Explore the innovative UniTraj model, a pioneering framework that revolutionizes multi-agent trajectory modeling by unifying trajectory prediction, missing data recovery, and spatiotemporal analysis. This model utilizes the Ghost Spatial Masking module integrated within a Transformer-based architecture to manage trajectory data, offering a robust approach to handling spatial features and dependencies. Additionally, it incorporates bidirectional temporal encoding via the Mamba model for comprehensive long-term trajectory generation. Discover how this approach improves algorithmic trading and enhances performance for traders and developers. MQL5 implementation details highlight effective handling of incomplete data and efficient use of resources, providing insightful advancements in algorithmic trading technology.

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