valuest app
structuring complex financial data for active traders
Designing high-density financial dashboards from 96 raw indicators into decision-ready interfaces.
fintech · dashboard architecture
overview
Valuest is a portfolio tracking app designed for active traders. We received access to ~96 financial indicators from a market data provider before any product launch or user validation.
The challenge was transforming high-density financial data into a structured, decision-ready dashboard without overwhelming users.
context
The primary audience was active traders who rely on speed and analytical clarity. Therefore we defined the First Usable Product as:
find a company
add a transaction
build a portfolio
Everything else was secondary.
challenge
Nearly 96 financial indicators were available for each company. The initial ambition was to expose as much data as possible, but this quickly led to overloaded screens and unclear hierarchy. The real problem wasn’t missing data — it was structuring it.
prioritization
We focused on decision-critical financial structures:
income statement
cash flow
balance sheet
From ~96 indicators, I structured the company dashboard into:
3 financial groups
3 key metrics per group
3 primary charts
This reduced visible metrics to ~9 while preserving analytical depth.
architecture decisions
The company dashboard was organized into:
overview (company & market data)
financial metrics (prioritized, grouped indicators)
secondary data (layered disclosure)
At one stage, we tested collapsible sections to reduce density.
While this reduced clutter, it introduced friction for active traders.
Final decision:
Keep key metrics visible. Hide only secondary data.
adjustment
Portfolio previews initially displayed multiple metrics per card, creating visual overload. I replaced metric-heavy cards with a selector-based interaction and moved detailed data into focused views, improving scanability.
my role
I defined:
dashboard architecture
metric grouping logic
navigation structure
density reduction strategy
The client defined data scope and validated domain assumptions.
outcome
structured dashboard system
reduced visible indicators from ~96 to ~9
scalable data grouping logic
clear decision hierarchy
reflection
Designing analytical products without live users requires disciplined prioritization and architectural thinking. This project strengthened my ability to manage complex financial data and design for speed in high-density environments.