  The majority of studies in the field of AI guided financial trading focus on
purely applying machine learning algorithms to continuous historical price and
technical analysis data. However, due to non-stationary and high volatile
nature of Forex market most algorithms fail when put into real practice. We
developed novel event-driven features which indicate a change of trend in
direction. We then build long deep learning models to predict a retracement
point providing a perfect entry point to gain maximum profit. We use a simple
recurrent neural network (RNN) as our baseline model and compared with
short-term memory (LSTM), bidirectional long short-term memory (BiLSTM) and
gated recurrent unit (GRU). Our experiment results show that the proposed
event-driven feature selection together with the proposed models can form a
robust prediction system which supports accurate trading strategies with
minimal risk. Our best model on 15-minutes interval data for the EUR/GBP
currency achieved RME 0.006x10^(-3) , RMSE 2.407x10^(-3), MAE 1.708x10^(-3),
MAPE 0.194% outperforming previous studies.
