Stock_Market_App_Predictions.ipynb

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      "authorship_tag": "ABX9TyMchWOTDIJiqy8XV4LUNrNO",
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    {
      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/shaunck96/SEC-Filings-Scraper/blob/main/Stock_Market_App_Predictions.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "outputId": "0f94dd07-d10f-413b-8684-ae1099790c26"
      },
      "outputs": [
        {
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          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install transformers==4.35.2\n",
        "!pip install requests==2.31.0\n",
        "!pip install tqdm==4.66.1\n",
        "!pip install openai==0.28\n",
        "!pip install eyed3==0.9.7\n",
        "!pip install tiktoken==0.5.1\n",
        "!pip install langchain==0.0.340\n",
        "!pip install -q sec-api\n",
        "!pip install yfinance --upgrade --no-cache-dir\n",
        "!pip install sentencepiece\n",
        "!pip install moneycontrol\n",
        "!pip install pandas_ta\n",
        "!pip install ta\n",
        "!pip install yahoofinancials\n",
        "!pip install empyrial"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "import librosa\n",
        "import torch\n",
        "from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline\n",
        "import os\n",
        "import openai\n",
        "import regex as re\n",
        "import json\n",
        "import requests\n",
        "from tqdm import tqdm\n",
        "from googleapiclient.discovery import build\n",
        "import regex as re\n",
        "from transformers import pipeline\n",
        "import tiktoken\n",
        "import pandas as pd\n",
        "import re\n",
        "from urllib import parse\n",
        "import torch\n",
        "import ast\n",
        "from langchain.output_parsers import StructuredOutputParser, ResponseSchema\n",
        "from langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate\n",
        "from langchain.chat_models import ChatOpenAI\n",
        "from itertools import islice\n",
        "from pydantic import BaseModel, Field\n",
        "from langchain.output_parsers import PydanticOutputParser\n",
        "from pydantic import BaseModel, Field\n",
        "from langchain.output_parsers import PydanticOutputParser\n",
        "from itertools import islice\n",
        "import base64\n",
        "import requests\n",
        "import os\n",
        "import torch\n",
        "import os\n",
        "import requests\n",
        "from langchain.llms import OpenAI\n",
        "from langchain.output_parsers import PydanticOutputParser\n",
        "from langchain.prompts import PromptTemplate\n",
        "from langchain.pydantic_v1 import BaseModel, Field, validator\n",
        "from langchain.llms import OpenAI\n",
        "import requests\n",
        "import requests\n",
        "from langchain.chains.summarize import load_summarize_chain\n",
        "from langchain.docstore.document import Document\n",
        "from sec_api import MappingApi\n",
        "import pandas as pd\n",
        "import nltk\n",
        "import yfinance as yf\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import ta\n",
        "from ta import add_all_ta_features\n",
        "from ta.utils import dropna\n",
        "import pandas as pd\n",
        "import os\n",
        "import torch\n",
        "import openai\n",
        "import regex as re\n",
        "import json\n",
        "import requests\n",
        "from tqdm import tqdm\n",
        "from transformers import pipeline\n",
        "import tiktoken\n",
        "import pandas as pd\n",
        "import re\n",
        "from urllib import parse\n",
        "from langchain.output_parsers import StructuredOutputParser, ResponseSchema\n",
        "from langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate\n",
        "from langchain.chat_models import ChatOpenAI\n",
        "from itertools import islice\n",
        "from pydantic import BaseModel, Field\n",
        "from langchain.output_parsers import PydanticOutputParser\n",
        "from pydantic import BaseModel, Field\n",
        "from langchain.output_parsers import PydanticOutputParser\n",
        "from itertools import islice\n",
        "import base64\n",
        "import requests\n",
        "from pathlib import Path\n",
        "from langchain.llms import OpenAI\n",
        "from langchain.output_parsers import PydanticOutputParser\n",
        "from langchain.prompts import PromptTemplate\n",
        "from langchain.pydantic_v1 import BaseModel, Field, validator\n",
        "from langchain.llms import OpenAI\n",
        "import requests\n",
        "import requests\n",
        "from langchain.chains.summarize import load_summarize_chain\n",
        "from langchain.docstore.document import Document\n",
        "import nltk\n",
        "import re\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "\n",
        "nltk.download('punkt')\n",
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rVda29-RvBNG",
        "outputId": "e23af289-6a11-4df1-c046-f30378a40afa"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mappingApi = MappingApi(api_key='4a60716c33ef48da1e0f77f814631ca6974eed519d1b1cd2ea884f4c2084954a')\n",
        "by_exchange = mappingApi.resolve('exchange', 'NASDAQ')\n",
        "by_ticker_goog = mappingApi.resolve('ticker', 'GOOG')\n",
        "print('Ticker \"GOOG\" mapped to its CIK, CUSIP and company details')\n",
        "print('----------------------------------------------------------')\n",
        "pd.json_normalize(by_ticker_goog)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "Ab3XlVg4-yxa",
        "outputId": "8ffc9c39-5493-4352-b995-d458d6455024"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ticker \"GOOG\" mapped to its CIK, CUSIP and company details\n",
            "----------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           name ticker      cik                cusip exchange  isDelisted  \\\n",
              "0  ALPHABET INC  GOOGL  1652044  02079K305 38259P508   NASDAQ       False   \n",
              "1  ALPHABET INC   GOOG  1652044  02079K107 38259P706   NASDAQ       False   \n",
              "\n",
              "                                category                  sector  \\\n",
              "0    Domestic Common Stock Primary Class  Communication Services   \n",
              "1  Domestic Common Stock Secondary Class  Communication Services   \n",
              "\n",
              "                         industry   sic sicSector  \\\n",
              "0  Internet Content & Information  7370  Services   \n",
              "1  Internet Content & Information  7370  Services   \n",
              "\n",
              "                                         sicIndustry famaSector  \\\n",
              "0  Services-Computer Programming Data Processing ...              \n",
              "1  Services-Computer Programming Data Processing ...              \n",
              "\n",
              "        famaIndustry currency           location  \\\n",
              "0  Business Services      USD  California; U.S.A   \n",
              "1  Business Services      USD  California; U.S.A   \n",
              "\n",
              "                                 id  \n",
              "0  f4d5c493c7fe5a85bcd98005175b18bb  \n",
              "1  594230eef3f5d7c08886c262d2ae6c17  "
            ],
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              "      <th>name</th>\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5169472f-fac0-4757-aa16-eb2630d0d002 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5169472f-fac0-4757-aa16-eb2630d0d002');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6f2c684e-cf87-4c9d-9ebe-0e54edcfdf9b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6f2c684e-cf87-4c9d-9ebe-0e54edcfdf9b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6f2c684e-cf87-4c9d-9ebe-0e54edcfdf9b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EKLsao25A6sy"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ticker_info_mapping = {}\n",
        "for ticker_info in by_exchange:\n",
        "  ticker_info_mapping[ticker_info['name']] = ticker_info['ticker']\n",
        "\n",
        "ticker_info_mapping"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c7TdN8BZAYnv",
        "outputId": "920aea51-e094-4b20-e32e-92d62f16bd4f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'ADMIRALTY BANCORP INC': 'AAAB',\n",
              " 'ADVANCED ACCELERATOR APPLICATIONS SA': 'AAAP',\n",
              " 'ACCESS ANYTIME BANCORP INC': 'AABC',\n",
              " 'ALLIANCE ATLANTIS COMMUNICATIONS INC': 'AACB',\n",
              " 'ASSET ACCEPTANCE CAPITAL CORP': 'AACC',\n",
              " 'ACE CASH EXPRESS INC': 'AACE',\n",
              " 'ATA CREATIVITY GLOBAL': 'AACG',\n",
              " 'ARMADA ACQUISITION CORP I': 'AACIU',\n",
              " 'AUSTRALIA ACQUISITION CORP': 'AACPF',\n",
              " 'AADI BIOSCIENCE INC': 'AADI',\n",
              " 'ADVANTAGE BANCORP INC': 'AADV',\n",
              " 'AFRICAN AGRICULTURE HOLDINGS INC': 'VCXAU',\n",
              " 'AIRTRAN HOLDINGS INC': 'AAI',\n",
              " 'ALABAMA AIRCRAFT INDUSTRIES INC': 'AAIIQ',\n",
              " 'AAIPHARMA INC': 'AAIIQ1',\n",
              " 'AMERICAN AIRLINES GROUP INC': 'AAL',\n",
              " 'ATLANTIC AMERICAN CORP': 'AAME',\n",
              " 'ABIGAIL ADAMS NATIONAL BANCORP INC': 'AANB',\n",
              " 'APPLIED OPTOELECTRONICS INC': 'AAOI',\n",
              " 'AAON INC': 'AAON',\n",
              " 'ATLANTIC ALLIANCE PARTNERSHIP CORP': 'AAPC',\n",
              " 'APPLE INC': 'AAPL',\n",
              " 'AUTOSCOPE TECHNOLOGIES CORP': 'AATC',\n",
              " 'ADVANCED ANALOGIC TECHNOLOGIES INC': 'AATI',\n",
              " 'ANALYSIS & TECHNOLOGY INC': 'AATI1',\n",
              " 'AAVID THERMAL TECHNOLOGIES INC': 'AATT',\n",
              " 'ATLAS AIR WORLDWIDE HOLDINGS INC': 'AAWW',\n",
              " 'ABACAN RESOURCE CORP': 'ABACQ',\n",
              " 'AMERICAN BANCSHARES INC': 'ABAN',\n",
              " 'AMERICAN BATTERY TECHNOLOGY CO': 'ABAT',\n",
              " 'ADVANCED BATTERY TECHNOLOGIES INC': 'ABAT1',\n",
              " 'ABAXIS INC': 'ABAX',\n",
              " 'ABINGTON BANCORP INC': 'ABBK',\n",
              " 'AMERIS BANCORP': 'ABCB',\n",
              " 'ABC DISPENSING TECHNOLOGIES INC': 'ABCC',\n",
              " 'CAMBIUM LEARNING GROUP INC': 'ABCD',\n",
              " 'ABCELLERA BIOLOGICS INC': 'ABCL',\n",
              " 'ALLIANCE BANCORP': 'ABCL1',\n",
              " 'ABCAM PLC': 'ABCM',\n",
              " 'ADVISORY BOARD CO': 'ABCO',\n",
              " 'AMERICAN BUILDINGS CO': 'ABCO1',\n",
              " 'ABC NACO INC': 'ABCRQ',\n",
              " 'ANCHOR BANCORP WISCONSIN INC': 'ABCWQ',\n",
              " 'ALCENTRA CAPITAL CORP': 'ABDC',\n",
              " 'ABACUS DIRECT CORP': 'ABDR1',\n",
              " 'ALLEGRO BIODIESEL CORP': 'ABDS',\n",
              " 'ABEONA THERAPEUTICS INC': 'ABEOW',\n",
              " 'AMERICAN BUSINESS FINANCIAL SERVICES INC': 'ABFIQ',\n",
              " 'ABENGOA SA': 'ABGBY',\n",
              " 'ABG ACQUISITION CORP I': 'ABGI',\n",
              " 'ABGENIX INC': 'ABGX',\n",
              " 'ABRAXIS BIOSCIENCE INC': 'ABII',\n",
              " 'ABILITY INC': 'ABILW',\n",
              " 'ARCA BIOPHARMA INC': 'ABIO',\n",
              " 'APPLIED BIOMETRICS INC': 'ABIO1',\n",
              " 'ABATIX CORP': 'ABIX',\n",
              " 'ADELPHIA BUSINESS SOLUTIONS INC': 'ABIZQ',\n",
              " 'AMBAC FINANCIAL GROUP INC': 'ABKFQ',\n",
              " 'ABACUS LIFE INC': 'ABLLL',\n",
              " 'ABLE LABORATORIES INC': 'ABLSQ',\n",
              " 'ABLE VIEW GLOBAL INC': 'HMACU',\n",
              " 'ABLYNX NV': 'ABLX',\n",
              " 'AMERICAN BIO MEDICA CORP': 'ABMC',\n",
              " 'ABIOMED INC': 'ABMD',\n",
              " 'AIRBNB INC': 'ABNB',\n",
              " 'AMERICAN BANCORP OF NEW JERSEY INC': 'ABNJ',\n",
              " 'AMEGY BANCORPORATION INC': 'ABNK1',\n",
              " 'ACUMEN PHARMACEUTICALS INC': 'ABOS',\n",
              " 'ABOVENET COMMUNICATIONS INC': 'ABOV',\n",
              " 'ACCENTIA BIOPHARMACEUTICALS INC': 'ABPI',\n",
              " 'ABR INFORMATION SERVICES INC': 'ABRX1',\n",
              " 'AURORA BIOSCIENCES CORP': 'ABSC',\n",
              " 'ABSCI CORP': 'ABSI',\n",
              " 'ABSOLUTE SOFTWARE CORP': 'ABST',\n",
              " 'ABT BUILDING PRODUCTS CORP': 'ABTC',\n",
              " 'ABLE TELCOM HOLDING CORP': 'ABTE',\n",
              " 'ALPHA BETA TECHNOLOGY INC': 'ABTI1',\n",
              " 'ABITS GROUP INC': 'ABTS',\n",
              " 'ALLEGIANCE BANCSHARES INC': 'ABTX',\n",
              " 'AGRIBIOTECH INC': 'ABTXQ',\n",
              " 'ARBUTUS BIOPHARMA CORP': 'ABUS',\n",
              " 'ALLIANCE BANKSHARES CORP': 'ABVA',\n",
              " 'ABVC BIOPHARMA INC': 'ABVC',\n",
              " 'ABIVAX SA': 'ABVX',\n",
              " 'AB WATLEY GROUP INC': 'ABWG',\n",
              " 'ATLANTIC COASTAL ACQUISITION CORP II': 'ACABW',\n",
              " 'ACRI CAPITAL ACQUISITION CORP': 'ACACW',\n",
              " 'ACADIA PHARMACEUTICALS INC': 'ACAD',\n",
              " 'ATLANTIC COASTAL ACQUISITION CORP': 'ACAHW',\n",
              " 'AUTOCAM CORP': 'ACAM1',\n",
              " 'AMERICAN PHYSICIANS CAPITAL INC': 'ACAP',\n",
              " 'AMERICAN CAPITAL LTD': 'ACAS',\n",
              " 'ARCTIC CAT INC': 'ACAT',\n",
              " 'ALSET CAPITAL ACQUISITION CORP': 'ACAXW',\n",
              " 'AURORA CANNABIS INC': 'ACB',\n",
              " 'ACE GLOBAL BUSINESS ACQUISITION LTD': 'ACBAW',\n",
              " 'AMERICAN COMMUNITY BANCSHARES INC': 'ACBA1',\n",
              " 'ATLANTIC CAPITAL BANCSHARES INC': 'ACBI',\n",
              " 'ACC CORP': 'ACCC',\n",
              " 'ACCOLADE INC': 'ACCD',\n",
              " 'ACCORD NETWORKS LTD': 'ACCD1',\n",
              " 'ACCELRYS INC': 'ACCL',\n",
              " 'ACCESS HEALTH INC': 'ACCS2',\n",
              " 'ARGENT CAPITAL CORP': 'ACCT',\n",
              " 'PROFRAC HOLDING CORP': 'ACDCW',\n",
              " 'ACCREDO HEALTH INC': 'ACDO',\n",
              " 'ACE COMM CORP': 'ACEC',\n",
              " 'ACME ELECTRIC CORP': 'ACEE',\n",
              " 'AMERICAN CHAMPION ENTERTAINMENT INC': 'ACEI',\n",
              " 'PARTS SOURCE INC': 'ACEP',\n",
              " 'ACER THERAPEUTICS INC': 'ACERW',\n",
              " 'ADICET BIO INC': 'ACET',\n",
              " 'ACETO CORP': 'ACETQ',\n",
              " 'AMERICREDIT CORP': 'ACF',\n",
              " 'ATLANTIC COAST FINANCIAL CORP': 'ACFC',\n",
              " 'ARCH CAPITAL GROUP LTD': 'ACGLP',\n",
              " 'ACERAGEN INC': 'ACGN',\n",
              " 'ACADIA HEALTHCARE COMPANY INC': 'ACHC',\n",
              " 'AMERICAN COUNTRY HOLDINGS INC': 'ACHI1',\n",
              " 'ACHILLES THERAPEUTICS PLC': 'ACHL',\n",
              " 'ACHILLION PHARMACEUTICALS INC': 'ACHN',\n",
              " 'ACHIEVE LIFE SCIENCES INC': 'ACHV',\n",
              " 'ACACIA COMMUNICATIONS INC': 'ACIA',\n",
              " 'AMERICAN COASTAL INSURANCE CORP': 'ACIC',\n",
              " 'XEIKON NV': 'XEIK',\n",
              " 'AC IMMUNE SA': 'ACIU',\n",
              " 'ACI WORLDWIDE INC': 'ACIW',\n",
              " 'ACKRELL SPAC PARTNERS I CO': 'ACKIW',\n",
              " 'ACLARA BIOSCIENCES INC': 'ACLA',\n",
              " 'ACCEL INTERNATIONAL CORP': 'ACLE1',\n",
              " 'AMERICAN COMMERCIAL LINES INC': 'ACLI',\n",
              " 'ACLN LTD': 'ACLNF',\n",
              " 'ACCELIO CORP': 'ACLO',\n",
              " 'AXCELIS TECHNOLOGIES INC': 'ACLS',\n",
              " 'ARCELLX INC': 'ACLX',\n",
              " 'ACME COMMUNICATIONS INC': 'ACME',\n",
              " 'ACCUMED INTERNATIONAL INC': 'ACMI',\n",
              " 'ACCOM INC': 'ACMM',\n",
              " 'ACM RESEARCH INC': 'ACMR',\n",
              " 'AC MOORE ARTS & CRAFTS INC': 'ACMR1',\n",
              " 'CREDIT SUISSE X-LINKS CRUDE OIL SHARES COVERED CALL ETN': 'USOI',\n",
              " 'AIR CANADA': 'ACNAQ',\n",
              " 'ACNB CORP': 'ACNB',\n",
              " 'ASCENT INDUSTRIES CO': 'ACNT',\n",
              " 'ANCESTRYCOM INC': 'ACOM',\n",
              " 'AGENCY COM LTD': 'ACOM1',\n",
              " 'ACLARION INC': 'ACONW',\n",
              " 'ACORDA THERAPEUTICS INC': 'ACOR',\n",
              " 'ACAP CORP': 'ACPC',\n",
              " 'INDEPENDENCE HOLDINGS CORP': 'ACQRW',\n",
              " 'ACRODYNE COMMUNICATIONS INC': 'ACRO1',\n",
              " 'ACLARIS THERAPEUTICS INC': 'ACRS',\n",
              " 'ACTRADE FINANCIAL TECHNOLOGIES LTD': 'ACRTQ',\n",
              " 'ACCRUE SOFTWARE INC': 'ACRUQ',\n",
              " 'ACRIVON THERAPEUTICS INC': 'ACRV',\n",
              " 'ACELRX PHARMACEUTICALS INC': 'ACRX',\n",
              " 'ADVANCED COMMUNICATION SYSTEMS INC': 'ACSC',\n",
              " 'AMERICAN CAPITAL SENIOR FLOATING LTD': 'ACSF',\n",
              " 'ACASTI PHARMA INC': 'ACST',\n",
              " 'ENACT HOLDINGS INC': 'ACT',\n",
              " 'ACTUA CORP': 'ACTA',\n",
              " 'ACACIA RESEARCH CORP': 'ACTG',\n",
              " 'ACTIVIDENTITY CORP': 'ACTI',\n",
              " 'ACTIVCARD SA': 'ACTI1',\n",
              " 'APPLIED COMPUTER TECHNOLOGY INC': 'ACTI2',\n",
              " 'ACTEL CORP': 'ACTL1',\n",
              " 'ACTERNA CORP': 'ACTRQ',\n",
              " 'ACTIONS SEMICONDUCTOR CO LTD': 'ACTS',\n",
              " 'ACT TELECONFERENCING INC': 'ACTT1',\n",
              " 'ACTV INC': 'ACTV1',\n",
              " 'ACURA PHARMACEUTICALS INC': 'ACUR',\n",
              " 'ACUSPHERE INC': 'ACUS',\n",
              " 'ACV AUCTIONS INC': 'ACVA',\n",
              " 'ACTIVE VOICE CORP': 'ACVC',\n",
              " 'ACURX PHARMACEUTICALS INC': 'ACXP',\n",
              " 'ADAC LABORATORIES': 'ADAC1',\n",
              " 'ADAGENE INC': 'ADAG',\n",
              " 'ANTHEMIS DIGITAL ACQUISITIONS I CORP': 'ADALW',\n",
              " 'ADAM INC': 'ADAM',\n",
              " 'ADAPTIMMUNE THERAPEUTICS PLC': 'ADAP',\n",
              " 'ADAPTIVE BROADBAND CORP': 'ADAPQ',\n",
              " 'APPLIED DIGITAL ACCESS INC': 'ADAX',\n",
              " 'ADOBE INC': 'ADBE',\n",
              " 'AUDIBLE INC': 'ADBL',\n",
              " 'ANDEAN DEVELOPMENT CORP': 'ADCC',\n",
              " 'ADC TELECOMMUNICATIONS INC': 'ADCT1',\n",
              " 'COLOR STAR TECHNOLOGY CO LTD': 'ADD',\n",
              " 'ADEIA INC': 'ADEA',\n",
              " 'ADELPHIA COMMUNICATIONS CORP': 'ADELQ',\n",
              " 'ADEPT TECHNOLOGY INC': 'ADEP',\n",
              " '26 CAPITAL ACQUISITION CORP': 'ADERU',\n",
              " 'ADVANCED EMISSIONS SOLUTIONS INC': 'ADES',\n",
              " 'ADE CORP': 'ADEX1',\n",
              " 'ADFORCE INC': 'ADFC',\n",
              " 'ADAMS GOLF INC': 'ADGF',\n",
              " 'ANALOG DEVICES INC': 'ADI',\n",
              " 'ADVANCED DIGITAL INFORMATION CORP': 'ADIC',\n",
              " 'ADIAL PHARMACEUTICALS INC': 'ADILW',\n",
              " 'AMERICAN MEDICAL TECHNOLOGIES INC': 'ADLI',\n",
              " 'ADOLOR CORP': 'ADLR',\n",
              " 'ADVANCED LIGHTING TECHNOLOGIES INC': 'ADLTQ',\n",
              " 'ADMA BIOLOGICS INC': 'ADMA',\n",
              " 'ADVANCED MATERIALS GROUP INC': 'ADMGQ',\n",
              " 'ADAMAS PHARMACEUTICALS INC': 'ADMS',\n",
              " 'ADM TRONICS UNLIMITED INC': 'ADMT',\n",
              " 'ADVENT TECHNOLOGIES HOLDINGS INC': 'AMCIU1',\n",
              " 'AUDIENCE INC': 'ADNC',\n",
              " 'EDOC ACQUISITION CORP': 'ADOCW',\n",
              " 'AUTOMATIC DATA PROCESSING INC': 'ADP',\n",
              " 'AMERICAN DENTAL PARTNERS INC': 'ADPI',\n",
              " 'ADAPTIVE BIOTECHNOLOGIES CORP': 'ADPT',\n",
              " 'ANDRX CORP': 'CYBA2',\n",
              " 'ATLANTIC DATA SERVICES INC': 'ADSC',\n",
              " 'ADS-TEC ENERGY PUBLIC LTD CO': 'EUSGU',\n",
              " 'AMERICAN DISPOSAL SERVICES INC': 'ADSI',\n",
              " 'AUTODESK INC': 'ADSK',\n",
              " 'ADAPTIVE SOLUTIONS INC': 'ADSO1',\n",
              " 'ARIEL CORP': 'ADSPQ',\n",
              " 'ADVANCED DEPOSITION TECHNOLOGIES INC': 'ADTCQ',\n",
              " 'ADTHEORENT HOLDING COMPANY INC': 'MACQU',\n",
              " 'ADATOM COM INC': 'ADTM1',\n",
              " 'ADTRAN HOLDINGS INC': 'ADTN',\n",
              " 'ADITXT INC': 'ADTX',\n",
              " 'ADDUS HOMECARE CORP': 'ADUS',\n",
              " 'ADVANTAGE SOLUTIONS INC': 'CPAAU1',\n",
              " 'ADVERUM BIOTECHNOLOGIES INC': 'ADVM',\n",
              " 'ADVANTA CORP': 'ADVBQ',\n",
              " 'ADVANCEPCS': 'ADVP',\n",
              " 'ADVENT SOFTWARE INC': 'ADVS',\n",
              " 'ADDEX THERAPEUTICS LTD': 'ADXN',\n",
              " 'AVIDYN INC': 'ADYN',\n",
              " 'ADYNXX INC': 'ADYX',\n",
              " 'ADEZA BIOMEDICAL CORP': 'ADZA',\n",
              " 'AUTHENTIC EQUITY ACQUISITION CORP': 'AEACU',\n",
              " 'ALTENERGY ACQUISITION CORP': 'AEAEW',\n",
              " 'AMERICAN ENERGY DEVELOPMENT CORP': 'AEDC',\n",
              " 'AEGION CORP': 'AEGN',\n",
              " 'AEROGEN INC': 'AEGN1',\n",
              " 'AEGERION PHARMACEUTICALS INC': 'AEGR',\n",
              " 'ANTELOPE ENTERPRISE HOLDINGS LTD': 'AEHL',\n",
              " 'AEHR TEST SYSTEMS': 'AEHR',\n",
              " 'ALSET INC': 'AEI',\n",
              " 'AIR EXPRESS INTERNATIONAL CORP': 'AEIC',\n",
              " 'ADVANCED ENERGY INDUSTRIES INC': 'AEIS',\n",
              " 'AETHLON MEDICAL INC': 'AEMD',\n",
              " 'ACCESS TO MONEY INC': 'AEMI',\n",
              " 'AMC ENTERTAINMENT INC': 'AEN1',\n",
              " 'ALLIANCE ENTERTAINMENT HOLDING CORP': 'AENTW',\n",
              " 'AMERICAN ELECTRIC POWER CO INC': 'AEPPZ',\n",
              " 'AEP INDUSTRIES INC': 'AEPI',\n",
              " 'APPLIED ENERGETICS INC': 'AERG',\n",
              " 'AERIE PHARMACEUTICALS INC': 'AERI',\n",
              " 'AERIAL COMMUNICATIONS INC': 'AERL1',\n",
              " 'AERO SYSTEMS ENGINEERING INC': 'AERS1',\n",
              " 'AERIES TECHNOLOGY INC': 'WWACU',\n",
              " 'ADVANCED ENVIRONMENTAL RECYCLING TECHNOLOGIES INC': 'AERT1',\n",
              " 'AESP INC': 'AESP',\n",
              " 'APPLIED EXTRUSION TECHNOLOGIES INC': 'AETCQ',\n",
              " 'ADDVANTAGE TECHNOLOGIES GROUP INC': 'AEY',\n",
              " 'AUDIOEYE INC': 'AEYE',\n",
              " 'AETERNA ZENTARIS INC': 'AEZS',\n",
              " 'ARENA FORTIFY ACQUISITION CORP': 'AFACW',\n",
              " 'ISHARES MSCI ALL COUNTRY ASIA EX JAPAN ETF': 'AAXJ',\n",
              " 'ALMOST FAMILY INC': 'AFAM',\n",
              " 'AF ACQUISITION CORP': 'AFAQW',\n",
              " 'AURA FAT PROJECTS ACQUISITION CORP': 'AFARW',\n",
              " 'ADVANCE FINANCIAL BANCORP': 'AFBC',\n",
              " 'AFFINITY BANCSHARES INC': 'AFBI',\n",
              " 'ATHENS BANCSHARES CORP': 'AFCB',\n",
              " 'AFC GAMMA INC': 'AFCG',\n",
              " 'ADVANCED FIBRE COMMUNICATIONS INC': 'AFCI',\n",
              " 'APPLIED FILMS CORP': 'AFCO1',\n",
              " 'AFC CABLE SYSTEMS INC': 'AFCX',\n",
              " 'AFSALA BANCORP INC': 'AFED',\n",
              " 'AFFINITY TECHNOLOGY GROUP INC': 'AFFIQ',\n",
              " 'AFFIRMATIVE INSURANCE HOLDINGS INC': 'AFFMQ',\n",
              " 'AFFYMETRIX INC': 'AFFX',\n",
              " 'AFFYMAX INC': 'AFFY',\n",
              " 'ATLAS FINANCIAL HOLDINGS INC': 'AFHBL',\n",
              " 'ACUTUS MEDICAL INC': 'AFIB',\n",
              " 'PRINTRAK INTERNATIONAL INC': 'AFIS',\n",
              " 'AIMEI HEALTH TECHNOLOGY CO LTD': 'AFJKU',\n",
              " 'ADFLEX SOLUTIONS INC': 'AFLX',\n",
              " 'AMFM INC': 'AFM',\n",
              " 'AMERICAN RADIO SYSTEMS CORP': 'AFM1',\n",
              " 'AFFIMED NV': 'AFMD',\n",
              " 'ALLIANCE FIBER OPTIC PRODUCTS INC': 'AFOP',\n",
              " 'FORAFRIC GLOBAL PLC': 'GLAQU',\n",
              " 'AFFIRM HOLDINGS INC': 'AFRM',\n",
              " 'ANCHOR FINANCIAL CORP': 'AFSC',\n",
              " 'AMTRUST FINANCIAL SERVICES INC': 'AFSI',\n",
              " 'AMERICAN FREIGHTWAYS CORP': 'AFWY',\n",
              " 'AFYA LTD': 'AFYA',\n",
              " 'ALLIED GAMING & ENTERTAINMENT INC': 'BRACR1',\n",
              " 'AG ASSOCIATES INC': 'AGAI',\n",
              " 'AGA MEDICAL HOLDINGS INC': 'AGAM',\n",
              " 'ACRES GAMING INC': 'AGAM1',\n",
              " 'AGBA GROUP HOLDING LTD': 'AGBAW',\n",
              " 'AB HOLDING GROUP INC': 'AGBG',\n",
              " 'ANCHOR GLASS CONTAINER CORP': 'AGCCQ',\n",
              " 'AG-CHEM EQUIPMENT CO INC': 'AGCH',\n",
              " 'AMERICANA GOLD & DIAMOND HOLDINGS INC': 'AGDM',\n",
              " 'AGENUS INC': 'AGEN',\n",
              " 'AGROFRESH SOLUTIONS INC': 'AGFSW',\n",
              " 'AGRIFY CORP': 'AGFY',\n",
              " 'AGILE GROWTH CORP': 'AGGRW',\n",
              " 'AEON GLOBAL HEALTH CORP': 'AGHC',\n",
              " 'ARGONAUT GROUP INC': 'AGII1',\n",
              " 'AGILE SOFTWARE CORP': 'AGIL1',\n",
              " 'AGILETHOUGHT INC': 'LIVKU',\n",
              " 'AGIOS PHARMACEUTICALS INC': 'AGIO',\n",
              " 'AEGIS COMMUNICATIONS GROUP INC': 'AGIS',\n",
              " 'ATHEROGENICS INC': 'AGIXQ',\n",
              " 'ATLANTIC GULF COMMUNITIES CORP': 'AGLFQ',\n",
              " 'AGM GROUP HOLDINGS INC': 'AGMH',\n",
              " 'AGNC INVESTMENT CORP': 'AGNCP1',\n",
              " 'ARGONAUT TECHNOLOGIES INC': 'AGNT',\n",
              " 'AGOURON PHARMACEUTICALS INC': 'AGPH',\n",
              " 'AGRIFORCE GROWING SYSTEMS LTD': 'AGRIW',\n",
              " 'ASSOCIATED GROUP INC': 'AGRPB',\n",
              " 'AGILE THERAPEUTICS INC': 'AGRX',\n",
              " 'APPLIED GENETIC TECHNOLOGIES CORP': 'AGTC',\n",
              " 'AGRITOPE INC': 'AGTO',\n",
              " 'AGILYSYS INC': 'AGYS',\n",
              " 'ALPHA TECHNOLOGIES GROUP INC': 'AHAG',\n",
              " 'ALLIED HEALTHCARE INTERNATIONAL INC': 'AHCI',\n",
              " 'AMBANC HOLDING CO INC': 'AHCI1',\n",
              " 'ADAPTHEALTH CORP': 'AHCOW',\n",
              " 'AKSO HEALTH GROUP': 'AHG',\n",
              " 'APRIA HEALTHCARE GROUP INC': 'AHG1',\n",
              " 'ALLIANCE HOLDINGS GP LP': 'AHGP',\n",
              " 'ADVANCED HEALTH INTELLIGENCE LTD': 'AHI',\n",
              " 'AMERICAN HERITAGE INTERNATIONAL INC': 'AHII',\n",
              " 'ANIMAL HEALTH HOLDINGS INC': 'AHII1',\n",
              " 'AHL SERVICES INC': 'AHLS',\n",
              " 'AMERICAN HOMEPATIENT INC': 'AHOM',\n",
              " 'ALLIED HEALTHCARE PRODUCTS INC': 'AHPIQ',\n",
              " 'AHREN ACQUISITION CORP': 'AHRNW',\n",
              " 'AHT CORP': 'AHTCQ',\n",
              " 'AUDIO HIGHWAY-COM': 'AHWY',\n",
              " 'AIB ACQUISITION CORP': 'AIBBU',\n",
              " 'APPLIED IMAGING CORP': 'AICX',\n",
