The volatile and complex market of agricultural futures' contracts creates difficulty in predicting future price movements. Many things affect commodity data, including weather, sentiment, supply, and demand. Hence, by its very nature, this data is non-linear. Charting and technical trading are used by brokers to find trends and movements in the market; however, trading using these strategies with uncertain situations can become risky. Moreover, technical and charting strategies tend to create signals to buy and sell in the market later, rather than sooner. Some commodity traders still feel as though computer generated trades can lead to greater losses compared to more traditional methods. Thus, the purpose of this research is to use a hybrid neuro-fuzzy model to create predictive entry and exit trades in the soy market. Data from the CME soybean, soy oil, and soymeal futures markets will be used to show that computational intelligence methods are comparable, facilitating profitability in complex markets.