K²VAE Probabilistic Forecasting as a Trading-Agent State Encoder

Summary

This article describes integrating the K²VAE framework into an Actor–Director–Critic trading agent. The model combines a Koopman representation for approximating nonlinear dynamics in latent space, a stabilized Kalman filter to update estimates with new observations, and a variational autoencoder to represent possible future outcomes as a probability distribution. The forecast is framed as one input to a useful state representation for the agent, rather than the system's sole objective.

The implementation discussion covers constructing encoder, forecast, actor, director, and critic components, along with preprocessing and feature transformations. The article also reports a historical evaluation: the balance curve generally rose over part of the period, but March was loss-making, despite a roughly balanced share of winning and losing positions. The author presents this as evidence of viability while acknowledging weaker performance over a longer testing interval and the need to improve generalization.

The account is a single model and historical test description, not proof of robust future performance. The article provides no broad comparative evidence establishing that the architecture will generalize across markets or conditions.

Key ideas

• K²VAE combines latent linear-dynamics modeling, Kalman correction, and probabilistic decoding.
• The framework is used as an encoder of market state within an Actor–Director–Critic trading agent.
• The variational component represents multiple possible future scenarios rather than one point forecast.
• The historical evaluation reports periods of account growth alongside a loss-making month.
• Declining results over an extended test interval point to unresolved generalization limits.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.