Classification of Trading Strategies in Adaptive Markets

In the CAT Tournament, agents facilitate transactions between buyers and sellers with the intention of maximizing profit from commission and other fees. The agent must find a well-balanced strategy that allows it to sign buyers and sellers to trade in its market while also maintaining the buyers and sellers that are currently subscribed to it. One approach is to classify the traders that interact with the agent with respect to the trading strategies they utilize. Although the set of trading strategies is very small (traders are assigned one of four previously-defined strategies) the data available to the agent does not provide an explicit correlation of strategies to traders during the competition. As a result, agents must gather data in which the relationship between agents and strategies is known and then construct a probabilistic model based on the acquired data. Different strategies yielded varying frequency and quantity of data, making its " raw " form unusable for the k-means clustering technique. Only after discarding most of the data (thus deserting its integrity) was it possible to pass it to the k-means algorithm, which performed very poorly. A support vector machine (SVM) was also utilized and yielded varying results. While the linear and sigmoid kernels performed nominally better than classification based on complete randomness (28.2% and 32.8% respectively), the SVM employing a radial kernel was able to predict strategies with nearly 60% accuracy. Classification using a Hidden Markov Model was even more successful, predicting strategies correctly 62.28% of the time. It was observed that a moderate number of states and mixtures yielded the best results when classifying using the HMM. Additionally, two of the four strategies were observed to be more easily predictable using both the SVM and HMM classification techniques, suggesting that better classification could be achieved if the raw data from the two strategies could be separated into two disjoint sets before performing classification.