[0:numb]\n",
        "  y_train = newDf['p'].iloc[0:numb]\n",
        "  X_test = newDf.drop('p', axis=1).iloc[numb:]\n",
        "  y_test = newDf['p'].iloc[numb:]\n",
        "\n",
        "  if toModel == 'NN':\n",
        "    model = KerasRegressor(make_model, batch_size=32, epochs=100)\n",
        "    params = [{'num_hidden_layers': [1, 2],\n",
        "              'hidden_layer_size': [64, 128],\n",
        "              'activation_function': ['linear', 'sigmoid', 'tanh']}]\n",
        "    grid = GridSearchCV(model,\n",
        "                        param_grid=params,\n",
        "                        scoring='neg_mean_squared_error', #sklearn optimizing by maximizing negative MSE\n",
        "                        n_jobs=1,\n",
        "                        verbose=0,\n",
        "                        cv=5 )\n",
        "    # print('The parameters of the best model are: ')\n",
        "    # print(grid.best_params_)\n",
        "    # print(grid.best_estimator_)\n",
        "\n",
        "  elif toModel == 'SVC':\n",
        "    parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}\n",
        "    svc = svm.SVC()\n",
        "    grid = GridSearchCV(svc, parameters)\n",
        "\n",
        "  # Fit and Run chosen Model\n",
        "  grid.fit(np.array(X_train), np.array(y_train))\n",
        "  best_model = grid.best_estimator_ #scikit-wrapped best model\n",
        "  # print(best_model)\n",
        "  prediction = best_model.predict(X_test)\n",
        "\n",
        "  b = (prediction > 0).astype(int)\n",
        "  # print(b)\n",
        "  c = (y_test > 0).astype(int)\n",
        "  # print(c)\n",
        "  direction = ((b-c) == 0)\n",
        "\n",
        "  if ((b-c) == 0).unique()[0] == True:\n",
        "    print(list(Counter(direction).values())[0]/len(direction))\n",
        "  else:\n",
        "    print(list(Counter(direction).values())[1]/len(direction))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GME\n",
            "0.8\n",
            "AMC\n",
            "0.8857142857142857\n",
            "NOK\n",
            "0.9428571428571428\n",
            "TSLA\n",
            "0.9714285714285714\n",
            "BB\n",
            "0.9142857142857143\n",
            "KOSS\n",
            "1.0\n",
            "EXPR\n",
            "1.0\n",
            "MSFT\n",
            "1.0\n",
            "FB\n",
            "1.0\n",
            "AMZN\n",
            "1.0\n",
            "GOOGL\n",
            "1.0\n",
            "AAPL\n",
            "1.0\n",
            "NFLX\n",
            "0.9714285714285714\n",
            "BABA\n",
            "1.0\n",
            "RCL\n",
            "1.0\n",
            "CCL\n",
            "1.0\n",
            "BYND\n",
            "1.0\n",
            "ZM\n",
            "1.0\n",
            "AAL\n",
            "1.0\n",
            "BA\n",
            "1.0\n",
            "MRNA\n",
            "1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vBdwnImcoo0Z"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lsDt1Gfboo2j"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SnY_ufyTPSoP"
      },
      "source": [
        "filename = r\"/content/drive/Shareddrives/COS424/stocks.csv\"\n",
        "stocks.to_csv(filename)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z3y-VcKFuB-l"
      },
      "source": [
        "# Scatter Plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BIk4EmCTuF7r"
      },
      "source": [
        "# Create Scatter plots of correlation between stock price and sentiment or trading volumes"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s7XeDGdeu9l1"
      },
      "source": [
        "scatter = pd.DataFrame([], columns = ['pred'])\n",
        "scatter=pd.DataFrame()\n",
        "\n",
        "# Choose X-Axis (from gme df - created in prediction)\n",
        "scatter['a'] = gme['positive score']\n",
        "x=scatter['a']\n",
        "\n",
        "# Choose Y-Axis\n",
        "scatter['b'] =gme['trading']\n",
        "\n",
        "m, b = np.polyfit(x, scatter['b'], 1)\n",
        "plt.plot(x, m*x + b)\n",
        "\n",
        "plt.plot(x, scatter['b'], 'o', color='green')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CHsqPs4amIYi"
      },
      "source": [
        "# Granger Causality"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yWtq56D5mXb8"
      },
      "source": [
