Financial markets produce terabytes of data daily, the vast majority of which is noise. Extracting actionable signal—alpha—requires sophisticated mathematical frameworks that go beyond traditional technical analysis.
Our recent research into high-frequency order book dynamics reveals that short-term mean reversion strategies are becoming less effective due to the proliferation of predatory algorithms. Instead, we find more robust alpha in medium-term momentum anomalies, properly filtered for fundamental regime context.
By utilizing advanced machine learning techniques, specifically attention-based neural networks, we can teach our models to focus only on data streams that historically precede regime shifts, ignoring the high-volume noise that traps many quantitative strategies.