When people hear the phrase Polymarket arbitrage bot, they usually imagine finding pricing inefficiencies and collecting risk-free profits.
Reality is much more complicated.
Building an arbitrage bot requires solving problems that have very little to do with arbitrage itself.
Over the past several months I've been building trading infrastructure for Polymarket, and most of the engineering work has gone into making the system reliable rather than making it "smarter."
Core Components
My bot consists of several independent services:
Real-time market data
WebSocket subscriptions
Order book synchronization
Opportunity detection
Risk management
Order execution
Settlement monitoring
Separating these layers keeps the system maintainable as Polymarket evolves.
Arbitrage Is Mostly an Execution Problem
Finding opportunities isn't usually the hardest part.
Executing before those opportunities disappear is.
Execution introduces challenges such as:
Network latency
Order queue position
Partial fills
Liquidity constraints
Exchange downtime
Market rule changes
Each of these affects profitability far more than a simple arbitrage formula.
Infrastructure Never Stops Changing
Recent changes such as Polymarket's move toward TWAP settlement highlight why trading systems must be designed for change rather than hard-coded assumptions.
A flexible architecture makes these transitions significantly easier.
Final Thoughts
If you're building a Polymarket arbitrage bot, don't optimize only for opportunity detection.
Invest just as much time in:
System architecture
Execution quality
Risk management
Monitoring
Logging
Testing
A trading bot isn't just an algorithm.
It's an engineering system that has to keep working as the market evolves.
Top comments (1)
This matches what a lot of people discover once they move past the prototype stage.
Most discussions about Polymarket bots focus on signal quality or “finding the edge,” but the real bottleneck almost always ends up being execution reliability and how gracefully the system handles platform changes. The TWAP transition is a perfect example; any bot that treated resolution as a fixed last-tick assumption needed non-trivial rework.
Separating the layers (data, detection, risk, execution, settlement) is the right call. It makes adapting to things like the new Chainlink TWAP feeds or rate-limit changes much less painful.
One area I’d be curious to hear more about: how you’re handling the shift from snapshot to TWAP in the opportunity detection layer itself. Are you projecting the running average in real time, or still primarily reacting to the final window?
Thanks for sharing the architecture perspective, more of this kind of practical engineering content is needed.