Stock Dashboard

An interactive stock analysis dashboard built with Python and Streamlit. Multi-ticker comparison, candlestick and line charts, key metrics, volume overlay, and a 1-hour data cache — built to demonstrate moving quickly from concept to a working data application.

---

Features

• Live market data — real-time and historical price data via Yahoo Finance
• Candlestick or line chart — toggle from the sidebar
• Key metrics — last close, day change, 52-week high/low, average volume
• Configurable time range — 1 month, 3 months, 6 months, 1 year, 2 years
• Volume overlay — color-coded bars (green = up day, red = down day) beneath the price chart
• Multi-ticker comparison — normalized indexed chart to compare relative performance across any set of tickers
• Data caching — 1-hour TTL cache on data fetches keeps the UI fast and avoids redundant API calls

---

Stack

Layer	Library
UI / App framework	Streamlit
Market data	yfinance
Charting	Plotly

Why Plotly over Matplotlib — Plotly charts are interactive out of the box in Streamlit: hover tooltips, zoom, pan, and range selectors with no extra configuration. Matplotlib produces static images that would need additional work to match the same user experience.

---

Getting Started

git clone https://github.com/worthbeer/simple-streamlit-stockl-app.git
cd simple-streamlit-stockl-app
pip install -r requirements.txt
streamlit run app.py

App opens at http://localhost:8501.

---

Usage

1. Enter a ticker symbol in the sidebar (default: AAPL)
2. Select a time period and chart type
3. View key metrics and the interactive price + volume chart
4. Scroll to Compare Tickers and enter a comma-separated list (e.g. AAPL, MSFT, GOOGL) for normalized side-by-side comparison

---

Project Structure

simple-streamlit-stockl-app/
├── app.py              # Main application
├── requirements.txt    # Python dependencies
└── README.md

---

Background

Built to answer a direct ask: demonstrate Python and Streamlit proficiency by delivering a working data application. The workflow — from planning through implementation — used AI-assisted development, which is how this went from brief to deployed in a single session.