High-frequency trading (HFT) employs cutting-edge hardware for rapid decision-making and order execution but often relies on simpler algorithms that may miss deeper market trends. Conversely, lower-frequency algorithmic trading uses machine learning (ML) for better market predictions but higher latency can negate its strategic benefits. To achieve the best of both worlds, we present an in-network ML solution that embeds ML processes into programmable network devices, accelerating feature engineering and extraction as well as ML inference. In this paper, we design and develop a solution that supports both stock mid-price and volatility movement forecasting using commodity switches. Our approach achieves microsecond-scale, ultra-low latency, significantly lowering it by 64% to 97% compared to previous works, while upholding the same level of ML performance as server models. Additionally, by combining network hardware and servers, a hybrid deployment strategy can keep the misclassification rate change below 0.8% relative to the server baseline while processing 49% of the traffic directly on the switch and achieving a 45% average reduction in end-to-end latency.