Abstract There are various indicators i.e. Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD) , Stochastic Oscillator which have advantages in applications to determine not only market movements with buying and selling decisions in Computational Finance, but have significant drawbacks that discrepancies are easy to match against the best trading times due to fixed order-triggering boundaries and delay problems. For example, RSI ’s 70 and 30 overbuy and oversell are fixed boundaries. Orders can only be triggered when RSI’s value exceeds one of the boundaries. Its computation only considers past market situation prompting indicators like RSI to trigger orders with delay. In this paper, we proposed a method to reduce these problems with advanced AI technologies to generate indicators’ buy and sell signals executed in the best trading time. Recurrent Neural Network (RNN) has outstanding performance to learn time-series data automatic with long-time sequences but ordinary RNN units such as Long-Short-Term-Memory(LSTM) are unable to decipher the relationships between time units, so-called context. Hence, researchers have proposed an algorithm based on RNNs’ Attention Mechanism allowing RNNs to learn information such as chaotic attributes and Quantum properties contained in time sequences. Chaos Theory and Quantum Finance Theory (QFT) are also proposed to simulate these two features. One of the well-performed QFT models is Quantum Price Level (QPL) to simulate all possible vibration levels to locate price. The system used in this paper consists of two components - neural network and fuzzy logic. Neural networks are used to predict future data and to solve indicators lagging problem whereas fuzzy logic is used to solve fixed order-triggering boundaries problem. By combining these two core components, the proposed model has obtained remarkable results in backtesting previous data that it is possible for these methods to make better investment decisions when market changes constantly.