This paper builds an innovative composite world trade cycle index (WTI) by means of a dynamic factor model to monitor and perform short-term forecasts in real time of world trade growth of both goods and (usually neglected) services. The selection of trade indicator series is made using a multidimensional approach, including Bayesian model averaging techniques, dynamic correlations and Granger non-causality tests in a linear VAR framework. To overcome real-time forecasting challenges, the dynamic factor model is extended to account for mixed frequencies, to deal with asynchronous data publication and to include hard and survey data along with leading indicators. Nonlinearities are addressed with a Markov switching model. Simulations analysis in pseudo real-time suggests that: i) the global trade index is a useful tool to track and forecast world trade in real time; ii) the model is able to infer global trade cycles precisely and better than the few competing alternatives; and iii) global trade finance conditions seem to lead the trade cycle, in line with the theoretical literature.