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Technical analysis in stock trading addresses the crucial matter of making optimal trading decisions promptly. Predicting directional movement in the target market using technical indicators is quite common. Besides its many other applications, machine learning helps to solve the algorithmic trading problem of determining optimal trading positions,...
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... that has fallen sharply. A trading strategy is a set of predetermined rules that decides how to approach stock trading to achieve profitable returns. UTS is a trend-following strategy that works on a breakout model, buying strength, and selling weakness. The following points help to understand the methodology of UTS, which is also illustrated in Fig. 1. Technical Indicators (TI's) are applied on close prices of available historical data. Each of these TI's try to determine and mark a trend. a) A trend period may be determined by some TI's early, and others may observe it late or may not observe it at all. The algorithm scans adjacent days around a trend period to search and count ...
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Citations
... Meanwhile, the implementation of deep learning became possible with the help of modern computing resources, like powerful machines, advanced algorithms, and availability of larger datasets. It served many disciplines and solved complex problems in computer vision, healthcare, stock trading, social networks, and real-world problems from numerous branches of engineering (Mahfooz et al., 2023;Mahfooz et al., 2022;Zhong et al., 2021). In chemical engineering, the use of DNN is highly valuable for solving complex problems. ...
Accurate vapor-liquid equilibria (VLE) calculations of carbon dioxide and hydrogen sulfide mixtures are critical to gas processing and the affordable, safe design of flow assurance technologies. Inaccurate VLE predictions can lead to inaccurate gas hydrate phase equilibria predictions and ensuing safety and economic risks. This research paper explores the potential incorporation of Deep Neural Networks (DNNs) to support conventional expert systems within the context of predicting VLE. It facilitates more flexible and data-driven approaches that are required due to the growing intricacy and dynamic character of chemical processes. Moreover, various cubic and non-cubic equation of state (EoS) models (such as SRK, PR, CPA, SAFT, and PC-SAFT) were also examined to compare predicted VLE for various mixtures of CO2 and H2S. Prior to the comparison of DNN-predicted VLE with EOS models, binary interaction parameters were optimized for all EOS with the available experimental phase equilibria measurements. Model accuracies were compared and analyzed for various binary systems containing CO2/H2S + other associative and non-associative components. The absolute average deviation in vapor and liquid phase composition/bubble pressure was calculated and compared for all five-state EOS with DNN predictions. The DNN and equation of states with BIP gave a reliable illustration of the phase behavior of CO2/H2S-containing systems compared to others as indicated by the lower AADP values. By contrasting the applied DNN model with conventional techniques, we explore the promising channel for future research directions and industry applications, as well as an opportunity for innovation and field advancement for modern expert systems.
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... Unified Trading Strategy benefits from the features of multiple indicators and it helps to automate stock trading by providing potential opportunities of buying and selling shares in stock market [24]. Five different technical indicators, including EMA, ADX, MACD, RSI, and TRIX are simultaneously applied on the stock data and if three or more technical indicators are able to mark a potential trend, UTS generate buy/sell signals accordingly. ...
... Five different technical indicators, including EMA, ADX, MACD, RSI, and TRIX are simultaneously applied on the stock data and if three or more technical indicators are able to mark a potential trend, UTS generate buy/sell signals accordingly. The marked up/down trends and corresponding buy/sell signals are generated under the combined influence of collaborated technical indicators [24]. ...
... Later, the same neural network model works as stand-alone system to predict buy and sell signals. We adopt the default configuration of the neural network model [24]. The overall strategy is depicted through Fig. 2. 3. Mark trend days based on the output of each indicator. ...
In this article we evaluate the deployment of a smart trading system that exploits the features of different technical indicators for intelligent stock trading.
Depending on their behaviors, these indicators help in trading under various market conditions. Our smart trading system uses a unified trading strategy that
selects five indicators from three well-known categories referred as leading, lagging, and volatility indicators. The trading system looks for common trend
signals from at least three indicators within a certain period of time. Collectively generated signals from the technical indicators are used to train a neural
network model. The trained neural network model is then used to produce buy and sell signals for trading in stocks. The system is efficient and convenient to
use for both individual traders and fund managers. We tested the model on actual data collected from Saudi Stock Exchange and New York Stock Exchange.
The performance of the model was checked in terms of percentage returns. The results of the proposed trading model were compared with the benchmark
trading strategy. The deployed smart trading system is efficient to produce significant returns over the longer and shorter timeframes.
... Unified Trading Strategy benefits from the features of multiple indicators and it helps to automate stock trading by providing potential opportunities of buying and selling shares in stock market [24]. Five different technical indicators, including EMA, ADX, MACD, RSI, and TRIX are simultaneously applied on the stock data and if three or more technical indicators are able to mark a potential trend, UTS generate buy/sell signals accordingly. ...
... Five different technical indicators, including EMA, ADX, MACD, RSI, and TRIX are simultaneously applied on the stock data and if three or more technical indicators are able to mark a potential trend, UTS generate buy/sell signals accordingly. The marked up/down trends and corresponding buy/sell signals are generated under the combined influence of collaborated technical indicators [24]. ...
... Later, the same neural network model works as stand-alone system to predict buy and sell signals. We adopt the default configuration of the neural network model [24]. The overall strategy is depicted through Fig. 2. 3. Mark trend days based on the output of each indicator. ...
In this article we evaluate the deployment of a smart trading system that exploits the features of different technical indicators for intelligent stock trading. Depending on their behaviors, these indicators help in trading under various market conditions. Our smart trading system uses a unified trading strategy that selects five indicators from three well-known categories referred as leading, lagging, and volatility indicators. The trading system looks for common trend signals from at least three indicators within a certain period of time. Collectively generated signals from the technical indicators are used to train a neural network model. The trained neural network model is then used to produce buy and sell signals for trading in stocks. The system is efficient and convenient to use for both individual traders and fund managers. We tested the model on actual data collected from Saudi Stock Exchange and New York Stock Exchange. The performance of the model was checked in terms of percentage returns. The results of the proposed trading model were compared with the benchmark trading strategy. The deployed smart trading system is efficient to produce significant returns over the longer and shorter timeframes.
Options trading is a process of speculating the strike price of an underlying security or index on the expiration date. To finalize the options contract, a trader pays a small percentage as premium. This paper is to maximmize the profits of trader and minimize their loses which is generally manual based process, but this research paper helps integrates finance with technology. It fills the gap between the implementation of deep learning and algorithm trading, with option trading. We have named this model as Option Trading Prediction Model (OTP). This paper can be referred to develop an option trading tool or platform. The paper contains the information in the following manner, Introduction, describes an overview of Model Features, followed by an overview of Evaluation which includes data collection and preprocessing. Later on, discusses Model training and about the modelling and prediction.
