stocks and return values.ipynb

{
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-04-04</td>\n",
       "      <td>178.190002</td>\n",
       "      <td>178.190002</td>\n",
       "      <td>175.520004</td>\n",
       "      <td>176.470001</td>\n",
       "      <td>171.820190</td>\n",
       "      <td>6860600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-04-05</td>\n",
       "      <td>176.470001</td>\n",
       "      <td>179.649994</td>\n",
       "      <td>176.289993</td>\n",
       "      <td>177.610001</td>\n",
       "      <td>172.930145</td>\n",
       "      <td>7446100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-04-06</td>\n",
       "      <td>179.389999</td>\n",
       "      <td>182.600006</td>\n",
       "      <td>178.619995</td>\n",
       "      <td>182.229996</td>\n",
       "      <td>177.428421</td>\n",
       "      <td>10330900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-04-07</td>\n",
       "      <td>181.669998</td>\n",
       "      <td>182.949997</td>\n",
       "      <td>180.229996</td>\n",
       "      <td>181.759995</td>\n",
       "      <td>176.970779</td>\n",
       "      <td>7385200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-04-08</td>\n",
       "      <td>181.330002</td>\n",
       "      <td>183.190002</td>\n",
       "      <td>180.429993</td>\n",
       "      <td>182.119995</td>\n",
       "      <td>177.321320</td>\n",
       "      <td>7144500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>154.199997</td>\n",
       "      <td>154.539993</td>\n",
       "      <td>153.110001</td>\n",
       "      <td>153.300003</td>\n",
       "      <td>153.300003</td>\n",
       "      <td>6772300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>152.389999</td>\n",
       "      <td>153.449997</td>\n",
       "      <td>151.729996</td>\n",
       "      <td>151.820007</td>\n",
       "      <td>151.820007</td>\n",
       "      <td>6115300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>152.240005</td>\n",
       "      <td>153.380005</td>\n",
       "      <td>152.070007</td>\n",
       "      <td>153.309998</td>\n",
       "      <td>153.309998</td>\n",
       "      <td>6012000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>153.580002</td>\n",
       "      <td>153.800003</td>\n",
       "      <td>152.809998</td>\n",
       "      <td>153.429993</td>\n",
       "      <td>153.429993</td>\n",
       "      <td>4555700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>153.789993</td>\n",
       "      <td>155.190002</td>\n",
       "      <td>153.240005</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>9888000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 7 columns</p>\n",
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       "           Date        Open        High         Low       Close   Adj Close  \\\n",
       "0    2022-04-04  178.190002  178.190002  175.520004  176.470001  171.820190   \n",
       "1    2022-04-05  176.470001  179.649994  176.289993  177.610001  172.930145   \n",
       "2    2022-04-06  179.389999  182.600006  178.619995  182.229996  177.428421   \n",
       "3    2022-04-07  181.669998  182.949997  180.229996  181.759995  176.970779   \n",
       "4    2022-04-08  181.330002  183.190002  180.429993  182.119995  177.321320   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "245  2023-03-27  154.199997  154.539993  153.110001  153.300003  153.300003   \n",
       "246  2023-03-28  152.389999  153.449997  151.729996  151.820007  151.820007   \n",
       "247  2023-03-29  152.240005  153.380005  152.070007  153.309998  153.309998   \n",
       "248  2023-03-30  153.580002  153.800003  152.809998  153.429993  153.429993   \n",
       "249  2023-03-31  153.789993  155.190002  153.240005  155.000000  155.000000   \n",
       "\n",
       "       Volume  \n",
       "0     6860600  \n",
       "1     7446100  \n",
       "2    10330900  \n",
       "3     7385200  \n",
       "4     7144500  \n",
       "..        ...  \n",
       "245   6772300  \n",
       "246   6115300  \n",
       "247   6012000  \n",
       "248   4555700  \n",
       "249   9888000  \n",
       "\n",
       "[250 rows x 7 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "jnj_hc=pd.read_csv(\"C:/Users/91897/Downloads/JNJ_HC.csv\")\n",
    "jnj_hc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "901d9be7",
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   "outputs": [
    {
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       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>84.459999</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>84.059998</td>\n",
       "      <td>84.680000</td>\n",
       "      <td>76.710106</td>\n",
       "      <td>3456700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>84.739998</td>\n",
       "      <td>85.419998</td>\n",
       "      <td>84.269997</td>\n",
       "      <td>85.139999</td>\n",
       "      <td>77.126816</td>\n",
       "      <td>3078400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>85.279999</td>\n",
       "      <td>85.800003</td>\n",
       "      <td>84.739998</td>\n",
       "      <td>85.519997</td>\n",
       "      <td>77.471046</td>\n",
       "      <td>2462500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>86.089996</td>\n",
       "      <td>86.750000</td>\n",
       "      <td>85.930000</td>\n",
       "      <td>86.570000</td>\n",
       "      <td>78.422241</td>\n",
       "      <td>2946300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>86.690002</td>\n",
       "      <td>86.709999</td>\n",
       "      <td>85.400002</td>\n",
       "      <td>86.129997</td>\n",
       "      <td>78.023643</td>\n",
       "      <td>3251400</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>327.799988</td>\n",
       "      <td>336.220001</td>\n",
       "      <td>323.260010</td>\n",
       "      <td>334.600006</td>\n",
       "      <td>334.600006</td>\n",
       "      <td>3102300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>335.799988</td>\n",
       "      <td>338.829987</td>\n",
       "      <td>334.720001</td>\n",
       "      <td>337.649994</td>\n",
       "      <td>337.649994</td>\n",
       "      <td>2433200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>339.000000</td>\n",
       "      <td>339.769989</td>\n",
       "      <td>334.579987</td>\n",
       "      <td>335.869995</td>\n",
       "      <td>335.869995</td>\n",
       "      <td>2743800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>336.070007</td>\n",
       "      <td>341.309998</td>\n",
       "      <td>336.070007</td>\n",
       "      <td>340.690002</td>\n",
       "      <td>340.690002</td>\n",
       "      <td>2146900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>343.649994</td>\n",
       "      <td>340.690002</td>\n",
       "      <td>343.420013</td>\n",
       "      <td>343.420013</td>\n",
       "      <td>2932800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1321 rows × 7 columns</p>\n",
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      "text/plain": [
       "            Date        Open        High         Low       Close   Adj Close  \\\n",
       "0     2018-01-02   84.459999   85.000000   84.059998   84.680000   76.710106   \n",
       "1     2018-01-03   84.739998   85.419998   84.269997   85.139999   77.126816   \n",
       "2     2018-01-04   85.279999   85.800003   84.739998   85.519997   77.471046   \n",
       "3     2018-01-05   86.089996   86.750000   85.930000   86.570000   78.422241   \n",
       "4     2018-01-08   86.690002   86.709999   85.400002   86.129997   78.023643   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "1316  2023-03-27  327.799988  336.220001  323.260010  334.600006  334.600006   \n",
       "1317  2023-03-28  335.799988  338.829987  334.720001  337.649994  337.649994   \n",
       "1318  2023-03-29  339.000000  339.769989  334.579987  335.869995  335.869995   \n",
       "1319  2023-03-30  336.070007  341.309998  336.070007  340.690002  340.690002   \n",
       "1320  2023-03-31  342.000000  343.649994  340.690002  343.420013  343.420013   \n",
       "\n",
       "       Volume  \n",
       "0     3456700  \n",
       "1     3078400  \n",
       "2     2462500  \n",
       "3     2946300  \n",
       "4     3251400  \n",
       "...       ...  \n",
       "1316  3102300  \n",
       "1317  2433200  \n",
       "1318  2743800  \n",
       "1319  2146900  \n",
       "1320  2932800  \n",
       "\n",
       "[1321 rows x 7 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "lly_hc=pd.read_csv(\"C:/Users/91897/Downloads/LLY_HC.csv\")\n",
    "lly_hc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a64aa8fc",
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
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       "      <th>Volume</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>54.490002</td>\n",
       "      <td>54.520000</td>\n",
       "      <td>53.990002</td>\n",
       "      <td>54.160000</td>\n",
       "      <td>49.106178</td>\n",
       "      <td>741400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>54.540001</td>\n",
       "      <td>54.750000</td>\n",
       "      <td>54.380001</td>\n",
       "      <td>54.680000</td>\n",
       "      <td>49.577660</td>\n",
       "      <td>752400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>54.650002</td>\n",
       "      <td>54.680000</td>\n",
       "      <td>54.360001</td>\n",
       "      <td>54.490002</td>\n",
       "      <td>49.405388</td>\n",
       "      <td>604200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>54.470001</td>\n",
       "      <td>54.759998</td>\n",
       "      <td>54.450001</td>\n",
       "      <td>54.740002</td>\n",
       "      <td>49.632057</td>\n",
       "      <td>645400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>54.560001</td>\n",
       "      <td>54.740002</td>\n",
       "      <td>54.380001</td>\n",
       "      <td>54.689999</td>\n",
       "      <td>49.586723</td>\n",
       "      <td>614000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>153.539993</td>\n",
       "      <td>155.440002</td>\n",
       "      <td>153.100006</td>\n",
       "      <td>154.699997</td>\n",
       "      <td>154.699997</td>\n",
       "      <td>1164500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>155.660004</td>\n",
       "      <td>155.860001</td>\n",
       "      <td>154.529999</td>\n",
       "      <td>155.320007</td>\n",
       "      <td>155.320007</td>\n",
       "      <td>916300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>156.240005</td>\n",
       "      <td>157.190002</td>\n",
       "      <td>155.940002</td>\n",
       "      <td>156.580002</td>\n",
       "      <td>156.580002</td>\n",
       "      <td>822400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>157.229996</td>\n",
       "      <td>158.110001</td>\n",
       "      <td>157.059998</td>\n",
       "      <td>158.089996</td>\n",
       "      <td>158.089996</td>\n",
       "      <td>1033200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>158.559998</td>\n",
       "      <td>159.740005</td>\n",
       "      <td>158.050003</td>\n",
       "      <td>159.139999</td>\n",
       "      <td>159.139999</td>\n",
       "      <td>1142800</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>1321 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date        Open        High         Low       Close   Adj Close  \\\n",
       "0     2018-01-02   54.490002   54.520000   53.990002   54.160000   49.106178   \n",
       "1     2018-01-03   54.540001   54.750000   54.380001   54.680000   49.577660   \n",
       "2     2018-01-04   54.650002   54.680000   54.360001   54.490002   49.405388   \n",
       "3     2018-01-05   54.470001   54.759998   54.450001   54.740002   49.632057   \n",
       "4     2018-01-08   54.560001   54.740002   54.380001   54.689999   49.586723   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "1316  2023-03-27  153.539993  155.440002  153.100006  154.699997  154.699997   \n",
       "1317  2023-03-28  155.660004  155.860001  154.529999  155.320007  155.320007   \n",
       "1318  2023-03-29  156.240005  157.190002  155.940002  156.580002  156.580002   \n",
       "1319  2023-03-30  157.229996  158.110001  157.059998  158.089996  158.089996   \n",
       "1320  2023-03-31  158.559998  159.740005  158.050003  159.139999  159.139999   \n",
       "\n",
       "       Volume  \n",
       "0      741400  \n",
       "1      752400  \n",
       "2      604200  \n",
       "3      645400  \n",
       "4      614000  \n",
       "...       ...  \n",
       "1316  1164500  \n",
       "1317   916300  \n",
       "1318   822400  \n",
       "1319  1033200  \n",
       "1320  1142800  \n",
       "\n",
       "[1321 rows x 7 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "nvo_hc=pd.read_csv(\"C:/Users/91897/Downloads/NVO_HC.csv\")\n",
    "nvo_hc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "cf8f042c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>42.540001</td>\n",
       "      <td>43.075001</td>\n",
       "      <td>42.314999</td>\n",
       "      <td>43.064999</td>\n",
       "      <td>40.888058</td>\n",
       "      <td>102223600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>43.132500</td>\n",
       "      <td>43.637501</td>\n",
       "      <td>42.990002</td>\n",
       "      <td>43.057499</td>\n",
       "      <td>40.880947</td>\n",
       "      <td>118071600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>43.134998</td>\n",
       "      <td>43.367500</td>\n",
       "      <td>43.020000</td>\n",
       "      <td>43.257500</td>\n",
       "      <td>41.070835</td>\n",
       "      <td>89738400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>43.360001</td>\n",
       "      <td>43.842499</td>\n",
       "      <td>43.262501</td>\n",
       "      <td>43.750000</td>\n",
       "      <td>41.538437</td>\n",
       "      <td>94640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>43.587502</td>\n",
       "      <td>43.902500</td>\n",
       "      <td>43.482498</td>\n",
       "      <td>43.587502</td>\n",
       "      <td>41.384163</td>\n",
       "      <td>82271200</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>159.940002</td>\n",
       "      <td>160.770004</td>\n",
       "      <td>157.869995</td>\n",
       "      <td>158.279999</td>\n",
       "      <td>158.279999</td>\n",
       "      <td>52390300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>157.970001</td>\n",
       "      <td>158.490005</td>\n",
       "      <td>155.979996</td>\n",
       "      <td>157.649994</td>\n",
       "      <td>157.649994</td>\n",
       "      <td>45992200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>159.369995</td>\n",
       "      <td>161.050003</td>\n",
       "      <td>159.350006</td>\n",
       "      <td>160.770004</td>\n",
       "      <td>160.770004</td>\n",
       "      <td>51305700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>161.529999</td>\n",
