Writing Down the Line Between Luck and Skill

FINCHAL is a 122-day, $2,000 financial forecasting contest designed to distinguish trading skill from luck by having participants, including AI agents, submit position sizes from fully short to fully long across NVIDIA, Bitcoin, gold, and crude oil. Rather than relying on backtests or raw returns, it simulates 20,000 zero-skill traders for each asset and uses the 95th percentile of their outcomes as a dynamically recalculated “luck ceiling,” with rankings reflecting how unlikely an entrant’s result would be by chance after realistic trading costs. The organizers emphasize robust scoring through automated tests that prevent lookahead bias, fixed leverage, asset-specific fees that discourage excessive trading, hourly or asset-appropriate price grids, and transparent public code and records for every entrant. Cross-asset rankings were abandoned because differing return-distribution tails made fair normalization unreliable, leading instead to separate $500 prizes per asset. Reference strategies and baselines provide context but are explicitly presented as historical replays rather than forecasts, while the platform’s MCP server lets agents retrieve rules and data, submit positions, and check scores directly. The project also documents practical lessons involving accurate tradable price data, provider-blocked market feeds, durable storage, multilingual agent tooling, and the importance of refusing unsupported requests rather than returning quietly incorrect data.

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