OrderbookTrade LitePaper
The Matching Engine & Liquidity Layer for Prediction Markets
1. Overview
In the last cycle, prediction markets quietly graduated from a niche toy to a piece of real financial infrastructure capable of handling billions of dollars in volume.
During the 2024 U.S. presidential election, a single Polymarket (Trump vs. Harris) processed billions of dollars in cumulative trading volume, with every bet and settlement transparently recorded on-chain.
For many, this was the first time it became clear that:
Uncertainty itself can be turned into a tradable asset.
At the same time, the sector exposed a shared weakness:
There are many teams building new prediction products,
But very few platforms with real users, deep liquidity, and CEX-grade execution.
OrderbookTrade is not “yet another prediction platform.”
Instead, it is the matching engine and liquidity infrastructure behind prediction markets:
A plug-and-play infrastructure stack for new L1s, L2s, and prediction projects,
so teams can launch robust, scalable prediction products in weeks, not months.
Our mission is simple:
Make prediction markets a core layer of the internet’s probability markets
Make OrderbookTrade the shared matching layer that powers them.
2. Market & Problem
Bear Markets, Liquidity Crunch, and New Primitives
We are at the end of 2025, coming out of:
A sharp drawdown in
1011and severe short-term liquidity stressRepeated U.S. government shutdowns and fiscal tightening—macro “bad news” everywhere
Depressed secondary markets, slower fundraising, and more conservative token launches
Looking back at previous cycles, a pattern emerges:
Post-2018/19 bear:
Ethereum DeFi, AMMs, and Uniswap emerged as core "financial Legos."
Post-2022/23 winter:
Rollups and L2s came online, and 2024’s Meme wave brought a new user cohort.
In 2025:
Prices retrace, sentiment cools, but prediction markets & event derivatives quietly accelerate.
Historically, the worst markets are often when the most important infrastructure quietly ships.
What’s Broken in Today’s Prediction Markets?
From our research across ~200+ projects, most prediction platforms share similar pain points:
For end users
Thin liquidity, shallow books – even mid-sized orders can move the price aggressively
High slippage and slow fills – UX is far below CEX standards; pros don’t stick around
Downtime and degradation during volatility – exactly when users most want to trade
Limited product surface area
Most markets are simple yes/no or fixed-odds,
Lacking leverage, combinatorial markets, conditional markets, and proper risk tools.
For Prediction product builders / new L1s / infra teams
Every team reimplements the same stack:
matching engine, orderbook, clearing logic, monitoring, risk, etc.
To get “real” performance, you either:
Go fully centralized, or
Build your own exchange-grade engine from scratch.
Cross-chain / multi-chain – every chain needs a fresh integration.
Compliance, logging, reporting and audit – all reinvented per project.
The result is :
Most teams are busy building exchanges, not better prediction products.
3. The rise of Prediction Markets
The 2024 U.S. election was prediction markets’ first real breakout moment:
A single market processed billions in notional volume
Every position and settlement was verifiable on-chain
Media outlets, institutions, and poll trackers began treating prediction prices as a serious signal
The mental model shift:
For retail users:
“I can express my view on an event with $100, not just bet on sports or meme coins.”
For institutions and builders:
“Any uncertain event can be turned into a market and priced in real-time.”
We estimate that 200+ teams are now building prediction-related products:
On-chain prediction markets
Event contracts embedded in CEXs
“Prediction + social,” “prediction + meme,” “prediction + games,” and more
But the number of platforms with meaningful, sticky volume and recurring users is probably <10.
4. The Difference between Prediction / Gambling
On the surface, the UI looks similar to sportsbooks—yes/no, team A vs team B, fixed odds.
Underneath, there are two crucial differences:
Who are you trading against?
Sports betting: you are mostly trading against the house.
Prediction markets: you trade in a common pool or orderbook against all other participants.
What does the price mean?
Betting odds: capture the bookmaker’s risk model and business objectives.
Prediction prices: aggregate market participants’ beliefs about the probability of an event.
In other words:
A well-designed prediction market is a machine that compresses information into prices.
5. The OrderbookTrade Solution
The market does not need ten more frontends or a dozen more prediction tokens.
What it needs is reliable, high-performance infrastructure that everyone can build on.
OrderbookTrade chooses to sit at this lower layer:
We don’t compete for users with our customers.