        "# read in stock data and match to sentiment data\n",
        "filename = r\"/content/drive/Shareddrives/COS424/percent.csv\" \n",
        "stock = pd.read_csv(filename)\n",
        "\n",
        "stock['Date']= pd.to_datetime(stock['Date'])\n",
        "stock = trading.set_index('date')\n",
        "for date in gme.index:\n",
        "  if date in trading.index.values:\n",
        "    gme.at[date,'stockPrice'] = stock.at[date,'GME']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eVfKT_wqmNmS"
      },
      "source": [
        "#source: https://rishi-a.github.io/2020/05/25/granger-causality.html\n",
        "# for each sentiment score, compute Granger causality score between GME sentiment and stock \n",
        "\n",
        "labels = ['pos','comp','neu','neg']\n",
        "for label in labels:\n",
        "  newDF= pd.DataFrame()\n",
        "\n",
        "  #shift data so it is stationary\n",
        "  newDF[label] = gme[label] - gme[label].shift(1)\n",
        "  newDF['stockPrice'] = gme['stockPrice'] - gme['stockPrice'].shift(1)\n",
        "  newDF = newDF.dropna()\n",
        "\n",
        "\n",
        "  # two tests to check for for stationarity\n",
        "\n",
        "  ## ADF Null hypothesis: there is a unit root, meaning series is non-stationary\n",
        "  from statsmodels.tsa.stattools import adfuller\n",
        "\n",
        "  X1 = np.array(newDF[label])\n",
        "  X1 = X1[~np.isnan(X1)]\n",
        "\n",
        "  result = adfuller(X1)\n",
        "  print('ADF Statistic: %f' % result[0])\n",
        "  print('p-value: %f' % result[1])\n",
        "  print('Critical Values:')\n",
        "  for key, value in result[4].items():\n",
        "      print('\\t%s: %.3f' % (key, value))\n",
        "      \n",
        "      \n",
        "  X2 = np.array(newDF['stockPrice'])\n",
        "  X2 = X2[~np.isnan(X2)]\n",
        "\n",
        "  result = adfuller(X2)\n",
        "  print('ADF Statistic: %f' % result[0])\n",
        "  print('p-value: %f' % result[1])\n",
        "  print('Critical Values:')\n",
        "  for key, value in result[4].items():\n",
        "      print('\\t%s: %.3f' % (key, value))\n",
        "\n",
        "  ## KPSS Null hypothesis: there is a no unit root, meaning series is stationary\n",
        "  from statsmodels.tsa.stattools import kpss\n",
        "  def kpss_test(series, **kw):    \n",
        "      statistic, p_value, n_lags, critical_values = kpss(series, **kw)\n",
        "      # Format Output\n",
        "      print(f'KPSS Statistic: {statistic}')\n",
        "      print(f'p-value: {p_value}')\n",
        "      print(f'num lags: {n_lags}')\n",
        "      print('Critial Values:')\n",
        "      for key, value in critical_values.items():\n",
        "          print(f'   {key} : {value}')\n",
        "      print(f'Result: The series is {\"not \" if p_value < 0.05 else \"\"}stationary')\n",
        "\n",
        "  kpss_test(X1)\n",
        "  kpss_test(X2)\n",
        "\n",
        "  # perform granger causality\n",
        "  stationary_df=pd.DataFrame(columns=['stock','sent'])\n",
        "  stationary_df['stock']=X2\n",
        "  stationary_df['sent']=X1\n",
        "  print(label)\n",
        "  res = grangercausalitytests(stationary_df, maxlag=5 )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jaK_vDENMgMz"
      },
      "source": [
        "# Extra Code"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_HycE5vFapNT"
      },
      "source": [
        "Extra code is mostly uncommented, because it is not used for any significant findings in our paper. It is included solely for convenience and to explore our process. It includes Flair Sentiment Analysis, bc we chose to go with VADER instead"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YqaODoJZ4c_v"
      },
      "source": [
        "## Graph Sentiment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a5uAhZbT6CFz"
      },
      "source": [
        "gme_df['weighted compound']=gme_df[\"comp\"] * gme_df['score']\n",
        "gme_df['weighted compound (body)']=gme_df[\"compB\"] * gme_df['score']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aNM_CiI24s7-"
      },
      "source": [
        "gme_df['date'] = pd.to_datetime(gme_df['est'],errors='coerce').dt.date"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "aDBmQVeG9frg",
        "outputId": "3be181fe-910c-4248-cc99-27d457c3b475"