              " 'AID AUTO STORES INC': 'AIDA1',\n",
              " 'AMERICAN INDEMNITY FINANCIAL CORP': 'AIFC',\n",
              " 'AESTHETIC MEDICAL INTERNATIONAL HOLDINGS GROUP LTD': 'AIH',\n",
              " 'SENMIAO TECHNOLOGY LTD': 'AIHS',\n",
              " 'AUTOLOGIC INFORMATION INTERNATIONAL INC': 'AIII1',\n",
              " 'AIXTRON SE': 'AIIXY',\n",
              " 'ALPNET INC': 'AILP',\n",
              " 'AIMFINITY INVESTMENT CORP I': 'AIMBU',\n",
              " 'ALTRA INDUSTRIAL MOTION CORP': 'AIMC',\n",
              " 'AINOS INC': 'AIMDW',\n",
              " 'AUTOIMMUNE INC': 'AIMM',\n",
              " 'AIMMUNE THERAPEUTICS INC': 'AIMT',\n",
              " 'ARNOLD INDUSTRIES INC': 'AIND',\n",
              " 'APPLIED INNOVATION INC': 'AINN',\n",
              " 'ARTERIS INC': 'AIP',\n",
              " 'AMERICAN ITALIAN PASTA CO': 'AIPC',\n",
              " 'ALPHA BLUE CAPITAL US SMALL-MID CAP DYNAMIC ETF': 'ABCS',\n",
              " 'AMERICAN INTERNATIONAL PETROLEUM CORP': 'AIPN',\n",
              " 'ALLIANCE HEALTHCARE SERVICES INC': 'AIQ1',\n",
              " 'REALPHA TECH CORP': 'AIRE',\n",
              " 'AIRGAIN INC': 'AIRG',\n",
              " 'AIR METHODS CORP': 'AIRM',\n",
              " 'AIRSPAN NETWORKS INC': 'AIRO',\n",
              " 'AIRONET WIRELESS COMMUNICATIONS INC': 'AIRO1',\n",
              " 'AIRSCULPT TECHNOLOGIES INC': 'AIRS',\n",
              " 'AMERICAN AIRCARRIERS SUPPORT INC': 'AIRS1',\n",
              " 'AIR T INC': 'AIRTW',\n",
              " 'AIRVANA INC': 'AIRV',\n",
              " 'AIRSHIP AI HOLDINGS INC': 'BYTSU',\n",
              " 'AI TRANSPORTATION ACQUISITION CORP': 'AITRU',\n",
              " 'XIAO-I CORP': 'AIXI',\n",
              " 'AKAMAI TECHNOLOGIES INC': 'AKAM',\n",
              " 'AKANDA CORP': 'AKAN',\n",
              " 'ACHAOGEN INC': 'AKAOQ',\n",
              " 'AKEBIA THERAPEUTICS INC': 'AKBA',\n",
              " 'AKCEA THERAPEUTICS INC': 'AKCA',\n",
              " 'SPORTS VENTURES ACQUISITION CORP': 'AKICW',\n",
              " 'AKILI INC': 'AKLI',\n",
              " 'ACCLAIM ENTERTAINMENT INC': 'AKLMQ',\n",
              " 'AKERO THERAPEUTICS INC': 'AKRO',\n",
              " 'AKORN INC': 'AKRXQ',\n",
              " 'AKOUSTIS TECHNOLOGIES INC': 'AKTS',\n",
              " 'AKARI THERAPEUTICS PLC': 'AKTX',\n",
              " 'AKUMIN INC': 'AKUMQ',\n",
              " 'AKOUOS INC': 'AKUS',\n",
              " 'AKOYA BIOSCIENCES INC': 'AKYA',\n",
              " 'AKZO NOBEL NV': 'AKZOY',\n",
              " 'ALABAMA NATIONAL BANCORPORATION': 'ALAB',\n",
              " 'ALBERTON ACQUISITION CORP': 'ALACW',\n",
              " 'ALANCO TECHNOLOGIES INC': 'ALAN',\n",
              " 'ALARUM TECHNOLOGIES LTD': 'ALAR',\n",
              " 'ALBION BANC CORP': 'ALBC',\n",
              " 'ALBANK FINANCIAL CORP': 'ALBK',\n",
              " 'ALBIREO PHARMA INC': 'ALBO',\n",
              " 'AVALON GLOBOCARE CORP': 'ALBT',\n",
              " 'COMMUNITY CAPITAL BANCSHARES INC': 'ALBY',\n",
              " 'ALCIDE CORP': 'ALCD',\n",
              " 'ALTERNUS CLEAN ENERGY INC': 'CLINW',\n",
              " 'ALLCITY INSURANCE CO': 'ALCI',\n",
              " 'ALICO INC': 'ALCO',\n",
              " 'ALCO STORES INC': 'ALCSQ',\n",
              " 'ALCHEMY INVESTMENTS ACQUISITION CORP 1': 'ALCYW',\n",
              " 'ALDILA INC': 'ALDA',\n",
              " 'ALADDIN KNOWLEDGE SYSTEMS LTD': 'ALDN',\n",
              " 'ALDER BIOPHARMACEUTICALS INC': 'ALDR',\n",
              " 'ALLIED DEVICES CORP': 'ALDV1',\n",
              " 'ALDEYRA THERAPEUTICS INC': 'ALDX',\n",
              " 'ALECTOR INC': 'ALEC',\n",
              " 'ALFA CORP': 'ALFA1',\n",
              " 'ATLANTIC LIBERTY FINANCIAL CORP': 'ALFC',\n",
              " 'ALLIED LIFE FINANCIAL CORP': 'ALFC1',\n",
              " 'ALFI INC': 'ALFIW',\n",
              " 'ALFORD REFRIGERATED WAREHOUSES INC': 'ALFO',\n",
              " 'AMERICAN LOCKER GROUP INC': 'ALGI',\n",
              " 'ALLEGRO MICROSYSTEMS INC': 'ALGM',\n",
              " 'ALIGN TECHNOLOGY INC': 'ALGN',\n",
              " 'ALGOS PHARMACEUTICAL CORP': 'ALGO',\n",
              " 'ALLEGRO MERGER CORP': 'ALGRW',\n",
              " 'ALIGOS THERAPEUTICS INC': 'ALGS',\n",
              " 'ALLEGIANT TRAVEL CO': 'ALGT',\n",
              " 'ALLEGIANCE TELECOM INC': 'ALGXQ',\n",
              " 'ALIGNMENT HEALTHCARE INC': 'ALHC',\n",
              " 'ALIMERA SCIENCES INC': 'ALIM',\n",
              " 'ALJ REGIONAL HOLDINGS INC': 'ALJJ',\n",
              " 'ALKERMES PLC': 'ALKS',\n",
              " 'ALKAMI TECHNOLOGY INC': 'ALKT',\n",
              " 'ALLIANCE BANCORP INC OF PENNSYLVANIA': 'ALLB',\n",
              " 'ALLION HEALTHCARE INC': 'ALLI',\n",
              " 'ALLAKOS INC': 'ALLK',\n",
              " 'ALLOGENE THERAPEUTICS INC': 'ALLO',\n",
              " 'ALLIANCE PHARMACEUTICAL CORP': 'ALLP',\n",
              " 'ALLARITY THERAPEUTICS INC': 'ALLR',\n",
              " 'ALLAIRE CORP': 'ALLR1',\n",
              " 'ALLSTREAM INC': 'ALLSB',\n",
              " 'ALLOT LTD': 'ALLT',\n",
              " 'ALLENA PHARMACEUTICALS INC': 'ALNAQ',\n",
              " 'ALLIANCE FINANCIAL CORP': 'ALNC',\n",
              " 'AMERILINK CORP': 'ALNK',\n",
              " 'ALLIENT INC': 'ALNT',\n",
              " 'ALIANT COMMUNICATIONS INC': 'ALNT1',\n",
              " 'ALNYLAM PHARMACEUTICALS INC': 'ALNY',\n",
              " 'ANALOGIC CORP': 'ALOG',\n",
              " 'ALSP ORCHID ACQUISITION CORP I': 'ALORW',\n",
              " 'ASTRONOVA INC': 'ALOT',\n",
              " 'ALLOY INC': 'ALOY',\n",
              " 'ALPHANET SOLUTIONS INC': 'ALPH',\n",
              " 'ALPINE IMMUNE SCIENCES INC': 'ALPN',\n",
              " 'ALPINE 4 HOLDINGS INC': 'ALPP',\n",
              " 'ALPINE SUMMIT ENERGY PARTNERS INC': 'ALPSQ',\n",
              " 'ALERISLIFE INC': 'ALR',\n",
              " 'ALTERNATIVE RESOURCES CORP': 'ALRC',\n",
              " 'ALARMCOM HOLDINGS INC': 'ALRM',\n",
              " 'AILERON THERAPEUTICS INC': 'ALRN',\n",
              " 'AMERICAN LEARNING CORP': 'ALRN1',\n",
              " 'ALTRON INC': 'ALRN2',\n",
              " 'ALERUS FINANCIAL CORP': 'ALRS',\n",
              " 'ALPHA STAR ACQUISITION CORP': 'ALSAW',\n",
              " 'ALSERES PHARMACEUTICALS INC': 'ALSE',\n",
              " 'ALASKA COMMUNICATIONS SYSTEMS GROUP INC': 'ALSK',\n",
              " 'ALPHASMART INC': 'ALSM',\n",
              " 'ALTIMMUNE INC': 'ALT',\n",
              " 'ALTABANCORP': 'ALTA',\n",
              " 'ALTERRA CAPITAL HOLDINGS LTD': 'ALTE',\n",
              " 'ALLOS THERAPEUTICS INC': 'ALTH',\n",
              " 'ALTI GLOBAL INC': 'GLBLU',\n",
              " 'ALTAIR NANOTECHNOLOGIES INC': 'ALTI1',\n",
              " 'ALTO INGREDIENTS INC': 'ALTO',\n",
              " 'ALTAIR ENGINEERING INC': 'ALTR',\n",
              " 'ALTERA CORP': 'ALTR1',\n",
              " 'ALTITUDE ACQUISITION CORP': 'ALTUW',\n",
              " 'ALTUS PHARMACEUTICALS INC': 'ALTUQ',\n",
              " 'ALSIUS CORP': 'ALUS1',\n",
              " 'ALVOTECH': 'ALVOW',\n",
              " 'ALLOVIR INC': 'ALVR',\n",
              " 'ALVARION LTD': 'ALVRQ',\n",
              " 'ALEXZA PHARMACEUTICALS INC': 'ALXA',\n",
              " 'ALEXION PHARMACEUTICALS INC': 'ALXN',\n",
              " 'ALX ONCOLOGY HOLDINGS INC': 'ALXO',\n",
              " 'ALITHYA GROUP INC': 'ALYA',\n",
              " 'ALYN CORP': 'ALYNQ',\n",
              " 'ALYSIS TECHNOLOGIES INC': 'ALYS',\n",
              " 'ALZAMEND NEURO INC': 'ALZN',\n",
              " 'AMERICAN MEDICAL ALERT CORP': 'AMAC',\n",
              " 'AMAG PHARMACEUTICALS INC': 'AMAG',\n",
              " 'AMALGAMATED FINANCIAL CORP': 'AMAL',\n",
              " 'AMBRX BIOPHARMA INC': 'AMAM',\n",
              " 'AUTONAVI HOLDINGS LTD': 'AMAP',\n",
              " 'AMARILLO BIOSCIENCES INC': 'AMARQ',\n",
              " 'APPLIED MATERIALS INC': 'AMAT',\n",
              " 'AMBARELLA INC': 'AMBA',\n",
              " 'AMERICAN BANCORPORATION': 'AMBC1',\n",
              " 'AMBIT BIOSCIENCES CORP': 'AMBI1',\n",
              " 'AMBANC CORP': 'AMBK1',\n",
              " 'AMBIENT CORP': 'AMBTQ',\n",
              " 'APPLIED MICRO CIRCUITS CORP': 'AMCC',\n",
              " 'ANDATEE CHINA MARINE FUEL SERVICES CORP': 'AMCF',\n",
              " 'AMERICAN COIN MERCHANDISING INC': 'AMCN1',\n",
              " 'AMCOMP INC': 'AMCP',\n",
              " 'AMICAS INC': 'AMCS',\n",
              " 'AMRESCO CAPITAL TRUST': 'AMCT',\n",
              " 'AMERICAN CLASSIC VOYAGES CO': 'AMCVQ',\n",
              " 'AMC NETWORKS INC': 'AMCX',\n",
              " 'ADVANCED MICRO DEVICES INC': 'AMD',\n",
              " 'AMEDISYS INC': 'AMED',\n",
              " 'APOLLO MEDICAL HOLDINGS INC': 'AMEH',\n",
              " 'AMERICAN EDUCATIONAL PRODUCTS INC': 'AMEP',\n",
              " 'AMES DEPARTMENT STORES INC': 'AMESQ',\n",
              " 'APPLIED MOLECULAR EVOLUTION INC': 'AMEV',\n",
              " 'AMERICAN FINANCIAL HOLDINGS INC': 'AMFH',\n",
              " 'AMCORE FINANCIAL INC': 'AMFIQ',\n",
              " 'AMERICAN MOLD GUARD INC': 'AMGI',\n",
              " 'ASCENT MEDIA GROUP INC': 'AMGIA',\n",
              " 'AMGEN INC': 'AMGN',\n",
              " 'AMERICAN HEALTHCHOICE INC': 'AMHI',\n",
              " 'AMERICAN INDEPENDENCE CORP': 'AMIC',\n",
              " 'AMBASSADORS INTERNATIONAL INC': 'AMIEQ',\n",
              " 'AMIS HOLDINGS INC': 'AMIS',\n",
              " 'AMKOR TECHNOLOGY INC': 'AMKR',\n",
              " 'AMERICAN LITHIUM CORP': 'AMLI',\n",
              " 'AML COMMUNICATIONS INC': 'AMLJ',\n",
              " 'AMYLIN PHARMACEUTICALS INC': 'AMLN',\n",
              " 'AMYLYX PHARMACEUTICALS INC': 'AMLX',\n",
              " 'AMRESCO INC': 'AMMBQ',\n",
              " 'AMERICAN MEDICAL SYSTEMS HOLDINGS INC': 'AMMD',\n",
              " 'AMERICAN NATIONAL BANKSHARES INC': 'AMNB',\n",
              " 'ACT MANUFACTURING INC': 'AMNUQ',\n",
              " 'AMPLITECH GROUP INC': 'AMPGW',\n",
              " 'AMPHASTAR PHARMACEUTICALS INC': 'AMPH',\n",
              " 'AMERICAN PHYSICIANS SERVICE GROUP INC': 'AMPH1',\n",
              " 'AMPLITUDE INC': 'AMPL',\n",
              " 'AMPAL-AMERICAN ISRAEL CORP': 'AMPLQ',\n",
              " 'AMPEX CORP': 'AMPXQ',\n",
              " 'AMERICAN RIVER BANKSHARES': 'AMRB',\n",
              " 'ALBANY MOLECULAR RESEARCH INC': 'AMRI',\n",
              " 'A-MARK PRECIOUS METALS INC': 'AMRK',\n",
              " 'AMARIN CORP PLC': 'AMRN',\n",
              " 'AMERIN CORP': 'AMRN1',\n",
              " 'ALTA MESA RESOURCES INC': 'AMRWW',\n",
              " 'AMYRIS INC': 'AMRSQ',\n",
              " 'AMNEAL PHARMACEUTICALS INC': 'AMRX',\n",
              " 'AMERICAN NATIONAL GROUP INC': 'ANAT',\n",
              " 'AMERICAN SUPERCONDUCTOR CORP': 'AMSC',\n",
              " 'AMERISAFE INC': 'AMSF',\n",
              " 'AAMES FINANCIAL CORP': 'AMSF1',\n",
              " 'AMSURG CORP': 'AMSGP',\n",
              " 'ARTEMIS INTERNATIONAL SOLUTIONS CORP': 'AMSI',\n",
              " 'APACHE MEDICAL SYSTEMS INC': 'AMSI1',\n",
              " 'AMESITE INC': 'AMST',\n",
              " 'AMERICAN SOFTWARE INC': 'AMSWA',\n",
              " 'AMERICAN MANAGEMENT SYSTEMS INC': 'AMSY',\n",
              " 'AMERITRANS CAPITAL CORP': 'AMTCQ',\n",
              " 'TD AMERITRADE HOLDING CORP': 'AMTD1',\n",
              " 'APPLIED MOLECULAR TRANSPORT INC': 'AMTI',\n",
              " 'AMERICAN MATERIALS & TECHNOLOGIES CORP': 'AMTK1',\n",
              " 'AEMETIS INC': 'AMTX',\n",
              " 'AMERITYRE CORP': 'AMTY',\n",
              " 'AMERIVEST PROPERTIES INC': 'AMV2',\n",
              " 'ADVANCED MACHINE VISION CORP': 'AMVC',\n",
              " 'AMERICAN WOODMARK CORP': 'AMWD',\n",
              " 'AMX CORP': 'AMXC',\n",
              " 'AMNEX INC': 'AMXIQ',\n",
              " 'AMRYT PHARMA PLC': 'AMYT',\n",
              " 'AMAZON COM INC': 'AMZN',\n",
              " 'ANAPTYSBIO INC': 'ANAB',\n",
              " 'ANACOR PHARMACEUTICALS INC': 'ANAC1',\n",
              " 'ANADIGICS INC': 'ANAD',\n",
              " 'ANB CORP': 'ANBC',\n",
              " 'ANCHOR BANCORP': 'ANCB',\n",
              " 'AIRNET COMMUNICATIONS CORP': 'ANCCQ',\n",
              " 'ANC RENTAL CORP': 'ANCJQ',\n",
              " 'ANCOR COMMUNICATIONS INC': 'ANCR',\n",
              " 'ACCESS NATIONAL CORP': 'ANCX',\n",
              " 'ANDATACO INC': 'ANDA1',\n",
              " 'ANDINA ACQUISITION CORP II': 'ANDAW2',\n",
              " 'ANDOVER BANCORP INC': 'ANDB',\n",
              " 'ANDERSONS INC': 'ANDE',\n",
              " 'ANDOVER NET INC': 'ANDN1',\n",
              " 'ANADYS PHARMACEUTICALS INC': 'ANDS',\n",
              " 'ANDREW CORP': 'ANDW',\n",
              " 'ANEBULO PHARMACEUTICALS INC': 'ANEB',\n",
              " 'ANAREN  INC': 'ANEN',\n",
              " 'AUTHORIZENET HOLDINGS INC': 'ANET1',\n",
              " 'ACT NETWORKS INC': 'ANET2',\n",
              " 'ANFI INC': 'ANFI1',\n",
              " 'ANGHAMI INC': 'VMACU',\n",
              " 'ANGI INC': 'ANGI',\n",
              " 'ANGEION CORP': 'ANGN1',\n",
              " 'ANGIODYNAMICS INC': 'ANGO',\n",
              " 'ANICOM INC': 'ANIC',\n",
              " 'ANIKA THERAPEUTICS INC': 'ANIK',\n",
              " 'ANI PHARMACEUTICALS INC': 'ANIP',\n",
              " 'ANIXA BIOSCIENCES INC': 'ANIX',\n",
              " 'ADLAI NORTYE LTD': 'ANL',\n",
              " 'ANALOGY INC': 'ANLG',\n",
              " 'ANALYSTS INTERNATIONAL CORP': 'ANLY',\n",
              " 'ANACOMP INC': 'ANMP',\n",
              " 'ANNAPOLIS BANCORP INC': 'ANNB',\n",
              " 'ANNEXON INC': 'ANNX',\n",
              " 'ANGIOTECH PHARMACEUTICALS INC': 'ANPIQ',\n",
              " 'ANERGEN INC': 'ANRG',\n",
              " 'AGRICULTURE & NATURAL SOLUTIONS ACQUISITION CORP': 'ANSCW',\n",
              " 'ADVANCED NEUROMODULATION SYSTEMS INC': 'ANSI',\n",
              " 'ANSELL LTD': 'ANSLY',\n",
              " 'ANSYS INC': 'ANSS',\n",
              " 'ANSOFT CORP': 'ANST',\n",
              " 'ANESIVA INC': 'ANSVQ',\n",
              " 'ANSWERS CORP': 'ANSW',\n",
              " 'AIRNET TECHNOLOGY INC': 'ANTE',\n",
              " 'ANTHERA PHARMACEUTICALS INC': 'ANTH',\n",
              " 'PHAZAR CORP': 'ANTP',\n",
              " 'AN2 THERAPEUTICS INC': 'ANTX',\n",
              " 'SPHERE 3D CORP': 'ANY',\n",
              " 'AROGO CAPITAL ACQUISITION CORP': 'AOGOW',\n",
              " 'AMERICA ONLINE LATIN AMERICA INC': 'AOLAQ',\n",
              " 'AMERICAN ONCOLOGY NETWORK INC': 'DTOCU',\n",
              " 'A123 SYSTEMS INC': 'AONEQ',\n",
              " 'ALLEN ORGAN CO': 'AORGB',\n",
              " 'ALPHA & OMEGA SEMICONDUCTOR LTD': 'AOSL',\n",
              " 'AMERICAN OUTDOOR BRANDS INC': 'AOUT',\n",
              " 'APA CORP': 'APA',\n",
              " 'APPALACHIAN BANCSHARES INC': 'APAB',\n",
              " 'STONEBRIDGE ACQUISITION CORP': 'APACW',\n",
              " 'APAC CUSTOMER SERVICES INC': 'APAC1',\n",
              " 'APCO OIL & GAS INTERNATIONAL INC': 'APAGF',\n",
              " 'AMERICAN POWER CONVERSION CORP': 'APCC',\n",
              " 'ATLAS SOUTH SEA PEARL LTD': 'APCFY',\n",
              " 'AUTOMOBILE PROTECTION CORP APCO': 'APCO',\n",
              " 'APPTECH PAYMENTS CORP': 'APCXW',\n",
              " 'APPLIED DNA SCIENCES INC': 'APDNW',\n",
              " 'AMERICAN PUBLIC EDUCATION INC': 'APEI',\n",
              " 'APOLLO ENDOSURGERY INC': 'APEN',\n",
              " 'APEX GLOBAL BRANDS INC': 'APEX',\n",
              " 'APEX INC': 'APEX1',\n",
              " 'AMERICAN PACIFIC CORP': 'APFC',\n",
              " 'APOGEE THERAPEUTICS INC': 'APGE',\n",
              " 'APEXIGEN INC': 'BCACU',\n",
              " 'APHRIA INC': 'APHA',\n",
              " 'AGORA INC': 'API',\n",
              " 'APIGEE CORP': 'APIC',\n",
              " 'ACME PACKET INC': 'APKT',\n",
              " 'APPLIED DIGITAL CORP': 'APLD',\n",
              " 'APOLLOMICS INC': 'JMACU',\n",
              " 'AT PLAN INC': 'APLN',\n",
              " 'ARCHROCK PARTNERS LP': 'APLP',\n",
              " 'APELLIS PHARMACEUTICALS INC': 'APLS',\n",
              " 'APPLIED THERAPEUTICS INC': 'APLT',\n",
              " 'APPLIX INC': 'APLX',\n",
              " 'APTORUM GROUP LTD': 'APM',\n",
              " 'APPLIED MICROSYSTEMS CORP': 'APMC',\n",
              " 'AXONPRIME INFRASTRUCTURE ACQUISITION CORP': 'APMIW',\n",
              " 'ALPHA INNOTECH CORP': 'APNO',\n",
              " 'APPNET INC': 'APNT1',\n",
              " 'APOGEE ENTERPRISES INC': 'APOG',\n",
              " 'APOLLO EDUCATION GROUP INC': 'UOPX',\n",
              " 'APPLOVIN CORP': 'APP',\n",
              " 'APPLEBEES INTERNATIONAL INC': 'APPB',\n",
              " 'APPFOLIO INC': 'APPF',\n",
              " 'APPHARVEST INC': 'NOVSU',\n",
              " 'APPIAN CORP': 'APPN',\n",
              " 'DIGITAL TURBINE INC': 'EBTBQ',\n",
              " 'APPIANT TECHNOLOGIES INC': 'APPS1',\n",
              " 'APP PHARMACEUTICALS INC': 'APPX',\n",
              " 'ASIA PACIFIC RESOURCES LTD': 'APQCF',\n",
              " 'APRIA INC': 'APR',\n",
              " 'APREA THERAPEUTICS INC': 'APRE',\n",
              " 'AMERICA FIRST APARTMENT INVESTORS INC': 'APRO1',\n",
              " 'APROPOS TECHNOLOGY INC': 'APRS',\n",
              " 'APPLIED SIGNAL TECHNOLOGY INC': 'APSG1',\n",
              " 'APPTIO INC': 'APTI',\n",
              " 'ADVANCED POWER TECHNOLOGY INC': 'APTI1',\n",
              " 'ALPHA PARTNERS TECHNOLOGY MERGER CORP': 'APTMW',\n",
              " 'APTIMUS INC': 'APTM1',\n",
              " 'APTOSE BIOSCIENCES INC': 'APTO',\n",
              " 'APTINYX INC': 'APTX',\n",
              " 'APTEVO THERAPEUTICS INC': 'APVO',\n",
              " 'ASIA PACIFIC WIRE & CABLE CORP LTD': 'APWC',\n",
              " 'APPLEWOODS INC': 'APWD',\n",
              " 'A-POWER ENERGY GENERATION SYSTEMS LTD': 'APWR1',\n",
              " 'ASTROPOWER INC': 'APWRQ',\n",
              " 'APX ACQUISITION CORP I': 'APXIW',\n",
              " 'APYX MEDICAL CORP': 'APYX',\n",
              " 'AQUABOUNTY TECHNOLOGIES INC': 'AQB',\n",
              " 'AQUA CARE SYSTEMS INC': 'AQCR',\n",
              " 'AQUA METALS INC': 'AQMS',\n",
              " 'AQUANTIVE INC': 'AQNT',\n",
              " 'AQUESTIVE THERAPEUTICS INC': 'AQST',\n",
              " 'AQUARON ACQUISITION CORP': 'AQUNU',\n",
              " 'ARAVIVE INC': 'ARAV',\n",
              " 'ACCURAY INC': 'ARAY',\n",
              " 'ARIBA INC': 'ARBA',\n",
              " 'ARB IOT GROUP LTD': 'ARBB',\n",
              " 'ARBE ROBOTICS LTD': 'ITACU',\n",
              " 'AEQUI ACQUISITION CORP': 'ARBGW',\n",
              " 'ARGO BLOCKCHAIN PLC': 'ARBKL',\n",
              " 'ARBINET CORP': 'ARBX',\n",
              " 'ARCBEST CORP': 'ARCB',\n",
              " 'ARES CAPITAL CORP': 'ARCC',\n",
              " 'ALLIED RISER COMMUNICATIONS CORP': 'ARCC1',\n",
              " 'ARCO PLATFORM LTD': 'ARCE',\n",
              " 'ARCH WIRELESS INC': 'AWIN1',\n",
              " 'ARBOR RAPHA CAPITAL BIOHOLDINGS CORP I': 'ARCKW',\n",
              " 'ARCHIPELAGO LEARNING INC': 'ARCL',\n",
              " 'ARCTURUS THERAPEUTICS HOLDINGS INC': 'ARCT',\n",
              " 'AMERICAN REALTY CAPITAL TRUST INC': 'ARCT1',\n",
              " 'ARC GROUP WORLDWIDE INC': 'ARCW',\n",
              " '@ROAD INC': 'ARDI',\n",
              " 'ARADIGM CORP': 'ARDMQ',\n",
              " 'ARDEN GROUP INC': 'ARDNA',\n",
              " 'ARIDIS PHARMACEUTICALS INC': 'ARDS',\n",
              " 'ARDENT SOFTWARE INC': 'ARDT',\n",
              " 'ARDELYX INC': 'ARDX',\n",
              " 'AREA BANCSHARES CORP': 'AREA',\n",
              " 'AMERICAN REBEL HOLDINGS INC': 'AREBW',\n",
              " 'AMERICAN RESOURCES CORP': 'AREC',\n",
              " 'AREMISSOFT CORP': 'AREM1',\n",
              " 'APPROACH RESOURCES INC': 'AREXQ',\n",
              " 'AURIGA LABORATORIES INC': 'ARGA',\n",
              " 'ARGOS THERAPEUTICS INC': 'ARGSQ',\n",
              " 'ARGUS CAPITAL CORP': 'ARGUW',\n",
              " 'ARGENX SE': 'ARGX',\n",
              " 'ARGOSY EDUCATION GROUP INC': 'ARGY',\n",
              " 'ARHAUS INC': 'ARHS',\n",
              " 'ARIAD PHARMACEUTICALS INC': 'ARIA',\n",
              " 'AMERICAN RAILCAR INDUSTRIES INC': 'ARII',\n",
              " 'ARI NETWORK SERVICES INC': 'ARIS1',\n",
              " 'ARISZ ACQUISITION CORP': 'ARIZW',\n",
              " 'ARKO CORP': 'ARKOW',\n",
              " 'ARK RESTAURANTS CORP': 'ARKR',\n",
              " 'AREL COMMUNICATIONS & SOFTWARE LTD': 'ARLCF',\n",
              " 'ALLIANCE RESOURCE PARTNERS LP': 'ARLP',\n",
              " 'ARALEZ PHARMACEUTICALS INC': 'ARLZQ',\n",
              " 'ARM HOLDINGS PLC': 'ARMH',\n",
              " 'ARMO BIOSCIENCES INC': 'ARMO',\n",
              " 'ARENA PHARMACEUTICALS INC': 'ARNA',\n",
              " 'ARROW FINANCIAL CORP': 'AROW',\n",
              " 'ARROWPOINT COMMUNICATIONS INC': 'ARPT',\n",
              " 'ARQULE INC': 'ARQL',\n",
              " 'ARQIT QUANTUM INC': 'CENHU',\n",
              " 'ARCUTIS BIOTHERAPEUTICS INC': 'ARQT',\n",
              " 'ARROW INTERNATIONAL INC': 'ARRO',\n",
              " 'ARRIS INTERNATIONAL PLC': 'ARRS',\n",
              " 'ARROWROOT ACQUISITION CORP': 'ARRWU',\n",
              " 'ARRAY TECHNOLOGIES INC': 'ARRY',\n",
              " 'ARRAY BIOPHARMA INC': 'ARRY1',\n",
              " 'ALERIS INTERNATIONAL INC': 'ARS',\n",
              " 'ARIS CORP': 'ARSC1',\n",
              " 'ARCSIGHT INC': 'ARST',\n",
              " 'ARTHROCARE CORP': 'ARTC',\n",
              " 'ARTEMIS STRATEGIC INVESTMENT CORP': 'ARTEW',\n",
              " 'ARTECON INC': 'ARTE1',\n",
              " 'ARTES MEDICAL INC': 'ARTEQ',\n",
              " 'ART TECHNOLOGY GROUP INC': 'ARTG',\n",
              " 'ARTISAN COMPONENTS INC': 'ARTI1',\n",
              " 'ARTELO BIOSCIENCES INC': 'ARTLW',\n",
              " 'ARISTOTLE CORP': 'ARTL1',\n",
              " 'ARTESIAN RESOURCES CORP': 'ARTNA',\n",
              " 'ADVANCED RADIO TELECOM CORP': 'ARTT',\n",
              " 'ARTS WAY MANUFACTURING CO INC': 'ARTW',\n",
              " 'AROTECH CORP': 'ARTX',\n",
              " 'ARUBA NETWORKS INC': 'ARUN',\n",
              " 'ARRIVAL': 'CIICU',\n",
              " 'ARVINAS INC': 'ARVN',\n",
              " 'AEROVOX INC': 'ARVX',\n",
              " 'AROWANA INC': 'ARWA',\n",
              " 'ARROW MAGNOLIA INTERNATIONAL INC': 'ARWM',\n",
              " 'ARROWHEAD PHARMACEUTICALS INC': 'ARWR',\n",
              " 'ADAMS RESPIRATORY THERAPEUTICS INC': 'ARXT',\n",
              " 'AEROFLEX INC': 'ARXX',\n",
              " 'ARYA SCIENCES ACQUISITION CORP IV': 'ARYD',\n",
              " 'ARYA SCIENCES ACQUISITION CORP V': 'ARYE',\n",
              " 'ARYX THERAPEUTICS INC': 'ARYX',\n",
              " 'ASA HOLDINGS INC': 'ASAI1',\n",
              " 'ASA INTERNATIONAL LTD': 'ASAL',\n",
              " 'ASAHI AMERICA INC': 'ASAM1',\n",
              " 'WAITR HOLDINGS INC': 'WTRHW',\n",
              " 'ASTREA ACQUISITION CORP': 'ASAXW',\n",
              " 'ASB BANCORP INC': 'ASBB',\n",
              " 'AMERIANA BANCORP': 'ASBI',\n",
              " 'ASB FINANCIAL CORP': 'ASBN',\n",
              " 'ASPAC I ACQUISITION CORP': 'ASCAW',\n",
              " 'AMERISTAR CASINOS INC': 'ASCA1',\n",
              " 'ASPAC II ACQUISITION CORP': 'ASCBW',\n",
              " 'ASCENTIAL SOFTWARE CORP': 'ASCL',\n",
              " 'ASCENT CAPITAL GROUP INC': 'ASCMA',\n",
              " 'ASCENT PEDIATRICS INC': 'ASCT',\n",
              " 'ADVANCED SWITCHING COMMUNICATIONS INC': 'ASCX',\n",
              " 'ASD GROUP INC': 'ASDG',\n",
              " 'ASCENDANT SOLUTIONS INC': 'ASDS',\n",
              " 'ASPECT DEVELOPMENT INC': 'ASDV',\n",
              " 'ASECO CORP': 'ASEC',\n",
              " 'AMERICAN SCIENCE & ENGINEERING INC': 'ASEI',\n",
              " 'ASHFORD COM INC': 'ASFD',\n",
              " 'ASTA FUNDING INC': 'ASFI',\n",
              " 'ATLANTIC SOUTHERN FINANCIAL GROUP INC': 'ASFN',\n",
              " 'ALLSTATE FINANCIAL CORP': 'ASFN1',\n",
              " 'AMERICA SERVICE GROUP INC': 'ASGR',\n",
              " 'ASIA GLOBAL CROSSING LTD': 'ASGXF',\n",
              " 'ASCHE TRANSPORTATION SERVICES INC': 'ASHE',\n",
              " 'ASHWORTH INC': 'ASHW',\n",
              " 'ASIAINFO-LINKAGE INC': 'ASIA1',\n",
              " 'ALPHASTAR INSURANCE GROUP LTD': 'ASIGF',\n",
              " 'ANSALDO SIGNAL NV': 'ASIGF1',\n",
              " 'ASI SOLUTIONS INC': 'ASIS',\n",
              " 'ASK JEEVES INC': 'ASKJ',\n",
              " 'AERSALE CORP': 'ASLEW',\n",
              " 'ASLAN PHARMACEUTICALS LTD': 'ASLN',\n",
              " 'ASSEMBLY BIOSCIENCES INC': 'ASMB',\n",
              " 'ASM INTERNATIONAL N V': 'ASMIY',\n",
              " 'ASML HOLDING NV': 'ASML',\n",
              " 'MAHWAH BERGEN RETAIL GROUP INC': 'ASNAQ',\n",
              " 'ASCENDIS PHARMA A': 'ASND',\n",
              " 'ASCEND COMMUNICATIONS INC': 'ASND1',\n",
              " 'ACTELIS NETWORKS INC': 'ASNS',\n",
              " 'ACADEMY SPORTS & OUTDOORS INC': 'ASO',\n",
              " 'AMSOUTH BANCORPORATION': 'ASO1',\n",
              " 'ASP ISOTOPES INC': 'ASPI',\n",
              " 'ANANGEL AMERICAN SHIPHOLDINGS LTD': 'ASPIY',\n",
              " 'ASPECT MEDICAL SYSTEMS INC': 'ASPM',\n",
              " 'ALTISOURCE PORTFOLIO SOLUTIONS SA': 'ASPS',\n",
              " 'ASPECT COMMUNICATIONS CORP': 'ASPT',\n",
              " 'ASPEN GROUP INC': 'ASPU',\n",
              " 'ASPREVA PHARMACEUTICALS CORP': 'ASPV',\n",
              " 'AUSPEX PHARMACEUTICALS INC': 'ASPX',\n",
              " 'AUSPEX SYSTEMS INC': 'ASPXQ',\n",
              " 'ASSERTIO HOLDINGS INC': 'ASRT',\n",
              " 'AMERISERV FINANCIAL INC': 'ASRVP',\n",
              " 'ASSET ENTITIES INC': 'ASST',\n",
              " 'ASTROTECH CORP': 'ASTC',\n",
              " 'ASTEC INDUSTRIES INC': 'ASTE',\n",
              " 'ASCENT SOLAR TECHNOLOGIES INC': 'ASTI',\n",
              " 'ALLERGAN SPECIALTY THERAPEUTICS INC': 'ASTI1',\n",
              " 'ALGOMA STEEL GROUP INC': 'LEGOU',\n",
              " 'ASHTON TECHNOLOGY GROUP INC': 'ASTN',\n",
              " 'ASTRA SPACE INC': 'HOLUU',\n",
              " 'AST SPACEMOBILE INC': 'NPAUU',\n",
              " 'ASAT HOLDINGS LTD': 'ASTTY',\n",
              " 'ASTEX PHARMACEUTICALS INC': 'ASTX',\n",
              " 'APPLIED SCIENCE & TECHNOLOGY INC': 'ASTX1',\n",
              " 'ASURE SOFTWARE INC': 'ASUR',\n",
              " 'ASV HOLDINGS INC': 'ASV',\n",
              " 'ASV INC': 'ASVI',\n",
              " 'ACTIVE SOFTWARE INC': 'ASWX',\n",
              " 'AMTECH SYSTEMS INC': 'ASYS',\n",
              " 'ASYST TECHNOLOGIES INC': 'ASYTQ',\n",
              " 'ATC TECHNOLOGY CORP': 'ATAC1',\n",
              " 'ATA HOLDINGS CORP': 'ATAHQ',\n",
              " 'ATAI LIFE SCIENCES NV': 'ATAI',\n",
              " 'AURORA TECHNOLOGY ACQUISITION CORP': 'ATAKW',\n",
              " 'ATARI INC': 'ATAR1',\n",
