📑 Table of Contents

• Description
• Installation
• TechStack
• API Reference
• Workflows
• Deployment

Description

Welcome to Metafin – Your Intelligent, Jargon‑Free Investment Companion

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. 🚀

Installation

Clone the repo

git clone https://github.com/HrushikeshAnandSarangi/Project_MetaFin
cd Project_MetaFin

Copy and update the environment

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.

Build the docker image and run it

docker-compose up --build

TechStack

🌟 Enhanced Tech Stack for Complex ML Model Deployment 🌟

🖥️ 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.

🚀 Deployment Workflow:

1. Frontend Interaction (Next.js): Users interact with the interface to upload data or query predictions.
2. Intermediate Processing (Node.js + Express): The frontend sends data to the Node.js server, which preprocesses it before forwarding the request.
3. 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.
4. Result Display: The prediction results are routed back through Node.js to Next.js and displayed to the user.

🔧 Why This Stack?

• 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!

API Reference

Data Sources

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

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Workflows

Recommendation System Workflow

Sentiment Analysis

Personalised News Recommendation

Deployment

You can deploy this service on services like Amazon Elastic Container Registry (ECR),Google Artifact Registry (GAR),GitHub Container Registry (GHCR)