Solving Unconverged Learning of Pairs Trading Strategies with Representation Labeling Mechanism

A pairs trading strategy (PTS) constructs a market-neutral portfolio whose value typically moves back and forth around a mean price level; investors short (long) the portfolio when its value reaches the upside (downside) opening threshold and close the position when the value reverts to the mean to earn the price difference. Recent machine learning models select the open and stop-loss thresholds either heuristically or chosen from a limited set, which significantly limits the investment performance. We address this by creating a wider set of open/stop-loss threshold recommendations that generally cover all possible scenarios; but regression- or classification-based deep learning methods for recommending thresholds fail to converge. Thus, we design a representative labeling mechanism that selects representative open and stop-loss thresholds from all possible optimal thresholds according to the selection frequencies of the thresholds and the k-means algorithm. Experiments suggest that training the multi-scale residual network with stock pairs relabeled by representative thresholds yields better investment performance than other methods in the literature.