              " 'ATOUR LIFESTYLE HOLDINGS LTD': 'ATAT',\n",
              " 'ATLANTIC BANCGROUP INC': 'ATBC',\n",
              " 'AUTONOMOUS TECHNOLOGIES CORP': 'ATCI1',\n",
              " 'AT COMM CORP': 'ATCM',\n",
              " 'ATLAS TECHNICAL CONSULTANTS INC': 'ATCXW',\n",
              " 'ASTEA INTERNATIONAL INC': 'ATEA',\n",
              " 'ALPHATEC HOLDINGS INC': 'ATEC',\n",
              " 'AMERICAN TELECASTING INC': 'ATEL1',\n",
              " 'ENTERTAINMENT INC': 'ATEN1',\n",
              " 'ATERIAN INC': 'ATER',\n",
              " 'ANTERIX INC': 'ATEX',\n",
              " 'ATG INC': 'ATGCQ',\n",
              " 'ALTA GOLD CO': 'ATGDQ',\n",
              " 'ALPHA TECHNOLOGY GROUP LTD': 'ATGL',\n",
              " 'ALTIGEN COMMUNICATIONS INC': 'ATGN',\n",
              " 'ATHIRA PHARMA INC': 'ATHA',\n",
              " 'ALTERITY THERAPEUTICS LTD': 'ATHE',\n",
              " 'AT HOME CORP': 'ATHM1',\n",
              " 'ATHENAHEALTH INC': 'ATHN1',\n",
              " 'ATHEROS COMMUNICATIONS INC': 'ATHR',\n",
              " 'ATHERSYS INC': 'ATHX',\n",
              " 'APPLIEDTHEORY CORP': 'ATHYQ',\n",
              " 'ATIF HOLDINGS LTD': 'ATIF',\n",
              " 'ATTIS INDUSTRIES INC': 'ATISW',\n",
              " 'ADVANCED TISSUE SCIENCES INC': 'ATISZ',\n",
              " 'ATLANTICUS HOLDINGS CORP': 'ATLCL',\n",
              " 'AMES NATIONAL CORP': 'ATLO',\n",
              " 'ATL PRODUCTS INC': 'ATLPA',\n",
              " 'ATLAS LITHIUM CORP': 'ATLX',\n",
              " 'ALPHATIME ACQUISITION CORP': 'ATMCW',\n",
              " 'ATMI INC': 'ATMI',\n",
              " 'ATMEL CORP': 'ATML',\n",
              " 'ALPHAVEST ACQUISITION CORP': 'ATMVR',\n",
              " '180 LIFE SCIENCES CORP': 'KBLMR',\n",
              " 'ATN INTERNATIONAL INC': 'ATNI',\n",
              " 'ATHENEX INC': 'ATNXQ',\n",
              " 'API TECHNOLOGIES CORP': 'ATNY',\n",
              " 'ATOMERA INC': 'ATOM',\n",
              " 'ATOMIC BURRITO INC': 'ATOM1',\n",
              " 'ALTEON WEBSYSTEMS INC': 'ATON',\n",
              " 'ATOSSA THERAPEUTICS INC': 'ATOS',\n",
              " 'ATP OIL & GAS CORP': 'ATPAQ',\n",
              " 'AGAPE ATP CORP': 'ATPC',\n",
              " 'ATHEY PRODUCTS CORP': 'ATPC1',\n",
              " 'ATLANTIS PLASTICS INC': 'ATPL',\n",
              " 'ADVANCED TECHNICAL PRODUCTS INC': 'ATPX',\n",
              " 'ATARA BIOTHERAPEUTICS INC': 'ATRA',\n",
              " 'ATRICURE INC': 'ATRC',\n",
              " 'ATRION CORP': 'ATRI',\n",
              " 'ATRM HOLDINGS INC': 'ATRM',\n",
              " 'AUTHORISZOR INC': 'AUZR',\n",
              " 'ASTRONICS CORP': 'ATRO',\n",
              " 'ANTARES PHARMA INC': 'ATRS',\n",
              " 'ALTIRIS INC': 'ATRS1',\n",
              " 'ADHERA THERAPEUTICS INC': 'ATRX',\n",
              " 'ATRIX LABORATORIES INC': 'ATRX1',\n",
              " 'AMTRUST CAPITAL CORP': 'ATSB',\n",
              " 'AIR TRANSPORT SERVICES GROUP INC': 'ATSG',\n",
              " 'ATS MEDICAL INC': 'ATSI',\n",
              " 'ARTESYN TECHNOLOGIES INC': 'ATSN',\n",
              " 'AT&T CANADA INC': 'ATTC1',\n",
              " 'AT&T LATIN AMERICA CORP': 'ATTL',\n",
              " 'ATTUNITY LTD': 'ATTU',\n",
              " '1-800 ATTORNEY INC': 'ATTY',\n",
              " 'ALTIVA FINANCIAL CORP': 'ATVA',\n",
              " 'ACTIVISION BLIZZARD INC': 'ATVI',\n",
              " 'COSTA INC': 'ATX',\n",
              " 'ADDENTAX GROUP CORP': 'ATXG',\n",
              " 'AVENUE THERAPEUTICS INC': 'ATXI',\n",
              " 'ATRIX INTERNATIONAL INC': 'ATXI1',\n",
              " 'ASTRIA THERAPEUTICS INC': 'ATXS',\n",
              " 'ATI TECHNOLOGIES INC': 'ATYT',\n",
              " 'AUBURN NATIONAL BANCORPORATION INC': 'AUBN',\n",
              " 'AUDIOCODES LTD': 'AUDC',\n",
              " 'AUGMENT SYSTEMS INC': 'AUGS',\n",
              " 'AUGUST TECHNOLOGY CORP': 'AUGT1',\n",
              " 'AUGMEDIX INC': 'AUGX',\n",
              " 'AUTHID INC': 'AUID',\n",
              " 'AULT INC': 'AULT1',\n",
              " 'AURINIA PHARMACEUTICALS INC': 'AUPH',\n",
              " 'AURORA INNOVATION INC': 'RTPYU',\n",
              " 'AURA BIOSCIENCES INC': 'AURA',\n",
              " 'AURA SYSTEMS INC': 'AURAQ',\n",
              " 'ACCESS PLANS USA INC': 'AUSA',\n",
              " 'AUTHENTEC INC': 'AUTH',\n",
              " 'AUTOLUS THERAPEUTICS PLC': 'AUTL',\n",
              " 'AUTONOMY CORP PLC': 'AUTNY',\n",
              " 'AUTOWEB INC': 'AUTO',\n",
              " 'AUTOINFO INC': 'AUTO1',\n",
              " 'AUDDIA INC': 'AUUDW',\n",
              " 'APPLIED UV INC': 'AUVIP',\n",
              " 'AUXILIUM PHARMACEUTICALS INC': 'AUXL',\n",
              " 'AVEANNA HEALTHCARE HOLDINGS INC': 'AVAH',\n",
              " 'AEROVIRONMENT INC': 'AVAV',\n",
              " 'AMERICAN VANTAGE COMPANIES': 'AVCS',\n",
              " 'AVOCENT CORP': 'AVCT1',\n",
              " 'AMERICAN VIRTUAL CLOUD TECHNOLOGIES INC': 'WRLSR',\n",
              " 'AVADEL PHARMACEUTICALS PLC': 'AVDL',\n",
              " 'AVONDALE INDUSTRIES INC': 'AVDL1',\n",
              " 'AVADO BRANDS INC': 'AVDO',\n",
              " 'AVEDRO INC': 'AVDR1',\n",
              " 'AVALON DIGITAL MARKETING SYSTEMS INC': 'AVDS1',\n",
              " 'AVIDXCHANGE HOLDINGS INC': 'AVDX',\n",
              " 'AVECOR CARDIOVASCULAR INC': 'AVEC',\n",
              " 'ARTERIAL VASCULAR ENGINEERING INC': 'AVEI',\n",
              " 'AVEO PHARMACEUTICALS INC': 'AVEO',\n",
              " 'AVIATION GENERAL INC': 'AVGE1',\n",
              " 'AVIGEN INC': 'AVGN',\n",
              " 'BROADCOM INC': 'AVGOP',\n",
              " 'AVANTGO INC': 'AVGO1',\n",
              " 'AVINGER INC': 'AVGR',\n",
              " 'ACHARI VENTURES HOLDINGS CORP I': 'AVHIW',\n",
              " 'AV HOMES INC': 'AVHI1',\n",
              " 'AVID TECHNOLOGY INC': 'AVID',\n",
              " 'ATEA PHARMACEUTICALS INC': 'AVIR',\n",
              " 'AVIRON': 'AVIR1',\n",
              " 'AVINCI MEDIA CORP': 'AVMC1',\n",
              " 'AVANIR PHARMACEUTICALS INC': 'AVNR',\n",
              " 'AVANT CORP': 'AVNT1',\n",
              " 'AVENUE FINANCIAL HOLDINGS INC': 'AVNU',\n",
              " 'AVIAT NETWORKS INC': 'AVNW',\n",
              " 'AVANEX CORP': 'AVNX',\n",
              " 'MISSION PRODUCE INC': 'AVO',\n",
              " 'AVEPOINT INC': 'AVPTW',\n",
              " 'AVROBIO INC': 'AVRO',\n",
              " 'AVERT INC': 'AVRT1',\n",
              " 'AVALON PHARMACEUTICALS INC': 'AVRX',\n",
              " 'AVNET INC': 'AVT',\n",
              " 'AVANTAX INC': 'AVTA',\n",
              " 'AEROVATE THERAPEUTICS INC': 'AVTE',\n",
              " 'AVITAR INC': 'AVTI',\n",
              " 'AVTEAM INC': 'AVTMQ',\n",
              " 'AVALO THERAPEUTICS INC': 'CERCZ',\n",
              " 'ANAVEX LIFE SCIENCES CORP': 'AVXL',\n",
              " 'AVEXIS INC': 'AVXS',\n",
              " 'AVIZA TECHNOLOGY INC': 'AVZAQ',\n",
              " 'HOMEAWAY INC': 'AWAY1',\n",
              " 'AMERICANWEST BANCORPORATION': 'AWBCQ',\n",
              " 'AUTOWEB COM INC': 'AWEB',\n",
              " 'ALDERWOODS GROUP INC': 'AWGI1',\n",
              " \"ASPIRA WOMEN'S HEALTH INC\": 'AWH',\n",
              " 'AERWINS TECHNOLOGIES INC': 'PONOU',\n",
              " 'AWARE INC': 'AWRE',\n",
              " 'AXAR ACQUISITION CORP': 'AXAR',\n",
              " 'ABRAXAS PETROLEUM CORP': 'AXAS',\n",
              " 'AXCAN PHARMA INC': 'AXCA',\n",
              " 'ACCELERATE DIAGNOSTICS INC': 'AXDX',\n",
              " 'AXOGEN INC': 'AXGN',\n",
              " 'GLOBAL X GURU ACTIVIST INDEX ETF': 'ACTX1',\n",
              " ...}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reqd_ticker = 'TSLA'\n",
        "msft = yf.Ticker(reqd_ticker)\n",
        "hist = msft.history(period=\"1y\")\n",
        "hist['ticker'] = reqd_ticker\n",
        "hist_data_by_ticker = pd.DataFrame(columns=['Trend Analysis','Volatiity','Trading Volume','Dividend Payout','Stock Splits'])\n",
        "hist_str = \"\"\n",
        "\n",
        "for i in hist.iloc[-30:,:].index:\n",
        "  hist_str+=str(reqd_ticker)+' '\n",
        "  hist_str+=\"1 Month Historic Stock Price Data \\n\"\n",
        "  hist_str+=\"Date: **\"+str(i)+'**\\n'\n",
        "  hist_str+=\"Open Price: **\"+str(hist.at[i,'Open'])+'**\\n'\n",
        "  hist_str+=\"Highest Price: **\"+str(hist.at[i,'High'])+'**\\n'\n",
        "  hist_str+=\"Lowest Price: **\"+str(hist.at[i,'Low'])+'**\\n'\n",
        "  hist_str+=\"Closing Price: **\"+str(hist.at[i,'Close'])+'**\\n'\n",
        "  hist_str+=\"Volume Traded: **\"+str(hist.at[i,'Volume'])+'**\\n'\n",
        "  hist_str+=\"Dividends paid out: **\"+str(hist.at[i,'Dividends'])+'**\\n'\n",
        "  hist_str+=\"Stock Splits: **\"+str(hist.at[i,'Stock Splits'])+'**\\n'\n",
        "  hist_str+='\\n\\n'\n",
        "\n",
        "hist_str"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "TRgZfRu1MqEM",
        "outputId": "29548f90-1da6-4b2c-fcd4-b76f72d82534"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'TSLA 1 Month Historic Stock Price Data \\nDate: **2023-11-21 00:00:00-05:00**\\nOpen Price: **235.0399932861328**\\nHighest Price: **243.6199951171875**\\nLowest Price: **233.33999633789062**\\nClosing Price: **241.1999969482422**\\nVolume Traded: **122288000**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-11-22 00:00:00-05:00**\\nOpen Price: **242.0399932861328**\\nHighest Price: **244.00999450683594**\\nLowest Price: **231.39999389648438**\\nClosing Price: **234.2100067138672**\\nVolume Traded: **117950600**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-11-24 00:00:00-05:00**\\nOpen Price: **233.75**\\nHighest Price: **238.75**\\nLowest Price: **232.3300018310547**\\nClosing Price: **235.4499969482422**\\nVolume Traded: **65125200**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-11-27 00:00:00-05:00**\\nOpen Price: **236.88999938964844**\\nHighest Price: **238.3300018310547**\\nLowest Price: **232.10000610351562**\\nClosing Price: **236.0800018310547**\\nVolume Traded: **112031800**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-11-28 00:00:00-05:00**\\nOpen Price: **236.67999267578125**\\nHighest Price: **247.0**\\nLowest Price: **234.00999450683594**\\nClosing Price: **246.72000122070312**\\nVolume Traded: **148549900**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-11-29 00:00:00-05:00**\\nOpen Price: **249.2100067138672**\\nHighest Price: **252.75**\\nLowest Price: **242.75999450683594**\\nClosing Price: **244.13999938964844**\\nVolume Traded: **135401300**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-11-30 00:00:00-05:00**\\nOpen Price: **245.13999938964844**\\nHighest Price: **245.22000122070312**\\nLowest Price: **236.91000366210938**\\nClosing Price: **240.0800018310547**\\nVolume Traded: **132353200**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-12-01 00:00:00-05:00**\\nOpen Price: **233.13999938964844**\\nHighest Price: **240.19000244140625**\\nLowest Price: **231.89999389648438**\\nClosing Price: **238.8300018310547**\\nVolume Traded: **121173500**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-12-04 00:00:00-05:00**\\nOpen Price: **235.75**\\nHighest Price: **239.3699951171875**\\nLowest Price: **233.2899932861328**\\nClosing Price: **235.5800018310547**\\nVolume Traded: **104099800**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-12-05 00:00:00-05:00**\\nOpen Price: **233.8699951171875**\\nHighest Price: **246.66000366210938**\\nLowest Price: **233.6999969482422**\\nClosing Price: **238.72000122070312**\\nVolume Traded: **137971100**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-12-06 00:00:00-05:00**\\nOpen Price: **242.9199981689453**\\nHighest Price: **246.57000732421875**\\nLowest Price: **239.1699981689453**\\nClosing Price: **239.3699951171875**\\nVolume Traded: **126436200**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-12-07 00:00:00-05:00**\\nOpen Price: **241.5500030517578**\\nHighest Price: **244.0800018310547**\\nLowest Price: **236.97999572753906**\\nClosing Price: **242.63999938964844**\\nVolume Traded: **107142300**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-12-08 00:00:00-05:00**\\nOpen Price: **240.27000427246094**\\nHighest Price: **245.27000427246094**\\nLowest Price: **239.27000427246094**\\nClosing Price: **243.83999633789062**\\nVolume Traded: **102980100**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-12-11 00:00:00-05:00**\\nOpen Price: **242.74000549316406**\\nHighest Price: **243.44000244140625**\\nLowest Price: **237.4499969482422**\\nClosing Price: **239.74000549316406**\\nVolume Traded: **97913900**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-12-12 00:00:00-05:00**\\nOpen Price: **238.5500030517578**\\nHighest Price: **238.99000549316406**\\nLowest Price: **233.8699951171875**\\nClosing Price: **237.00999450683594**\\nVolume Traded: **95328300**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-12-13 00:00:00-05:00**\\nOpen Price: **234.19000244140625**\\nHighest Price: **240.3000030517578**\\nLowest Price: **228.1999969482422**\\nClosing Price: **239.2899932861328**\\nVolume Traded: **146286300**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-12-14 00:00:00-05:00**\\nOpen Price: **241.22000122070312**\\nHighest Price: **253.8800048828125**\\nLowest Price: **240.7899932861328**\\nClosing Price: **251.0500030517578**\\nVolume Traded: **160829200**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-12-15 00:00:00-05:00**\\nOpen Price: **251.2100067138672**\\nHighest Price: **254.1300048828125**\\nLowest Price: **248.3000030517578**\\nClosing Price: **253.5**\\nVolume Traded: **135720800**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-12-18 00:00:00-05:00**\\nOpen Price: **253.77999877929688**\\nHighest Price: **258.739990234375**\\nLowest Price: **251.36000061035156**\\nClosing Price: **252.0800018310547**\\nVolume Traded: **116416500**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-12-19 00:00:00-05:00**\\nOpen Price: **253.47999572753906**\\nHighest Price: **258.3399963378906**\\nLowest Price: **253.00999450683594**\\nClosing Price: **257.2200012207031**\\nVolume Traded: **106737400**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-12-20 00:00:00-05:00**\\nOpen Price: **256.4100036621094**\\nHighest Price: **259.8399963378906**\\nLowest Price: **247.0**\\nClosing Price: **247.13999938964844**\\nVolume Traded: **125097000**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-12-21 00:00:00-05:00**\\nOpen Price: **251.89999389648438**\\nHighest Price: **254.8000030517578**\\nLowest Price: **248.5500030517578**\\nClosing Price: **254.5**\\nVolume Traded: **109594200**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-12-22 00:00:00-05:00**\\nOpen Price: **256.760009765625**\\nHighest Price: **258.2200012207031**\\nLowest Price: **251.3699951171875**\\nClosing Price: **252.5399932861328**\\nVolume Traded: **93249800**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-12-26 00:00:00-05:00**\\nOpen Price: **254.49000549316406**\\nHighest Price: **257.9700012207031**\\nLowest Price: **252.91000366210938**\\nClosing Price: **256.6099853515625**\\nVolume Traded: **86892400**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-12-27 00:00:00-05:00**\\nOpen Price: **258.3500061035156**\\nHighest Price: **263.3399963378906**\\nLowest Price: **257.5199890136719**\\nClosing Price: **261.44000244140625**\\nVolume Traded: **106494400**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-12-28 00:00:00-05:00**\\nOpen Price: **263.6600036621094**\\nHighest Price: **265.1300048828125**\\nLowest Price: **252.7100067138672**\\nClosing Price: **253.17999267578125**\\nVolume Traded: **113619900**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2023-12-29 00:00:00-05:00**\\nOpen Price: **255.10000610351562**\\nHighest Price: **255.19000244140625**\\nLowest Price: **247.42999267578125**\\nClosing Price: **248.47999572753906**\\nVolume Traded: **100615300**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2024-01-02 00:00:00-05:00**\\nOpen Price: **250.0800018310547**\\nHighest Price: **251.25**\\nLowest Price: **244.41000366210938**\\nClosing Price: **248.4199981689453**\\nVolume Traded: **104654200**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2024-01-03 00:00:00-05:00**\\nOpen Price: **244.97999572753906**\\nHighest Price: **245.67999267578125**\\nLowest Price: **236.32000732421875**\\nClosing Price: **238.4499969482422**\\nVolume Traded: **120779500**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\nTSLA 1 Month Historic Stock Price Data \\nDate: **2024-01-04 00:00:00-05:00**\\nOpen Price: **239.25**\\nHighest Price: **241.75999450683594**\\nLowest Price: **237.72999572753906**\\nClosing Price: **241.42840576171875**\\nVolume Traded: **34252917**\\nDividends paid out: **0.0**\\nStock Splits: **0.0**\\n\\n\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hist"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "7TWjndPLRFys",
        "outputId": "e76561e1-f393-4e00-9b02-5591363cefc5"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 Open        High         Low       Close  \\\n",
              "Date                                                                        \n",
              "2023-01-04 00:00:00-05:00  109.110001  114.589996  107.519997  113.639999   \n",
              "2023-01-05 00:00:00-05:00  110.510002  111.750000  107.160004  110.339996   \n",
              "2023-01-06 00:00:00-05:00  103.000000  114.389999  101.809998  113.059998   \n",
              "2023-01-09 00:00:00-05:00  118.959999  123.519997  117.110001  119.769997   \n",
              "2023-01-10 00:00:00-05:00  121.070000  122.760002  114.919998  118.849998   \n",
              "...                               ...         ...         ...         ...   \n",
              "2023-12-28 00:00:00-05:00  263.660004  265.130005  252.710007  253.179993   \n",
              "2023-12-29 00:00:00-05:00  255.100006  255.190002  247.429993  248.479996   \n",
              "2024-01-02 00:00:00-05:00  250.080002  251.250000  244.410004  248.419998   \n",
              "2024-01-03 00:00:00-05:00  244.979996  245.679993  236.320007  238.449997   \n",
              "2024-01-04 00:00:00-05:00  239.250000  241.759995  237.729996  241.428406   \n",
              "\n",
              "                              Volume  Dividends  Stock Splits ticker  \n",
              "Date                                                                  \n",
              "2023-01-04 00:00:00-05:00  180389000        0.0           0.0   TSLA  \n",
              "2023-01-05 00:00:00-05:00  157986300        0.0           0.0   TSLA  \n",
              "2023-01-06 00:00:00-05:00  220911100        0.0           0.0   TSLA  \n",
              "2023-01-09 00:00:00-05:00  190284000        0.0           0.0   TSLA  \n",
              "2023-01-10 00:00:00-05:00  167642500        0.0           0.0   TSLA  \n",
              "...                              ...        ...           ...    ...  \n",
              "2023-12-28 00:00:00-05:00  113619900        0.0           0.0   TSLA  \n",
              "2023-12-29 00:00:00-05:00  100615300        0.0           0.0   TSLA  \n",
              "2024-01-02 00:00:00-05:00  104654200        0.0           0.0   TSLA  \n",
              "2024-01-03 00:00:00-05:00  120779500        0.0           0.0   TSLA  \n",
              "2024-01-04 00:00:00-05:00   34252917        0.0           0.0   TSLA  \n",
              "\n",
              "[252 rows x 8 columns]"
            ],
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              "\n",
              "  <div id=\"df-715a795f-82cf-4042-acd6-8f38b69dd11d\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Dividends</th>\n",
              "      <th>Stock Splits</th>\n",
              "      <th>ticker</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2023-01-04 00:00:00-05:00</th>\n",
              "      <td>109.110001</td>\n",
              "      <td>114.589996</td>\n",
              "      <td>107.519997</td>\n",
              "      <td>113.639999</td>\n",
              "      <td>180389000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-01-05 00:00:00-05:00</th>\n",
              "      <td>110.510002</td>\n",
              "      <td>111.750000</td>\n",
              "      <td>107.160004</td>\n",
              "      <td>110.339996</td>\n",
              "      <td>157986300</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-01-06 00:00:00-05:00</th>\n",
              "      <td>103.000000</td>\n",
              "      <td>114.389999</td>\n",
              "      <td>101.809998</td>\n",
              "      <td>113.059998</td>\n",
              "      <td>220911100</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-01-09 00:00:00-05:00</th>\n",
              "      <td>118.959999</td>\n",
              "      <td>123.519997</td>\n",
              "      <td>117.110001</td>\n",
              "      <td>119.769997</td>\n",
              "      <td>190284000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-01-10 00:00:00-05:00</th>\n",
              "      <td>121.070000</td>\n",
              "      <td>122.760002</td>\n",
              "      <td>114.919998</td>\n",
              "      <td>118.849998</td>\n",
              "      <td>167642500</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-12-28 00:00:00-05:00</th>\n",
              "      <td>263.660004</td>\n",
              "      <td>265.130005</td>\n",
              "      <td>252.710007</td>\n",
              "      <td>253.179993</td>\n",
              "      <td>113619900</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-12-29 00:00:00-05:00</th>\n",
              "      <td>255.100006</td>\n",
              "      <td>255.190002</td>\n",
              "      <td>247.429993</td>\n",
              "      <td>248.479996</td>\n",
              "      <td>100615300</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-01-02 00:00:00-05:00</th>\n",
              "      <td>250.080002</td>\n",
              "      <td>251.250000</td>\n",
              "      <td>244.410004</td>\n",
              "      <td>248.419998</td>\n",
              "      <td>104654200</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-01-03 00:00:00-05:00</th>\n",
              "      <td>244.979996</td>\n",
              "      <td>245.679993</td>\n",
              "      <td>236.320007</td>\n",
              "      <td>238.449997</td>\n",
              "      <td>120779500</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-01-04 00:00:00-05:00</th>\n",
              "      <td>239.250000</td>\n",
              "      <td>241.759995</td>\n",
              "      <td>237.729996</td>\n",
              "      <td>241.428406</td>\n",
              "      <td>34252917</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>252 rows × 8 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ta_hist = add_all_ta_features(\n",
        "    hist, open=\"Open\", high=\"High\", low=\"Low\", close=\"Close\", volume=\"Volume\")\n",
        "\n",
        "ta_hist.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "D868fZd5DKAE",
        "outputId": "0ef26050-987e-4cdd-e101-bbc0e7fc0a3b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 Open        High         Low       Close  \\\n",
              "Date                                                                        \n",
              "2023-01-04 00:00:00-05:00  109.110001  114.589996  107.519997  113.639999   \n",
              "2023-01-05 00:00:00-05:00  110.510002  111.750000  107.160004  110.339996   \n",
              "2023-01-06 00:00:00-05:00  103.000000  114.389999  101.809998  113.059998   \n",
              "\n",
              "                              Volume  Dividends  Stock Splits ticker  \\\n",
              "Date                                                                   \n",
              "2023-01-04 00:00:00-05:00  180389000        0.0           0.0   TSLA   \n",
              "2023-01-05 00:00:00-05:00  157986300        0.0           0.0   TSLA   \n",
              "2023-01-06 00:00:00-05:00  220911100        0.0           0.0   TSLA   \n",
              "\n",
              "                             volume_adi  volume_obv  ...  momentum_ppo  \\\n",
              "Date                                                 ...                 \n",
              "2023-01-04 00:00:00-05:00  1.319112e+08   180389000  ...           NaN   \n",
              "2023-01-05 00:00:00-05:00  1.928337e+08    22402700  ...           NaN   \n",
              "2023-01-06 00:00:00-05:00  3.670338e+08   243313800  ...           NaN   \n",
              "\n",
              "                           momentum_ppo_signal  momentum_ppo_hist  \\\n",
              "Date                                                                \n",
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              "                           momentum_pvo  momentum_pvo_signal  \\\n",
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              "                           momentum_pvo_hist  momentum_kama  others_dr  \\\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4ec57285-fbbc-4ec7-8abb-067e3389e644 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4ec57285-fbbc-4ec7-8abb-067e3389e644');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6f348f12-6c07-4edf-9f8a-037313e1347e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6f348f12-6c07-4edf-9f8a-037313e1347e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6f348f12-6c07-4edf-9f8a-037313e1347e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "summary = ta_hist.iloc[-7:,:].reset_index()\n",
        "summary_str = \"\"\n",
        "columns = summary.columns\n",
        "\n",
        "for index in summary.index:\n",
        "  for column in columns:\n",
        "    summary_str+=(column+\": \")\n",
        "    summary_str+=str(summary.at[index,column])\n",
        "    summary_str+=', '\n",
        "  summary_str+=\"\\n\\n\""
      ],
      "metadata": {
        "id": "wrZRp6kkGbCW"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "summary_str"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "njNrHxJNICva",
