Quant_invest.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# Import Libraries"],"metadata":{"id":"ioNTEOSzcNAo"}},{"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","import yfinance as yf"],"metadata":{"id":"1e5cmdmQcSJk","executionInfo":{"status":"ok","timestamp":1683829424805,"user_tz":-120,"elapsed":239,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}}},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":["# Import Dataset"],"metadata":{"id":"63542p52cdzy"}},{"cell_type":"code","source":["# Access files from Google Drive\n","from google.colab import drive \n","drive.mount('/content/gdrive')\n","import os\n","os.chdir(\"gdrive/MyDrive/quant_invest/\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xB2plKbpdZZx","executionInfo":{"status":"ok","timestamp":1683829444170,"user_tz":-120,"elapsed":17780,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}},"outputId":"78c6ce32-9ffa-4884-9af9-4c0eeedf62a6"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive\n"]}]},{"cell_type":"code","source":["# Creating list of the year and dates we plan to include\n","YYYY_MM = ['2014-01', '2015-01' , '2016-01', '2017-01', '2018-01', '2019-01', '2020-01', '2021-01', '2022-01'] \n","factor_scores = pd.read_csv('Portfolio_Ratings_New.csv') # Import .csv"],"metadata":{"id":"mkWbGefVch1n","executionInfo":{"status":"ok","timestamp":1683829448058,"user_tz":-120,"elapsed":1923,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["factor_scores.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"pLfkWyg4gVgS","executionInfo":{"status":"ok","timestamp":1683829452307,"user_tz":-120,"elapsed":756,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}},"outputId":"c605e383-8ab7-4833-ad60-113081d62872"},"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         Date EXCHANGE_TICKER  Price12Mom  Value_EY      Beta   Mom_Val  \\\n","0  31/01/2014            SBUX    2.610566 -0.003794 -1.111369  1.303386   \n","1  31/01/2014             WFM    1.210209  0.042937 -1.087502  0.626573   \n","2  31/01/2014             WBA    2.585360  0.070482 -0.939167  1.327921   \n","3  31/01/2014             HAL    2.001988  0.061307 -1.230365  1.031647   \n","4  31/01/2014            HBAN    2.749435  0.120163 -1.421205  1.434799   \n","\n","   Val_Beta   Mom_Bet  Mom_Val_Quad  Val_Beta_Quad  Mom_Bet_Quad  Mom_Val_Log  \\\n","0 -0.557582  0.749598      5.508603       3.130171      5.261234     2.969554   \n","1 -0.522283  0.061353      4.334524       3.181851      3.997401     2.587439   \n","2 -0.434342  0.823097      5.520337       3.294603      5.302243     3.000897   \n","3 -0.584529  0.385811      5.006735       3.113182      4.651742     2.835252   \n","4 -0.650521  0.664115      5.688765       3.059060      5.290500     3.069673   \n","\n","   Val_Beta_Log  Mom_Bet_Log  \n","0      1.960947     2.630775  \n","1      1.987617     2.286298  \n","2      2.062786     2.726284  \n","3      1.927734     2.414647  \n","4      1.852718     2.463464  "],"text/html":["\n","  <div id=\"df-bf53e5e2-06e6-42d9-85fd-6b53ed724ed5\">\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","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Date</th>\n","      <th>EXCHANGE_TICKER</th>\n","      <th>Price12Mom</th>\n","      <th>Value_EY</th>\n","      <th>Beta</th>\n","      <th>Mom_Val</th>\n","      <th>Val_Beta</th>\n","      <th>Mom_Bet</th>\n","      <th>Mom_Val_Quad</th>\n","      <th>Val_Beta_Quad</th>\n","      <th>Mom_Bet_Quad</th>\n","      <th>Mom_Val_Log</th>\n","      <th>Val_Beta_Log</th>\n","      <th>Mom_Bet_Log</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>31/01/2014</td>\n","      <td>SBUX</td>\n","      <td>2.610566</td>\n","      <td>-0.003794</td>\n","      <td>-1.111369</td>\n","      <td>1.303386</td>\n","      <td>-0.557582</td>\n","      <td>0.749598</td>\n","      <td>5.508603</td>\n","      <td>3.130171</td>\n","      <td>5.261234</td>\n","      <td>2.969554</td>\n","      <td>1.960947</td>\n","      <td>2.630775</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>31/01/2014</td>\n","      <td>WFM</td>\n","      <td>1.210209</td>\n","      <td>0.042937</td>\n","      <td>-1.087502</td>\n","      <td>0.626573</td>\n","      <td>-0.522283</td>\n","      <td>0.061353</td>\n","      <td>4.334524</td>\n","      <td>3.181851</td>\n","      <td>3.997401</td>\n","      <td>2.587439</td>\n","      <td>1.987617</td>\n","      <td>2.286298</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>31/01/2014</td>\n","      <td>WBA</td>\n","      <td>2.585360</td>\n","      <td>0.070482</td>\n","      <td>-0.939167</td>\n","      <td>1.327921</td>\n","      <td>-0.434342</td>\n","      <td>0.823097</td>\n","      <td>5.520337</td>\n","      <td>3.294603</td>\n","      <td>5.302243</td>\n","      <td>3.000897</td>\n","      <td>2.062786</td>\n","      <td>2.726284</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>31/01/2014</td>\n","      <td>HAL</td>\n","      <td>2.001988</td>\n","      <td>0.061307</td>\n","      <td>-1.230365</td>\n","      <td>1.031647</td>\n","      <td>-0.584529</td>\n","      <td>0.385811</td>\n","      <td>5.006735</td>\n","      <td>3.113182</td>\n","      <td>4.651742</td>\n","      <td>2.835252</td>\n","      <td>1.927734</td>\n","      <td>2.414647</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>31/01/2014</td>\n","      <td>HBAN</td>\n","      <td>2.749435</td>\n","      <td>0.120163</td>\n","      <td>-1.421205</td>\n","      <td>1.434799</td>\n","      <td>-0.650521</td>\n","      <td>0.664115</td>\n","      <td>5.688765</td>\n","      <td>3.059060</td>\n","      <td>5.290500</td>\n","      <td>3.069673</td>\n","      <td>1.852718</td>\n","      <td>2.463464</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bf53e5e2-06e6-42d9-85fd-6b53ed724ed5')\"\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","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </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","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [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-bf53e5e2-06e6-42d9-85fd-6b53ed724ed5 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-bf53e5e2-06e6-42d9-85fd-6b53ed724ed5');\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","  </div>\n","  "]},"metadata":{},"execution_count":5}]},{"cell_type":"code","source":["import datetime\n","\n","factor_scores['YYYY'] = pd.to_datetime(factor_scores['Date']).dt.year\n","factor_scores['Date'] = pd.to_datetime(factor_scores['Date'])\n","factor_scores['MM_YYYY'] = factor_scores['Date'].dt.to_period('M')\n","factor_scores.head()"],"metadata":{"id":"5zuxku22gmzo","executionInfo":{"status":"ok","timestamp":1683829455537,"user_tz":-120,"elapsed":201,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}},"colab":{"base_uri":"https://localhost:8080/","height":298},"outputId":"62c8dbac-9a12-48c4-a1c1-87fa342847b7"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-6-cc1b69eb9fba>:3: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.\n","  factor_scores['YYYY'] = pd.to_datetime(factor_scores['Date']).dt.year\n","<ipython-input-6-cc1b69eb9fba>:4: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.\n","  factor_scores['Date'] = pd.to_datetime(factor_scores['Date'])\n"]},{"output_type":"execute_result","data":{"text/plain":["        Date EXCHANGE_TICKER  Price12Mom  Value_EY      Beta   Mom_Val  \\\n","0 2014-01-31            SBUX    2.610566 -0.003794 -1.111369  1.303386   \n","1 2014-01-31             WFM    1.210209  0.042937 -1.087502  0.626573   \n","2 2014-01-31             WBA    2.585360  0.070482 -0.939167  1.327921   \n","3 2014-01-31             HAL    2.001988  0.061307 -1.230365  1.031647   \n","4 2014-01-31            HBAN    2.749435  0.120163 -1.421205  1.434799   \n","\n","   Val_Beta   Mom_Bet  Mom_Val_Quad  Val_Beta_Quad  Mom_Bet_Quad  Mom_Val_Log  \\\n","0 -0.557582  0.749598      5.508603       3.130171      5.261234     2.969554   \n","1 -0.522283  0.061353      4.334524       3.181851      3.997401     2.587439   \n","2 -0.434342  0.823097      5.520337       3.294603      5.302243     3.000897   \n","3 -0.584529  0.385811      5.006735       3.113182      4.651742     2.835252   \n","4 -0.650521  0.664115      5.688765       3.059060      5.290500     3.069673   \n","\n","   Val_Beta_Log  Mom_Bet_Log  YYYY  MM_YYYY  \n","0      1.960947     2.630775  2014  2014-01  \n","1      1.987617     2.286298  2014  2014-01  \n","2      2.062786     2.726284  2014  2014-01  \n","3      1.927734     2.414647  2014  2014-01  \n","4      1.852718     2.463464  2014  2014-01  "],"text/html":["\n","  <div id=\"df-7ee25c73-a280-4eec-b258-1a167f5a80a3\">\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","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Date</th>\n","      <th>EXCHANGE_TICKER</th>\n","      <th>Price12Mom</th>\n","      <th>Value_EY</th>\n","      <th>Beta</th>\n","      <th>Mom_Val</th>\n","      <th>Val_Beta</th>\n","      <th>Mom_Bet</th>\n","      <th>Mom_Val_Quad</th>\n","      <th>Val_Beta_Quad</th>\n","      <th>Mom_Bet_Quad</th>\n","      <th>Mom_Val_Log</th>\n","      <th>Val_Beta_Log</th>\n","      <th>Mom_Bet_Log</th>\n","      <th>YYYY</th>\n","      <th>MM_YYYY</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2014-01-31</td>\n","      <td>SBUX</td>\n","      <td>2.610566</td>\n","      <td>-0.003794</td>\n","      <td>-1.111369</td>\n","      <td>1.303386</td>\n","      <td>-0.557582</td>\n","      <td>0.749598</td>\n","      <td>5.508603</td>\n","      <td>3.130171</td>\n","      <td>5.261234</td>\n","      <td>2.969554</td>\n","      <td>1.960947</td>\n","      <td>2.630775</td>\n","      <td>2014</td>\n","      <td>2014-01</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2014-01-31</td>\n","      <td>WFM</td>\n","      <td>1.210209</td>\n","      <td>0.042937</td>\n","      <td>-1.087502</td>\n","      <td>0.626573</td>\n","      <td>-0.522283</td>\n","      <td>0.061353</td>\n","      <td>4.334524</td>\n","      <td>3.181851</td>\n","      <td>3.997401</td>\n","      <td>2.587439</td>\n","      <td>1.987617</td>\n","      <td>2.286298</td>\n","      <td>2014</td>\n","      <td>2014-01</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2014-01-31</td>\n","      <td>WBA</td>\n","      <td>2.585360</td>\n","      <td>0.070482</td>\n","      <td>-0.939167</td>\n","      <td>1.327921</td>\n","      <td>-0.434342</td>\n","      <td>0.823097</td>\n","      <td>5.520337</td>\n","      <td>3.294603</td>\n","      <td>5.302243</td>\n","      <td>3.000897</td>\n","      <td>2.062786</td>\n","      <td>2.726284</td>\n","      <td>2014</td>\n","      <td>2014-01</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2014-01-31</td>\n","      <td>HAL</td>\n","      <td>2.001988</td>\n","      <td>0.061307</td>\n","      <td>-1.230365</td>\n","      <td>1.031647</td>\n","      <td>-0.584529</td>\n","      <td>0.385811</td>\n","      <td>5.006735</td>\n","      <td>3.113182</td>\n","      <td>4.651742</td>\n","      <td>2.835252</td>\n","      <td>1.927734</td>\n","      <td>2.414647</td>\n","      <td>2014</td>\n","      <td>2014-01</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2014-01-31</td>\n","      <td>HBAN</td>\n","      <td>2.749435</td>\n","      <td>0.120163</td>\n","      <td>-1.421205</td>\n","      <td>1.434799</td>\n","      <td>-0.650521</td>\n","      <td>0.664115</td>\n","      <td>5.688765</td>\n","      <td>3.059060</td>\n","      <td>5.290500</td>\n","      <td>3.069673</td>\n","      <td>1.852718</td>\n","      <td>2.463464</td>\n","      <td>2014</td>\n","      <td>2014-01</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7ee25c73-a280-4eec-b258-1a167f5a80a3')\"\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","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </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","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [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-7ee25c73-a280-4eec-b258-1a167f5a80a3 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-7ee25c73-a280-4eec-b258-1a167f5a80a3');\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","  </div>\n","  "]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["import yfinance as yf\n","import pandas as pd\n","\n","def get_stock_data(ticker, start_date, end_date):\n","\n","    ticker_df = yf.download(ticker,start_date,end_date)\n","    return ticker_df['Adj Close']\n"],"metadata":{"id":"m_B7eWQOxm9c","executionInfo":{"status":"ok","timestamp":1683829459042,"user_tz":-120,"elapsed":2,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# defining the list of all the unique stocks in the dataset\n","STOCKS = (pd.DataFrame(set(factor_scores['EXCHANGE_TICKER']))).dropna()"],"metadata":{"id":"72py9qiY2wVe","executionInfo":{"status":"ok","timestamp":1683829461542,"user_tz":-120,"elapsed":186,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["factor_scores = factor_scores[factor_scores['EXCHANGE_TICKER'].notna()]"],"metadata":{"id":"QpmaHNbb_LrO","executionInfo":{"status":"ok","timestamp":1683829463711,"user_tz":-120,"elapsed":1104,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["returns = pd.DataFrame()\n","\n","start = datetime.datetime(2014, 1, 1)\n","end = datetime.datetime(2022, 12, 31)\n","\n","# Extracting return data for the entire period for all stocks\n","# This can glitch sometimes since Yahoo limits how much information we can call sometimes so reset workspace\n","for ticker in STOCKS[0]:\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"],"metadata":{"id":"vK1gzZR4yWe4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1683829576738,"user_tz":-120,"elapsed":112167,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}},"outputId":"8fb30642-a165-4824-f060-9cdc94d50ca7"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- LO: No data found for this date range, symbol may be delisted\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- WINMQ: No timezone found, symbol may be delisted\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- FRX: No timezone found, symbol may be delisted\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- SIAL: No data found for this date range, symbol may be delisted\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- STI: No timezone found, symbol may be delisted\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- WPX: No timezone found, symbol may be delisted\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- CTXS: No timezone found, symbol may be delisted\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- TE: No data found for this date range, symbol may be delisted\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- QEP: No timezone found, symbol may be delisted\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- LLL: No timezone found, symbol may be delisted\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- TWC: No data found for this date range, symbol may be delisted\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- BRK.B: No timezone found, symbol may be delisted\n","[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- CELG: No timezone found, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- MON: No timezone found, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- BRCM: No data found for this date range, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- ETFC: No timezone found, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- MXIM: No timezone found, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- GMCR: No data found for this date range, symbol may be delisted\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- VIAB: No timezone found, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- DTV: No timezone found, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- NLSN: No timezone found, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- RDC: No timezone found, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- APC: No timezone found, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- STJ: No data found for this date range, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- SPLS: No data found for this date range, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- FLIR: No timezone found, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- SNDK: No data found for this date range, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- NBL: No timezone found, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- DOFSQ: No timezone found, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- VAR: No timezone found, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- LLTC: No data found for this date range, symbol may be delisted\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- ALXN: No timezone found, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- HCBK: No data found for this date range, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\n","\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- WFM: No data found for this date range, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- KRFT: No data found for this date range, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- MJN: No data found for this date range, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- MWV: No data found for this date range, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- BF.B: No data found for this date range, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- PBCT: No timezone found, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- RTN: No timezone found, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\n","\r[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- CAM: No data found for this date range, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["[*********************100%***********************]  1 of 1 completed\n","[*********************100%***********************]  1 of 1 completed\n","\n","1 Failed download:\n","- CERN: No timezone found, symbol may be delisted\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n","<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]},{"output_type":"stream","name":"stdout","text":["\r[*********************100%***********************]  1 of 1 completed\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-10-b2f118ec6179>:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n","  returns[ticker] = get_stock_data(ticker, start,end).pct_change()\n"]}]},{"cell_type":"code","source":["# We should have 445 Columns\n","\n","# Currently, the index is the 'date' therefore we will reset the index and add 'Date' to be a column\n","returns = returns.reset_index(inplace=False)\n","returns.