We present novel approaches, including latent liquidity modeling, Bayesian toxicity scoring, and reinforcement learning-enhanced high frequency trading Hidden Markviol Models (HMMs), which remain largely undocumented in public literature.

In the context of Ethereum Markets (ETH) and other cryptocurrencies, HFT strategies are increasingly sophisticated, often employing proprietary and undisclosed quantitative models