This project was a part of IIT Dhanbad's Hackfest 2025, the problem statement from ILNB financial services. My contribution to this project includes:
- Developed the complete recommendation system workflow where the task was to recommend the best stock based on the user history and the current market trends. From user history I got data of user stocks interaction and from yahoo finance I got the stock data and on vectorizing the stocks description and stocks performance values muliplication I got the best stock for the user.
- Developed the complete sentiment analysis workflow where the task was to analyze the sentiment of the stock market based on the news headlines and the reddit posts. I used the pretrained distilroberta financial sentiment analysis model to analyze the sentiment. The single valued percentage is calculated based on the number of positive and negative sentiments in the news headlines and the reddit posts.
- Developed the complete personalized news recommendation workflow from the user history we get the stocks name and get the relevant news articles for those stocks from the Yahoo Finance news API. Further passed through a LLM to get the important and relevent new for the user.
- Developed the complete stock comparator workflow where the task was to compare two stocks and to explain the non jagron user why it is best. The llm is trained on huge financial data so on passing the two stocks and based on thier beta, forwardPE, trailingPE etc values able to understand what these values mean and which stock is better and why.
Metafin is an AI‑powered platform built to simplify investing with a completely no‑jargon UI, featuring clear iconography and logos for every section—so you always know exactly where to go.
-
Personalized Dashboard
Your home base, tailored to your goals, with custom logos guiding you through your portfolio at a glance. -
Mutual Funds & ETFs
Discover top performers and the single best pick for you. Each fund page sports its own logo, and our “i” button decodes any remaining complexity in plain English. -
Stock Performance Recommendations
Powered by both cutting‑edge LLMs and a custom from‑scratch model, get concise summaries, interactive graphs, and a clear Invest/Pass verdict—each with a dedicated logo for easy navigation. -
Ongoing Trends
Real‑time candlestick charts for stocks, forex, and more, driven by our proprietary ML model that predicts optimal buy/sell windows. Look for the trend‑tracker logo to dive in. -
Sentiment Analysis Engine
Aggregates insights from Yahoo Finance and Reddit APIs to gauge market mood. Spot the sentiment‑meter icon wherever you need a pulse check. -
Detailed Stock Analysis
A complete stock analysis based on Real‑time values and a comprehensive summary with feedback to a non-jargon user. -
Stock Recommendations
A custom built hybrid(content based+Performance based) Recommendation system that gives the most performing stocks based on user history. -
Stock Comparator
Side‑by‑side stock comparator based on Real‑time analysis from YahooFinance Api and a feedback from a llm to a non-jargon user. -
Custom News Hub
Curates headlines and deep dives on your past and current investments, all under one news‑feed logo.
Metafin isn’t just another fintech tool—it’s your clear, icon‑driven co‑pilot for smarter investing. 🚀
git clone https://github.com/kunalkushwahatg/project_MetaFin.git
cd project_MetaFin
cd MetaFin-Frontend
cp .env.example .env
cd ..
cd MetaFin-Backend
cp .env.example .env
cd ..
Then update the api keys in the environment file(.env) of both frontend and the backend.
docker-compose up --build
🖥️ Client-Side:
- React: For dynamic and interactive user interfaces.
- Redux: To manage application state efficiently.
- TailwindCSS: For sleek, responsive, and modern styling.
- Next.js: Optimized for server-side rendering (SSR) and seamless integration with APIs.
🌐 Server-Side:
- Node.js: Acts as an intermediary server for preprocessing and routing between the frontend and backend APIs.
- Express.js: Facilitates robust API creation and request handling.
- Flask: Lightweight Python framework to serve ML models as RESTful APIs, ideal for quick deployments and prototyping.
- Django: A robust Python framework for scalable and feature-rich backend applications, suitable for handling complex ML model deployments.
- Frontend Interaction (Next.js): Users interact with the interface to upload data or query predictions.
- Intermediate Processing (Node.js + Express): The frontend sends data to the Node.js server, which preprocesses it before forwarding the request.
- Model Inference (Flask/Django):
- Flask handles lightweight deployments with RESTful APIs for quick responses.
- Django is used for larger-scale applications requiring advanced features like caching or database integration.
- Result Display: The prediction results are routed back through Node.js to Next.js and displayed to the user.
- Next.js + Flask/Django Integration: Combines fast rendering capabilities of Next.js with Flask/Django’s ability to serve complex ML models efficiently.
- Scalability: Django ensures scalability for high-demand applications, while Flask is perfect for lightweight prototypes.
- Flexibility: Flask allows custom routes and preprocessing, while Django supports advanced features like caching and authentication.
This stack is ideal for deploying machine learning models in real-world applications, ensuring performance, scalability, and user-friendly interfaces!
This API aggregates and serves financial data from the following sources:
- FMP (Financial Modeling Prep)
- Alpha Vantage
- TradersView
- Reddit (r/stocks, r/investing, etc.)
- Yahoo Finance
You can deploy this service on services like Amazon Elastic Container Registry (ECR),Google Artifact Registry (GAR),GitHub Container Registry (GHCR)