Stock Ticker Analysis and Prediction System using Stacked LSTM

Description

• Our project works on concepts of deep learning to predict values based on time-series model. It includes use of LSTM (Long Short Term Memory) with XG-Boost to enhance the performance of prediction.

• This repository invloves deployment of model with two-interfaces. One with Streamlit-App [Link] and React App

Install and Run the project

1. Running project for first time

To run our site. Follow the steps -

• Navigate to site_integration folder.

 cd "site_integration"

• Run following command if project is cloned for first time. This will install necessary node_modules folder in current folder.

npm install

npm run start

• Simultaneously, Run a command in another terminal to start waitress_server.py file in /backend/waitress_server.py folder.

python waitress_server.py

Site Image

2. Running project on local system

No need to type npm install. Just type

cd "site_integration"
npm run start

• Simultaneously, Run a command in another terminal to start waitress_server.py file in /backend/waitress_server.py folder.

python waitress_server.py

Note: This project is still in production and will not resemble the final product.

Steps to deploy on Github Packages

1. Create a replica of Docker image with different tag.
2. Check the image created.
3. Authenticate by using PAT (Personal Access Token).
4. Push that image in GitHub Packages.

docker tag debasishray/streamlit-app:v1.0 webapp

docker tag webapp ghcr.io/debasishray16/stockpredictor/webapp:latest
docker image ls

# For authentication
echo "pat-value" | docker login ghcr.io -u debasishray16 --password-stdin

# ghcr.io/<username>/<repository>
docker push ghcr.io/debasishray16/stockpredictor/webapp:latest

Note (Information)

For this project, we have included a different repository with different models trained on different epoch cycles and parameters, which are usable and integratable in this project.

Link to Model's Repository

docker run debasishray/streamlit-app:v1.0
docker stop debasishray/streamlit-app:v1.0

Contributors