      },
      "source": [
        "names = ['date','daily score']\n",
        "gme_time = pd.DataFrame(columns = names)\n",
        "df.index = pd.to_datetime(df.index)\n",
        "\n",
        "for row in gme_df.iterrows():\n",
        "  date = row[1][25]\n",
        "  print(type(date))\n",
        "  df['score'][date]+=row[1][22]\n",
        "  df['num'][date]+=1\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'datetime.date'>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-114-89b8e56f61e2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m   \u001b[0mdate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdate\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----> 8\u001b[0;31m   \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'score'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m22\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      9\u001b[0m   \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'num'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    880\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    881\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mkey_is_scalar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 882\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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    883\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    884\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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/usr/local/lib/python3.7/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m    988\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    989\u001b[0m         \u001b[0;31m# Similar to Index.get_value, but we do not fall back to positional\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 990\u001b[0;31m         \u001b[0mloc\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[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\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    991\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values_for_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    992\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/datetimes.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    620\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    621\u001b[0m             \u001b[0;31m# unrecognized type\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 622\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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    623\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    624\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[0;31mKeyError\u001b[0m: datetime.date(2020, 10, 8)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "oHcbqqBfD9bz",
        "outputId": "6ed38fad-9413-4786-dccd-c937832edb14"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "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>date</th>\n",
              "      <th>daily score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-10-08</td>\n",
              "      <td>[0.5464]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-10-09</td>\n",
              "      <td>[0.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-10-08</td>\n",
              "      <td>[0.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-10-11</td>\n",
              "      <td>[-0.5464]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>[0.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2020-11-15</td>\n",
              "      <td>[15.909999999999998]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2020-11-15</td>\n",
              "      <td>[-11.7476]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2020-11-15</td>\n",
              "      <td>[0.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2020-11-15</td>\n",
              "      <td>[-1.2248]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2020-11-16</td>\n",
              "      <td>[-12.24]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2020-11-16</td>\n",
              "      <td>[-14.0998]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2020-11-16</td>\n",
              "      <td>[0.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2020-11-16</td>\n",
              "      <td>[2.9562]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2020-11-16</td>\n",
