Some use the terms "stop” order and "stop-loss” order interchangeably.

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“The dataset” section provides details on the data that we extracted from the public data sources and the dataset prepared. “Methods” section presents the research problems, methods, and design of the proposed solution. Detailed technical design with algorithms and how the model implemented are also included in this section. “Results” section presents comprehensive results and evaluation of our proposed model, and by comparing it with the models used in most of the related works.

What is the stock market?

We believe that by extracting new features from data, then combining such features with existed common technical indices will significantly benefit the existing and well-tested prediction models. Kara et T stock al. in also exploited ANN and SVM in predicting the movement of stock price index. The data set they used covers a time period from January 2, 1997, to December 31, 2007, of the Istanbul Stock Exchange.

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  • The system achieves overall high accuracy for stock market trend prediction.
  • Our strong capital position, conservative balance sheet and automated risk controls are designed to protect IBKR and our clients from large trading losses.
  • We conducted comprehensive evaluations on frequently used machine learning models and conclude that our proposed solution outperforms due to the comprehensive feature engineering that we built.

Nekoeiqachkanloo et al. in proposed a system with two different approaches for stock investment. First, it is a comprehensive system that consists of data pre-processing and two different algorithms to suggest the best investment portions. Second, the system also embedded with a forecasting component, which also retains the features of the time series. Last but not least, their input features are a mix of fundamental features and technical T stock forecast indices that aim to fill in the gap between the financial domain and technical domain. Instead of evaluating the proposed system on a large dataset, they chose 25 well-known stocks. There is a high possibility that the well-known stocks might potentially share some common hidden features. Thakur and Kumar in also developed a hybrid financial trading support system by exploiting multi-category classifiers and random forest .

Detailed technical design elaboration

We observe from the previous works and find the gaps and proposed a solution architecture with a comprehensive feature engineering procedure before training the prediction model. It proved the effectiveness of our proposed feature extension as feature engineering. We further introduced our customized LSTM model and further improved the prediction scores in all the evaluation metrics. The proposed solution outperformed the machine learning and deep learning-based models in similar previous works. Tsai and Hsiao in proposed a solution as a combination of different feature selection methods for prediction of stocks. In their work, they used a sliding window method and combined it with multi layer perceptron based artificial neural networks with back propagation, as their prediction model.

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In Table6 we can conclude that feature pre-processing does not have a significant impact on training efficiency, but it does influence the model prediction AT&T stock accuracy. If it performs the normalization before PCA, both true positive rate and true negative rate are decreasing by approximately 10%.

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We recorded the confusion matrices comparison between training the model by 29 features and by five principal components in Fig.11. The model training using the full 29 features takes 28.5 s per epoch on average. While it only takes 18 s on average per epoch training on the feature set of five principal components.

The Table1 lists the field information of each data table as well as which category the data table belongs to.