This paper presents a scalable parallel implementation of genetic programming on distributed memory machines. The system runs multiple master-slave instances each mapped on all the allocated nodes and multithreading is used to overlap message latencies with useful computation. Load balancing is achieved using a dynamic scheduling algorithm and comparison with a static algorithm is reported. To alleviate premature convergence, asynchronous migration of individuals is performed among processes. We show that nearly linear speedups can be obtained for problems of large enough size. The system has been applied to infer robust trading strategies which is a compute-intensive financial application.