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":236},"id":"Zs_KE7Uqz5il","executionInfo":{"status":"ok","timestamp":1683829617062,"user_tz":-120,"elapsed":193,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}},"outputId":"1e5c3986-1de8-4e66-ff70-14572be3da49"},"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        Date       RTX       HUM       ALL       PFG  LO  WINMQ      ENPH  \\\n","0 2014-01-02       NaN       NaN       NaN       NaN NaN    NaN       NaN   \n","1 2014-01-03  0.003644 -0.010502 -0.004108  0.006993 NaN    NaN  0.020649   \n","2 2014-01-06 -0.000974 -0.009827 -0.003938  0.001021 NaN    NaN  0.017341   \n","3 2014-01-07  0.006383 -0.002084  0.004330  0.000612 NaN    NaN  0.071023   \n","4 2014-01-08  0.001233 -0.011437  0.003187  0.001835 NaN    NaN -0.038462   \n","\n","   FRX        CA  ...       TXN  FOXA      CHTR       OKE      VRSN       AFL  \\\n","0  NaN       NaN  ...       NaN   NaN       NaN       NaN       NaN       NaN   \n","1  NaN -0.001207  ...  0.004408   NaN -0.010315  0.002288 -0.002999  0.002881   \n","2  NaN -0.005436  ... -0.008316   NaN  0.012222  0.007828 -0.006350 -0.019199   \n","3  NaN  0.015791  ... -0.005358   NaN  0.022815  0.011003  0.028254  0.003237   \n","4  NaN  0.004185  ...  0.013818   NaN -0.014412  0.009283  0.001636 -0.007067   \n","\n","   CERN      SCHW       CDW         R  \n","0   NaN       NaN       NaN       NaN  \n","1   NaN  0.003098 -0.000428  0.002065  \n","2   NaN -0.003475  0.016260 -0.016348  \n","3   NaN -0.010461  0.009684  0.010195  \n","4   NaN  0.011746  0.000000  0.003457  \n","\n","[5 rows x 443 columns]"],"text/html":["\n","  <div id=\"df-426569fa-b337-473f-9832-c1d329babaee\">\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","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Date</th>\n","      <th>RTX</th>\n","      <th>HUM</th>\n","      <th>ALL</th>\n","      <th>PFG</th>\n","      <th>LO</th>\n","      <th>WINMQ</th>\n","      <th>ENPH</th>\n","      <th>FRX</th>\n","      <th>CA</th>\n","      <th>...</th>\n","      <th>TXN</th>\n","      <th>FOXA</th>\n","      <th>CHTR</th>\n","      <th>OKE</th>\n","      <th>VRSN</th>\n","      <th>AFL</th>\n","      <th>CERN</th>\n","      <th>SCHW</th>\n","      <th>CDW</th>\n","      <th>R</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2014-01-02</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2014-01-03</td>\n","      <td>0.003644</td>\n","      <td>-0.010502</td>\n","      <td>-0.004108</td>\n","      <td>0.006993</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.020649</td>\n","      <td>NaN</td>\n","      <td>-0.001207</td>\n","      <td>...</td>\n","      <td>0.004408</td>\n","      <td>NaN</td>\n","      <td>-0.010315</td>\n","      <td>0.002288</td>\n","      <td>-0.002999</td>\n","      <td>0.002881</td>\n","      <td>NaN</td>\n","      <td>0.003098</td>\n","      <td>-0.000428</td>\n","      <td>0.002065</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2014-01-06</td>\n","      <td>-0.000974</td>\n","      <td>-0.009827</td>\n","      <td>-0.003938</td>\n","      <td>0.001021</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.017341</td>\n","      <td>NaN</td>\n","      <td>-0.005436</td>\n","      <td>...</td>\n","      <td>-0.008316</td>\n","      <td>NaN</td>\n","      <td>0.012222</td>\n","      <td>0.007828</td>\n","      <td>-0.006350</td>\n","      <td>-0.019199</td>\n","      <td>NaN</td>\n","      <td>-0.003475</td>\n","      <td>0.016260</td>\n","      <td>-0.016348</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2014-01-07</td>\n","      <td>0.006383</td>\n","      <td>-0.002084</td>\n","      <td>0.004330</td>\n","      <td>0.000612</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.071023</td>\n","      <td>NaN</td>\n","      <td>0.015791</td>\n","      <td>...</td>\n","      <td>-0.005358</td>\n","      <td>NaN</td>\n","      <td>0.022815</td>\n","      <td>0.011003</td>\n","      <td>0.028254</td>\n","      <td>0.003237</td>\n","      <td>NaN</td>\n","      <td>-0.010461</td>\n","      <td>0.009684</td>\n","      <td>0.010195</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2014-01-08</td>\n","      <td>0.001233</td>\n","      <td>-0.011437</td>\n","      <td>0.003187</td>\n","      <td>0.001835</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>-0.038462</td>\n","      <td>NaN</td>\n","      <td>0.004185</td>\n","      <td>...</td>\n","      <td>0.013818</td>\n","      <td>NaN</td>\n","      <td>-0.014412</td>\n","      <td>0.009283</td>\n","      <td>0.001636</td>\n","      <td>-0.007067</td>\n","      <td>NaN</td>\n","      <td>0.011746</td>\n","      <td>0.000000</td>\n","      <td>0.003457</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 443 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-426569fa-b337-473f-9832-c1d329babaee')\"\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","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </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","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [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-426569fa-b337-473f-9832-c1d329babaee 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-426569fa-b337-473f-9832-c1d329babaee');\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","  </div>\n","  "]},"metadata":{},"execution_count":11}]},{"cell_type":"markdown","source":["# Momentum and Value"],"metadata":{"id":"XLL89r1Wnx6v"}},{"cell_type":"code","source":["# This chunk is for Momentum and Value\n","Mom_Val_returns = pd.DataFrame()\n","\n","for month_year in YYYY_MM:\n","\n","  # Extracting only data associated to those month and dates\n","  Data = factor_scores.loc[factor_scores['MM_YYYY'] == month_year,:] \n","\n","  # Calculate Decile Groups\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val'], 10, labels = False)\n","\n","  # Select TICKERS of TOP and BOTTOM decile of stocks based on score\n","  top_decile =  Data.loc[Data['Quantile_rank'] == 9,'EXCHANGE_TICKER']\n","  bottom_decile =  Data.loc[Data['Quantile_rank'] == 0,'EXCHANGE_TICKER']\n","  \n","  # Creating Empty DataFrame\n","  top_returns = pd.DataFrame()\n","  bottom_returns = pd.DataFrame()\n","\n","  # Extracts the year from the 'Date'\n","  year = (Data.reset_index(drop=True).loc[0,'Date']).year\n","\n","  # Defining the first and last day of the year\n","  start = datetime.datetime(year, 1, 1)\n","  end = datetime.datetime(year, 12, 31)\n","\n","  # Extract Returns for the tickers\n","  for ticker in top_decile:\n","    top_returns[ticker] = returns.loc[((returns['Date'] >= start) & (returns['Date'] <= end)), ticker]\n","\n","  for ticker in bottom_decile:\n","    bottom_returns[ticker] = returns.loc[ ((returns['Date'] >= start) & (returns['Date'] <= end)), ticker]\n","\n","  # Taking the row sum for each returns\n","\n","\n","  #portfolio_returns = top_returns.mean(axis=1) - bottom_returns.mean(axis=1) # Allowing shorting\n","  portfolio_returns = top_returns.mean(axis=1)  # LONG ONLY\n","\n","  # Mergining poortfolio returns with previous years\n","  Mom_Val_returns = [Mom_Val_returns, portfolio_returns]\n","  Mom_Val_returns = pd.concat(Mom_Val_returns)\n","\n"],"metadata":{"id":"L6wieKAnlqSe","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1683829619994,"user_tz":-120,"elapsed":827,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}},"outputId":"2f0c9f7f-4144-4d49-bae6-d9d31a188edf"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-12-0d39039ee8ce>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val'], 10, labels = False)\n","<ipython-input-12-0d39039ee8ce>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val'], 10, labels = False)\n","<ipython-input-12-0d39039ee8ce>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val'], 10, labels = False)\n","<ipython-input-12-0d39039ee8ce>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val'], 