We deploy computational models to identify trading opportunities and anticipate future price fluctuations.
Our models are built after extensive investigation on the different sources of financial data which essentially affect asset prices. Discriminating established causality from occasional and random correlation relationships is of paramount importance. By adhering to different realms of scientific study, we ensure that investment strategies stem from principles with solid academic premise and rationale.
Algorithmic strategies execute orders from a predefined set of programming commands, so their potential outcome can be evaluated on historical data, a process commonly referred to as backtesting. Through trading simulation on past prices, comprehensive risk-reward analytic reports can be obtained in order to assess a given model's repeatability in producing competitive returns within an acceptable risk margin.
Realistic paper trading sessions require several factors to be taken into consideration to make the simulated outcome applicable to real-time conditions. Examples include transaction costs, accurate slippage estimation and order time delay. Modifying individual risk parameters like leverage, order size and capital allocation among different, multiple strategies offers different layers of personalized portfolio management.