DeepVue

Making dense market data easy to read

Accessibility and data clarity for traders, through data-centric visualization design.

Role
Experience Designer
Product
Trading analysis platform
Surfaces
Mobile, web, desktop
Tools
Figma, VS Code, Tableau, Jira, Google Workspace
DeepVue watchlist screens in light and dark themes, including table, chart and alerts views

Problem breakdown

Traders work fast, with a lot of data on screen.

The product had to stay deep without feeling heavy.

Customizing the trading experience

Every trader runs a different strategy. The tools needed to adapt to individual preferences and surface real-time, actionable insight.

Smart alerts and AI recommendations

AI-driven notifications flag trading opportunities early, cutting reaction time.

Seamless multi-platform experience

Traders move between mobile, web and desktop. Transitions had to be smooth so they can act on any device.

Data-driven decision making

Traders don't just need numbers. They need clarity. Complex datasets had to read as trends.

My First Watchlist in table view with symbol, 1D chart, price and RV 20d columns

Watchlist, table view

A whole watchlist in one scan

Each row pairs a symbol with its 1D sparkline, price and daily change.

Losers switch to pink, so a drop stands out without reading numbers.

  • Symbol
  • 1D chart
  • Price
  • RV 20d

Key features

The focus was accessibility and insight, so traders can decide faster.

  • Advanced screenerFilter the market down to what fits a strategy.
  • Accessibility complianceWCAG 2.1 AA standards applied to data views.
  • Real-time market dataLive prices and changes across the watchlist.

Design process

Trading platforms need a balance between depth and simplicity.

The job was turning massive financial data into something intuitive.

  1. User research, testing and auditing

    • Ran 15+ usability tests, contextual inquiries and A/B tests to refine navigation and layout.
    • Studied trader behavior so the interface matched real decision-making and real-time needs.
  2. Wireframes and iterations

    • Sketched low-fidelity options to explore several interaction models.
    • Iterated on research insights to improve accessibility and usability.
    • Set up a component-based design system that scales across devices.
  3. High-fidelity prototypes

    • Built interactive Figma prototypes to test responsiveness and usability.
    • Refined transitions so mobile, web and desktop stay consistent.
    • Applied WCAG 2.1 AA standards to improve data visibility for all users.
DeepVue launch screen with the logo on a blue-to-lilac gradient

Launch screen

One gradient, from launch to list

The blue-to-lilac gradient on launch carries into the watchlist header.

The app feels like one continuous surface from the first second.

Interaction details

Small gestures that keep a watchlist quick to manage on mobile.

Long-press menu on a watchlist row with color tags, Add to, Move to, Copy, Add alert and Delete

Long press on a row

Every action on a symbol, one press away

Holding a row opens a menu right where the thumb already is.

Color tags sit on top, so organizing a list takes a single tap.

  • Color tag
  • Add to
  • Move to
  • Copy
  • Add alert
  • Delete

Clarity is power.

That's the lesson designing for traders reinforced.

Every iteration aimed to simplify workflows and make data easier to reach.

The goal: faster, more confident decisions.

Key learnings

  • Design systems matterA scalable, component-based UI improves efficiency and keeps every platform consistent.
  • Accessibility drives usabilityWCAG 2.1 AA compliance improved financial data discoverability by 15%, making insights more actionable for everyone.
  • User testing is everythingAcross 15+ tests, traders valued speed over aesthetics. The aim became cutting friction and cognitive load at every step.

What I'd do differently

  • Refine AI-driven trade recommendations for more personalization.
  • Streamline mobile interactions further for traders who rely on fast execution.
  • Explore motion and micro-interactions to make data exploration more intuitive.