Market discovery
How users find opportunities
PolymarketProbability-first cards, event categories, trending markets
KalshiEvent-first browsing and sports-oriented selections
Predict.funTrading-oriented market lists and Web3 incentives
01 — Problems
How can one platform connect financial trading with real-world event prediction?
Three audiences, one product. Switch between them to see how each one behaves — and the problems that behaviour runs into.
Behavioural profile
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Problems it runs into · {{audSel.count}}
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Different motivations. Different behaviors. One connected prediction market ecosystem.
02 — Research
Behaviour shapes the information architecture.
Four research areas, a competitor review of Polymarket, Kalshi and Predict.fun, and a behaviour matrix that compares the three audiences.
01
Market discovery
Asset categoriesTrending marketsSearchMarket filtersSports leaguesTeam preferencesUpcoming events
02
Trading behavior
Short-term price predictionsEvent-based positionsDirectional decisionsOutcome-based decisionsTrading frequencySettlement expectations
03
Engagement patterns
Team subscriptionsTournament schedulesCampaign participationFollowing tradersRepeat participation
04
Position management
Account balanceActive positionsPosition valueUnrealized PnLSettled marketsTransaction history
Competitor analysis
01 / Positioning & engagement
Different value propositions, different paths to repeat trading
PolymarketInformation-driven marketplace
Core audienceCrypto traders and event-driven participants
Primary advantageBroad event coverage, probabilities, and market discovery
Main challengeHigh information density across categories
What drives engagementDiscovery builds the funnelTreat discovery as a core product experience.
KalshiAccessible event trading
Core audienceMainstream consumers, sports and financial traders
Primary advantageSimplified order entry and regulated event contracts
Main challengeIncreasing complexity of combinations and advanced trading
What drives engagementFamiliar events drive frequencyReduce friction between event selection and order placement.
Predict.funDeFi-native prediction trading
Core audienceWeb3 users and DeFi-focused traders
Primary advantageOnchain trading, yield features, and incentives
Main challengeMore concepts to learn around assets, rewards, and risk
What drives engagementUtility extends participationClearly explain financial benefits and risks.
Market context: October 2026 reported notional-volume share — Kalshi ~80.9%, Polymarket ~16.8%, Predict.fun ~0.69%. Trading volume, not users or revenue.
02 / UX & UI audit
Discover differently. Trade consistently.
Trading interaction
How users place orders
PolymarketYes/No contracts with order-book / limit-trading patterns
KalshiQuick orders with progressive access to advanced trading
Predict.funYes/No trading with onchain and position-utility concepts
01 / Price clarity
Separate implied probability from actual execution price, fees, and slippage.
02 / Progressive complexity
Lead with simple orders; reveal limit price, shares, and depth when needed.
03 / Position transparency
Keep cost, potential payout, exposure, and settlement status visible.
03 / Research synthesis
Translate research into design decisions
ReferenceWhat to learnOur design response
PolymarketInformation-led discoveryExplore: categories, trending, upcoming events
KalshiAccessible event tradingMatches: league → game → outcome → trade
Predict.funCapital and position utilityPortfolio: clear value, P&L, exposure, and settlement
Opportunity gapMultiple user mental modelsUp/Down + Matches: market-specific discovery, shared order logic
Behaviour comparison
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Five product opportunities
Each becomes a goal and a feature03 — Goals
Create a unified platform where users can discover, trade and manage predictions across financial markets and real-world events.
User goals
- Discover relevant prediction markets.
- Understand market outcomes and conditions.
- Participate through familiar interaction patterns.
- Explore trading strategies.
- Manage positions transparently.
Business goals
- Expand participation beyond existing crypto traders.
- Improve market discoverability.
- Reduce unnecessary trading friction.
- Encourage event-based engagement.
- Support continued use of the prediction market ecosystem.
User problems → product goals → expected outcomes
User problem
Product goal
Expected outcome
Fragmented discovery
Centralize market navigation
Easier market discovery
Complex price prediction
Simplify directional trading
Reduced execution friction
Limited event engagement
Organize matches and subscriptions
More relevant participation
Low strategy visibility
Surface trader performance
Better strategy discovery
Portfolio complexity
Consolidate position information
Greater account transparency
Design principles
01Discovery first
Help users find markets relevant to their interests.
02Familiar interactions
Adapt patterns from financial trading, sports schedules and event predictions.
03Contextual complexity
Show what the task needs instead of every function at once.
04Speed with transparency
Efficient interactions that keep pricing, risk and settlement clear.
05Financial clarity
Balances, positions and outcomes that are easy to understand.
04 — Strategies
Five features, shown on the product.
Every screen is the live prototype: prices tick, countdowns run, and filters, tickets, sheets and toasts respond. Scroll inside a phone or tap through it.
Explore
One discovery feed with category chips, search and a Live filter; every market type shares a scannable card.
