Product

We talked to quants, PMs, and AI researchers across buy- and sell-side desks to map what actually runs in trading today—organized by time frame and market maturity. On the spectrum from event-driven LOB and imbalance signals (intraday) to legacy ML and macro TS (days→quarters), plus emerging LLM use for signals/portfolio, the winner is whatever beats a market-validated baseline. Our focus: intraday (minutes→hours) using Level-II LOB and finance-native transformer models trained solely on quantitative time-series.

At Quantum Signals, we are building AI-powered foundational models for financial time series, focusing on predicting short-term market dynamics such as price movements, volatility, and liquidity. Our platform goes beyond static predictions, allowing financial professionals to customize AI-driven insights by integrating proprietary data, introducing strategic biases, and fine-tuning models to align with specific trading strategies. By leveraging the latest advancements in AI, we aim to democratize access to powerful market forecasting tools and revolutionize how traders and institutions navigate the markets.

We established an initial benchmark to evaluate mid-price prediction models using Nasdaq order book data, replicating state-of-the-art methodologies from a key research study. By designing a scalable AI pipeline and testing leading models, we validated our approach and set the stage for further experimentation with larger datasets and custom models in AI and quantum-driven trading.