02_stock_portfolio_analysis.ipynb

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      "name": "python3",
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    {
      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/jidemaestri/Financial-Markets/blob/master/02_stock_portfolio_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
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      "execution_count": 1,
      "metadata": {
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: yfinance in /usr/local/lib/python3.8/dist-packages (0.1.87)\n",
            "Requirement already satisfied: appdirs>=1.4.4 in /usr/local/lib/python3.8/dist-packages (from yfinance) (1.4.4)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.8/dist-packages (from yfinance) (1.5.2)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.8/dist-packages (from yfinance) (0.0.11)\n",
            "Requirement already satisfied: requests>=2.26 in /usr/local/lib/python3.8/dist-packages (from yfinance) (2.28.1)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.8/dist-packages (from yfinance) (1.21.6)\n",
            "Requirement already satisfied: lxml>=4.5.1 in /usr/local/lib/python3.8/dist-packages (from yfinance) (4.9.1)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.8/dist-packages (from pandas>=0.24.0->yfinance) (2022.6)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.8/dist-packages (from pandas>=0.24.0->yfinance) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.8.1->pandas>=0.24.0->yfinance) (1.15.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests>=2.26->yfinance) (2022.9.24)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests>=2.26->yfinance) (2.10)\n",
            "Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.8/dist-packages (from requests>=2.26->yfinance) (2.1.1)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests>=2.26->yfinance) (1.24.3)\n"
          ]
        }
      ],
      "source": [
        "!pip install yfinance\n",
        "\n",
        "import yfinance as yf\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stocks = [\"AAPL\", \"GOOG\", \"MSFT\", \"VALE\", \"MELI\", \"DIS\", \"KO\", \"MCD\", \"NVDA\", \"PAM\", \"GGAL\", \"BBAR\"]\n"
      ],
      "metadata": {
        "id": "t_ivBYCtJMnG"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for stock in stocks:\n",
        "    info = yf.Ticker(stock).info\n",
        "    print(info[\"shortName\"], info[\"regularMarketPrice\"], info[\"sector\"], info[\"country\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iHxhX2pfJlW4",
        "outputId": "5138c533-3b74-423d-bd64-79e976252c7f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Apple Inc. 142.91 Technology United States\n",
            "Alphabet Inc. 97.31 Communication Services United States\n",
            "Microsoft Corporation 245.12 Technology United States\n",
            "VALE S.A. 16.76 Basic Materials Brazil\n",
            "MercadoLibre, Inc. 863.09 Consumer Cyclical Uruguay\n",
            "Walt Disney Company (The) 92.29 Communication Services United States\n",
            "Coca-Cola Company (The) 63.44 Consumer Defensive United States\n",
            "McDonald's Corporation 271.77 Consumer Cyclical United States\n",
            "NVIDIA Corporation 159.87 Technology United States\n",
            "Pampa Energia S.A. Pampa Energi 27.04 Utilities Argentina\n",
            "Grupo Financiero Galicia S.A. 7.63 Financial Services Argentina\n",
            "Banco BBVA Argentina S.A. 2.88 Financial Services Argentina\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "stock_portfolio = pd.DataFrame()\n"
      ],
      "metadata": {
        "id": "EtooonurJneS"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Definir una función que convierta los números a valores en millones\n",
        "# def to_millions(x):\n",
        "#     return \"{:.2f}M\".format(x / 1000000)\n"
      ],
      "metadata": {
        "id": "H1pJxBvaWYzQ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "for stock in stocks:\n",
        "    financials = yf.Ticker(stock).info\n",
        "\n",
        "    name = financials[\"shortName\"]\n",
        "    current_price = financials[\"regularMarketPrice\"]\n",
        "    roa = financials[\"returnOnAssets\"]\n",
        "    fcf_margin = financials[\"freeCashflow\"]\n",
        "    gross_margin = financials[\"grossMargins\"]\n",
        "    current_ratio = financials[\"currentRatio\"]\n",
        "    free_cash_flow = financials[\"freeCashflow\"]\n",
        "    pb_ratio = financials[\"priceToBook\"]\n",
        "    revenue_growth = financials[\"revenueGrowth\"] \n",
        "    earningsGrowth = financials[\"earningsGrowth\"]\n",
        "    targetLowPrice = financials[\"targetLowPrice\"]\n",
        "    targetMedianPrice = financials[\"targetMedianPrice\"]\n",
