Arbitron NASDAQ

Arbitron NASDAQ is a research-driven short term trading framework built around the relationship between NASDAQ equities and the NASDAQ index.

It’s not a black box, and it’s not magic, it’s a collection of machine learning models designed to interpret relative value dynamics and exploit short-term statistical inefficiencies between individual stocks and their benchmark.

The models were trained on M15 data from January 2022 to January 2025, using supervised and adaptive ML processes across multiple major NASDAQ components.

Everything beyond that period is out-of-sample a deliberate, design choice to assess robustness and generalization in unseen market conditions. Correlations evolve, volatility shifts, and regimes change, the framework adapts accordingly through continuous retraining and recalibration. Like any serious quantitative system, it needs to be updated regularly to keep pace with the market’s changing structure. And I'll provide those necessary regular updates.

Importantly, Arbitron’s models are trained under a unidirectional buy framework, focusing exclusively on long-side exposure. If a particular stock enters a structural decline or shows clear breakdown characteristics, it should be paused, the algorithm is a tool, not a guarantee.

Arbitron NASDAQ is built around this philosophy: that algorithmic trading is an assistant to human judgment, not a replacement. It provides structure, discipline, and an analytical edge, but the ultimate edge remains awareness, adaptation, and responsibility.