Prediction-Market Bots
Automated, risk-aware participation in on-chain prediction markets.
The brief
Participating effectively in prediction markets means reacting to fresh signals quickly and sizing positions sensibly across many simultaneous markets — work that is tedious and error-prone by hand, and unforgiving of slow reactions.
The client wanted disciplined automation: bots that translate a signal into a correctly sized, risk-bounded on-chain order and manage the position through resolution.
- Automate signal-to-order across many simultaneous prediction markets.
- Size positions to edge and bound exposure per market and overall.
- Settle transparently on-chain with a minimal, audited contract surface.
- Manage positions through to market resolution automatically.
- Validate strategies on historical markets before deployment.
- Keep every order, fill and exposure level observable.
Our AI-native approach
We built a Python strategy layer that converts signals into target positions, with a risk module that bounds exposure per market and overall. Orders settle on Polygon through audited contract interactions, and the whole loop is observable.
As with all our trading work, nothing went live without backtesting and a shadow period.
What we built
Signal processing
Inputs are turned into probability-aware target positions.
Position sizing
Stakes are bounded per market and across the portfolio.
On-chain settlement
Orders execute and settle transparently on Polygon.
Resolution handling
Positions are managed and closed through market resolution.
Backtest framework
Strategies are validated on historical markets first.
Observability
Every order, fill and exposure level is logged.
How we built it
We built the strategy layer to reason in probabilities and the risk module as a separate bound on exposure, so even aggressive signals could never translate into unbounded positions. Contract interactions were kept minimal and audited to limit on-chain risk.
A backtesting framework over historical markets gave a clear go/no-go basis, and a shadow period always preceded live capital. A specialist pod owned strategy, risk and the Polygon integration end to end.
How it works
Signal
Inputs are processed into target positions.
Size
Risk limits bound the stake.
Submit
Orders are placed on-chain.
Manage
Positions are tracked to resolution.
Review
Outcomes feed back into strategy evaluation.
The strategy layer reasons in probabilities, not certainties — positions are sized to edge and bounded by risk policy, which keeps drawdowns controlled across many concurrent markets. Contract interactions are kept minimal and audited to limit on-chain risk.
A backtesting framework over historical markets provides the evidence base before live deployment.
The impact
Signal-to-order became fully automated and consistent.
Exposure stayed within policy across many markets at once.
Settlement was transparent and verifiable on-chain.
Backtesting gave a clear basis for go/no-go decisions.
Technology stack
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