A machine learning based pairs trading investment strategy This book investigates the application of promising machine learning techniques to address two problems: (i) how to find profitable pairs while constraining the search space and (ii) how to avoid long decline periods due to prolonged divergent pairs. It also proposes the integration of an unsupervised learning algorithm OPTICS to handle problem (i) and demonstrates that the suggested technique can outperform the common pairs search methods achieving an average portfolio Sharpe ratio of 3.79 in comparison to 3.58 and 2.59 obtained using standard approaches. For problem (ii) the authors introduce a forecasting based trading model capable of reducing the periods of portfolio decline by 75%. However this comes at the expense of decreasing overall profitability. The authors also test the proposed strategy using an ARMA model an LSTM and an LSTM encoder decoder. Introduction Pairs Trading Background and Related Work Proposed Pairs Selection Framework Proposed Trading Model Implementation Results Conclusions and Future Work.