semvec/pypi-packages huggingface dataset:
pyaccelerator 0.2.0
Accelerator building blocks.
License: MIT
Requires Python: <4.0,>=3.9

# pyaccelerator

[![Tests](https://github.com/loiccoyle/accelerator/workflows/tests/badge.svg)](https://github.com/loiccoyle/accelerator/actions?query=workflow%3Atests)
[![Documentation Status](https://readthedocs.org/projects/pyaccelerator/badge/?version=latest)](https://pyaccelerator.readthedocs.io/en/latest/?badge=latest)
[![pypi](https://img.shields.io/pypi/v/pyaccelerator)](https://pypi.org/project/pyaccelerator/)

Python package to build simple toy accelerators.

# Dependencies:
  * `numpy`
  * `matplotlib`
  * `scipy`

# Installation:

```sh
pip install pyaccelerator
```

It usualy a good idea to use a [virtual environment](https://docs.python.org/3/tutorial/venv.html). To create a virtual environment in the `venv` folder (assuming python3):
```sh
python -m venv venv
```

To activate the virtual environment:
```sh
source venv/bin/activate
```

Proceed with the installation to install in the virtual environment.

To deactivate the virtual environment:
```sh
deactivate
```

# Usage:
See the `notebooks` folder for some examples.
lightning-toolbox 0.0.35
A collection of utilities for PyTorch Lightning.
License: MIT
Requires Python: >=3.8
Keywords: artificial intelligence, pytorch lightning, objective functions, regularization

# LightningToolbox: A PyTorch Lightning Facilitator
<p align="center">
  <a href="#installation">Installation</a> •
  <a href="https://github.com/vahidzee/lightning-toolbox/tree/main/docs/">Docs</a> •
  <a href="#license">License</a>
</p>

<p align="center" markdown="1">
    <a href="https://badge.fury.io/py/lightning_toolbox"><img src="https://badge.fury.io/py/lightning_toolbox.svg" alt="PyPI version" height="18"></a>
</p>


Welcome to lightning-toolbox, a python package that offers a set of automation tools built on top of PyTorch Lightning. As a deep learning researcher, I found that PyTorch Lightning offloads a significant portion of redundant work. However, I still found myself spending a considerable amount of time on writing boilerplate code for datamodules, training/validation steps, logging, and specially for not so complicated costum training loops. This is why I created lightning-toolbox - to make it easier to focus on writing experiment-specific code rather than dealing with tedious setup tasks.

By passing your PyTorch model onto a generic `lightning.LightningModule` (`lightning_toolbox.TrainingModule`), lightning-toolbox automatically populates the objective function, optimizer step, and more. In addition, lightning-toolbox's generic `lightning.LightningDataModule` (`lightning_toolbox.DataModule`) can turn any PyTorch dataset, into a experiment-ready lightning data module, completing the cycle for writing lightning deep learning experiments.

Most of the functionality provided in this package is based on [dypy](https://github.com/vahidzee/dypy), which enables lazy evaluation of variables and runtime code injections. Although lightning-toolbox is currently in its early stages and mainly serves as a facilitator for my personal research projects, I believe it can be helpful for many others who deal with similar deep learning experiments. Therefore, I decided to open-source this project and continue to add on to it as I move further in my research.

As a disclaimer this package does not intend to solve field-specific problems and provides more generic facilitator codes that the official [lightning-bolts](https://lightning-bolts.readthedocs.io/en/latest/), that you can easily mold into your desired deep learning experiment.

## Installation

```bash
pip install lightning-toolbox
```
Lightning toolbox is tested on `lightning==1.9.0`, although there's no version restriction setup for this package, things might break down if the community decides to roll backward incompatible changes to the core Pytorch Lightning API (as they usually do).

## License

This project is licensed under the terms of the Apache 2.0 license. See [LICENSE](https://github.com/vahidzee/lightning-toolbox/tree/main/LICENSE) for more details.
py-alpaca-daily-losers 2.2.8
Daily Losers strategy, uses py-alpaca-api for Alpaca Markets integration.
License: MIT
Requires Python: <4.0,>=3.12

# Py-Alpaca-Daily-Losers 📉🐍

**Py-Alpaca-Daily-Losers** is a Python package that integrates with the Alpaca Markets API to identify and analyze daily stock market losers. This tool leverages various market data indicators and advanced AI analysis to provide insightful trading information.

* [PyAlpacaApi](https://github.com/TexasCoding/py-alpaca-api) is used to communicate with Alpaca Markets API.

## Features 🌟

- **Market Data Extraction**: Retrieves daily stock market data using the Alpaca Markets API.
- **Technical Analysis**: Analyzes data with indicators like Bollinger Bands and RSI.
- **News and Sentiment Analysis**: Extracts and analyzes news for sentiment using OpenAI.
- **Notifications**: Sends alerts via Slack.
- **Modular Design**: Structured for easy maintenance and scalability.

## Getting Started 🏁

### Installation

Clone the repository and install dependencies:
```bash
git clone https://github.com/TexasCoding/py-alpaca-daily-losers.git
cd py-alpaca-daily-losers
pip install -r requirements.txt
```

### Usage

Run the application:
```bash
python main.py
```

### Tests

Run the test suite:
```bash
pytest
```

## Project Structure 📂

```
py-alpaca-daily-losers/
├── README.md
├── poetry.lock
├── py_alpaca_daily_losers/
│   ├── __init__.py
│   ├── daily_losers.py
│   └── src/
│       ├── article_extractor.py
│       ├── global_functions.py
│       ├── marketaux.py
│       ├── openai.py
│       └── slack.py
├── pyproject.toml
└── tests/
    └── __init__.py
```

## Contributing 🤝

Contributions are welcome! Here’s how you can help:

1. Fork the repository.
2. Create a new branch (`git checkout -b feature-branch`).
3. Make your changes.
4. Commit your changes (`git commit -m 'Add new feature'`).
5. Push to the branch (`git push origin feature-branch`).
6. Open a Pull Request.

## License 📜

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

## Acknowledgments 🙏

Special thanks to the contributors and the open-source community for their support and resources.

For more details, visit the [repository](https://github.com/TexasCoding/py-alpaca-daily-losers).