Defining financial constraints for implementation and backtesting

Express objectives and constraints in a way that is relevant to the portfolio management process using the Financial Query Language

These constraints are then converted into a mixed-integer conic optimization problem and the solution appears as your rebalanced portfolio.

Alternately, the same optimization parameter objects can be used in the backtestingtool, allowing for perfectly realistic simulations.

Objectives include: expected-return, volatility, tracking-error, active share, turnover, market impact, number of trades, number of instruments and more...

Constraints include: min/max weights, maximum volatility, maximum tracking-error, minimum sharperatio, minimum information ratio, maximum market impact, maximum number of trades, maximum number of instruments and more...

Screen Captures


Create complex optimization constraints visually

Leverage the power of FQL to use any other object in the system

State of the art mixed-integer conic optimization

Use the same optimization constraints for rebalancing and for backtesting