stock.ipynb

{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport yfinance as yf\nimport datetime\nimport matplotlib.pyplot as plt\n\nstart = '2016-01-01'\nend = ''\n\ndf = yf.download(['AaPL', 'MSFT', 'TSLA'], start)\n#print(tesla_df)\n\nprint(df.head())\nprint(f'\\n{df.tail()}')\n\n# Getting the Adj Close stock prices\ndf2 = df.loc[:, ['Adj Close']]\ndf2.rename(columns={'Adj Close':'adj_close'}, inplace=True)\n#print(df2.head())\n\nprint(df2.adj_close.AAPL)\n\ndf2[\"simple_rtn\"] = df2.adj_close.AAPL.pct_change()\ndf2[\"log_rtn\"] = np.log(df2.adj_close.AAPL/df2.adj_close.AAPL.shift(1))\nprint(df2)","execution_count":4,"outputs":[{"output_type":"stream","text":"[*********************100%***********************]  3 of 3 completed\n            Adj Close                            Close                        \\\n                 AAPL       MSFT       TSLA       AAPL       MSFT       TSLA   \nDate                                                                           \n2016-01-04  24.400942  50.006744  44.681999  26.337500  54.799999  44.681999   \n2016-01-05  23.789471  50.234879  44.686001  25.677500  55.049999  44.686001   \n2016-01-06  23.323915  49.322346  43.807999  25.174999  54.049999  43.807999   \n2016-01-07  22.339539  47.606789  43.130001  24.112499  52.169998  43.130001   \n2016-01-08  22.457672  47.752792  42.200001  24.240000  52.330002  42.200001   \n\n                 High                              Low                        \\\n                 AAPL       MSFT       TSLA       AAPL       MSFT       TSLA   \nDate                                                                           \n2016-01-04  26.342501  54.799999  46.276001  25.500000  53.389999  43.799999   \n2016-01-05  26.462500  55.389999  45.377998  25.602501  54.540001  44.000000   \n2016-01-06  25.592501  54.400002  44.009998  24.967501  53.639999  43.195999   \n2016-01-07  25.032499  53.490002  43.688000  24.107500  52.070000  42.734001   \n2016-01-08  24.777500  53.279999  44.088001  24.190001  52.150002  42.153999   \n\n                 Open                           Volume                      \n                 AAPL       MSFT       TSLA       AAPL      MSFT      TSLA  \nDate                                                                        \n2016-01-04  25.652500  54.320000  46.144001  270597600  53778000  34135500  \n2016-01-05  26.437500  54.930000  45.271999  223164000  34079700  15934000  \n2016-01-06  25.139999  54.320000  44.000000  273829600  39518900  18895500  \n2016-01-07  24.670000  52.700001  42.838001  324377600  56564900  17771500  \n2016-01-08  24.637501  52.369999  43.571999  283192000  48754000  18140500  \n\n             Adj Close                               Close              \\\n                  AAPL        MSFT        TSLA        AAPL        MSFT   \nDate                                                                     \n2021-01-14  128.910004  213.020004  845.000000  128.910004  213.020004   \n2021-01-15  127.139999  212.649994  826.159973  127.139999  212.649994   \n2021-01-19  127.830002  216.440002  844.549988  127.830002  216.440002   \n2021-01-20  132.029999  224.339996  850.450012  132.029999  224.339996   \n2021-01-21  136.869995  224.970001  844.989990  136.869995  224.970001   \n\n                              High                                 Low  \\\n                  TSLA        AAPL        MSFT        TSLA        AAPL   \nDate                                                                     \n2021-01-14  845.000000  131.000000  217.460007  863.000000  128.759995   \n2021-01-15  826.159973  130.220001  214.509995  859.900024  127.000000   \n2021-01-19  844.549988  128.710007  216.979996  850.000000  126.940002   \n2021-01-20  850.450012  132.490005  225.789993  859.500000  128.550003   \n2021-01-21  844.989990  137.339996  226.300003  855.719910  133.589996   \n\n                                          Open                          \\\n                  MSFT        TSLA        AAPL        MSFT        TSLA   \nDate                                                                     \n2021-01-14  212.740005  838.750000  130.800003  215.910004  843.390015   \n2021-01-15  212.029999  819.099976  128.779999  213.520004  852.000000   \n2021-01-19  212.630005  833.000000  127.779999  213.750000  837.799988   \n2021-01-20  217.289993  837.280029  128.660004  217.699997  858.739990   \n2021-01-21  222.419998  841.420105  133.800003  224.699997  855.000000   \n\n               Volume                      \n                 AAPL      MSFT      TSLA  \nDate                                       \n2021-01-14   90221800  29480800  31266300  \n2021-01-15  111598500  31746500  38777600  \n2021-01-19   90757300  30480900  25367000  \n2021-01-20  103900700  37751500  25598200  \n2021-01-21  119690717  30685750  20463294  \nDate\n2016-01-04     24.400942\n2016-01-05     23.789471\n2016-01-06     23.323915\n2016-01-07     22.339539\n2016-01-08     22.457672\n                 ...    \n2021-01-14    128.910004\n2021-01-15    127.139999\n2021-01-19    127.830002\n2021-01-20    132.029999\n2021-01-21    136.869995\nName: AAPL, Length: 1272, dtype: float64\n             adj_close                         simple_rtn   log_rtn\n                  AAPL        MSFT        TSLA                     \nDate                                                               \n2016-01-04   24.400942   50.006744   44.681999        NaN       NaN\n2016-01-05   23.789471   50.234879   44.686001  -0.025059 -0.025379\n2016-01-06   23.323915   49.322346   43.807999  -0.019570 -0.019764\n2016-01-07   22.339539   47.606789   43.130001  -0.042205 -0.043121\n2016-01-08   22.457672   47.752792   42.200001   0.005288  0.005274\n...                ...         ...         ...        ...       ...\n2021-01-14  128.910004  213.020004  845.000000  -0.015127 -0.015243\n2021-01-15  127.139999  212.649994  826.159973  -0.013731 -0.013826\n2021-01-19  127.830002  216.440002  844.549988   0.005427  0.005412\n2021-01-20  132.029999  224.339996  850.450012   0.032856  0.032328\n2021-01-21  136.869995  224.970001  844.989990   0.036658  0.036002\n\n[1272 rows x 5 columns]\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.8","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4}