        "outputId": "955beb46-f69a-4d23-f3f3-9994ddadfa56"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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trend_adx: 14.211912877798586, trend_adx_pos: 24.020041207164937, trend_adx_neg: 17.78524006176888, trend_cci: 110.53683428843387, trend_visual_ichimoku_a: 227.96750259399414, trend_visual_ichimoku_b: 236.52500915527344, trend_aroon_up: 88.0, trend_aroon_down: 0.0, trend_aroon_ind: 88.0, trend_psar_up: 238.68407594099176, trend_psar_down: nan, trend_psar_up_indicator: 0.0, trend_psar_down_indicator: 0.0, momentum_rsi: 60.82403526667754, momentum_stoch_rsi: 0.6878413669580687, momentum_stoch_rsi_k: 0.6059250522596701, momentum_stoch_rsi_d: 0.5715868657531326, momentum_tsi: 17.7148208498978, momentum_uo: 49.84618777881947, momentum_stoch: 89.79136836714075, momentum_stoch_signal: 83.28064614189098, momentum_wr: -10.208631632859253, momentum_ao: 15.634382270364199, momentum_roc: 5.75749505318786, momentum_ppo: 2.251270927902377, momentum_ppo_signal: 1.9197332458786462, momentum_ppo_hist: 0.3315376820237308, momentum_pvo: -4.46490701216564, momentum_pvo_signal: -1.5145834299496677, 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84.00863325823649, momentum_wr: -32.358542213701504, momentum_ao: 15.360824315688205, momentum_roc: 5.606067771196583, momentum_ppo: 2.326938234162587, momentum_ppo_signal: 2.087271990094865, momentum_ppo_hist: 0.23966624406772175, momentum_pvo: -4.007137685576264, momentum_pvo_signal: -2.4941020842824018, momentum_pvo_hist: -1.5130356012938626, momentum_kama: 245.16224469753553, others_dr: -3.1594284304201747, others_dlr: -3.2104151730160346, others_cr: 122.79126543082648, \\n\\nDate: 2023-12-29 00:00:00-05:00, Open: 255.10000610351562, High: 255.19000244140625, Low: 247.42999267578125, Close: 248.47999572753906, Volume: 100615300, Dividends: 0.0, Stock Splits: 0.0, ticker: TSLA, volume_adi: 3626955880.758614, volume_obv: 4458201700, volume_cmf: 0.0535760308094523, volume_fi: -31160601.29750848, volume_em: -58.692602672671455, volume_sma_em: 3.249183463333673, volume_vpt: 170851493.92506716, volume_vwap: 250.12297111920662, volume_mfi: 58.279211242470296, volume_nvi: 1403.9444260828473, volatility_bbm: 247.13799819946288, volatility_bbh: 262.5108544987171, volatility_bbl: 231.76514190020868, volatility_bbw: 12.440706335127723, volatility_bbp: 0.5436482818141376, volatility_bbhi: 0.0, volatility_bbli: 0.0, volatility_kcc: 254.4183319091797, volatility_kch: 261.9723327636719, volatility_kcl: 246.8643310546875, volatility_kcw: 5.938252010227594, volatility_kcp: 0.10694099087179433, volatility_kchi: 0.0, volatility_kcli: 0.0, volatility_dcl: 228.1999969482422, volatility_dch: 265.1300048828125, volatility_dcm: 246.66500091552734, volatility_dcw: 14.943071564723297, volatility_dcp: 0.5491468838898541, volatility_atr: 8.544687185541951, volatility_ui: 2.4967646862187465, trend_macd: 5.042504854773938, trend_macd_signal: 5.070092571220867, trend_macd_diff: -0.027587716446928923, trend_sma_fast: 252.25249735514322, trend_sma_slow: 245.36307584322415, trend_ema_fast: 251.2098880291519, trend_ema_slow: 246.16738317437796, trend_vortex_ind_pos: 1.0160018913604618, trend_vortex_ind_neg: 0.8712216069845978, trend_vortex_ind_diff: 0.144780284375864, trend_trix: 0.32638816435982354, trend_mass_index: 24.150501678279987, trend_dpo: -7.848004913330072, trend_kst: 99.41876025206173, trend_kst_sig: 93.95755874267414, trend_kst_diff: 5.4612015093875925, trend_ichimoku_conv: 256.06500244140625, trend_ichimoku_base: 246.66500091552734, trend_ichimoku_a: 251.3650016784668, trend_ichimoku_b: 229.60000610351562, trend_stc: 49.99999999970026, trend_adx: 13.786956685148215, trend_adx_pos: 22.879325601338564, trend_adx_neg: 22.09675628056553, trend_cci: 28.70125015688772, trend_visual_ichimoku_a: 225.9099998474121, trend_visual_ichimoku_b: 236.52500915527344, trend_aroon_up: 96.0, trend_aroon_down: 56.00000000000001, trend_aroon_ind: 39.99999999999999, trend_psar_up: 245.36774752528487, trend_psar_down: nan, trend_psar_up_indicator: 0.0, trend_psar_down_indicator: 0.0, momentum_rsi: 52.07011803740926, momentum_stoch_rsi: 0.07909066993557154, momentum_stoch_rsi_k: 0.44144747838408893, momentum_stoch_rsi_d: 0.5934045987161823, momentum_tsi: 15.975186530568925, momentum_uo: 39.76227790540575, momentum_stoch: 54.91468838898541, momentum_stoch_signal: 72.38307326551805, momentum_wr: -45.08531161101459, momentum_ao: 14.182823226031104, momentum_roc: 4.839458877913396, momentum_ppo: 2.0484049469713748, momentum_ppo_signal: 2.079498581470167, momentum_ppo_hist: -0.031093634498792433, momentum_pvo: -4.49172210988359, momentum_pvo_signal: -2.8936260894026398, momentum_pvo_hist: -1.5980960204809507, momentum_kama: 245.19237713172635, others_dr: -1.8563856087399966, others_dlr: -1.8738327073791083, others_cr: 118.65540044183889, \\n\\nDate: 2024-01-02 00:00:00-05:00, Open: 250.0800018310547, High: 251.25, Low: 244.41000366210938, Close: 248.4199981689453, Volume: 104654200, Dividends: 0.0, Stock Splits: 0.0, ticker: TSLA, volume_adi: 3645010157.894065, volume_obv: 4353547500, volume_cmf: 0.026150979296487586, volume_fi: -27606086.32594756, volume_em: -22.744579799226607, volume_sma_em: 2.4220487726264417, volume_vpt: 170826224.29932445, volume_vwap: 250.59203996477947, volume_mfi: 57.92530416876587, volume_nvi: 1403.9444260828473, volatility_bbm: 247.61749801635742, volatility_bbh: 262.5147855061244, volatility_bbl: 232.72021052659045, volatility_bbw: 12.032499810480166, volatility_bbp: 0.5269344386734545, volatility_bbhi: 0.0, volatility_bbli: 0.0, volatility_kcc: 254.02333170572916, volatility_kch: 261.67833201090497, volatility_kcl: 246.36833140055336, volatility_kcw: 6.027005672096029, volatility_kcp: 0.13400827476157987, volatility_kchi: 0.0, volatility_kcli: 0.0, volatility_dcl: 228.1999969482422, volatility_dch: 265.1300048828125, volatility_dcm: 246.66500091552734, volatility_dcw: 14.914134998703018, volatility_dcp: 0.5475222549783183, volatility_atr: 8.374218100776819, volatility_ui: 2.726474550533229, trend_macd: 4.446430660158825, trend_macd_signal: 4.945360189008459, trend_macd_diff: -0.4989295288496338, trend_sma_fast: 253.0133310953776, trend_sma_slow: 245.90961397611179, trend_ema_fast: 250.78067420450475, trend_ema_slow: 246.33424354434592, trend_vortex_ind_pos: 1.0091936229713676, trend_vortex_ind_neg: 0.8928810490568088, trend_vortex_ind_diff: 0.11631257391455885, trend_trix: 0.3197522414042056, trend_mass_index: 24.170287648850767, trend_dpo: 3.4325050354003963, trend_kst: 92.85191544556068, trend_kst_sig: 95.40253506717949, trend_kst_diff: -2.5506196216188073, trend_ichimoku_conv: 254.77000427246094, trend_ichimoku_base: 246.66500091552734, trend_ichimoku_a: 250.71750259399414, trend_ichimoku_b: 229.60000610351562, trend_stc: 24.99999999985013, trend_adx: 13.082851710036607, trend_adx_pos: 21.581926703071506, trend_adx_neg: 23.34741953788471, trend_cci: 2.697708877386231, trend_visual_ichimoku_a: 225.27250289916992, trend_visual_ichimoku_b: 236.52500915527344, trend_aroon_up: 92.0, trend_aroon_down: 52.0, trend_aroon_ind: 40.0, trend_psar_up: nan, trend_psar_down: 265.1300048828125, trend_psar_up_indicator: 0.0, trend_psar_down_indicator: 1.0, momentum_rsi: 52.02018350997815, momentum_stoch_rsi: 0.07561820411449417, momentum_stoch_rsi_k: 0.16853728446117866, momentum_stoch_rsi_d: 0.41811638007895197, momentum_tsi: 14.211520953315596, momentum_uo: 46.227121915597465, momentum_stoch: 54.75222549783183, momentum_stoch_signal: 59.10279055770525, momentum_wr: -45.24777450216817, momentum_ao: 11.692912247601697, momentum_roc: 3.815456199163008, momentum_ppo: 1.8050396064233607, momentum_ppo_signal: 2.024606786460806, momentum_ppo_hist: -0.21956718003744546, momentum_pvo: -4.538360072966888, momentum_pvo_signal: -3.2225728861154894, momentum_pvo_hist: -1.3157871868513982, momentum_kama: 245.24559235260003, others_dr: -0.02414583049958896, others_dlr: -0.0241487460745482, others_cr: 118.60260427947003, \\n\\nDate: 2024-01-03 00:00:00-05:00, Open: 244.97999572753906, High: 245.67999267578125, Low: 236.32000732421875, Close: 238.4499969482422, Volume: 120779500, Dividends: 0.0, Stock Splits: 0.0, ticker: TSLA, volume_adi: 3579200633.1946764, volume_obv: 4232768000, volume_cmf: 0.008474407914810116, volume_fi: -195686897.1987998, volume_em: -52.930105762829726, volume_sma_em: 0.1816309235895537, volume_vpt: 165978902.1380185, volume_vwap: 250.6353277888805, volume_mfi: 57.00932203472245, volume_nvi: 1403.9444260828473, volatility_bbm: 247.7609977722168, volatility_bbh: 262.24116207092504, volatility_bbl: 233.28083347350858, volatility_bbw: 11.688816584457584, volatility_bbp: 0.17849118863916297, volatility_bbhi: 0.0, volatility_bbli: 0.0, volatility_kcc: 252.63233184814453, volatility_kch: 260.4853317260742, volatility_kcl: 244.77933197021486, volatility_kcw: 6.21693970877018, volatility_kcp: -0.40298835606510286, volatility_kchi: 0.0, volatility_kcli: 1.0, volatility_dcl: 228.1999969482422, volatility_dch: 265.1300048828125, volatility_dcm: 246.66500091552734, volatility_dcw: 14.905496937222754, volatility_dcp: 0.2775520660098461, volatility_atr: 8.746795375171793, volatility_ui: 3.442482063019632, trend_macd: 3.1334216563426196, trend_macd_signal: 4.582972482475291, trend_macd_diff: -1.449550826132671, trend_sma_fast: 251.9633305867513, trend_sma_slow: 246.0249985914964, trend_ema_fast: 248.8836469343105, trend_ema_slow: 245.75022527796787, trend_vortex_ind_pos: 0.9566471461643515, trend_vortex_ind_neg: 0.8912571520592636, trend_vortex_ind_diff: 0.06538999410508795, trend_trix: 0.3002323180898506, trend_mass_index: 24.270892267882317, trend_dpo: 5.739002227783203, trend_kst: 84.53594667528428, trend_kst_sig: 95.29961304749375, trend_kst_diff: -10.763666372209471, trend_ichimoku_conv: 250.72500610351562, trend_ichimoku_base: 246.66500091552734, trend_ichimoku_a: 248.69500350952148, trend_ichimoku_b: 229.60000610351562, trend_stc: 12.499999999925064, trend_adx: 13.379419483114267, trend_adx_pos: 19.477746759028843, trend_adx_neg: 27.58973301385354, trend_cci: -79.67990002146638, trend_visual_ichimoku_a: 224.77000045776367, trend_visual_ichimoku_b: 236.52500915527344, trend_aroon_up: 88.0, trend_aroon_down: 48.0, trend_aroon_ind: 40.0, trend_psar_up: nan, trend_psar_down: 264.7156048583984, trend_psar_up_indicator: 0.0, trend_psar_down_indicator: 0.0, momentum_rsi: 44.40035584624422, momentum_stoch_rsi: 0.0, momentum_stoch_rsi_k: 0.05156962468335532, momentum_stoch_rsi_d: 0.22051812917620775, momentum_tsi: 10.098568217731623, momentum_uo: 37.87123294369392, momentum_stoch: 27.75520660098461, momentum_stoch_signal: 45.80737349593395, momentum_wr: -72.24479339901539, momentum_ao: 8.143294031479797, momentum_roc: -5.018922903943539, momentum_ppo: 1.2750432488102132, momentum_ppo_signal: 1.8746940789306878, momentum_ppo_hist: -0.5996508301204746, momentum_pvo: -3.357825482798581, momentum_pvo_signal: -3.249623405452108, momentum_pvo_hist: -0.10820207734647314, momentum_kama: 244.9460251116442, others_dr: -4.013364984377277, others_dlr: -4.09612227993259, others_cr: 109.82928390438093, \\n\\nDate: 2024-01-04 00:00:00-05:00, Open: 239.25, High: 241.75999450683594, Low: 237.72999572753906, Close: 241.42840576171875, Volume: 34252917, Dividends: 0.0, Stock Splits: 0.0, ticker: TSLA, volume_adi: 3607816882.5991516, volume_obv: 4267020917, volume_cmf: 0.036123938781996844, volume_fi: -153157456.1875311, volume_em: -14.765656734420581, volume_sma_em: 0.4149251111229914, volume_vpt: 166406745.25475472, volume_vwap: 251.82175576416662, volume_mfi: 63.09439733891579, volume_nvi: 1421.4806831840963, volatility_bbm: 247.89641799926758, volatility_bbh: 262.0835327218387, volatility_bbl: 233.70930327669643, volatility_bbw: 11.446002194846603, volatility_bbp: 0.2720462418176379, volatility_bbhi: 0.0, volatility_bbli: 0.0, volatility_kcc: 251.04394531249994, volatility_kch: 258.7669448852539, volatility_kcl: 243.3209457397461, volatility_kcw: 6.152707298429606, volatility_kcp: -0.12252622573643962, volatility_kchi: 0.0, volatility_kcli: 1.0, volatility_dcl: 228.1999969482422, volatility_dch: 265.1300048828125, volatility_dcm: 246.66500091552734, volatility_dcw: 14.897354400126678, volatility_dcp: 0.3582021654832465, volatility_atr: 8.2751157155843, volatility_ui: 3.92273399959587, trend_macd: 2.306596254925296, trend_macd_signal: 4.127697236965292, trend_macd_diff: -1.8211009820399964, trend_sma_fast: 250.95736440022787, trend_sma_slow: 246.23070643498346, trend_ema_fast: 247.73668675391175, trend_ema_slow: 245.43009049898646, trend_vortex_ind_pos: 1.0143075965933372, trend_vortex_ind_neg: 0.9285467299695412, trend_vortex_ind_diff: 0.085760866623796, trend_trix: 0.2748691716013316, trend_mass_index: 24.19708032488417, trend_dpo: 4.183583831787104, trend_kst: 75.55843189958499, trend_kst_sig: 93.86416005304042, trend_kst_diff: -18.305728153455433, trend_ichimoku_conv: 250.72500610351562, trend_ichimoku_base: 246.66500091552734, trend_ichimoku_a: 248.69500350952148, trend_ichimoku_b: 229.60000610351562, trend_stc: 6.249999999962532, trend_adx: 13.654803843829239, trend_adx_pos: 18.81962216619187, trend_adx_neg: 26.65751626255985, trend_cci: -78.8427825734388, trend_visual_ichimoku_a: 228.50249862670898, trend_visual_ichimoku_b: 236.52500915527344, trend_aroon_up: 84.0, trend_aroon_down: 44.0, trend_aroon_ind: 40.0, trend_psar_up: nan, trend_psar_down: 263.57978095703123, trend_psar_up_indicator: 0.0, trend_psar_down_indicator: 0.0, momentum_rsi: 46.90254839879652, momentum_stoch_rsi: 0.11965021151793487, momentum_stoch_rsi_k: 0.06508947187747642, momentum_stoch_rsi_d: 0.09506546034067025, momentum_tsi: 7.531677450739754, momentum_uo: 40.80710704674236, momentum_stoch: 17.731339362700545, momentum_stoch_signal: 33.41292382050566, momentum_wr: -82.26866063729945, momentum_ao: 3.8499711429371644, momentum_roc: -4.761970113720414, momentum_ppo: 0.9398180354477852, momentum_ppo_signal: 1.6877188702341073, momentum_ppo_hist: -0.7479008347863221, momentum_pvo: -8.895793919831062, momentum_pvo_signal: -4.378857508327899, momentum_pvo_hist: -4.516936411503163, momentum_kama: 244.74385543588235, others_dr: 1.2490706024722797, others_dlr: 1.2413340721586508, others_cr: 112.4501998050087, \\n\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "', '.join(ta_hist.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "jydGC5DPCf8F",
        "outputId": "ccc34b49-faf3-4450-b0e6-f3b8223f1b91"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Open, High, Low, Close, Volume, Dividends, Stock Splits, ticker, volume_adi, volume_obv, volume_cmf, volume_fi, volume_em, volume_sma_em, volume_vpt, volume_vwap, volume_mfi, volume_nvi, volatility_bbm, volatility_bbh, volatility_bbl, volatility_bbw, volatility_bbp, volatility_bbhi, volatility_bbli, volatility_kcc, volatility_kch, volatility_kcl, volatility_kcw, volatility_kcp, volatility_kchi, volatility_kcli, volatility_dcl, volatility_dch, volatility_dcm, volatility_dcw, volatility_dcp, volatility_atr, volatility_ui, trend_macd, trend_macd_signal, trend_macd_diff, trend_sma_fast, trend_sma_slow, trend_ema_fast, trend_ema_slow, trend_vortex_ind_pos, trend_vortex_ind_neg, trend_vortex_ind_diff, trend_trix, trend_mass_index, trend_dpo, trend_kst, trend_kst_sig, trend_kst_diff, trend_ichimoku_conv, trend_ichimoku_base, trend_ichimoku_a, trend_ichimoku_b, trend_stc, trend_adx, trend_adx_pos, trend_adx_neg, trend_cci, trend_visual_ichimoku_a, trend_visual_ichimoku_b, trend_aroon_up, trend_aroon_down, trend_aroon_ind, trend_psar_up, trend_psar_down, trend_psar_up_indicator, trend_psar_down_indicator, momentum_rsi, momentum_stoch_rsi, momentum_stoch_rsi_k, momentum_stoch_rsi_d, momentum_tsi, momentum_uo, momentum_stoch, momentum_stoch_signal, momentum_wr, momentum_ao, momentum_roc, momentum_ppo, momentum_ppo_signal, momentum_ppo_hist, momentum_pvo, momentum_pvo_signal, momentum_pvo_hist, momentum_kama, others_dr, others_dlr, others_cr'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def technical_analysis_definition(input_to_llm):\n",
        "    response_schemas = [\n",
        "        ResponseSchema(name=\"Technical Analysis Indicator\", description=\"One Line Description of the Technical Analysis Indicator\")\n",
        "    ]\n",
        "    output_parser = StructuredOutputParser.from_response_schemas(response_schemas)\n",
        "    format_instructions = output_parser.get_format_instructions()\n",
        "\n",
        "    chat_model = ChatOpenAI(model_name=\"gpt-3.5-turbo-16k\", temperature=0.5, openai_api_key = \"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\")\n",
        "\n",
        "    prompt = ChatPromptTemplate(\n",
        "        messages=[\n",
        "            HumanMessagePromptTemplate.from_template(\"\"\"\n",
        "            Assume the role as a leading Technical Analysis (TA) expert in the stock market, a modern counterpart to Charles Dow, John Bollinger, and Alan Andrews. Give brief descriptions of the following technical analysis indicators delimited by \",\":\n",
        "            {format_instructions}\n",
        "            Technical Analysis Indicators: {question}\"\"\")\n",
        "        ],\n",
        "        input_variables=[\"question\"],\n",
        "        partial_variables={\"format_instructions\": format_instructions}\n",
        "    )\n",
        "\n",
        "    clean_transcript = input_to_llm\n",
        "    _input = prompt.format_prompt(question=clean_transcript)\n",
        "    output = chat_model(_input.to_messages())\n",
        "    return(output)\n",
        "\n",
        "definitions = technical_analysis_definition(', '.join(ta_hist.columns))\n",
        "print(definitions)"
      ],
      "metadata": {
        "id": "ieRHRWEE8dJU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3510bf14-c507-4fa7-df57-08b92ac4a9a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "content='```json\\n{\\n\\t\"Open\": \"The opening price of a stock or asset for a given period of time.\",\\n\\t\"High\": \"The highest price reached by a stock or asset during a given period of time.\",\\n\\t\"Low\": \"The lowest price reached by a stock or asset during a given period of time.\",\\n\\t\"Close\": \"The closing price of a stock or asset for a given period of time.\",\\n\\t\"Volume\": \"The total number of shares or contracts traded for a stock or asset during a given period of time.\",\\n\\t\"Dividends\": \"The amount of dividends paid out to shareholders of a stock during a given period of time.\",\\n\\t\"Stock Splits\": \"The division of existing shares of a stock into multiple shares, resulting in a lower price per share.\",\\n\\t\"ticker\": \"The symbol or abbreviation used to uniquely identify a stock or asset.\",\\n\\t\"volume_adi\": \"Accumulation/Distribution Line (ADI) is a momentum indicator that uses volume to confirm price trends.\",\\n\\t\"volume_obv\": \"On-Balance Volume (OBV) is a cumulative indicator that uses volume to predict price movements.\",\\n\\t\"volume_cmf\": \"Chaikin Money Flow (CMF) is a momentum indicator that measures the flow of money into or out of a stock.\",\\n\\t\"volume_fi\": \"Force Index (FI) is a trend indicator that measures the strength of buying or selling pressure.\",\\n\\t\"volume_em\": \"Ease of Movement (EM) is a volume-based oscillator that measures the relationship between price and volume.\",\\n\\t\"volume_sma_em\": \"Simple Moving Average (SMA) of the Ease of Movement (EM) indicator.\",\\n\\t\"volume_vpt\": \"Volume Price Trend (VPT) is a cumulative indicator that measures the relationship between volume and price.\",\\n\\t\"volume_vwap\": \"Volume Weighted Average Price (VWAP) is the average price of a stock or asset weighted by volume.\",\\n\\t\"volume_mfi\": \"Money Flow Index (MFI) is a momentum indicator that measures the strength of buying or selling pressure.\",\\n\\t\"volume_nvi\": \"Negative Volume Index (NVI) is a cumulative indicator that uses volume to predict price movements.\",\\n\\t\"volatility_bbm\": \"Bollinger Bands Middle Band (BBM) is a trend indicator that represents the middle moving average line of Bollinger Bands.\",\\n\\t\"volatility_bbh\": \"Bollinger Bands High Band (BBH) is a trend indicator that represents the upper moving average line of Bollinger Bands.\",\\n\\t\"volatility_bbl\": \"Bollinger Bands Low Band (BBL) is a trend indicator that represents the lower moving average line of Bollinger Bands.\",\\n\\t\"volatility_bbw\": \"Bollinger Bands Width (BBW) is a volatility indicator that measures the width between the upper and lower Bollinger Bands.\",\\n\\t\"volatility_bbp\": \"Bollinger Bands Percent (BBP) is a volatility indicator that represents the position of the current price relative to the Bollinger Bands.\",\\n\\t\"volatility_bbhi\": \"Bollinger Bands High Indicator (BBHI) is a volatility indicator that signals when the price is above the upper Bollinger Band.\",\\n\\t\"volatility_bbli\": \"Bollinger Bands Low Indicator (BBLI) is a volatility indicator that signals when the price is below the lower Bollinger Band.\",\\n\\t\"volatility_kcc\": \"Keltner Channels Central Line (KCC) is a trend indicator that represents the middle moving average line of Keltner Channels.\",\\n\\t\"volatility_kch\": \"Keltner Channels High Line (KCH) is a trend indicator that represents the upper moving average line of Keltner Channels.\",\\n\\t\"volatility_kcl\": \"Keltner Channels Low Line (KCL) is a trend indicator that represents the lower moving average line of Keltner Channels.\",\\n\\t\"volatility_kcw\": \"Keltner Channels Width (KCW) is a volatility indicator that measures the width between the upper and lower Keltner Channels.\",\\n\\t\"volatility_kcp\": \"Keltner Channels Percent (KCP) is a volatility indicator that represents the position of the current price relative to the Keltner Channels.\",\\n\\t\"volatility_kchi\": \"Keltner Channels High Indicator (KCHI) is a volatility indicator that signals when the price is above the upper Keltner Channel.\",\\n\\t\"volatility_kcli\": \"Keltner Channels Low Indicator (KCLI) is a volatility indicator that signals when the price is below the lower Keltner Channel.\",\\n\\t\"volatility_dcl\": \"Donchian Channels Lower Line (DCL) is a trend indicator that represents the lower moving average line of Donchian Channels.\",\\n\\t\"volatility_dch\": \"Donchian Channels Higher Line (DCH) is a trend indicator that represents the upper moving average line of Donchian Channels.\",\\n\\t\"volatility_dcm\": \"Donchian Channels Middle Line (DCM) is a trend indicator that represents the middle moving average line of Donchian Channels.\",\\n\\t\"volatility_dcw\": \"Donchian Channels Width (DCW) is a volatility indicator that measures the width between the upper and lower Donchian Channels.\",\\n\\t\"volatility_dcp\": \"Donchian Channels Percent (DCP) is a volatility indicator that represents the position of the current price relative to the Donchian Channels.\",\\n\\t\"volatility_atr\": \"Average True Range (ATR) is a volatility indicator that measures the average range between the high and low prices.\",\\n\\t\"volatility_ui\": \"Ultimate Oscillator (UI) is a momentum indicator that combines multiple timeframes to predict price movements.\",\\n\\t\"trend_macd\": \"Moving Average Convergence Divergence (MACD) is a trend indicator that uses moving averages to identify potential buy or sell signals.\",\\n\\t\"trend_macd_signal\": \"Signal line of the Moving Average Convergence Divergence (MACD) indicator.\",\\n\\t\"trend_macd_diff\": \"Difference between the MACD line and the signal line of the Moving Average Convergence Divergence (MACD) indicator.\",\\n\\t\"trend_sma_fast\": \"Simple Moving Average (SMA) of the fast line of a trend indicator.\",\\n\\t\"trend_sma_slow\": \"Simple Moving Average (SMA) of the slow line of a trend indicator.\",\\n\\t\"trend_ema_fast\": \"Exponential Moving Average (EMA) of the fast line of a trend indicator.\",\\n\\t\"trend_ema_slow\": \"Exponential Moving Average (EMA) of the slow line of a trend indicator.\",\\n\\t\"trend_vortex_ind_pos\": \"Vortex Indicator Positive (VI+) is a trend indicator that measures the strength of positive price movements.\",\\n\\t\"trend_vortex_ind_neg\": \"Vortex Indicator Negative (VI-) is a trend indicator that measures the strength of negative price movements.\",\\n\\t\"trend_vortex_ind_diff\": \"Difference between the Vortex Indicator Positive (VI+) and the Vortex Indicator Negative (VI-) of a trend indicator.\",\\n\\t\"trend_trix\": \"Triple Exponential Average (TRIX) is a trend indicator that uses multiple moving averages to identify potential buy or sell signals.\",\\n\\t\"trend_mass_index\": \"Mass Index is a volatility indicator that identifies potential reversals in price movements.\",\\n\\t\"trend_dpo\": \"Detrended Price Oscillator (DPO) is a trend indicator that removes the trend component from the price to identify potential buy or sell signals.\",\\n\\t\"trend_kst\": \"Know Sure Thing (KST) is a momentum indicator that combines multiple moving averages to predict price movements.\",\\n\\t\"trend_kst_sig\": \"Signal line of the Know Sure Thing (KST) indicator.\",\\n\\t\"trend_kst_diff\": \"Difference between the Know Sure Thing (KST) line and the signal line of the Know Sure Thing (KST) indicator.\",\\n\\t\"trend_ichimoku_conv\": \"Conversion Line of the Ichimoku Cloud indicator.\",\\n\\t\"trend_ichimoku_base\": \"Base Line of the Ichimoku Cloud indicator.\",\\n\\t\"trend_ichimoku_a\": \"Leading Span A of the Ichimoku Cloud indicator.\",\\n\\t\"trend_ichimoku_b\": \"Leading Span B of the Ichimoku Cloud indicator.\",\\n\\t\"trend_stc\": \"Schaff Trend Cycle (STC) is a momentum indicator that combines multiple moving averages to predict price movements.\",\\n\\t\"trend_adx\": \"Average Directional Index (ADX) is a trend indicator that measures the strength of a trend.\",\\n\\t\"trend_adx_pos\": \"Positive Directional Indicator (+DI) of the Average Directional Index (ADX) indicator.\",\\n\\t\"trend_adx_neg\": \"Negative Directional Indicator (-DI) of the Average Directional Index (ADX) indicator.\",\\n\\t\"trend_cci\": \"Commodity Channel Index (CCI) is a momentum indicator that measures the deviation of a stock\\'s price from its statistical average.\",\\n\\t\"trend_visual_ichimoku_a\": \"Visual Ichimoku A is a visual representation of the Ichimoku Cloud indicator.\",\\n\\t\"trend_visual_ichimoku_b\": \"Visual Ichimoku B is a visual representation of the Ichimoku Cloud indicator.\",\\n\\t\"trend_aroon_up\": \"Aroon Up is a trend indicator that measures the strength of an uptrend.\",\\n\\t\"trend_aroon_down\": \"Aroon Down is a trend indicator that measures the strength of a downtrend.\",\\n\\t\"trend_aroon_ind\": \"Aroon Indicator is a trend indicator that combines Aroon Up and Aroon Down to predict price movements.\",\\n\\t\"trend_psar_up\": \"Parabolic SAR (PSAR) Up is a trend indicator that represents the upper line of the Parabolic SAR.\",\\n\\t\"trend_psar_down\": \"Parabolic SAR (PSAR) Down is a trend indicator that represents the lower line of the Parabolic SAR.\",\\n\\t\"trend_psar_up_indicator\": \"Parabolic SAR (PSAR) Up Indicator is a trend indicator that signals when the price is above the Parabolic SAR.\",\\n\\t\"trend_psar_down_indicator\": \"Parabolic SAR (PSAR) Down Indicator is a trend indicator that signals when the price is below the Parabolic SAR.\",\\n\\t\"momentum_rsi\": \"Relative Strength Index (RSI) is a momentum indicator that measures the speed and change of price movements.\",\\n\\t\"momentum_stoch_rsi\": \"Stochastic RSI is a momentum indicator that combines Stochastic Oscillator and Relative Strength Index (RSI) to predict price movements.\",\\n\\t\"momentum_stoch_rsi_k\": \"Fast line of the Stochastic RSI indicator.\",\\n\\t\"momentum_stoch_rsi_d\": \"Signal line of the Stochastic RSI indicator.\",\\n\\t\"momentum_tsi\": \"True Strength Index (TSI) is a momentum indicator that combines multiple moving averages to predict price movements.\",\\n\\t\"momentum_uo\": \"Ultimate Oscillator (UO) is a momentum indicator that combines multiple timeframes to predict price movements.\",\\n\\t\"momentum_stoch\": \"Stochastic Oscillator is a momentum indicator that measures the current price relative to the price range over a given period of time.\",\\n\\t\"momentum_stoch_signal\": \"Signal line of the Stochastic Oscillator indicator.\",\\n\\t\"momentum_wr\": \"Williams %R (WR) is a momentum indicator that measures the current price relative to the high and low prices over a given period of time.\",\\n\\t\"momentum_ao\": \"Awesome Oscillator (AO) is a momentum indicator that measures the difference between a 34-period and a 5-period simple moving average.\",\\n\\t\"momentum_roc\": \"Rate of Change (ROC) is a momentum indicator that measures the percentage change in price over a given period of time.\",\\n\\t\"momentum_ppo\": \"Percentage Price Oscillator (PPO) is a momentum indicator that measures the difference between two moving averages as a percentage of the larger moving average.\",\\n\\t\"momentum_ppo_signal\": \"Signal line of the Percentage Price Oscillator (PPO) indicator.\",\\n\\t\"momentum_ppo_hist\": \"Histogram of the Percentage Price Oscillator (PPO) indicator.\",\\n\\t\"momentum_pvo\": \"Percentage Volume Oscillator (PVO) is a momentum indicator that measures the difference between two volume moving averages as a percentage of the larger volume moving average.\",\\n\\t\"momentum_pvo_signal\": \"Signal line of the Percentage Volume Oscillator (PVO) indicator.\",\\n\\t\"momentum_pvo_hist\": \"Histogram of the Percentage Volume Oscillator (PVO) indicator.\",\\n\\t\"momentum_kama\": \"Kaufman\\'s Adaptive Moving Average (KAMA) is a trend indicator that adapts to the market conditions to identify potential buy or sell signals.\",\\n\\t\"others_dr\": \"Daily Return (DR) is the percentage change in price from the previous day\\'s closing price.\",\\n\\t\"others_dlr\": \"Daily Log Return (DLR) is the logarithmic percentage change in price from the previous day\\'s closing price.\",\\n\\t\"others_cr\": \"Cumulative Return (CR) is the total percentage change in price over a given period of time.\"\\n}\\n```'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def technical_analysis_trends_evaluator(input_to_llm):\n",
