When applying artificial intelligence technology to quantitative trading, high noise and unpredictability of market environment are the first practical problems to be considered. Therefore, how to select the learning features of the market based on rapidly changing financial data is particularly important. In this paper, the real time financial data are first processed by K-line theory, which uses candlesticks as a generalization of price movements over a period of time, so this process can play the role of de-noising. Then, the candlesticks are decomposed into different subparts by mean of a specified spatio-temporal relationship, based on which cluster analysis of the subparts to get the learning features. Further, the learning features that are clustered by the above K-lines are put into the model, and the online adaptive control of the parameters in the unknown environment is realized by the deep reinforcement learning method, so as to realize the high frequency transaction strategy. In order to verify the performance of the model, the data on different financial derivatives transactions such as stocks, financial futures and commodity futures are used. The proposal approach is compared with other methods which are based on price, fuzzified price and K-lines for features learning. In order to verify the accuracy of the proposal approach, prediction-based methods such as recurrent neural network and fuzzy neural network are used for comparison. Experimental results show that the proposed method has higher robustness and prediction accuracy.

In this article, the controllability of asynchronous Boolean multiplex control networks (ABMCNs) with time delay is studied. Firstly, dynamical model of Boolean multiplex control networks is constructed, which is assumed to be under Harvey' asynchronous update and time delay is introduced both in states and controls. By using of semi-tensor product (STP) approach, the logical dynamics is converted into an equivalent algebraic form by obtaining the control-depending network transition matrices of delayed system. Secondly, a necessary and sufficient condition is proved that only control-depending fixed points of the studied dynamics can be controlled with probability one. Thirdly, respectively for two types of controls, the controllability of dynamical control system is investigated. When initial states and time delay are given, formulae are obtained to show a) the reachable set at time s under specified controls; b) the reachable set at time s under arbitrary controls; c) the reachable probabilities to different destination states. Furthermore, an approach is discussed to find a precise control sequence which can steer dynamical system into a specified target with the maximum reachable probability. Examples are shown to illustrate the feasibility of the proposed scheme.

Traffic flow Forecasting is essential in intelligent transportation systems. Although graph neural networks perform well with non-Euclidean traffic data, they exhibit limits in accurately capturing complex spatio-temporal dependencies. Most current methodologies assume instantaneous propagation of traffic flow information, neglecting the delay in information propagation between nodes. Moreover, traditional graph convolutional models capture spatial dependencies using static adjacency matrices, overlooking the dynamic correlations among nodes over time. To address these challenges, this paper proposes a delay propagation spatio-temporal graph convolutional network (DPSTGC) for traffic prediction. By incorporating the delay propagation of traffic flow information into a directed graph of the road network, this model accurately captures the spatio-temporal dependencies of traffic flows. To refine the relationships between nodes, adaptive graph convolution network is employed to learn the dynamic correlation between nodes. The alternating application of temporal gated convolutions and spatial structure significantly enhances the model’s capacity to interpret spatio-temporal information. Furthermore, the effectiveness of the Delay-aware Directed Graph Attention (DDGA) is further interpreted from a causality perspective. Finally, the proposed model is evaluated on four real-world traffic datasets for experimental validation. Experimental results demonstrate that DPSTGC proficiently captures spatio-temporal information and achieves excellent performance.