We have engineered several different approaches in designing market signals. Each approach is built from a data-driven, quantitative methodology and is designed to provide stable and powerful insights into the future.
Our high-frequency optimisation framework allows us to calibrate models and design signals for integration in market-making and high-frequency arbitrage strategies. Advanced techniques such as machine learning and statistical modelling are deployed to discover inefficiencies and opportunities.:
Our mid-frequency signal optimisation pipeline. The goal is to identify robust, high-capacity signals which can be adapted for strategies with holding periods from 1-minute to several hours:
Our cutting-edge portfolio optimisation framework. The focus is on engineering a dynamically weighted optimal portfolio to benefit from an array of signals, while complying with rule-based constraints: