StockSync

This project is a stock market application built using Flask, a Python-based web framework. It includes functionalities for user registration, login, adding stocks to a wishlist, predicting stock prices, and fetching stock data. The application uses MongoDB for data storage, yfinance for fetching stock data, and scikit-learn for linear regression modeling.

Main Components

1. Flask Web Framework

   • Flask is used to create the web application with various routes to handle user interactions.

2. MongoDB

   • MongoDB stores user data and wishlist data. pymongo is used to interact with MongoDB.
   • Collections:
     • users_collection: Stores user credentials.
     • wishlist_collection: Stores users' stock wishlists.

3. YFinance

   • yfinance library is used to fetch stock data for a given ticker.

4. Scikit-Learn

   • The LinearRegression model from scikit-learn is used to predict the next day's stock price based on historical data.

Routes and Their Functions

1. User Registration

   • @app.route('/register', methods=['GET', 'POST'])
   • Handles user registration. Checks if the username already exists, then saves the new user if the username is unique.

2. User Login

   • @app.route('/login', methods=['GET', 'POST'])
   • Handles user login. Verifies user credentials and starts a session if the credentials are valid.

3. Index Page

   • @app.route('/', methods=['GET'])
   • The landing page of the application. Redirects to the dashboard if the user is logged in.

4. Dashboard

   • @app.route('/dashboard', methods=['GET', 'POST'])
   • Displays the user's dashboard with stock data and wishlist.

5. Add to Wishlist

   • @app.route('/add_to_wishlist', methods=['POST'])
   • Allows logged-in users to add a stock ticker to their wishlist.

6. Logout

   • @app.route('/logout')
   • Logs out the user by clearing the session.

7. Predict Price

   • @app.route('/predict_price', methods=['POST'])
   • Uses the predict_next_day_price function to predict the next day's stock price for a given ticker.

8. Search Ticker

   • @app.route('/search_ticker', methods=['POST'])
   • Fetches and returns stock data for a given ticker.

Key Functions

1. Predict Next Day Price

   • predict_next_day_price(ticker)
   • Fetches the last month's stock data, trains a linear regression model, and predicts the next day's closing price.

2. Search Ticker Data

   • search_ticker_data(ticker)
   • Fetches the last 6 months of stock data and the current day's stock data. It also predicts the next day's closing price using the predict_next_day_price function.

Prediction Model

• Linear Regression Model
  • The model is trained using the historical closing prices of a stock.
  • The date is converted to ordinal format to serve as the feature for the model.
  • The trained model is used to predict the next day's closing price based on the most recent date.

Technologies Used

1. Flask: Web framework to handle routing and web requests.
2. MongoDB: Database to store user and wishlist information.
3. YFinance: Library to fetch historical stock data.
4. Scikit-Learn: Machine learning library to build the linear regression model.
5. Gunicorn: WSGI HTTP server for running the Flask app in production.