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.