        "    targetHighPrice = financials[\"targetHighPrice\"]\n",
        "    net_profit_margin = financials[\"netIncomeToCommon\"] / financials[\"totalRevenue\"]\n",
        "    #evfcf_ratio = financials[\"enterpriseValue\"]/financials[\"freeCashflow\"]\n",
        "    debt_to_equity = financials[\"debtToEquity\"]\n",
        "    priceToSalesTrailing12Months = financials[\"priceToSalesTrailing12Months\"]\n",
        "    twoHundredDayAverage  = financials[\"twoHundredDayAverage\"]\n",
        "    totalCashPerShare = financials[\"totalCashPerShare\"]\n",
        "\n",
        "    \n",
        "    \n",
        "    stock_portfolio = stock_portfolio.append({\"Stock\": name, \n",
        "                    \"Current Price\": current_price,\n",
        "                    #\"Debt/Equity\": debt_to_equity, \n",
        "                    \"Current Ratio\": current_ratio, \n",
        "                    \"Price To Sales 12M Ratio\": priceToSalesTrailing12Months, \n",
        "                    #\"EV/FCF Ratio\": evfcf_ratio, \n",
        "                    \"P/B Ratio\": pb_ratio, \n",
        "                    \"Revenue Growth\": revenue_growth, \n",
        "                    \"ROA\": roa, \n",
        "                    \"target High Price\": targetHighPrice,\n",
        "                    \"target Median Price\": targetMedianPrice,\n",
        "                    \"target Low Price\": targetLowPrice,\n",
        "                    \"Gross Margin\": gross_margin, \n",
        "                    \"Free Cash Flow\": free_cash_flow,\n",
        "                    #\"FCF Margin\": fcf_margin, \n",
        "                    \"Net Profit Margin\": net_profit_margin,\n",
        "                    \"total Cash Per Share\": totalCashPerShare, \n",
        "                    \"twoHundred Day Avg\": twoHundredDayAverage,\n",
        "                    },\n",
        "                   ignore_index=True)\n",
        "\n",
        "    \n",
        "    \n",
        "\n",
        "\n",
        "\n",
        "    \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RG-cElH9LUuR",
        "outputId": "da5a96fd-57d8-42e4-d4f9-74f6d315a2ac"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-3cdbe25ddf97>:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  stock_portfolio = stock_portfolio.append({\"Stock\": name,\n",
            "<ipython-input-6-3cdbe25ddf97>:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  stock_portfolio = stock_portfolio.append({\"Stock\": name,\n",
            "<ipython-input-6-3cdbe25ddf97>:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  stock_portfolio = stock_portfolio.append({\"Stock\": name,\n",
            "<ipython-input-6-3cdbe25ddf97>:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  stock_portfolio = stock_portfolio.append({\"Stock\": name,\n",
            "<ipython-input-6-3cdbe25ddf97>:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  stock_portfolio = stock_portfolio.append({\"Stock\": name,\n",
            "<ipython-input-6-3cdbe25ddf97>:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  stock_portfolio = stock_portfolio.append({\"Stock\": name,\n",
            "<ipython-input-6-3cdbe25ddf97>:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  stock_portfolio = stock_portfolio.append({\"Stock\": name,\n",
            "<ipython-input-6-3cdbe25ddf97>:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  stock_portfolio = stock_portfolio.append({\"Stock\": name,\n",
            "<ipython-input-6-3cdbe25ddf97>:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  stock_portfolio = stock_portfolio.append({\"Stock\": name,\n",
            "<ipython-input-6-3cdbe25ddf97>:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  stock_portfolio = stock_portfolio.append({\"Stock\": name,\n",
            "<ipython-input-6-3cdbe25ddf97>:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  stock_portfolio = stock_portfolio.append({\"Stock\": name,\n",
            "<ipython-input-6-3cdbe25ddf97>:26: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  stock_portfolio = stock_portfolio.append({\"Stock\": name,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stock_portfolio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "7_dT28uJhGTC",
        "outputId": "f53fb1da-40d4-481a-86f8-f7895870f873"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              Stock  Current Price Current Ratio  \\\n",
              "0                        Apple Inc.         142.91         0.879   \n",
              "1                     Alphabet Inc.          97.31         2.518   \n",
              "2             Microsoft Corporation         245.12          1.84   \n",
              "3                         VALE S.A.          16.76         1.071   \n",
