Metadata-Version: 2.1
Name: binpan
Version: 0.0.9
Summary: Binance API wrapper with backtesting tools.
Home-page: https://github.com/nand0san/binpan_studio
Author: Fernando Alfonso
Author-email: hancaidolosdos@hotmail.com
License: MIT
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/markdown
License-File: LICENSE

Welcome to BinPan's documentation!
==================================

BinPan is a Python wrapper for Binance API, creating objects with many capabilities to analyze data.

BinPan can make plots easily and grab API requests in the same object. It can also obtain some technical indicators.

The objective of this module is to have a fast tool to collect and handle data from the Binance API
in an agile way.

It is intended to be useful in Jupyter Notebooks or even the python console, but it can be used in
many other ways.

BinPan manages symbol objects that can:

- get candles with time zone and indexing options.
- get trades.
- calculate technical indicators.
- plot candles, histograms, indicators, etc in a very simple and beautiful way.
- check applied fees.

An example of a plot for candles and indicators:

![](https://raw.githubusercontent.com/nand0san/binpan_studio/main/docs/images/candles.png)


Documentation
-------------
Take a look to the basic **tutorial**. Find it in the Jupyter Notebook file **tutorial.ipynb**

Also, can be found Sphinx documentation at: 

https://nand0san.github.io/binpan_studio/

Hope you find it useful breaking the market!!!

Python Version
--------------

Google colab ready: `python 3.7.9`

GitHub repo
-----------

https://github.com/nand0san/binpan_studio


Installation
------------

```
   pip install binpan
```

Usage
-----

Importing just like this:

```
    import binpan

    btcusdt = binpan.Symbol(symbol='btcusdt',
                            tick_interval='15m',
                            time_zone='Europe/Madrid',
                            start_time='2021-10-31 01:00:00',
                            end_time='2021-10-31 03:00:00')
                            
    btcusdt.sma(21)
    
    btcusdt.plot()
    
```

### Greetings

Thank you for the pandas_ta people for that great library.
