Rebuilt Binance Asset Page for Pro Traders
Redesign for Clarity, Insight, and Precision
Shallow analysis
and attribution
Generic suggestions lacking context
Unstructured asset presentation
Scattered information makes it hard to scan asset structure at a glance.
Analysis lacks depth and progression, failing to support clear insights.
Suggestions are detached from user context, adding friction instead of value.
Built for pros.
But not pro enough.
BINANCE Pro
Low in volume, high in value.
Pro users matter.
Retail users dominate in size, but revenue is driven by professional and institutional users. Designing for them brings higher returns with fewer resources.
Source: Chainalysis, 2023. Exchange user segmentation and inflow analysis.
Mapping user profiles.
Involved, active, diverse, distributed.
Significant asset holding
Typically holds $10,000–$10,000,000 with strong market involement.
Complex asset distribution
Holds multiple accounts and tokens, with higher demand for unified asset views.
High trading frequency
Trades almost daily and has been active in crypto for over a year.
Cross-platform trading
Familiar with platform rules, frequently trades across CEXs.
Values data integrity under density
Requires complete, verifiable data with clear source logic, even at high information density.
Structured analytical mindset
Specializes in structured thinking and comparison, evaluating assets from multiple perspectives.
Habit of reflective analysis
Evaluates not just current asset status but also strategies and trading behavior to improve decision quality.
Learns fast, open to advanced tools
Willing to explore new tools, strategies, and analytical methods, with high acceptance of complex interactions.
Zoom into user patterns.
Precise, structured, reflective, adaptive.
Crafted for pro users’ workflow.
Designed for long-term stickness.
Design Strategy
Beyond numbers.
A new asset perspective.
Structured asset view
Seamless user flow
Layered interpretation
Deeper analysis
Contextual Suggestions
Tied to trading personality
Structured asset view
Seamless user flow
Asset page features a more structured layout and a more fluid flow, enabling users to scan, compare and quickly dive, supporting execution through clarity.
Overview.
Just at first glance.
Before
After
Mid-term performance data and charts are surfaced on the asset homepage, allowing users to scan recent trends. Margin assets are separated from spot due to their distinct risk profile, improving accuracy in statistics and asset browsing.
After
Before
Visual separation: PNL and total amount are distinguished by layout and color, making it easier to compare across tokens.
Simplified view: Secondary details like average price and buttons are folded by default to to emphasize the important data.
Faster access: Clicking expands account distribution, avoiding repeated page jumps.
Wasted space: Low-value elements like token full names, headers, and buttons are displayed repeatedly.
Hard to distinguish and compare: Total amount and average cost share the similar style, and with all data aligned to the right, it’s difficult to quickly tell them apart.
Messy browsing flow: Once confused, the eye shifts back and forth, breaking the scanning rhythm.
Smarter list.
Efficient even at scale.
Seamless switching.
Smooth multi-token browsing.
After
Before
Frequent switching disrupted browsing flow. Users had to switch repeatedly between the list and detail pages to view different tokens.
Swipe navigation keeps the browsing flow smooth. Users can switch between tokens directly within the detail page, without returning to the list.
Crytpo allocation
in asset report
Account allocation
in asset homepage
Limited dimensional view, scattered across pages.
Before
More granular chart.
Clarity in layered asset structure.
Chart entries are placed beside both the crypto and account list
Allocation is included as one of the modules in the asset report
Contextual entries, effortless to find.
Allows users to view breakdowns within a single dimension, when browsing by token, they can see its distribution across accounts.
A new Crypto + Account dimension is added, enabling more granular analysis of asset allocation.
Multi-dimensional view.
Detailed down to crypto-account level.
After
Explanation.
Now with better transparency.
Technical language with unclear structure. Complex formulas and jargon made it hard to understand.
Asset chart spikes from transfers may be mistaken for profits.
Simple, user-friendly language with clear breakdowns. Users can easily trace and verify their PNL now.
Transfer activity is clearly flagged to avoid confusion.
After
After
Before
Before
Layered interpretation
Deeper analysis
Asset report integrates multi dimensions such as crypto account, market and time for specific attribution and insight, driving retention and guiding users into deeper trading.
From data to insight.
Information architecture matters.
Fragmented structure
disrupts continuous analysis.
Before
Overall Performance
PNL Breakdown
Trading Detail
Holding Asset Detail
User Persona Summary
Asset Analysis
Total asset trend
Trade Analysis
Trading PNL trend, Trading PNL calendar
Trading PNL and times in account
Crypto Allocation
Crypto allocation
Top 3 PNL in crypto
Inflow & Outflow Analysis
Inflow & outflow amount
Risk Analysis
User risk profile
Risk allocation
Key Categories
Information Architecture
Similar insights like profit and loss are scattered across modules. Without a logic aligned with users’ analysis flow, it’s hard to build consistent understanding.
