About The Project

Our project aims to learn and apply machine learning techniques to predict stock prices and trends in the stock market. We will be building a web application that will predict U.S. stock market prices. We will focus on the US stock market and the large companies that are part of the Standards and Poor (S&P) 500, a stock market index that tracks the performance of these large companies. Some of these are Apple, Google, Amazon, and Microsoft.

We divided our project into several strategies. In each one, we used different machine learning techniques that we have learned in this course to improve and get the best-predicted trading results that simulate the actual trending. Some of the strategies that will be used to predict the direction of the stock trend will be based on the dataset, and others can indicate a stock price for a specific day. Moreover, we will compare the results between the strategies to see which one was more accurate.

Built With

This section lists all major frameworks/libraries used to bootstrap this project.

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Getting Started

Following the instructions below should get you up and running and quickly as possible without googling around to run the code.

Prerequisites

Below is the list things you need to use the software and how to install them. Note, these instructions assume you are using a Mac OS. If you are using Windows you will need to go through these instructions yourself and update this READ for future users.

1. pyenv

   brew update
   brew install pyenv
   echo 'export PYENV_ROOT="$HOME/.pyenv"' >> ~/.zshrc
   echo 'command -v pyenv >/dev/null || export PATH="$PYENV_ROOT/bin:$PATH"' >> ~/.zshrc
   echo 'eval "$(pyenv init -)"' >> ~/.zshrc

2. python

   pyenv install 3.9.5   
   pyenv global 3.9.5 

3. poetry

   curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python -

4. miniconda

   cd /tmp
   curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-$(uname)-$(uname -m).sh"
   bash Mambaforge-$(uname)-$(uname -m).sh

5. Restart new terminal session in order to initiate mini conda environmental setup

6. Git LFS

   brew install git-lfs

7. Docker, including docker-compose

Installation

Below is the list of steps for installing and setting up the app. These instructions do not rely on any external dependencies or services outside of the prerequisites above.

1. Clone the repo

   git clone git@github.com:fall2023csce5214/lstm_stock_market_prediction.git

2. Install notebook

   conda env create -f environment.yml
   conda activate lstm_stock_market_prediction

3. Build Docker Image (Note, you should be in the same dire)

   docker-compose build

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Usage

In order to view or execute the various notebooks run the following command on any of the sub folders in this directory.

Here is an example to launch the LSTM Notebook.

jupyter notebook

Once inside the notebook use the following link on examples of how to use the notebook.

Here is an example to launch docker to run the unit test on the command line.

docker-compose up -d
docker-compose exec lstm-web-service bash
poetry run python -m pytest -k test_appl_lstm_60_day_model

Here is an example to launch docker to populate the database and launch the web service.

docker-compose up -d
docker-compose logs -f lstm-web-service

Then hit the following URL to test the web service. Note, wait until the web service starts. You will see a message like the following:

INFO:     Will watch for changes in these directories: ['/lstm']
INFO:     Uvicorn running on http://0.0.0.0:10000 (Press CTRL+C to quit)
INFO:     Started reloader process [142] using statreload
INFO:     Started server process [146]
INFO:     Waiting for application startup.
INFO:     Application startup complete.

http://localhost:10000/predict?ticker_symbol=AAPL&date=2023-09-22

The response would look something like the following:

{
  "trading_date": "2023-09-22",
  "closing_price": 177.7
}

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Acknowledgements

Stock Market Analysis + Prediction using LSTM

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Contact

Jibril Moalim
Lakshmi Prasanna Valdas
Larry Johnson
Monish Galla
Vamshidhar Reddy Venumula

Project Link: https://github.com/fall2023csce5214/ltsm_stock_market_prediction/

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