When Seconds Cost Millions

Visualizing Trading Data for DZ Bank

FinTech

Data Visualization

B2B SaaS

Enterprise

Traders at DZ Bank have seconds to price bonds worth millions. Their challenge: scan historical data, spot patterns, make the deal. I redesigned how they visualize their trading history for split-second clarity.

Client

DZ Bank

Role

Product Designer

Problem

Data existed. Insight didn't.

Traders couldn't answer the questions that drive million-dollar pricing decisions.

Three Critical Gaps

Temporal Blindness

"I need to know if we're getting better or worse at pricing this security"

Can't see if pricing patterns improve or degrade over time

Risk: Using outdated strategies without realizing it

Pattern Invisibility

"I want to see where our 'sweet spot' is—where do we win most often?"

Can't identify which spreads consistently win vs. lose

Risk: Gut feel instead of data-driven decisions

Mental Model Mismatch

"Sometimes I need the timeline, sometimes I need to see it spatially"

Different questions need different views

Risk: Single-view solutions miss critical insights

Key Insights from Research

  • Traders think in "spread from current price" (not absolute values)

  • Win/loss outcomes matter more than deal size

  • Recency and frequency both critical

  • Buy side vs. sell side behave differently

Solution

Smart data visualization for fast decision-making

I designed four visualization approaches, then iterated based on real trader feedback. Here's how each evolved:

Direction 1

Timeline Swim Lanes

Answers: How is our performance trending over time?

version 1
Initial Concept
Key Features

  • Horizontal bars = individual deals

  • X-axis = spread from current price

  • Y-axis = time (recent at top)

  • Bar width = deal size

  • Bar color = outcome (won/lost/covered/pass)

  • Green vertical line = current price reference

Stakeholder Feedback

  • "Keep this—but enhance it"

  • Add buy/sell distinction

  • Include cover price margin

version 2
Final Solution
What changed

  • Buy/sell indicators:
    Blue border + ▲ (buy)
    Orange border + ▼ (sell)
    → Solves: Different risk profiles for buy vs. sell side

  • Cover price ghosts:
    Yellow ghost bars show competitive margin
    → Solves: "How much did I leave on the table?" or "How close was I?"

  • 8 Enhanced KPIs:
    Performance, Trend, Avg Volume, Bid/Ask Balance, 24h Volume, Total Volume, Frequency, Frequency Change
    → Solves: Temporal blindness.
    See performance evolution at a glance

Direction 2

Distribution Analysis

Answers: Statistical clustering of spreads

version 1
Initial Concept
Key Features

  • Bubble chart

  • Spread on X-axis

  • Bubble size = deal size

Stakeholder Feedback

  • Killed

  • "Bubbles are too hard to compare visually"

  • Decision: Removed from product entirely

  • Learning: Visual accuracy > aesthetic novelty

Direction 3

Decision Matrix

Answers: Where do we win vs. lose?

version 1
Initial Concept
Key Features

  • 2×2 grid: Premium/Discount × Won/Lost

  • Deal cards in each quadrant

  • Quadrant counts

  • AI-generated insights

Stakeholder Feedback

  • "Keep—add buy/sell distinction"

  • Improve KPIs

version 2
Final Solution
What changed

  • Buy/sell indicators in cards: ▲ / ▼ symbolsSolves: Pattern invisibility—see if buy/sell sides behave differently

  • Cover price in tooltips: Hover shows competitive marginSolves: "How much margin did I have?" context

  • 4 Performance KPIs: Premium Win Rate | Discount Win Rate | Bid/Ask Balance | Total VolumeSolves: Pattern recognition—which position wins more?

Direction 4

Performance Spectrum

Answers: What's my pricing sweet spot?

version 1
Initial Concept
Key Features

  • Horizontal spectrum of all spreads

  • Bars "float" (won) or "sink" (lost)

  • Visual metaphor: good rises, bad sinks

  • Baseline = neutral

Stakeholder Feedback

  • "Keep—fix overlaps and show recency"

version 2
Final Solution
What changed

  • Recency gradient: Brightness = age (bright = recent, dim = old)Solves: Time visibility—recency matters for decision-making

  • Z-index layering: Newer bars render on topSolves: Overlap issue—nothing hidden

  • Buy/sell borders: Blue top / Orange bottomSolves: Side distinction in spatial view

  • Cover price tooltips: Shows margin on hoverSolves: "How optimal was my pricing?"

  • 4 Quick-Decision KPIs: Sweet Spot | Frequency | Bid/Ask Balance | Total VolumeSolves: Fast gut-check—"Should I price here?"

Cross-Direction Enhancements

Answers: What's my pricing sweet spot?

Unified Design System:

  • Horizontal spectrum of all spreads

  • Bars "float" (won) or "sink" (lost)

  • Visual metaphor: good rises, bad sinks

  • Baseline = neutral

Accessibility:

  • "Keep—fix overlaps and show recency"

Outcome

Faster decisions = pricing advantage

Impact

  • Pattern recognition: Seconds instead of minutes

  • Market shifts: Automatic visual warnings (bid/ask balance alerts)

  • Decision confidence: Data-driven pricing, not gut feel

By the Numbers

  • 3 visualization approaches (1 killed, 3 shipped)

  • 16 total KPIs across all views

  • 2 iterations in 1 week

  • 100% data accuracy, zero visual ambiguity

Validation

"This is exactly what I need—I can see the patterns immediately."

Ralf Henke, Lead Consultant

Success Criteria Met

  • Identify trends in <5 seconds

  • Visual hierarchy guides attention

  • Every element has clear meaning

  • Actionable without explanation

Let's Connect

I'm always open to discussing new projects, creative ideas, or opportunities to be part of your vision.

Get in touch

Adriana Rodriguez-Conto

Product Designer · Systems Builder · Design Lead

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