Stock AI Anlysis APP

Description

An AI-powered stock market analysis and prediction engine that uses OpenAI's GPT-3.5/4/4o to analyze historic financial data, news headlines, and YouTube videos and predict future stock prices and performance.

Features

• Search for stock symbols and get detailed information about the stock
• AI sentiment analysis of stock news headlines
• AI future price predictions
• Get the latest news about a stock
• Get the latest YouTube videos about a stock
• Options data
• Custom Themes
• User Authentication - Coming soon...
• User Posts - Coming soon...
• User Sentiment - Coming soon...
• Market fear and greed - Coming soon...

Installation:

First things first, some info...

This is a full-stack repository that contains both the client and server code.

You can run both the client and the server code locally. For deployment, I recommend using a cloud service like Vercel for the client and Heroku or a VPS for the server. The current deployment of this application is as follows:

• Client: Vercel
• Server: Ubuntu 20.04 VPS (Nginx, Uvicorn, SystemD)

Prerequisites

Client

Environment Variables

1. YOUTUBE_API_KEY: You will need to create a YouTube API key and set it as an environment variable. You can do this by creating a .env file in the root of the project and adding the following line:

2. OPENAI_API_KEY: You will need to create an OpenAI API key and set it as an environment variable. You can do this by creating a .env file in the root of the project and adding the following line:

3. SERVER_URL: You will need to set the server url as an environment variable. You can do this by creating a .env file in the root of the project and adding the following line:

SERVER_URL=YOUR_SERVER_URL (e.g. http://localhost:8000)

Server

1. A way to run Python code (e.g. a VPS, Heroku, etc.)
2. A way to run FastAPI (e.g. Uvicorn, Gunicorn, etc.)
3. A way to proxy requests to the FastAPI server (e.g. Nginx, Caddy, etc.)

Getting Started:

Running client

1. Clone the repository

git clone https://github.com/bagus/stonk-ai.git
cd stonk-ai/client

2. Create a .env (or .env.local) file in the root of the project and add the following line:

YOUTUBE_API_KEY=YOUR_YOUTUBE_API_KEY
OPENAI_API_KEY=YOUR_OPENAI_API_KEY
SERVER_URL=YOUR_SERVER_URL

3. Install the dependencies

pnpm install

4. Run the development server

pnpm run dev

Deployment

Client

Build client

pnpm run build

Server

1. Navigate to the server directory

cd stonk-ai/server

2. Create a new Python virtual environment

python3 -m venv venv && source venv/bin/activate

3. Install the dependencies

pip3 install -r requirements.txt

4. Run the server code

python3 main.py

NOTE: Take note of the server URL. You will need it to set the SERVER_URL environment variable in the client.

Logs

server logs can be found in /stonk-ai/server/log.txt.

Contributing

1. Fork the repository
2. Create a new branch (git checkout -b feature)
3. Commit your changes (git commit -am 'Add new feature')
4. Push to the branch (git push origin feature)
5. Create a new Pull Request

License

MIT License

Acknowledgements

• OpenAI
• YouTube Data API
• Financial Modeling Prep API
• Alpha Vantage API
• IEX Cloud API
• Polygon.io API
• Yahoo Finance API
• Finage API
• Finnhub API
• Stock News API
• Stocktwits API
• Twitter API
• Reddit API
• News API