ohlc: open-high-low-close types and tools
Project description
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ohlc: data type and tools
=========================
ohlc provides `Ohlc`, a `namedtuple` for storing and efficiently processing
open-high-low-close data, used for financial charts and calculations.
It also provides tools for processing and visualizing lists of `Ohlc` values
in the console.
Examples
--------
```python
from ohlc import Ohlc
o = Ohlc.from_values([2,5,4,7,11,7,2,9,5]) # Ohlc(open=2, high=11, low=2, close=5)
o.spread() # 9
o = Ohlc(3,4,1,2) # Ohlc(open=3, high=4, low=1, close=2)
o == (3,4,1,2) # True -- Yeay! it is a regular tuple!
o1 = Ohlc.from_values(range(5,15))
o2 = Ohlc.from_values(range(14,3,-1), prev=o1)
o3 = Ohlc.from_values(range(5,20), prev=o2)
[o1,o2,o3] # [Ohlc(open=5, high=14, low=5, close=14),
# Ohlc(open=14, high=14, low=4, close=4),
# Ohlc(open=5, high=19, low=5, close=19)]
o3.heikin() # compute Heikin-Ashi candles from Ohlc chain
# Ohlc(open=9.0, high=19, low=5, close=12.0)
```
Visualization
-------------
![ohlc demo screen](https://github.com/ubunatic/ohlc/blob/master/ohlc-ui.gif)
For visualizations it provides raw colored console output using terminal colors and also
supports styled output for embedding in [urwid](http://urwid.org) console apps.
It provides a simple ohlc grapher built using the [widdy](https://github.com/ubunatic/widdy/)
widgets urwid-wrapper.
Installation
------------
pip install ohlc
Usage (WIP)
-----------
The cli usage is not final, please be patient!
ohlc candles --name "random values" # start the ohlc candlestick visualization using random values
shuf -i0-1000 | ohlc candles --name "shuf 1000" # visualize raw input data
Development
-----------
First clone the repo.
git clone https://github.com/ubunatic/ohlc
cd ohlc
Then install the cloned version and install missing tools.
make # clean and run all tests
make install # install the checked-out dev version
make build # transpile Py3 to Py2
You may need to install some tools and modules, i.e., `flake8`, `pytest-3`, `twine`, `urwid`, and maybe others.
[Pull requests](https://github.com/ubunatic/ohlc/pulls) are welcome!
ohlc: data type and tools
=========================
ohlc provides `Ohlc`, a `namedtuple` for storing and efficiently processing
open-high-low-close data, used for financial charts and calculations.
It also provides tools for processing and visualizing lists of `Ohlc` values
in the console.
Examples
--------
```python
from ohlc import Ohlc
o = Ohlc.from_values([2,5,4,7,11,7,2,9,5]) # Ohlc(open=2, high=11, low=2, close=5)
o.spread() # 9
o = Ohlc(3,4,1,2) # Ohlc(open=3, high=4, low=1, close=2)
o == (3,4,1,2) # True -- Yeay! it is a regular tuple!
o1 = Ohlc.from_values(range(5,15))
o2 = Ohlc.from_values(range(14,3,-1), prev=o1)
o3 = Ohlc.from_values(range(5,20), prev=o2)
[o1,o2,o3] # [Ohlc(open=5, high=14, low=5, close=14),
# Ohlc(open=14, high=14, low=4, close=4),
# Ohlc(open=5, high=19, low=5, close=19)]
o3.heikin() # compute Heikin-Ashi candles from Ohlc chain
# Ohlc(open=9.0, high=19, low=5, close=12.0)
```
Visualization
-------------
![ohlc demo screen](https://github.com/ubunatic/ohlc/blob/master/ohlc-ui.gif)
For visualizations it provides raw colored console output using terminal colors and also
supports styled output for embedding in [urwid](http://urwid.org) console apps.
It provides a simple ohlc grapher built using the [widdy](https://github.com/ubunatic/widdy/)
widgets urwid-wrapper.
Installation
------------
pip install ohlc
Usage (WIP)
-----------
The cli usage is not final, please be patient!
ohlc candles --name "random values" # start the ohlc candlestick visualization using random values
shuf -i0-1000 | ohlc candles --name "shuf 1000" # visualize raw input data
Development
-----------
First clone the repo.
git clone https://github.com/ubunatic/ohlc
cd ohlc
Then install the cloned version and install missing tools.
make # clean and run all tests
make install # install the checked-out dev version
make build # transpile Py3 to Py2
You may need to install some tools and modules, i.e., `flake8`, `pytest-3`, `twine`, `urwid`, and maybe others.
[Pull requests](https://github.com/ubunatic/ohlc/pulls) are welcome!
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