       "      <td>162.470001</td>\n",
       "      <td>161.270004</td>\n",
       "      <td>162.360001</td>\n",
       "      <td>162.360001</td>\n",
       "      <td>49501700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>162.440002</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>161.910004</td>\n",
       "      <td>164.899994</td>\n",
       "      <td>164.899994</td>\n",
       "      <td>68694700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1321 rows × 7 columns</p>\n",
       "</div>"
      ],
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       "            Date        Open        High         Low       Close   Adj Close  \\\n",
       "0     2018-01-02   42.540001   43.075001   42.314999   43.064999   40.888058   \n",
       "1     2018-01-03   43.132500   43.637501   42.990002   43.057499   40.880947   \n",
       "2     2018-01-04   43.134998   43.367500   43.020000   43.257500   41.070835   \n",
       "3     2018-01-05   43.360001   43.842499   43.262501   43.750000   41.538437   \n",
       "4     2018-01-08   43.587502   43.902500   43.482498   43.587502   41.384163   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "1316  2023-03-27  159.940002  160.770004  157.869995  158.279999  158.279999   \n",
       "1317  2023-03-28  157.970001  158.490005  155.979996  157.649994  157.649994   \n",
       "1318  2023-03-29  159.369995  161.050003  159.350006  160.770004  160.770004   \n",
       "1319  2023-03-30  161.529999  162.470001  161.270004  162.360001  162.360001   \n",
       "1320  2023-03-31  162.440002  165.000000  161.910004  164.899994  164.899994   \n",
       "\n",
       "         Volume  \n",
       "0     102223600  \n",
       "1     118071600  \n",
       "2      89738400  \n",
       "3      94640000  \n",
       "4      82271200  \n",
       "...         ...  \n",
       "1316   52390300  \n",
       "1317   45992200  \n",
       "1318   51305700  \n",
       "1319   49501700  \n",
       "1320   68694700  \n",
       "\n",
       "[1321 rows x 7 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "aapl_it=pd.read_csv(\"C:/Users/91897/Downloads/AAPL_IT.csv\")\n",
    "aapl_it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "bbc7ba10",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>86.129997</td>\n",
       "      <td>86.309998</td>\n",
       "      <td>85.500000</td>\n",
       "      <td>85.949997</td>\n",
       "      <td>80.738167</td>\n",
       "      <td>22483800</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>86.059998</td>\n",
       "      <td>86.510002</td>\n",
       "      <td>85.970001</td>\n",
       "      <td>86.349998</td>\n",
       "      <td>81.113922</td>\n",
       "      <td>26061400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>86.589996</td>\n",
       "      <td>87.660004</td>\n",
       "      <td>86.570000</td>\n",
       "      <td>87.110001</td>\n",
       "      <td>81.827827</td>\n",
       "      <td>21912000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>87.660004</td>\n",
       "      <td>88.410004</td>\n",
       "      <td>87.430000</td>\n",
       "      <td>88.190002</td>\n",
       "      <td>82.842339</td>\n",
       "      <td>23407100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>88.199997</td>\n",
       "      <td>88.580002</td>\n",
       "      <td>87.599998</td>\n",
       "      <td>88.279999</td>\n",
       "      <td>82.926888</td>\n",
       "      <td>22113000</td>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>280.500000</td>\n",
       "      <td>281.459991</td>\n",
       "      <td>275.519989</td>\n",
       "      <td>276.380005</td>\n",
       "      <td>276.380005</td>\n",
       "      <td>26840200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>275.790009</td>\n",
       "      <td>276.140015</td>\n",
       "      <td>272.049988</td>\n",
       "      <td>275.230011</td>\n",
       "      <td>275.230011</td>\n",
       "      <td>21878600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>278.959991</td>\n",
       "      <td>281.140015</td>\n",
       "      <td>278.410004</td>\n",
       "      <td>280.510010</td>\n",
       "      <td>280.510010</td>\n",
       "      <td>25087000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>284.230011</td>\n",
       "      <td>284.459991</td>\n",
       "      <td>281.480011</td>\n",
       "      <td>284.049988</td>\n",
       "      <td>284.049988</td>\n",
       "      <td>25053400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>283.730011</td>\n",
       "      <td>289.269989</td>\n",
       "      <td>283.000000</td>\n",
       "      <td>288.299988</td>\n",
       "      <td>288.299988</td>\n",
       "      <td>32740300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1321 rows × 7 columns</p>\n",
       "</div>"
      ],
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       "            Date        Open        High         Low       Close   Adj Close  \\\n",
       "0     2018-01-02   86.129997   86.309998   85.500000   85.949997   80.738167   \n",
       "1     2018-01-03   86.059998   86.510002   85.970001   86.349998   81.113922   \n",
       "2     2018-01-04   86.589996   87.660004   86.570000   87.110001   81.827827   \n",
       "3     2018-01-05   87.660004   88.410004   87.430000   88.190002   82.842339   \n",
       "4     2018-01-08   88.199997   88.580002   87.599998   88.279999   82.926888   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "1316  2023-03-27  280.500000  281.459991  275.519989  276.380005  276.380005   \n",
       "1317  2023-03-28  275.790009  276.140015  272.049988  275.230011  275.230011   \n",
       "1318  2023-03-29  278.959991  281.140015  278.410004  280.510010  280.510010   \n",
       "1319  2023-03-30  284.230011  284.459991  281.480011  284.049988  284.049988   \n",
       "1320  2023-03-31  283.730011  289.269989  283.000000  288.299988  288.299988   \n",
       "\n",
       "        Volume  \n",
       "0     22483800  \n",
       "1     26061400  \n",
       "2     21912000  \n",
       "3     23407100  \n",
       "4     22113000  \n",
       "...        ...  \n",
       "1316  26840200  \n",
       "1317  21878600  \n",
       "1318  25087000  \n",
       "1319  25053400  \n",
       "1320  32740300  \n",
       "\n",
       "[1321 rows x 7 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "msft_it=pd.read_csv(\"C:/Users/91897/Downloads/MSFT_IT.csv\")\n",
    "msft_it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "32c48cdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>47.570000</td>\n",
       "      <td>47.799999</td>\n",
       "      <td>46.169998</td>\n",
       "      <td>46.630001</td>\n",
       "      <td>42.764431</td>\n",
       "      <td>25380000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>47.529999</td>\n",
       "      <td>48.070000</td>\n",
       "      <td>47.439999</td>\n",
       "      <td>47.709999</td>\n",
       "      <td>43.754902</td>\n",
       "      <td>24165000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>47.990002</td>\n",
       "      <td>48.189999</td>\n",
       "      <td>47.720001</td>\n",
       "      <td>48.180000</td>\n",
       "      <td>44.185932</td>\n",
       "      <td>19815100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>48.419998</td>\n",
       "      <td>48.630001</td>\n",
       "      <td>48.279999</td>\n",
       "      <td>48.470001</td>\n",
       "      <td>44.451900</td>\n",
       "      <td>14496400</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>48.299999</td>\n",
       "      <td>49.070000</td>\n",
       "      <td>47.939999</td>\n",
       "      <td>48.980000</td>\n",
       "      <td>44.919617</td>\n",
       "      <td>15075000</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>88.620003</td>\n",
       "      <td>90.500000</td>\n",
       "      <td>88.550003</td>\n",
       "      <td>90.139999</td>\n",
       "      <td>90.139999</td>\n",
       "      <td>8170100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>90.220001</td>\n",
       "      <td>90.879997</td>\n",
       "      <td>89.480003</td>\n",
       "      <td>89.849998</td>\n",
       "      <td>89.849998</td>\n",
       "      <td>6378900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>90.129997</td>\n",
       "      <td>90.660004</td>\n",
       "      <td>89.809998</td>\n",
       "      <td>90.620003</td>\n",
       "      <td>90.620003</td>\n",
       "      <td>5233600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>90.849998</td>\n",
       "      <td>91.180000</td>\n",
       "      <td>89.709999</td>\n",
       "      <td>90.510002</td>\n",
       "      <td>90.510002</td>\n",
       "      <td>5640700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>90.559998</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>90.410004</td>\n",
       "      <td>92.919998</td>\n",
       "      <td>92.919998</td>\n",
       "      <td>10475300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1321 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date       Open       High        Low      Close  Adj Close  \\\n",
       "0     2018-01-02  47.570000  47.799999  46.169998  46.630001  42.764431   \n",
       "1     2018-01-03  47.529999  48.070000  47.439999  47.709999  43.754902   \n",
       "2     2018-01-04  47.990002  48.189999  47.720001  48.180000  44.185932   \n",
       "3     2018-01-05  48.419998  48.630001  48.279999  48.470001  44.451900   \n",
       "4     2018-01-08  48.299999  49.070000  47.939999  48.980000  44.919617   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "1316  2023-03-27  88.620003  90.500000  88.550003  90.139999  90.139999   \n",
       "1317  2023-03-28  90.220001  90.879997  89.480003  89.849998  89.849998   \n",
       "1318  2023-03-29  90.129997  90.660004  89.809998  90.620003  90.620003   \n",
       "1319  2023-03-30  90.849998  91.180000  89.709999  90.510002  90.510002   \n",
       "1320  2023-03-31  90.559998  93.000000  90.410004  92.919998  92.919998   \n",
       "\n",
       "        Volume  \n",
       "0     25380000  \n",
       "1     24165000  \n",
       "2     19815100  \n",
       "3     14496400  \n",
       "4     15075000  \n",
       "...        ...  \n",
       "1316   8170100  \n",
       "1317   6378900  \n",
       "1318   5233600  \n",
       "1319   5640700  \n",
       "1320  10475300  \n",
       "\n",
       "[1321 rows x 7 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "orcl_it=pd.read_csv(\"C:/Users/91897/Downloads/ORCL_IT.csv\")\n",
    "orcl_it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d713fa9f",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>28.920000</td>\n",
       "      <td>28.920000</td>\n",
       "      <td>28.629999</td>\n",
       "      <td>28.750000</td>\n",
       "      <td>20.367199</td>\n",
       "      <td>87837</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>28.799999</td>\n",
       "      <td>28.850000</td>\n",
       "      <td>28.600000</td>\n",
       "      <td>28.680000</td>\n",
       "      <td>20.317612</td>\n",
       "      <td>61386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>28.740000</td>\n",
       "      <td>28.790001</td>\n",
       "      <td>28.659901</td>\n",
       "      <td>28.719999</td>\n",
       "      <td>20.345945</td>\n",
       "      <td>37062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>28.790001</td>\n",
       "      <td>28.850000</td>\n",
       "      <td>28.700001</td>\n",
       "      <td>28.850000</td>\n",
       "      <td>20.438038</td>\n",
       "      <td>30642</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>28.920000</td>\n",
       "      <td>28.920000</td>\n",
       "      <td>28.770000</td>\n",
       "      <td>28.790001</td>\n",
       "      <td>20.395536</td>\n",
       "      <td>42057</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>25.330000</td>\n",
       "      <td>25.450001</td>\n",
       "      <td>25.110001</td>\n",
       "      <td>25.170000</td>\n",
       "      <td>25.170000</td>\n",
       "      <td>86237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>25.230000</td>\n",
       "      <td>25.260000</td>\n",
       "      <td>25.125000</td>\n",
       "      <td>25.170000</td>\n",
       "      <td>25.170000</td>\n",
       "      <td>54619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>25.170000</td>\n",
       "      <td>25.330000</td>\n",
       "      <td>25.150000</td>\n",
       "      <td>25.150000</td>\n",
       "      <td>25.150000</td>\n",
       "      <td>110085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>25.260000</td>\n",
       "      <td>25.280001</td>\n",
       "      <td>25.150000</td>\n",
       "      <td>25.190001</td>\n",
       "      <td>25.190001</td>\n",
       "      <td>77456</td>\n",
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       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>25.230000</td>\n",
       "      <td>25.230000</td>\n",
       "      <td>25.030001</td>\n",
       "      <td>25.030001</td>\n",
       "      <td>25.030001</td>\n",
       "      <td>229008</td>\n",
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       "<p>1321 rows × 7 columns</p>\n",
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       "            Date       Open       High        Low      Close  Adj Close  \\\n",
       "0     2018-01-02  28.920000  28.920000  28.629999  28.750000  20.367199   \n",
       "1     2018-01-03  28.799999  28.850000  28.600000  28.680000  20.317612   \n",
       "2     2018-01-04  28.740000  28.790001  28.659901  28.719999  20.345945   \n",
       "3     2018-01-05  28.790001  28.850000  28.700001  28.850000  20.438038   \n",
       "4     2018-01-08  28.920000  28.920000  28.770000  28.790001  20.395536   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "1316  2023-03-27  25.330000  25.450001  25.110001  25.170000  25.170000   \n",
       "1317  2023-03-28  25.230000  25.260000  25.125000  25.170000  25.170000   \n",
       "1318  2023-03-29  25.170000  25.330000  25.150000  25.150000  25.150000   \n",
       "1319  2023-03-30  25.260000  25.280001  25.150000  25.190001  25.190001   \n",
       "1320  2023-03-31  25.230000  25.230000  25.030001  25.030001  25.030001   \n",
       "\n",
       "      Volume  \n",
       "0      87837  \n",
       "1      61386  \n",
       "2      37062  \n",
       "3      30642  \n",
       "4      42057  \n",
       "...      ...  \n",
       "1316   86237  \n",
       "1317   54619  \n",
       "1318  110085  \n",
       "1319   77456  \n",
       "1320  229008  \n",
       "\n",
       "[1321 rows x 7 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "cpj_fn=pd.read_csv(\"C:/Users/91897/Downloads/C-PJ_FN.csv\")\n",
    "cpj_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "92a7ebb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>51.889999</td>\n",
       "      <td>52.410000</td>\n",
       "      <td>40.521835</td>\n",
