Directional Diffusion Models for Market Data Representation

Summary

This article adapts Directional Diffusion Models, originally developed for graph representation learning, to financial market data. Standard diffusion adds isotropic Gaussian noise, which can erase structure quickly in anisotropic data. The proposed method makes noise depend on batch feature statistics and aligns its direction with the data, aiming to preserve structure as the signal-to-noise ratio declines. A denoising model is trained to predict the original sequence because the reverse process is not available in closed form.

The practical section describes an MQL5 implementation that inserts directional noise between normalization and the scaling and shifting steps. The authors say they trained on historical market data and evaluated out of sample, reporting potential but also a need for further optimization. The document gives no detailed performance figures or trading rules, so it does not establish that the learned representations improve trade quality. Its contribution is chiefly a modeling approach for representation learning, with the trading application presented as a promising research direction.

Key ideas

• Directional noise is intended to preserve anisotropic data structure better than isotropic Gaussian noise.
• The method scales noise using feature statistics computed from the current mini-batch.
• An angular constraint aligns noise with the data object's direction.
• The denoising model predicts the original sequence because the reverse process has no closed-form expression.
• The reported market-data evaluation is described as preliminary and still needing optimization.

Tags

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