Predicting the future trend of the Lima Stock Exchange market is challenging because of its high volatility, transaction costs, and illiquidity. In this work, we investigate machine learning models able to use technical indicators, economic variables, and financial news sentiments to forecast the daily return trend of the S&P/BVL Peru General Index. To the best of our knowledge, no other published S&P/BVL predicting tool considered these joint sources of information as relevant input features. To do so, fifteen economic indicators relevant to the local market and sentiment-tagged financial news headlines were used as extra input features for multiple machine learning classification models and feature selection methods. In addition, the performance of the static learning approach (the only one used for this particular problem so far) was compared against an online learning approach, which could dynamically better adapt to such a volatile, emergent market. The results showed an increase in performance when using the economic variables and news sentiment in comparison to existing predicting tools of the local market. When comparing both learning approaches, online learning yielded better predictive accuracy than its static counterpart. To the best of our knowledge, this is the first effort to include all these novel features for predicting trends in the Lima Stock Exchange.