        "    response_schemas = [\n",
        "        ResponseSchema(name=\"Techincal Analysis Indicator 1\", description=\"Bullish/Neutral/Bearish\"),\n",
        "        ResponseSchema(name=\"Techincal Analysis Indicator 2\", description=\"Bullish/Neutral/Bearish\")\n",
        "    ]\n",
        "    output_parser = StructuredOutputParser.from_response_schemas(response_schemas)\n",
        "    format_instructions = output_parser.get_format_instructions()\n",
        "\n",
        "    chat_model = ChatOpenAI(model_name=\"gpt-3.5-turbo-16k\", temperature=0.5, openai_api_key = \"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\")\n",
        "\n",
        "    prompt = ChatPromptTemplate(\n",
        "        messages=[\n",
        "            HumanMessagePromptTemplate.from_template(\"\"\"\n",
        "            Assume the role as a leading Technical Analysis (TA) expert in the stock market, a modern counterpart to Charles Dow, John Bollinger, and Alan Andrews. Give a brief description of future estimated market trends for the stock given the following technical analysis indicators for the given day:\n",
        "\n",
        "            1 Month Historical Stock Price Data: {hist}\n",
        "\n",
        "            Technical Analysis Context: {context}\n",
        "\n",
        "            Technical Analysis Indicators for last 7 days: {question}\n",
        "\n",
        "            {format_instructions}\"\"\")\n",
        "        ],\n",
        "        input_variables=[\"hist\",\"context\",\"question\"],\n",
        "        partial_variables={\"format_instructions\": format_instructions}\n",
        "    )\n",
        "\n",
        "    clean_transcript = input_to_llm\n",
        "    _input = prompt.format_prompt(hist=hist_str, context=definitions, question=clean_transcript)\n",
        "    output = chat_model(_input.to_messages())\n",
        "    return(output)\n",
        "\n",
        "response = technical_analysis_trends_evaluator(summary_str)\n",
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2vH21xc8DESl",
        "outputId": "3104c5ec-fa20-47f7-cc5f-5d79a32ca32c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "content='```json\\n{\\n\\t\"volume_adi\": \"Bullish\",\\n\\t\"volume_obv\": \"Bullish\",\\n\\t\"volume_cmf\": \"Bullish\",\\n\\t\"volume_fi\": \"Bullish\",\\n\\t\"volume_em\": \"Bullish\",\\n\\t\"volume_sma_em\": \"Bullish\",\\n\\t\"volume_vpt\": \"Bullish\",\\n\\t\"volume_vwap\": \"Bullish\",\\n\\t\"volume_mfi\": \"Bullish\",\\n\\t\"volume_nvi\": \"Bullish\",\\n\\t\"volatility_bbm\": \"Bullish\",\\n\\t\"volatility_bbh\": \"Bullish\",\\n\\t\"volatility_bbl\": \"Bullish\",\\n\\t\"volatility_bbw\": \"Bullish\",\\n\\t\"volatility_bbp\": \"Bullish\",\\n\\t\"volatility_bbhi\": \"Bullish\",\\n\\t\"volatility_bbli\": \"Bullish\",\\n\\t\"volatility_kcc\": \"Bullish\",\\n\\t\"volatility_kch\": \"Bullish\",\\n\\t\"volatility_kcl\": \"Bullish\",\\n\\t\"volatility_kcw\": \"Bullish\",\\n\\t\"volatility_kcp\": \"Bullish\",\\n\\t\"volatility_kchi\": \"Bullish\",\\n\\t\"volatility_kcli\": \"Bullish\",\\n\\t\"volatility_dcl\": \"Bullish\",\\n\\t\"volatility_dch\": \"Bullish\",\\n\\t\"volatility_dcm\": \"Bullish\",\\n\\t\"volatility_dcw\": \"Bullish\",\\n\\t\"volatility_dcp\": \"Bullish\",\\n\\t\"volatility_atr\": \"Bullish\",\\n\\t\"volatility_ui\": \"Bullish\",\\n\\t\"trend_macd\": \"Bullish\",\\n\\t\"trend_macd_signal\": \"Bullish\",\\n\\t\"trend_macd_diff\": \"Bullish\",\\n\\t\"trend_sma_fast\": \"Bullish\",\\n\\t\"trend_sma_slow\": \"Bullish\",\\n\\t\"trend_ema_fast\": \"Bullish\",\\n\\t\"trend_ema_slow\": \"Bullish\",\\n\\t\"trend_vortex_ind_pos\": \"Bullish\",\\n\\t\"trend_vortex_ind_neg\": \"Bullish\",\\n\\t\"trend_vortex_ind_diff\": \"Bullish\",\\n\\t\"trend_trix\": \"Bullish\",\\n\\t\"trend_mass_index\": \"Bullish\",\\n\\t\"trend_dpo\": \"Bullish\",\\n\\t\"trend_kst\": \"Bullish\",\\n\\t\"trend_kst_sig\": \"Bullish\",\\n\\t\"trend_kst_diff\": \"Bullish\",\\n\\t\"trend_ichimoku_conv\": \"Bullish\",\\n\\t\"trend_ichimoku_base\": \"Bullish\",\\n\\t\"trend_ichimoku_a\": \"Bullish\",\\n\\t\"trend_ichimoku_b\": \"Bullish\",\\n\\t\"trend_stc\": \"Bullish\",\\n\\t\"trend_adx\": \"Bullish\",\\n\\t\"trend_adx_pos\": \"Bullish\",\\n\\t\"trend_adx_neg\": \"Bullish\",\\n\\t\"trend_cci\": \"Bullish\",\\n\\t\"trend_visual_ichimoku_a\": \"Bullish\",\\n\\t\"trend_visual_ichimoku_b\": \"Bullish\",\\n\\t\"trend_aroon_up\": \"Bullish\",\\n\\t\"trend_aroon_down\": \"Bullish\",\\n\\t\"trend_aroon_ind\": \"Bullish\",\\n\\t\"trend_psar_up\": \"Bullish\",\\n\\t\"trend_psar_down\": \"Bearish\",\\n\\t\"trend_psar_up_indicator\": \"Bullish\",\\n\\t\"trend_psar_down_indicator\": \"Bearish\",\\n\\t\"momentum_rsi\": \"Neutral\",\\n\\t\"momentum_stoch_rsi\": \"Neutral\",\\n\\t\"momentum_stoch_rsi_k\": \"Neutral\",\\n\\t\"momentum_stoch_rsi_d\": \"Neutral\",\\n\\t\"momentum_tsi\": \"Neutral\",\\n\\t\"momentum_uo\": \"Neutral\",\\n\\t\"momentum_stoch\": \"Neutral\",\\n\\t\"momentum_stoch_signal\": \"Neutral\",\\n\\t\"momentum_wr\": \"Neutral\",\\n\\t\"momentum_ao\": \"Neutral\",\\n\\t\"momentum_roc\": \"Neutral\",\\n\\t\"momentum_ppo\": \"Neutral\",\\n\\t\"momentum_ppo_signal\": \"Neutral\",\\n\\t\"momentum_ppo_hist\": \"Neutral\",\\n\\t\"momentum_pvo\": \"Neutral\",\\n\\t\"momentum_pvo_signal\": \"Neutral\",\\n\\t\"momentum_pvo_hist\": \"Neutral\",\\n\\t\"momentum_kama\": \"Bullish\",\\n\\t\"others_dr\": \"Neutral\",\\n\\t\"others_dlr\": \"Neutral\",\\n\\t\"others_cr\": \"Bullish\"\\n}\\n```'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "technical_indicators_trend = ', '.join(response.content.split(\"\\n\\t\")[1:]).replace(\"\\n}\\n```\",\"\")"
      ],
      "metadata": {
        "id": "hCDNwW18NRMF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def final_evaluation(input_to_llm):\n",
        "    response_schemas = [\n",
        "        ResponseSchema(name=\"Prediction of Stock Price Movement\", description=\"Description of Stock Price Movement\")\n",
        "    ]\n",
        "    output_parser = StructuredOutputParser.from_response_schemas(response_schemas)\n",
        "    format_instructions = output_parser.get_format_instructions()\n",
        "\n",
        "    chat_model = ChatOpenAI(model_name=\"gpt-3.5-turbo-16k\", temperature=0.5, openai_api_key = \"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\")\n",
        "\n",
        "    prompt = ChatPromptTemplate(\n",
        "        messages=[\n",
        "            HumanMessagePromptTemplate.from_template(\"\"\"\n",
        "            Assume the role as a leading Technical Analysis (TA) expert in the stock market, a modern counterpart to Charles Dow, John Bollinger, and Alan Andrews. Give a brief description of future estimated market trends for the stock given the following technical analysis indicators for the given day:\n",
        "\n",
        "            1 Month Historical Stock Price Data: {hist}\n",
        "\n",
        "            Technical Analysis Context: {context}\n",
        "\n",
        "            Technical Analysis Indicators for last 7 days: {question}\n",
        "\n",
        "            {format_instructions}\"\"\")\n",
        "        ],\n",
        "        input_variables=[\"hist\",\"context\",\"question\"],\n",
        "        partial_variables={\"format_instructions\": format_instructions}\n",
        "    )\n",
        "\n",
        "    clean_transcript = input_to_llm\n",
        "    _input = prompt.format_prompt(hist=hist_str, context=definitions, question=technical_indicators_trend)\n",
        "    output = chat_model(_input.to_messages())\n",
        "    return(output)\n",
        "\n",
        "final_eval = final_evaluation(summary_str)\n",
        "print(final_eval)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "duJJe3VrFNGm",
        "outputId": "b8d3c31a-cdc0-49b0-f44a-0898bf5b7528"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "content='```json\\n{\\n\\t\"Prediction of Stock Price Movement\": \"Based on the technical analysis indicators for the last 7 days, the stock price of TSLA is expected to continue its bullish trend.\"\\n}\\n```'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_eval.content.split(\"\\n\\t\")[1:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h1qQxdF1OGU5",
        "outputId": "22459a50-c764-4de2-8c29-90e1a9f85d41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['\"Prediction of Stock Price Movement\": \"Based on the technical analysis indicators for the last 7 days, the stock price of TSLA is expected to continue its bullish trend.\"\\n}\\n```']"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def price_trend_eval(input_to_llm):\n",
        "    response_schemas = [\n",
        "        ResponseSchema(name=\"price trend\", description=\"Description of Stock price trend\")\n",
        "    ]\n",
        "    output_parser = StructuredOutputParser.from_response_schemas(response_schemas)\n",
        "    format_instructions = output_parser.get_format_instructions()\n",
        "\n",
        "    chat_model = ChatOpenAI(model_name=\"gpt-3.5-turbo-16k\", temperature=0.5, openai_api_key = \"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\")\n",
        "\n",
        "    prompt = ChatPromptTemplate(\n",
        "        messages=[\n",
        "            HumanMessagePromptTemplate.from_template(\"\"\"\n",
        "            Assume the role as a leading Technical Analysis (TA) expert in the stock market, a modern counterpart to Charles Dow, John Bollinger, and Alan Andrews. Analyze the price trend over the past one month:\n",
        "\n",
        "            1 Month Historical Stock Price Data: {hist}\n",
        "\n",
        "            {format_instructions}\"\"\")\n",
        "        ],\n",
        "        input_variables=[\"hist\",\"context\",\"question\"],\n",
        "        partial_variables={\"format_instructions\": format_instructions}\n",
        "    )\n",
        "\n",
        "    clean_transcript = input_to_llm\n",
        "    _input = prompt.format_prompt(hist=hist_str)\n",
        "    output = chat_model(_input.to_messages())\n",
        "    return(output)\n",
        "\n",
        "price_trend_eval = price_trend_eval(summary_str)\n",
        "print(price_trend_eval)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y4q3bj7GR05-",
        "outputId": "50fddc40-e663-4d79-ea1e-48480dd9bae6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "content='```json\\n{\\n\\t\"price trend\": \"The stock price of TSLA has shown a mixed trend over the past one month. It started at an open price of $234.04 on 2023-11-20 and reached its highest point of $263.34 on 2023-12-27. However, it experienced some fluctuations and closed at $239.80 on 2024-01-03. Overall, the stock price has shown volatility with both upward and downward movements.\"\\n}\\n```'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def seasonality_identifier(input_to_llm):\n",
        "    response_schemas = [\n",
        "        ResponseSchema(name=\"seasonality trend\", description=\"Detect any seasonality patterns in the price of [Commodity Name] over the past 3 years\")\n",
        "    ]\n",
        "    output_parser = StructuredOutputParser.from_response_schemas(response_schemas)\n",
        "    format_instructions = output_parser.get_format_instructions()\n",
        "\n",
        "    chat_model = ChatOpenAI(model_name=\"gpt-3.5-turbo-16k\", temperature=0.5, openai_api_key = \"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\")\n",
        "\n",
        "    prompt = ChatPromptTemplate(\n",
        "        messages=[\n",
        "            HumanMessagePromptTemplate.from_template(\"\"\"\n",
        "            Assume the role as a leading Technical Analysis (TA) expert in the stock market, a modern counterpart to Charles Dow, John Bollinger, and Alan Andrews. Analyze the price trend over the past 3 years:\n",
        "\n",
        "            3 year Historical Stock Price Data: {hist}\n",
        "\n",
        "            {format_instructions}\"\"\")\n",
        "        ],\n",
        "        input_variables=[\"hist\",\"context\",\"question\"],\n",
        "        partial_variables={\"format_instructions\": format_instructions}\n",
        "    )\n",
        "\n",
        "    clean_transcript = input_to_llm\n",
        "    _input = prompt.format_prompt(hist=hist_str)\n",
        "    output = chat_model(_input.to_messages())\n",
        "    return(output)\n",
        "\n",
        "seasonality = ta_hist.iloc[-365*3:,:].reset_index()\n",
        "seasonality_str = \"\"\n",
        "columns = seasonality.columns\n",
        "\n",
        "for index in seasonality.index:\n",
        "  for column in columns[:4]:\n",
        "    seasonality_str+=(column+\": \")\n",
        "    seasonality_str+=str(seasonality.at[index,column])\n",
        "    seasonality_str+=', '\n",
        "  seasonality_str+=\"\\n\\n\"\n",
        "seasonality_trend_eval = seasonality_identifier(seasonality_str)\n",
        "print(seasonality_trend_eval)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FoP7K5ImSka6",
        "outputId": "b6124918-9a40-461a-e12f-10dd2f26738d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "content='```json\\n{\\n    \"seasonality trend\": \"There does not appear to be a clear seasonality pattern in the price of TSLA over the past 3 years.\"\\n}\\n```'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from yahoofinancials import YahooFinancials\n",
        "tickers = ['AAPL']\n",
        "yahoo_financials = YahooFinancials(tickers, concurrent=True, max_workers=8, country=\"US\")\n",
        "balance_sheet_data_qt = yahoo_financials.get_financial_stmts('quarterly', 'balance')"
      ],
      "metadata": {
        "id": "GCW21T_pVDmP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(balance_sheet_data_qt)['balanceSheetHistoryQuarterly'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HrG7SZ39XfhR",
        "outputId": "add5d6c3-85a5-4322-9b90-1d22ff24547a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'2022-12-31': {'currentDebt': 11483000000.0,\n",
              "   'totalEquityGrossMinorityInterest': 56727000000.0,\n",
              "   'inventory': 6820000000.0,\n",
              "   'cashFinancial': 17908000000.0,\n",
              "   'accumulatedDepreciation': -68044000000.0,\n",
              "   'investmentsAndAdvances': 114095000000.0,\n",
              "   'totalDebt': 111110000000.0,\n",
              "   'otherNonCurrentAssets': 60924000000.0,\n",
              "   'tangibleBookValue': 56727000000.0,\n",
              "   'totalLiabilitiesNetMinorityInterest': 290020000000.0,\n",
              "   'cashEquivalents': 2627000000.0,\n",
              "   'otherReceivables': 30428000000.0,\n",
              "   'otherCurrentBorrowings': 9740000000.0,\n",
              "   'commercialPaper': 1743000000.0,\n",
              "   'currentAssets': 128777000000.0,\n",
              "   'currentLiabilities': 137286000000.0,\n",
              "   'grossPPE': 110995000000.0,\n",
              "   'retainedEarnings': 3240000000.0,\n",
              "   'shareIssued': 15842407000.0,\n",
              "   'longTermDebtAndCapitalLeaseObligation': 99627000000.0,\n",
              "   'ordinarySharesNumber': 15842407000.0,\n",
              "   'netPPE': 42951000000.0,\n",
              "   'otherCurrentLiabilities': 59893000000.0,\n",
              "   'payablesAndAccruedExpenses': 57918000000.0,\n",
              "   'otherShortTermInvestments': 30820000000.0,\n",
              "   'netDebt': 90575000000.0,\n",
              "   'workingCapital': -8509000000.0,\n",
              "   'stockholdersEquity': 56727000000.0,\n",
              "   'accountsReceivable': 23752000000.0,\n",
              "   'commonStock': 66399000000.0,\n",
              "   'netTangibleAssets': 56727000000.0,\n",
              "   'totalAssets': 346747000000.0,\n",
              "   'finishedGoods': 4307000000.0,\n",
              "   'totalNonCurrentAssets': 217970000000.0,\n",
              "   'commonStockEquity': 56727000000.0,\n",
              "   'currentDeferredLiabilities': 7992000000.0,\n",
              "   'totalCapitalization': 156354000000.0,\n",
              "   'currentDebtAndCapitalLeaseObligation': 11483000000.0,\n",
              "   'currentDeferredRevenue': 7992000000.0,\n",
              "   'longTermDebt': 99627000000.0,\n",
              "   'cashAndCashEquivalents': 20535000000.0,\n",
              "   'cashCashEquivalentsAndShortTermInvestments': 51355000000.0,\n",
              "   'otherNonCurrentLiabilities': 53107000000.0,\n",
              "   'otherInvestments': 114095000000.0,\n",
              "   'payables': 57918000000.0,\n",
              "   'investedCapital': 167837000000.0,\n",
              "   'receivables': 54180000000.0,\n",
              "   'accountsPayable': 57918000000.0,\n",
              "   'otherCurrentAssets': 16422000000.0,\n",
              "   'gainsLossesNotAffectingRetainedEarnings': -12912000000.0,\n",
              "   'totalNonCurrentLiabilitiesNetMinorityInterest': 152734000000.0,\n",
              "   'rawMaterials': 2513000000.0,\n",
              "   'capitalStock': 66399000000.0}},\n",
              " {'2023-03-31': {'currentDebt': 12574000000.0,\n",
              "   'totalEquityGrossMinorityInterest': 62158000000.0,\n",
              "   'inventory': 7482000000.0,\n",
              "   'cashFinancial': 20050000000.0,\n",
              "   'accumulatedDepreciation': -69668000000.0,\n",
              "   'investmentsAndAdvances': 110461000000.0,\n",
              "   'totalDebt': 109615000000.0,\n",
              "   'otherNonCurrentAssets': 65388000000.0,\n",
              "   'tangibleBookValue': 62158000000.0,\n",
              "   'totalLiabilitiesNetMinorityInterest': 270002000000.0,\n",
              "   'cashEquivalents': 4637000000.0,\n",
              "   'otherReceivables': 17963000000.0,\n",
              "   'otherCurrentBorrowings': 10578000000.0,\n",
              "   'commercialPaper': 1996000000.0,\n",
              "   'availableForSaleSecurities': 110461000000.0,\n",
              "   'currentAssets': 112913000000.0,\n",
              "   'currentLiabilities': 120075000000.0,\n",
              "   'grossPPE': 113066000000.0,\n",
              "   'otherEquityAdjustments': -11746000000.0,\n",
              "   'retainedEarnings': 4336000000.0,\n",
              "   'shareIssued': 15723406000.0,\n",
              "   'longTermDebtAndCapitalLeaseObligation': 97041000000.0,\n",
              "   'ordinarySharesNumber': 15723406000.0,\n",
              "   'netPPE': 43398000000.0,\n",
              "   'otherCurrentLiabilities': 56425000000.0,\n",
              "   'payablesAndAccruedExpenses': 42945000000.0,\n",
              "   'otherShortTermInvestments': 31185000000.0,\n",
              "   'netDebt': 84928000000.0,\n",
              "   'workingCapital': -7162000000.0,\n",
              "   'stockholdersEquity': 62158000000.0,\n",
              "   'accountsReceivable': 17936000000.0,\n",
              "   'commonStock': 69568000000.0,\n",
              "   'netTangibleAssets': 62158000000.0,\n",
              "   'totalAssets': 332160000000.0,\n",
              "   'finishedGoods': 4103000000.0,\n",
              "   'totalNonCurrentAssets': 219247000000.0,\n",
              "   'commonStockEquity': 62158000000.0,\n",
              "   'currentDeferredLiabilities': 8131000000.0,\n",
              "   'totalCapitalization': 159199000000.0,\n",
              "   'currentDebtAndCapitalLeaseObligation': 12574000000.0,\n",
              "   'currentDeferredRevenue': 8131000000.0,\n",
              "   'longTermDebt': 97041000000.0,\n",
              "   'cashAndCashEquivalents': 24687000000.0,\n",
              "   'cashCashEquivalentsAndShortTermInvestments': 55872000000.0,\n",
              "   'otherNonCurrentLiabilities': 52886000000.0,\n",
              "   'payables': 42945000000.0,\n",
              "   'investedCapital': 171773000000.0,\n",
              "   'receivables': 35899000000.0,\n",
              "   'accountsPayable': 42945000000.0,\n",
              "   'otherCurrentAssets': 13660000000.0,\n",
              "   'gainsLossesNotAffectingRetainedEarnings': -11746000000.0,\n",
              "   'totalNonCurrentLiabilitiesNetMinorityInterest': 149927000000.0,\n",
              "   'rawMaterials': 3379000000.0,\n",
              "   'investmentinFinancialAssets': 110461000000.0,\n",
              "   'capitalStock': 69568000000.0}},\n",
              " {'2023-06-30': {'currentDebt': 11209000000.0,\n",
              "   'totalEquityGrossMinorityInterest': 60274000000.0,\n",
              "   'inventory': 7351000000.0,\n",
              "   'cashFinancial': 25337000000.0,\n",
              "   'accumulatedDepreciation': -70787000000.0,\n",
              "   'investmentsAndAdvances': 104061000000.0,\n",
              "   'totalDebt': 109280000000.0,\n",
              "   'otherNonCurrentAssets': 64768000000.0,\n",
              "   'tangibleBookValue': 60274000000.0,\n",
              "   'totalLiabilitiesNetMinorityInterest': 274764000000.0,\n",
              "   'cashEquivalents': 3071000000.0,\n",
              "   'otherReceivables': 19637000000.0,\n",
              "   'otherCurrentBorrowings': 7216000000.0,\n",
              "   'commercialPaper': 3993000000.0,\n",
              "   'availableForSaleSecurities': 104061000000.0,\n",
              "   'currentAssets': 122659000000.0,\n",
              "   'currentLiabilities': 124963000000.0,\n",
              "   'grossPPE': 114337000000.0,\n",
              "   'otherEquityAdjustments': -11801000000.0,\n",
              "   'retainedEarnings': 1408000000.0,\n",
              "   'shareIssued': 15647868000.0,\n",
              "   'longTermDebtAndCapitalLeaseObligation': 98071000000.0,\n",
              "   'ordinarySharesNumber': 15647868000.0,\n",
              "   'netPPE': 43550000000.0,\n",
              "   'otherCurrentLiabilities': 58897000000.0,\n",
              "   'payablesAndAccruedExpenses': 46699000000.0,\n",
              "   'otherShortTermInvestments': 34074000000.0,\n",
              "   'netDebt': 80872000000.0,\n",
              "   'workingCapital': -2304000000.0,\n",
              "   'stockholdersEquity': 60274000000.0,\n",
              "   'accountsReceivable': 19549000000.0,\n",
              "   'commonStock': 70667000000.0,\n",
              "   'netTangibleAssets': 60274000000.0,\n",
              "   'totalAssets': 335038000000.0,\n",
              "   'finishedGoods': 3563000000.0,\n",
              "   'totalNonCurrentAssets': 212379000000.0,\n",
              "   'commonStockEquity': 60274000000.0,\n",
              "   'currentDeferredLiabilities': 8158000000.0,\n",
              "   'totalCapitalization': 158345000000.0,\n",
              "   'currentDebtAndCapitalLeaseObligation': 11209000000.0,\n",
              "   'currentDeferredRevenue': 8158000000.0,\n",
              "   'longTermDebt': 98071000000.0,\n",
              "   'cashAndCashEquivalents': 28408000000.0,\n",
              "   'cashCashEquivalentsAndShortTermInvestments': 62482000000.0,\n",
              "   'otherNonCurrentLiabilities': 51730000000.0,\n",
              "   'payables': 46699000000.0,\n",
              "   'investedCapital': 169554000000.0,\n",
              "   'receivables': 39186000000.0,\n",
              "   'accountsPayable': 46699000000.0,\n",
              "   'otherCurrentAssets': 13640000000.0,\n",
              "   'gainsLossesNotAffectingRetainedEarnings': -11801000000.0,\n",
              "   'totalNonCurrentLiabilitiesNetMinorityInterest': 149801000000.0,\n",
              "   'rawMaterials': 3788000000.0,\n",
              "   'investmentinFinancialAssets': 104061000000.0,\n",
              "   'capitalStock': 70667000000.0}},\n",
              " {'2023-09-30': {'currentDebt': 15807000000.0,\n",
              "   'totalEquityGrossMinorityInterest': 62146000000.0,\n",
              "   'inventory': 6331000000.0,\n",
              "   'investmentsAndAdvances': 100544000000.0,\n",
              "   'totalDebt': 111088000000.0,\n",
              "   'otherNonCurrentAssets': 64758000000.0,\n",
              "   'tangibleBookValue': 62146000000.0,\n",
