8.2% Better Than Market Pricing đŻ
Iâm watching prediction markets and perps become a real-time test of who can process information fastest.
On $HYPE traders operate in an environment where one headline can reprice a position within seconds. The same can be said for $POL where Polymarket has captured over $64.9B in volume this year.
But volume and one-off headlines arenât the problem. The problem is that market price only shows the current consensus. It does not show when a structured forecast disagrees with that consensus.
Thatâs a problem that becomes more valuable when the forecasting system is scored after every market resolves.
Over the last 60 days, Quotientâs agent Q has been 8.2% more accurate than Polymarket across geopolitics and global elections.
More than half of traders using Qâs intelligence were profitable as of August 31.
Bankr is helping Quotient turn Qâs forecasting loop into infrastructure for AI agents and traders.
Q produces forecasts, then Quotient publishes Signals showing where those forecasts differ from market prices.
Builders can use that intelligence in a few ways.
âą Retrieve structured forecasts, evidence, and historical results
âą Request new forecasts through a skill, CLI, MCP, or API
âą Bring cross-venue market data into their own products and workflows
Coverage includes Polymarket and Kalshi prediction markets, with perps intelligence live on Hyperliquid.
Teams can buy API credits or pay for individual requests through x402.
What stands out to me is the feedback loop. Every resolved market gives Q another scored outcome to learn from, making forecast quality measurable over time.
#AI #DeFi
Iâm watching prediction markets and perps become a real-time test of who can process information fastest.
On $HYPE traders operate in an environment where one headline can reprice a position within seconds. The same can be said for $POL where Polymarket has captured over $64.9B in volume this year.
But volume and one-off headlines arenât the problem. The problem is that market price only shows the current consensus. It does not show when a structured forecast disagrees with that consensus.
Thatâs a problem that becomes more valuable when the forecasting system is scored after every market resolves.
Over the last 60 days, Quotientâs agent Q has been 8.2% more accurate than Polymarket across geopolitics and global elections.
More than half of traders using Qâs intelligence were profitable as of August 31.
Bankr is helping Quotient turn Qâs forecasting loop into infrastructure for AI agents and traders.
Q produces forecasts, then Quotient publishes Signals showing where those forecasts differ from market prices.
Builders can use that intelligence in a few ways.
âą Retrieve structured forecasts, evidence, and historical results
âą Request new forecasts through a skill, CLI, MCP, or API
âą Bring cross-venue market data into their own products and workflows
Coverage includes Polymarket and Kalshi prediction markets, with perps intelligence live on Hyperliquid.
Teams can buy API credits or pay for individual requests through x402.
What stands out to me is the feedback loop. Every resolved market gives Q another scored outcome to learn from, making forecast quality measurable over time.
#AI #DeFi
