Candlestick Patterns Recognition using CNN-LSTM Model to Predict Financial Trading Position in Stock Market

Ramadhan, Aditya Rifky; Palupi, Irma; Wahyudi, Bambang

doi:10.47065/josyc.v3i4.2133

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…The classification results have an accuracy rate of 90.72% and a precision of 87.69%. In this section, the researcher compared the accuracy results between the SVM method that was carried out by researchers with CNN and CNN-LSTM, which was carried out in the study [14], which can be seen in Figure 7. It can be seen in Figure 7 that SVM has a better accuracy rate than CNN, with an accuracy rate of 60%, and the combination of CNN and LSTM, with an accuracy rate of 82.7%, was carried out by [14].…”

Section: Figure 5 Svm Training Results In 3d Modelmentioning

confidence: 99%

“…The performance of the model that has been made is measured using a confusion matrix. Table 1 shows the classification results represented as True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) [14]. : negative data are classified as positive data False Negative (FN) : positive data that is classified as negative data Accuracy is the degree of closeness between actual data and predictive data.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…In 2022, a candlestick pattern prediction ability test was carried out by [14] to determine trading positions. The Gramian Angular Field (GAF) presents candlestick patterns as images to identify 3 hours and 5 hours of 6 candlestick patterns using a Convolutional Neural Network (CNN).…”

Section: Introductionmentioning

confidence: 99%

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Support Vector Machine for Predicting Candlestick Chart Movement on Foreign Exchange

Puteri,

Syamsu,

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et al. 2023

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Foreign Exchange, commonly called Forex, is a form of investment in the non-real sector in great demand. Forex is a marketplace that specializes in foreign exchange trading. Technology advancements have made it easy to monitor investment conditions in real time and present them in an easyto - understand graphical form. As a result, predictions are closely related to investment, starting from market sentiment and economic conditions to technical matters. One of the Artificial Intelligence methods that can be used in classifying is the Support Vector Machine (SVM). SVM is a machine learning classification method based on the Structural Risk Minimization (SRM) principle to find the best hyperplane that separates two classes in the input space that determines the classification decision function by minimizing empirical risk. This study used candlestick patterns to predict foreign exchange chart movements using the Support Vector Machine (SVM) classification method. The purpose of this study was to measure the accuracy of the Support Vector Machine method in making predictions using candlestick patterns so that it can assist traders in making decisions in forex trading. The accuracy level obtained from the data classification results reached 90.72% with a precision of 87.69%. With a relatively good level of accuracy, the Support Vector Machine (SVM) method can be used to predict chart movements in foreign exchange using candlesticks to indicate the current trend’s direction.

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Section: Figure 5 Svm Training Results In 3d Modelmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

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Support Vector Machine for Predicting Candlestick Chart Movement on Foreign Exchange

Puteri,

Syamsu,

Putra

et al. 2023

matrik

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An Automatic Detection of Citrus Fruits and Leaves Diseases Using Enhanced Convolutional Neural Network

Shastri

Chaturvedi

Mouleswararao

et al. 2023

Remote Sens Earth Syst Sci

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A structural VAR and VECM modeling method for open-high-low-close data contained in candlestick chart

Huang,

Wang,

Wang

2024

Financ Innov

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The structural modeling of open-high-low-close (OHLC) data contained within the candlestick chart is crucial to financial practice. However, the inherent constraints in OHLC data pose immense challenges to its structural modeling. Models that fail to process these constraints may yield results deviating from those of the original OHLC data structure. To address this issue, a novel unconstrained transformation method, along with its explicit inverse transformation, is proposed to properly handle the inherent constraints of OHLC data. A flexible and effective framework for structurally modeling OHLC data is designed, and the detailed procedure for modeling OHLC data through the vector autoregression and vector error correction model are provided as an example of multivariate time-series analysis. Extensive simulations and three authentic financial datasets from the Kweichow Moutai, CSI 100 index, and 50 ETF of the Chinese stock market demonstrate the effectiveness and stability of the proposed modeling approach. The modeling results of support vector regression provide further evidence that the proposed unconstrained transformation not only ensures structural forecasting of OHLC data but also is an effective feature-extraction method that can effectively improve the forecasting accuracy of machine-learning models for close prices.

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Candlestick Patterns Recognition using CNN-LSTM Model to Predict Financial Trading Position in Stock Market

Cited by 4 publications

References 11 publications

Support Vector Machine for Predicting Candlestick Chart Movement on Foreign Exchange

Support Vector Machine for Predicting Candlestick Chart Movement on Foreign Exchange

An Automatic Detection of Citrus Fruits and Leaves Diseases Using Enhanced Convolutional Neural Network

A structural VAR and VECM modeling method for open-high-low-close data contained in candlestick chart

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