       "      <td>2119600</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>51.669998</td>\n",
       "      <td>52.169998</td>\n",
       "      <td>51.669998</td>\n",
       "      <td>52.150002</td>\n",
       "      <td>40.320816</td>\n",
       "      <td>2283600</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>52.049999</td>\n",
       "      <td>52.310001</td>\n",
       "      <td>52.029999</td>\n",
       "      <td>52.259998</td>\n",
       "      <td>40.405857</td>\n",
       "      <td>2857400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>52.020000</td>\n",
       "      <td>52.180000</td>\n",
       "      <td>51.889999</td>\n",
       "      <td>52.169998</td>\n",
       "      <td>40.336273</td>\n",
       "      <td>1871100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>51.630001</td>\n",
       "      <td>51.849998</td>\n",
       "      <td>51.619999</td>\n",
       "      <td>51.750000</td>\n",
       "      <td>40.011543</td>\n",
       "      <td>1255300</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>33.410000</td>\n",
       "      <td>33.650002</td>\n",
       "      <td>33.299999</td>\n",
       "      <td>33.639999</td>\n",
       "      <td>33.639999</td>\n",
       "      <td>2071800</td>\n",
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       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>33.320000</td>\n",
       "      <td>33.619999</td>\n",
       "      <td>33.250000</td>\n",
       "      <td>33.459999</td>\n",
       "      <td>33.459999</td>\n",
       "      <td>1620200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>34.200001</td>\n",
       "      <td>34.349998</td>\n",
       "      <td>34.070000</td>\n",
       "      <td>34.220001</td>\n",
       "      <td>34.220001</td>\n",
       "      <td>2183500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>34.560001</td>\n",
       "      <td>34.560001</td>\n",
       "      <td>34.240002</td>\n",
       "      <td>34.290001</td>\n",
       "      <td>34.290001</td>\n",
       "      <td>1263000</td>\n",
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       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>34.130001</td>\n",
       "      <td>34.220001</td>\n",
       "      <td>33.990002</td>\n",
       "      <td>34.130001</td>\n",
       "      <td>34.130001</td>\n",
       "      <td>1716500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1321 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date       Open       High        Low      Close  Adj Close  \\\n",
       "0     2018-01-02  52.000000  52.439999  51.889999  52.410000  40.521835   \n",
       "1     2018-01-03  51.669998  52.169998  51.669998  52.150002  40.320816   \n",
       "2     2018-01-04  52.049999  52.310001  52.029999  52.259998  40.405857   \n",
       "3     2018-01-05  52.020000  52.180000  51.889999  52.169998  40.336273   \n",
       "4     2018-01-08  51.630001  51.849998  51.619999  51.750000  40.011543   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "1316  2023-03-27  33.410000  33.650002  33.299999  33.639999  33.639999   \n",
       "1317  2023-03-28  33.320000  33.619999  33.250000  33.459999  33.459999   \n",
       "1318  2023-03-29  34.200001  34.349998  34.070000  34.220001  34.220001   \n",
       "1319  2023-03-30  34.560001  34.560001  34.240002  34.290001  34.290001   \n",
       "1320  2023-03-31  34.130001  34.220001  33.990002  34.130001  34.130001   \n",
       "\n",
       "       Volume  \n",
       "0     2119600  \n",
       "1     2283600  \n",
       "2     2857400  \n",
       "3     1871100  \n",
       "4     1255300  \n",
       "...       ...  \n",
       "1316  2071800  \n",
       "1317  1620200  \n",
       "1318  2183500  \n",
       "1319  1263000  \n",
       "1320  1716500  \n",
       "\n",
       "[1321 rows x 7 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "hsbc_fn=pd.read_csv(\"C:/Users/91897/Downloads/HSBC_FN.csv\")\n",
    "hsbc_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "870b33d3",
   "metadata": {},
   "outputs": [
    {
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       "      <td>6946100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>116.250000</td>\n",
       "      <td>117.360001</td>\n",
       "      <td>116.029999</td>\n",
       "      <td>116.080002</td>\n",
       "      <td>112.111008</td>\n",
       "      <td>7805300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>116.989998</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>116.419998</td>\n",
       "      <td>118.860001</td>\n",
       "      <td>114.795944</td>\n",
       "      <td>8590500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>118.610001</td>\n",
       "      <td>120.480003</td>\n",
       "      <td>118.070000</td>\n",
       "      <td>119.339996</td>\n",
       "      <td>115.259537</td>\n",
       "      <td>6660700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>222.589996</td>\n",
       "      <td>224.080002</td>\n",
       "      <td>221.389999</td>\n",
       "      <td>221.809998</td>\n",
       "      <td>221.809998</td>\n",
       "      <td>7210900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>222.820007</td>\n",
       "      <td>223.330002</td>\n",
       "      <td>218.649994</td>\n",
       "      <td>220.330002</td>\n",
       "      <td>220.330002</td>\n",
       "      <td>6418400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>222.289993</td>\n",
       "      <td>223.550003</td>\n",
       "      <td>220.919998</td>\n",
       "      <td>223.300003</td>\n",
       "      <td>223.300003</td>\n",
       "      <td>4483000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>223.919998</td>\n",
       "      <td>224.240005</td>\n",
       "      <td>221.210007</td>\n",
       "      <td>222.360001</td>\n",
       "      <td>222.360001</td>\n",
       "      <td>5383900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>223.600006</td>\n",
       "      <td>225.839996</td>\n",
       "      <td>223.289993</td>\n",
       "      <td>225.460007</td>\n",
       "      <td>225.460007</td>\n",
       "      <td>9507200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1321 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date        Open        High         Low       Close   Adj Close  \\\n",
       "0     2018-01-02  114.570000  115.080002  113.949997  114.510002  110.594673   \n",
       "1     2018-01-03  114.949997  115.790001  114.660004  115.650002  111.695686   \n",
       "2     2018-01-04  116.250000  117.360001  116.029999  116.080002  112.111008   \n",
       "3     2018-01-05  116.989998  119.000000  116.419998  118.860001  114.795944   \n",
       "4     2018-01-08  118.610001  120.480003  118.070000  119.339996  115.259537   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "1316  2023-03-27  222.589996  224.080002  221.389999  221.809998  221.809998   \n",
       "1317  2023-03-28  222.820007  223.330002  218.649994  220.330002  220.330002   \n",
       "1318  2023-03-29  222.289993  223.550003  220.919998  223.300003  223.300003   \n",
       "1319  2023-03-30  223.919998  224.240005  221.210007  222.360001  222.360001   \n",
       "1320  2023-03-31  223.600006  225.839996  223.289993  225.460007  225.460007   \n",
       "\n",
       "       Volume  \n",
       "0     6080900  \n",
       "1     6946100  \n",
       "2     7805300  \n",
       "3     8590500  \n",
       "4     6660700  \n",
       "...       ...  \n",
       "1316  7210900  \n",
       "1317  6418400  \n",
       "1318  4483000  \n",
       "1319  5383900  \n",
       "1320  9507200  \n",
       "\n",
       "[1321 rows x 7 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "v_fn=pd.read_csv(\"C:/Users/91897/Downloads/V_FN.csv\")\n",
    "v_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "85964dd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         0\n",
       "Open         0\n",
       "High         0\n",
       "Low          0\n",
       "Close        0\n",
       "Adj Close    0\n",
       "Volume       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jnj_hc.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6e4ce884",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         0\n",
       "Open         0\n",
       "High         0\n",
       "Low          0\n",
       "Close        0\n",
       "Adj Close    0\n",
       "Volume       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lly_hc.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6fb2abd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         0\n",
       "Open         0\n",
       "High         0\n",
       "Low          0\n",
       "Close        0\n",
       "Adj Close    0\n",
       "Volume       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nvo_hc.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0186fb4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         0\n",
       "Open         0\n",
       "High         0\n",
       "Low          0\n",
       "Close        0\n",
       "Adj Close    0\n",
       "Volume       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_it.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "062f7e2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         0\n",
       "Open         0\n",
       "High         0\n",
       "Low          0\n",
       "Close        0\n",
       "Adj Close    0\n",
       "Volume       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msft_it.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d6018e78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         0\n",
       "Open         0\n",
       "High         0\n",
       "Low          0\n",
       "Close        0\n",
       "Adj Close    0\n",
       "Volume       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orcl_it.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a73650f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         0\n",
       "Open         0\n",
       "High         0\n",
       "Low          0\n",
       "Close        0\n",
       "Adj Close    0\n",
       "Volume       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpj_fn.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0b0e0956",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         0\n",
       "Open         0\n",
       "High         0\n",
       "Low          0\n",
       "Close        0\n",
       "Adj Close    0\n",
       "Volume       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hsbc_fn.isnull().sum()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "4c622b4f",
   "metadata": {},
   "source": [
    "v_fn.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "97bf63c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         0\n",
       "Open         0\n",
       "High         0\n",
       "Low          0\n",
       "Close        0\n",
       "Adj Close    0\n",
       "Volume       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v_fn.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "8aca2b3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Close</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-04-04</td>\n",
       "      <td>176.470001</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-04-05</td>\n",
       "      <td>177.610001</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-04-06</td>\n",
       "      <td>182.229996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-04-07</td>\n",
       "      <td>181.759995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-04-08</td>\n",
       "      <td>182.119995</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>153.300003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>151.820007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>153.309998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>153.429993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>155.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date       Close\n",
       "0    2022-04-04  176.470001\n",
       "1    2022-04-05  177.610001\n",
       "2    2022-04-06  182.229996\n",
       "3    2022-04-07  181.759995\n",
       "4    2022-04-08  182.119995\n",
       "..          ...         ...\n",
       "245  2023-03-27  153.300003\n",
       "246  2023-03-28  151.820007\n",
       "247  2023-03-29  153.309998\n",
       "248  2023-03-30  153.429993\n",
       "249  2023-03-31  155.000000\n",
       "\n",
       "[250 rows x 2 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jnj_hc['hc1']= np.log(jnj_hc['Close'] / jnj_hc['Close'].shift(1))\n",
    "jnj_hc['hc1']\n",
    "df=pd.DataFrame(jnj_hc['hc1'])\n",
    "df\n",
    "df=jnj_hc[['Date','Close']]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "519e2c0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>84.680000</td>\n",
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       "    <tr>\n",
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       "      <td>2018-01-03</td>\n",
       "      <td>85.139999</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>85.519997</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>86.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>86.129997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>334.600006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>337.649994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>335.869995</td>\n",
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       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>340.690002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>343.420013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1321 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date       Close\n",
       "0     2018-01-02   84.680000\n",
       "1     2018-01-03   85.139999\n",
       "2     2018-01-04   85.519997\n",
       "3     2018-01-05   86.570000\n",
       "4     2018-01-08   86.129997\n",
       "...          ...         ...\n",
       "1316  2023-03-27  334.600006\n",
       "1317  2023-03-28  337.649994\n",
       "1318  2023-03-29  335.869995\n",
       "1319  2023-03-30  340.690002\n",
       "1320  2023-03-31  343.420013\n",
       "\n",
       "[1321 rows x 2 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lly_hc['hc2']= np.log(lly_hc['Close'] / lly_hc['Close'].shift(1))\n",
    "lly_hc['hc2']\n",
    "df1=pd.DataFrame(lly_hc['hc2'])\n",
    "df1\n",
    "df1=lly_hc[['Date','Close']]\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "12632e15",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2018-01-02</td>\n",
       "      <td>54.160000</td>\n",
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       "      <td>2018-01-03</td>\n",
       "      <td>54.680000</td>\n",
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       "      <th>2</th>\n",
