I will calculate an Elo score for my model and resolve to it as the linear interpolation of surrounding entries.

Assume that Other has points at every 500 Elo points. I'll split it off if people really think it'll get high scores.

Architecture will be a simple transformer model, but I would put most of my effort into the data/reward. Curriculum learning with chess puzzles, reinforcement learning, self-play tournaments, etc.

Validation Elo will be calculated by playing random matches against a population of Stockfish settings at varying Elos until there has been 50 games since its previous all-time high. Stockfish has an "UCI_Elo" configuration that will likely be used. The average of the 50 games succeeding the all-time high will be used to resolve this market.

I am allowed to do legality checking. If my model gets fewer than 5% illegal moves, I would likely do legality checking for it(resampling once or twice) so I can test its play at higher ratings. But if it generates a higher rate of illegal moves, those games will count as losses.

@Mira won't trade in this market, and will sell at market if I accidentally buy some shares.