ResNeXt Multi-Task Learning for Trading Decisions and Trend Forecasting

Summary

The article describes a trading model that combines a ResNeXt-based market encoder with multiple task-specific outputs. One output acts as an agent to generate trade parameters, while another estimates the probability of future price direction. Sharing the encoder is intended to reuse learned market representations, reduce duplicated computation, and let related tasks inform one another. The implementation is presented in MQL5, with raw market inputs normalized before convolutional processing and a sequence of grouped-convolution residual blocks used to extract features.

The article explains the design and implementation approach rather than establishing a reliable trading edge. It argues that multi-task learning may improve generalization and training stability, but these benefits are not demonstrated with detailed comparative results in the available text. The author says the implementation showed potential for time-series analysis and trend forecasting, while emphasizing that deployment requires training on a more representative dataset and comprehensive testing. Performance may depend on task definitions, data quality, and market conditions.

Key ideas

• A shared encoder can supply representations to both trade-parameter generation and price-direction prediction.
• The Actor incorporates market-state encoding, while a separate predictive head uses its latent representation.
• Batch normalization is applied to heterogeneous raw inputs before convolutional feature extraction.
• Grouped convolutions and residual connections form the ResNeXt encoder blocks.
• The described implementation requires representative training data and thorough testing before live use.

Tags

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