Result-Analysis-Foundation
A progressive structure deepens insight.
After
Based on a chain of reasoning, the structure starts from PNL results, progressively expands across multiple perspectives, and concludes with user behavior patterns. It combines breadth and depth of analysis.
Start with the full picture.
A multi-layer asset overview.
After
Before
Tapping today’s PNL leads to asset trend chart cause mislead to actual earnings, as asset trend includes transfers.
A misleading overview with limited angle
PNL, market, and trade.
All in one overview.
Using line chart for daily PNL may mislead to cumulative PNL. Bar chart is more suitable since daily PNL is independent
Cumulative trade PNL was shown at the top instead of asset trend, matching the intention after tapping the PNL.
BTC trend was added to help comparison between users’ return with broader market conditions.
Overlay recent trades on the chart to support a rough review and attribution in the context of overall market movement.
Using multiple colors on red and green curves reduced clarity.
Using shapes reduced visual clutter, but six types remained too complex to distinguish.
Simplified legends.
Better chart readability.
Grouping actions by direction reduced the legend to four symbols, making the chart easier to read.
Not just what, but why.
A more specific PNL attribution.
PNL only shown by account or crypto,
blocking accurate attribution.
Direct attribution by crypto-account pair.
Deeper analysis, more readable design.
A crypto may have different PNL across accounts, so does the account dimension, single-dimension analysis makes it hard to trace specific cause. PNL attribution is split across different modules in the page, disrupting a continuous analysis.
Before
After
Further breakdown on one tap. Market, trades, and position shifts unfold on tap for deeper attribution.
Bar chart for intuitive PNL amount comparison. Aligning in a single direction with different colors enables clearer comparison than traditional opposite-direction layouts.
Deepen insight into results.
A multi-angle trade review.
Condensed insights for review
Full trade history for search
Review actions, not just positions.
Detail in labeling.
Colors indicate results, not market direction.
Not just buy and hold, sell actions are equally reviewed. Hypothetical PNL helps evaluate sell timing, profit-taking, or missed opportunities.
Traditional red and green for price changes can be misleading. Selling in a falling market is often a smart move, but red may falsely imply a loss.
Colors reflecting outcomes directly, helping users judge if a result is favorable or not at a glance.
Ongoing and completed trades are both included. Current P&L shows real-time outcomes, while completed trades reflect full-cycle results.
Largest amount, highest gain, and biggest loss trades (including actual and hypothetical PNL) distilled from a large trading history enabling focused post-trade review.
Distilled highlights in 3 angles.
Multi-metric view for quality and timing assessment.
Each trade includes three metrics and one chart.PNL and price change reflect trade quality,while 24h post-trade change and the price chart evaluate timing.
Zoom into asset structure.
A transparent interpretation.
Before
After
Single-layer allocation.
Followed by a black-box risk result.
Precise, multi-dimensional allocation.
Summarized through transparent risk analysis.
Risk analysis displays only the user’s risk profile and overall risk proportions. Strategy suggestions are generic, lacking contextual relevance and situational alignment.
Risk analysis shows detailed risk breakdown by token and product, with max drawdown data, enabling targeted rebalancing. Strategy advice is tied to specific analysis, with two levels of recommendation for better acceptance.
Conclude with introspection.
A profile beyond daily moves.
Trading personality is distilled from user patterns, shifting focus from behaviors to traits. Its low update frequency makes it a fitting conclusion and offers a macro view of overall performance.
Contextual Suggestions
Tied to trading personality
summarize users‘ behavior through trading psychology to deliver more acceptable, contextual suggestions.
Detached suggestions.
Interrupting rather than guiding.
Generic suggestions are not aligned with user behavior, appearing more like intrusive ads rather than actionable insights. Their placement among key data points disrupts the analytical flow and reduces user trust.
Before
Build empathy first.
Deliver suggestions later.
Web3 users tend to be cautious about suggestions. With the belief in decentralization and the overload of complex information, they rely more on independent judgment. Only when a suggestion triggers empathy can it lower their defenses.
Trading psychology
Bridging suggestions and user behavior.
Neutral framing to reduce resistance
Low-cost generation with emotional resonance
After
Four independent dimensions profile user behavior. Each reflects a typical trading pattern, and together they form a comprehensive portrait that evokes a sense of being understood by the product.
Personality is shown as a spectrum, with both ends in percentages to reflect tendencies without fixed labels. Neutral language avoids judgment, lowering user defensiveness.
By tracking only key behaviors and mapping score ranges to generalized descriptions, the system reduces computational complexity and privacy concerns. The vague yet relatable language helps trigger a sense of being understood, making users more open to accepting suggestions.
