Deep reinforcement learning (DRL) algorithm is often used to find the best trading strategy in algorithmic trading. However, the classical DRL model is difficult to achieve rapid convergence, and the features extracted from the market data are relatively simple, resulting in incomplete DRL learning information. In this paper, we propose a supervised reinforcement learning method, a hybrid optimal investment strategy formation method consisting of long short-term memory neural network (LSTM) and deep deterministic policy gradient (DDPG). By participating in reinforcement learning in the early stage of supervised learning, agents can obtain guiding prior experience, thus reducing the cost of agent learning and accelerating convergence. In addition, multi-feature state input is added to the model to optimize the agent’s learning of the environment. Compared with DDPG algorithm, LSTM-DDPG algorithm achieves convergence faster. Experiments on three regional stock markets in China, the United States and Europe show that LSTM-DDPG algorithm has higher profit and lower risk than B&H, MACD and LSTM trading strategies. 

Multi-Feature Supervised Reinforcement Learning for Stock Trading

Nowadays, deep learning-based methods are used in single image super resolution tasks. Even though these methods yield acceptable results, they have the problems of requiring more data and memory space. In this paper, a method based on convolutional neural network is proposed, which not only requires less data and memory space for learning, but also leads to accurate results. In the proposed approach, a three-path network is presented which has the same subnetworks fed by different Low Resolution (LR) images. The original LR data are fed into the first path and its high frequency-discarded versions are fed into the other paths. In this way, the network is forced to detect and learn high level features from low information inputs via parallel feature extractors. Furthermore, multi-scale manner is utilized to simultaneously detect coarse and fine features. In addition, feature maps have been fully exploited by concatenating and merging them throughout the network. Moreover, a strategy of parallelizing upsampling methods is proposed to ensure the reconstruction of the High Resolution (HR) image is accurately verified. Experimental results demonstrate that the proposed network outperforms most of the state-of-the-art methods in terms of the depth, the number of parameters and input data, and overall complexity of the network. The proposed method also improves visual performance and reaches higher evaluation metrics compared to the most of the existing methods.

Single Image Super Resolution Using Multi-path Convolutional Neural Network

It is widely acknowledged that stock price prediction is a job full of challenges due to the highly unpredictable existence of financial markets. Many market participants or analysts, however, attempt to predict stock prices using different mathematical, econometric, or even neural network models in order to make money or understand the nature of the equity market. In the past few years, a lot of models based on deep learning have been gaining popularity for predicting the volatility of the stock market prices. In this paper, the outcomes of many classical deep learning models such as LSTMs, GRUs, CNNs, and their several common variants are contrasted with two distinct stock price prediction targets: absolute stock price and volatility. The aim of the comparative study is to find out which model is the best fit for stock market prediction. We also attempt to research the relationship between news and stock trends, believing that news stories have an impact on the stock market by incorporating sentiment analysis into our model. Our methodology was to scrape news articles of a particular stock and use the corpus gathered to generate a sentiment score which is further used as an input to the model.

Stock Direction Prediction Using Sentiment Analysis of News Articles

Investors must devise an effective stock trading plan. The stock market is complex and dynamic, making it difficult to find an optimal approach. A reinforcement learning strategy is used because it can manage domains with stochastic transitions without a model. This work will investigate how RL may be used to maximise market returns whilst keeping the human element in mind. The learning agent is trained on stock prices and public interest patterns collected from Google Trends. A convolution neural network is trained on stock data and Google Trends using deep Q-learning (DQN). This paper investigates the use of graphical social media trends to train a reinforcement learning agent for stock trading. The agent’s performance was tested using a variety of stocks and phrases. Preliminary results demonstrate the agent performs effectively when trained on associated positive data sets. 

Convolution Neural Network (CNN) Based Deep Q-Learning to Maximise the Returns from Stock Market

Aiming at the problems of the existing single image super-resolution (SISR) reconstruction methods, such as insufficient feature utilization and weak ability to recover image high-frequency details, we propose a multi-scale feature feedback network (MFN) for SISR. First, multi-scale convolutional kernels are used to extract features of different dimensions in the image. Thus, the ability of the network to extract features of different scales early is improved. Then, we combine the dense network to design a group of multi-scale projection units to explore the correlation between high and low-resolution image features from multiple scales. Each group of projection units consists of a multi-scale up-sampling block and a multi-scale attention block (MAB). The MAB focuses on high-frequency features related to reconstructed image edges and textures. Finally, we used a constrained recurrent neural network to implement the feedback mechanism to improve the feature extraction capability of the network. Extensive experimental results demonstrate the effectiveness of our approach, which has advantages over other state-of-the-art SISR methods in terms of restoration quality and network complexity. 

Multi-scale feature feedback network for single image super-resolution

With the increasingly close connection between the market and computer technology. Stock forecasting is one of the task studies on the market economy. However, the information about the market economy contains a lot of noise and uncertainty, which makes the economic forecast more and more challenging. Ensemble learning and deep learning are the main methods to solve the stock forecast problem. In this paper, we forward a model combination of two methods, the advantages of two methods to forecast the change of stock price. The proposed method combines CNN and GBoost. The results of two market indexes show that this method has better performance for current popular methods.

Prediction of Stock Based on Convolution Neural Network

Aiming at the problem of low performance of the Single Image Super-Resolution (SISR) method, a method based on residual dense network is proposed. The shallow convolution features are input to the residual dense block to obtain local and global features, and the reconstruction is performed on the images to obtain a clear high-resolution image. To verify the effectiveness of the method, quantitative experiments were performed on three public datasets including Set5, Set14 and B100. Experimental results show that this method can better recover high-resolution images.

Optimization of Single Image Super-Resolution Reconstruction Algorithm Based on Residual Dense Network

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Prediction of Stock Based on Convolution Neural Network

Optimization of Single Image Super-Resolution Reconstruction Algorithm Based on Residual Dense Network