semvec/pypi-packages huggingface dataset:
aiida-scheduler 0.1.2
Process scheduler for AiiDA.

# AiiDA-Scheduler
[![PyPI version](https://badge.fury.io/py/aiida-scheduler.svg)](https://badge.fury.io/py/aiida-scheduler)
[![Unit test](https://github.com/superstar54/aiida-scheduler/actions/workflows/ci.yaml/badge.svg)](https://github.com/superstar54/aiida-scheduler/actions/workflows/ci.yaml)
[![codecov](https://codecov.io/gh/superstar54/aiida-scheduler/branch/main/graph/badge.svg)](https://codecov.io/gh/superstar54/aiida-scheduler)
[![Docs status](https://readthedocs.org/projects/aiida-scheduler/badge)](http://aiida-scheduler.readthedocs.io/)

Efficiently control the execution of AiiDA processes with AiiDA Scheduler:
* enforce dynamic concurrency limits per HPC allocation;
* implement priority scheduling so urgent workflows start first; and
* obtain live visibility into queue state and utilisation in a GUI.

## Installation

```console
    pip install aiida-scheduler
```


## Documentation
Explore the comprehensive [documentation](https://aiida-scheduler.readthedocs.io/en/latest/) to discover all the features and capabilities of AiiDA Scheduler.


## License
[MIT](http://opensource.org/licenses/MIT)
finsaber 0.1.2
Financial INvesting Strategy Assessment with Bias mitigation, Expanded time, and Range of symbols (FINSABER)
Requires Python: >=3.10

# FINSABER

[![Arxiv link](https://img.shields.io/static/v1?label=arXiv&message=2505.07078&color=red&logo=arxiv)](https://arxiv.org/abs/2505.07078)

FINSABER is a comprehensive framework for evaluating trading strategies with a specific focus on comparing traditional technical analysis approaches with modern machine learning and large language model (LLM) based strategies. 

<img src="https://github.com/waylonli/FINSABER/blob/main/figs/framework.png" width="600">


## Citation

```
@misc{li2025llmbasedfinancialinvestingstrategies,
      title={Can LLM-based Financial Investing Strategies Outperform the Market in Long Run?}, 
      author={Weixian Waylon Li and Hyeonjun Kim and Mihai Cucuringu and Tiejun Ma},
      year={2025},
      eprint={2505.07078},
      archivePrefix={arXiv},
      primaryClass={q-fin.TR},
      url={https://arxiv.org/abs/2505.07078}, 
}
```