10, labels = False)\n","<ipython-input-12-0d39039ee8ce>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val'], 10, labels = False)\n","<ipython-input-12-0d39039ee8ce>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val'], 10, labels = False)\n","<ipython-input-12-0d39039ee8ce>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val'], 10, labels = False)\n","<ipython-input-12-0d39039ee8ce>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val'], 10, labels = False)\n","<ipython-input-12-0d39039ee8ce>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val'], 10, labels = False)\n"]}]},{"cell_type":"code","source":["Mom_Val_returns = (Mom_Val_returns+1).cumprod()\n","\n","import matplotlib.pyplot as plt\n","\n","x_axis = range(2266)\n","y_axis = Mom_Val_returns\n","\n","plt.plot(x_axis,y_axis)\n","plt.xlabel(\"date from 2014 to 2022\")\n","plt.ylabel(\"portfolio return\")\n","plt.title(\"Momentum Value strategy\")"],"metadata":{"id":"0C2_s9qX5dTL","colab":{"base_uri":"https://localhost:8080/","height":490},"executionInfo":{"status":"ok","timestamp":1683829623767,"user_tz":-120,"elapsed":599,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}},"outputId":"5f8de345-432a-40ca-f42d-8d1a674c38cf"},"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Momentum Value strategy')"]},"metadata":{},"execution_count":13},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# Momentum and Low Beta"],"metadata":{"id":"vIpNxpvE9_HY"}},{"cell_type":"code","source":["# This chunk is for Momentum and Value\n","Mom_Beta_returns = pd.DataFrame()\n","\n","for month_year in YYYY_MM:\n","\n","  # Extracting only data associated to those month and dates\n","  Data = factor_scores.loc[factor_scores['MM_YYYY'] == month_year,:] \n","\n","  # Calculate Decile Groups\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet'], 10, labels = False)\n","\n","  # Select TICKERS of TOP and BOTTOM decile of stocks based on score\n","  top_decile =  Data.loc[Data['Quantile_rank'] == 9,'EXCHANGE_TICKER']\n","  bottom_decile =  Data.loc[Data['Quantile_rank'] == 0,'EXCHANGE_TICKER']\n","  \n","  # Creating Empty DataFrame\n","  top_returns = pd.DataFrame()\n","  bottom_returns = pd.DataFrame()\n","\n","  # Extracts the year from the 'Date'\n","  year = (Data.reset_index(drop=True).loc[0,'Date']).year\n","\n","  # Defining the first and last day of the year\n","  start = datetime.datetime(year, 1, 1)\n","  end = datetime.datetime(year, 12, 31)\n","\n","  # Extract Returns for the tickers\n","  for ticker in top_decile:\n","    top_returns[ticker] = returns.loc[((returns['Date'] >= start) & (returns['Date'] <= end)), ticker]\n","\n","  for ticker in bottom_decile:\n","    bottom_returns[ticker] = returns.loc[ ((returns['Date'] >= start) & (returns['Date'] <= end)), ticker]\n","\n","  # Taking the row sum for each returns\n","\n","\n","  #portfolio_returns = top_returns.mean(axis=1) - bottom_returns.mean(axis=1) # Allowing shorting\n","  portfolio_returns = top_returns.mean(axis=1)  # LONG ONLY\n","\n","  # Mergining poortfolio returns with previous years\n","  Mom_Beta_returns = [Mom_Beta_returns, portfolio_returns]\n","  Mom_Beta_returns = pd.concat(Mom_Beta_returns)\n","\n"],"metadata":{"id":"eEGHu-jg-CRV","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1683829629757,"user_tz":-120,"elapsed":1864,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}},"outputId":"d106b40d-f611-4751-8344-e08eebc907a6"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-14-4d66139db99b>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet'], 10, labels = False)\n","<ipython-input-14-4d66139db99b>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet'], 10, labels = False)\n","<ipython-input-14-4d66139db99b>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet'], 10, labels = False)\n","<ipython-input-14-4d66139db99b>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet'], 10, labels = False)\n","<ipython-input-14-4d66139db99b>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet'], 10, labels = False)\n","<ipython-input-14-4d66139db99b>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet'], 10, labels = False)\n","<ipython-input-14-4d66139db99b>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet'], 10, labels = False)\n","<ipython-input-14-4d66139db99b>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet'], 10, labels = False)\n","<ipython-input-14-4d66139db99b>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet'], 10, labels = False)\n"]}]},{"cell_type":"code","source":["Mom_Beta_returns = (Mom_Beta_returns+1).cumprod()\n","\n","x_axis = range(2266)\n","y_axis = Mom_Beta_returns\n","\n","plt.plot(x_axis,y_axis)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"id":"UMEcLOtc-JKA","executionInfo":{"status":"ok","timestamp":1683822467317,"user_tz":-60,"elapsed":308,"user":{"displayName":"Anthony Li","userId":"16806347570419169042"}},"outputId":"56b6dab2-2f1e-4c2d-b7be-a09ba842245a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7fd4fc83e200>]"]},"metadata":{},"execution_count":49},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# Value and Low Beta"],"metadata":{"id":"57Q5aorf-y_o"}},{"cell_type":"code","source":["# This chunk is for Momentum and Value\n","Val_Beta_returns = pd.DataFrame()\n","\n","for month_year in YYYY_MM:\n","\n","  # Extracting only data associated to those month and dates\n","  Data = factor_scores.loc[factor_scores['MM_YYYY'] == month_year,:] \n","\n","  # Calculate Decile Groups\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta'], 10, labels = False)\n","\n","  # Select TICKERS of TOP and BOTTOM decile of stocks based on score\n","  top_decile =  Data.loc[Data['Quantile_rank'] == 9,'EXCHANGE_TICKER']\n","  bottom_decile =  Data.loc[Data['Quantile_rank'] == 0,'EXCHANGE_TICKER']\n","  \n","  # Creating Empty DataFrame\n","  top_returns = pd.DataFrame()\n","  bottom_returns = pd.DataFrame()\n","\n","  # Extracts the year from the 'Date'\n","  year = (Data.reset_index(drop=True).loc[0,'Date']).year\n","\n","  # Defining the first and last day of the year\n","  start = datetime.datetime(year, 1, 1)\n","  end = datetime.datetime(year, 12, 31)\n","\n","  # Extract Returns for the tickers\n","  for ticker in top_decile:\n","    top_returns[ticker] = returns.loc[((returns['Date'] >= start) & (returns['Date'] <= end)), ticker]\n","\n","  for ticker in bottom_decile:\n","    bottom_returns[ticker] = returns.loc[ ((returns['Date'] >= start) & (returns['Date'] <= end)), ticker]\n","\n","  # Taking the row sum for each returns\n","\n","\n","  #portfolio_returns = top_returns.mean(axis=1) - bottom_returns.mean(axis=1) # Allowing shorting\n","  portfolio_returns = top_returns.mean(axis=1)  # LONG ONLY\n","\n","  # Mergining poortfolio returns with previous years\n","  Val_Beta_returns = [Val_Beta_returns, portfolio_returns]\n","  Val_Beta_returns = pd.concat(Val_Beta_returns)\n","\n"],"metadata":{"id":"JI84Lb0b-1jt","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1683829634113,"user_tz":-120,"elapsed":2220,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}},"outputId":"0dd89178-53b8-44c0-8863-8f848ad1573e"},"execution_count":15,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-15-1160913e096a>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta'], 10, labels = False)\n","<ipython-input-15-1160913e096a>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta'], 10, labels = False)\n","<ipython-input-15-1160913e096a>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta'], 10, labels = False)\n","<ipython-input-15-1160913e096a>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta'], 10, labels = False)\n","<ipython-input-15-1160913e096a>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta'], 10, labels = False)\n","<ipython-input-15-1160913e096a>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta'], 10, labels = False)\n","<ipython-input-15-1160913e096a>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta'], 10, labels = False)\n","<ipython-input-15-1160913e096a>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta'], 10, labels = False)\n","<ipython-input-15-1160913e096a>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta'], 10, labels = False)\n"]}]},{"cell_type":"code","source":["Val_Beta_returns = (Val_Beta_returns+1).cumprod()\n","\n","x_axis = range(2266)\n","y_axis =Val_Beta_returns\n","\n","plt.plot(x_axis,y_axis)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"W9Nv67yn_o_V","executionInfo":{"status":"ok","timestamp":1683829636930,"user_tz":-120,"elapsed":642,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}},"outputId":"45ff0cd5-3278-4515-e2c4-8fcb8af9e2f1"},"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7fd4dc2eb160>]"]},"metadata":{},"execution_count":16},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["fig, ax = plt.subplots()\n","x_axis = np.linspace(2014, 2022, 2266)\n","y_axis =Mom_Val_returns\n","\n","x_axis2 = np.linspace(2014, 2022, 2266)\n","y_axis2 =Mom_Beta_returns\n","\n","x_axis3 = np.linspace(2014, 2022, 2266)\n","y_axis3 =Val_Beta_returns\n","\n","ax.plot(x_axis, y_axis, color='red', label='Mon-Val')\n","ax.plot(x_axis2, y_axis2, color='blue', label='Mon-Beta')\n","ax.plot(x_axis3, y_axis3, color='yellow', label='Val-Beta')\n","\n","ax.legend()\n","ax.legend()\n","ax.legend()\n","ax.set_xlabel('Date between 2014-2022')\n","ax.set_ylabel('The portfolio return')\n","ax.set_title('Strategies Performance')\n","\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"QPn21m2MS2fZ","executionInfo":{"status":"ok","timestamp":1683829642931,"user_tz":-120,"elapsed":2191,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}},"outputId":"71e6cfce-b519-43ce-b86e-667088fd83f5"},"execution_count":17,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# SINGLE FACTOR PORTFOLIOS : Momentum"],"metadata":{"id":"jOl1EKSI__4p"}},{"cell_type":"code","source":["Mom_returns = pd.DataFrame()\n","\n","for month_year in YYYY_MM:\n","\n","  # Extracting only data associated to those month and dates\n","  Data = factor_scores.loc[factor_scores['MM_YYYY'] == month_year,:] \n","\n","  # Calculate Decile Groups\n","  Data['Quantile_rank'] = pd.qcut(Data['Price12Mom'], 10, labels = False)\n","\n","  # Select TICKERS of TOP and BOTTOM decile of stocks based on score\n","  top_decile =  Data.loc[Data['Quantile_rank'] == 9,'EXCHANGE_TICKER']\n","  bottom_decile =  Data.loc[Data['Quantile_rank'] == 0,'EXCHANGE_TICKER']\n","  \n","  # Creating Empty DataFrame\n","  top_returns = pd.DataFrame()\n","  bottom_returns = pd.DataFrame()\n","\n","  # Extracts the year from the 'Date'\n","  year = (Data.reset_index(drop=True).loc[0,'Date']).year\n","\n","  # Defining the first and last day of the year\n","  start = datetime.datetime(year, 1, 1)\n","  end = datetime.datetime(year, 12, 31)\n","\n","  # Extract Returns for the tickers\n","  for ticker in top_decile:\n","    top_returns[ticker] = returns.loc[((returns['Date'] >= start) & (returns['Date'] <= end)), ticker]\n","\n","  for ticker in bottom_decile:\n","    bottom_returns[ticker] = returns.loc[ ((returns['Date'] >= start) & (returns['Date'] <= end)), ticker]\n","\n","  # Taking the row sum for each returns\n","\n","\n","  portfolio_returns = top_returns.mean(axis=1) - bottom_returns.mean(axis=1) # Allowing shorting\n","  #portfolio_returns = top_returns.mean(axis=1)  # LONG ONLY\n","\n","  # Mergining poortfolio returns with previous years\n","  Mom_returns  = [Mom_returns , portfolio_returns]\n","  Mom_returns  = pd.concat(Mom_returns )"],"metadata":{"id":"EymnFEX5__QH","executionInfo":{"status":"ok","timestamp":1683829645076,"user_tz":-120,"elapsed":1462,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"b366a9e7-cc71-4e93-e940-be4286a9d038"},"execution_count":18,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-18-8a519b812e4f>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Price12Mom'], 10, labels = False)\n","<ipython-input-18-8a519b812e4f>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Price12Mom'], 10, labels = False)\n","<ipython-input-18-8a519b812e4f>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Price12Mom'], 10, labels = False)\n","<ipython-input-18-8a519b812e4f>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Price12Mom'], 10, labels = False)\n","<ipython-input-18-8a519b812e4f>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Price12Mom'], 10, labels = False)\n","<ipython-input-18-8a519b812e4f>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Price12Mom'], 10, labels = False)\n","<ipython-input-18-8a519b812e4f>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Price12Mom'], 10, labels = False)\n","<ipython-input-18-8a519b812e4f>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Price12Mom'], 10, labels = False)\n","<ipython-input-18-8a519b812e4f>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Price12Mom'], 10, labels = False)\n"]}]},{"cell_type":"code","source":["Mom_returns = (Mom_returns+1).cumprod()\n","\n","x_axis = range(2266)\n","y_axis =Mom_returns\n","\n","plt.plot(x_axis,y_axis)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"id":"qPXC_hnYAgJe","executionInfo":{"status":"ok","timestamp":1683822364752,"user_tz":-60,"elapsed":548,"user":{"displayName":"Anthony Li","userId":"16806347570419169042"}},"outputId":"4a9ff7bd-f934-4544-ef18-dd10c6a2ded8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7fd4fcbc9900>]"]},"metadata":{},"execution_count":39},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# Value"],"metadata":{"id":"9zNTLlEOAtZ2"}},{"cell_type":"code","source":["Val_returns = pd.DataFrame()\n","\n","for month_year in YYYY_MM:\n","\n","  # Extracting only data associated to those month and dates\n","  Data = factor_scores.loc[factor_scores['MM_YYYY'] == month_year,:] \n","\n","  # Calculate Decile Groups\n","  Data['Quantile_rank'] = pd.qcut(Data['Value_EY'], 10, labels = False)\n","\n","  # Select TICKERS of TOP and BOTTOM decile of stocks based on score\n","  top_decile =  Data.loc[Data['Quantile_rank'] == 9,'EXCHANGE_TICKER']\n","  bottom_decile =  Data.loc[Data['Quantile_rank'] == 0,'EXCHANGE_TICKER']\n","  \n","  # Creating Empty DataFrame\n","  top_returns = pd.DataFrame()\n","  bottom_returns = pd.DataFrame()\n","\n","  # Extracts the year from the 'Date'\n","  year = (Data.reset_index(drop=True).loc[0,'Date']).year\n","\n","  # Defining the first and last day of the year\n","  start = datetime.datetime(year, 1, 1)\n","  end = datetime.datetime(year, 12, 31)\n","\n","  # Extract Returns for the tickers\n","  for ticker in top_decile:\n","    top_returns[ticker] = returns.loc[((returns['Date'] >= start) & (returns['Date'] <= end)), ticker]\n","\n","  for ticker in bottom_decile:\n","    bottom_returns[ticker] = returns.loc[ ((returns['Date'] >= start) & (returns['Date'] <= end)), ticker]\n","\n","  # Taking the row sum for each returns\n","\n","\n","  portfolio_returns = top_returns.mean(axis=1) - bottom_returns.mean(axis=1) # Allowing shorting\n","  #portfolio_returns = top_returns.mean(axis=1)  # LONG ONLY\n","\n","  # Mergining poortfolio returns with previous years\n","  Val_returns  = [Val_returns , portfolio_returns]\n","  Val_returns  = pd.concat(Val_returns )"],"metadata":{"id":"1G-7T242AmRT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1683829653626,"user_tz":-120,"elapsed":1210,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}},"outputId":"d4d3f97a-fbdb-41c2-f22e-dba1948b0cd7"},"execution_count":20,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-20-684f9210498b>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Value_EY'], 10, labels = False)\n","<ipython-input-20-684f9210498b>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Value_EY'], 10, labels = False)\n","<ipython-input-20-684f9210498b>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Value_EY'], 10, labels = False)\n","<ipython-input-20-684f9210498b>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Value_EY'], 10, labels = False)\n","<ipython-input-20-684f9210498b>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Value_EY'], 10, labels = False)\n","<ipython-input-20-684f9210498b>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Value_EY'], 10, labels = False)\n","<ipython-input-20-684f9210498b>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Value_EY'], 10, labels = False)\n","<ipython-input-20-684f9210498b>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Value_EY'], 10, labels = False)\n","<ipython-input-20-684f9210498b>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Value_EY'], 10, labels = False)\n"]}]},{"cell_type":"code","source":["Val_returns = (Val_returns+1).cumprod()\n","\n","x_axis = range(2266)\n","y_axis =Val_returns\n","\n","plt.plot(x_axis,y_axis)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"XOXaPiUMA5dc","executionInfo":{"status":"ok","timestamp":1683829657640,"user_tz":-120,"elapsed":1069,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}},"outputId":"b43ff1bf-e851-49a4-911f-1b1ca60916a9"},"execution_count":21,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7fd4dc0b8bb0>]"]},"metadata":{},"execution_count":21},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# Low Beta"],"metadata":{"id":"k7JzhNjqA96z"}},{"cell_type":"code","source":["Beta_returns = pd.DataFrame()\n","\n","for month_year in YYYY_MM:\n","\n","  # Extracting only data associated to those month and dates\n","  Data = factor_scores.loc[factor_scores['MM_YYYY'] == month_year,:] \n","\n","  # Calculate Decile Groups\n","  Data['Quantile_rank'] = pd.qcut(Data['Beta'], 10, labels = False)\n","\n","  # Select TICKERS of TOP and BOTTOM decile of stocks based on score\n","  top_decile =  Data.loc[Data['Quantile_rank'] == 