Speaker
Description
The increasing complexity of financial markets demands analytical tools that combine real-time data access, rigorous statistical modelling, and intuitive visual communication within a single, reproducible framework. This study presents FinDash Pro, a production-grade interactive dashboard developed entirely in R using the Shiny ecosystem, designed to bridge the gap between institutional-level financial analytics and open-source accessibility.
FinDash Pro aggregates live market data from multiple sources, including Yahoo Finance via quantmod, the Tiingo API via riingo, and web-scraped fundamental metrics, and renders them through a polished dark-themed interface built with navbarPage, plotly, and gt/gtExtras. The dashboard encompasses four analytical modules, whcih are a Market Overview panel displaying S&P 500 capitalisation rankings with 30-day sparkline trends; a Stock Analysis panel featuring candlestick charts, technical indicators (RSI, MACD), and multi-stock comparison; a Portfolio Calculator with CAGR, annualised volatility, and Sharpe ratio metrics benchmarked against the S&P 500 index; and a News & Sentiment panel delivering real-time headlines with lexicon-based sentiment scoring via sentimentr.
The central methodological contribution is the Machine Learning Predictions module, which implements two complementary forecasting paradigms: a Long Short-Term Memory (LSTM) neural network via keras3 for 7-day-ahead price prediction with bootstrapped confidence intervals, and a Random Forest ensemble via randomForest for iterative daily return forecasting with feature importance attribution. The model were selected based on the literature review. Furthermore, the dashboard integrates a context-aware large language model (Google Gemini via ellmer) whose system prompt is dynamically populated with live price action and technical signals, enabling natural-language stock analysis grounded in real-time dashboard state.
FinDash Pro demonstrates how modern R tooling can deliver a coherent, extensible, and fully reproducible analytical platform that meets the demands of quantitative finance practitioners and data science educators alike.
If you used AI tools or services to support the preparation of this submission, please state the name and reason for using each of them.
Claude Opus 4.6 is used to debug errors encountered during application development.
| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | Shiny, financial dashboard, LSTM, Random Forest, large language models, quantmod, real-time analytics, time series forecasting |
|---|---|
| Virtual Option | This submission is for onsite presentation primarily, but I would also like it to be considered for pre-recorded virtual presentation if I don't get an onsite slot |
| Video Recording | Video sharing is fine |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |
| Interested in serving as reviewer? | o.ozdemir@rug.nl |