Discussing the article: "Neural Networks in Trading: A Hybrid Trading Framework with Predictive Coding (Final Part)"
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Check out the new article: Neural Networks in Trading: A Hybrid Trading Framework with Predictive Coding (Final Part).
In the previous article, we examined in detail the theoretical aspects of the hybrid trading system StockFormer, which combines predictive coding and reinforcement learning algorithms to forecast market trends and the dynamics of financial assets. StockFormer is a hybrid framework that brings together several key technologies and approaches to address complex challenges in financial markets. Its core feature is the use of three modified Transformer branches, each responsible for capturing different aspects of market dynamics. The first branch extracts hidden interdependencies between assets, while the second and third focus on short-term and long-term forecasting, enabling the system to account for both current and future market trends.
The integration of these branches is achieved through a cascade of attention mechanisms, which enhance the model’s ability to learn from multi-head blocks, improving its processing and detection of latent patterns in the data. As a result, the system can not only analyze and predict trends based on historical data but also take into account dynamic relationships between various assets. This is particularly important for developing trading strategies capable of adapting to rapidly changing market conditions.
The original visualization of the StockFormer framework is provided below.
Author: Dmitriy Gizlyk