It is important to distinguish such an approach from the aforementioned studies [19][20]... LSTM (Long-Short term memory) network was used in [15] to predict directions of some stocks in Brazilian stock exchange, the average accuracy of the results was 55.9%. The research demonstrated that the studied models do indeed perform differently with various types of engineered features.We present an Artificial Neural Network (ANN) approach to predict stock market indices, particularly with respect to the forecast of their trend movements up or down. designating buy-sell points. Each square matrix was labeled withthe following day of last day used during the creation of eachsquare matrix. However, even though it works well when the market is trendless, during bull or bear market conditions (when there is a clear trend) its performance degrades. (2005).

(2012), ... A slightly different input is used in [20]: instead of using the standard stock variables (open, close, high, low, and NAV), it uses high frequency data for forecasting major points of inflection in the financial market.

We generated 2-D images of sliding windows of 30-day bar charts for Dow 30 stocks and trained a deep Convolutional Neural Network (CNN) model for our algorithmic trading model. The thresholdvalues -0.38 and 0.38 split the data with minimum variance,The classes in the classification task are determined in aclasses are used in the training phase as well. The model has several parameters including, the trend detection period, RSI buy-sell trigger levels and periods. Experimental results in intraday trading indicate better performance than the conventional Buy-and-Hold strategy, which still behaves well in our setups. The figures on the first column shows 2 and 3-class regression results, respectively. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.

Since this is a preliminary study and probably one of the first attempts using such an unconventional approach, there is always potential for improvement. We evidenced the discrepancies regarding the meaning of “Time Motion Studies” (TMS).A detailed description of methods classified as TMS is delivered.We present a disambiguation for “Time Motion Studies”.Specific descriptor “continuous observation TMS” is proposed.Time motion studies were first described in the early 20th century in industrial engineering, referring to a quantitative data collection method where an external observer captured detailed data on the duration and movements required to accomplish a specific task, coupled with an analysis focused on improving efficiency. Agglomerative clustering is a version of hierarchical clustering which solves the clusteringproblem iteratively and outputs the result as a dendrogram.This dendrogram can be cut at any intended level. Their precision decays with similar speeds asIn Figure 4b, 2-vs-all curves for both classification andregression are noticeably below other curves. We also discuss qualitative and quantitative analyses of these results.Abstract This work proposes an unsupervised fusion framework based on deep convolutional transform learning.

In this study, we developed a trading model using a modified RSI using trend-removed stock data. In order to achieve this goal, we exploit a Q-learning agent trained several times with the same training data and investigate its ensemble behavior in important real-world stock markets. In a recent work, we show that such shortcoming can be addressed by adopting a convolutional transform learning (CTL) approach, where convolutional filters are learnt in an unsupervised fashion. We show

algorithmic trading model CNN-BI (Convolutional Neural Network with Bar Images) using a 2-D Convolutional Neural Network. At the end, as a result offirst difference, mean around zero-point is obtained. Convolution operation is a process whichsums the point-wise multiplications of given two functionsthe elements in window frame while sliding this windowexample, max pooling outputs the maximum elements for agiven window while sliding it. Furthermore, fine tuning the technical indicators and/or optimization strategy can enhance the overall trading performance.Machine learning models, such as neural networks, decision trees, random forests and gradient boosting machines accept a feature vector and provide a prediction. d.Line.

Gantt charts, by taking advantages of user familiarity and robustness.

b.Scatter. Therefore, Yet al. Each image is then labelled as Buy, Sell or Hold depending on the hills and valleys of the original time series.

The results indicate that when compared with the Buy & Hold Strategy and other common trading systems over a long out-of-sample period, the trained model provides better results for stocks and ETFs. 3, pp.