9,'EXCHANGE_TICKER']\n","  bottom_decile =  Data.loc[Data['Quantile_rank'] == 0,'EXCHANGE_TICKER']\n","  \n","  # Creating Empty DataFrame\n","  top_returns = pd.DataFrame()\n","  bottom_returns = pd.DataFrame()\n","\n","  # Extracts the year from the 'Date'\n","  year = (Data.reset_index(drop=True).loc[0,'Date']).year\n","\n","  # Defining the first and last day of the year\n","  start = datetime.datetime(year, 1, 1)\n","  end = datetime.datetime(year, 12, 31)\n","\n","  # Extract Returns for the tickers\n","  for ticker in top_decile:\n","    top_returns[ticker] = returns.loc[((returns['Date'] >= start) & (returns['Date'] <= end)), ticker]\n","\n","  for ticker in bottom_decile:\n","    bottom_returns[ticker] = returns.loc[ ((returns['Date'] >= start) & (returns['Date'] <= end)), ticker]\n","\n","  # Taking the row sum for each returns\n","\n","\n","  portfolio_returns = top_returns.mean(axis=1) - bottom_returns.mean(axis=1) # Allowing shorting\n","  #portfolio_returns = top_returns.mean(axis=1)  # LONG ONLY\n","\n","  # Mergining poortfolio returns with previous years\n","  Beta_returns  = [Beta_returns, portfolio_returns]\n","  Beta_returns  = pd.concat(Beta_returns )"],"metadata":{"id":"ZiJBJpISBArV","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1683829660490,"user_tz":-120,"elapsed":683,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}},"outputId":"f0e061eb-8fc6-4196-c679-fac809bd1046"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-22-d24f928df08e>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Beta'], 10, labels = False)\n","<ipython-input-22-d24f928df08e>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Beta'], 10, labels = False)\n","<ipython-input-22-d24f928df08e>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Beta'], 10, labels = False)\n","<ipython-input-22-d24f928df08e>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Beta'], 10, labels = False)\n","<ipython-input-22-d24f928df08e>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Beta'], 10, labels = False)\n","<ipython-input-22-d24f928df08e>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Beta'], 10, labels = False)\n","<ipython-input-22-d24f928df08e>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Beta'], 10, labels = False)\n","<ipython-input-22-d24f928df08e>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Beta'], 10, labels = False)\n","<ipython-input-22-d24f928df08e>:9: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Beta'], 10, labels = False)\n"]}]},{"cell_type":"code","source":["Beta_returns = (Beta_returns+1).cumprod()\n","\n","x_axis = range(2266)\n","y_axis =Beta_returns\n","\n","plt.plot(x_axis,y_axis)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"PCby6guLBFqG","executionInfo":{"status":"ok","timestamp":1683829664577,"user_tz":-120,"elapsed":568,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}},"outputId":"4b9fc343-95c0-4896-944f-97e9cbe17e31"},"execution_count":23,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7fd4dc1d72e0>]"]},"metadata":{},"execution_count":23},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["fig, ax = plt.subplots(figsize=(12,7))\n","\n","x_axis = np.linspace(2014, 2022, 2266)\n","y_axis =Mom_Val_returns\n","\n","x_axis2 = np.linspace(2014, 2022, 2266)\n","y_axis2 =Mom_Beta_returns\n","\n","x_axis3 = np.linspace(2014, 2022, 2266)\n","y_axis3 =Val_Beta_returns\n","\n","x_axis4 = np.linspace(2014, 2022, 2266)\n","y_axis4 =Mom_returns\n","\n","x_axis5 = np.linspace(2014, 2022, 2266)\n","y_axis5 =Val_returns\n","\n","x_axis6 = np.linspace(2014, 2022, 2266)\n","y_axis6 =Beta_returns\n","\n","ax.plot(x_axis, y_axis, color='red', label='Momentum-Value')\n","ax.plot(x_axis2, y_axis2, color='blue', label='Momentum-Beta')\n","ax.plot(x_axis3, y_axis3, color='yellow', label='Value-Beta')\n","ax.plot(x_axis4, y_axis4, color='green', label='Momentum')\n","ax.plot(x_axis5, y_axis5, color='black', label='Value')\n","ax.plot(x_axis6, y_axis6, color='orange', label='Beta')\n","\n","ax.legend()\n","ax.legend()\n","ax.legend()\n","ax.legend()\n","ax.legend()\n","ax.legend()\n","ax.set_xlabel('Date')\n","ax.set_ylabel('Portfolio Return')\n","ax.set_title('Long/Short + Linear Performance')\n","\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":641},"id":"6rJQzobccMRa","executionInfo":{"status":"ok","timestamp":1683829666963,"user_tz":-120,"elapsed":719,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}},"outputId":"45799eab-7eeb-4e14-af0f-e88d6071ad5b"},"execution_count":24,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x700 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":[],"metadata":{"id":"X_LPM_vkpHHg"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Log\n","\n"],"metadata":{"id":"X3X1_c7tpHo-"}},{"cell_type":"code","source":["# This chunk is for Momentum and Value\n","Mom_Val_Log_returns = pd.DataFrame()\n","\n","for month_year in YYYY_MM:\n","\n","  # Extracting only data associated to those month and dates\n","  Data = factor_scores.loc[factor_scores['MM_YYYY'] == month_year,:] \n","\n","  # Calculate Decile Groups\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val_Log'], 10, labels = False)\n","\n","  # Select TICKERS of TOP and BOTTOM decile of stocks based on score\n","  top_decile =  Data.loc[Data['Quantile_rank'] == 9,'EXCHANGE_TICKER']\n","  bottom_decile =  Data.loc[Data['Quantile_rank'] == 0,'EXCHANGE_TICKER']\n","  \n","  # Creating Empty DataFrame\n","  top_returns = pd.DataFrame()\n","  bottom_returns = pd.DataFrame()\n","\n","  # Extracts the year from the 'Date'\n","  year = (Data.reset_index(drop=True).loc[0,'Date']).year\n","\n","  # Defining the first and last day of the year\n","  start = datetime.datetime(year, 1, 1)\n","  end = datetime.datetime(year, 12, 31)\n","\n","  # Extract Returns for the tickers\n","  for ticker in top_decile:\n","    top_returns[ticker] = returns.loc[((returns['Date'] >= start) & (returns['Date'] <= end)), ticker]\n","\n","  for ticker in bottom_decile:\n","    bottom_returns[ticker] = returns.loc[ ((returns['Date'] >= start) & (returns['Date'] <= end)), ticker]\n","\n","  # Taking the row sum for each returns\n","\n","\n","  #portfolio_returns = top_returns.mean(axis=1) - bottom_returns.mean(axis=1) # Allowing shorting\n","  portfolio_returns = top_returns.mean(axis=1)  # LONG ONLY\n","\n","  # Mergining poortfolio returns with previous years\n","  Mom_Val_Log_returns = [Mom_Val_Log_returns, portfolio_returns]\n","  Mom_Val_Log_returns = pd.concat(Mom_Val_Log_returns)\n"],"metadata":{"id":"E2e0kM0HpKGB","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1683829668948,"user_tz":-120,"elapsed":282,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}},"outputId":"57f18c6f-7f09-439f-c6bc-f7a41a5e339f"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-25-c29bd568874b>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val_Log'], 10, labels = False)\n","<ipython-input-25-c29bd568874b>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val_Log'], 10, labels = False)\n","<ipython-input-25-c29bd568874b>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val_Log'], 10, labels = False)\n","<ipython-input-25-c29bd568874b>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val_Log'], 10, labels = False)\n","<ipython-input-25-c29bd568874b>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val_Log'], 10, labels = False)\n","<ipython-input-25-c29bd568874b>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val_Log'], 10, labels = False)\n","<ipython-input-25-c29bd568874b>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val_Log'], 10, labels = False)\n","<ipython-input-25-c29bd568874b>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val_Log'], 10, labels = False)\n","<ipython-input-25-c29bd568874b>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val_Log'], 10, labels = False)\n"]}]},{"cell_type":"code","source":["# This chunk is for Momentum and Value\n","Mom_Beta_Log_returns = pd.DataFrame()\n","\n","for month_year in YYYY_MM:\n","\n","  # Extracting only data associated to those month and dates\n","  Data = factor_scores.loc[factor_scores['MM_YYYY'] == month_year,:] \n","\n","  # Calculate Decile Groups\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet_Log'], 10, labels = False)\n","\n","  # Select TICKERS of TOP and BOTTOM decile of stocks based on score\n","  top_decile =  Data.loc[Data['Quantile_rank'] == 9,'EXCHANGE_TICKER']\n","  bottom_decile =  Data.loc[Data['Quantile_rank'] == 0,'EXCHANGE_TICKER']\n","  \n","  # Creating Empty DataFrame\n","  top_returns = pd.DataFrame()\n","  bottom_returns = pd.DataFrame()\n","\n","  # Extracts the year from the 'Date'\n","  year = (Data.reset_index(drop=True).loc[0,'Date']).year\n","\n","  # Defining the first and last day of the year\n","  start = datetime.datetime(year, 1, 1)\n","  end = datetime.datetime(year, 12, 