Metadata-Version: 2.1
Name: bamboo-ta
Version: 0.0.1
Summary: TA library for Pandas
Home-page: https://github.com/DutchCryptoDad/bamboo-ta
Author: DutchCryptoDad (DCD)
Author-email: <dutchcryptodad@gmail.com>
License: UNKNOWN
Description: # bamboo-ta
        
        A library with technical analysis indicators for trading. Especially made for use with Pandas dataframes.
        
        ## Installation
        
        ### Stable version
        
        Install bamboo-ta from Pypi with:
        
        ``pip install bamboo-ta``
        
        Or you can install this directly from the Github repository with the following command:
        
        ``$ pip install -U git+https://github.com/DutchCryptoDad/bamboo-ta``
        
        ### Development version
        
        The bleeding edge development version can be installed with:
        
        ``$ pip install -U git+https://github.com/DutchCryptoDad/bamboo-ta.git@development``
        
        ## Using the library
        
        Import the library into your Python scripts or Notebook as follows:
        
        ``import bamboo_ta as bta``
        
        After this, you can use the libraries technical indicators with:
        
        ``df['lsma'] = bta.calculate_lsma(df, 14)``
        
        Example script:
        
        ```
        # -*- coding: utf-8 -*-
        # Import necessary libraries
        import pandas_ta as pta
        import bamboo_ta as bta
        import pandas as pd
        from pandas import DataFrame
        import numpy as np
        
        # create dataframe and read the json data in the datasets directory
        df = pd.read_json("./testdata/BTC_USDT-1d.json")
        # name the columns that are loaded into the dataframe
        df.columns = ['date', 'open', 'high', 'low', 'close', 'volume']
        # the date column consists of unix time in milliseconds, so this command changes this data into human readable form.
        df['date'] = (pd.to_datetime(df['date'], unit='ms'))
        
        print(df)  # This command outputs the dataframe
        
        # Using the pandas_ta library
        df['imi_ema'] = pta.ema(close=df['close'], length=7)
        
        df['lsma'] = bta.calculate_lsma(df, 14)  # Using the bamboo_ta library
        
        print(df)
        ```
        
        Output:
        
        ```
         /dev
        ➜  python test.py 
                   date      open      high       low     close         volume
        0    2017-08-17   4261.48   4485.39   4200.74   4285.08     795.150377
        1    2017-08-18   4285.08   4371.52   3938.77   4108.37    1199.888264
        2    2017-08-19   4108.37   4184.69   3850.00   4139.98     381.309763
        3    2017-08-20   4120.98   4211.08   4032.62   4086.29     467.083022
        4    2017-08-21   4069.13   4119.62   3911.79   4016.00     691.743060
        ...         ...       ...       ...       ...       ...            ...
        1967 2023-01-05  16850.36  16879.82  16753.00  16831.85  163473.566410
        1968 2023-01-06  16831.85  17041.00  16679.00  16950.65  207401.284150
        1969 2023-01-07  16950.31  16981.91  16908.00  16943.57  104526.568800
        1970 2023-01-08  16943.83  17176.99  16911.00  17127.83  135155.896950
        1971 2023-01-09  17127.83  17398.80  17104.66  17178.26  266211.527230
        
        [1972 rows x 6 columns]
                   date      open      high       low     close         volume       imi_ema          lsma
        0    2017-08-17   4261.48   4485.39   4200.74   4285.08     795.150377           NaN           NaN
        1    2017-08-18   4285.08   4371.52   3938.77   4108.37    1199.888264           NaN           NaN
        2    2017-08-19   4108.37   4184.69   3850.00   4139.98     381.309763           NaN           NaN
        3    2017-08-20   4120.98   4211.08   4032.62   4086.29     467.083022           NaN           NaN
        4    2017-08-21   4069.13   4119.62   3911.79   4016.00     691.743060           NaN           NaN
        ...         ...       ...       ...       ...       ...            ...           ...           ...
        1967 2023-01-05  16850.36  16879.82  16753.00  16831.85  163473.566410  16737.537534  16633.800286
        1968 2023-01-06  16831.85  17041.00  16679.00  16950.65  207401.284150  16790.815651  16678.202286
        1969 2023-01-07  16950.31  16981.91  16908.00  16943.57  104526.568800  16829.004238  16746.722286
        1970 2023-01-08  16943.83  17176.99  16911.00  17127.83  135155.896950  16903.710678  16816.734571
        1971 2023-01-09  17127.83  17398.80  17104.66  17178.26  266211.527230  16972.348009  16930.485143
        
        [1972 rows x 8 columns]
        ```
        
        
        ## Creating the Python pip package (personal notes)
        
        After creating and testing the code, make a Python pip package as follows:
        
        In the library folder, create the package
        
        ``python3 setup.py sdist bdist_wheel``
        
        Before uploading the package to Pypi it is wise to test the package on your system.
        
        Load the package to the system with:
        
        ``pip install .``
        
        After you've checked that everything is worknig correctly, then use the following command to upload to Pypi.
        You'll have to install twine for this (``pip install twine`` or ``sudo apt install twine``).
        
        ```
        # Check first
        
        twine check dist/*
        
        # Test upload first
        
        twine upload -r testpypi dist/*
        
        # Upload to Pypi
        
        twine upload dist/*
        ```
        
        ### Uploading with 2FA enabled
        
        First create an API token (at https://test.pypi.org/manage/account/token/).
        
        Create a file .pypirc in your home folder (e.g. ``nano $HOME/.pypirc``)
        
        Add the given token to the file like this:
        
        ```
        [testpypi]
          username = __token__
          password = pypi-AgENdalaljdljhdalkHTaddsdSQtMjBjOS00ZjgxLWIyZDMtYWViMDAwOTk3MWZmAAIqWzMsImU3YjkzMGVmLWQzMFmZkZCJdAAAGIB6NZ-rSrzc8UXj38ijwCRmZwkFLnhhNP
        ```
        
        Save the file and reload environment if necessary.
        
        Now you an upload libraries without having to use the password.
Keywords: python,pandas,numpy,trading,indicator,technical analysis
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/markdown
Provides-Extra: def
