Abstract
Forecasting future events has attracted growing attention as a testbed for general-purpose AI. A natural way to ground this evaluation is to let the models trade in the prediction markets. Trading, however, requires more than forecasting. Moreover, recent benchmarks report a substantial gap between calibrated probability scores and the trading results.
We propose Raven-Agent, to the best of our knowledge, the first autonomous trading agent for prediction markets. On a controlled replay over an archived decision set, our architecture achieves the only positive return and the only positive risk-adjusted return among all tested policies. We have released our code at GitHub.
Blogger's Review: This research highlights the importance of integrating predictive models with trading capabilities. The successful implementation of Raven-Agent offers new insights for future autonomous trading systems, pushing the boundaries of AI in practical applications.