Our portfolio is composed of several investment algorithms, built in-house and also in partnership with traders and researchers around the world (learn more). With the use of the machine learning, deep learning and AI, and by operating with large datasets, we are constantly refining and enhancing the predictive power of our models.
We have a proactive risk management tool, built as a Complexity Based Systemic Risk Alert. Our risk-off indicators are complexity based, not volatility driven. It means that they are derived by some selection of the general properties of systems in transition, according to complexity science, using the analytical tools available to non-linear systems.
Our investment models are primarily focused on US and EU stocks, global future markets, fixed income and derivatives. By trading these instruments, we have the ideal market liquidity to execute our trading strategies and to offer daily liquidity to our investors via our UCITS fund.
Our investment research process is systematic and scientific, generating sets of robust trading strategies for multiple asset classes. Our data-driven investment approach allows for greater speed and flexibility, enabling us to promptly respond to opportunities as they arise.
Some examples of our trading models.
Random Forest classification algorithm to predict the short-term movement in the Vix futures.
Deep learning and AI to deploy alternative data from social media and to derive trading signals on US stocks.
Algos built to identify investors’ behavioural biases. Intraday dynamic allocation on global index futures.
Machine learning model (deep neural network) trained to build a long-short equity portfolio in EU stocks.