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https://github.com/pravincoder/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.
https://github.com/pravincoder/stock_analysis_investment
crewai exa flask flask-cors groq-api javascript nextjs ollama openai-api python serpapi
Last synced: about 1 month ago
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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.
- Host: GitHub
- URL: https://github.com/pravincoder/stock_analysis_investment
- Owner: pravincoder
- License: mit
- Created: 2024-08-07T15:54:26.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-10-02T08:25:58.000Z (about 1 month ago)
- Last Synced: 2024-10-10T18:42:52.711Z (about 1 month ago)
- Topics: crewai, exa, flask, flask-cors, groq-api, javascript, nextjs, ollama, openai-api, python, serpapi
- Language: Python
- Homepage:
- Size: 5.9 MB
- Stars: 2
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
- License: LICENSE
Awesome Lists containing this project
README
# 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 .
https://github.com/user-attachments/assets/26c7ec92-955e-4a6e-98af-ac9a8404d9f3
## 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](#features)
- [Tech Stack](#tech-stack)
- [Installation](#installation)
- [Fast Installation](#fast-installation-via-docker)
- [Normal Installation](#normal-installation)
- [Usage](#usage)
- [API Endpoints](#api-endpoints)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)
## 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
- **Docker**: Make sure Docker is installed on your system. You can download it from the [official Docker website](https://www.docker.com/get-started).
### Steps to Run Using Docker
1. Clone the repository: See [Backend Setup Step 1 Command](#backend-setup)
2. Setup Environments : See [Env Setup](#environment-variable-setup)
3. Change Directory to Stock_Analyzer:-
```bash
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*)
```bash
docker-compose build
```
5.Docker Run build (replace 'up' with 'down' before recreating build)
```bash
docker-compose up
```
## Normal Installation
### Prerequisites
- Python 3.10+
- Node.js 14+
- npm or yarn
- Conda 20+
### Backend Setup
1. Clone the repository:
```bash
git clone https://github.com/pravincoder/Stock_investment_Analysis_Agent.git
```
2. Navigate to the Main directory:
```bash
cd Stock_investment_Analysis_Agent/Stock_Analyzer
```
3. Create a conda environment:
```bash
conda env create -f environment.yml
```
4. Activate the Conda Environment:
```bash
conda activate stock_guru
```
5. Run the Flask development server:
```bash
python app.py
```
### Frontend Setup
1. Navigate to the frontend directory:
```bash
cd nextjs_app
```
2. Install the dependencies:
```bash
npx create-next-app@latest --js --tailwind --eslint
```
Keep every option default while setting up the app.
3. Run the Next.js development server:
```bash
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.*
```dotenv
# 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](LICENSE) file for details.
## Contact
For any questions, issues, or suggestions, please contact:
- **Project Maintainer**: [Pravin Maurya](mailto:[email protected])
- **GitHub**: [PravinCoder](https://github.com/pravincoder)
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