| Name | fintorch JSON |
| Version |
0.5.0
JSON |
| download |
| home_page | None |
| Summary | AI4FinTech project repository |
| upload_time | 2025-01-10 21:52:48 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | MIT License Copyright (c) 2024, Marcel Boersma Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
fintorch
|
| VCS |
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| bugtrack_url |
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alembic
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kaggle
kiwisolver
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lightning-utilities
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mako
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mpmath
msgpack
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multitasking
networkx
neuralforecast
numpy
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optuna-integration
packaging
pandas
peewee
pillow
platformdirs
polars
protobuf
psutil
pyarrow
pyparsing
python-dateutil
python-slugify
pytorch-lightning
pytz
pyyaml
ray
referencing
requests
rpds-py
scikit-learn
scipy
seaborn
sentry-sdk
setproctitle
six
smmap
soupsieve
sqlalchemy
sympy
tenacity
tensorboardx
text-unidecode
threadpoolctl
torch
torch-geometric
torchmetrics
torchvision
tqdm
typing-extensions
tzdata
urllib3
utilsforecast
wandb
webencodings
yarl
yfinance
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
========
FinTorch
========
.. image:: https://img.shields.io/pypi/v/fintorch.svg
:target: https://pypi.python.org/pypi/fintorch
.. image:: https://readthedocs.org/projects/fintorch/badge/?version=latest
:target: https://fintorch.readthedocs.io/en/latest/?version=latest
:alt: Documentation Status
.. image:: https://codecov.io/gh/AI4FinTech/FinTorch/graph/badge.svg?token=OBD2MHP5SE
:target: https://codecov.io/gh/AI4FinTech/FinTorch
AI4FinTech project repository
* Free software: MIT license
* Documentation: https://fintorch.readthedocs.io.
FinTorch - Machine Learning for FinTech
=========================================
The integration of AI in the financial sector demands specialized tools that can handle the unique challenges of this field, especially in regulatory compliance and risk management. Building on the familiarity and robustness of PyTorch, FinTorch aims to bridge the gap between AI technology and the financial industry needs.
Goal
----
Develop FinTorch, an open-source machine learning library as an extension of PyTorch, specifically tailored for the FinTech industry's compliance and risk management requirements.
Key Objectives
--------------
1. Specialized Financial AI Models
Implement state-of-the-art machine learning models for financial data analysis, fraud detection, risk assessment, and regulatory compliance, seamlessly integrating with PyTorch's existing framework.
2. Regulatory Compliance Toolkit
Provide tools specifically designed for monitoring and ensuring adherence to financial regulations using AI.
3. User-Friendly API
Maintain a tensor-centric API, consistent with PyTorch, ensuring ease of use for those familiar with PyTorch. Aim for simplicity, where basic models can be implemented in as few as 10-20 lines of code.
4. Extensibility for Research
Offer a flexible platform for academic and industry researchers to develop and test new AI models for FinTech, with support for custom architectures and novel strategies.
5. Scalability and Real-World Application
Focus on scalability to handle large-scale financial data and real-world scenarios.
6. Ethical and Responsible AI Practices
Embed principles of sustainable and responsible AI, ensuring that models adhere to ethical standards and contribute positively to the FinTech ecosystem.
7. Educational Resources and Community Support
Provide comprehensive documentation, tutorials, and masterclasses to facilitate learning and collaboration within the AI4FinTech community.
Impact
------
FinTorch will not only streamline the process of regulatory compliance for FinTech companies but also foster innovation and research in AI-driven financial technologies. It will serve as a crucial tool for industry professionals, researchers, and government institutions, aligning with the AI4FinTech community's objectives of knowledge dissemination and development of responsible, cutting-edge financial solutions.
Getting started
---------------
Please install the package as follows
.. code-block:: bash
pip install fintorch
**Required Dependencies**
Run
.. code-block:: bash
python -c "import torch; print(torch.__version__)"
and set
.. code-block:: bash
export TORCH={your_pytorch_version}
export CUDA={your_cuda_version}
The following dependencies must be installed:
.. code-block:: bash
pip install pyg-lib -f https://data.pyg.org/whl/torch-${TORCH}+${CUDA}.html
pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-${TORCH}+${CUDA}.html
**Important Notes**
* Replace `${TORCH}` and `${CUDA}` with the appropriate version numbers for your environment (e.g., "1.12.0" and "cu113").
* These installation commands use custom index URLs provided by PyTorch Geometric (PyG).
Description of the Structure
-----------------------------
- `fintorch` Directory: Contains the core library modules.
- `models`: Core models for compliance monitoring, fraud detection, risk assessment, and sustainable finance.
- `datasets`: Financial datasets and data processing utilities.
- `utils`: Helper tools and functions for compliance and other financial applications.
- `training`: Training and evaluation scripts for the models.
- `examples` Directory: Example scripts demonstrating the use of FinTorch in different scenarios.
- `tests` Directory: Unit and integration tests for the library.
- `benchmarks` Directory: Benchmark scripts and resources for testing the performance of the library.
- `docs` Directory: Documentation files, including build scripts and source files.
- `docker` Directory: Dockerfile and related resources for containerizing the FinTorch library.
- `conda` Directory: Scripts and files needed for building a Conda package of the library.
- `tutorials` Directory: Jupyter notebooks that provide tutorials on how to use the library for various FinTech applications.
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Building on the familiarity and robustness of PyTorch, FinTorch aims to bridge the gap between AI technology and the financial industry needs.\n\nGoal\n----\nDevelop FinTorch, an open-source machine learning library as an extension of PyTorch, specifically tailored for the FinTech industry's compliance and risk management requirements.\n\nKey Objectives\n--------------\n\n1. Specialized Financial AI Models\n Implement state-of-the-art machine learning models for financial data analysis, fraud detection, risk assessment, and regulatory compliance, seamlessly integrating with PyTorch's existing framework.\n\n2. Regulatory Compliance Toolkit\n Provide tools specifically designed for monitoring and ensuring adherence to financial regulations using AI.\n\n3. User-Friendly API\n Maintain a tensor-centric API, consistent with PyTorch, ensuring ease of use for those familiar with PyTorch. Aim for simplicity, where basic models can be implemented in as few as 10-20 lines of code.\n\n4. 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