              "4                MercadoLibre, Inc.         863.09         1.335   \n",
              "5         Walt Disney Company (The)          92.29         1.001   \n",
              "6           Coca-Cola Company (The)          63.44         1.126   \n",
              "7            McDonald's Corporation         271.77         1.647   \n",
              "8                NVIDIA Corporation         159.87         3.388   \n",
              "9   Pampa Energia S.A. Pampa Energi          27.04         2.459   \n",
              "10    Grupo Financiero Galicia S.A.           7.63          None   \n",
              "11        Banco BBVA Argentina S.A.           2.88          None   \n",
              "\n",
              "    Price To Sales 12M Ratio  P/B Ratio  Revenue Growth      ROA  \\\n",
              "0                   5.811708  44.968536           0.081  0.21214   \n",
              "1                   4.498129    4.97673           0.061  0.13917   \n",
              "2                   8.997856  10.531019           0.106  0.15223   \n",
              "3                   0.332782   0.424057          -0.192  0.14835   \n",
              "4                   4.491173   26.61476           0.448  0.04799   \n",
              "5                   2.034515   1.771809           0.087  0.02097   \n",
              "6                   6.479157   12.02882           0.102  0.08355   \n",
              "7                   8.555734       None          -0.053  0.12554   \n",
              "8                  13.946511   18.48208          -0.165  0.11235   \n",
              "9                   0.005243   0.004638           0.628  0.09482   \n",
              "10                  0.007288   0.002249           0.248  0.00870   \n",
              "11                  0.002693   0.001979           1.525  0.02286   \n",
              "\n",
              "    target High Price  target Median Price  target Low Price  Gross Margin  \\\n",
              "0               214.0               180.00            122.00       0.43310   \n",
              "1               160.0               134.50            115.00       0.56099   \n",
              "2               411.0               290.00            234.00       0.68260   \n",
              "3                25.0                17.25             12.00       0.48170   \n",
              "4              1650.0              1280.00            800.00       0.53046   \n",
              "5               229.0               120.00             94.00       0.34236   \n",
              "6                77.0                66.00             59.00       0.58494   \n",
              "7               328.0               290.00            250.00       0.56132   \n",
              "8               320.0               200.00            110.00       0.57838   \n",
              "9                31.7                30.00             21.00       0.36202   \n",
              "10               21.3                 8.15              5.00       0.00000   \n",
              "11                7.2                 3.10              2.81       0.00000   \n",
              "\n",
              "   Free Cash Flow  Net Profit Margin  total Cash Per Share  twoHundred Day Avg  \n",
              "0     90215251968           0.253096                 3.036          153.856700  \n",
              "1     48262373376           0.237458                 8.982          113.709496  \n",
              "2     46155874304           0.343661                14.387          264.977600  \n",
              "3     35882250240           0.408950                 6.298           15.502950  \n",
              "4     -1272036096           0.028070                47.420          903.731570  \n",
              "5      5532874752           0.038599                 6.369          111.072000  \n",
              "6      9524750336           0.234395                 3.057           61.669250  \n",
              "7      4133400064           0.254148                 3.862          251.574250  \n",
              "8      5152999936           0.208535                 5.343          176.466100  \n",
              "9     -8726375424           0.222900              1826.376           23.352350  \n",
              "10           None           0.051345              5568.762            8.464700  \n",
              "11           None           0.133885              1100.625            2.985350  "
            ],
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              "\n",
              "  <div id=\"df-187e0797-a795-4b74-a788-45a240eb96b7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Stock</th>\n",
              "      <th>Current Price</th>\n",
              "      <th>Current Ratio</th>\n",
              "      <th>Price To Sales 12M Ratio</th>\n",
              "      <th>P/B Ratio</th>\n",
