Step 2. Preparing_the_dataset_with_price.ipynb

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          "text": [
            "Requirement already satisfied: yfinance in /usr/local/lib/python3.7/dist-packages (0.1.68)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.10)\n",
            "Requirement already satisfied: lxml>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from yfinance) (4.7.1)\n",
            "Requirement already satisfied: requests>=2.26 in /usr/local/lib/python3.7/dist-packages (from yfinance) (2.27.1)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.19.5)\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.1.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24->yfinance) (1.15.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.10)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.0.9)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2021.10.8)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (1.24.3)\n"
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      ],
      "source": [
        "import pandas as pd\n",
        "!pip install yfinance\n",
        "import yfinance as yf\n",
        "import numpy as np\n",
        "from sklearn import preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('scraped_data.csv')"
      ],
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      "source": [
        "price_trend = []\n",
        "price_score = []\n",
        "for i in df['ticker']:\n",
        "  try:\n",
        "    stock = yf.Ticker(i)\n",
        "    df1 = stock.history(period='1y')\n",
        "    df1.reset_index(inplace=True)\n",
        "\n",
        "    df1['year'] = pd.DatetimeIndex(df1['Date']).year\n",
        "    df1['month'] = pd.DatetimeIndex(df1['Date']).month\n",
        "\n",
        "    df1 = df1[df1.year != 2022]\n",
        "    df1 = df1[['month', 'Close']]\n",
        "    df1 = df1.groupby(['month']).median()\n",
        "\n",
        "\n",
        "    def trendline(index,data, order=1):\n",
        "        coeffs = np.polyfit(index, list(data), order)\n",
        "        slope = coeffs[-2]\n",
        "        return float(slope)\n",
        "\n",
        "    index=[1,2,3,4,5,6,7,8,9,10,11,12]\n",
        "    resultant=trendline(index,df1['Close'])\n",
        "    price_trend.append(resultant)\n",
        "\n",
        "    df1.reset_index(inplace=True)\n",
        "    initial = df1['Close'][df1['month'] == 1].values[0]\n",
        "    final = df1['Close'][df1['month'] == 12].values[0]\n",
        "\n",
        "    price_change = ((final - initial)/initial)*100\n",
        "    price_score.append(price_change)\n",
        "\n",
        "  except:\n",
        "    price_trend.append('NA')\n",
        "    price_score.append('NA')\n",
        "\n",
        "df['price_trend'] = price_trend\n",
        "df['price_score'] = price_score"
      ],
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          "text": [
            "- BRK.B: No data found, symbol may be delisted\n"
          ]
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      ]
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      "cell_type": "code",
      "source": [
        "df = df[df.price_trend != 'NA']\n",
        "df = df[df.price_score != 'NA']"
      ],
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        "id": "Ldm8khU912es"
      },
      "execution_count": null,
      "outputs": []
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        "x = df['price_trend'].values #returns a numpy array\n",
        "min_max_scaler = preprocessing.MinMaxScaler()\n",
        "x = x.reshape(-1,1)\n",
        "x_scaled = min_max_scaler.fit_transform(x)\n",
        "df['price_trend'] = x_scaled * 100\n",
        "df"
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          "data": {
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>ticker</th>\n",
              "      <th>company_name</th>\n",
              "      <th>pe_ratio</th>\n",
              "      <th>current_ratio</th>\n",
              "      <th>roce</th>\n",
              "      <th>debt_to_equity_ratio</th>\n",
              "      <th>inventory_turnover_ratio</th>\n",
              "      <th>free_cash_flow</th>\n",
              "      <th>reserves_trend</th>\n",
              "      <th>quick_ratio</th>\n",
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              "      <th>return_on_investment</th>\n",
              "      <th>price_trend</th>\n",
              "      <th>price_score</th>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>ABT</td>\n",
              "      <td>abbott_laboratories</td>\n",
              "      <td>17.43</td>\n",
              "      <td>1.71</td>\n",
              "      <td>0.181770</td>\n",
              "      <td>2.32</td>\n",
              "      <td>0.98</td>\n",
              "      <td>6740.00</td>\n",
              "      <td>1472.8</td>\n",
              "      <td>1.19</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.2471</td>\n",
              "      <td>13.154162</td>\n",
              "      <td>22.1353</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>ABBV</td>\n",
              "      <td>abbvie</td>\n",
              "      <td>34.92</td>\n",
              "      <td>1.83</td>\n",
              "      <td>0.088340</td>\n",
              "      <td>1.13</td>\n",
              "      <td>0.84</td>\n",
              "      <td>5724.00</td>\n",
              "      <td>599.3</td>\n",
              "      <td>1.42</td>\n",
              "      <td>0.2139</td>\n",
              "      <td>0.1643</td>\n",
              "      <td>13.362954</td>\n",
              "      <td>23.6857</td>\n",
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              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>ABMD</td>\n",
              "      <td>abiomed</td>\n",
              "      <td>32.24</td>\n",
              "      <td>1.02</td>\n",
              "      <td>0.093213</td>\n",
