Yuna

Yuna

Yuna

Prediction Market

FinTech

Data Visualization

Flex Prediction

Empowering traders with flexible risk control

*To comply with NDA, sensitive data and branding have been obfuscated or modified.

THE CHALLENGE

From Binary Guesses to Precise Predictions

Current markets limit traders to a static Over/Under decision. Users with strong convictions need a way to customize their risk for better rewards.

THE CHALLENGE

From Binary Guesses to Precise Predictions

Current markets limit traders to a static Over/Under decision. Users with strong convictions need a way to customize their risk for better rewards.

THE SOLUTION

Calibrate Risk. Amplify Reward

Don't settle for the market line. Shift the target score to define your own risk-to-reward ratio, turning a simple prediction into a precise strategy.

Don't settle for the market line. Shift the target score to define your own risk-to-reward ratio, turning a simple prediction into a precise strategy.

Don't settle for the market line. Shift the target score to define your own risk-to-reward ratio, turning a simple prediction into a precise strategy.

INFORMATION ARCHITECTURE

Structured for Clarity

The interface is divided into three distinct cognitive zones. This hierarchy anchors users in the context first, visualizes the risk second, and empowers action last.

THE LIVE DEMO

Explore the Live Interface

Freely adjust the target score, stake, and toggles to experience how the interface adapts to your decisions in real-time.

IMPACT AND REFLECTION

Empowering Market Conviction

Transforming binary outcomes into granular investment opportunities.

  • Unlocking Granular Market Positions

Standard prediction markets are often binary (Over/Under), which limits users who have strong specific insights. For example, a user might believe a team will not just "win," but "win by a landslide."

This feature solves a key commercial problem: under-monetization of high-conviction users. By allowing users to adjust the target score, we empower them to customize their risk exposure based on their confidence level. This transforms a passive "guessing" experience into an active "strategy" experience, directly increasing the trading volume per user.

Standard prediction markets are often binary (Over/Under), which limits users who have strong specific insights. For example, a user might believe a team will not just "win," but "win by a landslide."

This feature solves a key commercial problem: under-monetization of high-conviction users. By allowing users to adjust the target score, we empower them to customize their risk exposure based on their confidence level. This transforms a passive "guessing" experience into an active "strategy" experience, directly increasing the trading volume per user.

  • Competitive Differentiation & Engagement

While most competitors offer static contracts with fixed probabilities, Flex Prediction introduces a dynamic interaction layer that is currently unique in the market.

This innovation serves as a strong retention hook. Early testing/projections indicate that this feature could significantly boost Average Order Value (AOV), as users are motivated to pursue higher multipliers when they identify a discrepancy between the market consensus and their own analysis. It creates a "sticky" experience where users feel they have an edge over the standard market price.

While most competitors offer static contracts with fixed probabilities, Flex Prediction introduces a dynamic interaction layer that is currently unique in the market.

This innovation serves as a strong retention hook. Early testing/projections indicate that this feature could significantly boost Average Order Value (AOV), as users are motivated to pursue higher multipliers when they identify a discrepancy between the market consensus and their own analysis. It creates a "sticky" experience where users feel they have an edge over the standard market price.

  • UX Reflection: Visualizing the "Risk-Reward" Curve

The primary design challenge was abstracting the complex mathematical model behind the changing multipliers into an intuitive interface for non-expert users. If the UI is too complex, users will abandon the trade.

I utilized a direct manipulation slider paired with a real-time dynamic curve. I used color transitions (e.g., Green to Orange) and immediate data feedback (Implied Probability) to visually communicate risk without requiring users to do the math. This reduces cognitive load, allowing users to "feel" the trade's risk profile instantly rather than calculating it.

The primary design challenge was abstracting the complex mathematical model behind the changing multipliers into an intuitive interface for non-expert users. If the UI is too complex, users will abandon the trade.

I utilized a direct manipulation slider paired with a real-time dynamic curve. I used color transitions (e.g., Green to Orange) and immediate data feedback (Implied Probability) to visually communicate risk without requiring users to do the math. This reduces cognitive load, allowing users to "feel" the trade's risk profile instantly rather than calculating it.

Want to dive deeper into my work? I’m currently available for new opportunities – let’s talk.

Linkedin

Email @

© 2026 Yuna Yang. All rights reserved.

Want to dive deeper into my work? I’m currently available for new opportunities – let’s talk.

Linkedin

Email @

© 2026 Yuna Yang. All rights reserved.

Want to dive deeper into my work? I’m currently available for new opportunities – let’s talk.

Linkedin

Email @

© 2026 Yuna Yang. All rights reserved.

Create a free website with Framer, the website builder loved by startups, designers and agencies.