Predicting Stock Market Trends for Japanese Candlestick Using Cloud Model

Madbouly, Magda M.; Elkholy, Mohamed; Gharib, Yasser M.; Darwish, Saad M.

doi:10.1007/978-3-030-44289-7_59

Cited by 9 publications

(4 citation statements)

References 37 publications

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“…Udagawa et al [13] proposed a hybrid algorithm that combines candlesticks sharing a specific price range into a single candlestick, thereby eliminating noisy candlesticks. Madbouly et al [25] combined a cloud model, fuzzy time series and Heikin-Ashi candlesticks to forecast stock trends, thereby enhancing the accuracy of predictions. Wang et al [26] proposed a quantification method for stock market candlestick charts based on the Hough variation.…”

Section: Candlestick Patterns Analysismentioning

confidence: 99%

Stock Price Prediction Using Candlestick Patterns and Sparrow Search Algorithm

Chen,

Hu,

Xue

2024

Electronics

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Accurately forecasting the trajectory of stock prices holds crucial significance for investors in mitigating investment risks and making informed decisions. Candlestick charts visually depict price information and the trends in stocks, harboring valuable insights for predicting stock price movements. Therefore, the challenge lies in efficiently harnessing candlestick patterns to forecast stock prices. Furthermore, the selection of hyperparameters in network models has a profound impact on the forecasting outcomes. Building upon this foundation, we propose a stock price prediction model SSA-CPBiGRU that integrates candlestick patterns and a sparrow search algorithm (SSA). The incorporation of candlestick patterns endows the input data with structural characteristics and time series relationships. Moreover, the hyperparameters of the CPBiGRU model are optimized using an SSA. Subsequently, the optimized hyperparameters are employed within the network model to conduct predictions. We selected six stocks from different industries in the Chinese stock market for experimentation. The experimental results demonstrate that the model proposed in this paper can effectively enhance the prediction accuracy and has universal applicability. In comparison to the LSTM model, the proposed model produces an average of 31.13%, 24.92%, and 30.42% less test loss in terms of MAPE, RMSE and MAE, respectively. Moreover, it achieves an average improvement of 2.05% in R2.

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Section: Candlestick Patterns Analysismentioning

confidence: 99%

Stock Price Prediction Using Candlestick Patterns and Sparrow Search Algorithm

Chen,

Hu,

Xue

2024

Electronics

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“…Heikin-Ashi candlesticks are used in literature for different purposes. Madbouly et al (16) introduces a method for forecasting stock prices by integrating cloud models, fuzzy time series, and Heikin-Ashi candlesticks. The devised model tackles the complexities of nonlinearity, uncertainty, and noise inherent in stock market trends.…”

Section: Related Workmentioning

confidence: 99%

Utilizing Machine Learning and Deep Learning for Predicting Crypto-currency Trends

El Youssefi,

Hessane,

Zeroual

et al. 2024

Salud, Ciencia y Tecnología - Serie de Conferencias

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In the dynamic and often volatile world of the cryptocurrency market, accurately predicting future market movements is crucial for making informed trading decisions. While manual trading involves traders making subjective judgments based on market observations, the development of algorithmic trading systems, incorporating Machine Learning and Deep Learning, has introduced a more systematic approach to trading. These systems often employ technical analysis and machine learning techniques to analyze historical price data and generate trading signals. This study delves into a comparative analysis of two charting techniques, Heikin-Ashi and alternate candlestick patterns, in the context of forecasting single-step future price movements of cryptocurrency pairs. Utilizing a range of time windows (1 day, 12 hours, 8 hours, ..., 5 minutes) and various regression algorithms (Huber regressor, k-nearest neighbors regressor, Light Gradient Boosting Machine, linear regression, and random forest regressor), the study evaluates the effectiveness of each technique in forecasting future price movements. The primary outcomes of the research indicate that the application of ensemble learning methods to the alternate candlestick patterns consistently surpasses the performance of Heikin-Ashi candlesticks across all examined time windows. This suggests that alternate candlestick patterns provide more reliable information for predicting short-term price movements. Additionally, the study highlights the varying behavior of Heikin-Ashi candlesticks over different time windows

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“…Hence, CNN is able to classify eye images that are considered complex and have multiple pixel dependencies throughout (Karthik and Mahadevappa, 2023). Accurate classification leads to accurate prediction of different diseases (Madbouly et al, 2020). Similar to the human brain, CNN contains several neurons that are arranged in layers (Ting et al, 2018;Zhao et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based classification of eye diseases using Convolutional Neural Network for OCT images

Elkholy,

Marzouk

2024

Front. Comput. Sci.

Self Cite

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Deep learning shows promising results in extracting useful information from medical images. The proposed work applies a Convolutional Neural Network (CNN) on retinal images to extract features that allow early detection of ophthalmic diseases. Early disease diagnosis is critical to retinal treatment. Any damage that occurs to retinal tissues that cannot be recovered can result in permanent degradation or even complete loss of sight. The proposed deep-learning algorithm detects three different diseases from features extracted from Optical Coherence Tomography (OCT) images. The deep-learning algorithm uses CNN to classify OCT images into four categories. The four categories are Normal retina, Diabetic Macular Edema (DME), Choroidal Neovascular Membranes (CNM), and Age-related Macular Degeneration (AMD). The proposed work uses publicly available OCT retinal images as a dataset. The experimental results show significant enhancement in classification accuracy while detecting the features of the three listed diseases.

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Predicting Stock Market Trends for Japanese Candlestick Using Cloud Model

Cited by 9 publications

References 37 publications

Stock Price Prediction Using Candlestick Patterns and Sparrow Search Algorithm

Stock Price Prediction Using Candlestick Patterns and Sparrow Search Algorithm

Utilizing Machine Learning and Deep Learning for Predicting Crypto-currency Trends

Deep learning-based classification of eye diseases using Convolutional Neural Network for OCT images

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