There are many researches aimed to predict times series of various financial markets. Some of these papers have shown that it is possible to obtain satisfactory results, thereby contradicting the theory that financial time series follow a random walk model. This study applies an architecture based on two stages for trading with two of the most traded foreign exchange rates (forex), the EUR/USD and GBP/USD. It also proposes a trading system to evaluate the model under a financial perspective, both in terms of profitability and risk, and to compare the application of the model in different timeframes (daily or intraday). The architecture consists of a GHSOM network, whose goal is to divide the dataset into regions with similar statistical distribution in order to circumvent the problem of nonstationarity, and a support vector regression machine (SVR), to make forecasts for the regions defined by GHSOM. We report on experiments that the SVR+GHSOM architecture performance is far superior compared to a model based solely on SVR. The comparison considered performance measures such as profitability (ROI) and the maximum drawdown (MD) and has shown that the best results are obtained in daily timeframe. The experiments have also shown that it is possible to increase profit by adjusting the risk parameter (number of lots), at the expense of increasing the risk. Furthermore, the proposed model proved to be much more profitable than a buy-and-hold model using the same time series (EUR/USD and GBP/USD); it also outperformed buy-and-hold with the Dow Jones in the same period.