Quantitative Trading Technologies

Our History


Deep Nexus was formed in 2017 as a Research & Development firm based on a core hypothesis: financial markets are not totally random and they generate repeating anomalies.


We began by developing simple neural networks to test this idea. By mid-2018, our work evolved into deep learning models integrated with a full-scale production stack for automated algorithmic trading.


In January 2020, we expanded into portfolio modeling to address cross-asset relationships and scaling across large universes of instruments. While the results frequently exceeded expectations, our exhaustive research also uncovered serious limitations in traditional machine learning when faced with market non-stationarity, regime shifts, and noise.


In early 2025, we completed a proprietary system that unifies all of our prior research. This framework resolves the core limitations of conventional AI/ML and enables an information-theoretic approach to trading.


Opportunities


We are currently seeking exceptional scientists and engineers to help scale our models and trading infrastructure to their limits. Knowledge of financial markets may be helpful but is not required.


Ideal candidates will likely have:


  •   BS/MS/PhD with experience in the field of Information Theory. Electrical Engineering, Physics, Computer Science, and Mathematics are the most relevant fields of study.


  •   A track record of applied research or building complex systems.


  •   Strong inductive reasoning capabilities.


  •   Experience with backward reasoning and layered technology design.


  •   An interest in working within a flat, collaborative team where the best ideas win.


If you are interested in working together, please get in touch.



web@deepnexus.com

DISCLAIMER: None of the information presented herein are intended to form the basis for any investment decision, and no specific recommendations are intended. This presentation does not constitute investment advice or counsel or solicitation for investment in any security. 

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