ITS-SentARL

Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning Approach research project implementation used in:

1. Second ACM International Conference on AI in Finance (ICAIF'21) article
   • ACM Official Version
   • Free pre-print version (arXiv)
2. F. C. Lima Paiva (aka @xicocaio) Master's Thesis.

ITS-SentARL general architecture

Image source: Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning Approach article presented in the Second ACM International Conference on AI in Finance (ICAIF'21)

Stack

The stack bellow was used mostly due to its ease of installation, configuration, and also efficiency and portability.

• Language: Python (3.8.10)
• RL Environment: OpenAI gym (0.21.0)
• RL engine: StableBaselines3 (1.6.0)

Pre-installation

This system was originally developed in Ubuntu 16.04 but will work properly on any other Operational System(OS X, Windows, etc.).

However, this guide will only include instructions for plugins and packages that are not already installed on this OS. For this reason, we assume that technologies like a python interpreter and pipenv are ready for use, and should not be on the scope of this document.

• Now install pipenv dependency manager:

$ pip install --user pipenv

Project configuration

Now we'll start setting up the project.

• Clone the repo from GitHub and change to project root directory. After that, install project dependencies and go to python virtual env, by running:

$ pipenv install
$ pipenv shell

Running the project

The system will do the following:

1. Load financial data and prepare the trading environment. 2. Run a trading strategy and collect results. 3. Store all results regarding training, testing and validation in .csv files. 4. Optional: Save model versions at each training episode.

The base command to run in terminal is:

$ python app

There are three modes for running the project, which can be selected using the following arg

• mode with the allowed values: single for running a single configuration; routine for running several configurations; consolidation for grouping all results files into one single large file.

Then the arguments according to the desired modes are:

Default

• stg for selecting the strategy to run. Allows: bh for Buy and Hold (BH); sentarl fpr the sentiment-aware RL algorithm; vanilla for the sentiment-free version of the algorithm.
• asset for selecting the asset to run trade. Allows: any asset present in the data/__init__.py file, with the corresponding .csv file in the data folder.
• algo for selecting the RL algorithm to run. Allows: a2c, ppo, dqn. To add more algorithms add code to the models\__init__.py and settings.py files, given it is implemented by StableBaselines3.
• episodes for selecting the number of episodes to train. Allows: integer values only
• setup for selecting the train/val window type of setup. Allows: static, rolling.

Example to run default mode with ITS-SentARL using A2C

python app mode=single setup=rolling stg=sentarl asset=aapl algo=A2C episodes=1 setup='rolling'

Routine

• routine_name for selecting which routine to run when mode arg is set to routine. Allows: default for running the most basic routine, load_model for running routines that load a model trained up to a given episode.

Example to run default routine

python app mode=routine routine_name=default

Beware this mode can take a long time to run depending on how many assets, tcs, rolling windows, stgs and other characteristics are selected.

Consolidation

Example to consolidate all results files

python app mode=consolidation

Folder Structure

its-sentarl/
├── app
│   ├── common
│   │   ├── __init__.py
│   │   ├── config.py
│   │   ├── logger.py
│   │   └── utils.py
│   ├── data
│   │   ├── __init__.py
│   │   └── *.csv
│   ├── envs
│   │   ├── __init__.py
│   │   ├── exchange.py
│   │   └── stock.py
│   ├── __main__.py
│   ├── models
│   │   ├── __init__.py
│   │   └── /**/*.zip
│   ├── results
│   │   ├── __init__.py
│   │   ├── consolidated
│   │   │   └── *.csv
│   │   ├── hour
│   │   │   └── /**/*.csv
│   │   ├── monitor.py
│   │   └── organizer.py
│   ├── settings.py
│   └── setups
│       ├── base_setup.py
│       ├── __init__.py
│       ├── rolling_window.py
│       └── static.py
├── LICENSE
├── Pipfile
├── Pipfile.lock
└── README.md

• Root folder: Dependencies list and other metadata info.
  • Pipfile: Information about dependencies.
  • README.md: Hi.
• app: Project settings and other general use files.
  • settings.py: Information regarding allowed assets, stgs and others.

Citing the Project

To cite this repository in publications, please use the following bibtex formated text:

@inproceedings{LimaPaiva2021,
    author = {{Lima Paiva}, Francisco Caio and Felizardo, Leonardo Kanashiro and Bianchi, Reinaldo Augusto da Costa Bianchi and Costa, Anna Helena Reali},
    title = {Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning Approach},
    year = {2021},
    isbn = {9781450391481},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3490354.3494445},
    doi = {10.1145/3490354.3494445},
    booktitle = {Proceedings of the Second ACM International Conference on AI in Finance},
    articleno = {40},
    numpages = {9},
    keywords = {deep reinforcement learning, sentiment analysis, stock markets},
    location = {Virtual Event},
    series = {ICAIF '21},
    archivePrefix = {arXiv},
    eprint = {2112.02095},
    primaryClass = {q-fin.TR}
}

Suplementary Material and Components

Reading:

• Full Article (ACM)
• pre-print version (arXiv)

Sentiment extractor module details:

• Article
• Source code

Financial news webcrawler:

• Source code

Acknowledgments

This work was financed in part by Itaú Unibanco S.A. through the Programa de Bolsas Itaú (PBI) of the Centro de Ciência de Dados (C2D), EP-USP), by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES Finance Code 001), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq grant 310085/2020-9 and 88882.333380/2019-01),and by the Center for Artificial Intelligence (C4AI-USP), with support from FAPESP (grant 2019/07665-4) and \textit{IBM Corporation}. Any views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of the funding companies.

Final considerations

• Go play around! =)