Governed AI Execution for Digital-Asset Markets: A Risk-Bounded Architecture for Auditable Trading Agents

Digital-asset markets create opportunities for artificial intelligence while magnifying the failure modes of algorithmic trading, including overfitting, unstable execution, liquidity shocks, unbounded automation, and misleading performance claims. This paper synthesizes research on financial machine learning, cryptocurrency trading, limit-order-book modelling, reinforcement learning, large language models, synthetic market generation, and backtest-overfitting control into a general architecture for governed AI execution. 

The proposed Governed AI Execution Stack separates probabilistic signal generation, policy proposals, deterministic risk constraints, execution optimization, supervision, and audit. A cleared anonymized pre-launch validation data room is used to demonstrate the evidence format. The reported results are validation evidence, not a guarantee of future returns or an independently audited investment-performance claim.