              "      <th>Revenue Growth</th>\n",
              "      <th>ROA</th>\n",
              "      <th>target High Price</th>\n",
              "      <th>target Median Price</th>\n",
              "      <th>target Low Price</th>\n",
              "      <th>Gross Margin</th>\n",
              "      <th>Free Cash Flow</th>\n",
              "      <th>Net Profit Margin</th>\n",
              "      <th>total Cash Per Share</th>\n",
              "      <th>twoHundred Day Avg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Apple Inc.</td>\n",
              "      <td>142.91</td>\n",
              "      <td>0.879</td>\n",
              "      <td>5.811708</td>\n",
              "      <td>44.968536</td>\n",
              "      <td>0.081</td>\n",
              "      <td>0.21214</td>\n",
              "      <td>214.0</td>\n",
              "      <td>180.00</td>\n",
              "      <td>122.00</td>\n",
              "      <td>0.43310</td>\n",
              "      <td>90215251968</td>\n",
              "      <td>0.253096</td>\n",
              "      <td>3.036</td>\n",
              "      <td>153.856700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Alphabet Inc.</td>\n",
              "      <td>97.31</td>\n",
              "      <td>2.518</td>\n",
              "      <td>4.498129</td>\n",
              "      <td>4.97673</td>\n",
              "      <td>0.061</td>\n",
              "      <td>0.13917</td>\n",
              "      <td>160.0</td>\n",
              "      <td>134.50</td>\n",
              "      <td>115.00</td>\n",
              "      <td>0.56099</td>\n",
              "      <td>48262373376</td>\n",
              "      <td>0.237458</td>\n",
              "      <td>8.982</td>\n",
              "      <td>113.709496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Microsoft Corporation</td>\n",
              "      <td>245.12</td>\n",
              "      <td>1.84</td>\n",
              "      <td>8.997856</td>\n",
              "      <td>10.531019</td>\n",
              "      <td>0.106</td>\n",
              "      <td>0.15223</td>\n",
              "      <td>411.0</td>\n",
              "      <td>290.00</td>\n",
              "      <td>234.00</td>\n",
              "      <td>0.68260</td>\n",
              "      <td>46155874304</td>\n",
              "      <td>0.343661</td>\n",
              "      <td>14.387</td>\n",
              "      <td>264.977600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>VALE S.A.</td>\n",
              "      <td>16.76</td>\n",
              "      <td>1.071</td>\n",
              "      <td>0.332782</td>\n",
              "      <td>0.424057</td>\n",
              "      <td>-0.192</td>\n",
              "      <td>0.14835</td>\n",
              "      <td>25.0</td>\n",
              "      <td>17.25</td>\n",
              "      <td>12.00</td>\n",
              "      <td>0.48170</td>\n",
              "      <td>35882250240</td>\n",
              "      <td>0.408950</td>\n",
              "      <td>6.298</td>\n",
              "      <td>15.502950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>MercadoLibre, Inc.</td>\n",
              "      <td>863.09</td>\n",
              "      <td>1.335</td>\n",
              "      <td>4.491173</td>\n",
              "      <td>26.61476</td>\n",
              "      <td>0.448</td>\n",
              "      <td>0.04799</td>\n",
              "      <td>1650.0</td>\n",
              "      <td>1280.00</td>\n",
              "      <td>800.00</td>\n",
              "      <td>0.53046</td>\n",
              "      <td>-1272036096</td>\n",
              "      <td>0.028070</td>\n",
              "      <td>47.420</td>\n",
              "      <td>903.731570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Walt Disney Company (The)</td>\n",
              "      <td>92.29</td>\n",
              "      <td>1.001</td>\n",
              "      <td>2.034515</td>\n",
              "      <td>1.771809</td>\n",
              "      <td>0.087</td>\n",
              "      <td>0.02097</td>\n",
              "      <td>229.0</td>\n",
              "      <td>120.00</td>\n",
              "      <td>94.00</td>\n",
              "      <td>0.34236</td>\n",
              "      <td>5532874752</td>\n",
              "      <td>0.038599</td>\n",
              "      <td>6.369</td>\n",
              "      <td>111.072000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Coca-Cola Company (The)</td>\n",
              "      <td>63.44</td>\n",
              "      <td>1.126</td>\n",
              "      <td>6.479157</td>\n",
              "      <td>12.02882</td>\n",
              "      <td>0.102</td>\n",
              "      <td>0.08355</td>\n",
              "      <td>77.0</td>\n",
              "      <td>66.00</td>\n",
              "      <td>59.00</td>\n",
