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Momentum and mean reversion trading strategies have opposite characteristics.The former is generally better with trending assets, and the latter isgenerally better with mean reverting assets. Using the Hurst exponent, whichclassifies time series as trending or mean reverting, we attempt to trade witheach strategy when it is advantageous to generate higher returns on average. Weultimately find that trading with the Hurst exponent can achieve higherreturns, but it also comes at a higher risk. Finally, we consider limitationsof our study and propose a method using Q-learning to improve our strategy andimplementation of individual algorithms.

Optimizing Returns Using the Hurst Exponent and Q Learning on Momentum and Mean Reversion Strategies