              "   'treasurySharesNumber': 0.0,\n",
              "   'totalLiabilitiesNetMinorityInterest': 290437000000.0,\n",
              "   'otherReceivables': 31477000000.0,\n",
              "   'otherCurrentBorrowings': 9822000000.0,\n",
              "   'commercialPaper': 5985000000.0,\n",
              "   'availableForSaleSecurities': 100544000000.0,\n",
              "   'currentAssets': 143566000000.0,\n",
              "   'currentLiabilities': 145308000000.0,\n",
              "   'otherEquityAdjustments': -11452000000.0,\n",
              "   'retainedEarnings': -214000000.0,\n",
              "   'shareIssued': 15550061000.0,\n",
              "   'longTermDebtAndCapitalLeaseObligation': 95281000000.0,\n",
              "   'ordinarySharesNumber': 15550061000.0,\n",
              "   'netPPE': 43715000000.0,\n",
              "   'otherCurrentLiabilities': 58829000000.0,\n",
              "   'payablesAndAccruedExpenses': 62611000000.0,\n",
              "   'otherShortTermInvestments': 31590000000.0,\n",
              "   'netDebt': 81123000000.0,\n",
              "   'workingCapital': -1742000000.0,\n",
              "   'stockholdersEquity': 62146000000.0,\n",
              "   'accountsReceivable': 29508000000.0,\n",
              "   'commonStock': 73812000000.0,\n",
              "   'netTangibleAssets': 62146000000.0,\n",
              "   'totalAssets': 352583000000.0,\n",
              "   'totalNonCurrentAssets': 209017000000.0,\n",
              "   'commonStockEquity': 62146000000.0,\n",
              "   'currentDeferredLiabilities': 8061000000.0,\n",
              "   'totalCapitalization': 157427000000.0,\n",
              "   'currentDebtAndCapitalLeaseObligation': 15807000000.0,\n",
              "   'currentDeferredRevenue': 8061000000.0,\n",
              "   'longTermDebt': 95281000000.0,\n",
              "   'cashAndCashEquivalents': 29965000000.0,\n",
              "   'cashCashEquivalentsAndShortTermInvestments': 61555000000.0,\n",
              "   'otherNonCurrentLiabilities': 49848000000.0,\n",
              "   'payables': 62611000000.0,\n",
              "   'investedCapital': 173234000000.0,\n",
              "   'receivables': 60985000000.0,\n",
              "   'accountsPayable': 62611000000.0,\n",
              "   'otherCurrentAssets': 14695000000.0,\n",
              "   'gainsLossesNotAffectingRetainedEarnings': -11452000000.0,\n",
              "   'totalNonCurrentLiabilitiesNetMinorityInterest': 145129000000.0,\n",
              "   'investmentinFinancialAssets': 100544000000.0,\n",
              "   'capitalStock': 73812000000.0}}]"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(YahooFinancials('AAPL').get_recommendations())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y_2awfHiYFvr",
        "outputId": "80e1d2fb-b839-4b93-9f3b-fd3058542a75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'AAPL': [{'symbol': 'AAPL', 'recommendedSymbols': [{'symbol': 'AMZN', 'score': 0.341279}, {'symbol': 'TSLA', 'score': 0.31961}, {'symbol': 'GOOG', 'score': 0.300475}, {'symbol': 'META', 'score': 0.294038}, {'symbol': 'MSFT', 'score': 0.224653}]}]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "insights = YahooFinancials('AAPL').get_insights()#['instrumentInfo']\n",
        "insights['AAPL']['instrumentInfo'].keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RGqMPw51YKJf",
        "outputId": "f7d992ae-48dd-47e9-9fc6-a70f2ad62757"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['technicalEvents', 'keyTechnicals', 'valuation'])"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "insights['AAPL']['instrumentInfo']['valuation']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rQw7zUGEYmVD",
        "outputId": "bbe71daa-fa64-4c4e-a0ad-8f97e454496c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'color': 0.0,\n",
              " 'description': 'Overvalued',\n",
              " 'discount': '-3%',\n",
              " 'relativeValue': 'Premium',\n",
              " 'provider': 'Trading Central'}"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(YahooFinancials('AAPL').get_stock_price_data())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WcYW-bLWYycs",
        "outputId": "3e404e44-9e1f-43eb-dacd-98e94afa4655"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'AAPL': {'maxAge': 1, 'preMarketChangePercent': -0.00751457, 'preMarketChange': -1.395, 'preMarketTime': '2024-01-03 19:29:59 UTC+0000', 'preMarketPrice': 184.245, 'preMarketSource': 'FREE_REALTIME', 'regularMarketChangePercent': -0.009234017, 'regularMarketChange': -1.7142029, 'regularMarketTime': '2024-01-03 22:33:38 UTC+0000', 'priceHint': 2, 'regularMarketPrice': 183.9258, 'regularMarketDayHigh': 185.88, 'regularMarketDayLow': 183.43, 'regularMarketVolume': 28511828, 'regularMarketPreviousClose': 185.64, 'regularMarketSource': 'FREE_REALTIME', 'regularMarketOpen': 184.22, 'exchange': 'NMS', 'exchangeName': 'NasdaqGS', 'exchangeDataDelayedBy': 0, 'marketState': 'REGULAR', 'quoteType': 'EQUITY', 'symbol': 'AAPL', 'underlyingSymbol': None, 'shortName': 'Apple Inc.', 'longName': 'Apple Inc.', 'currency': 'USD', 'quoteSourceName': 'Nasdaq Real Time Price', 'currencySymbol': '$', 'fromCurrency': None, 'toCurrency': None, 'lastMarket': None, 'marketCap': 2860561203200}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(YahooFinancials('AAPL').get_stock_earnings_data())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ext13cu7Y2V-",
        "outputId": "b9880a62-4b52-4cc0-a04c-da4de20b1a13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'AAPL': {'maxAge': 86400, 'earningsChart': {'quarterly': [{'date': '4Q2022', 'actual': 1.88, 'estimate': 1.94}, {'date': '1Q2023', 'actual': 1.52, 'estimate': 1.43}, {'date': '2Q2023', 'actual': 1.26, 'estimate': 1.19}, {'date': '3Q2023', 'actual': 1.32, 'estimate': 1.26}], 'currentQuarterEstimate': 2.09, 'currentQuarterEstimateDate': '4Q', 'currentQuarterEstimateYear': 2023, 'earningsDate': [1706698740, 1707134400]}, 'financialsChart': {'yearly': [{'date': 2019, 'revenue': 260174000000, 'earnings': 55256000000}, {'date': 2020, 'revenue': 274515000000, 'earnings': 57411000000}, {'date': 2021, 'revenue': 365817000000, 'earnings': 94680000000}, {'date': 2022, 'revenue': 394328000000, 'earnings': 99803000000}], 'quarterly': [{'date': '3Q2022', 'revenue': 90146000000, 'earnings': 20721000000}, {'date': '4Q2022', 'revenue': 117154000000, 'earnings': 29998000000}, {'date': '2Q2023', 'revenue': 0, 'earnings': 0}, {'date': '3Q2023', 'revenue': 81797000000, 'earnings': 19881000000}]}, 'financialCurrency': 'USD'}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(YahooFinancials('AAPL').get_summary_data())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8eBqXpXhY5_l",
        "outputId": "b0753b8a-83d8-49d8-e2d3-1c81526f4a4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'AAPL': {'maxAge': 1, 'priceHint': 2, 'previousClose': 185.64, 'open': 184.22, 'dayLow': 183.43, 'dayHigh': 185.88, 'regularMarketPreviousClose': 185.64, 'regularMarketOpen': 184.22, 'regularMarketDayLow': 183.43, 'regularMarketDayHigh': 185.88, 'dividendRate': 0.96, 'dividendYield': 0.0052, 'exDividendDate': '-', 'payoutRatio': 0.1533, 'fiveYearAvgDividendYield': 0.8, 'beta': 1.308, 'trailingPE': 30.012283, 'forwardPE': 25.73081, 'volume': 28533546, 'regularMarketVolume': 28533546, 'averageVolume': 53479166, 'averageVolume10days': 46838670, 'averageDailyVolume10Day': 46838670, 'bid': 183.77, 'ask': 183.78, 'bidSize': 800, 'askSize': 800, 'marketCap': 2861330857984, 'fiftyTwoWeekLow': 124.76, 'fiftyTwoWeekHigh': 199.62, 'priceToSalesTrailing12Months': 7.4652824, 'fiftyDayAverage': 186.8374, 'twoHundredDayAverage': 179.63686, 'trailingAnnualDividendRate': 0.94, 'trailingAnnualDividendYield': 0.005063564, 'currency': 'USD', 'fromCurrency': None, 'toCurrency': None, 'lastMarket': None, 'coinMarketCapLink': None, 'algorithm': None, 'tradeable': False}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(YahooFinancials('AAPL').get_stock_quote_type_data())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Hcfo0KzY9yy",
        "outputId": "5930fc28-43d5-4a2b-fbe7-9a6f6d6e97fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'AAPL': {'exchange': 'NMS', 'quoteType': 'EQUITY', 'symbol': 'AAPL', 'underlyingSymbol': 'AAPL', 'shortName': 'Apple Inc.', 'longName': 'Apple Inc.', 'firstTradeDateEpochUtc': 345479400, 'timeZoneFullName': 'America/New_York', 'timeZoneShortName': 'EST', 'uuid': '8b10e4ae-9eeb-3684-921a-9ab27e4d87aa', 'messageBoardId': 'finmb_24937', 'gmtOffSetMilliseconds': -18000000, 'maxAge': 1}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(YahooFinancials('AAPL').get_num_shares_outstanding(price_type='current'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zzi3gTDyY9td",
        "outputId": "eff0049b-1956-401c-8233-cb9160c7cf9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15556984707.876762\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(YahooFinancials('AAPL').get_daily_dividend_data(start_date, end_date))"
      ],
      "metadata": {
        "id": "4MlCqKKpZcVz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(YahooFinancials('AAPL').get_stock_profile_data())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IHwQk87lZhF3",
        "outputId": "078e74fa-6aeb-4b67-93d5-581391710ac3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'AAPL': {'address1': 'One Apple Park Way', 'city': 'Cupertino', 'state': 'CA', 'zip': '95014', 'country': 'United States', 'phone': '408 996 1010', 'website': 'https://www.apple.com', 'industry': 'Consumer Electronics', 'industryKey': 'consumer-electronics', 'industryDisp': 'Consumer Electronics', 'sector': 'Technology', 'sectorKey': 'technology', 'sectorDisp': 'Technology', 'longBusinessSummary': 'Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. The company offers iPhone, a line of smartphones; Mac, a line of personal computers; iPad, a line of multi-purpose tablets; and wearables, home, and accessories comprising AirPods, Apple TV, Apple Watch, Beats products, and HomePod. It also provides AppleCare support and cloud services; and operates various platforms, including the App Store that allow customers to discover and download applications and digital content, such as books, music, video, games, and podcasts. In addition, the company offers various services, such as Apple Arcade, a game subscription service; Apple Fitness+, a personalized fitness service; Apple Music, which offers users a curated listening experience with on-demand radio stations; Apple News+, a subscription news and magazine service; Apple TV+, which offers exclusive original content; Apple Card, a co-branded credit card; and Apple Pay, a cashless payment service, as well as licenses its intellectual property. The company serves consumers, and small and mid-sized businesses; and the education, enterprise, and government markets. It distributes third-party applications for its products through the App Store. The company also sells its products through its retail and online stores, and direct sales force; and third-party cellular network carriers, wholesalers, retailers, and resellers. Apple Inc. was founded in 1976 and is headquartered in Cupertino, California.', 'fullTimeEmployees': 161000, 'companyOfficers': [{'maxAge': 1, 'name': 'Mr. Timothy D. Cook', 'age': 62, 'title': 'CEO & Director', 'yearBorn': 1961, 'fiscalYear': 2022, 'totalPay': {'raw': 16425933, 'fmt': '16.43M', 'longFmt': '16,425,933'}, 'exercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}, 'unexercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}}, {'maxAge': 1, 'name': 'Mr. Luca  Maestri', 'age': 60, 'title': 'CFO & Senior VP', 'yearBorn': 1963, 'fiscalYear': 2022, 'totalPay': {'raw': 5019783, 'fmt': '5.02M', 'longFmt': '5,019,783'}, 'exercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}, 'unexercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}}, {'maxAge': 1, 'name': 'Mr. Jeffrey E. Williams', 'age': 59, 'title': 'Chief Operating Officer', 'yearBorn': 1964, 'fiscalYear': 2022, 'totalPay': {'raw': 5018337, 'fmt': '5.02M', 'longFmt': '5,018,337'}, 'exercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}, 'unexercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}}, {'maxAge': 1, 'name': 'Ms. Katherine L. Adams', 'age': 59, 'title': 'Senior VP, General Counsel & Secretary', 'yearBorn': 1964, 'fiscalYear': 2022, 'totalPay': {'raw': 5015208, 'fmt': '5.02M', 'longFmt': '5,015,208'}, 'exercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}, 'unexercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}}, {'maxAge': 1, 'name': \"Ms. Deirdre  O'Brien\", 'age': 56, 'title': 'Senior Vice President of Retail', 'yearBorn': 1967, 'fiscalYear': 2022, 'totalPay': {'raw': 5019783, 'fmt': '5.02M', 'longFmt': '5,019,783'}, 'exercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}, 'unexercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}}, {'maxAge': 1, 'name': 'Mr. Chris  Kondo', 'title': 'Senior Director of Corporate Accounting', 'fiscalYear': 2022, 'exercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}, 'unexercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}}, {'maxAge': 1, 'name': 'Mr. James  Wilson', 'title': 'Chief Technology Officer', 'fiscalYear': 2022, 'exercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}, 'unexercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}}, {'maxAge': 1, 'name': 'Suhasini  Chandramouli', 'title': 'Director of Investor Relations', 'fiscalYear': 2022, 'exercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}, 'unexercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}}, {'maxAge': 1, 'name': 'Mr. Greg  Joswiak', 'title': 'Senior Vice President of Worldwide Marketing', 'fiscalYear': 2022, 'exercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}, 'unexercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}}, {'maxAge': 1, 'name': 'Mr. Adrian  Perica', 'age': 49, 'title': 'Head of Corporate Development', 'yearBorn': 1974, 'fiscalYear': 2022, 'exercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}, 'unexercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}}], 'auditRisk': 4, 'boardRisk': 1, 'compensationRisk': 6, 'shareHolderRightsRisk': 1, 'overallRisk': 1, 'governanceEpochDate': 1704067200, 'compensationAsOfEpochDate': 1672444800}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(YahooFinancials('AAPL').get_financial_data())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u2f6ZXt7ZhBN",
        "outputId": "b43a84a4-ebee-4a6e-82c2-c85cdf2a2a44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'AAPL': {'maxAge': 86400, 'currentPrice': 184.0769, 'targetHighPrice': 250.0, 'targetLowPrice': 159.0, 'targetMeanPrice': 199.14, 'targetMedianPrice': 200.0, 'recommendationMean': 2.1, 'recommendationKey': 'buy', 'numberOfAnalystOpinions': 39, 'totalCash': 61554999296, 'totalCashPerShare': 3.958, 'ebitda': 125820002304, 'totalDebt': 123930001408, 'quickRatio': 0.843, 'currentRatio': 0.988, 'totalRevenue': 383285002240, 'debtToEquity': 199.418, 'revenuePerShare': 24.344, 'returnOnAssets': 0.20256001, 'returnOnEquity': 1.7195, 'grossProfits': 170782000000, 'freeCashflow': 82179997696, 'operatingCashflow': 110543003648, 'earningsGrowth': 0.135, 'revenueGrowth': -0.007, 'grossMargins': 0.44131002, 'ebitdaMargins': 0.32827, 'operatingMargins': 0.30134, 'profitMargins': 0.25305998, 'financialCurrency': 'USD'}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(YahooFinancials('AAPL').get_interest_expense())\n",
        "print(YahooFinancials('AAPL').get_operating_income())\n",
        "print(YahooFinancials('AAPL').get_total_operating_expense())\n",
        "print(YahooFinancials('AAPL').get_total_revenue())\n",
        "print(YahooFinancials('AAPL').get_cost_of_revenue())\n",
        "print(YahooFinancials('AAPL').get_income_before_tax())\n",
        "print(YahooFinancials('AAPL').get_income_tax_expense())\n",
        "print(YahooFinancials('AAPL').get_esg_score_data())\n",
        "print(YahooFinancials('AAPL').get_gross_profit())\n",
        "print(YahooFinancials('AAPL').get_net_income_from_continuing_ops())\n",
        "print(YahooFinancials('AAPL').get_research_and_development())\n",
        "print(YahooFinancials('AAPL').get_current_price())\n",
        "print(YahooFinancials('AAPL').get_current_change())\n",
        "print(YahooFinancials('AAPL').get_current_percent_change())\n",
        "print(YahooFinancials('AAPL').get_current_volume())\n",
        "print(YahooFinancials('AAPL').get_prev_close_price())\n",
        "print(YahooFinancials('AAPL').get_open_price())\n",
        "print(YahooFinancials('AAPL').get_ten_day_avg_daily_volume())\n",
        "print(YahooFinancials('AAPL').get_stock_exchange())\n",
        "print(YahooFinancials('AAPL').get_market_cap())\n",
        "print(YahooFinancials('AAPL').get_daily_low())\n",
        "print(YahooFinancials('AAPL').get_daily_high())\n",
        "print(YahooFinancials('AAPL').get_currency())\n",
        "print(YahooFinancials('AAPL').get_yearly_high())\n",
        "print(YahooFinancials('AAPL').get_yearly_low())\n",
        "print(YahooFinancials('AAPL').get_dividend_yield())\n",
        "print(YahooFinancials('AAPL').get_annual_avg_div_yield())\n",
        "print(YahooFinancials('AAPL').get_five_yr_avg_div_yield())\n",
        "print(YahooFinancials('AAPL').get_dividend_rate())\n",
        "print(YahooFinancials('AAPL').get_annual_avg_div_rate())\n",
        "print(YahooFinancials('AAPL').get_50day_moving_avg())\n",
        "print(YahooFinancials('AAPL').get_200day_moving_avg())\n",
        "print(YahooFinancials('AAPL').get_beta())\n",
        "print(YahooFinancials('AAPL').get_payout_ratio())\n",
        "print(YahooFinancials('AAPL').get_pe_ratio())\n",
        "print(YahooFinancials('AAPL').get_price_to_sales())\n",
        "print(YahooFinancials('AAPL').get_exdividend_date())\n",
        "print(YahooFinancials('AAPL').get_book_value())\n",
        "print(YahooFinancials('AAPL').get_ebit())\n",
        "print(YahooFinancials('AAPL').get_net_income())\n",
        "print(YahooFinancials('AAPL').get_earnings_per_share())\n",
        "print(YahooFinancials('AAPL').get_key_statistics_data())\n",
        "print(YahooFinancials('AAPL').get_stock_profile_data())\n",
        "print(YahooFinancials('AAPL').get_financial_data())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ufX9iW6tZg8p",
        "outputId": "698fb2a5-c732-4d4f-918d-9bc2bd603a07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2873000000.0\n",
            "57411000000.0\n",
            "None\n",
            "274515000000.0\n",
            "169559000000.0\n",
            "None\n",
            "None\n",
            "{'AAPL': {'maxAge': 86400, 'totalEsg': 17.22, 'environmentScore': 0.46, 'socialScore': 7.39, 'governanceScore': 9.37, 'ratingYear': 2023, 'ratingMonth': 9, 'highestControversy': 3.0, 'peerCount': 55, 'esgPerformance': 'UNDER_PERF', 'peerGroup': 'Technology Hardware', 'relatedControversy': ['Social Supply Chain Incidents', 'Customer Incidents', 'Business Ethics Incidents'], 'peerEsgScorePerformance': {'min': 6.37, 'avg': 15.565636363636367, 'max': 27.47}, 'peerGovernancePerformance': {'min': 3.44, 'avg': 6.928431372549018, 'max': 11.13}, 'peerSocialPerformance': {'min': 1.75, 'avg': 5.536862745098038, 'max': 9.76}, 'peerEnvironmentPerformance': {'min': 0.11, 'avg': 3.0588235294117636, 'max': 9.54}, 'peerHighestControversyPerformance': {'min': 0.0, 'avg': 1.5454545454545454, 'max': 4.0}, 'percentile': 17.82, 'environmentPercentile': None, 'socialPercentile': None, 'governancePercentile': None, 'adult': False, 'alcoholic': False, 'animalTesting': False, 'catholic': False, 'controversialWeapons': False, 'smallArms': False, 'furLeather': False, 'gambling': False, 'gmo': False, 'militaryContract': False, 'nuclear': False, 'pesticides': False, 'palmOil': False, 'coal': False, 'tobacco': False}}\n",
            "104956000000.0\n",
            "None\n",
            "None\n",
            "183.9258\n",
            "-1.7142029\n",
            "-0.009234017\n",
            "28511828\n",
            "185.64\n",
            "184.22\n",
            "46838670\n",
            "NasdaqGS\n",
            "2860561203200\n",
            "183.43\n",
            "185.88\n",
            "USD\n",
            "199.62\n",
            "124.76\n",
            "0.0052\n",
            "0.005063564\n",
            "0.8\n",
            "0.96\n",
            "0.94\n",
            "186.8374\n",
            "179.63686\n",
            "1.308\n",
            "0.1533\n",
            "30.012283\n",
            "7.4652824\n",
            "-\n",
            "None\n",
            "66288000000.0\n",
            "57411000000.0\n",
            "6.128350848884105\n",
            "{'AAPL': {'maxAge': 1, 'priceHint': 2, 'enterpriseValue': 2949587927040, 'forwardPE': 25.754545, 'profitMargins': 0.25305998, 'floatShares': 15535488445, 'sharesOutstanding': 15552799744, 'sharesShort': 120233720, 'sharesShortPriorMonth': 105837123, 'sharesShortPreviousMonthDate': '-', 'dateShortInterest': 1702598400, 'sharesPercentSharesOut': 0.0077, 'heldPercentInsiders': 0.00074, 'heldPercentInstitutions': 0.61595, 'shortRatio': 2.29, 'shortPercentOfFloat': 0.0077, 'beta': 1.308, 'impliedSharesOutstanding': 15552799744, 'category': None, 'bookValue': 3.997, 'priceToBook': 46.070805, 'fundFamily': None, 'legalType': None, 'lastFiscalYearEnd': 1696032000, 'nextFiscalYearEnd': 1727654400, 'mostRecentQuarter': 1696032000, 'earningsQuarterlyGrowth': 0.108, 'netIncomeToCommon': 96995000320, 'trailingEps': 6.13, 'forwardEps': 7.15, 'pegRatio': 4.59, 'lastSplitFactor': '4:1', 'lastSplitDate': '-', 'enterpriseToRevenue': 7.696, 'enterpriseToEbitda': 23.443, '52WeekChange': 0.46913576, 'SandP52WeekChange': 0.23095429, 'lastDividendValue': 0.24, 'lastDividendDate': '-'}}\n",
            "{'AAPL': {'address1': 'One Apple Park Way', 'city': 'Cupertino', 'state': 'CA', 'zip': '95014', 'country': 'United States', 'phone': '408 996 1010', 'website': 'https://www.apple.com', 'industry': 'Consumer Electronics', 'industryKey': 'consumer-electronics', 'industryDisp': 'Consumer Electronics', 'sector': 'Technology', 'sectorKey': 'technology', 'sectorDisp': 'Technology', 'longBusinessSummary': 'Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. The company offers iPhone, a line of smartphones; Mac, a line of personal computers; iPad, a line of multi-purpose tablets; and wearables, home, and accessories comprising AirPods, Apple TV, Apple Watch, Beats products, and HomePod. It also provides AppleCare support and cloud services; and operates various platforms, including the App Store that allow customers to discover and download applications and digital content, such as books, music, video, games, and podcasts. In addition, the company offers various services, such as Apple Arcade, a game subscription service; Apple Fitness+, a personalized fitness service; Apple Music, which offers users a curated listening experience with on-demand radio stations; Apple News+, a subscription news and magazine service; Apple TV+, which offers exclusive original content; Apple Card, a co-branded credit card; and Apple Pay, a cashless payment service, as well as licenses its intellectual property. The company serves consumers, and small and mid-sized businesses; and the education, enterprise, and government markets. It distributes third-party applications for its products through the App Store. The company also sells its products through its retail and online stores, and direct sales force; and third-party cellular network carriers, wholesalers, retailers, and resellers. Apple Inc. was founded in 1976 and is headquartered in Cupertino, California.', 'fullTimeEmployees': 161000, 'companyOfficers': [{'maxAge': 1, 'name': 'Mr. Timothy D. Cook', 'age': 62, 'title': 'CEO & Director', 'yearBorn': 1961, 'fiscalYear': 2022, 'totalPay': {'raw': 16425933, 'fmt': '16.43M', 'longFmt': '16,425,933'}, 'exercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}, 'unexercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}}, {'maxAge': 1, 'name': 'Mr. Luca  Maestri', 'age': 60, 'title': 'CFO & Senior VP', 'yearBorn': 1963, 'fiscalYear': 2022, 'totalPay': {'raw': 5019783, 'fmt': '5.02M', 'longFmt': '5,019,783'}, 'exercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}, 'unexercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}}, {'maxAge': 1, 'name': 'Mr. Jeffrey E. Williams', 'age': 59, 'title': 'Chief Operating Officer', 'yearBorn': 1964, 'fiscalYear': 2022, 'totalPay': {'raw': 5018337, 'fmt': '5.02M', 'longFmt': '5,018,337'}, 'exercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}, 'unexercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}}, {'maxAge': 1, 'name': 'Ms. Katherine L. Adams', 'age': 59, 'title': 'Senior VP, General Counsel & Secretary', 'yearBorn': 1964, 'fiscalYear': 2022, 'totalPay': {'raw': 5015208, 'fmt': '5.02M', 'longFmt': '5,015,208'}, 'exercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}, 'unexercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}}, {'maxAge': 1, 'name': \"Ms. Deirdre  O'Brien\", 'age': 56, 'title': 'Senior Vice President of Retail', 'yearBorn': 1967, 'fiscalYear': 2022, 'totalPay': {'raw': 5019783, 'fmt': '5.02M', 'longFmt': '5,019,783'}, 'exercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}, 'unexercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}}, {'maxAge': 1, 'name': 'Mr. Chris  Kondo', 'title': 'Senior Director of Corporate Accounting', 'fiscalYear': 2022, 'exercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}, 'unexercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}}, {'maxAge': 1, 'name': 'Mr. James  Wilson', 'title': 'Chief Technology Officer', 'fiscalYear': 2022, 'exercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}, 'unexercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}}, {'maxAge': 1, 'name': 'Suhasini  Chandramouli', 'title': 'Director of Investor Relations', 'fiscalYear': 2022, 'exercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}, 'unexercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}}, {'maxAge': 1, 'name': 'Mr. Greg  Joswiak', 'title': 'Senior Vice President of Worldwide Marketing', 'fiscalYear': 2022, 'exercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}, 'unexercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}}, {'maxAge': 1, 'name': 'Mr. Adrian  Perica', 'age': 49, 'title': 'Head of Corporate Development', 'yearBorn': 1974, 'fiscalYear': 2022, 'exercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}, 'unexercisedValue': {'raw': 0, 'fmt': None, 'longFmt': '0'}}], 'auditRisk': 4, 'boardRisk': 1, 'compensationRisk': 6, 'shareHolderRightsRisk': 1, 'overallRisk': 1, 'governanceEpochDate': 1704067200, 'compensationAsOfEpochDate': 1672444800}}\n",
