EduInvest

EduInvest is a lightweight educational desktop application created for a hackathon. It blends a simple PyQt6 GUI with market data from yfinance and a placeholder AI model to help students learn about stock movement prediction in a fun, game-like environment.

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Project overview

• Name: EduInvest
• Type: Desktop application (Python, PyQt6)
• Purpose: Teach basic market intuition by letting students guess whether a selected stock will go up or down; compare with an AI prediction and display price history.
• Hackathon: Quick-prototyped for learning, demo, and discussion.

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Features

• Interactive GUI built with PyQt6.
• Trending stock list and random stock selection for a short game.
• AI model (placeholder) that predicts Up/Down for the next trading day.
• Embedded matplotlib chart showing recent close prices for the selected ticker.
• Simple scoring and streak system to make learning rewarding.
• Modular code to swap-in a real model in the future.

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How it works (high level)

1. The GUI selects a stock (random from a curated list).
2. It fetches historical price data using yfinance and displays a 1-year close-price chart.
3. A placeholder AI function returns a direction (Up / Down) for the next day.
4. The user guesses whether the price will go up or down. The app reveals whether the AI was right and updates the score and streak.

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Installation (macOS-focused)

This project depends on Python and several packages. The heavy ML dependencies (torch, torch_geometric) are intentionally left for advanced users to install separately.

Prerequisites

• macOS (Intel or Apple Silicon)
• Python 3.10 or 3.11 (recommend using pyenv or the python.org installer)
• Xcode command line tools (for compiling wheels when necessary): xcode-select --install

Quick install (recommended for demo / student use)

1. Open a Terminal and clone or extract the project folder.
2. Create a virtual environment and activate it:

python3 -m venv .venv
source .venv/bin/activate

3. Install the lightweight dependencies (does not include torch or torch_geometric):

pip install -r requirements.txt

4. Install PyTorch (CPU-only recommended for ease):

# Example: CPU wheel install (check https://pytorch.org/get-started/locally/ for latest)
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu

5. If you need GNN features, follow the instructions at https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html to install torch_geometric and its dependencies.

6. Run the app:

python eduinvest.py

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How to run the app

• With the venv active, run python eduinvest.py.
• The GUI will open. Click the Up or Down buttons to make guesses. The app picks a new random stock after each guess and updates the price chart.

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The AI component

• The repository includes a simple placeholder function that returns Up or Down (and previously a confidence). This is intentionally lightweight for the hackathon demo.
• The placeholder is located in eduinvest.py (the model_predict_direction function delegates to a model wrapper). The real model would be a PyTorch model that consumes three modalities (time-series transformer, GNN, sentiment LSTM) in model.py.
• For the demo, the AI provides a direction that the UI compares against the user's guess.