We help them launch better prediction products, faster and safer
OrderbookTrade provides a purpose-built matching and liquidity layer for prediction markets, including:
A high-performance matching engine (targeting 100k+ TPS, millisecond latency)
Asset and settlement models for yes/no and multi-outcome events
Risk controls, margin & liquidation logic, limit systems, and audit-ready logs
APIs, SDKs, and white-label UIs
Integration patterns for new L1s, L2s, and exchanges
Our Goal :
Take a team from “idea” to “first production prediction product” in weeks, not quarters.
6. Core Architecture
OrderbookTrade uses a hybrid model:
on-chain settlement, off-chain matching
On-chain:
Market creation, collateral custody, settlement, and final clearing
Asset issuance and redemption (yes/no tokens, multi-outcome tokens)
Governance over key parameters and upgrades
Off-chain:
High-performance orderbook and matching engine
Risk checks and real-time risk monitoring
Streaming market data, order flow analytics, and historical data
This design offers:
CEX-like performance and UX
On-chain verifiability and auditability of funds and results
Multi-chain flexibility: a unified API on top, chain-specific adapters below
6.1 Matching Engine
Implemented in a high-performance language (e.g. Go / Rust)
Supports:
Limit, market, post-only, IOC orders
Multi-market concurrent matching
Pre-trade risk checks (balances, margin, exposure limits)
Provides:
WebSocket market data feeds
Trade confirmations and book snapshots
Fault-tolerance and replay mechanisms
6.2 Market Engine (Event Logic)
A market engine tailored to prediction use-cases:
Binary events (Yes/No)
Multi-outcome events (candidates, teams, ranges, scenarios)
Supports:
Event lifecycle management: create → trade → lock → pending resolution → settled
Market parameterization: tick size, leverage caps, margin requirements
Exposure tracking: per-event and cross-event risk
6.3. Settlement Layer
Settlement logic:
Payout based on resolved outcome
P&L attribution and balance updates
Handling of disputes, delays, and cancellations
6.4. Developer APIs & White-Label Frontends
REST and WebSocket APIs
SDKs in multiple languages (TypeScript, Python, Rust, etc.)
Ready-to-deploy white-label frontends:
Generic prediction market interface
Sports / esports vertical templates
Politics / macro / crypto events templates
7. ICP & Integrations
Who We Serve
OrderbookTrade is built for a few core customer profiles:
Vertical prediction product startups
Focused on specific verticals: sports, macro, NFTs, esports, social, etc.
Want to focus on UX, growth, and novel market design instead of infra.
New L1 / L2 / Rollup teams
Need real transaction flow and a compelling “volume & TVL story”
Can use prediction markets as a flagship application for ecosystem growth
Integration Capabilities
Chain integrations:
Major EVM chains, L2s, and rollups
Selected high-performance non-EVM chains
External modules:
Logging, monitoring, and analytics services
Reporting and dashboards for internal teams and institutional clients
8. OrderBookTrade Roadmap
OrderbookTrade is designed as a platform capability, not a single app backend:
New markets can be created via templates by project teams or ecosystem partners
Multiple frontends can share the same liquidity and orderbooks
Prediction products across different chains can plug into a unified settlement and account mapping layer
Ultimately, the goal is:
Make OrderbookTrade the shared matching & liquidity layer of the prediction ecosystem.
9. Conclusion
Prediction markets are in the middle of a structural upgrade:
From passive to active, intelligent, composable liquidity
From isolated apps to shared, cross-chain liquidity layers
From simple bets to complex event portfolios, hedging, leverage, governance, and social use-cases
Along this path, many toC platforms will appear, spike, and be replaced.
OrderbookTrade chooses a different position:
We sit at the infrastructure layer
Helping anyone who wants to build prediction products ship faster and more safer .
If you are:
An L1 / L2 ecosystem looking for a flagship, narrative-strong application,
A vertical prediction startup that wants to innovate on product, not infra
this Litepaper is your starting point.
The rest of our documentation will go deeper into:
Detailed system architecture and safety model
Pricing, leverage, and margin design
Case studies and launch templates
How to go from 0 → 1 using OrderbookTrade to power your own prediction product
We believe prediction markets will be to “uncertainty” what AMMs were to spot trading
OrderbookTrade is building the matching and liquidity brick !!!
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