Our Offerings

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.:

  • Agent-based Reinforcement Learning Approaches
  • L2/L3 Orderbook Replay
  • Market Impact and Order Execution Optimisation


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:

  • Distributed optimisation framework
  • Feature Engineering/Selection
  • Concept drift adaptation
  • Wide variety of model/optimisation approaches

Portfolio Optimisation

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:

  • Dynamic weighting
  • Portfolio constraints