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       "      <td>2018-01-05</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>54.689999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>154.699997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>155.320007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>156.580002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>158.089996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>159.139999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1321 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date       Close\n",
       "0     2018-01-02   54.160000\n",
       "1     2018-01-03   54.680000\n",
       "2     2018-01-04   54.490002\n",
       "3     2018-01-05   54.740002\n",
       "4     2018-01-08   54.689999\n",
       "...          ...         ...\n",
       "1316  2023-03-27  154.699997\n",
       "1317  2023-03-28  155.320007\n",
       "1318  2023-03-29  156.580002\n",
       "1319  2023-03-30  158.089996\n",
       "1320  2023-03-31  159.139999\n",
       "\n",
       "[1321 rows x 2 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nvo_hc['hc3']= np.log(nvo_hc['Close'] / nvo_hc['Close'].shift(1))\n",
    "nvo_hc['hc3']\n",
    "df2=pd.DataFrame(nvo_hc['hc3'])\n",
    "df2\n",
    "df2=nvo_hc[['Date','Close']]\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "37bdd8e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2018-01-04</td>\n",
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       "      <th>1317</th>\n",
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       "      <td>157.649994</td>\n",
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       "      <td>2023-03-29</td>\n",
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       "      <td>2023-03-30</td>\n",
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       "<p>1321 rows × 2 columns</p>\n",
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      "text/plain": [
       "            Date       Close\n",
       "0     2018-01-02   43.064999\n",
       "1     2018-01-03   43.057499\n",
       "2     2018-01-04   43.257500\n",
       "3     2018-01-05   43.750000\n",
       "4     2018-01-08   43.587502\n",
       "...          ...         ...\n",
       "1316  2023-03-27  158.279999\n",
       "1317  2023-03-28  157.649994\n",
       "1318  2023-03-29  160.770004\n",
       "1319  2023-03-30  162.360001\n",
       "1320  2023-03-31  164.899994\n",
       "\n",
       "[1321 rows x 2 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl_it['it1']= np.log(aapl_it['Close'] / aapl_it['Close'].shift(1))\n",
    "aapl_it['it1']\n",
    "df3=pd.DataFrame(aapl_it['it1'])\n",
    "df3\n",
    "df3=aapl_it[['Date','Close']]\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "40ba9f5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2018-01-05</td>\n",
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       "      <td>88.279999</td>\n",
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       "      <th>1316</th>\n",
       "      <td>2023-03-27</td>\n",
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       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>275.230011</td>\n",
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       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>2023-03-29</td>\n",
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       "<p>1321 rows × 2 columns</p>\n",
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      "text/plain": [
       "            Date       Close\n",
       "0     2018-01-02   85.949997\n",
       "1     2018-01-03   86.349998\n",
       "2     2018-01-04   87.110001\n",
       "3     2018-01-05   88.190002\n",
       "4     2018-01-08   88.279999\n",
       "...          ...         ...\n",
       "1316  2023-03-27  276.380005\n",
       "1317  2023-03-28  275.230011\n",
       "1318  2023-03-29  280.510010\n",
       "1319  2023-03-30  284.049988\n",
       "1320  2023-03-31  288.299988\n",
       "\n",
       "[1321 rows x 2 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msft_it['it2']= np.log(msft_it['Close'] / msft_it['Close'].shift(1))\n",
    "msft_it['it2']\n",
    "df4=pd.DataFrame(msft_it['it2'])\n",
    "df4\n",
    "df4=msft_it[['Date','Close']]\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e2eae9b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2018-01-03</td>\n",
       "      <td>47.709999</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>48.180000</td>\n",
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       "      <th>3</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>48.470001</td>\n",
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       "      <td>48.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>90.139999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>89.849998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>90.620003</td>\n",
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       "      <th>1319</th>\n",
       "      <td>2023-03-30</td>\n",
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       "      <th>1320</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>92.919998</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1321 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date      Close\n",
       "0     2018-01-02  46.630001\n",
       "1     2018-01-03  47.709999\n",
       "2     2018-01-04  48.180000\n",
       "3     2018-01-05  48.470001\n",
       "4     2018-01-08  48.980000\n",
       "...          ...        ...\n",
       "1316  2023-03-27  90.139999\n",
       "1317  2023-03-28  89.849998\n",
       "1318  2023-03-29  90.620003\n",
       "1319  2023-03-30  90.510002\n",
       "1320  2023-03-31  92.919998\n",
       "\n",
       "[1321 rows x 2 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orcl_it['it3']= np.log(orcl_it['Close'] / orcl_it['Close'].shift(1))\n",
    "orcl_it['it3']\n",
    "df5=pd.DataFrame(orcl_it['it3'])\n",
    "df5\n",
    "df5=orcl_it[['Date','Close']]\n",
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "367d7c71",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91897\\AppData\\Local\\Temp\\ipykernel_9704\\2200073611.py:1: 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",
      "  df['hc1']= np.log(df['Close'])- np.log(df['Close']-1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1bac4c54160>]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['hc1']= np.log(df['Close'])- np.log(df['Close']-1)\n",
    "plt.plot(df['hc1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "13256ac1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91897\\AppData\\Local\\Temp\\ipykernel_9704\\330131369.py:1: 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",
      "  df1['hc2']= np.log(df1['Close'])- np.log(df1['Close']-1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1bac4cb9040>]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1['hc2']= np.log(df1['Close'])- np.log(df1['Close']-1)\n",
    "plt.plot(df1['hc2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "c93aa844",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91897\\AppData\\Local\\Temp\\ipykernel_9704\\3531773710.py:1: 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",
      "  df2['hc3']= np.log(df2['Close'])- np.log(df2['Close']-1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1bac4d11dc0>]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2['hc3']= np.log(df2['Close'])- np.log(df2['Close']-1)\n",
    "plt.plot(df2['hc3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "5c272027",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91897\\AppData\\Local\\Temp\\ipykernel_9704\\2422692702.py:1: 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",
      "  df3['it1']= np.log(df3['Close'])- np.log(df3['Close']-1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1bac4d7c6a0>]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3['it1']= np.log(df3['Close'])- np.log(df3['Close']-1)\n",
    "plt.plot(df3['it1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "bac3f5f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91897\\AppData\\Local\\Temp\\ipykernel_9704\\1154107901.py:1: 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",
      "  df4['it2']= np.log(df4['Close'])- np.log(df4['Close']-1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1bac4dd9ee0>]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df4['it2']= np.log(df4['Close'])- np.log(df4['Close']-1)\n",
    "plt.plot(df4['it2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "f5b2c17e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91897\\AppData\\Local\\Temp\\ipykernel_9704\\2438354461.py:1: 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",
      "  df5['it3']= np.log(df5['Close'])- np.log(df5['Close']-1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1bac4e3f760>]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df5['it3']= np.log(df5['Close'])- np.log(df5['Close']-1)\n",
    "plt.plot(df5['it3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "4c1126fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>28.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>28.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>28.719999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>28.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>28.790001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>25.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>25.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>25.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>25.190001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>25.030001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1321 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date      Close\n",
       "0     2018-01-02  28.750000\n",
       "1     2018-01-03  28.680000\n",
       "2     2018-01-04  28.719999\n",
       "3     2018-01-05  28.850000\n",
       "4     2018-01-08  28.790001\n",
       "...          ...        ...\n",
       "1316  2023-03-27  25.170000\n",
       "1317  2023-03-28  25.170000\n",
       "1318  2023-03-29  25.150000\n",
       "1319  2023-03-30  25.190001\n",
       "1320  2023-03-31  25.030001\n",
       "\n",
       "[1321 rows x 2 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpj_fn['fn1']= np.log(cpj_fn['Close'] / cpj_fn['Close'].shift(1))\n",
    "cpj_fn['fn1']\n",
    "df6=pd.DataFrame(cpj_fn['fn1'])\n",
    "df6\n",
    "df6=cpj_fn[['Date','Close']]\n",
    "df6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "3b788da5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>52.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>52.150002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>52.259998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>52.169998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>51.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>33.639999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>33.459999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>34.220001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>34.290001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>34.130001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1321 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date      Close\n",
       "0     2018-01-02  52.410000\n",
       "1     2018-01-03  52.150002\n",
       "2     2018-01-04  52.259998\n",
       "3     2018-01-05  52.169998\n",
       "4     2018-01-08  51.750000\n",
       "...          ...        ...\n",
       "1316  2023-03-27  33.639999\n",
       "1317  2023-03-28  33.459999\n",
       "1318  2023-03-29  34.220001\n",
       "1319  2023-03-30  34.290001\n",
       "1320  2023-03-31  34.130001\n",
       "\n",
       "[1321 rows x 2 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hsbc_fn['fn2']= np.log(hsbc_fn['Close'] / hsbc_fn['Close'].shift(1))\n",
    "hsbc_fn['fn2']\n",
    "df7=pd.DataFrame(hsbc_fn['fn2'])\n",
    "df7\n",
    "df7=hsbc_fn[['Date','Close']]\n",
    "df7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "8e91e0e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>114.510002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>115.650002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>116.080002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>118.860001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>119.339996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>221.809998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>220.330002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>223.300003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>222.360001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>225.460007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1321 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date       Close\n",
       "0     2018-01-02  114.510002\n",
       "1     2018-01-03  115.650002\n",
       "2     2018-01-04  116.080002\n",
       "3     2018-01-05  118.860001\n",
       "4     2018-01-08  119.339996\n",
       "...          ...         ...\n",
       "1316  2023-03-27  221.809998\n",
       "1317  2023-03-28  220.330002\n",
       "1318  2023-03-29  223.300003\n",
       "1319  2023-03-30  222.360001\n",
       "1320  2023-03-31  225.460007\n",
       "\n",
       "[1321 rows x 2 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v_fn['fn3']= np.log(v_fn['Close'] / v_fn['Close'].shift(1))\n",
    "v_fn['fn3']\n",
    "df8=pd.DataFrame(v_fn['fn3'])\n",
    "df8\n",
    "df8=v_fn[['Date','Close']]\n",
    "df8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "867af8ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91897\\AppData\\Local\\Temp\\ipykernel_9704\\1145704698.py:1: 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",
      "  df6['fn1']= np.log(df6['Close'])- np.log(df6['Close']-1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1bac4ead970>]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df6['fn1']= np.log(df6['Close'])- np.log(df6['Close']-1)\n",
    "plt.plot(df6['fn1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "1aec5a0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91897\\AppData\\Local\\Temp\\ipykernel_9704\\992782333.py:1: 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",
      "  df7['fn2']= np.log(df7['Close'])- np.log(df7['Close']-1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1bac4f20310>]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df7['fn2']= np.log(df7['Close'])- np.log(df7['Close']-1)\n",
    "plt.plot(df7['fn2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "2a894ee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91897\\AppData\\Local\\Temp\\ipykernel_9704\\1552126854.py:1: 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",
      "  df8['fn3']= np.log(df8['Close'])- np.log(df8['Close']-1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1bac4a8c070>]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df8['fn3']= np.log(df8['Close'])- np.log(df8['Close']-1)\n",
