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
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
Requires-Dist: numpy
Requires-Dist: pandas
Provides-Extra: def
Requires-Dist: pytest ; extra == 'def'
Requires-Dist: twine ; extra == 'def'

# 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.

