iStockProMax

iStockProMax is a Flask-based stock analysis project built to demonstrate a practical full-stack data workflow for interviews and recruiters. It combines live stock quote scraping, historical market data retrieval, and a machine learning prediction pipeline to show how a web app can move from user input to data processing to a visual result.

Demo link

https://istock-pro-max.vercel.app/

Project Overview

The app opens with a stock dashboard inspired by the Apple Investor Relations experience. It shows Apple’s live price and daily change, then lets a user enter a ticker, a historical date range, and a future date to generate a forecast. Behind the scenes, the app pulls data from YFinance, engineers stock features, trains a Random Forest Regressor, and returns a predicted price movement.

What It Demonstrates

• Flask routing and template rendering
• Live market data lookup with YFinance
• Financial time-series handling with Pandas and YFinance
• Machine learning with scikit-learn
• A polished frontend with a stock-market style interface and TradingView chart embed

Key Features

• Live stock price lookup for Apple on the homepage
• Historical stock data download for supported tickers
• Feature engineering using returns, moving averages, volatility, and volume change
• Future price prediction with a Random Forest model
• Simple visual output showing predicted price, percentage change, and movement direction

Tech Stack

• Backend: Flask, Python
• Data: Pandas, NumPy, YFinance
• ML: scikit-learn
• Frontend: HTML, CSS, Jinja templates
• Deployment: Vercel serverless functions

How It Works

1. The homepage loads the current Apple price and daily change.
2. The user enters a ticker symbol, a start year, an end year, and a target future date.
3. The app downloads historical market data and builds technical features.
4. A Random Forest Regressor is trained on the historical data.
5. The app returns a predicted price, percentage movement, and an up/down signal.

Setup

Install the required packages:

pip install -r requirements.txt

Run the app locally:

python app.py

Notes

• The homepage quote currently targets Apple only.
• The quote lookup now uses YFinance, which is more suitable for Vercel than Selenium-based browser scraping.
• This project is intended for educational and interview showcase purposes, not real trading decisions.

Screenshots

Homepage

Prediction Page

Disclaimer

iStockProMax is for educational and informational use only. The predictions are based on historical data and a machine learning model, so they are not guaranteed to be accurate or suitable for trading.