Crypto rounds, fixtures, multi-outcome and yes/no markets share one card system, filtered by Watchlist, Live and category.
Chance, volume and price history above the ticket. Yes and No show price per share and what the amount would win.
Multi-outcome markets rank every outcome by chance and chart the leaders over time.
1 / 3One feed
- User problem
- Users need to find relevant opportunities across market categories without excessive navigation or information overload.
- Design decision
- One discovery feed with category chips, search and a Live filter; every market type shares a scannable card.
- UX rationale
- Audiences enter through different doors — a coin, a match, a headline. One feed with consistent cards lets each find theirs without learning separate sections.
- Expected user benefit
- Faster discovery with less navigation.
Up/Down
Directional trading on fixed rounds for crypto, stocks and ETFs: a price to beat, a countdown and two buttons.
Live price against the price to beat, a countdown ring and the current split. One-Tap is on: the stake (5, 10 or 50 USDT) is set once.
Up or Down buys at the chosen stake straight away — no confirm step. A notification confirms the order and the position appears on the card.
Live price, move against target, the round timeline and a Buy/Sell ticket.
The same round format for stocks and ETFs.
1 / 4Rounds at a glance
- User problem
- Users interested in price movements need a fast, understandable way to choose a direction, trade and know how it settles.
- Design decision
- Directional trading on fixed rounds for crypto, stocks and ETFs: a price to beat, a countdown and two buttons.
- UX rationale
- Framing a price prediction as Up or Down against a visible target removes order-book concepts while keeping pricing transparent.
- Expected user benefit
- Fewer decisions per trade, with payout and settlement in view.
Matches
An event-centred schedule: sport and game rail, league filters, a date strip, followed teams and campaigns.
Game rail, leagues and a date strip, with the tournament campaign above the schedule.
Followed teams and fixtures in one list, with kick-off alerts.
Each side’s chance over time, then moneyline, game winner and totals in one ticket.
1 / 3Fixture list
- User problem
- Sports audiences navigate by games, leagues, teams and schedules rather than financial market structures.
- Design decision
- An event-centred schedule: sport and game rail, league filters, a date strip, followed teams and campaigns.
- UX rationale
- Meeting fans in the structure they already use — a fixture list — lowers the cost of a first prediction.
- Expected user benefit
- Familiar navigation from a match to its markets.
Leaderboard
Category rankings and trader profiles with PnL, ROI and win rate over selectable ranges, public positions and Follow.
24-hour PnL by category opens the rankings.
A podium and list with PnL, ROI and win rate; star a trader to watch.
PnL, ROI and win rate over selectable ranges, open positions, copy and follow.
1 / 3Where profits are
- User problem
- Users who want to learn from other traders need transparent performance data to decide whom to follow.
- Design decision
- Category rankings and trader profiles with PnL, ROI and win rate over selectable ranges, public positions and Follow.
- UX rationale
- Comparable, labelled metrics make performance legible. Past results do not predict future ones, so the design shows evidence, not promises.
- Expected user benefit
- Informed strategy discovery.
Portfolio
One dashboard: total value, today’s PnL, position value and available balance, with Active, Closed and Open orders.
Total value, today’s PnL, position value and available USDT; active positions with payout if won.
Won or lost, amount returned, PnL and settlement date.
Resting limit orders with fill progress and cancel.
1 / 3One dashboard
- User problem
- Users across many predictions need a clear view of exposure, balances and settlement outcomes.
- Design decision
- One dashboard: total value, today’s PnL, position value and available balance, with Active, Closed and Open orders.
- UX rationale
- Financial clarity builds the confidence to keep trading; every position restates price, shares, value, PnL and payout.
- Expected user benefit
- Transparent monitoring in one place.
The prototype’s Up/Down also lists commodities (gold, crude oil) under its own filter; the strategy scope is crypto, stocks and ETFs.
05 — Key metrics
Measuring participation, trading activity and product performance.
An evidence-based view of how users interact with the prediction market ecosystem. Every figure comes from the analytics dashboard, May–November 2026; derived values are marked as calculated.
Hatched bar: 2026-11 is month-to-date at the time of the report.
Dashed points: not labelled in the source chart — values unverified, shown without a number.
Only months with both values labelled in the source. Same unit as volume.
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Trader shares are of the category total as reported (a trader active in two categories counts in both).
Metrics and the five strategies
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Product insights
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Conclusion
Participation scaled fast and is now split almost evenly between crypto and event markets; trading volume kept growing after the July peak in active traders.
The data shows what happened, not why. It doesn’t isolate the effect of any single feature, so no design change is credited with these results. Next: cohort retention after July, and category-level conversion for culture and politics markets.
Source: analytics dashboard — “Daily Trading Volume” (monthly dimension) and “Daily Trading Volume Share by Source” (2026-11). Volume unit not shown in the source.