# Stock-X

![License](https://img.shields.io/github/license/Circle-1/Stock-X)
![Stars](https://img.shields.io/github/stars/Circle-1/Stock-X)
![Release](https://img.shields.io/github/v/release/Circle-1/Stock-X)
[![Heroku](https://img.shields.io/badge/Heroku-Active-blue?logo=heroku)](https://stock-x-proj.herokuapp.com/)

Related arXiv paper: arxiv.org/abs/2305.14378

This project is all about analysis of Stock Market and providing suggestions to stockholders to invest in right company

Note: The notebook used here (IPYNB) is made using Kaggle, a data-science and ML community website which provides free Jupyter Notebook environment to work on programs and GPUs and TPUs to work on Neural Networks easily.

Here's the ref link to [Kaggle](https://www.kaggle.com/)

Notebook link for CNN-LSTM: [Click here](https://www.kaggle.com/aadhityaa/stock-cnn-lstm)

Docker Image link (contains bundled libraries): [Click here](https://hub.docker.com/r/aerox86/stock-x) ![Size](https://img.shields.io/docker/image-size/aerox86/stock-x/latest-stable)

Helm charts: [![Artifact Hub](https://img.shields.io/endpoint?url=https://artifacthub.io/badge/repository/stock-x)](https://artifacthub.io/packages/search?repo=stock-x)

## Libraries used:
    - Tensorflow
    - Keras
    - Pandas
    - Scikit-learn
    - Matplotlib
    - Seaborn

## Neural Network type

Here CNN (with Time Distributed function) and Bi-LSTM combined Neural Network is used to train. Other algorithms like XGBoost, RNN-LSTM, LSTM-GRU are also added for comparison. Here are the links to view the notebooks directly. You can also view the results in the app created using [Mercury](https://mljar.com/mercury/) which is deployed over [Heroku (free dyno)](https://stock-x-proj.herokuapp.com/).

 - [CNN-LSTM](stock-market-prediction-using-cnn-lstm.ipynb)
 - [LSTM-GRU](lstm_gru_model.ipynb)
 - [RNN-LSTM](RNN-LSTM.ipynb)
 - [XGBoost](regressor-model.ipynb)
{"language": ["en"], "license": "apache-2.0", "library_name": "keras", "tags": ["finance", "code"], "metrics": ["accuracy"]}
keras finance code en arxiv:2305.14378 doi:10.57967/hf/0650 license:apache-2.0 region:us
TAGS
#keras #finance #code #en #arxiv-2305.14378 #doi-10.57967/hf/0650 #license-apache-2.0 #region-us 

# Stock-X

!License
!Stars
!Release
![Heroku](URL

Related arXiv paper: URL

This project is all about analysis of Stock Market and providing suggestions to stockholders to invest in right company

Note: The notebook used here (IPYNB) is made using Kaggle, a data-science and ML community website which provides free Jupyter Notebook environment to work on programs and GPUs and TPUs to work on Neural Networks easily.

Here's the ref link to Kaggle

Notebook link for CNN-LSTM: Click here

Docker Image link (contains bundled libraries): Click here !Size

Helm charts: ![Artifact Hub](URL

## Libraries used:
    - Tensorflow
    - Keras
    - Pandas
    - Scikit-learn
    - Matplotlib
    - Seaborn

## Neural Network type

Here CNN (with Time Distributed function) and Bi-LSTM combined Neural Network is used to train. Other algorithms like XGBoost, RNN-LSTM, LSTM-GRU are also added for comparison. Here are the links to view the notebooks directly. You can also view the results in the app created using Mercury which is deployed over Heroku (free dyno).

 - CNN-LSTM
 - LSTM-GRU
 - RNN-LSTM
 - XGBoost
# Stock-X

!License
!Stars
!Release
![Heroku](URL

Related arXiv paper: URL

This project is all about analysis of Stock Market and providing suggestions to stockholders to invest in right company

Note: The notebook used here (IPYNB) is made using Kaggle, a data-science and ML community website which provides free Jupyter Notebook environment to work on programs and GPUs and TPUs to work on Neural Networks easily.

Here's the ref link to Kaggle

Notebook link for CNN-LSTM: Click here

Docker Image link (contains bundled libraries): Click here !Size

Helm charts: ![Artifact Hub](URL ## Libraries used:
    - Tensorflow
    - Keras
    - Pandas
    - Scikit-learn
    - Matplotlib
    - Seaborn ## Neural Network type

Here CNN (with Time Distributed function) and Bi-LSTM combined Neural Network is used to train. Other algorithms like XGBoost, RNN-LSTM, LSTM-GRU are also added for comparison. Here are the links to view the notebooks directly. You can also view the results in the app created using Mercury which is deployed over Heroku (free dyno).

 - CNN-LSTM
 - LSTM-GRU
 - RNN-LSTM
 - XGBoost
TAGS
#keras #finance #code #en #arxiv-2305.14378 #doi-10.57967/hf/0650 #license-apache-2.0 #region-us 
 # Stock-X

!License
!Stars
!Release
![Heroku](URL

Related arXiv paper: URL

This project is all about analysis of Stock Market and providing suggestions to stockholders to invest in right company

Note: The notebook used here (IPYNB) is made using Kaggle, a data-science and ML community website which provides free Jupyter Notebook environment to work on programs and GPUs and TPUs to work on Neural Networks easily.

Here's the ref link to Kaggle

Notebook link for CNN-LSTM: Click here

Docker Image link (contains bundled libraries): Click here !Size

Helm charts: ![Artifact Hub](URL ## Libraries used:
    - Tensorflow
    - Keras
    - Pandas
    - Scikit-learn
    - Matplotlib
    - Seaborn ## Neural Network type

Here CNN (with Time Distributed function) and Bi-LSTM combined Neural Network is used to train. Other algorithms like XGBoost, RNN-LSTM, LSTM-GRU are also added for comparison. Here are the links to view the notebooks directly. You can also view the results in the app created using Mercury which is deployed over Heroku (free dyno).

 - CNN-LSTM
 - LSTM-GRU
 - RNN-LSTM
 - XGBoost