Using Genetic Algorithm for Trading Strategy Optimization - Kramoliš, Richard (2023)
This thesis applies a genetic algorithm to the trading strategy optimization problem. Genetic algorithms have potential to thoroughly search the state space of the trading strategies and find the one that works the best. The thesis aims to design and develop a genetic algorithm, train it and test the performance of a particular strategy generated by the genetic algorithm on the stock price data. Two different objective functions are used to train the algorithm. The results of the study show that even though the algorithm can achieve good results on the training set, it is unable to outperform the buy and hold strategy on the test set.