We apply Machine Learning algorithms and neural networks to generate information from the huge amount of market data, in order to:

  • Check the “non-linear” dynamics between different assets.
  • Improve the ability and competitiveness of trading
  • Analyze anomalies in the pricing of correlated instruments (from interest rate curves to equity markets).
  • Highlight anomalies in trading (volumes, actors, algorithms) and obtain warning signals.

Our focus is:

  • Pattern recognition
  • Long-term forecasting (interday data) and related activities (tactical asset allocation);
  • Relative value and trade discovery process.

The area of application is fixed-income, cross-asset, liquid instruments and derivatives.