    "plt.plot(df8['fn3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "64e05ced",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAD8CAYAAABTjp5OAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAWFUlEQVR4nO3df6wlZX3H8ffHhVX8kbqowMrSupFNU4hA2y1aqalU1ywrump/BGLoFjUXjJu2xjRdY2K1/lGqbU0q1M1ViJigVKOUjd0KSLTUIu0uBGFX3LJsQa67gPxQ1K0s955P/zhzyXg895w59/y4O3M/LzI5M/M8z8xzh+S73zzzzIxsExERR79nLXUHIiKimgTsiIiaSMCOiKiJBOyIiJpIwI6IqIkE7IiImkjAjohYgKSrJD0iac8C5ZL0j5L2S7pL0m+UyjZK2leUbRtFfxKwIyIW9hlgY4/y84B1xTIFfBJA0grgiqL8NOBCSacN25kE7IiIBdi+BXi8R5XNwGfddhvwQkmrgbOB/bYP2D4CXFvUHcoxwx6gn6cfPZBHKcfsvevfv9RdWBY+9uG1S92Fxjtuy2Ua9hiDxJyVL3n5JbQz43nTtqcHON3JwIOl7ZliX7f9rxzguF2NPWBHRExUa65y1SI4DxKgO3X7B8Y99g8lATsimsWtSZ5tBjiltL0GOAisXGD/UDKGHRHN0mpVX4a3A/jjYrbIq4Af2T4E7ALWSVoraSVwQVF3KMmwI6JRPMIMW9LngdcCL5Y0A/wVcGz7PN4O7AQ2AfuBw8DFRdmspK3ADcAK4Crbe4ftTwJ2RDTL3OzIDmX7wj7lBt6zQNlO2gF9ZBKwI6JZBrjpWDcJ2BHRLJO96ThRCdgR0SyjuZl4VErAjohGGeVNx6NNAnZENEsy7IiImph7eql7MDYJ2BHRLBkSiYioiQyJRETURDLsiIiaSIYdEVEPbuWmY0REPSTDjoioiYxhR0TURF7+FBFRE8mwIyJqImPYERE1McIPGBxtErAjolkanGHnI7wR0Sj2XOWlH0kbJe2TtF/Sti7lfyHpzmLZI2lO0vFF2f2S7i7Kdo/ib0uGHRHNMqIMW9IK4ApgAzAD7JK0w/Z35uvY/hjwsaL+m4D32n68dJhzbT86kg6RDDsimsat6ktvZwP7bR+wfQS4Ftjco/6FwOdH9Fd0lYAdEc3SalVfejsZeLC0PVPs+wWSngtsBL5U2m3gRkm3S5oa4i96RoZEIqJZBpglUgTScjCdtj09X9yliRc41JuA/+wYDjnH9kFJJwA3Sfqu7Vsqd66LBOyIaJYBHpwpgvP0AsUzwCml7TXAwQXqXkDHcIjtg8XvI5Kuoz3EMlTAzpBIRDTL6IZEdgHrJK2VtJJ2UN7RWUnSLwG/C1xf2vc8SS+YXwfeAOwZ9k/rm2FLOgnA9kOSXgK8Bthne++wJ4+IGLkRzRKxPStpK3ADsAK4yvZeSZcW5duLqm8FbrT901LzE4HrJEE7zn7O9leH7VPPgC3pEmBbe1V/C/wJsBf4G0kftX3lsB2IiBipEb5LxPZOYGfHvu0d258BPtOx7wBw5sg6UuiXYW8FTgeOAx4ATi0y7VXA14EE7Ig4uizjR9Oftn0YOCzpPtsPAdh+QtJCd0sjIpZOgx9N7xewW5KOtf008Mb5nZKeQ25YRsTRaBm/XvVtFPMObc+U9r8IeN+4OhURsWgNzrB7Zsm2v1fcKV1bZNXzHgf2L9RO0pSk3ZJ2f/qzY31SMyLi541uWt9Rp+qDM18EXl3aniv2/Va3yuXJ6E8/eiBj3RExOW5uyKkasI8pXn4CgO0jxUTyiIijy2xzZ4lUvXH4A0lvnt+QtBkY2SsDIyJGZnRv6zvqVM2wLwWukXR5sT0DXDSeLkVEDKGGY9NVVQ3YM8AngLXAKuBJ4O3AX4+pXxERi5MxbK4HfgjcwcJvq4qIWHrJsFlje+NYexIRMQoJ2Nwq6RW27x5rbyIihuS5/h/Xrat+b+u7m/aTjscAF0s6ADxF+0sMtn3G+LsYETGAZZxhnz+RXkREjEoNp+tV1TNg235gUh2JiBiJVmaJRETUwzIeEomIqJfletMxIqJ2Gpxh5yMEEdEsLVdf+pC0UdI+SfslbetS/lpJP5J0Z7F8sGrbxUiGHRHNMqJZIpJWAFcAG2i/nmOXpB22v9NR9T9sn7/ItgNJhh0RzTK6DPtsYL/tA8Xrpa8FNlfsxTBtF5SAHRGN4lar8lL+OlaxTJUOdTLwYGl7ptjX6bclfVvSv0k6fcC2A8mQSEQ0ywCzRMpfx+pC3Zp0bN8B/Irtn0jaBPwLsK5i24Elw46IZhndkMgMcEppew0dbyu1/aTtnxTrO4FjJb24StvFSMCOiGYZ3Ud4dwHrio+QrwQuAHaUK0g6SZKK9bNpx9THqrRdjAyJRESzjOjRdNuzkrYCNwArgKts75V0aVG+HfgD4N2SZoH/Ay6wbaBr22H7lIAdEc0ywpc/FcMcOzv2bS+tXw5c3tluobbDSsCOiGbJy58iIurBs3mXSEREPSTDjoioieX6AYOIiNpJhh0RUQ9OwI6IqIncdIyIqIlk2BERNZGAHRFRD+0nw5spATsimiUZdkRETSRgL957179/3KdY9j6++2+WugvLgn/206XuQlTg2Tw4ExFRD82N1wnYEdEseXAmIqIuErAjImoiQyIREfXQ5CGRfIQ3IhrFs6689CNpo6R9kvZL2tal/O2S7iqWWyWdWSq7X9Ldku6UtHsUf1sy7IholhENiUhaAVwBbABmgF2Sdtj+Tqna/wK/a/sJSecB08ArS+Xn2n50ND1KwI6Ihhnh9wvOBvbbPgAg6VpgM/BMwLZ9a6n+bcCakZ29iwyJRESztKovkqYk7S4tU6UjnQw8WNqeKfYt5J3Av5W2Ddwo6faO4y5aMuyIaJRBMmzb07SHMbpRtyZdK0rn0g7Yv1PafY7tg5JOAG6S9F3bt1Tv3S9Khh0RjeLZ6ksfM8Appe01wMHOSpLOAD4NbLb92DP9sA8Wv48A19EeYhlKAnZENIpb1Zc+dgHrJK2VtBK4ANhRriDpl4EvAxfZ/p/S/udJesH8OvAGYM+wf1uGRCKiUUZ109H2rKStwA3ACuAq23slXVqUbwc+CLwI+CdJALO21wMnAtcV+44BPmf7q8P2KQE7IprF3YaeF3koeyews2Pf9tL6u4B3dWl3ADizc/+wErAjolFGOK3vqJOAHRGN4tboMuyjTQJ2RDRKay4BOyKiFjIkEhFRExkSiYioCTf37aoJ2BHRLMmwIyJqIjcdIyJqIhl2RERNeIRPOh5tErAjolEyrS8ioiZaybAjIuohQyIRETWRWSIRETWxrGeJSDoJwPZDkl4CvAbYZ3vvuDsXETGoJo9h9/xEmKRLgG8Bt0l6N/AV4Hzgy5LeOYH+RUQMxFblpW76ZdhbgdOB44AHgFOLTHsV8HXgyjH3LyJiIE1+l0i/j/A+bftw8SXg+2w/BGD7CRb43HtExFJqWZWXfiRtlLRP0n5J27qUS9I/FuV3SfqNqm0Xo1/Abkk6tlh/Y6kjz6nQNiJi4lotVV56kbQCuAI4DzgNuFDSaR3VzgPWFcsU8MkB2g6sX9B9G0UmbXumtP9FwPuGPXlExKiNMMM+G9hv+4DtI8C1wOaOOpuBz7rtNuCFklZXbDuwngHb9veKT72vLbLqeY8D+xdqJ2lK0m5Ju/f++L5h+xgRUdkgNx3LsapYpkqHOhl4sLQ9U+yjQp0qbQdWdVjji0D5Cf25Yl9Xtqdtr7e9/vQXvHyY/kVEDGSQDLscq4plunSobil45727hepUaTuwqg/OHFOk9e2z2kckrRz25BERozbC2RAzwCml7TXAwYp1VlZoO7CqGfYPJL15fkPSZuDRYU8eETFqc61nVV762AWsK4aEVwIXADs66uwA/riYLfIq4Ee2D1VsO7CqGfalwDWSLi+2Z4CLhj15RMSojertqsX9u63ADcAK4CrbeyVdWpRvB3YCm2jf0zsMXNyr7bB9qhqwZ4BPAGuBVcCTwNuBvx62AxERo+Suw8eLPJa9k3ZQLu/bXlo38J6qbYdVNWBfD/wQuIMRjMNERIxLq8GP9FUN2GtsbxxrTyIiRqA1wgz7aFP1puOtkl4x1p5ERIyAUeWlbnpm2JLupj1L5hjgYkkHgKdozzG07TPG38WIiOrmahiIq+o3JHL+RHoRETEiDf4Gb++AbfuBSXUkImIUlm3AjoiomzqOTVeVgB0RjdLgTzomYEdEszR5Wl8CdkQ0ytxSd2CMErAjolFaSoYdEVELDX4yPQE7Ipol0/oiImois0QiImpiOT+aHhFRK8mwIyJqImPYERE10eRZIlXfhx0RUQstVV+GIel4STdJurf4XdWlzimSvi7pHkl7Jf1ZqexDkr4v6c5i2dTvnAnYEdEorQGWIW0Dbra9Dri52O40C7zP9q8BrwLeI+m0UvnHbZ9VLH2//5iAHRGNMqfqy5A2A1cX61cDb+msYPuQ7TuK9R8D9wAnL/aECdgR0SiDZNiSpiTtLi1TA5zqRNuHoB2YgRN6VZb0MuDXgf8q7d4q6S5JV3UbUumUm44R0SiDDHXYngamFyqX9DXgpC5FHxikT5KeD3wJ+HPbTxa7Pwl8hPZ90o8Afw+8o9dxErAjolFGOUvE9usXKpP0sKTVtg9JWg08skC9Y2kH62tsf7l07IdLdT4FfKVffzIkEhGNMqlZIsAOYEuxvgW4vrOCJAFXAvfY/oeOstWlzbcCe/qdMAE7IhplgrNELgM2SLoX2FBsI+mlkuZnfJwDXAT8Xpfpex+VdLeku4Bzgff2O2GGRCKiUSb1AQPbjwGv67L/ILCpWP8mdH+5ie2LBj1nAnZENEreJRIRURN5l0hERE00+V0iYw/YH/vw2nGfYtnzz3661F1YFvSc5y11F6KCVoNDdjLsiGiUfDU9IqImMoYdEVETmSUSEVETGcOOiKiJ5obrBOyIaJiMYUdE1MRcg3PsBOyIaJRk2BERNZGbjhERNdHccJ2AHRENkyGRiIiayE3HiIiayBh2RERNNDdcJ2BHRMNMKsOWdDzwz8DLgPuBP7L9RJd69wM/pv0iwVnb6wdpX5aP8EZEo0zwI7zbgJttrwNuLrYXcq7ts+aD9SLaAwnYEdEwHuC/IW0Gri7WrwbeMu72CdgR0ShzuPIiaUrS7tIyNcCpTrR9CKD4PWGBegZulHR7x/Grtn9GxrAjolEGGeqwPQ1ML1Qu6WvASV2KPjDAac6xfVDSCcBNkr5r+5YB2j8jATsiGqXl0d10tP36hcokPSxpte1DklYDjyxwjIPF7yOSrgPOBm4BKrUvy5BIRDSKB1iGtAPYUqxvAa7vrCDpeZJeML8OvAHYU7V9pwTsiGiUFq68DOkyYIOke4ENxTaSXippZ1HnROCbkr4N/Dfwr7a/2qt9LxkSiYhGGcHsj2rnsR8DXtdl/0FgU7F+ADhzkPa9JGBHRKPMNvhZxwTsiGiUSWXYSyEBOyIaJa9XjYioCY9wWt/RJgE7Iholr1eNiKiJfMAgIqImlnWGLekkANsPSXoJ8Bpgn+294+5cRMSgmjyG3fNJR0mXAN8CbpP0buArwPnAlyW9cwL9i4gYyATfhz1x/TLsrcDpwHHAA8CpRaa9Cvg6cOWY+xcRMZDlPA/7aduHgcOS7rP9EIDtJyQ196pERG0t5zHslqRjbT8NvHF+p6TnkBdHRcRRaM51HOyopl/AfhvFWwhtz5T2vwh437g6FRGxWE0eEumZJdv+nu1ZSWuLrHre48D+hdqVP7tz5TfuHFFXIyL6a9mVl7qpOqzxRX7+pupcsa8r29O219te/87XnjVE9yIiBjPBDxhMXNUHZ46xfWR+w/YRSSvH1KeIiEVr8k3Hqhn2DyS9eX5D0mbg0fF0KSJi8Sb4xZmJq5phXwpcI+nyYnsGuGg8XYqIWLwmzxKpmmHPAJ+g/Tn4LwI3AG8fV6ciIhbLA/w3DEnHS7pJ0r3F76oudX5V0p2l5UlJf16UfUjS90tlm/qds2rAvh54E3AEOAj8BPhp5b8sImJCbFdehrQNuNn2OuDmYruzL/tsn2X7LOA3gcPAdaUqH58vt72zs32nqkMia2xvrFg3ImLJTHBsejPw2mL9auAbwF/2qP864D7bDyz2hFUz7FslvWKxJ4mImJQJZtgn2j5UnPMQcEKf+hcAn+/Yt1XSXZKu6jak0qlnhi3pbtrTFY8BLpZ0AHgKULuPPqPfCSIiJmlugPfwSZoCpkq7pm1Pl8q/BpzUpekHBulTMQ36zcD7S7s/CXyEdoz9CPD3wDt6HaffkMj5g3QqImKpDfIEYxGcp3uUv36hMkkPS1pt+5Ck1cAjPU51HnCH7YdLx35mXdKnaL++uqeeAXuYsZaIiKUwwXeJ7AC2AJcVv9f3qHshHcMh88G+2HwrsKffCfOJsIholAm+I+Qy4AvFx1y+B/whgKSXAp+2vanYfi6wAbiko/1HJZ1Fe0jk/i7lvyABOyIaZVIZtu3HaM/86Nx/ENhU2j5M+w2nnfUGfvgwATsiGqWOb+GrKgE7IhqlyY+mJ2BHRKM0+QMGCdgR0ShOhh0RUQ91fG1qVQnYEdEoI3jk/KiVgB0RjZIMOyKiJuZaGcOOiKiFzBKJiKiJjGFHRNRExrAjImoiGXZERE3kpmNERE1kSCQioiYyJBIRURN5vWpERE1kHnZERE0kw46IqIlWg1+v+qyl7kBExCjZrrwMQ9IfStorqSVpfY96GyXtk7Rf0rbS/uMl3STp3uJ3Vb9zJmBHRKNMKmADe4C3AbcsVEHSCuAK4DzgNOBCSacVxduAm22vA24utntKwI6IRvEAy1Dnse+xva9PtbOB/bYP2D4CXAtsLso2A1cX61cDb+l3zrGPYR+35TKN+xyjJmnK9vRS96PJco3Hb7le49kj368ccyRNAVOlXdMjvmYnAw+WtmeAVxbrJ9o+BGD7kKQT+h0sGXZ3U/2rxJByjccv17gP29O215eWnwvWkr4maU+XZfNCx+zQ7R+PRSf3mSUSEbEA268f8hAzwCml7TXAwWL9YUmri+x6NfBIv4Mlw46IGJ9dwDpJayWtBC4AdhRlO4AtxfoW4Pp+B0vA7m7ZjfstgVzj8cs1HiNJb5U0A/w28K+Sbij2v1TSTgDbs8BW4AbgHuALtvcWh7gM2CDpXmBDsd37nE1+UUpERJMkw46IqIkE7IiImliWAVvSyyTtqVj3VyTdLunO4jHUS8fdvybINR6/XOPlJ9P6+jsEvNr2U5KeD+yRtMP2wX4No7Jc4/HLNW6AZZlhF1ZI+lSRbdwo6ThJpxYT5b8t6Q5JL7d9xPZTRZtns7yv2aByjccv13gZWc7/09YBV9g+Hfgh8PvANcW+M4FX085KkHSKpLtoP2L6t8lKKss1Hr9c42VkOQfs/7V9Z7F+O7AWONn2dQC2f2b7cLH+oO0zgFOBLZJOXIoO11Cu8fjlGi8jyzlgP1VanwP6vou2yEj2Aq8ZV6caJtd4/HKNl5HlHLA7PQnMSHoLgKRnS3qupDWSjiv2rQLOAfq9UjG6yzUev1zjBkvA/nkXAX9ajPPdCpwE/BrwX5K+Dfw78He2717CPtZdrvH45Ro3VB5Nj4ioiWTYERE1kYAdEVETCdgRETWRgB0RURMJ2BERNZGAHRFREwnYERE18f/zzNCaG7LexgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n=[df['Close'],df1['Close'],df2['Close']]\n",
    "hc=pd.DataFrame(n)\n",
    "tr=hc.T\n",
    "tr.rename(columns={\"Close\":\"hc1\",\"Close\":\"hc2\",\"Close\":\"hc3\"},inplace=True)\n",
    "health=tr.corr()\n",
    "sns.heatmap(health,vmin=-1,vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "80c279a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m=[df3['Close'],df4['Close'],df5['Close']]\n",
    "fn=pd.DataFrame(m)\n",
    "sr=fn.T\n",
    "sr.rename(columns={\"Close\":\"fn1\",\"Close\":\"fn2\",\"Close\":\"fn3\"},inplace=True)\n",
    "finance=sr.corr()\n",
    "sns.heatmap(finance,vmin=-1,vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "3b135b95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p=[df6['Close'],df7['Close'],df8['Close']]\n",
    "it=pd.DataFrame(p)\n",
    "rr=it.T\n",
    "rr.rename(columns={\"Close\":\"fn1\",\"Close\":\"fn2\",\"Close\":\"fn3\"},inplace=True)\n",
    "finance=rr.corr()\n",
    "sns.heatmap(finance,vmin=-1,vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "9e37d484",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91897\\AppData\\Local\\Temp\\ipykernel_9704\\1888222685.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df.rename(columns={\"Close\": \"jnj_hc-close\"}, inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>jnj_hc-close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-04-04</td>\n",
       "      <td>176.470001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-04-05</td>\n",
       "      <td>177.610001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-04-06</td>\n",
       "      <td>182.229996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-04-07</td>\n",
       "      <td>181.759995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-04-08</td>\n",
       "      <td>182.119995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>153.300003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>151.820007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>153.309998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>153.429993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>155.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  jnj_hc-close\n",
       "0    2022-04-04    176.470001\n",
       "1    2022-04-05    177.610001\n",
       "2    2022-04-06    182.229996\n",
       "3    2022-04-07    181.759995\n",
       "4    2022-04-08    182.119995\n",
       "..          ...           ...\n",
       "245  2023-03-27    153.300003\n",
       "246  2023-03-28    151.820007\n",
       "247  2023-03-29    153.309998\n",
       "248  2023-03-30    153.429993\n",
       "249  2023-03-31    155.000000\n",
       "\n",
       "[250 rows x 2 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= df[['Date', 'Close']]\n",
    "df.rename(columns={\"Close\": \"jnj_hc-close\"}, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "cf492b33",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91897\\AppData\\Local\\Temp\\ipykernel_9704\\2886914674.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1.rename(columns={\"Close\": \"lly_hc-close\"}, inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>lly_hc-close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>84.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>85.139999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>85.519997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>86.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>86.129997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>334.600006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>337.649994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>335.869995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>340.690002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>343.420013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1321 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  lly_hc-close\n",
       "0     2018-01-02     84.680000\n",
       "1     2018-01-03     85.139999\n",
       "2     2018-01-04     85.519997\n",
       "3     2018-01-05     86.570000\n",
       "4     2018-01-08     86.129997\n",
       "...          ...           ...\n",
       "1316  2023-03-27    334.600006\n",
       "1317  2023-03-28    337.649994\n",
       "1318  2023-03-29    335.869995\n",
       "1319  2023-03-30    340.690002\n",
       "1320  2023-03-31    343.420013\n",
       "\n",
       "[1321 rows x 2 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1= df1[['Date', 'Close']]\n",
    "df1.rename(columns={\"Close\": \"lly_hc-close\"}, inplace=True)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "ccec9a7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91897\\AppData\\Local\\Temp\\ipykernel_9704\\3956741137.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2.rename(columns={\"Close\": \"nvo_hc-close\"}, inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>nvo_hc-close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>54.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>54.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>54.490002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>54.740002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>54.689999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>154.699997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>155.320007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>156.580002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>158.089996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>159.139999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1321 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  nvo_hc-close\n",
       "0     2018-01-02     54.160000\n",
       "1     2018-01-03     54.680000\n",
       "2     2018-01-04     54.490002\n",
       "3     2018-01-05     54.740002\n",
       "4     2018-01-08     54.689999\n",
       "...          ...           ...\n",
       "1316  2023-03-27    154.699997\n",
       "1317  2023-03-28    155.320007\n",
       "1318  2023-03-29    156.580002\n",
       "1319  2023-03-30    158.089996\n",
       "1320  2023-03-31    159.139999\n",
       "\n",
       "[1321 rows x 2 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2= df2[['Date', 'Close']]\n",
    "df2.rename(columns={\"Close\": \"nvo_hc-close\"}, inplace=True)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "109b8d85",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91897\\AppData\\Local\\Temp\\ipykernel_9704\\326933454.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df3.rename(columns={\"Close\": \"aapl_it-close\"}, inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>aapl_it-close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>43.064999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>43.057499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>43.257500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>43.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>43.587502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>158.279999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>157.649994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>160.770004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>162.360001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>164.899994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1321 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  aapl_it-close\n",
       "0     2018-01-02      43.064999\n",
       "1     2018-01-03      43.057499\n",
       "2     2018-01-04      43.257500\n",
