Stock Trend Prediction Using Candlestick Charting and Ensemble Machine Learning Techniques With a Novelty Feature Engineering Scheme

Lin, Yaohu; Liu, Shancun; Yang, Haijun; Wu, Hao

doi:10.1109/access.2021.3096825

Cited by 38 publications

(13 citation statements)

References 49 publications

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“…For instance, Lin et al constructed a novel ensemble machine learning method with six commonly used machine learning algorithms including SVM (Support Vector Machine), RF (Random Forest), and KNN (K-Nearest Neighbor) to predict the daily price movements of stocks in the Chinese stock market. The experimental results show that the accuracy and profitability of their proposed method outperformed the traditional methods [21]. Kamalov proposed a Neural Network-(NN-) based method for significant change prediction in stock price, and the experimental results show that the proposed method obtained the best accuracy [22].…”

Section: Related Workmentioning

confidence: 99%

Financial Futures Prediction Using Fuzzy Rough Set and Synthetic Minority Oversampling Technique

Zhu

Liu

2022

Advances in Mathematical Physics

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In this research, a novel approach called SMOTE-FRS is proposed for movement prediction and trading simulation of the Chinese Stock Index 300 (CSI300) futures, which is the most crucial financial futures in the Chinese A-share market. First, the SMOTE- (Synthetic Minority Oversampling Technique-) based method is employed to address the sample unbalance problem by oversampling the minority class and undersampling the majority class of the futures price change. Then, the FRS- (fuzzy rough set-) based method, as an efficient tool for analyzing complex and nonlinear information with high noise and uncertainty of financial time series, is adopted for the price change multiclassification of the CSI300 futures. Next, based on the multiclassification results of the futures price movement, a trading strategy is developed to execute a one-year simulated trading for an out-of-sample test of the trained model. From the experimental results, it is found that the proposed method averagely yielded an accumulated return of 6.36%, a F1-measure of 65.94%, and a hit ratio of 62.39% in the four testing periods, indicating that the proposed method is more accurate and more profitable than the benchmarks. Therefore, the proposed method could be applied by the market participants as an alternative prediction and trading system to forecast and trade in the Chinese financial futures market.

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Section: Related Workmentioning

confidence: 99%

Financial Futures Prediction Using Fuzzy Rough Set and Synthetic Minority Oversampling Technique

Zhu

Liu

2022

Advances in Mathematical Physics

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“…Prediction based on deep learning is a new prediction method in recent years. [9] It uses deep neural network model to represent and learn data, and can deal with more complex data structures and patterns. This method is excellent in prediction accuracy and generalization ability, but it takes a long time to train the model and requires high computing resources.…”

Section: Analysis Of Smart Grid Forecasting Strategymentioning

confidence: 99%

Research on Smart Grid Forecasting Strategy Based on Artificial Intelligence

2024

IJNDES

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This paper deeply discusses the smart grid forecasting strategy based on artificial intelligence technology. Firstly, the development status and integration trend of smart grid and artificial intelligence technology are summarized. Then the existing smart grid forecasting strategies are analyzed, and the application of artificial intelligence in forecasting strategies is emphatically discussed. On this basis, the framework of smart grid forecasting strategy based on artificial intelligence is constructed, and the key links of strategy construction are expounded in detail, including data collection and processing, model selection and training, and forecasting result evaluation. Through the application of practical cases, the effectiveness of this strategy in improving prediction accuracy and stability is verified. Finally, the research results are summarized, and the existing problems of the current strategy and the future research direction are pointed out. The research in this paper provides technical support for the optimal scheduling and decision-making of smart grid, and helps to promote the sustainable development of smart grid.

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“…In the empirical stage, they were unable to discover more complex candlestick patterns since more complex patterns necessitate larger data sets. To anticipate stock patterns, [12] utilised ensemble machine learning algorithms with candlestick charting. Kusuma et al [13]…”

Section: Related Workmentioning

confidence: 99%

Quantitative study of candlestick pattern & identifying candlestick patterns using deep learning for the Indian stock market

Upreti

Agrawal

Joshi

et al. 2022

ijhs

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The stock market is an integral aspect of any country’s economic infrastructure. Analyzing and attempting to play the markets to maximize profits is an endeavor a large fraction of the population aspires to. Candlestick patterns are the backbone of Technical Analysis, used for trading in the stock market. There are a number of candlestick patterns in the market, each with its own benefits and downsides. Due to this, the task that befalls the hands of analysts is deciding which patterns provide the most effective gauge of the current market situation. Due to the large level of noise and widely recognized semi-strong form of market efficiency, analyzing and forecasting the stock market is infamously difficult. For traders that use Technical Analysis to trade, it's critical to be able to recognize candlestick patterns quickly. We will be attempting to determine their respective effectiveness with respect to the Indian Stock Market via exploratory analysis conducted on real-world market data. Also, we'll use candlestick charts to train neural networks and subsequently find patterns. Deep Learning will be used to recognize candlestick patterns in large-cap Indian equities.

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Stock Trend Prediction Using Candlestick Charting and Ensemble Machine Learning Techniques With a Novelty Feature Engineering Scheme

Cited by 38 publications

References 49 publications

Financial Futures Prediction Using Fuzzy Rough Set and Synthetic Minority Oversampling Technique

Financial Futures Prediction Using Fuzzy Rough Set and Synthetic Minority Oversampling Technique

Research on Smart Grid Forecasting Strategy Based on Artificial Intelligence

Quantitative study of candlestick pattern & identifying candlestick patterns using deep learning for the Indian stock market

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