The VIX index is a powerful tool used by institutional traders to gauge market ‘fear’ and ‘greed’. Since VIX futures provide a measure of the implied volatility of 30-day at the money of the S&P 500 index options, traders often use long VIX futures as a hedge for their positions, to speculate in different financial instruments and effectively deal with risk (Traub, Ferreira, McArdle, & Antognelli, 2000). This research suggests a new model to predict NASDAQ 100 (IXIC) index direction using the VIX futures term structure, which generally negatively correlates with asset returns. The VIX futures term structure follows a Markov Process, meaning that each state or value depends probabilistically on the previous state or value. Computationally, two strategies based on Ensemble Machine Learning Methods were used to predict market directions using VIX Futures term structure historical data including current VIX value, and the Convergence-divergence between VIX futures and popular index futures contracts mainly S&P 500 and NASDAQ 100. Performance reports on test datasets and back-testing evaluate the efficiency of our models and how does their predictability vary among Contango versus Backwardation. Our key finding suggests that the model trained using combined information about VIX futures term structure and historical VIX data performed better than the model trained using only information about VIX futures term structure in terms of cumulative returns and the number of trading signals generated.