              "      <td>[1.2057]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2020-11-16</td>\n",
              "      <td>[0.0]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          date           daily score\n",
              "0   2020-10-08              [0.5464]\n",
              "1   2020-10-09                 [0.0]\n",
              "2   2020-10-08                 [0.0]\n",
              "3   2020-10-11             [-0.5464]\n",
              "4   2020-11-07                 [0.0]\n",
              "5   2020-11-15  [15.909999999999998]\n",
              "6   2020-11-15            [-11.7476]\n",
              "7   2020-11-15                 [0.0]\n",
              "8   2020-11-15             [-1.2248]\n",
              "9   2020-11-16              [-12.24]\n",
              "10  2020-11-16            [-14.0998]\n",
              "11  2020-11-16                 [0.0]\n",
              "12  2020-11-16              [2.9562]\n",
              "13  2020-11-16              [1.2057]\n",
              "14  2020-11-16                 [0.0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q_gpM4hGlWNA"
      },
      "source": [
        "## Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "54bvPB32li7J"
      },
      "source": [
        "filename = r\"/content/drive/Shareddrives/COS424/closing.csv\" # Path to Train_dataset.csv\n",
        "closing_df = pd.read_csv(filename)\n",
        "filename = r\"/content/drive/Shareddrives/COS424/gme_sentiments.csv\" # Path to Train_dataset.csv\n",
        "sentiment = pd.read_csv(filename)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZGbc1_QkoaAc",
        "outputId": "56d88efa-b133-442c-b131-e10e10171d56"
      },
      "source": [
        "index = 0\n",
        "for date in sentiment['date']:\n",
        "  sentiment['date'][index] = datetime.strptime(sentiment['date'][index], '%Y-%m-%d')\n",
        "  index += 1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w0_3OIFAnq71",
        "outputId": "daa76c99-d360-412a-a49a-236ab03b18d5"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                   GME    AMC   NOK  ...        MRNA        BYND        CCL\n",
            "Date                                 ...                                   \n",
            "2020-10-01    9.770000   4.65  3.87  ...   70.029999  170.600006  15.030000\n",
            "2020-10-02    9.390000   4.65  3.82  ...   68.809998  166.240005  15.160000\n",
            "2020-10-05    9.460000   4.13  3.93  ...   71.949997  175.490005  14.960000\n",
            "2020-10-06    9.130000   4.06  3.91  ...   71.040001  181.860001  15.190000\n",
            "2020-10-07    9.360000   4.04  3.90  ...   72.370003  189.139999  15.990000\n",
            "...                ...    ...   ...  ...         ...         ...        ...\n",
            "2021-04-22  151.169998   9.99  4.16  ...  168.100006  132.830002  26.910000\n",
            "2021-04-23  151.179993  10.16  4.20  ...  173.630005  131.419998  27.270000\n",
            "2021-04-26  168.929993  11.50  4.20  ...  177.610001  132.059998  27.549999\n",
            "2021-04-27  177.770004  11.46  4.22  ...  183.410004  133.500000  27.809999\n",
            "2021-04-28  178.580002  10.85  4.20  ...  178.679993  134.220001  27.799999\n",
            "\n",
            "[144 rows x 22 columns]\n",
            "DatetimeIndex(['2020-10-01', '2020-10-02', '2020-10-05', '2020-10-06',\n",
            "               '2020-10-07', '2020-10-08', '2020-10-09', '2020-10-12',\n",
            "               '2020-10-13', '2020-10-14',\n",
            "               ...\n",
            "               '2021-04-15', '2021-04-16', '2021-04-19', '2021-04-20',\n",
            "               '2021-04-21', '2021-04-22', '2021-04-23', '2021-04-26',\n",
            "               '2021-04-27', '2021-04-28'],\n",
            "              dtype='datetime64[ns]', name='Date', length=144, freq=None)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2trxqHzVmXfC"
      },
      "source": [
        "start_date = datetime.date(2020, 10, 1)\n",
        "end_date = datetime.date(2021, 4, 29)\n",
        "delta = datetime.timedelta(days=1)\n",
        "while start_date <= end_date:\n",
        "  \n",
        "  cur = start_date\n",
        "  start_date += delta\n",
        "  next = start_date"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JJJEYdJpDSg9"
      },
      "source": [