31)\n","\n","  # Extract Returns for the tickers\n","  for ticker in top_decile:\n","    top_returns[ticker] = returns.loc[((returns['Date'] >= start) & (returns['Date'] <= end)), ticker]\n","\n","  for ticker in bottom_decile:\n","    bottom_returns[ticker] = returns.loc[ ((returns['Date'] >= start) & (returns['Date'] <= end)), ticker]\n","\n","  # Taking the row sum for each returns\n","\n","\n","  #portfolio_returns = top_returns.mean(axis=1) - bottom_returns.mean(axis=1) # Allowing shorting\n","  portfolio_returns = top_returns.mean(axis=1)  # LONG ONLY\n","\n","  # Mergining poortfolio returns with previous years\n","  Mom_Beta_Log_returns = [Mom_Beta_Log_returns, portfolio_returns]\n","  Mom_Beta_Log_returns = pd.concat(Mom_Beta_Log_returns)"],"metadata":{"id":"DvNy0ygHplj7","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1683829672368,"user_tz":-120,"elapsed":1568,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}},"outputId":"200d3bcb-50c6-4406-e11d-6487b9c25356"},"execution_count":26,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-26-a5d8d16ad80d>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet_Log'], 10, labels = False)\n","<ipython-input-26-a5d8d16ad80d>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet_Log'], 10, labels = False)\n","<ipython-input-26-a5d8d16ad80d>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet_Log'], 10, labels = False)\n","<ipython-input-26-a5d8d16ad80d>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet_Log'], 10, labels = False)\n","<ipython-input-26-a5d8d16ad80d>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet_Log'], 10, labels = False)\n","<ipython-input-26-a5d8d16ad80d>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet_Log'], 10, labels = False)\n","<ipython-input-26-a5d8d16ad80d>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet_Log'], 10, labels = False)\n","<ipython-input-26-a5d8d16ad80d>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet_Log'], 10, labels = False)\n","<ipython-input-26-a5d8d16ad80d>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet_Log'], 10, labels = False)\n"]}]},{"cell_type":"code","source":["# This chunk is for Momentum and Value\n","Val_Beta_Log_returns = pd.DataFrame()\n","\n","for month_year in YYYY_MM:\n","\n","  # Extracting only data associated to those month and dates\n","  Data = factor_scores.loc[factor_scores['MM_YYYY'] == month_year,:] \n","\n","  # Calculate Decile Groups\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta_Log'], 10, labels = False)\n","\n","  # Select TICKERS of TOP and BOTTOM decile of stocks based on score\n","  top_decile =  Data.loc[Data['Quantile_rank'] == 9,'EXCHANGE_TICKER']\n","  bottom_decile =  Data.loc[Data['Quantile_rank'] == 0,'EXCHANGE_TICKER']\n","  \n","  # Creating Empty DataFrame\n","  top_returns = pd.DataFrame()\n","  bottom_returns = pd.DataFrame()\n","\n","  # Extracts the year from the 'Date'\n","  year = (Data.reset_index(drop=True).loc[0,'Date']).year\n","\n","  # Defining the first and last day of the year\n","  start = datetime.datetime(year, 1, 1)\n","  end = datetime.datetime(year, 12, 31)\n","\n","  # Extract Returns for the tickers\n","  for ticker in top_decile:\n","    top_returns[ticker] = returns.loc[((returns['Date'] >= start) & (returns['Date'] <= end)), ticker]\n","\n","  for ticker in bottom_decile:\n","    bottom_returns[ticker] = returns.loc[ ((returns['Date'] >= start) & (returns['Date'] <= end)), ticker]\n","\n","  # Taking the row sum for each returns\n","\n","\n","  #portfolio_returns = top_returns.mean(axis=1) - bottom_returns.mean(axis=1) # Allowing shorting\n","  portfolio_returns = top_returns.mean(axis=1)  # LONG ONLY\n","\n","  # Mergining poortfolio returns with previous years\n","  Val_Beta_Log_returns = [Val_Beta_Log_returns, portfolio_returns]\n","  Val_Beta_Log_returns = pd.concat(Val_Beta_Log_returns)"],"metadata":{"id":"0CCN_Kl2p2lO","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1683829675301,"user_tz":-120,"elapsed":708,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}},"outputId":"c8baff8e-a08d-41a3-c3a8-d3fa446fbd6f"},"execution_count":27,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-27-9caf86612082>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta_Log'], 10, labels = False)\n","<ipython-input-27-9caf86612082>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta_Log'], 10, labels = False)\n","<ipython-input-27-9caf86612082>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta_Log'], 10, labels = False)\n","<ipython-input-27-9caf86612082>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta_Log'], 10, labels = False)\n","<ipython-input-27-9caf86612082>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta_Log'], 10, labels = False)\n","<ipython-input-27-9caf86612082>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta_Log'], 10, labels = False)\n","<ipython-input-27-9caf86612082>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta_Log'], 10, labels = False)\n","<ipython-input-27-9caf86612082>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta_Log'], 10, labels = False)\n","<ipython-input-27-9caf86612082>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta_Log'], 10, labels = False)\n"]}]},{"cell_type":"code","source":["Mom_Beta_Log_returns = (Mom_Beta_Log_returns+1).cumprod()\n","\n","Mom_Val_Log_returns = (Mom_Val_Log_returns+1).cumprod()\n","\n","Val_Beta_Log_returns = (Val_Beta_Log_returns+1).cumprod()"],"metadata":{"id":"vHgtgrSIpXCB","executionInfo":{"status":"ok","timestamp":1683829677512,"user_tz":-120,"elapsed":184,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}}},"execution_count":28,"outputs":[]},{"cell_type":"code","source":["# This chunk is for Momentum and Value\n","Mom_Val_Quad_returns = pd.DataFrame()\n","\n","for month_year in YYYY_MM:\n","\n","  # Extracting only data associated to those month and dates\n","  Data = factor_scores.loc[factor_scores['MM_YYYY'] == month_year,:] \n","\n","  # Calculate Decile Groups\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val_Quad'], 10, labels = False)\n","\n","  # Select TICKERS of TOP and BOTTOM decile of stocks based on score\n","  top_decile =  Data.loc[Data['Quantile_rank'] == 9,'EXCHANGE_TICKER']\n","  bottom_decile =  Data.loc[Data['Quantile_rank'] == 0,'EXCHANGE_TICKER']\n","  \n","  # Creating Empty DataFrame\n","  top_returns = pd.DataFrame()\n","  bottom_returns = pd.DataFrame()\n","\n","  # Extracts the year from the 'Date'\n","  year = (Data.reset_index(drop=True).loc[0,'Date']).year\n","\n","  # Defining the first and last day of the year\n","  start = datetime.datetime(year, 1, 1)\n","  end = datetime.datetime(year, 12, 31)\n","\n","  # Extract Returns for the tickers\n","  for ticker in top_decile:\n","    top_returns[ticker] = returns.loc[((returns['Date'] >= start) & (returns['Date'] <= end)), ticker]\n","\n","  for ticker in bottom_decile:\n","    bottom_returns[ticker] = returns.loc[ ((returns['Date'] >= start) & (returns['Date'] <= end)), ticker]\n","\n","  # Taking the row sum for each returns\n","\n","\n","  #portfolio_returns = top_returns.mean(axis=1) - bottom_returns.mean(axis=1) # Allowing shorting\n","  portfolio_returns = top_returns.mean(axis=1)  # LONG ONLY\n","\n","  # Mergining poortfolio returns with previous years\n","  Mom_Val_Quad_returns = [Mom_Val_Quad_returns, portfolio_returns]\n","  Mom_Val_Quad_returns = pd.concat(Mom_Val_Quad_returns)"],"metadata":{"id":"jiRcN1d7sZ0H","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1683829680940,"user_tz":-120,"elapsed":1703,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}},"outputId":"31f24d44-d658-4f78-b266-ec67aaa6e285"},"execution_count":29,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-29-864184d48fdf>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val_Quad'], 10, labels = False)\n","<ipython-input-29-864184d48fdf>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val_Quad'], 10, labels = False)\n","<ipython-input-29-864184d48fdf>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val_Quad'], 10, labels = False)\n","<ipython-input-29-864184d48fdf>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val_Quad'], 10, labels = False)\n","<ipython-input-29-864184d48fdf>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val_Quad'], 10, labels = False)\n","<ipython-input-29-864184d48fdf>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val_Quad'], 10, labels = False)\n","<ipython-input-29-864184d48fdf>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val_Quad'], 10, labels = False)\n","<ipython-input-29-864184d48fdf>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val_Quad'], 10, labels = False)\n","<ipython-input-29-864184d48fdf>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Val_Quad'], 10, labels = False)\n"]}]},{"cell_type":"code","source":["# This chunk is