              "      <td>0.58494</td>\n",
              "      <td>9524750336</td>\n",
              "      <td>0.234395</td>\n",
              "      <td>3.057</td>\n",
              "      <td>61.669250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>McDonald's Corporation</td>\n",
              "      <td>271.77</td>\n",
              "      <td>1.647</td>\n",
              "      <td>8.555734</td>\n",
              "      <td>None</td>\n",
              "      <td>-0.053</td>\n",
              "      <td>0.12554</td>\n",
              "      <td>328.0</td>\n",
              "      <td>290.00</td>\n",
              "      <td>250.00</td>\n",
              "      <td>0.56132</td>\n",
              "      <td>4133400064</td>\n",
              "      <td>0.254148</td>\n",
              "      <td>3.862</td>\n",
              "      <td>251.574250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>NVIDIA Corporation</td>\n",
              "      <td>159.87</td>\n",
              "      <td>3.388</td>\n",
              "      <td>13.946511</td>\n",
              "      <td>18.48208</td>\n",
              "      <td>-0.165</td>\n",
              "      <td>0.11235</td>\n",
              "      <td>320.0</td>\n",
              "      <td>200.00</td>\n",
              "      <td>110.00</td>\n",
              "      <td>0.57838</td>\n",
              "      <td>5152999936</td>\n",
              "      <td>0.208535</td>\n",
              "      <td>5.343</td>\n",
              "      <td>176.466100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Pampa Energia S.A. Pampa Energi</td>\n",
              "      <td>27.04</td>\n",
              "      <td>2.459</td>\n",
              "      <td>0.005243</td>\n",
              "      <td>0.004638</td>\n",
              "      <td>0.628</td>\n",
              "      <td>0.09482</td>\n",
              "      <td>31.7</td>\n",
              "      <td>30.00</td>\n",
              "      <td>21.00</td>\n",
              "      <td>0.36202</td>\n",
              "      <td>-8726375424</td>\n",
              "      <td>0.222900</td>\n",
              "      <td>1826.376</td>\n",
              "      <td>23.352350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Grupo Financiero Galicia S.A.</td>\n",
              "      <td>7.63</td>\n",
              "      <td>None</td>\n",
              "      <td>0.007288</td>\n",
              "      <td>0.002249</td>\n",
              "      <td>0.248</td>\n",
              "      <td>0.00870</td>\n",
              "      <td>21.3</td>\n",
              "      <td>8.15</td>\n",
              "      <td>5.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>None</td>\n",
              "      <td>0.051345</td>\n",
              "      <td>5568.762</td>\n",
              "      <td>8.464700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Banco BBVA Argentina S.A.</td>\n",
              "      <td>2.88</td>\n",
              "      <td>None</td>\n",
              "      <td>0.002693</td>\n",
              "      <td>0.001979</td>\n",
              "      <td>1.525</td>\n",
              "      <td>0.02286</td>\n",
              "      <td>7.2</td>\n",
              "      <td>3.10</td>\n",
              "      <td>2.81</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>None</td>\n",
              "      <td>0.133885</td>\n",
              "      <td>1100.625</td>\n",
              "      <td>2.985350</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-187e0797-a795-4b74-a788-45a240eb96b7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stock_portfolio.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eYQI4l63deiY",
        "outputId": "605f4d15-d69a-46d5-edc7-935a109dcf51"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Stock', 'Current Price', 'Current Ratio', 'Price To Sales 12M Ratio',\n",
              "       'P/B Ratio', 'Revenue Growth', 'ROA', 'target High Price',\n",
              "       'target Median Price', 'target Low Price', 'Gross Margin',\n",
              "       'Free Cash Flow', 'Net Profit Margin', 'total Cash Per Share',\n",
              "       'twoHundred Day Avg'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Backlog API - Keys YFINANCE API"
      ],
      "metadata": {
        "id": "AhqcX6GBVw5s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stock = \"AAPL\"\n",
        "info = yf.Ticker(stock).info\n",
        "\n",
        "for key in info.keys():\n",
        "    print(key)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FidV1FRYMZm2",
        "outputId": "f88cfe56-22c8-4e88-b4d3-1fe99c788182"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "zip\n",
            "sector\n",
            "fullTimeEmployees\n",
            "longBusinessSummary\n",
            "city\n",
            "phone\n",
