📈 Real-Time Trading Terminal — Dash & Yahoo Finance

A modern, Yahoo Finance–styled real-time trading terminal built with Dash, Plotly, and pandas. Includes dynamic KPIs, sparklines, volatility metrics, and interactive visual analytics.

🎬 Live Demo (Preview)

📖 Project Overview

This project demonstrates a full interactive analytics workflow:

Data Fetch (yfinance) → Transform (pandas) → Visualize (Dash + Plotly)

The app connects directly to Yahoo Finance APIs to retrieve live OHLCV market data and compute rolling KPIs such as cumulative returns and volatility.

📂 Project Structure

realtime-trading-terminal/ ├─ app/ │ ├─ init.py # Dash app factory │ ├─ layout.py # UI layout, components, navbar │ └─ callbacks.py # Callbacks & reactive logic │ ├─ src/ │ ├─ init.py # Public API for transforms │ ├─ fetch.py # Data download (yfinance) │ ├─ transform.py # Returns & volatility calculations │ ├─ forecast.py # Simple linear forecast (demo) │ └─ metrics.py # Extra Plotly chart builders │ ├─ assets/ │ ├─ styles.css # Custom theme & branding │ └─ logo.png # Yahoo-style logo │ ├─ data/
│ ├─ darkyahoo.png # Dashboard Dark Yahoo │ ├─ lightyahoo.png # Dashboard Light Yahoo │ └─ demo_dashboard.gif # Dashboard Yahoo Gif │ ├─ notebooks/ # Exploratory notebooks │ └─ EDA.ipynb
│ ├─ test/ # unit tests
│ ├─ run.py # App entry point ├─ requirements.txt ├─ .gitignore ├─ LICENSE └─ README.md

⚙️ Core Features

🧩 Key Modules

Module	Description
fetch.py	Fetches OHLCV data for multiple tickers via yfinance
transform.py	Computes returns, cumulative performance, and volatility
callbacks.py	Defines all Dash callbacks and interactivity
layout.py	Builds responsive UI with Bootstrap components
metrics.py	Optional reusable Plotly chart generators

📊 Dashboard Components

Section	Description
💹 Main Chart	Price, Cumulative Return, or Candlestick (per tab)
⚙️ Controls	Ticker selector, date range, and time interval
📈 KPI Cards	Last Close, Cumulative Return, and 20D Volatility
🔍 Mini Analytics	Daily returns bar chart and correlation heatmap
🌗 Theme Toggle	Seamless switch between light and dark mode

🧮 Calculations

Returns & Volatility

daily_return = close.pct_change() cum_return = (1 + daily_return).cumprod() - 1 vol_20d_ann = daily_return.rolling(20).std() * (252 ** 0.5)

Forecast (optional)

from sklearn.linear_model import LinearRegression model = LinearRegression().fit(X_time, close_prices) preds = model.predict(future_time_index)

🧠 Tech Stack

Area	Tools Used
Frontend	Dash, Plotly, Bootstrap (dash-bootstrap-components)
Data	pandas, numpy, yfinance
Forecasting	scikit-learn (LinearRegression)
Visualization	Plotly Express, Graph Objects
Environment	Python 3.11, virtualenv or conda

▶️ Quick Start

1️⃣ Clone this repository git clone https://github.com/MonicaVenzor/realtime-trading-terminal.git cd realtime-trading-terminal

2️⃣ Install dependencies pip install -r requirements.txt

3️⃣ Run the Dash app python run.py

4️⃣ Open in browser http://127.0.0.1:8050/

🖼️ Dashboard Preview

Dashboard Dark theme

Dashboard light theme

💡 Key Highlights

✅ Live market data directly from Yahoo Finance

✅ KPI sparklines with custom Plotly templates

✅ Dual-theme design (light/dark) with smooth transitions

✅ Correlation heatmap for cross-ticker analysis

✅ Modular architecture (src + app separation)

🧩 Next Steps

🚀 Add moving averages & Bollinger Bands

🧠 Integrate advanced ML-based forecasting

📈 Include portfolio backtesting features

☁️ Deploy on Render / AWS EC2 with HTTPS

💾 Add caching layer for improved performance

👩‍💻 Author

Mónica Venzor 📍 Data Analyst Jr | SQL | Excel | Power BI | Python | Data Visualization | Machine Learning Enthusiast

🔗LinkedIn | — GitHub

📜 License

This project is licensed under the MIT License — free for educational and personal use.

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