        "## Preprocessing (Old)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cunU8zjb5CTB"
      },
      "source": [
        "# Gets the part of speech tag of word for lemmatization\n",
        "# This function is based on code from:\n",
        "#   https://www.machinelearningplus.com/nlp/lemmatization-examples-python/\n",
        "def get_wordnet_pos(word):\n",
        "    tag = nltk.pos_tag([word])[0][1][0].upper()\n",
        "    tag_dict = {\"J\": wordnet.ADJ,\n",
        "                \"N\": wordnet.NOUN,\n",
        "                \"V\": wordnet.VERB,\n",
        "                \"R\": wordnet.ADV}\n",
        "    return tag_dict.get(tag, wordnet.NOUN)\n",
        "\n",
        "# Preprocesses the tweets text\n",
        "# This function is based on code from:\n",
        "#   https://www.pluralsight.com/guides/building-a-twitter-sentiment-analysis-in-python\n",
        "\n",
        "# Preprocessing takes in a tweet and a boolean determining if the preprocessing\n",
        "# is for the GloVe embeddings or for other classifiers\n",
        "# default value is: False\n",
        "\n",
        "def preprocess_text(tweet, glove):\n",
        "    # Changes emojis to words\n",
        "    tweet = emoji.demojize(tweet,  delimiters=(' ', ' '))\n",
        "    # Removes 'RT' from tweet\n",
        "    tweet = re.sub(r'RT[\\s]+', '', tweet)\n",
        "    # Removes capitalization\n",
        "    tweet = tweet.lower()\n",
        "    # Removes single characters\n",
        "    if glove: tweet = re.sub(r'\\s+[a-zA-Z]\\s+', ' ', tweet)\n",
        "    # Removes all hashtags\n",
        "    if glove: tweet = re.sub(r'#[a-zA-Z0-9]+\\s+', ' ', tweet)\n",
        "    # Removes all special characters\n",
        "    if glove: tweet = re.sub(r'\\W', ' ', tweet)\n",
        "    #Removes Punctuation\n",
        "    tweet = re.sub(r'\\p{P}+', '', tweet)\n",
        "    #Removes multiple spaces with single space\n",
        "    if glove: tweet = re.sub(r'\\s+', ' ', tweet, flags=re.MULTILINE)\n",
        "    #Remove \"blacklivesmatter\"\n",
        "    if glove: tweet = re.sub(r'blacklivesmatter', '', tweet)\n",
        "    # Removes all special characters\n",
        "    if glove: tweet = re.sub(r'\\W', ' ', tweet)\n",
        "    #Removes Punctuation\n",
        "    tweet = re.sub(r'\\p{P}+', '', tweet)\n",
        "    #Removes multiple spaces with single space\n",
        "    if glove: tweet = re.sub(r'\\s+', ' ', tweet, flags=re.MULTILINE)\n",
        "    # Removes stopwords\n",
        "    tokens = [w for w in word_tokenize(tweet) if not w in stop_words]\n",
        "    # Perfoms lemmatization on tokens\n",
        "    lemmatizer = WordNetLemmatizer()\n",
        "    lemma_words = [lemmatizer.lemmatize(w, get_wordnet_pos(w)) for w in tokens]\n",
        "    return \" \".join(lemma_words)\n",
        "\n",
        "# Preprocesses the text of the Tweets in the df and returns the df\n",
        "# By default, this removes the Tweets with the \"neither\" label\n",
        "def preprocess_df(df, glove, remove_neither=True):\n",
        "  idx = \"title\"\n",
        "  length = len(df[idx])\n",
        "  for ii in range(length):\n",
        "    tweet = str(df[idx][ii])\n",
        "    df.loc[ii, idx] = preprocess_text(tweet, glove)\n",
        "  if (remove_neither):\n",
        "    return df[df['BLM'] != \"neither\"]\n",
        "  else:\n",
        "    return df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ow-WoTXPkSts",
        "scrolled": false
      },
      "source": [
        "\n",
        "# Retrieves and preprocesses the training dataset\n",
        "\n",
        "# path = files.upload() # if using Google Colab directly\n",
        "reddit = r\"/content/drive/Shareddrives/COS424/reddit_wsb.csv\" # Path to Train_dataset.csv\n",
        "rtrain_df = pd.read_csv(reddit)\n",
        "rtrain_df.fillna(\"\", inplace=True) # fills any NaN values with empty strings\n",
        "glove_boolean = False; # optimize preprocessing for GloVe (default: false)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bkw7WDcyOBXu"
      },
      "source": [
        "rtrain_df = preprocess_df(rtrain_df, glove_boolean)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "noEV2VuUgzmj"
      },
      "source": [
        "\n",
        "# Uses a TfidfVectorizer to construct bag-of-words matrix\n",
