Welcome to QuantScrape, my personal project tailored for efficient and intelligent web scraping of financial data. I embarked on this project to streamline the process of gathering valuable insights for quantitative analysis, modeling, and research within the finance domain.
QuantScrape harnesses the power of Large Language Models (LLMs) to intelligently parse financial data from diverse web sources. This ensures a high level of efficiency and accuracy in extracting relevant information for your quantitative endeavors. The project is implemented in Python and exposes its capabilities through a Flask API, making it effortlessly integrable into various applications and services.
QuantScrape employs advanced LLMs to comprehend and parse financial data, ensuring the extraction of pertinent information with precision and speed.
The core functionality of QuantScrape is made accessible through a Flask API, facilitating seamless integration into other applications and services.
QuantScrape intelligently parses and validates scraped data, ensuring consistency and ease of use for downstream applications.
QuantScrape provides a set of intuitive and versatile API endpoints to cater to your diverse financial data needs. Here's a breakdown of each endpoint:
Retrieve detailed historical data for a specific stock by replacing <ticker>
with the corresponding stock symbol. This endpoint is perfect for conducting
in-depth analysis and trend assessments.
Fetch real-time quote details for a particular stock using its ticker symbol.
Get a curated list of top gaining stocks in the market. Identify potential investment opportunities and stay ahead of market trends.
Access a comprehensive list of companies with upcoming earnings calls. Stay informed about crucial financial events that might impact your investment strategy.
Each endpoint is designed for ease of use, delivering relevant data efficiently
to empower your quantitative analysis and decision-making processes. Simply make
a GET request to the desired endpoint and let QuantScrape handle the rest.
QuantScrape is crafted using the following technologies:
- Python: The primary programming language used.
- Flask: A lightweight web framework powering the API.
- Pydantic: A data validation library for parsing and validating scraped data intelligently.
- Redis: A caching database to enhance performance.
This project is licensed under the Apache License.
Feel free to explore, integrate, and enhance QuantScrape for your financial data needs. Happy coding!