semvec/pypi-packages huggingface dataset:
vocably 0.0.9
Vocably is a Natural Language Framework written in Python for Language based Tasks.
License: MIT
Requires Python: >=3.7,<4

<div>
<a href="https://colab.research.google.com/drive/1q56gWXSik2XCT2pqjSD8kv3fLvfvoI1n?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
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# Vocably

<img src="data/images/vocably.png" height =300px></img>

Vocably is a Natural Language Processing (NLP) Framework written with the purpose to increase the flow of our development in daily basis.
## Quick Installation

```bash
$ pip install vocably
```
## Installation from source 
```bash
$ git clone https://github.com/Nandhini25S/Vocably.git
$ cd Vocably
$ conda env create -f environment.yml
$ export PYTHONPATH=./src
$ python3 setup.py install
```

## Using Makefile (for Linux or Mac Users)
```bash
$ git clone https://github.com/Nandhini25S/Vocably.git
$ cd Vocably
$ conda env create -f environment.yml
```
now we will activate this environment
```bash
$ conda activate vocably
```
to install and config

```bash
$ export PYTHONPATH=./src
$ make install
$ make configure
```
or 
```bash
$ make all
```
## Using batch file (for Windows users)

```bash
$ git clone https://github.com/Nandhini25S/Vocably.git
$ cd Vocably
$ conda env create -f environment.yml
```
now we will activate this environment
```bash
$ conda activate vocably
```
to install and config

```bash
$ Scripts/install.bat
$ Scripts/config.bat
```
enular 1.0.0
Enular Library
License: MIT

# Enular Library
 
pip install enular

NOTE: Alpha version (v1.0.0) and development in progress. Expect beta release in late 2023.

The Enular Library contains tools for backtesting, evaluating and visualising algorithmic trading strategies. It allows the user to easily combine indicators with complex operations into strategies, similar to neural networks. It also provides indicators, data sources, and paper trading capabilities. Documentation coming soon. Enular.com

Details:
- Uses parts of Backtrader's Cerebro engine with fixes from Backtrader2
- Data streaming from Yahoo Finance
- Indicator and strategy collection
- Improve accessiblity with simplified architecture
- Highly scalable strategies: extend classes and redefine trade logic
- Live trading capabilities

Architecture:

- 1 category of base indicators (INPUT: market data, OUTPUT: single indicator signal)
    - Indicator library with existing technical analysis indicators

- 3 categories of indicator operations (INPUT: two indicator signals, OUTPUT: single indicator signal):
    - Scalar inputs to scalar output
    - Scalar inputs to boolean output
    - Boolean inputs to boolean output (7 basic logic gates)

- 2 categories of strategy operations (INPUT two indicator signals, OUTPUT: order instructions):
    - Scalar inputs to order instructions
    - Boolean inputs to order instructions (7 basic logic gates)

Development in progress:
- Live trading via IB
- Indicator library
- Strategy library
- Data feed improvements
- Machine learning capabities
- Instructional articles on Medium
- Templates
- Documentation
- User forum
- Resaerch
- Data hosting and supply