        "# vectorizer = TfidfVectorizer(ngram_range=(1, 2), min_df=2, binary=True, stop_words=None) # default max_features (i.e. none) & 1 to 3-gram_rang\n",
        "default = True # set to False for SVC-optimized vectorizer\n",
        "\n",
        "if default: vectorizer = TfidfVectorizer() # for Naive Bayes\n",
        "else: vectorizer = TfidfVectorizer(ngram_range=(1,2), max_features=12000) # for SVC\n",
        "\n",
        "# train_vocab is an 2d array of the vocab from the training dataset \n",
        "rtrain_vocab = vectorizer.fit_transform(rtrain_df['body'])\n",
        "# train_hash = vectorizer.fit_transform(train_df['hashtags'])\n",
        "\n",
        "# train_vocab_df is a dataframe where the element ij is the number of times word j occurred in Tweet i\n",
        "# train_vocab_df = pd.DataFrame(train_vocab, columns=vectorizer.get_feature_names())\n",
        "rtrain_labels = train_df['BLM']\n",
        "\n",
        "# Retrieves and preprocesses the test dataset\n",
        "# test = files.upload()\n",
        "path = r\"/content/drive/Shareddrives/COS424/combined_test.csv\" # Path to Test_dataset.csv\n",
        "test_df = pd.read_csv(path)\n",
        "test_df.fillna(\"\", inplace=True) # fills any NaN values with empty strings\n",
        "test_df = preprocess_df(test_df, glove_boolean)\n",
        "\n",
        "# Uses the vocab from the training dataset to vectorize the test dataset\n",
        "test_vocab = vectorizer.transform(test_df['text'])\n",
        "# test_hash = vectorizer.fit_transform(test_df['hashtags'])\n",
        "\n",
        "test_labels = test_df['BLM']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "46b8nwyAa8Wr"
      },
      "source": [
        "## Flair Sentiment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FdwNFLj1IABo",
        "outputId": "acdf8340-cb89-44ae-8b44-01c3d4d5480b"
      },
      "source": [
        "!pip install flair\n",
        "import flair\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting flair\n",
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            "\u001b[31mERROR: torchvision 0.9.1+cu101 has requirement torch==1.8.1, but you'll have torch 1.7.1 which is incompatible.\u001b[0m\n",
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      "source": [
        "sentiment_model = flair.models.TextClassifier.load('en-sentiment')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-04-30 16:51:33,521 https://nlp.informatik.hu-berlin.de/resources/models/sentiment-curated-distilbert/sentiment-en-mix-distillbert_4.pt not found in cache, downloading to /tmp/tmpw1b7iga0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 265512723/265512723 [00:25<00:00, 10595880.76B/s]"
          ],
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          "text": [
            "2021-04-30 16:51:59,073 copying /tmp/tmpw1b7iga0 to cache at /root/.flair/models/sentiment-en-mix-distillbert_4.pt\n"
          ],
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        },
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          "text": [
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2021-04-30 16:51:59,598 removing temp file /tmp/tmpw1b7iga0\n",
            "2021-04-30 16:52:00,101 loading file /root/.flair/models/sentiment-en-mix-distillbert_4.pt\n"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "fVNBtF8aW3An"
      },
      "source": [
        "tweets = r\"/content/drive/Shareddrives/COS424/final project/Tweet.csv\" # Path to Train_dataset.csv\n",
        "tweets_df = pd.read_csv(tweets)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Ms1NETwIJYn"
      },
      "source": [
        "#https://towardsdatascience.com/sentiment-analysis-for-stock-price-prediction-in-python-bed40c65d178\n",
        "# we will append probability and sentiment preds later\n",
        "probs = []\n",
        "sentiments = []\n",
        "\n",
        "# use regex expressions (in clean function) to clean tweets\n",
        "#rtrain_df['title'] = rtrain_df['title'].apply(clean)\n",
        "\n",
        "counter = 0\n",
        "\n",
        "for tweet in sentences:\n",
        "    # make prediction\n",
        "    sentence = flair.data.Sentence(tweet)\n",
        "    sentiment_model.predict(sentence)\n",
        "    # extract sentiment prediction\n",
        "    probs.append(sentence.labels[0].score)  # numerical score 0-1\n",
