This paper aims at constructing protable trading strategies based on yields forecasts from interest rates models with macroeconomic data. I first construct and estimate AR, VAR, Dynamic Nelson Siegel (DNS) and Affine Term Structure Model (ATSM) . The Monte Carlo simulation results together with the estimation results suggest that little benefit is achieved by adding macro factors to the DNS and ATSM in the estimation stage. In this regard, I propose a new way of exploiting predictability contained in macro factors, the 3PRFRFapproach, to extract efficient macro information. I evaluate the forecast performance of each model with and without macro factors and combine forecasts using the Model Confidence Set (MCS) to arrive at an optimal forecast that I show to be able to out-perform the Random Walk. I give a comprehensive evaluation of the interest rate models as well as different macro factor incorporation approaches based on the estimation and forecasting results. I show that the3PRF residual forecasting approach performs better than conventional principal components augmented forecasting approach. Furthermore, I construct trading strategies using individual model forecasts and use Model Confidence Set to yield combined trading strategies. I show that macro factors play a crucial role in the profitability of trading strategies. Lastly, I apply the forecast model tothe period of recent financial crisis and find that neither no-arbitrage assumptions nor macro fundamentals significantly improve the forecast or the trading strategies performance as they do in the pre-crisis period.