  The study seeks to develop an effective strategy based on the novel framework
of statistical arbitrage based on graph clustering algorithms. Amalgamation of
quantitative and machine learning methods, including the Kelly criterion, and
an ensemble of machine learning classifiers have been used to improve
risk-adjusted returns and increase immunity to transaction costs over existing
approaches. The study seeks to provide an integrated approach to optimal signal
detection and risk management. As a part of this approach, innovative ways of
optimizing take profit and stop loss functions for daily frequency trading
strategies have been proposed and tested. All of the tested approaches
outperformed appropriate benchmarks. The best combinations of the techniques
and parameters demonstrated significantly better performance metrics than the
relevant benchmarks. The results have been obtained under the assumption of
realistic transaction costs, but are sensitive to changes in some key
parameters.
