Aplikace umělé inteligence při obchodování na kapitálových trzích - Kotelnikov, Vladislav (2023)
This thesis aims to analyze the profitability of the LSTM neural-network-based models in FX Spot trading. The focus is EURGBP, EURUSD, USDJPY, and USDTRY currency pairs with 1-minute frequency data in the period from 11/11/2019 to 11/11/2022. It also analyzes various methods of data preparation. Overall, over 200 models are trained and analyzed. As a result, the five best models are backtested and described in detail. LSTM models trained using fractionally differenced data were profitable in cases of EURUSD and EURGBP pairs. The majority of selected models were able to successfully predict the direction of the price movement as well as its magnitude. Some were also able to predict well the exact future bid and ask prices. The backtests proved that three LSTM models can be profitable for EURUSD and two LSTM models can be profitable for EURGBP. The best EURUSD model was able to reach annualized profitability of 48% and the best EURGBP model reached profitability of 28%. Models trained on other currency pairs were not profitable.