       "3     2018-01-05      43.750000\n",
       "4     2018-01-08      43.587502\n",
       "...          ...            ...\n",
       "1316  2023-03-27     158.279999\n",
       "1317  2023-03-28     157.649994\n",
       "1318  2023-03-29     160.770004\n",
       "1319  2023-03-30     162.360001\n",
       "1320  2023-03-31     164.899994\n",
       "\n",
       "[1321 rows x 2 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3= df3[['Date', 'Close']]\n",
    "df3.rename(columns={\"Close\": \"aapl_it-close\"}, inplace=True)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "23571ee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91897\\AppData\\Local\\Temp\\ipykernel_9704\\1615004501.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df4.rename(columns={\"Close\": \"msft_it-close\"}, inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>msft_it-close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>85.949997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>86.349998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>87.110001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>88.190002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>88.279999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>276.380005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>275.230011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>280.510010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>284.049988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>288.299988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1321 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  msft_it-close\n",
       "0     2018-01-02      85.949997\n",
       "1     2018-01-03      86.349998\n",
       "2     2018-01-04      87.110001\n",
       "3     2018-01-05      88.190002\n",
       "4     2018-01-08      88.279999\n",
       "...          ...            ...\n",
       "1316  2023-03-27     276.380005\n",
       "1317  2023-03-28     275.230011\n",
       "1318  2023-03-29     280.510010\n",
       "1319  2023-03-30     284.049988\n",
       "1320  2023-03-31     288.299988\n",
       "\n",
       "[1321 rows x 2 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4= df4[['Date', 'Close']]\n",
    "df4.rename(columns={\"Close\": \"msft_it-close\"}, inplace=True)\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "e853014e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91897\\AppData\\Local\\Temp\\ipykernel_9704\\3940635207.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df5.rename(columns={\"Close\": \"orcl_it-close\"}, inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>46.630001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>47.709999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>48.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>48.470001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>48.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>90.139999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>89.849998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>90.620003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>90.510002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>92.919998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1321 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  orcl_it-close\n",
       "0     2018-01-02      46.630001\n",
       "1     2018-01-03      47.709999\n",
       "2     2018-01-04      48.180000\n",
       "3     2018-01-05      48.470001\n",
       "4     2018-01-08      48.980000\n",
       "...          ...            ...\n",
       "1316  2023-03-27      90.139999\n",
       "1317  2023-03-28      89.849998\n",
       "1318  2023-03-29      90.620003\n",
       "1319  2023-03-30      90.510002\n",
       "1320  2023-03-31      92.919998\n",
       "\n",
       "[1321 rows x 2 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5= df5[['Date', 'Close']]\n",
    "df5.rename(columns={\"Close\": \"orcl_it-close\"}, inplace=True)\n",
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "a85f3b81",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91897\\AppData\\Local\\Temp\\ipykernel_9704\\2869257678.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df6.rename(columns={\"Close\": \"cpj_fn-close\"}, inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>cpj_fn-close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>28.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>28.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>28.719999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>28.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>28.790001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>25.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>25.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>25.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>25.190001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>25.030001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1321 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  cpj_fn-close\n",
       "0     2018-01-02     28.750000\n",
       "1     2018-01-03     28.680000\n",
       "2     2018-01-04     28.719999\n",
       "3     2018-01-05     28.850000\n",
       "4     2018-01-08     28.790001\n",
       "...          ...           ...\n",
       "1316  2023-03-27     25.170000\n",
       "1317  2023-03-28     25.170000\n",
       "1318  2023-03-29     25.150000\n",
       "1319  2023-03-30     25.190001\n",
       "1320  2023-03-31     25.030001\n",
       "\n",
       "[1321 rows x 2 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6= df6[['Date', 'Close']]\n",
    "df6.rename(columns={\"Close\": \"cpj_fn-close\"}, inplace=True)\n",
    "df6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "c4065c69",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91897\\AppData\\Local\\Temp\\ipykernel_9704\\1980405041.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df7.rename(columns={\"Close\": \"hsbc_fn-close\"}, inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Date</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>52.410000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>52.150002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>52.259998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>52.169998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>51.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>33.639999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>33.459999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>34.220001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>34.290001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>34.130001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1321 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  hsbc_fn-close\n",
       "0     2018-01-02      52.410000\n",
       "1     2018-01-03      52.150002\n",
       "2     2018-01-04      52.259998\n",
       "3     2018-01-05      52.169998\n",
       "4     2018-01-08      51.750000\n",
       "...          ...            ...\n",
       "1316  2023-03-27      33.639999\n",
       "1317  2023-03-28      33.459999\n",
       "1318  2023-03-29      34.220001\n",
       "1319  2023-03-30      34.290001\n",
       "1320  2023-03-31      34.130001\n",
       "\n",
       "[1321 rows x 2 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7= df7[['Date', 'Close']]\n",
    "df7.rename(columns={\"Close\": \"hsbc_fn-close\"}, inplace=True)\n",
    "df7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "eeda0d9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91897\\AppData\\Local\\Temp\\ipykernel_9704\\1105699136.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df8.rename(columns={\"Close\": \"v_fn-close\"}, inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "      <td>2018-01-03</td>\n",
       "      <td>115.650002</td>\n",
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       "      <th>2</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>116.080002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>118.860001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>119.339996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>221.809998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>220.330002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>223.300003</td>\n",
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       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>2023-03-30</td>\n",
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       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>225.460007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1321 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  v_fn-close\n",
       "0     2018-01-02  114.510002\n",
       "1     2018-01-03  115.650002\n",
       "2     2018-01-04  116.080002\n",
       "3     2018-01-05  118.860001\n",
       "4     2018-01-08  119.339996\n",
       "...          ...         ...\n",
       "1316  2023-03-27  221.809998\n",
       "1317  2023-03-28  220.330002\n",
       "1318  2023-03-29  223.300003\n",
       "1319  2023-03-30  222.360001\n",
       "1320  2023-03-31  225.460007\n",
       "\n",
       "[1321 rows x 2 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8= df8[['Date', 'Close']]\n",
    "df8.rename(columns={\"Close\": \"v_fn-close\"}, inplace=True)\n",
    "df8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "5b16ec44",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <td>176.470001</td>\n",
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       "      <td>177.610001</td>\n",
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       "      <td>182.229996</td>\n",
       "      <td>85.519997</td>\n",
       "      <td>54.490002</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-04-07</td>\n",
       "      <td>181.759995</td>\n",
       "      <td>86.570000</td>\n",
       "      <td>54.740002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-04-08</td>\n",
       "      <td>182.119995</td>\n",
       "      <td>86.129997</td>\n",
       "      <td>54.689999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th>245</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>153.300003</td>\n",
       "      <td>109.419998</td>\n",
       "      <td>44.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>151.820007</td>\n",
       "      <td>106.750000</td>\n",
       "      <td>43.990002</td>\n",
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       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>153.309998</td>\n",
       "      <td>111.089996</td>\n",
       "      <td>45.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>153.429993</td>\n",
       "      <td>112.889999</td>\n",
       "      <td>45.590000</td>\n",
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       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>114.199997</td>\n",
       "      <td>45.830002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  jnj_hc-close  lly_hc-close  nvo_hc-close\n",
       "0    2022-04-04    176.470001     84.680000     54.160000\n",
       "1    2022-04-05    177.610001     85.139999     54.680000\n",
       "2    2022-04-06    182.229996     85.519997     54.490002\n",
       "3    2022-04-07    181.759995     86.570000     54.740002\n",
       "4    2022-04-08    182.119995     86.129997     54.689999\n",
       "..          ...           ...           ...           ...\n",
       "245  2023-03-27    153.300003    109.419998     44.480000\n",
       "246  2023-03-28    151.820007    106.750000     43.990002\n",
       "247  2023-03-29    153.309998    111.089996     45.070000\n",
       "248  2023-03-30    153.429993    112.889999     45.590000\n",
       "249  2023-03-31    155.000000    114.199997     45.830002\n",
       "\n",
       "[250 rows x 4 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hc=pd.DataFrame()\n",
    "hc['Date'] = df['Date'].values\n",
    "hc['jnj_hc-close'] = pd.Series(df['jnj_hc-close'])\n",
    "hc['lly_hc-close'] =pd.Series(df1['lly_hc-close'])\n",
    "hc['nvo_hc-close'] = pd.Series(df2['nvo_hc-close'])\n",
    "hc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "0423b866",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>43.064999</td>\n",
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       "      <td>46.630001</td>\n",
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       "      <td>2022-04-05</td>\n",
       "      <td>43.057499</td>\n",
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       "      <td>43.257500</td>\n",
       "      <td>87.110001</td>\n",
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       "      <td>48.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>37.682499</td>\n",
       "      <td>98.230003</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>36.707500</td>\n",
       "      <td>94.129997</td>\n",
       "      <td>42.689999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>39.292500</td>\n",
       "      <td>100.559998</td>\n",
       "      <td>44.590000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>39.037498</td>\n",
       "      <td>101.180000</td>\n",
       "      <td>44.970001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>39.057499</td>\n",
       "      <td>100.389999</td>\n",
       "      <td>44.820000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  aapl_it-close  msft_it-close  orcl_it-close\n",
       "0    2022-04-04      43.064999      85.949997      46.630001\n",
       "1    2022-04-05      43.057499      86.349998      47.709999\n",
       "2    2022-04-06      43.257500      87.110001      48.180000\n",
       "3    2022-04-07      43.750000      88.190002      48.470001\n",
       "4    2022-04-08      43.587502      88.279999      48.980000\n",
       "..          ...            ...            ...            ...\n",
       "245  2023-03-27      37.682499      98.230003      44.000000\n",
       "246  2023-03-28      36.707500      94.129997      42.689999\n",
       "247  2023-03-29      39.292500     100.559998      44.590000\n",
       "248  2023-03-30      39.037498     101.180000      44.970001\n",
       "249  2023-03-31      39.057499     100.389999      44.820000\n",
       "\n",
       "[250 rows x 4 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "it=pd.DataFrame()\n",
    "it['Date'] = df['Date'].values\n",
    "it['aapl_it-close'] = pd.Series(df3['aapl_it-close'])\n",
    "it['msft_it-close'] =pd.Series(df4['msft_it-close'])\n",
    "it['orcl_it-close'] = pd.Series(df5['orcl_it-close'])\n",
    "it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "312e6e70",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>cpj_fn-close</th>\n",
       "      <th>hsbc_fn-close</th>\n",
       "      <th>v_fn-close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-04-04</td>\n",
       "      <td>28.750000</td>\n",
       "      <td>52.410000</td>\n",
       "      <td>114.510002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-04-05</td>\n",