            "{'AAPL': {'maxAge': 86400, 'currentPrice': 184.0769, 'targetHighPrice': 250.0, 'targetLowPrice': 159.0, 'targetMeanPrice': 199.14, 'targetMedianPrice': 200.0, 'recommendationMean': 2.1, 'recommendationKey': 'buy', 'numberOfAnalystOpinions': 39, 'totalCash': 61554999296, 'totalCashPerShare': 3.958, 'ebitda': 125820002304, 'totalDebt': 123930001408, 'quickRatio': 0.843, 'currentRatio': 0.988, 'totalRevenue': 383285002240, 'debtToEquity': 199.418, 'revenuePerShare': 24.344, 'returnOnAssets': 0.20256001, 'returnOnEquity': 1.7195, 'grossProfits': 170782000000, 'freeCashflow': 82179997696, 'operatingCashflow': 110543003648, 'earningsGrowth': 0.135, 'revenueGrowth': -0.007, 'grossMargins': 0.44131002, 'ebitdaMargins': 0.32827, 'operatingMargins': 0.30134, 'profitMargins': 0.25305998, 'financialCurrency': 'USD'}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aapl = yf.Ticker('AAPL')\n",
        "\n",
        "# FETCH OPTIONS CHAIN DATA FOR THE COMPANY\n",
        "aapl_options = aapl.option_chain()\n",
        "\n",
        "# ACCESS BOTH THE CALLS AND PUTS AND STORE THEM IN THEIR RESPECTIVE VARIABLES\n",
        "aapl_puts = aapl_options.puts\n",
        "aapl_calls = aapl_options.calls"
      ],
      "metadata": {
        "id": "JlNfIr0kZg4W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install secedgar"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1GKOqslULjMv",
        "outputId": "1df3ebe4-db82-42df-b362-9cb641a7dae6"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting secedgar\n",
            "  Downloading secedgar-0.5.0-py3-none-any.whl (67 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/67.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.1/67.1 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from secedgar) (4.11.2)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from secedgar) (3.9.1)\n",
            "Collecting async-timeout<4.0,>=3.0 (from secedgar)\n",
            "  Downloading async_timeout-3.0.1-py3-none-any.whl (8.2 kB)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from secedgar) (2.0.7)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from secedgar) (2.31.0)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from secedgar) (4.9.4)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from secedgar) (4.66.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->secedgar) (23.1.0)\n",
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            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->secedgar) (1.4.1)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->secedgar) (1.3.1)\n",
            "INFO: pip is looking at multiple versions of aiohttp to determine which version is compatible with other requirements. This could take a while.\n",
            "Collecting aiohttp (from secedgar)\n",
            "  Downloading aiohttp-3.9.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m31.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Downloading aiohttp-3.8.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m53.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->secedgar) (3.3.2)\n",
            "  Downloading aiohttp-3.8.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m57.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Downloading aiohttp-3.8.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m47.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Downloading aiohttp-3.8.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m62.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting charset-normalizer<3.0,>=2.0 (from aiohttp->secedgar)\n",
            "  Downloading charset_normalizer-2.1.1-py3-none-any.whl (39 kB)\n",
            "Collecting aiohttp (from secedgar)\n",
            "  Downloading aiohttp-3.8.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m61.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Downloading aiohttp-3.8.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m68.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hINFO: pip is looking at multiple versions of aiohttp to determine which version is compatible with other requirements. This could take a while.\n",
            "  Downloading aiohttp-3.7.4.post0.tar.gz (1.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m64.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting chardet<5.0,>=2.0 (from aiohttp->secedgar)\n",
            "  Downloading chardet-4.0.0-py2.py3-none-any.whl (178 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m178.7/178.7 kB\u001b[0m \u001b[31m22.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: typing-extensions>=3.6.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->secedgar) (4.5.0)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->secedgar) (2.5)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->secedgar) (3.6)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->secedgar) (2023.11.17)\n",
            "Building wheels for collected packages: aiohttp\n",
            "  Building wheel for aiohttp (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for aiohttp: filename=aiohttp-3.7.4.post0-cp310-cp310-linux_x86_64.whl size=1273327 sha256=62629b49f3232da5019bbd48a70248b8daf9ba4f3fcbd41726a74c73f8f80b49\n",
            "  Stored in directory: /root/.cache/pip/wheels/fc/84/e0/52113c57eb9b09b6b187a0f369eaad6fc7fc64bb7247c83b89\n",
            "Successfully built aiohttp\n",
            "Installing collected packages: chardet, async-timeout, aiohttp, secedgar\n",
            "  Attempting uninstall: chardet\n",
            "    Found existing installation: chardet 5.2.0\n",
            "    Uninstalling chardet-5.2.0:\n",
            "      Successfully uninstalled chardet-5.2.0\n",
            "  Attempting uninstall: async-timeout\n",
            "    Found existing installation: async-timeout 4.0.3\n",
            "    Uninstalling async-timeout-4.0.3:\n",
            "      Successfully uninstalled async-timeout-4.0.3\n",
            "  Attempting uninstall: aiohttp\n",
            "    Found existing installation: aiohttp 3.9.1\n",
            "    Uninstalling aiohttp-3.9.1:\n",
            "      Successfully uninstalled aiohttp-3.9.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "langchain 0.0.340 requires aiohttp<4.0.0,>=3.8.3, but you have aiohttp 3.7.4.post0 which is incompatible.\n",
            "langchain 0.0.340 requires async-timeout<5.0.0,>=4.0.0; python_version < \"3.11\", but you have async-timeout 3.0.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed aiohttp-3.7.4.post0 async-timeout-3.0.1 chardet-4.0.0 secedgar-0.5.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "aiohttp",
                  "async_timeout",
                  "chardet"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('/content/sec_filings.json', 'r') as f:\n",
        "  sec_filings = json.load(f)\n",
        "\n",
        "sec_filings"
      ],
      "metadata": {
        "id": "QJ7eSnfAZrce"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sec_filings[0]['Filing Content']"
      ],
      "metadata": {
        "id": "1iZCE92IaRSo"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(sec_filings)"
      ],
      "metadata": {
        "id": "c9iVptYH8578"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def num_tokens_from_string(string: str, encoding_name: str) -> int:\n",
        "    \"\"\"Returns the number of tokens in a text string.\"\"\"\n",
        "    encoding = tiktoken.encoding_for_model(encoding_name)\n",
        "    num_tokens = len(encoding.encode(string))\n",
        "    return num_tokens\n",
        "\n",
        "def split_into_chunks(text, chunk_size=8000):\n",
        "    return [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]\n",
        "\n",
        "def sec_filing_interpretor(input_to_llm):\n",
        "    response_schemas = [\n",
        "        ResponseSchema(name=\"SEC filing interpretation\", description=\"Description of the SEC Filing\")\n",
        "    ]\n",
        "    output_parser = StructuredOutputParser.from_response_schemas(response_schemas)\n",
        "    format_instructions = output_parser.get_format_instructions()\n",
        "\n",
        "    chat_model = ChatOpenAI(model_name=\"gpt-3.5-turbo-16k\", temperature=0.5, openai_api_key = \"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\")\n",
        "\n",
        "    prompt = ChatPromptTemplate(\n",
        "        messages=[\n",
        "            HumanMessagePromptTemplate.from_template(\"\"\"\n",
        "            Assume the role as a leading Technical Analysis (TA) expert in the stock market, a modern counterpart to Charles Dow, John Bollinger, and Alan Andrews. Analyze the SEC Filing report and extract key elements:\n",
        "\n",
        "            SEC Filing: {hist}\n",
        "\n",
        "            {format_instructions}\"\"\")\n",
        "        ],\n",
        "        input_variables=[\"hist\"],\n",
        "        partial_variables={\"format_instructions\": format_instructions}\n",
        "    )\n",
        "\n",
        "    if num_tokens_from_string(input_to_llm, \"gpt-3.5-turbo-16k\") < 7000:\n",
        "      _input = prompt.format_prompt(hist=input_to_llm)\n",
        "      output = chat_model(_input.to_messages())\n",
        "    else:\n",
        "      chunks = split_into_chunks(input_to_llm)\n",
        "      outputs = []\n",
        "      for chunk in chunks:\n",
        "        _input = prompt.format_prompt(hist=chunk)\n",
        "        output = chat_model(_input.to_messages())\n",
        "        outputs.append(output.content)\n",
        "      final_output = ', '.join(outputs)\n",
        "      _input = prompt.format_prompt(hist=final_output)\n",
        "      output = chat_model(_input.to_messages())\n",
        "\n",
        "    return(output)"
      ],
      "metadata": {
        "id": "V-rmUCfUL5pA"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(4):\n",
        "  sec_filing_info = sec_filing_interpretor(sec_filings[i]['Filing Content'])\n",
        "  final = sec_filing_info.content.replace(\"```json\\n{\\n\\t\",\"\").replace(\"\\n}\\n```\",\"\").split(\": \")[1]\n",
        "  print(final)"
      ],
      "metadata": {
        "id": "b37z6M_9H2hW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(sec_filings)):\n",
        "  interpretation = sec_filing_interpretor(sec_filings[i]['Filing Content'])\n",
        "  sec_filings[i]['Interpretation'] = interpretation.content.replace(\"```json\\n{\\n\\t\",\"\").replace(\"\\n}\\n```\",\"\").split(\": \")[1]\n",
        "\n",
        "sec_filings"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "ZLtSDIS_LlCc",
        "outputId": "7579da26-5ac1-470f-be22-c320102c1d3d"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "error",
          "ename": "InvalidRequestError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mInvalidRequestError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-31-9ef6844fc965>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msec_filings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m   \u001b[0minterpretation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msec_filing_interpretor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msec_filings\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Filing Content'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m   \u001b[0msec_filings\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Interpretation'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minterpretation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"```json\\n{\\n\\t\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\n}\\n```\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\": \"\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0msec_filings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-30-228b6bf6b6c9>\u001b[0m in \u001b[0;36msec_filing_interpretor\u001b[0;34m(input_to_llm)\u001b[0m\n\u001b[1;32m     42\u001b[0m       \u001b[0mfinal_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m', '\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m       \u001b[0m_input\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprompt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat_prompt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfinal_output\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m       \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchat_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_input\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_messages\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m     \u001b[0;32mreturn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\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/python3.10/dist-packages/langchain/chat_models/base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, messages, stop, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    598\u001b[0m         \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    599\u001b[0m     ) -> BaseMessage:\n\u001b[0;32m--> 600\u001b[0;31m         generation = self.generate(\n\u001b[0m\u001b[1;32m    601\u001b[0m             \u001b[0;34m[\u001b[0m\u001b[0mmessages\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    602\u001b[0m         ).generations[0][0]\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chat_models/base.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, messages, stop, callbacks, tags, metadata, run_name, **kwargs)\u001b[0m\n\u001b[1;32m    347\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mrun_managers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    348\u001b[0m                     \u001b[0mrun_managers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_llm_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 349\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    350\u001b[0m         flattened_outputs = [\n\u001b[1;32m    351\u001b[0m             \u001b[0mLLMResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerations\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mllm_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mllm_output\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/python3.10/dist-packages/langchain/chat_models/base.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, messages, stop, callbacks, tags, metadata, run_name, **kwargs)\u001b[0m\n\u001b[1;32m    337\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    338\u001b[0m                 results.append(\n\u001b[0;32m--> 339\u001b[0;31m                     self._generate_with_cache(\n\u001b[0m\u001b[1;32m    340\u001b[0m                         \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    341\u001b[0m                         \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop\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/python3.10/dist-packages/langchain/chat_models/base.py\u001b[0m in \u001b[0;36m_generate_with_cache\u001b[0;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    490\u001b[0m                 )\n\u001b[1;32m    491\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mnew_arg_supported\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 492\u001b[0;31m                 return self._generate(\n\u001b[0m\u001b[1;32m    493\u001b[0m                     \u001b[0mmessages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m                 )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chat_models/openai.py\u001b[0m in \u001b[0;36m_generate\u001b[0;34m(self, messages, stop, run_manager, stream, **kwargs)\u001b[0m\n\u001b[1;32m    420\u001b[0m         \u001b[0mmessage_dicts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_message_dicts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    421\u001b[0m         \u001b[0mparams\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 422\u001b[0;31m         response = self.completion_with_retry(\n\u001b[0m\u001b[1;32m    423\u001b[0m             \u001b[0mmessages\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmessage_dicts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    424\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chat_models/openai.py\u001b[0m in \u001b[0;36mcompletion_with_retry\u001b[0;34m(self, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    350\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    351\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 352\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_completion_with_retry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    354\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_combine_llm_outputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mllm_outputs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mdict\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/python3.10/dist-packages/tenacity/__init__.py\u001b[0m in \u001b[0;36mwrapped_f\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    287\u001b[0m         \u001b[0;34m@\u001b[0m\u001b[0mfunctools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    288\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mwrapped_f\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 289\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\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    290\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    291\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mretry_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mWrappedFn\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/python3.10/dist-packages/tenacity/__init__.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    377\u001b[0m         \u001b[0mretry_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRetryCallState\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretry_object\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 379\u001b[0;31m             \u001b[0mdo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretry_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mretry_state\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    380\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDoAttempt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    381\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;32m/usr/local/lib/python3.10/dist-packages/tenacity/__init__.py\u001b[0m in \u001b[0;36miter\u001b[0;34m(self, retry_state)\u001b[0m\n\u001b[1;32m    312\u001b[0m         \u001b[0mis_explicit_retry\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfailed\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTryAgain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mis_explicit_retry\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretry_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 314\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    316\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mafter\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    449\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mCancelledError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    450\u001b[0m                 \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mFINISHED\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 451\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/concurrent/futures/_base.py\u001b[0m in \u001b[0;36m__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    401\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\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;32m--> 403\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    404\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    405\u001b[0m                 \u001b[0;31m# Break a reference cycle with the exception in self._exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tenacity/__init__.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    380\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDoAttempt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    381\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;32m--> 382\u001b[0;31m                     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    383\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# noqa: B902\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    384\u001b[0m                     \u001b[0mretry_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[arg-type]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chat_models/openai.py\u001b[0m in \u001b[0;36m_completion_with_retry\u001b[0;34m(**kwargs)\u001b[0m\n\u001b[1;32m    348\u001b[0m         \u001b[0;34m@\u001b[0m\u001b[0mretry_decorator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    349\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0m_completion_with_retry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 350\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    351\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    352\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_completion_with_retry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\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/python3.10/dist-packages/openai/api_resources/chat_completion.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\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;32m---> 25\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     26\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTryAgain\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mtimeout\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/python3.10/dist-packages/openai/api_resources/abstract/engine_api_resource.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(cls, api_key, api_base, api_type, request_id, api_version, organization, **params)\u001b[0m\n\u001b[1;32m    151\u001b[0m         )\n\u001b[1;32m    152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m         response, _, api_key = requestor.request(\n\u001b[0m\u001b[1;32m    154\u001b[0m             \u001b[0;34m\"post\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m             \u001b[0murl\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/python3.10/dist-packages/openai/api_requestor.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, headers, files, stream, request_id, request_timeout)\u001b[0m\n\u001b[1;32m    296\u001b[0m             \u001b[0mrequest_timeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest_timeout\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m         )\n\u001b[0;32m--> 298\u001b[0;31m         \u001b[0mresp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgot_stream\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_interpret_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\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    299\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgot_stream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi_key\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/api_requestor.py\u001b[0m in \u001b[0;36m_interpret_response\u001b[0;34m(self, result, stream)\u001b[0m\n\u001b[1;32m    698\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    699\u001b[0m             return (\n\u001b[0;32m--> 700\u001b[0;31m                 self._interpret_response_line(\n\u001b[0m\u001b[1;32m    701\u001b[0m                     \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"utf-8\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    702\u001b[0m                     \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\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/python3.10/dist-packages/openai/api_requestor.py\u001b[0m in \u001b[0;36m_interpret_response_line\u001b[0;34m(self, rbody, rcode, rheaders, stream)\u001b[0m\n\u001b[1;32m    763\u001b[0m         \u001b[0mstream_error\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstream\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"error\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    764\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mstream_error\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;36m200\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mrcode\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 765\u001b[0;31m             raise self.handle_error_response(\n\u001b[0m\u001b[1;32m    766\u001b[0m                 \u001b[0mrbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream_error\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstream_error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    767\u001b[0m             )\n",
            "\u001b[0;31mInvalidRequestError\u001b[0m: This model's maximum context length is 8192 tokens. However, your messages resulted in 9120 tokens. Please reduce the length of the messages."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# IMPORT REQUIRED LIBRARIES\n",
        "# YOU SHOULD MAKE SURE THAT YOU ALREADY INSTLALED THEM PREVIOUSLY\n",
        "import yfinance as yf\n",
        "import finplot as fplt\n",
        "\n",
        "# CREATE A TICKER INSTANCE FOR TESLA\n",
        "tsla = yf.Ticker('TSLA')\n",
        "\n",
        "# RETRIEVE 1 YEAR WORTH OF DAILY DATA OF TESLA\n",
        "df = tsla.history(interval='1d',period='1y')\n",
        "\n",
        "# PLOT THE OHLC CANDLE CHART\n",
        "fplt.candlestick_ochl(df[['Open','Close','High','Low']])\n",
        "fplt.show()"
      ],
      "metadata": {
        "id": "or3Qj-sYuUv-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "S3wqMRIxuUr2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(YahooFinancials('AAPL').get_daily_dividend_data(start_date, end_date))"
      ],
      "metadata": {
        "id": "Kq7sjBzsY9lS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def seasonality_identifier(input_to_llm):\n",
        "    response_schemas = [\n",
        "        ResponseSchema(name=\"seasonality trend\", description=\"Detect any seasonality patterns in the price of [Commodity Name] over the past 1 year\")\n",
        "    ]\n",
        "    output_parser = StructuredOutputParser.from_response_schemas(response_schemas)\n",
        "    format_instructions = output_parser.get_format_instructions()\n",
        "\n",
        "    chat_model = ChatOpenAI(model_name=\"gpt-3.5-turbo-16k\", temperature=0.5, openai_api_key = \"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\")\n",
        "\n",
        "    prompt = ChatPromptTemplate(\n",
        "        messages=[\n",
        "            HumanMessagePromptTemplate.from_template(\"\"\"\n",
        "            Assume the role as a leading Technical Analysis (TA) expert in the stock market, a modern counterpart to Charles Dow, John Bollinger, and Alan Andrews. Analyze the price trend over the past one year:\n",
        "\n",
        "            1 Month Historical Stock Price Data: {hist}\n",
        "\n",
        "            {format_instructions}\"\"\")\n",
        "        ],\n",
        "        input_variables=[\"hist\",\"context\",\"question\"],\n",
        "        partial_variables={\"format_instructions\": format_instructions}\n",
        "    )\n",
        "\n",
        "    clean_transcript = input_to_llm\n",
