Online trading algorithms can facilitate the investment decisions in financial markets. This paper presents a dynamic adaptive algorithm selection framework for online trading with the goal of maximizing overall revenue (lower competitive ratio). We integrate the algorithm selection ([1]) and probabilistic graphs to transform the algorithm selection model of offline algorithms for a dynamically adaptive model of online trading. Unlike the traditional static approach of algorithm selection, where a single algorithm is executed over the whole investment horizon, we dynamically select and update the trading algorithm by analyzing the time series features. The time series is partitioned in different windows and each window’s features are extracted sequentially using real time hybrid pattern matching approach. The extracted features of current window w _i are analyzed and the decision making module determines the best suitable algorithm for next window w _i + 1 on the basis of underlying probabilistic graphical model. The process is repeated until all windows in a time series are processed. Our dynamic adaptive algorithm selection model outperforms the static model on real world datasets of DAX30 and S&P500.