# bamboo-ta
<p align="center">
<a href="https://github.com/DutchCryptoDad/bamboo-ta">
<img src="images/bamboo.png" alt="Bamboo TA" width="250">
</a>
</p>
A library with technical analysis indicators for trading. Especially made for use with Pandas dataframes.
[](#license)
[](https://pypi.org/project/bamboo-ta/)
[](https://pypi.org/project/bamboo-ta/)
[](https://pypi.org/project/bamboo-ta/)
[](https://pypistats.org/packages/bamboo_ta)
[](#stars)
[](#forks)
[](#usedby)
[](#contributors)
[](#issues)
[](#closed-issues)
[](https://patreon.com/dutchalgotrading)
[
](https://www.youtube.com/@dutchalgotrading)
[](https://www.youtube.com/@dutchalgotrading)
## 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/*
```
Note: uploading new versions requires to delete the older versions from the /dist folder.
Another option is to use the ``--skip-existing`` option like this:
```
twine upload -r --skip-existing testpypi dist/*
twine upload --skip-existing 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 $HOME/.pypirc``)
Add the given token to the file like this:
```
[testpypi]
username = __token__
password = pypi-AgENdalaljdljhdalkHTaddsdSQtMjBjOS00ZjgxLWIyZDMtYWViMDAwOTk3MWZmAAIqWzMsImU3YjkzMGVmLWQzMFmZkZCJdAAAGIB6NZ-rSrzc8UXj38ijwCRmZwkFLnhhNP
```
Save the file and reload environment if necessary.
Now you an upload libraries without having to use the password.
## Other sources
* [ThinkOrSwim Tech indicators](https://tlc.thinkorswim.com/center/reference/Tech-Indicators)
Raw data
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"description": "# bamboo-ta\n\n<p align=\"center\">\n <a href=\"https://github.com/DutchCryptoDad/bamboo-ta\">\n <img src=\"images/bamboo.png\" alt=\"Bamboo TA\" width=\"250\">\n </a>\n</p>\n\n\nA library with technical analysis indicators for trading. Especially made for use with Pandas dataframes.\n\n[](#license)\n[](https://pypi.org/project/bamboo-ta/)\n[](https://pypi.org/project/bamboo-ta/)\n[](https://pypi.org/project/bamboo-ta/)\n[](https://pypistats.org/packages/bamboo_ta)\n[](#stars)\n[](#forks)\n[](#usedby)\n[](#contributors)\n[](#issues)\n[](#closed-issues)\n[](https://patreon.com/dutchalgotrading)\n[\n](https://www.youtube.com/@dutchalgotrading)\n[](https://www.youtube.com/@dutchalgotrading)\n\n\n## Installation\n\n### Stable version\n\nInstall bamboo-ta from Pypi with:\n\n``pip install bamboo-ta``\n\nOr you can install this directly from the Github repository with the following command:\n\n``$ pip install -U git+https://github.com/DutchCryptoDad/bamboo-ta``\n\n### Development version\n\nThe bleeding edge development version can be installed with:\n\n``$ pip install -U git+https://github.com/DutchCryptoDad/bamboo-ta.git@development``\n\n## Using the library\n\nImport the library into your Python scripts or Notebook as follows:\n\n``import bamboo_ta as bta``\n\nAfter this, you can use the libraries technical indicators with:\n\n``df['lsma'] = bta.calculate_lsma(df, 14)``\n\nExample script:\n\n```\n# -*- coding: utf-8 -*-\n# Import necessary libraries\nimport pandas_ta as pta\nimport bamboo_ta as bta\nimport pandas as pd\nfrom pandas import DataFrame\nimport numpy as np\n\n# create dataframe and read the json data in the datasets directory\ndf = pd.read_json(\"./testdata/BTC_USDT-1d.json\")\n# name the columns that are loaded into the dataframe\ndf.columns = ['date', 'open', 'high', 'low', 'close', 'volume']\n# the date column consists of unix time in milliseconds, so this command changes this data into human readable form.