            "state\n",
            "country\n",
            "companyOfficers\n",
            "website\n",
            "maxAge\n",
            "address1\n",
            "industry\n",
            "ebitdaMargins\n",
            "profitMargins\n",
            "grossMargins\n",
            "operatingCashflow\n",
            "revenueGrowth\n",
            "operatingMargins\n",
            "ebitda\n",
            "targetLowPrice\n",
            "recommendationKey\n",
            "grossProfits\n",
            "freeCashflow\n",
            "targetMedianPrice\n",
            "currentPrice\n",
            "earningsGrowth\n",
            "currentRatio\n",
            "returnOnAssets\n",
            "numberOfAnalystOpinions\n",
            "targetMeanPrice\n",
            "debtToEquity\n",
            "returnOnEquity\n",
            "targetHighPrice\n",
            "totalCash\n",
            "totalDebt\n",
            "totalRevenue\n",
            "totalCashPerShare\n",
            "financialCurrency\n",
            "revenuePerShare\n",
            "quickRatio\n",
            "recommendationMean\n",
            "exchange\n",
            "shortName\n",
            "longName\n",
            "exchangeTimezoneName\n",
            "exchangeTimezoneShortName\n",
            "isEsgPopulated\n",
            "gmtOffSetMilliseconds\n",
            "quoteType\n",
            "symbol\n",
            "messageBoardId\n",
            "market\n",
            "annualHoldingsTurnover\n",
            "enterpriseToRevenue\n",
            "beta3Year\n",
            "enterpriseToEbitda\n",
            "52WeekChange\n",
            "morningStarRiskRating\n",
            "forwardEps\n",
            "revenueQuarterlyGrowth\n",
            "sharesOutstanding\n",
            "fundInceptionDate\n",
            "annualReportExpenseRatio\n",
            "totalAssets\n",
            "bookValue\n",
            "sharesShort\n",
            "sharesPercentSharesOut\n",
            "fundFamily\n",
            "lastFiscalYearEnd\n",
            "heldPercentInstitutions\n",
            "netIncomeToCommon\n",
            "trailingEps\n",
            "lastDividendValue\n",
            "SandP52WeekChange\n",
            "priceToBook\n",
            "heldPercentInsiders\n",
            "nextFiscalYearEnd\n",
            "yield\n",
            "mostRecentQuarter\n",
            "shortRatio\n",
            "sharesShortPreviousMonthDate\n",
            "floatShares\n",
            "beta\n",
            "enterpriseValue\n",
            "priceHint\n",
            "threeYearAverageReturn\n",
            "lastSplitDate\n",
            "lastSplitFactor\n",
            "legalType\n",
            "lastDividendDate\n",
            "morningStarOverallRating\n",
            "earningsQuarterlyGrowth\n",
            "priceToSalesTrailing12Months\n",
            "dateShortInterest\n",
            "pegRatio\n",
            "ytdReturn\n",
            "forwardPE\n",
            "lastCapGain\n",
            "shortPercentOfFloat\n",
            "sharesShortPriorMonth\n",
            "impliedSharesOutstanding\n",
            "category\n",
            "fiveYearAverageReturn\n",
            "previousClose\n",
            "regularMarketOpen\n",
            "twoHundredDayAverage\n",
            "trailingAnnualDividendYield\n",
            "payoutRatio\n",
            "volume24Hr\n",
            "regularMarketDayHigh\n",
            "navPrice\n",
            "averageDailyVolume10Day\n",
            "regularMarketPreviousClose\n",
            "fiftyDayAverage\n",
            "trailingAnnualDividendRate\n",
            "open\n",
            "toCurrency\n",
            "averageVolume10days\n",
            "expireDate\n",
            "algorithm\n",
            "dividendRate\n",
            "exDividendDate\n",
            "circulatingSupply\n",
            "startDate\n",
            "regularMarketDayLow\n",
            "currency\n",
            "trailingPE\n",
            "regularMarketVolume\n",
            "lastMarket\n",
            "maxSupply\n",
            "openInterest\n",
            "marketCap\n",
            "volumeAllCurrencies\n",
            "strikePrice\n",
            "averageVolume\n",
            "dayLow\n",
            "ask\n",
            "askSize\n",
            "volume\n",
            "fiftyTwoWeekHigh\n",
            "fromCurrency\n",
            "fiveYearAvgDividendYield\n",
            "fiftyTwoWeekLow\n",
            "bid\n",
            "tradeable\n",
            "dividendYield\n",
            "bidSize\n",
            "dayHigh\n",
            "coinMarketCapLink\n",
            "regularMarketPrice\n",
            "preMarketPrice\n",
            "logo_url\n",
            "trailingPegRatio\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "C0WFc7T3SEJs"
      },
      "execution_count": 9,
      "outputs": []
    }
  ]
}