{"id":193874,"date":"2023-07-25T11:01:04","date_gmt":"2023-07-25T15:01:04","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=193874"},"modified":"2023-07-25T11:01:12","modified_gmt":"2023-07-25T15:01:12","slug":"python-download-stock-prices-using-the-yfinance-package","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/python-download-stock-prices-using-the-yfinance-package\/","title":{"rendered":"Python: Download Stock Prices Using the yfinance Package"},"content":{"rendered":"\n<p>This post shows how to read prices of stock prices with a list of symbols as a string using Python. Splitting data by price types or symbols are illustrated as examples.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-python-jupyter-notebook-code\">Python Jupyter notebook code<\/h3>\n\n\n\n<p>The following code downloads collection of stock prices.<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">import yfinance as yf\nimport pandas as pd\n \nsymbols = ['^GSPC','^VIX', '^FTSE', '^N225', '^HSI']\ndata = yf.download(symbols, start='2022-12-01', end  ='2022-12-06')\nprint(data)<\/pre>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">[*********************100%***********************]  5 of 5 completed\n              Adj Close                                                      \\\n                  ^FTSE        ^GSPC          ^HSI         ^N225       ^VIX   \nDate                                                                          \n2022-12-01  7558.500000  4076.570068  18736.439453  28226.080078  19.840000   \n2022-12-02  7556.200195  4071.699951  18675.349609  27777.900391  19.059999   \n2022-12-05  7567.500000  3998.840088  19518.289062  27820.400391  20.750000   \n \n                  Close                                                      \\\n                  ^FTSE        ^GSPC          ^HSI         ^N225       ^VIX   \nDate                                                                          \n2022-12-01  7558.500000  4076.570068  18736.439453  28226.080078  19.840000   \n2022-12-02  7556.200195  4071.699951  18675.349609  27777.900391  19.059999   \n2022-12-05  7567.500000  3998.840088  19518.289062  27820.400391  20.750000   \n \n            ...         Open                                           \\\n            ...        ^FTSE        ^GSPC          ^HSI         ^N225   \nDate        ...                                                         \n2022-12-01  ...  7573.100098  4087.139893  19058.900391  28273.130859   \n2022-12-02  ...  7558.500000  4040.169922  18785.279297  27983.179688   \n2022-12-05  ...  7556.200195  4052.020020  19221.679688  27752.990234   \n \n                          Volume                                         \n                 ^VIX      ^FTSE       ^GSPC        ^HSI     ^N225 ^VIX  \nDate                                                                     \n2022-12-01  20.830000  642843000  4527130000  4262000300  71400000    0  \n2022-12-02  20.420000  540219900  4012620000  3757394000  79400000    0  \n2022-12-05  20.299999  509145400  4280820000  4890142300  63900000    0  \n \n[3 rows x 30 columns]\n<\/pre>\n\n\n\n<p>We can&nbsp;<strong>split data by type of prices<\/strong>&nbsp;such as Adj Close, Close, Open, and the like.<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\"># Splitting the downloaded data into separate DataFrames\nadj_close_df = data['Adj Close']\nclose_df     = data['Close']\nhigh_df      = data['High']\nlow_df       = data['Low']\nopen_df      = data['Open']\nvolume_df    = data['Volume']\n \n# Printing the separate DataFrames\nprint(\"Adj Close:\"); print(adj_close_df.round(2))\nprint(\"\\nClose:\");   print(close_df.round(2))\nprint(\"\\nHigh:\");    print(high_df.round(2))\nprint(\"\\nLow:\");     print(low_df.round(2))\nprint(\"\\nOpen:\");    print(open_df.round(2))\nprint(\"\\nVolume:\");  print(volume_df)\n <\/pre>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">Adj Close:\n             ^FTSE    ^GSPC      ^HSI     ^N225   ^VIX\nDate                                                  \n2022-12-01  7558.5  4076.57  18736.44  28226.08  19.84\n2022-12-02  7556.2  4071.70  18675.35  27777.90  19.06\n2022-12-05  7567.5  3998.84  19518.29  27820.40  20.75\n \nClose:\n             ^FTSE    ^GSPC      ^HSI     ^N225   ^VIX\nDate                                                  \n2022-12-01  7558.5  4076.57  18736.44  28226.08  19.84\n2022-12-02  7556.2  4071.70  18675.35  27777.90  19.06\n2022-12-05  7567.5  3998.84  19518.29  27820.40  20.75\n \nHigh:\n             ^FTSE    ^GSPC      ^HSI     ^N225   ^VIX\nDate                                                  \n2022-12-01  7599.7  4100.51  19237.45  28423.46  21.06\n2022-12-02  7570.5  4080.48  18841.22  27983.18  20.96\n2022-12-05  7598.2  4052.45  19539.60  27854.11  21.29\n \nLow:\n             ^FTSE    ^GSPC      ^HSI     ^N225   ^VIX\nDate                                                  \n2022-12-01  7552.3  4050.87  18679.35  28226.08  19.80\n2022-12-02  7508.0  4026.63  18530.82  27662.12  18.95\n2022-12-05  7547.8  3984.49  19035.14  27700.86  19.78\n \nOpen:\n             ^FTSE    ^GSPC      ^HSI     ^N225   ^VIX\nDate                                                  \n2022-12-01  7573.1  4087.14  19058.90  28273.13  20.83\n2022-12-02  7558.5  4040.17  18785.28  27983.18  20.42\n2022-12-05  7556.2  4052.02  19221.68  27752.99  20.30\n \nVolume:\n                ^FTSE       ^GSPC        ^HSI     ^N225  ^VIX\nDate                                                         \n2022-12-01  642843000  4527130000  4262000300  71400000     0\n2022-12-02  540219900  4012620000  3757394000  79400000     0\n2022-12-05  509145400  4280820000  4890142300  63900000     0<\/pre>\n\n\n\n<p>We can also&nbsp;<strong>split data by each symbols<\/strong>.<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\"># Create a MultiIndex from the columns\ndata.columns = data.columns.swaplevel(0, 1)\ndata.sort_index(axis=1, level=0, inplace=True)\n \n# Split the data based on symbols\nsymbol_dfs = {}\nfor symbol in symbols:\n    # Create a copy of the DataFrame\n    symbol_dfs[symbol] = data[symbol].copy()  \n    # Divide 'Volume' column by 1000\n    symbol_dfs[symbol]['Volume'] \/= 1000000\n    symbol_dfs[symbol] = symbol_dfs[symbol].round(0)\n \n# Print the separate DataFrames\nfor symbol, df in symbol_dfs.items():\n    print(f\"Data for symbol: {symbol}\")\n    print(df)<\/pre>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">Data for symbol: ^GSPC\n            Adj Close   Close    High     Low    Open  Volume\nDate                                                         \n2022-12-01     4077.0  4077.0  4101.0  4051.0  4087.0  4527.0\n2022-12-02     4072.0  4072.0  4080.0  4027.0  4040.0  4013.0\n2022-12-05     3999.0  3999.0  4052.0  3984.0  4052.0  4281.0\nData for symbol: ^VIX\n            Adj Close  Close  High   Low  Open  Volume\nDate                                                  \n2022-12-01       20.0   20.0  21.0  20.0  21.0     0.0\n2022-12-02       19.0   19.0  21.0  19.0  20.0     0.0\n2022-12-05       21.0   21.0  21.0  20.0  20.0     0.0\nData for symbol: ^FTSE\n            Adj Close   Close    High     Low    Open  Volume\nDate                                                         \n2022-12-01     7558.0  7558.0  7600.0  7552.0  7573.0   643.0\n2022-12-02     7556.0  7556.0  7570.0  7508.0  7558.0   540.0\n2022-12-05     7568.0  7568.0  7598.0  7548.0  7556.0   509.0\nData for symbol: ^N225\n            Adj Close    Close     High      Low     Open  Volume\nDate                                                             \n2022-12-01    28226.0  28226.0  28423.0  28226.0  28273.0    71.0\n2022-12-02    27778.0  27778.0  27983.0  27662.0  27983.0    79.0\n2022-12-05    27820.0  27820.0  27854.0  27701.0  27753.0    64.0\nData for symbol: ^HSI\n            Adj Close    Close     High      Low     Open  Volume\nDate                                                             \n2022-12-01    18736.0  18736.0  19237.0  18679.0  19059.0  4262.0\n2022-12-02    18675.0  18675.0  18841.0  18531.0  18785.0  3757.0\n2022-12-05    19518.0  19518.0  19540.0  19035.0  19222.0  4890.0<\/pre>\n\n\n\n<p><em>Originally posted on <a href=\"https:\/\/shleeai.blogspot.com\/2023\/06\/python-download-stock-prices-using.html\">SHLee AI Financial Model<\/a> blog.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This post shows how to read prices of stock prices with a list of symbols as a string using Python.<\/p>\n","protected":false},"author":662,"featured_media":193877,"comment_status":"open","ping_status":"closed","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,343,349,338,341],"tags":[806,4922,1224,595,6674],"contributors-categories":[13728],"class_list":{"0":"post-193874","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-programing-languages","9":"category-python-development","10":"category-ibkr-quant-news","11":"category-quant-development","12":"tag-data-science","13":"tag-econometrics","14":"tag-pandas","15":"tag-python","16":"tag-yfinance","17":"contributors-categories-sh-fintech-modeling"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"<!-- This site is optimized with the Yoast 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