for Momentum and Value\n","Mom_Beta_Quad_returns = pd.DataFrame()\n","\n","for month_year in YYYY_MM:\n","\n","  # Extracting only data associated to those month and dates\n","  Data = factor_scores.loc[factor_scores['MM_YYYY'] == month_year,:] \n","\n","  # Calculate Decile Groups\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet_Quad'], 10, labels = False)\n","\n","  # Select TICKERS of TOP and BOTTOM decile of stocks based on score\n","  top_decile =  Data.loc[Data['Quantile_rank'] == 9,'EXCHANGE_TICKER']\n","  bottom_decile =  Data.loc[Data['Quantile_rank'] == 0,'EXCHANGE_TICKER']\n","  \n","  # Creating Empty DataFrame\n","  top_returns = pd.DataFrame()\n","  bottom_returns = pd.DataFrame()\n","\n","  # Extracts the year from the 'Date'\n","  year = (Data.reset_index(drop=True).loc[0,'Date']).year\n","\n","  # Defining the first and last day of the year\n","  start = datetime.datetime(year, 1, 1)\n","  end = datetime.datetime(year, 12, 31)\n","\n","  # Extract Returns for the tickers\n","  for ticker in top_decile:\n","    top_returns[ticker] = returns.loc[((returns['Date'] >= start) & (returns['Date'] <= end)), ticker]\n","\n","  for ticker in bottom_decile:\n","    bottom_returns[ticker] = returns.loc[ ((returns['Date'] >= start) & (returns['Date'] <= end)), ticker]\n","\n","  # Taking the row sum for each returns\n","\n","\n","  #portfolio_returns = top_returns.mean(axis=1) - bottom_returns.mean(axis=1) # Allowing shorting\n","  portfolio_returns = top_returns.mean(axis=1)  # LONG ONLY\n","\n","  # Mergining poortfolio returns with previous years\n","  Mom_Beta_Quad_returns = [Mom_Beta_Quad_returns, portfolio_returns]\n","  Mom_Beta_Quad_returns = pd.concat(Mom_Beta_Quad_returns)"],"metadata":{"id":"a5cXQCkIs9pb","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1683829684028,"user_tz":-120,"elapsed":865,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}},"outputId":"812e2e83-991a-4eda-ffaf-e51952bf886a"},"execution_count":30,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-30-8407549dfc69>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet_Quad'], 10, labels = False)\n","<ipython-input-30-8407549dfc69>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet_Quad'], 10, labels = False)\n","<ipython-input-30-8407549dfc69>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet_Quad'], 10, labels = False)\n","<ipython-input-30-8407549dfc69>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet_Quad'], 10, labels = False)\n","<ipython-input-30-8407549dfc69>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet_Quad'], 10, labels = False)\n","<ipython-input-30-8407549dfc69>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet_Quad'], 10, labels = False)\n","<ipython-input-30-8407549dfc69>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet_Quad'], 10, labels = False)\n","<ipython-input-30-8407549dfc69>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet_Quad'], 10, labels = False)\n","<ipython-input-30-8407549dfc69>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Mom_Bet_Quad'], 10, labels = False)\n"]}]},{"cell_type":"code","source":["# This chunk is for Momentum and Value\n","Val_Beta_Quad_returns = pd.DataFrame()\n","\n","for month_year in YYYY_MM:\n","\n","  # Extracting only data associated to those month and dates\n","  Data = factor_scores.loc[factor_scores['MM_YYYY'] == month_year,:] \n","\n","  # Calculate Decile Groups\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta_Quad'], 10, labels = False)\n","\n","  # Select TICKERS of TOP and BOTTOM decile of stocks based on score\n","  top_decile =  Data.loc[Data['Quantile_rank'] == 9,'EXCHANGE_TICKER']\n","  bottom_decile =  Data.loc[Data['Quantile_rank'] == 0,'EXCHANGE_TICKER']\n","  \n","  # Creating Empty DataFrame\n","  top_returns = pd.DataFrame()\n","  bottom_returns = pd.DataFrame()\n","\n","  # Extracts the year from the 'Date'\n","  year = (Data.reset_index(drop=True).loc[0,'Date']).year\n","\n","  # Defining the first and last day of the year\n","  start = datetime.datetime(year, 1, 1)\n","  end = datetime.datetime(year, 12, 31)\n","\n","  # Extract Returns for the tickers\n","  for ticker in top_decile:\n","    top_returns[ticker] = returns.loc[((returns['Date'] >= start) & (returns['Date'] <= end)), ticker]\n","\n","  for ticker in bottom_decile:\n","    bottom_returns[ticker] = returns.loc[ ((returns['Date'] >= start) & (returns['Date'] <= end)), ticker]\n","\n","  # Taking the row sum for each returns\n","\n","\n","  #portfolio_returns = top_returns.mean(axis=1) - bottom_returns.mean(axis=1) # Allowing shorting\n","  portfolio_returns = top_returns.mean(axis=1)  # LONG ONLY\n","\n","  # Mergining poortfolio returns with previous years\n","  Val_Beta_Quad_returns = [Val_Beta_Quad_returns, portfolio_returns]\n","  Val_Beta_Quad_returns = pd.concat(Val_Beta_Quad_returns)"],"metadata":{"id":"RryzFytVtKg_","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1683829687250,"user_tz":-120,"elapsed":1076,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}},"outputId":"7a886d78-1c97-416d-f8b6-3eb8ca6b94a9"},"execution_count":31,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-31-fa9b6e33b337>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta_Quad'], 10, labels = False)\n","<ipython-input-31-fa9b6e33b337>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta_Quad'], 10, labels = False)\n","<ipython-input-31-fa9b6e33b337>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta_Quad'], 10, labels = False)\n","<ipython-input-31-fa9b6e33b337>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta_Quad'], 10, labels = False)\n","<ipython-input-31-fa9b6e33b337>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta_Quad'], 10, labels = False)\n","<ipython-input-31-fa9b6e33b337>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta_Quad'], 10, labels = False)\n","<ipython-input-31-fa9b6e33b337>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta_Quad'], 10, labels = False)\n","<ipython-input-31-fa9b6e33b337>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta_Quad'], 10, labels = False)\n","<ipython-input-31-fa9b6e33b337>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  Data['Quantile_rank'] = pd.qcut(Data['Val_Beta_Quad'], 10, labels = False)\n"]}]},{"cell_type":"code","source":["Mom_Beta_Quad_returns = (Mom_Beta_Quad_returns+1).cumprod()\n","\n","Mom_Val_Quad_returns = (Mom_Val_Quad_returns+1).cumprod()\n","\n","Val_Beta_Quad_returns = (Val_Beta_Quad_returns+1).cumprod()"],"metadata":{"id":"z78iyYdKtfw5","executionInfo":{"status":"ok","timestamp":1683829689783,"user_tz":-120,"elapsed":2,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}}},"execution_count":32,"outputs":[]},{"cell_type":"code","source":["fig, ax = plt.subplots(figsize=(12,7))\n","\n","x_axis = np.linspace(2014, 2022, 2266)\n","y_axis =Mom_Val_returns\n","\n","x_axis2 = np.linspace(2014, 2022, 2266)\n","y_axis2 =Mom_Beta_returns\n","\n","x_axis3 = np.linspace(2014, 2022, 2266)\n","y_axis3 = Val_Beta_returns\n","\n","x_axis4 = np.linspace(2014, 2022, 2266)\n","y_axis4 = Mom_Val_Quad_returns\n","\n","x_axis5 = np.linspace(2014, 2022, 2266)\n","y_axis5 =Mom_Beta_Quad_returns\n","\n","x_axis6 = np.linspace(2014, 2022, 2266)\n","y_axis6 =Val_Beta_Quad_returns\n","\n","x_axis7 = np.linspace(2014, 2022, 2266)\n","y_axis7 = Mom_Val_Log_returns\n","\n","x_axis8 = np.linspace(2014, 2022, 2266)\n","y_axis8 =Mom_Beta_Log_returns\n","\n","x_axis9 = np.linspace(2014, 2022, 2266)\n","y_axis9 =Val_Beta_Log_returns\n","\n","ax.plot(x_axis, y_axis, color='red', label='Momentum-Value')\n","ax.plot(x_axis2, y_axis2, color='blue', label='Momentum-Beta')\n","ax.plot(x_axis3, y_axis3, color='yellow', label='Value-Beta')\n","ax.plot(x_axis4, y_axis4, color='green', label='Momentum-Value (Quadratic)')\n","ax.plot(x_axis5, y_axis5, color='black', label='Momentum-Beta (Quadratic)')\n","ax.plot(x_axis6, y_axis6, color='orange', label='Value-Beta (Quadratic)')\n","ax.plot(x_axis7, y_axis7, color='pink', label='Momentum-Value (Log)')\n","ax.plot(x_axis8, y_axis8, color='slategrey', label='Momentum-Beta (Log)')\n","ax.plot(x_axis9, y_axis9, color='lawngreen', label='Value-Beta (Log)')\n","\n","ax.legend()\n","ax.legend()\n","ax.legend()\n","ax.legend()\n","ax.legend()\n","ax.legend()\n","ax.legend()\n","ax.legend()\n","ax.legend()\n","ax.set_xlabel('Date')\n","ax.set_ylabel('Portfolio Return')\n","ax.set_title('Long-Only')\n","\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":641},"id":"tNUQRbwlqJlB","executionInfo":{"status":"ok","timestamp":1683829693877,"user_tz":-120,"elapsed":2029,"user":{"displayName":"Dmitry Tertychnyy","userId":"01455676677788782180"}},"outputId":"0425c3aa-9031-42e5-a38d-62980d9e2894"},"execution_count":33,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x700 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]}]}