In this paper, a stock trading model by integrating Technical Indicators and Convolutional Neural Network (TI-CNN) is developed and implemented. The stock data investigated in this work were collected from publicly available sources. Ten technical indicators are extracted from the historical data and ...
In this chapter Marina Resta demonstrates experimentally the great potential of neural networks for design of systems for trading stock markets. She suggests the use of a hybrid neural network architecture that combines the approach of Self-Organizing Maps together with that of Genetic Algorithms. She...
such as intraday trading, use of fundamental data and news, trading at market opening time, etc. I came across descriptions of neural network products, in which authors suggested using them to forecast prices, such as stocks, currencies and so on. ...
We present a futures trading system based on a nonstationary artificial neural network prediction model. The model is described and its design, as submitted to the first International Nonlinear Financial Forecasting Competition (INFFC) a , is reported. The performance is demonstrated to be significant...
We extend Neural Network (NN) trading models with an innovative and efficient volatility filter based on fuzzy c-means clustering algorithm, where the choice for the number of clusters, a frequent problem with cluster analysis, is selected by optimizing a global risk-return performance measure. Our...
In the previous sections, we got acquainted with the architecture of a fully connected perceptron and constructed our first neural network model. We tested it in various modes, received our first results, and gained our first experience. However, the fully connected neural layers used in the perc...
现代机器学习:with the booming of artificial intelligence technology, machine learning techniques have been introduced to handle complex financial market data and proved to be useful for making stock trendpredictions。 第三段:CNN简介 —— 近些年来使用图像特征的研究 —— 指出现在的不足就是欠缺考虑整个...
In this study, we propose a stock trading system based on optimized technical analysis parameters for creating buy-sell points using genetic algorithms. The model is developed utilizing Apache Spark big data platform. The optimized parameters are then passed to a deep MLP neural network for buy-se...
Since September 1996, a neural network based trading sys-tem, fully automatically and independently trades futures on the DTB. Being part of an overall expert system dedicated to the task of predict-ing short term price movements in order to assist professional market markers when posting quotes,...
Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria. The concept of neural networks, which has its roots inartificial intelligence, is swiftly gaining popularity in the development oftrading systems. ...