              "      <td>9.96</td>\n",
              "      <td>1.42</td>\n",
              "      <td>16790.00</td>\n",
              "      <td>-738.8</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.5686</td>\n",
              "      <td>0.1881</td>\n",
              "      <td>13.998170</td>\n",
              "      <td>-4.0271</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>ACN</td>\n",
              "      <td>accenture</td>\n",
              "      <td>110.85</td>\n",
              "      <td>7.64</td>\n",
              "      <td>0.168375</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.51</td>\n",
              "      <td>221.20</td>\n",
              "      <td>221.2</td>\n",
              "      <td>6.89</td>\n",
              "      <td>0.1128</td>\n",
              "      <td>inf</td>\n",
              "      <td>22.183968</td>\n",
              "      <td>50.5299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>AAP</td>\n",
              "      <td>advance_auto_parts</td>\n",
              "      <td>44.55</td>\n",
              "      <td>2.24</td>\n",
              "      <td>0.209167</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.17</td>\n",
              "      <td>777.00</td>\n",
              "      <td>707.6</td>\n",
              "      <td>1.71</td>\n",
              "      <td>0.5988</td>\n",
              "      <td>0.4384</td>\n",
              "      <td>17.394116</td>\n",
              "      <td>43.1487</td>\n",
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              "      <th>317</th>\n",
              "      <td>317</td>\n",
              "      <td>XLNX</td>\n",
              "      <td>xilinx</td>\n",
              "      <td>23.18</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.042560</td>\n",
              "      <td>2.79</td>\n",
              "      <td>2.23</td>\n",
              "      <td>-2521.00</td>\n",
              "      <td>497.2</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.1060</td>\n",
              "      <td>0.0615</td>\n",
              "      <td>18.107216</td>\n",
              "      <td>52.6718</td>\n",
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              "      <th>318</th>\n",
              "      <td>318</td>\n",
              "      <td>XYL</td>\n",
              "      <td>xylem</td>\n",
              "      <td>66.05</td>\n",
              "      <td>6.81</td>\n",
              "      <td>0.153830</td>\n",
              "      <td>0.77</td>\n",
              "      <td>1.06</td>\n",
              "      <td>1043.56</td>\n",
              "      <td>-77.2</td>\n",
              "      <td>6.35</td>\n",
              "      <td>0.2666</td>\n",
              "      <td>0.1852</td>\n",
              "      <td>14.393375</td>\n",
              "      <td>16.1458</td>\n",
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              "      <th>319</th>\n",
              "      <td>319</td>\n",
              "      <td>YUM</td>\n",
              "      <td>yum!_brands</td>\n",
              "      <td>47.03</td>\n",
              "      <td>2.33</td>\n",
              "      <td>0.054018</td>\n",
              "      <td>1.63</td>\n",
              "      <td>1.17</td>\n",
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              "      <td>243.3</td>\n",
              "      <td>1.81</td>\n",
              "      <td>0.1531</td>\n",
              "      <td>0.1138</td>\n",
              "      <td>14.191796</td>\n",
              "      <td>26.3353</td>\n",
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              "      <th>320</th>\n",
              "      <td>320</td>\n",
              "      <td>ZBH</td>\n",
              "      <td>zimmer_biomet</td>\n",
              "      <td>37.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.184733</td>\n",
              "      <td>1.08</td>\n",
              "      <td>1.80</td>\n",
              "      <td>895.00</td>\n",
              "      <td>397.6</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.3394</td>\n",
              "      <td>0.2886</td>\n",
              "      <td>9.304188</td>\n",
              "      <td>-21.2518</td>\n",
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              "    <tr>\n",
              "      <th>321</th>\n",
              "      <td>321</td>\n",
              "      <td>ZION</td>\n",
              "      <td>zions_bancorp</td>\n",
              "      <td>32.66</td>\n",
              "      <td>1.81</td>\n",
              "      <td>-0.004026</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0.23</td>\n",
              "      <td>795.30</td>\n",
              "      <td>364.4</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.0657</td>\n",
              "      <td>0.0481</td>\n",
              "      <td>13.095174</td>\n",
              "      <td>31.8065</td>\n",
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            "text/plain": [
              "     Unnamed: 0 ticker  ... price_trend  price_score\n",
              "0             0    ABT  ...   13.154162      22.1353\n",
              "1             1   ABBV  ...   13.362954      23.6857\n",
              "2             2   ABMD  ...   13.998170      -4.0271\n",
              "3             3    ACN  ...   22.183968      50.5299\n",
              "4             4    AAP  ...   17.394116      43.1487\n",
              "..          ...    ...  ...         ...          ...\n",
              "317         317   XLNX  ...   18.107216      52.6718\n",
              "318         318    XYL  ...   14.393375      16.1458\n",
              "319         319    YUM  ...   14.191796      26.3353\n",
              "320         320    ZBH  ...    9.304188     -21.2518\n",
              "321         321   ZION  ...   13.095174      31.8065\n",
              "\n",
              "[321 rows x 15 columns]"
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}