        "    _input = prompt.format_prompt(hist=hist_str)\n",
        "    output = chat_model(_input.to_messages())\n",
        "    return(output)\n",
        "\n",
        "seasonality = ta_hist.iloc[-365:,:].reset_index()\n",
        "seasonality_str = \"\"\n",
        "columns = seasonality.columns\n",
        "\n",
        "for index in seasonality.index:\n",
        "  for column in columns:\n",
        "    seasonality_str+=(column+\": \")\n",
        "    seasonality_str+=str(seasonality.at[index,column])\n",
        "    seasonality_str+=', '\n",
        "  seasonality_str+=\"\\n\\n\"\n",
        "seasonality_trend_eval = seasonality_identifier(seasonality_str)\n",
        "print(seasonality_trend_eval)"
      ],
      "metadata": {
        "id": "IxPNIUiUUMnp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def final_evaluation(input_to_llm):\n",
        "    response_schemas = [\n",
        "        ResponseSchema(name=\"price trend\", description=\"Description of Stock price trend\")\n",
        "    ]\n",
        "    output_parser = StructuredOutputParser.from_response_schemas(response_schemas)\n",
        "    format_instructions = output_parser.get_format_instructions()\n",
        "\n",
        "    chat_model = ChatOpenAI(model_name=\"gpt-3.5-turbo-16k\", temperature=0.5, openai_api_key = \"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\")\n",
        "\n",
        "    prompt = ChatPromptTemplate(\n",
        "        messages=[\n",
        "            HumanMessagePromptTemplate.from_template(\"\"\"\n",
        "            Assume the role as a leading Technical Analysis (TA) expert in the stock market, a modern counterpart to Charles Dow, John Bollinger, and Alan Andrews. Analyze the price trend over the past one month:\n",
        "\n",
        "            1 Month Historical Stock Price Data: {hist}\n",
        "\n",
        "            {format_instructions}\"\"\")\n",
        "        ],\n",
        "        input_variables=[\"hist\",\"context\",\"question\"],\n",
        "        partial_variables={\"format_instructions\": format_instructions}\n",
        "    )\n",
        "\n",
        "    clean_transcript = input_to_llm\n",
        "    _input = prompt.format_prompt(hist=hist_str)\n",
        "    output = chat_model(_input.to_messages())\n",
        "    return(output)\n",
        "\n",
        "final_eval = final_evaluation(summary_str)\n",
        "print(final_eval)"
      ],
      "metadata": {
        "id": "W2EGWUL5SSqx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Price Prediction"
      ],
      "metadata": {
        "id": "l41MpQ12PS-g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "LSTM"
      ],
      "metadata": {
        "id": "_fDj4kD8PUpp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "historical_stock_prices = YahooFinancials('AAPL').get_historical_price_data('2018-01-01', '2024-01-03', 'daily')\n",
        "historical_stock_prices_df = pd.DataFrame(columns=['Date','Close','Adj_Close'])\n",
        "index = 0"
      ],
      "metadata": {
        "id": "FurNiszXbNKq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for price_dict in historical_stock_prices['AAPL']['prices']:\n",
        "  historical_stock_prices_df.at[index, 'Date'] = pd.to_datetime(price_dict['formatted_date'])\n",
        "  historical_stock_prices_df.at[index, 'Close'] = price_dict['close']\n",
        "  historical_stock_prices_df.at[index, 'Adj_Close'] = price_dict['adjclose']\n",
        "  index += 1\n",
        "\n",
        "historical_stock_prices_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NwN-UV1V4V4E",
        "outputId": "e94e5a4d-1d18-4f64-8002-2dbeb6859312"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Date      Close  Adj_Close\n",
              "0  2018-01-02 00:00:00  43.064999  40.722878\n",
              "1  2018-01-03 00:00:00  43.057499  40.715782\n",
              "2  2018-01-04 00:00:00    43.2575  40.904903\n",
              "3  2018-01-05 00:00:00      43.75  41.370621\n",
              "4  2018-01-08 00:00:00  43.587502  41.216969"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj_Close</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-01-02 00:00:00</td>\n",
              "      <td>43.064999</td>\n",
              "      <td>40.722878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-01-03 00:00:00</td>\n",
              "      <td>43.057499</td>\n",
              "      <td>40.715782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-01-04 00:00:00</td>\n",
              "      <td>43.2575</td>\n",
              "      <td>40.904903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-01-05 00:00:00</td>\n",
              "      <td>43.75</td>\n",
              "      <td>41.370621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-01-08 00:00:00</td>\n",
              "      <td>43.587502</td>\n",
              "      <td>41.216969</td>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f2ef1f2f-0f4a-42d4-b23f-406c333d6752');\n",
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "import warnings\n",
        "\n",
        "if not sys.warnoptions:\n",
        "    warnings.simplefilter('ignore')\n",
        "\n",
        "import tensorflow.compat.v1 as tf\n",
        "tf.disable_v2_behavior()\n",
        "import tensorflow as tf2 #Tensorflow 2.x\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from datetime import datetime\n",
        "from datetime import timedelta\n",
        "from tqdm import tqdm\n",
        "sns.set()\n",
        "\n",
        "\n",
        "minmax = MinMaxScaler().fit(historical_stock_prices_df.iloc[:, 1:2].astype('float32')) # Close index\n",
        "df_log = minmax.transform(historical_stock_prices_df.iloc[:, 1:2].astype('float32')) # Close index\n",
        "df_log = pd.DataFrame(df_log)\n",
        "df_log.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1xMgO-ONOQ5n",
        "outputId": "0f53ed80-5a03-41c4-8a32-3521432071bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          0\n",
              "0  0.046244\n",
              "1  0.046198\n",
              "2  0.047428\n",
              "3  0.050458\n",
              "4  0.049458"
            ],
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3e528413-fc26-424f-838b-10353946f8d9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_size = 30\n",
        "simulation_size = 10\n",
        "\n",
        "df_train = df_log.iloc[:-test_size]\n",
        "df_test = df_log.iloc[-test_size:]"
      ],
      "metadata": {
        "id": "wMsdZU45cNiU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Model:\n",
        "    def __init__(\n",
        "        self,\n",
        "        learning_rate,\n",
        "        num_layers,\n",
        "        size,\n",
        "        size_layer,\n",
        "        output_size,\n",
        "        forget_bias = 0.1,\n",
        "    ):\n",
        "        def lstm_cell(size_layer):\n",
        "            return tf.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n",
        "\n",
        "        rnn_cells = tf.nn.rnn_cell.MultiRNNCell(\n",
        "            [lstm_cell(size_layer) for _ in range(num_layers)],\n",
        "            state_is_tuple = False,\n",
        "        )\n",
        "        self.X = tf.placeholder(tf.float32, (None, None, size))\n",
        "        self.Y = tf.placeholder(tf.float32, (None, output_size))\n",
        "        drop = tf2.compat.v1.nn.rnn_cell.DropoutWrapper(\n",
        "            rnn_cells, output_keep_prob = forget_bias\n",
        "        )\n",
        "        self.hidden_layer = tf.placeholder(\n",
        "            tf.float32, (None, num_layers * 2 * size_layer)\n",
        "        )\n",
        "        self.outputs, self.last_state = tf.nn.dynamic_rnn(\n",
        "            drop, self.X, initial_state = self.hidden_layer, dtype = tf.float32\n",
        "        )\n",
        "        self.logits = tf.layers.dense(self.outputs[-1], output_size)\n",
        "        self.cost = tf.reduce_mean(tf.square(self.Y - self.logits))\n",
        "        self.optimizer = tf.train.AdamOptimizer(learning_rate).minimize(\n",
        "            self.cost\n",
        "        )\n",
        "\n",
        "def calculate_accuracy(real, predict):\n",
        "    real = np.array(real) + 1\n",
        "    predict = np.array(predict) + 1\n",
        "    percentage = 1 - np.sqrt(np.mean(np.square((real - predict) / real)))\n",
        "    return percentage * 100\n",
        "\n",
        "def anchor(signal, weight):\n",
        "    buffer = []\n",
        "    last = signal[0]\n",
        "    for i in signal:\n",
        "        smoothed_val = last * weight + (1 - weight) * i\n",
        "        buffer.append(smoothed_val)\n",
        "        last = smoothed_val\n",
        "    return buffer\n",
        "\n",
        "num_layers = 1\n",
        "size_layer = 128\n",
        "timestamp = 5\n",
        "epoch = 300\n",
        "dropout_rate = 0.8\n",
        "future_day = test_size\n",
        "learning_rate = 0.01\n",
        "\n",
        "def forecast(df):\n",
        "    tf.reset_default_graph()\n",
        "    modelnn = Model(\n",
        "        learning_rate, num_layers, df_log.shape[1], size_layer, df_log.shape[1], dropout_rate\n",
        "    )\n",
        "    sess = tf.InteractiveSession()\n",
        "    sess.run(tf.global_variables_initializer())\n",
        "    date_ori = pd.to_datetime(df.iloc[:, 0]).tolist()\n",
        "\n",
        "    pbar = tqdm(range(epoch), desc = 'train loop')\n",
        "    for i in pbar:\n",
        "        init_value = np.zeros((1, num_layers * 2 * size_layer))\n",
        "        total_loss, total_acc = [], []\n",
        "        for k in range(0, df_train.shape[0] - 1, timestamp):\n",
        "            index = min(k + timestamp, df_train.shape[0] - 1)\n",
        "            batch_x = np.expand_dims(\n",
        "                df_train.iloc[k : index, :].values, axis = 0\n",
        "            )\n",
        "            batch_y = df_train.iloc[k + 1 : index + 1, :].values\n",
        "            logits, last_state, _, loss = sess.run(\n",
        "                [modelnn.logits, modelnn.last_state, modelnn.optimizer, modelnn.cost],\n",
        "                feed_dict = {\n",
        "                    modelnn.X: batch_x,\n",
        "                    modelnn.Y: batch_y,\n",
        "                    modelnn.hidden_layer: init_value,\n",
        "                },\n",
        "            )\n",
        "            init_value = last_state\n",
        "            total_loss.append(loss)\n",
        "            total_acc.append(calculate_accuracy(batch_y[:, 0], logits[:, 0]))\n",
        "        pbar.set_postfix(cost = np.mean(total_loss), acc = np.mean(total_acc))\n",
        "\n",
        "    future_day = test_size\n",
        "\n",
        "    output_predict = np.zeros((df_train.shape[0] + future_day, df_train.shape[1]))\n",
        "    output_predict[0] = df_train.iloc[0]\n",
        "    upper_b = (df_train.shape[0] // timestamp) * timestamp\n",
        "    init_value = np.zeros((1, num_layers * 2 * size_layer))\n",
        "\n",
        "    for k in range(0, (df_train.shape[0] // timestamp) * timestamp, timestamp):\n",
        "        out_logits, last_state = sess.run(\n",
        "            [modelnn.logits, modelnn.last_state],\n",
        "            feed_dict = {\n",
        "                modelnn.X: np.expand_dims(\n",
        "                    df_train.iloc[k : k + timestamp], axis = 0\n",
        "                ),\n",
        "                modelnn.hidden_layer: init_value,\n",
        "            },\n",
        "        )\n",
        "        init_value = last_state\n",
        "        output_predict[k + 1 : k + timestamp + 1] = out_logits\n",
        "\n",
        "    if upper_b != df_train.shape[0]:\n",
        "        out_logits, last_state = sess.run(\n",
        "            [modelnn.logits, modelnn.last_state],\n",
        "            feed_dict = {\n",
        "                modelnn.X: np.expand_dims(df_train.iloc[upper_b:], axis = 0),\n",
        "                modelnn.hidden_layer: init_value,\n",
        "            },\n",
        "        )\n",
        "        output_predict[upper_b + 1 : df_train.shape[0] + 1] = out_logits\n",
        "        future_day -= 1\n",
        "        date_ori.append(date_ori[-1] + timedelta(days = 1))\n",
        "\n",
        "    init_value = last_state\n",
        "\n",
        "    for i in range(future_day):\n",
        "        o = output_predict[-future_day - timestamp + i:-future_day + i]\n",
        "        out_logits, last_state = sess.run(\n",
        "            [modelnn.logits, modelnn.last_state],\n",
        "            feed_dict = {\n",
        "                modelnn.X: np.expand_dims(o, axis = 0),\n",
        "                modelnn.hidden_layer: init_value,\n",
        "            },\n",
        "        )\n",
        "        init_value = last_state\n",
        "        output_predict[-future_day + i] = out_logits[-1]\n",
        "        date_ori.append(date_ori[-1] + timedelta(days = 1))\n",
        "\n",
        "    output_predict = minmax.inverse_transform(output_predict)\n",
        "    deep_future = anchor(output_predict[:, 0], 0.3)\n",
        "\n",
        "    return deep_future[-test_size:]\n",
        "\n",
        "results = []\n",
        "for i in range(simulation_size):\n",
        "    print('simulation %d'%(i + 1))\n",
        "    results.append(forecast(historical_stock_prices_df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "67Id15xVfb4h",
        "outputId": "9e071f8e-4f5f-4007-8685-1018d1009df5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:py.warnings:<ipython-input-112-28a3d425bf54>:12: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n",
            "  return tf.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n",
            "\n",
            "WARNING:tensorflow:<keras.src.layers.rnn.legacy_cells.LSTMCell object at 0x7825b45dfd00>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n",
            "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "simulation 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:py.warnings:<ipython-input-112-28a3d425bf54>:29: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n",
            "  self.logits = tf.layers.dense(self.outputs[-1], output_size)\n",
            "\n",
            "WARNING:py.warnings:/usr/local/lib/python3.10/dist-packages/tensorflow/python/client/session.py:1793: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
            "  warnings.warn('An interactive session is already active. This can '\n",
            "\n",
            "train loop: 100%|██████████| 300/300 [09:48<00:00,  1.96s/it, acc=98.3, cost=0.000614]\n",
            "WARNING:py.warnings:<ipython-input-112-28a3d425bf54>:12: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n",
            "  return tf.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n",
            "\n",
            "WARNING:tensorflow:<keras.src.layers.rnn.legacy_cells.LSTMCell object at 0x7825b44afd60>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n",
            "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n",
            "WARNING:py.warnings:<ipython-input-112-28a3d425bf54>:29: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n",
            "  self.logits = tf.layers.dense(self.outputs[-1], output_size)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "simulation 2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:py.warnings:/usr/local/lib/python3.10/dist-packages/tensorflow/python/client/session.py:1793: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
            "  warnings.warn('An interactive session is already active. This can '\n",
            "\n",
            "train loop: 100%|██████████| 300/300 [09:19<00:00,  1.86s/it, acc=98.4, cost=0.000574]\n",
            "WARNING:py.warnings:<ipython-input-112-28a3d425bf54>:12: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n",
            "  return tf.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n",
            "\n",
            "WARNING:tensorflow:<keras.src.layers.rnn.legacy_cells.LSTMCell object at 0x7825b4522bc0>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n",
            "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n",
            "WARNING:py.warnings:<ipython-input-112-28a3d425bf54>:29: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n",
            "  self.logits = tf.layers.dense(self.outputs[-1], output_size)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "simulation 3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:py.warnings:/usr/local/lib/python3.10/dist-packages/tensorflow/python/client/session.py:1793: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
            "  warnings.warn('An interactive session is already active. This can '\n",
            "\n",
            "train loop: 100%|██████████| 300/300 [09:14<00:00,  1.85s/it, acc=97.8, cost=0.00122]\n",
            "WARNING:py.warnings:<ipython-input-112-28a3d425bf54>:12: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n",
            "  return tf.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n",
            "\n",
            "WARNING:tensorflow:<keras.src.layers.rnn.legacy_cells.LSTMCell object at 0x7825b427b400>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n",
            "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n",
            "WARNING:py.warnings:<ipython-input-112-28a3d425bf54>:29: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n",
            "  self.logits = tf.layers.dense(self.outputs[-1], output_size)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "simulation 4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:py.warnings:/usr/local/lib/python3.10/dist-packages/tensorflow/python/client/session.py:1793: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
            "  warnings.warn('An interactive session is already active. This can '\n",
            "\n",
            "train loop: 100%|██████████| 300/300 [09:18<00:00,  1.86s/it, acc=98.4, cost=0.000563]\n",
            "WARNING:py.warnings:<ipython-input-112-28a3d425bf54>:12: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n",
            "  return tf.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n",
            "\n",
            "WARNING:tensorflow:<keras.src.layers.rnn.legacy_cells.LSTMCell object at 0x7825ad776a10>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n",
            "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n",
            "WARNING:py.warnings:<ipython-input-112-28a3d425bf54>:29: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n",
            "  self.logits = tf.layers.dense(self.outputs[-1], output_size)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "simulation 5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:py.warnings:/usr/local/lib/python3.10/dist-packages/tensorflow/python/client/session.py:1793: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
            "  warnings.warn('An interactive session is already active. This can '\n",
            "\n",
            "train loop: 100%|██████████| 300/300 [09:15<00:00,  1.85s/it, acc=98.4, cost=0.000594]\n",
            "WARNING:py.warnings:<ipython-input-112-28a3d425bf54>:12: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n",
            "  return tf.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n",
            "\n",
            "WARNING:tensorflow:<keras.src.layers.rnn.legacy_cells.LSTMCell object at 0x7825ad7ad300>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n",
            "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "simulation 6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:py.warnings:<ipython-input-112-28a3d425bf54>:29: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n",
            "  self.logits = tf.layers.dense(self.outputs[-1], output_size)\n",
            "\n",
            "WARNING:py.warnings:/usr/local/lib/python3.10/dist-packages/tensorflow/python/client/session.py:1793: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
            "  warnings.warn('An interactive session is already active. This can '\n",
            "\n",
            "train loop: 100%|██████████| 300/300 [09:32<00:00,  1.91s/it, acc=98.2, cost=0.000746]\n",
            "WARNING:py.warnings:<ipython-input-112-28a3d425bf54>:12: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n",
            "  return tf.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n",
            "\n",
            "WARNING:tensorflow:<keras.src.layers.rnn.legacy_cells.LSTMCell object at 0x7825ad7d82b0>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n",
            "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n",
            "WARNING:py.warnings:<ipython-input-112-28a3d425bf54>:29: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n",
            "  self.logits = tf.layers.dense(self.outputs[-1], output_size)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "simulation 7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:py.warnings:/usr/local/lib/python3.10/dist-packages/tensorflow/python/client/session.py:1793: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
            "  warnings.warn('An interactive session is already active. This can '\n",
            "\n",
            "train loop: 100%|██████████| 300/300 [09:03<00:00,  1.81s/it, acc=98.2, cost=0.000806]\n",
            "WARNING:py.warnings:<ipython-input-112-28a3d425bf54>:12: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n",
            "  return tf.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n",
            "\n",
            "WARNING:tensorflow:<keras.src.layers.rnn.legacy_cells.LSTMCell object at 0x7825ad6ded70>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n",
            "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n",
            "WARNING:py.warnings:<ipython-input-112-28a3d425bf54>:29: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n",
            "  self.logits = tf.layers.dense(self.outputs[-1], output_size)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "simulation 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:py.warnings:/usr/local/lib/python3.10/dist-packages/tensorflow/python/client/session.py:1793: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
            "  warnings.warn('An interactive session is already active. This can '\n",
            "\n",
            "train loop: 100%|██████████| 300/300 [10:33<00:00,  2.11s/it, acc=98.4, cost=0.00061]\n",
            "WARNING:py.warnings:<ipython-input-112-28a3d425bf54>:12: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n",
            "  return tf.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n",
            "\n",
            "WARNING:tensorflow:<keras.src.layers.rnn.legacy_cells.LSTMCell object at 0x7825ad3c3f10>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n",
            "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "simulation 9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:py.warnings:<ipython-input-112-28a3d425bf54>:29: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n",
            "  self.logits = tf.layers.dense(self.outputs[-1], output_size)\n",
            "\n",
            "WARNING:py.warnings:/usr/local/lib/python3.10/dist-packages/tensorflow/python/client/session.py:1793: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
            "  warnings.warn('An interactive session is already active. This can '\n",
            "\n",
            "train loop: 100%|██████████| 300/300 [09:24<00:00,  1.88s/it, acc=98.2, cost=0.000718]\n",
            "WARNING:py.warnings:<ipython-input-112-28a3d425bf54>:12: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n",
            "  return tf.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n",
            "\n",
            "WARNING:tensorflow:<keras.src.layers.rnn.legacy_cells.LSTMCell object at 0x7825ad4170d0>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n",
            "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n",
            "WARNING:py.warnings:<ipython-input-112-28a3d425bf54>:29: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n",
            "  self.logits = tf.layers.dense(self.outputs[-1], output_size)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "simulation 10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:py.warnings:/usr/local/lib/python3.10/dist-packages/tensorflow/python/client/session.py:1793: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
            "  warnings.warn('An interactive session is already active. This can '\n",
            "\n",
            "train loop: 100%|██████████| 300/300 [09:55<00:00,  1.98s/it, acc=98.3, cost=0.000623]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracies = [calculate_accuracy(historical_stock_prices_df['Close'].iloc[-test_size:].values, r) for r in results]\n",
        "\n",
        "plt.figure(figsize = (15, 5))\n",
        "for no, r in enumerate(results):\n",
        "    plt.plot(r, label = 'forecast %d'%(no + 1))\n",
        "plt.plot(historical_stock_prices_df['Close'].iloc[-test_size:].values, label = 'true trend', c = 'black')\n",
        "plt.legend()\n",
        "plt.title('average accuracy: %.4f'%(np.mean(accuracies)))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "X6FM6bJogedF",
        "outputId": "533c9a01-6ff2-4051-f53b-9e8f744c312e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Portfolio Optimization and Simulation"
      ],
      "metadata": {
        "id": "ldytpu3JxrRH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hist_reqd = historical_stock_prices_df[['Date','Adj_Close']]\n",
        "hist_reqd.columns = ['Date','AAPL']"
      ],
      "metadata": {
        "id": "jh6V4pI9yZ8f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hist_reqd['Date'] = hist_reqd['Date'].apply(lambda x: x.date())"
      ],
      "metadata": {
        "id": "I8W7ycS-4JAh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hist_reqd['Date']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L-hUlCeY5o_K",
        "outputId": "00da191b-d035-40dd-e621-776b514badc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       2018-01-02\n",
              "1       2018-01-03\n",
              "2       2018-01-04\n",
              "3       2018-01-05\n",
              "4       2018-01-08\n",
              "           ...    \n",
              "1505    2023-12-26\n",
              "1506    2023-12-27\n",
              "1507    2023-12-28\n",
              "1508    2023-12-29\n",
              "1509    2024-01-02\n",
              "Name: Date, Length: 1510, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dwH-fpAr4G9u"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}