Stock Forecasting App

A Streamlit application for forecasting stock prices using Prophet and yfinance.

Features

• Data Loading: Fetches historical stock data (AAPL, GOOG, MSFT, GME) from Yahoo Finance.
• Data Visualization: Interactive plots of historical open and close prices using Plotly.
• Forecasting: Predicts future stock prices for up to 4 years using the Prophet model.
• Caching: Efficient data loading with Streamlit's caching mechanism.

Stack

• Python: 3.10+
• Streamlit: Web application framework.
• Prophet: Time series forecasting.
• yfinance: Market data downloader.
• Plotly: Interactive graphing library.
• Pandas: Data manipulation and analysis.

Setup

Prerequisites

• Python 3.10 or higher
• pip

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/stock-forecasting.git
   cd stock-forecasting

2. Create a virtual environment (optional but recommended):

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install the package in editable mode with development dependencies:

   pip install -e .[dev]

Usage

Run the Streamlit app:

streamlit run src/stock_forecasting/app.py

Open your browser at http://localhost:8501.

Development

Running Tests

pytest

Linting and Formatting

This project uses Ruff for linting and formatting.

ruff check .
ruff format .

Type Checking

mypy src

Architecture

The application is structured as follows:

• src/stock_forecasting/: Source code.
  • app.py: Main Streamlit application entry point.
  • utils.py: Utility functions for data loading, model training, and forecasting.
• tests/: Unit tests.

License

MIT