/Stock_Analysis_Investment

This project is a robust and scalable multi-agent stock investment and analysis platform built using a Flask backend and a Next.js frontend, leveraging CrewAI for sophisticated multi-agent interactions.

Primary LanguagePythonMIT LicenseMIT

Project Name: Multi-Agent Stock Investment and Analysis Platform

Output(Working Demo)

Once you have sucessfully done every step mentioned below.
You can also Play around .

Stock_guru_ai.-.Made.with.Clipchamp.1.mp4

Overview

This project is a robust and scalable multi-agent stock investment and analysis platform built using a Flask backend and a Next.js frontend, leveraging CrewAI for sophisticated multi-agent interactions. The platform is designed to provide detailed insights and recommendations for stock investments by utilizing a multi-agent approach, enabling users to make informed decisions even with limited prior knowledge of stocks and market analysis.

Table of Contents

Features

  • Multi-Agent Analysis: Utilizes CrewAI to deploy multiple agents for in-depth stock analysis and investment recommendations.
  • User-Friendly Interface: Intuitive and responsive interface built with Next.js to enhance user experience.
  • Real-Time Data: Provides real-time stock data and insights to aid in decision-making.
  • Secure and Scalable: Flask backend ensures the platform is secure and can scale as needed.

Tech Stack

  • Backend: Flask
  • Frontend: Next.js
  • AI/ML Integration: CrewAI,LangChain
  • Database: None (For Now)
  • APIs: YahooFinance Api using yfinance Package
  • Env:Conda

Installation

Fast Installation (Via Docker)

Prerequisites

Steps to Run Using Docker

  1. Clone the repository: See Backend Setup Step 1 Command
  2. Setup Environments : See Env Setup
  3. Change Directory to Stock_Analyzer:-
  cd Stock_investment_Analysis_Crew/Stock_Analyzer

4.Docker Build Both Frontend and Backend with Compose (Can even build Frontend and Backend Seperately using docker build Dockerfile)

   docker-compose build

5.Docker Run build (replace 'up' with 'down' before recreating build)

   docker-compose up

Normal Installation

Prerequisites

  • Python 3.10+
  • Node.js 14+
  • npm or yarn
  • Conda 20+

Backend Setup

  1. Clone the repository: 
    git clone https://github.com/pravincoder/Stock_investment_Analysis_Agent.git
  2. Navigate to the Main directory: 
    cd Stock_investment_Analysis_Agent/Stock_Analyzer
  3. Create a conda environment: 
    conda env create -f environment.yml
  4. Activate the Conda Environment: 
    conda activate stock_guru
  5. Run the Flask development server: 
    python app.py

Frontend Setup

  1. Navigate to the frontend directory: 
    cd nextjs_app
  2. Install the dependencies: 
    npx create-next-app@latest --js --tailwind --eslint
    Keep every option default while setting up the app.
  3. Run the Next.js development server: 
    npm run dev

Note:- You need to execute both the backend Flask app and the Frontend as we are using cross connection with flask-cors.

Environment Variable Setup

.env File Setup

Create a .env file and add the below code :- (Make sure to add the api keys from the specified platforms)

Note:- Use .sample.env by renaming as .env and add your Keys.

# API Key for GROQ
GROQ_API_KEY=your_groq_api_key_here

# API Key for OpenAI
OPENAI_API_KEY=your_openai_api_key_here

# Base URL for OpenAI API (default for local setup)
OPENAI_API_BASE=http://localhost:11434/v1

# Model name for OpenAI (adjust based on available models)
OPENAI_MODEL_NAME=mistral:latest

# Optional: API Key for LangChain
LANGCHAIN_API_KEY=your_langchain_api_key_here

# Optional: Enable LangChain tracing
LANGCHAIN_TRACING_V2=true

(Optional) Using Local LLM with Ollama

  • Use the files in the setup folder to create a LLama3 , Mistral8b ... or your own LLM model

  • Commands/steps to setup ollama llm comming soon!

Usage

Once the servers are running, you can access the application in your browser by navigating to http://localhost:3000.

  • Dashboard: Begin by exploring the dashboard for a quick overview of stock market trends.
  • Stock Analysis: Use the analysis tools to select specific stocks and view detailed insights provided by the AI agents.
  • Recommendations: Get tailored investment recommendations based on your portfolio and risk profile.

API Endpoints

The Flask backend exposes several API endpoints that can be used for data retrieval and interaction:

  • POST /analyze-stock: Analyze a stock and return Analysis and Investment reports in Markdown format.

(Detailed documentation for each endpoint can be provided here. In near future we might make a seperate api endpoints for both reports.)

Contributing

We welcome contributions! To get started:

  1. Fork the repository.
  2. Create a new feature branch (git checkout -b feature-name).
  3. Commit your changes (git commit -m 'Add some feature').
  4. Push to the branch (git push origin feature-name).
  5. Open a pull request.

Please ensure your code adheres to the project's coding standards and passes all tests.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For any questions, issues, or suggestions, please contact: