Discussing the article: "Neural Networks in Trading: A Hybrid Trading Framework with Predictive Coding (StockFormer)"

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Check out the new article: Neural Networks in Trading: A Hybrid Trading Framework with Predictive Coding (StockFormer).

In this article, we will discuss the hybrid trading system StockFormer, which combines predictive coding and reinforcement learning (RL) algorithms. The framework uses 3 Transformer branches with an integrated Diversified Multi-Head Attention (DMH-Attn) mechanism that improves on the vanilla attention module with a multi-headed Feed-Forward block, allowing it to capture diverse time series patterns across different subspaces.

StockFormer addresses forecasting and trading decision-making in financial markets through RL. A key limitation of conventional methods lies in their inability to effectively model dynamic dependencies between assets and their future trends. This is especially important in markets where conditions change rapidly and unpredictably. StockFormer resolves this challenge through two core stages: predictive coding and trading strategy learning.

In the first stage, StockFormer leverages self-supervised learning to extract hidden patterns from noisy market data. This allows the model to capture short- and long-term dynamics as well as cross-asset dependencies. Using this approach, the model extracts important hidden states, which are then used in the next step to make trading decisions.

Financial markets exhibit highly diverse temporal patterns across multiple assets, complicating the extraction of effective representations from raw data. To address this, StockFormer modifies the vanilla Transformer's multi-head attention mechanism by replacing the single FeedForward network (FFN) with a group of parallel FFNs. Without increasing the parameter count, this design strengthens the ability of multi-head attention to decompose features, thereby improving the modeling of heterogeneous temporal patterns across subspaces.

Author: Dmitriy Gizlyk