semvec/pypi-packages huggingface dataset:
etx-sum 0.1.1
ext-sum.py is intended to be a small utillity script that gives a few greate stats on your daytrading results
Requires Python: >=3.6

# Description

The intention of this Python project is to provide the basic statistics about my trading habbits, based on the data i get from my CFD broker [ETX capital](https://www.etxcapital.com/)


# Installation

## Requirements 

* Python 3.6 or newer


To install the package using pip simply run the following command

      pip3 install etx-sum

# Usage
The usage of this is intended to extremely simple

After you have installed the package see [Installation](#Installation) you simply log into your ETX account download youre trading history, to nice place on you computer.

In ETX this is done like seen on the below screenshot.

![How to get you're trading history ](./static/tradingHistory.png)

when the download completes, you simple run the following command in your terminal.

      etx-sum <path_to_where_you_downloaded_your_trading_history>





# Participate

## building

      python3 setup.py clean --all sdist bdist_wheel 

## Installing locally 

      python3 setup.py clean --all install

## Uploading to pypi

      python3 -m twine upload dist/*

Username: *__\_\_token\_\___*
>For the actual token you need to be part of the project
zorro-df 1.1.1
Package to mask pd.DataFrame data

[![codecov](https://codecov.io/gh/epw505/zorro_df/branch/master/graph/badge.svg)](https://codecov.io/gh/epw505/zorro_df)

![zorro logo](zorro_df_logo.png)
# Zorro DF
Zorro DF is a python package for masking pandas dataframe objects in order to 
anonymise data. It allows you to strip away identifiable column names and string 
values, replacing them with a generic naming convention. The package is built 
under the scikit-learn transformer framework and hence can be plugged into any 
scikit-learn Pipeline.

The package source-code can be found at http://github.com/epw505/zorro_df

## Getting Started
### Requirements
```
pandas>=0.25.3
scikit-learn>=0.22.1
```

### Installation
Zorro DF can be installed using `pip` with the following command:
```
pip install zorro_df
```

## Examples
Once the package is installed, you can load Zorro DF into your python session 
and use the Masker object to mask your data.
```
from zorro_df import mask_dataframe as mf

example_masker = mf.Masker()
example_masker.fit(data)
masked_data = example_masker.transform(data)
```

## Tests
The test suite for Zorro DF is built using `pytest` with the `pytest-mock` 
plugin. Install both as follows.
```
pip install pytest
pip install pytest-mock
```
Once they are installed, you can run the test suite from the root directory of
Zorro Df.
```
pytest tests/
```

## Future Development
* Reverse masking to allow retrieval of original data
* Additional numerical scaling techniques