Taking Neural Networks to the next level - page 41

[Sergey Golubev]

Neural Networks in Trading: Enhancing Transformer Efficiency by Reducing Sharpness (Final Part)

In the previous article we got acquainted with the theoretical aspects of the SAMformer (Sharpness-Aware Multivariate Transformer) framework. It is an innovative model designed to address the inherent limitations of traditional Transformers in long-term forecasting tasks for multivariate time series data. Some of the core issues with vanilla Transformers include high training complexity, poor generalization on small datasets, and a tendency to fall into suboptimal local minima. These limitations hinder the applicability of Transformer-based models in scenarios with limited input data and high demands for predictive accuracy.

[Sergey Golubev]

Neural Networks in Trading: Parameter-Efficient Transformer with Segmented Attention (Final Part)

In the previous article, we explored the theoretical aspects of the PSformer framework, which introduces two key innovations into the vanilla Transformer architecture: the Parameter Sharing (PS) mechanism and Spatial-Temporal Segmented Attention (SegAtt).

[Rafael Grecco]

My last reply/update in this thread was about 3 months ago. Since most of the posts here are just articles, I thought it would be nice to share something more practical and applied instead of just theory, so here’s a quick summary of my findings.

I’ve built and tested a bunch of different AI models, from time series models (market data) to more abstract approaches, like turning data matrices (price x indicators) into images for image analysis models. Basically, I’ve tried a bit of everything.

In the end, all the models I trained run into the same issue: they only work under specific conditions. Any strategy, whether created by AI or by human observation, works in certain market situations but not in others.

Since the market is constantly evolving, any strategy needs to adapt along with it. And that’s extremely hard, because it requires truly “understanding” the changes, something AI just can’t do on its own yet, at least not with the technology we currently have. For now, the best way to build a successful strategy is still human analysis combined with constant adaptation based on market observation.

[Sergey Golubev]

Neural Networks in Trading: A Hybrid Trading Framework with Predictive Coding (StockFormer)

Reinforcement Learning (RL) is increasingly being applied to complex problems in finance, including the development of trading strategies and portfolio management. Models are trained to analyze historical data on asset price movements, trading volumes, and technical indicators. However, most existing methods assume that the analyzed data fully capture all interdependencies between assets. In practice, this is rarely the case, especially in noisy and highly volatile market environments.

[Victor Paul Hamilton]

Most retail NN-based trading systems are curved fitted noise dressed up in math and might as well be true random. Where I dwell is true random and it makes a mockery of trading .