        "    sentiments.append(sentence.labels[0].value)  # 'POSITIVE' or 'NEGATIVE'\n",
        "    counter +=1\n",
        "    if counter > 10: break\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "U-bJk06XsoUI",
        "outputId": "5d189f8c-1ffb-4ede-b93e-9950dff56293"
      },
      "source": [
        "df = pd.DataFrame(list(zip(sentences, sentiments, probs)),\n",
        "               columns =['sentence', 'sentiment','probability'])\n",
        "df.head(25)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sentence</th>\n",
              "      <th>sentiment</th>\n",
              "      <th>probability</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>So GME just touched 14.50 a minute ago, 20c/sh...</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>0.999969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>So yeah $200k and its not my house, its my gra...</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>0.999850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Listen up ya autists that though gme was going...</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>0.999969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>DD here =&amp;gt; [https://pbs.twimg.com/media/EmK...</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>0.999882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>In the GameStop contest, employees were asked ...</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>0.999992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Source: www.wsj.com/amp/articles/gamestops-new...</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>0.999975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>[removed]</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>0.882164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>[deleted]</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>0.999269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>[removed]</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>0.882164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>GME Gang - New Article just dropped about GME....</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>0.997680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Its the gamestop subreddit. I might link some ...</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>0.961103</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
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            "text/plain": [
              "                                             sentence sentiment  probability\n",
              "0   So GME just touched 14.50 a minute ago, 20c/sh...  NEGATIVE     0.999969\n",
              "1   So yeah $200k and its not my house, its my gra...  NEGATIVE     0.999850\n",
              "2   Listen up ya autists that though gme was going...  NEGATIVE     0.999969\n",
              "3   DD here =&gt; [https://pbs.twimg.com/media/EmK...  NEGATIVE     0.999882\n",
              "4   In the GameStop contest, employees were asked ...  NEGATIVE     0.999992\n",
              "5   Source: www.wsj.com/amp/articles/gamestops-new...  NEGATIVE     0.999975\n",
              "6                                           [removed]  NEGATIVE     0.882164\n",
              "7                                           [deleted]  NEGATIVE     0.999269\n",
              "8                                           [removed]  NEGATIVE     0.882164\n",
              "9   GME Gang - New Article just dropped about GME....  NEGATIVE     0.997680\n",
              "10  Its the gamestop subreddit. I might link some ...  NEGATIVE     0.961103"
            ]
          },
          "metadata": {
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          "execution_count": 24
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    {
      "cell_type": "code",
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      },
      "source": [
        "tweets_copy = tweets_df.copy()\n",
        "first_n = tweets_copy.iloc[0:5001 , :]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "IuO3y7tVaPr_",
        "outputId": "bb1f2bab-c301-42ff-b500-fc0c6a3662ee"
      },
      "source": [
        "\n",
        "\n",
        "# add probability and sentiment predictions to tweets dataframe\n",
        "first_n['prob'] = probs\n",
        "first_n['sent'] = sentiments"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        }
      ]
    }
  ]
}