\ndf['date'] = (pd.to_datetime(df['date'], unit='ms'))\n\nprint(df) # This command outputs the dataframe\n\n# Using the pandas_ta library\ndf['imi_ema'] = pta.ema(close=df['close'], length=7)\n\ndf['lsma'] = bta.calculate_lsma(df, 14) # Using the bamboo_ta library\n\nprint(df)\n```\n\nOutput:\n\n```\n\ueb06 /dev\n\u279c python test.py \n date open high low close volume\n0 2017-08-17 4261.48 4485.39 4200.74 4285.08 795.150377\n1 2017-08-18 4285.08 4371.52 3938.77 4108.37 1199.888264\n2 2017-08-19 4108.37 4184.69 3850.00 4139.98 381.309763\n3 2017-08-20 4120.98 4211.08 4032.62 4086.29 467.083022\n4 2017-08-21 4069.13 4119.62 3911.79 4016.00 691.743060\n... ... ... ... ... ... ...\n1967 2023-01-05 16850.36 16879.82 16753.00 16831.85 163473.566410\n1968 2023-01-06 16831.85 17041.00 16679.00 16950.65 207401.284150\n1969 2023-01-07 16950.31 16981.91 16908.00 16943.57 104526.568800\n1970 2023-01-08 16943.83 17176.99 16911.00 17127.83 135155.896950\n1971 2023-01-09 17127.83 17398.80 17104.66 17178.26 266211.527230\n\n[1972 rows x 6 columns]\n date open high low close volume imi_ema lsma\n0 2017-08-17 4261.48 4485.39 4200.74 4285.08 795.150377 NaN NaN\n1 2017-08-18 4285.08 4371.52 3938.77 4108.37 1199.888264 NaN NaN\n2 2017-08-19 4108.37 4184.69 3850.00 4139.98 381.309763 NaN NaN\n3 2017-08-20 4120.98 4211.08 4032.62 4086.29 467.083022 NaN NaN\n4 2017-08-21 4069.13 4119.62 3911.79 4016.00 691.743060 NaN NaN\n... ... ... ... ... ... ... ... ...\n1967 2023-01-05 16850.36 16879.82 16753.00 16831.85 163473.566410 16737.537534 16633.800286\n1968 2023-01-06 16831.85 17041.00 16679.00 16950.65 207401.284150 16790.815651 16678.202286\n1969 2023-01-07 16950.31 16981.91 16908.00 16943.57 104526.568800 16829.004238 16746.722286\n1970 2023-01-08 16943.83 17176.99 16911.00 17127.83 135155.896950 16903.710678 16816.734571\n1971 2023-01-09 17127.83 17398.80 17104.66 17178.26 266211.527230 16972.348009 16930.485143\n\n[1972 rows x 8 columns]\n```\n\n\n## Creating the Python pip package (personal notes)\n\nAfter creating and testing the code, make a Python pip package as follows:\n\nIn the library folder, create the package\n\n``python3 setup.py sdist bdist_wheel``\n\nBefore uploading the package to Pypi it is wise to test the package on your system.\n\nLoad the package to the system with:\n\n``pip install .``\n\nAfter you've checked that everything is worknig correctly, then use the following command to upload to Pypi.\nYou'll have to install twine for this (``pip install twine`` or ``sudo apt install twine``).\n\n```\n# Check first\n\ntwine check dist/*\n\n# Test upload first\n\ntwine upload -r testpypi dist/*\n\n# Upload to Pypi\n\ntwine upload dist/*\n```\n\nNote: uploading new versions requires to delete the older versions from the /dist folder.\n\nAnother option is to use the ``--skip-existing`` option like this:\n\n```\ntwine upload -r --skip-existing testpypi dist/*\ntwine upload --skip-existing dist/*\n```\n\n### Uploading with 2FA enabled\n\nFirst create an API token (at https://test.pypi.org/manage/account/token/).\n\nCreate a file .pypirc in your home folder (e.g. ``nano $HOME/.pypirc``)\n\nAdd the given token to the file like this:\n\n```\n[testpypi]\n username = __token__\n password = pypi-AgENdalaljdljhdalkHTaddsdSQtMjBjOS00ZjgxLWIyZDMtYWViMDAwOTk3MWZmAAIqWzMsImU3YjkzMGVmLWQzMFmZkZCJdAAAGIB6NZ-rSrzc8UXj38ijwCRmZwkFLnhhNP\n```\n\nSave the file and reload environment if necessary.\n\nNow you an upload libraries without having to use the password.\n\n## Other sources\n\n* [ThinkOrSwim Tech indicators](https://tlc.thinkorswim.com/center/reference/Tech-Indicators)\n\n\n",
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