FINSCRAPER

Unleashing financial insights with cutting-edge tech!

<!-- TABLE OF CONTENTS -->

Overview

The finscraper project encompasses a suite of financial data extraction and processing functionalities. It includes modules for scraping SEC filings, model training with Fast R-CNN, and preprocessing financial statement images. The project manages dependencies via its pyproject.toml file, ensuring seamless integration for scraping tasks. With capabilities to download, store, analyze, and train models on financial data, finscraper offers a robust platform for financial data extraction, preparation, and analysis.

Features

| | Feature | Description | | --- | ----------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | ⚙️ | Architecture | The project uses a modular architecture with separate modules for scraping, testing, model training, and pre-processing financial data. It leverages external libraries and tools for specific tasks, promoting code separation and reusability. | | 🔩 | Code Quality | The codebase maintains high code quality standards with clear naming conventions, consistent formatting, and well-structured functions. Code comments are used effectively to enhance code readability and maintainability. | | 📄 | Documentation | The project includes informative documentation in the form of code comments, README files, and module descriptions. It provides detailed explanations of the project structure, functionality, and usage, making it easier for developers to onboard and contribute. | | 🔌 | Integrations | Key external dependencies include pytesseract, bs4, matplotlib, torch, and more for tasks such as image processing, web scraping, data visualization, and machine learning. These integrations enhance the project's capabilities and extend its functionality. | | 🧩 | Modularity | The codebase demonstrates high modularity and reusability by encapsulating distinct functionalities into separate modules. This design allows for easy maintenance, testing, and scalability of individual components without affecting the entire system. | | 🧪 | Testing | The project utilizes testing frameworks like pytest to ensure the correctness and reliability of various modules. Test cases are written to validate different functionalities, improving code robustness and facilitating future changes. | | ⚡️ | Performance | The project focuses on efficiency and resource optimization by using libraries like tensorflow and scikit-learn for machine learning tasks. It employs best practices for data processing and model training, enhancing performance and speed. | | 🛡️ | Security | Measures such as data encryption, secure access control, and data validation are implemented to ensure data protection and prevent unauthorized access. The project follows security best practices to safeguard sensitive financial information. | | 📦 | Dependencies | Key external libraries and dependencies include pytesseract, bs4, matplotlib, torch, scikit-learn, and more. These libraries provide essential functionalities for tasks such as image processing, web scraping, and machine learning model training. |

Repository Structure

sh └── finscraper/ ├── README.md ├── finscraper │ ├── __init__.py │ ├── model_training.py │ ├── preprocessor.py │ ├── scraper.py │ └── test_setup.py ├── poetry.lock ├── pyproject.toml └── tests └── __init__.py

Modules

Getting Started

System Requirements:

• Python: version x.y.z

Installation

From source

1. Clone the finscraper repository:

console $ git clone https://github.com/ChiragAgg5k/finscraper

1. Change to the project directory:

console $ cd finscraper

1. Install the dependencies:

console $ pip install -r requirements.txt

Usage

From source

Run finscraper using the command below:

console $ python main.py

Tests

Run the test suite using the command below:

console $ pytest

Project Roadmap

• [x] ► Create a pipeline for scraping financial data.
• [ ] ► Implement a model training module.
• [ ] ► Develop a preprocessor for financial statement images.

Contributing

Contributions are welcome! Here are several ways you can contribute:

• Report Issues: Submit bugs found or log feature requests for the finscraper project.
• Submit Pull Requests: Review open PRs, and submit your own PRs.
• Join the Discussions: Share your insights, provide feedback, or ask questions.

License

This project is protected under the SELECT-A-LICENSE License. For more details, refer to the LICENSE file.

Acknowledgments

• List any resources, contributors, inspiration, etc. here.

Return