financefunctions 0.0.2.1

a simple package that creates some convenience functions for financial data

Introduction

a simple package that creates some extensions to a pandas dataframe for handling financial functions

Getting Started

The main purpose of this simple lib is to have some convenience functions for easy handling of pandas dataframes when working with time series data. At the moment is this the focus of the implementation.

Example

>>> import financefunctions as ff
>>> import eod2pd
>>> df = eod2pd.get_symbols_prices(["BMW.XETRA","BAS.XETRA"], dict_result=False)
>>> df
            BAS.XETRA                                                                    BMW.XETRA                                                                  
               symbol      open      high       low     close adjusted_close     volume     symbol      open      high       low     close adjusted_close     volume
date                                                                                                                                                                
1994-02-01  BAS.XETRA  154.5124  155.6884  153.8990  154.5124         2.4273  1565840.0  BMW.XETRA  406.4767  410.5692  400.0884  406.4767         5.1169  2677750.0
1994-02-02  BAS.XETRA  154.4102  155.0748  154.1546  154.4102         2.4257  1044460.0  BMW.XETRA  404.1757  405.7123  401.8772  404.1757         5.0880  2083911.0
1994-02-03  BAS.XETRA  154.5636  156.6088  151.5980  154.5636         2.4281  2216020.0  BMW.XETRA  398.2944  402.3868  397.2726  398.2944         5.0139  1822376.0
1994-02-04  BAS.XETRA  151.3424  153.8990  149.6552  151.3424         2.3775  1569320.0  BMW.XETRA  386.5370  392.6705  383.4664  386.5370         4.8659  2529985.0
1994-02-07  BAS.XETRA  148.8882  150.3198  148.3770  148.8882         2.3390  2213040.0  BMW.XETRA  385.0030  394.7193  382.4472  385.0030         4.8466  1404435.0
...               ...       ...       ...       ...       ...            ...        ...        ...       ...       ...       ...       ...            ...        ...
2024-03-26  BAS.XETRA   52.9000   52.9400   52.4800   52.6300        52.6300  2278963.0  BMW.XETRA  106.6000  106.7600  105.5400  106.5600       106.5600   758874.0
2024-03-27  BAS.XETRA   52.7800   53.2600   52.1500   53.2500        53.2500  2540595.0  BMW.XETRA  105.5000  106.5200  104.6600  106.1600       106.1600   816350.0
2024-03-28  BAS.XETRA   53.3400   53.5300   52.7100   52.9300        52.9300  2646566.0  BMW.XETRA  106.3400  107.1800  106.3400  106.9600       106.9600   785748.0
2024-04-02  BAS.XETRA   53.4500   54.2300   53.2100   53.8200        53.8200  2903536.0  BMW.XETRA  107.2000  107.8000  105.8000  106.6500       106.6500  1084212.0
2024-04-03  BAS.XETRA   53.5800   54.7500   53.5700   54.5100        54.5100  2314791.0  BMW.XETRA  106.9500  111.9500  106.6000  111.8500       111.8500  1830218.0

[7634 rows x 14 columns]

>>> df.ff.norm()
             BAS.XETRA                                                                  BMW.XETRA                                                               
                  open        high         low       close adjusted_close      volume        open        high         low       close adjusted_close      volume
date                                                                                                                                                            
1994-02-01  100.000000  100.000000  100.000000  100.000000     100.000000  100.000000  100.000000  100.000000  100.000000  100.000000     100.000000  100.000000
1994-02-02   99.933856   99.605879  100.166083   99.933856      99.934083   66.702856   99.433916   98.817033  100.447101   99.433916      99.435205   77.823210
1994-02-03  100.033136  100.591181   98.504864  100.033136     100.032958  141.522761   97.987019   98.007059   99.296206   97.987019      97.987062   68.056241
1994-02-04   97.948385   98.850653   97.242477   97.948385      97.948338  100.222245   95.094504   95.640516   95.845418   95.094504      95.094686   94.481748
1994-02-07   96.360033   96.551702   96.411933   96.360033      96.362213  141.332448   94.717114   96.139530   95.590674   94.717114      94.717505   52.448324
...                ...         ...         ...         ...            ...         ...         ...         ...         ...         ...            ...         ...
2024-03-26   34.236734   34.003818   34.100287   34.061991    2168.252791  145.542520   26.225365   26.002925   26.379170   26.215525    2082.510895   28.339987
2024-03-27   34.159071   34.209357   33.885860   34.463253    2193.795575  162.251252   25.954747   25.944469   26.159219   26.117118    2074.693662   30.486416
2024-03-28   34.521501   34.382780   34.249735   34.256150    2180.612203  169.018929   26.161401   26.105222   26.579126   26.313931    2090.328128   29.343591
2024-04-02   34.592693   34.832396   34.574624   34.832156    2217.278458  185.429929   26.372975   26.256232   26.444156   26.237666    2084.269773   40.489665
2024-04-03   34.676829   35.166396   34.808543   35.278722    2245.705104  147.830621   26.311471   27.267023   26.644112   27.516952    2185.893803   68.349099

[7634 rows x 12 columns]

>>> df.ff.subcols("adjusted_close")
                BAS.XETRA      BMW.XETRA
           adjusted_close adjusted_close
date                                    
1994-02-01         2.4273         5.1169
1994-02-02         2.4257         5.0880
1994-02-03         2.4281         5.0139
1994-02-04         2.3775         4.8659
1994-02-07         2.3390         4.8466
...                   ...            ...
2024-03-26        52.6300       106.5600
2024-03-27        53.2500       106.1600
2024-03-28        52.9300       106.9600
2024-04-02        53.8200       106.6500
2024-04-03        54.5100       111.8500

[7634 rows x 2 columns]

Project Homepage

https://dev.azure.com/neuraldevelopment/financefunctions

Contribute

If you find a defect or suggest a new function, please send an eMail to [e-mail]