Nest.js application spiced up with some machine learning, to predict your favorite stock's price.
GET requests to stockhistory/:ticker return the stock's history in a JSON format.
stockmeta/page returns stocks' metadata in a JSON format according to the page size. The page size is 5 by default.
stockmeta/next returns the next page of the stocks' metadata. stockmeta/prev returns the previous page.
stockmeta/search/:ticker returns the metadata of the stock with the given ticker.
Triggering the /api/prediction/:ticker endpoint the backend runs a python script which loads a saved model for predicting stock prices. The script returns the last element of the prediction list, the backend captures it and sends back to the caller.
A POST request to /api/auth/ with a UserInterface entity in the body authenticates the user with Firebase.
Requests to operation/favorite/add/:ticker and operation/favorite/remove/:ticker add or remove the ticker from the user's favorite list.
Dataset dowloaded from: https://www.kaggle.com/datasets/jacksoncrow/stock-market-dataset