       "      <td>28.680000</td>\n",
       "      <td>52.150002</td>\n",
       "      <td>115.650002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-04-06</td>\n",
       "      <td>28.719999</td>\n",
       "      <td>52.259998</td>\n",
       "      <td>116.080002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-04-07</td>\n",
       "      <td>28.850000</td>\n",
       "      <td>52.169998</td>\n",
       "      <td>118.860001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-04-08</td>\n",
       "      <td>28.790001</td>\n",
       "      <td>51.750000</td>\n",
       "      <td>119.339996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>25.570000</td>\n",
       "      <td>40.560001</td>\n",
       "      <td>124.260002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>25.490000</td>\n",
       "      <td>40.369999</td>\n",
       "      <td>121.730003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>25.570000</td>\n",
       "      <td>41.230000</td>\n",
       "      <td>130.229996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>25.540001</td>\n",
       "      <td>40.700001</td>\n",
       "      <td>132.009995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>25.820000</td>\n",
       "      <td>41.070000</td>\n",
       "      <td>130.940002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  cpj_fn-close  hsbc_fn-close  v_fn-close\n",
       "0    2022-04-04     28.750000      52.410000  114.510002\n",
       "1    2022-04-05     28.680000      52.150002  115.650002\n",
       "2    2022-04-06     28.719999      52.259998  116.080002\n",
       "3    2022-04-07     28.850000      52.169998  118.860001\n",
       "4    2022-04-08     28.790001      51.750000  119.339996\n",
       "..          ...           ...            ...         ...\n",
       "245  2023-03-27     25.570000      40.560001  124.260002\n",
       "246  2023-03-28     25.490000      40.369999  121.730003\n",
       "247  2023-03-29     25.570000      41.230000  130.229996\n",
       "248  2023-03-30     25.540001      40.700001  132.009995\n",
       "249  2023-03-31     25.820000      41.070000  130.940002\n",
       "\n",
       "[250 rows x 4 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn=pd.DataFrame()\n",
    "fn['Date'] = df['Date'].values\n",
    "fn['cpj_fn-close'] = pd.Series(df6['cpj_fn-close'])\n",
    "fn['hsbc_fn-close'] =pd.Series(df7['hsbc_fn-close'])\n",
    "fn['v_fn-close'] = pd.Series(df8['v_fn-close'])\n",
    "fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "6b3697e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>jnj_hc-close</th>\n",
       "      <th>lly_hc-close</th>\n",
       "      <th>nvo_hc-close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-04-04</td>\n",
       "      <td>176.470001</td>\n",
       "      <td>84.680000</td>\n",
       "      <td>54.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-04-05</td>\n",
       "      <td>177.610001</td>\n",
       "      <td>85.139999</td>\n",
       "      <td>54.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-04-06</td>\n",
       "      <td>182.229996</td>\n",
       "      <td>85.519997</td>\n",
       "      <td>54.490002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-04-07</td>\n",
       "      <td>181.759995</td>\n",
       "      <td>86.570000</td>\n",
       "      <td>54.740002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-04-08</td>\n",
       "      <td>182.119995</td>\n",
       "      <td>86.129997</td>\n",
       "      <td>54.689999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>153.300003</td>\n",
       "      <td>109.419998</td>\n",
       "      <td>44.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>151.820007</td>\n",
       "      <td>106.750000</td>\n",
       "      <td>43.990002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>153.309998</td>\n",
       "      <td>111.089996</td>\n",
       "      <td>45.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>153.429993</td>\n",
       "      <td>112.889999</td>\n",
       "      <td>45.590000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>114.199997</td>\n",
       "      <td>45.830002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  jnj_hc-close  lly_hc-close  nvo_hc-close\n",
       "0    2022-04-04    176.470001     84.680000     54.160000\n",
       "1    2022-04-05    177.610001     85.139999     54.680000\n",
       "2    2022-04-06    182.229996     85.519997     54.490002\n",
       "3    2022-04-07    181.759995     86.570000     54.740002\n",
       "4    2022-04-08    182.119995     86.129997     54.689999\n",
       "..          ...           ...           ...           ...\n",
       "245  2023-03-27    153.300003    109.419998     44.480000\n",
       "246  2023-03-28    151.820007    106.750000     43.990002\n",
       "247  2023-03-29    153.309998    111.089996     45.070000\n",
       "248  2023-03-30    153.429993    112.889999     45.590000\n",
       "249  2023-03-31    155.000000    114.199997     45.830002\n",
       "\n",
       "[250 rows x 4 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "eca67285",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Date</th>\n",
       "      <th>jnj_hc-close</th>\n",
       "      <th>lly_hc-close</th>\n",
       "      <th>nvo_hc-close</th>\n",
       "      <th>aapl_it-close</th>\n",
       "      <th>msft_it-close</th>\n",
       "      <th>orcl_it-close</th>\n",
       "      <th>cpj_fn-close</th>\n",
       "      <th>hsbc_fn-close</th>\n",
       "      <th>v_fn-close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-04-04</td>\n",
       "      <td>176.470001</td>\n",
       "      <td>84.680000</td>\n",
       "      <td>54.160000</td>\n",
       "      <td>43.064999</td>\n",
       "      <td>85.949997</td>\n",
       "      <td>46.630001</td>\n",
       "      <td>28.750000</td>\n",
       "      <td>52.410000</td>\n",
       "      <td>114.510002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-04-05</td>\n",
       "      <td>177.610001</td>\n",
       "      <td>85.139999</td>\n",
       "      <td>54.680000</td>\n",
       "      <td>43.057499</td>\n",
       "      <td>86.349998</td>\n",
       "      <td>47.709999</td>\n",
       "      <td>28.680000</td>\n",
       "      <td>52.150002</td>\n",
       "      <td>115.650002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-04-06</td>\n",
       "      <td>182.229996</td>\n",
       "      <td>85.519997</td>\n",
       "      <td>54.490002</td>\n",
       "      <td>43.257500</td>\n",
       "      <td>87.110001</td>\n",
       "      <td>48.180000</td>\n",
       "      <td>28.719999</td>\n",
       "      <td>52.259998</td>\n",
       "      <td>116.080002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-04-07</td>\n",
       "      <td>181.759995</td>\n",
       "      <td>86.570000</td>\n",
       "      <td>54.740002</td>\n",
       "      <td>43.750000</td>\n",
       "      <td>88.190002</td>\n",
       "      <td>48.470001</td>\n",
       "      <td>28.850000</td>\n",
       "      <td>52.169998</td>\n",
       "      <td>118.860001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-04-08</td>\n",
       "      <td>182.119995</td>\n",
       "      <td>86.129997</td>\n",
       "      <td>54.689999</td>\n",
       "      <td>43.587502</td>\n",
       "      <td>88.279999</td>\n",
       "      <td>48.980000</td>\n",
       "      <td>28.790001</td>\n",
       "      <td>51.750000</td>\n",
       "      <td>119.339996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>153.300003</td>\n",
       "      <td>109.419998</td>\n",
       "      <td>44.480000</td>\n",
       "      <td>37.682499</td>\n",
       "      <td>98.230003</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>25.570000</td>\n",
       "      <td>40.560001</td>\n",
       "      <td>124.260002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>151.820007</td>\n",
       "      <td>106.750000</td>\n",
       "      <td>43.990002</td>\n",
       "      <td>36.707500</td>\n",
       "      <td>94.129997</td>\n",
       "      <td>42.689999</td>\n",
       "      <td>25.490000</td>\n",
       "      <td>40.369999</td>\n",
       "      <td>121.730003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>153.309998</td>\n",
       "      <td>111.089996</td>\n",
       "      <td>45.070000</td>\n",
       "      <td>39.292500</td>\n",
       "      <td>100.559998</td>\n",
       "      <td>44.590000</td>\n",
       "      <td>25.570000</td>\n",
       "      <td>41.230000</td>\n",
       "      <td>130.229996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>153.429993</td>\n",
       "      <td>112.889999</td>\n",
       "      <td>45.590000</td>\n",
       "      <td>39.037498</td>\n",
       "      <td>101.180000</td>\n",
       "      <td>44.970001</td>\n",
       "      <td>25.540001</td>\n",
       "      <td>40.700001</td>\n",
       "      <td>132.009995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>114.199997</td>\n",
       "      <td>45.830002</td>\n",
       "      <td>39.057499</td>\n",
       "      <td>100.389999</td>\n",
       "      <td>44.820000</td>\n",
       "      <td>25.820000</td>\n",
       "      <td>41.070000</td>\n",
       "      <td>130.940002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  jnj_hc-close  lly_hc-close  nvo_hc-close  aapl_it-close  \\\n",
       "0    2022-04-04    176.470001     84.680000     54.160000      43.064999   \n",
       "1    2022-04-05    177.610001     85.139999     54.680000      43.057499   \n",
       "2    2022-04-06    182.229996     85.519997     54.490002      43.257500   \n",
       "3    2022-04-07    181.759995     86.570000     54.740002      43.750000   \n",
       "4    2022-04-08    182.119995     86.129997     54.689999      43.587502   \n",
       "..          ...           ...           ...           ...            ...   \n",
       "245  2023-03-27    153.300003    109.419998     44.480000      37.682499   \n",
       "246  2023-03-28    151.820007    106.750000     43.990002      36.707500   \n",
       "247  2023-03-29    153.309998    111.089996     45.070000      39.292500   \n",
       "248  2023-03-30    153.429993    112.889999     45.590000      39.037498   \n",
       "249  2023-03-31    155.000000    114.199997     45.830002      39.057499   \n",
       "\n",
       "     msft_it-close  orcl_it-close  cpj_fn-close  hsbc_fn-close  v_fn-close  \n",
       "0        85.949997      46.630001     28.750000      52.410000  114.510002  \n",
       "1        86.349998      47.709999     28.680000      52.150002  115.650002  \n",
       "2        87.110001      48.180000     28.719999      52.259998  116.080002  \n",
       "3        88.190002      48.470001     28.850000      52.169998  118.860001  \n",
       "4        88.279999      48.980000     28.790001      51.750000  119.339996  \n",
       "..             ...            ...           ...            ...         ...  \n",
       "245      98.230003      44.000000     25.570000      40.560001  124.260002  \n",
       "246      94.129997      42.689999     25.490000      40.369999  121.730003  \n",
       "247     100.559998      44.590000     25.570000      41.230000  130.229996  \n",
       "248     101.180000      44.970001     25.540001      40.700001  132.009995  \n",
       "249     100.389999      44.820000     25.820000      41.070000  130.940002  \n",
       "\n",
       "[250 rows x 10 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yf=pd.DataFrame()\n",
    "yf['Date'] = hc['Date']\n",
    "yf['jnj_hc-close'] = pd.Series(hc['jnj_hc-close'])\n",
    "yf['lly_hc-close'] =pd.Series(hc['lly_hc-close'])\n",
    "yf['nvo_hc-close'] = pd.Series(hc['nvo_hc-close'])\n",
    "yf['aapl_it-close'] = pd.Series(it['aapl_it-close'])\n",
    "yf['msft_it-close'] =pd.Series(it['msft_it-close'])\n",
    "yf['orcl_it-close'] = pd.Series(it['orcl_it-close'])\n",
    "yf['cpj_fn-close'] = pd.Series(fn['cpj_fn-close'])\n",
    "yf['hsbc_fn-close'] =pd.Series(fn['hsbc_fn-close'])\n",
    "yf['v_fn-close'] = pd.Series(fn['v_fn-close'])\n",
    "yf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "eebd43ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>jnj_hc-close</th>\n",
       "      <th>lly_hc-close</th>\n",
       "      <th>nvo_hc-close</th>\n",
       "      <th>aapl_it-close</th>\n",
       "      <th>msft_it-close</th>\n",
       "      <th>orcl_it-close</th>\n",
       "      <th>cpj_fn-close</th>\n",
       "      <th>hsbc_fn-close</th>\n",
       "      <th>v_fn-close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>jnj_hc-close</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.445398</td>\n",
       "      <td>0.616733</td>\n",
       "      <td>0.138054</td>\n",
       "      <td>-0.328828</td>\n",
       "      <td>0.345394</td>\n",
       "      <td>0.767193</td>\n",
       "      <td>0.574766</td>\n",
       "      <td>-0.275785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lly_hc-close</th>\n",
       "      <td>-0.445398</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.560540</td>\n",
       "      <td>0.559028</td>\n",
       "      <td>0.834343</td>\n",
       "      <td>0.113831</td>\n",
       "      <td>-0.647651</td>\n",
       "      <td>-0.835881</td>\n",
       "      <td>0.770053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvo_hc-close</th>\n",
       "      <td>0.616733</td>\n",
       "      <td>-0.560540</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.355767</td>\n",
       "      <td>-0.550693</td>\n",
       "      <td>0.379516</td>\n",
       "      <td>0.624843</td>\n",
       "      <td>0.806034</td>\n",
       "      <td>-0.487597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aapl_it-close</th>\n",
       "      <td>0.138054</td>\n",
       "      <td>0.559028</td>\n",
       "      <td>-0.355767</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.785590</td>\n",
       "      <td>0.265434</td>\n",
       "      <td>0.060920</td>\n",
       "      <td>-0.472060</td>\n",
       "      <td>0.820282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>msft_it-close</th>\n",
       "      <td>-0.328828</td>\n",
       "      <td>0.834343</td>\n",
       "      <td>-0.550693</td>\n",
       "      <td>0.785590</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.166456</td>\n",
       "      <td>-0.393061</td>\n",
       "      <td>-0.739803</td>\n",
       "      <td>0.974583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orcl_it-close</th>\n",
       "      <td>0.345394</td>\n",
       "      <td>0.113831</td>\n",
       "      <td>0.379516</td>\n",
       "      <td>0.265434</td>\n",
       "      <td>0.166456</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.298372</td>\n",
       "      <td>0.175918</td>\n",
       "      <td>0.165786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cpj_fn-close</th>\n",
       "      <td>0.767193</td>\n",
       "      <td>-0.647651</td>\n",
       "      <td>0.624843</td>\n",
       "      <td>0.060920</td>\n",
       "      <td>-0.393061</td>\n",
       "      <td>0.298372</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.672710</td>\n",
       "      <td>-0.309487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hsbc_fn-close</th>\n",
       "      <td>0.574766</td>\n",
       "      <td>-0.835881</td>\n",
       "      <td>0.806034</td>\n",
       "      <td>-0.472060</td>\n",
       "      <td>-0.739803</td>\n",
       "      <td>0.175918</td>\n",
       "      <td>0.672710</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.653953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_fn-close</th>\n",
       "      <td>-0.275785</td>\n",
       "      <td>0.770053</td>\n",
       "      <td>-0.487597</td>\n",
       "      <td>0.820282</td>\n",
       "      <td>0.974583</td>\n",
       "      <td>0.165786</td>\n",
       "      <td>-0.309487</td>\n",
       "      <td>-0.653953</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               jnj_hc-close  lly_hc-close  nvo_hc-close  aapl_it-close  \\\n",
       "jnj_hc-close       1.000000     -0.445398      0.616733       0.138054   \n",
       "lly_hc-close      -0.445398      1.000000     -0.560540       0.559028   \n",
       "nvo_hc-close       0.616733     -0.560540      1.000000      -0.355767   \n",
       "aapl_it-close      0.138054      0.559028     -0.355767       1.000000   \n",
       "msft_it-close     -0.328828      0.834343     -0.550693       0.785590   \n",
       "orcl_it-close      0.345394      0.113831      0.379516       0.265434   \n",
       "cpj_fn-close       0.767193     -0.647651      0.624843       0.060920   \n",
       "hsbc_fn-close      0.574766     -0.835881      0.806034      -0.472060   \n",
       "v_fn-close        -0.275785      0.770053     -0.487597       0.820282   \n",
       "\n",
       "               msft_it-close  orcl_it-close  cpj_fn-close  hsbc_fn-close  \\\n",
       "jnj_hc-close       -0.328828       0.345394      0.767193       0.574766   \n",
       "lly_hc-close        0.834343       0.113831     -0.647651      -0.835881   \n",
       "nvo_hc-close       -0.550693       0.379516      0.624843       0.806034   \n",
       "aapl_it-close       0.785590       0.265434      0.060920      -0.472060   \n",
       "msft_it-close       1.000000       0.166456     -0.393061      -0.739803   \n",
       "orcl_it-close       0.166456       1.000000      0.298372       0.175918   \n",
       "cpj_fn-close       -0.393061       0.298372      1.000000       0.672710   \n",
       "hsbc_fn-close      -0.739803       0.175918      0.672710       1.000000   \n",
       "v_fn-close          0.974583       0.165786     -0.309487      -0.653953   \n",
       "\n",
       "               v_fn-close  \n",
       "jnj_hc-close    -0.275785  \n",
       "lly_hc-close     0.770053  \n",
       "nvo_hc-close    -0.487597  \n",
       "aapl_it-close    0.820282  \n",
       "msft_it-close    0.974583  \n",
       "orcl_it-close    0.165786  \n",
       "cpj_fn-close    -0.309487  \n",
       "hsbc_fn-close   -0.653953  \n",
       "v_fn-close       1.000000  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yf.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "402e74f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data=yf.corr(),vmin=-1,vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "4a6882a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91897\\anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3438: FutureWarning: In a future version, DataFrame.mean(axis=None) will return a scalar mean over the entire DataFrame. To retain the old behavior, use 'frame.mean(axis=0)' or just 'frame.mean()'\n",
      "  return mean(axis=axis, dtype=dtype, out=out, **kwargs)\n",
      "C:\\Users\\91897\\anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3438: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  return mean(axis=axis, dtype=dtype, out=out, **kwargs)\n",
      "C:\\Users\\91897\\anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3579: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  return std(axis=axis, dtype=dtype, out=out, ddof=ddof, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "jnj_hc-close    170.35092\n",
       "dtype: float64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean=np.mean(df)\n",
    "std=np.std(df)\n",
    "mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "ea7ae0f9",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid non-printable character U+00A0 (2459680600.py, line 7)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Input \u001b[1;32mIn [108]\u001b[1;36m\u001b[0m\n\u001b[1;33m    print('outlier in dataset is', outlier1)\u001b[0m\n\u001b[1;37m                                  ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid non-printable character U+00A0\n"
     ]
    }
   ],
   "source": [
    "threshold = 2\n",
    "outlier1 = []\n",
    "for i in df['jnj_hc-close']:\n",
    "    z = (i-mean)/std\n",
    "    if z > threshold:\n",
    "        outlier1.append(i)\n",
    "print('outlier in dataset is', outlier1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "18ced998",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'c' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [109]\u001b[0m, in \u001b[0;36m<cell line: 5>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmath\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m#we have converted correlation into distance and then using MDS, converted it to two dimensional coordinates\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m dist \u001b[38;5;241m=\u001b[39m\u001b[38;5;28mpow\u001b[39m(\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39m(\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[43mc\u001b[49m),\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmanifold\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MDS\n\u001b[0;32m      8\u001b[0m mds \u001b[38;5;241m=\u001b[39m MDS(random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'c' is not defined"
     ]
    }
   ],
   "source": [
    "c1=yf.corr()\n",
    "c1\n",
    "import math\n",
    "#we have converted correlation into distance and then using MDS, converted it to two dimensional coordinates\n",
    "dist =pow(2*(1-c),1/2)\n",
    "from sklearn.manifold import MDS\n",
    "\n",
    "mds = MDS(random_state=0)\n",
    "scaled_df = mds.fit_transform(dist) #for two dimension\n",
    "plt.scatter(scaled_df[:,0], scaled_df[:,1])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "d088cde6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>jnj_hc-close</th>\n",
       "      <th>lly_hc-close</th>\n",
       "      <th>nvo_hc-close</th>\n",
       "      <th>aapl_it-close</th>\n",
       "      <th>msft_it-close</th>\n",
       "      <th>orcl_it-close</th>\n",
       "      <th>cpj_fn-close</th>\n",
       "      <th>hsbc_fn-close</th>\n",
       "      <th>v_fn-close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>jnj_hc-close</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.445398</td>\n",
       "      <td>0.616733</td>\n",
       "      <td>0.138054</td>\n",
       "      <td>-0.328828</td>\n",
       "      <td>0.345394</td>\n",
       "      <td>0.767193</td>\n",
       "      <td>0.574766</td>\n",
       "      <td>-0.275785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lly_hc-close</th>\n",
       "      <td>-0.445398</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.560540</td>\n",
       "      <td>0.559028</td>\n",
       "      <td>0.834343</td>\n",
       "      <td>0.113831</td>\n",
       "      <td>-0.647651</td>\n",
       "      <td>-0.835881</td>\n",
       "      <td>0.770053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nvo_hc-close</th>\n",
       "      <td>0.616733</td>\n",
       "      <td>-0.560540</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.355767</td>\n",
       "      <td>-0.550693</td>\n",
       "      <td>0.379516</td>\n",
       "      <td>0.624843</td>\n",
       "      <td>0.806034</td>\n",
       "      <td>-0.487597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aapl_it-close</th>\n",
       "      <td>0.138054</td>\n",
       "      <td>0.559028</td>\n",
       "      <td>-0.355767</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.785590</td>\n",
       "      <td>0.265434</td>\n",
       "      <td>0.060920</td>\n",
       "      <td>-0.472060</td>\n",
       "      <td>0.820282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>msft_it-close</th>\n",
       "      <td>-0.328828</td>\n",
       "      <td>0.834343</td>\n",
       "      <td>-0.550693</td>\n",
       "      <td>0.785590</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.166456</td>\n",
       "      <td>-0.393061</td>\n",
       "      <td>-0.739803</td>\n",
       "      <td>0.974583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orcl_it-close</th>\n",
       "      <td>0.345394</td>\n",
       "      <td>0.113831</td>\n",
       "      <td>0.379516</td>\n",
       "      <td>0.265434</td>\n",
       "      <td>0.166456</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.298372</td>\n",
       "      <td>0.175918</td>\n",
       "      <td>0.165786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cpj_fn-close</th>\n",
       "      <td>0.767193</td>\n",
       "      <td>-0.647651</td>\n",
       "      <td>0.624843</td>\n",
       "      <td>0.060920</td>\n",
       "      <td>-0.393061</td>\n",
       "      <td>0.298372</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.672710</td>\n",
       "      <td>-0.309487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hsbc_fn-close</th>\n",
       "      <td>0.574766</td>\n",
       "      <td>-0.835881</td>\n",
       "      <td>0.806034</td>\n",
       "      <td>-0.472060</td>\n",
       "      <td>-0.739803</td>\n",
       "      <td>0.175918</td>\n",
       "      <td>0.672710</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.653953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_fn-close</th>\n",
       "      <td>-0.275785</td>\n",
       "      <td>0.770053</td>\n",
       "      <td>-0.487597</td>\n",
       "      <td>0.820282</td>\n",
       "      <td>0.974583</td>\n",
       "      <td>0.165786</td>\n",
       "      <td>-0.309487</td>\n",
       "      <td>-0.653953</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               jnj_hc-close  lly_hc-close  nvo_hc-close  aapl_it-close  \\\n",
       "jnj_hc-close       1.000000     -0.445398      0.616733       0.138054   \n",
       "lly_hc-close      -0.445398      1.000000     -0.560540       0.559028   \n",
       "nvo_hc-close       0.616733     -0.560540      1.000000      -0.355767   \n",
       "aapl_it-close      0.138054      0.559028     -0.355767       1.000000   \n",
       "msft_it-close     -0.328828      0.834343     -0.550693       0.785590   \n",
       "orcl_it-close      0.345394      0.113831      0.379516       0.265434   \n",
       "cpj_fn-close       0.767193     -0.647651      0.624843       0.060920   \n",
       "hsbc_fn-close      0.574766     -0.835881      0.806034      -0.472060   \n",
       "v_fn-close        -0.275785      0.770053     -0.487597       0.820282   \n",
       "\n",
       "               msft_it-close  orcl_it-close  cpj_fn-close  hsbc_fn-close  \\\n",
       "jnj_hc-close       -0.328828       0.345394      0.767193       0.574766   \n",
       "lly_hc-close        0.834343       0.113831     -0.647651      -0.835881   \n",
       "nvo_hc-close       -0.550693       0.379516      0.624843       0.806034   \n",
       "aapl_it-close       0.785590       0.265434      0.060920      -0.472060   \n",
       "msft_it-close       1.000000       0.166456     -0.393061      -0.739803   \n",
       "orcl_it-close       0.166456       1.000000      0.298372       0.175918   \n",
       "cpj_fn-close       -0.393061       0.298372      1.000000       0.672710   \n",
       "hsbc_fn-close      -0.739803       0.175918      0.672710       1.000000   \n",
       "v_fn-close          0.974583       0.165786     -0.309487      -0.653953   \n",
       "\n",
       "               v_fn-close  \n",
       "jnj_hc-close    -0.275785  \n",
       "lly_hc-close     0.770053  \n",
       "nvo_hc-close    -0.487597  \n",
       "aapl_it-close    0.820282  \n",
       "msft_it-close    0.974583  \n",
       "orcl_it-close    0.165786  \n",
       "cpj_fn-close    -0.309487  \n",
       "hsbc_fn-close   -0.653953  \n",
       "v_fn-close       1.000000  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1=yf.corr()\n",
    "c1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "9511a50f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91897\\anaconda3\\lib\\site-packages\\sklearn\\manifold\\_mds.py:517: UserWarning: The MDS API has changed. ``fit`` now constructs an dissimilarity matrix from data. To use a custom dissimilarity matrix, set ``dissimilarity='precomputed'``.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "#we have converted correlation into distance and then using MDS, converted it to two dimensional coordinates\n",
    "dist =pow(2*(1-c1),1/2)\n",
    "from sklearn.manifold import MDS\n",
    "\n",
    "mds = MDS(random_state=0)\n",
    "scaled_df = mds.fit_transform(dist) #for two dimension\n",
    "plt.scatter(scaled_df[:,0], scaled_df[:,1])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "cecefae3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91897\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1036: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "data = list(zip(scaled_df[:,0], scaled_df[:,1]))\n",
    "inertias = []\n",
    "\n",
    "for i in range(1,9):\n",
    "    kmeans = KMeans(n_clusters=i)\n",
    "    kmeans.fit(data)\n",
    "    inertias.append(kmeans.inertia_)\n",
    "\n",
    "plt.plot(range(1,9), inertias, marker='o')\n",
    "plt.title('Elbow method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Inertia')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "1f2bc98e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "kmeans = KMeans(n_clusters=3)\n",
    "kmeans.fit(data)\n",
    "\n",
    "plt.scatter(scaled_df[:,0], scaled_df[:,1], c=kmeans.labels_)\n",
    "for i, txt in enumerate(dist.index):\n",
    "    plt.annotate(txt, (scaled_df[:,0][i], scaled_df[:,1][i]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "2048729d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "bfdc1baa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1bac92c09a0>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scaled_df, y= make_blobs(n_samples=9,centers=4,cluster_std=0.20,random_state=0)\n",
    "plt.scatter(scaled_df[:,0], scaled_df[:,1], c=kmeans.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "f21c3cf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1bac90ddac0>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor=yf.corr()\n",
    "plt.imshow(cor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4117798",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}