Stacked Optimized Ensemble Machine Learning Model for Predicting Stock Trends through Candlestick Chart Analysis with Feature Engineering Approach Article Swipe
R. Sumathi
,
S Ashokkumar
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.14445/23488549/ijece-v11i6p107
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.14445/23488549/ijece-v11i6p107
The process of predicting stock trends through the analysis of Candlestick Charts (CCs) involves interpreting the patterns formed by these candlesticks to make informed predictions about future price movements. Utilizing Machine Learning (ML) for Stock Trend Prediction (STP) through CC analysis is common in algorithmic trading. CCs provide crucial information about the high, open, closed, and low prices within a specific time rate. However, stacked ensemble methods are employed to enhance reliability and stability, which combine the predictions of multiple models. Motivated by this objective, this work introduces the Stacked Optimized Ensemble ML Techniques with a Feature Engineering Approach for STP, referred to as SOEMLT-FEA. In the training phase, various models, including Random Forests (RF), SVM (Support Vector Machine), XGBoost, Decision Tree (DT), Adaboost, and ANN (Artificial Neural Network), are trained and optimized using the Chiroptera Algorithm (CA) to fine-tune their parameters. The optimized classifiers are then ranked, and the top three models are selected as the base classifiers for a stacking ensemble method. The efficacy of the developed feature engineering approach is confirmed by the experiential outcomes obtained (2000 and 2017) in China's stock market. This approach demonstrates promising economic returns for individual portfolios and stocks, achieving a prediction accuracy exceeding 90% for specific trend patterns.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.14445/23488549/ijece-v11i6p107
- OA Status
- diamond
- Cited By
- 1
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400656265
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https://openalex.org/W4400656265Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.14445/23488549/ijece-v11i6p107Digital Object Identifier
- Title
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Stacked Optimized Ensemble Machine Learning Model for Predicting Stock Trends through Candlestick Chart Analysis with Feature Engineering ApproachWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-06-30Full publication date if available
- Authors
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R. Sumathi, S AshokkumarList of authors in order
- Landing page
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https://doi.org/10.14445/23488549/ijece-v11i6p107Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.14445/23488549/ijece-v11i6p107Direct OA link when available
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Chart, Feature engineering, Feature (linguistics), Ensemble learning, Artificial intelligence, Computer science, Machine learning, Stock (firearms), Ensemble forecasting, Engineering, Statistics, Mathematics, Mechanical engineering, Deep learning, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(Support | 116 |
| abstract_inverted_index.Approach | 98 |
| abstract_inverted_index.Decision | 120 |
| abstract_inverted_index.Ensemble | 91 |
| abstract_inverted_index.However, | 63 |
| abstract_inverted_index.Learning | 31 |
| abstract_inverted_index.XGBoost, | 119 |
| abstract_inverted_index.accuracy | 200 |
| abstract_inverted_index.analysis | 8, 40 |
| abstract_inverted_index.approach | 171, 187 |
| abstract_inverted_index.economic | 190 |
| abstract_inverted_index.efficacy | 165 |
| abstract_inverted_index.employed | 68 |
| abstract_inverted_index.ensemble | 65, 162 |
| abstract_inverted_index.informed | 23 |
| abstract_inverted_index.involves | 13 |
| abstract_inverted_index.multiple | 79 |
| abstract_inverted_index.obtained | 178 |
| abstract_inverted_index.outcomes | 177 |
| abstract_inverted_index.patterns | 16 |
| abstract_inverted_index.referred | 101 |
| abstract_inverted_index.selected | 154 |
| abstract_inverted_index.specific | 60, 204 |
| abstract_inverted_index.stacking | 161 |
| abstract_inverted_index.trading. | 45 |
| abstract_inverted_index.training | 107 |
| abstract_inverted_index.Adaboost, | 123 |
| abstract_inverted_index.Algorithm | 136 |
| abstract_inverted_index.Machine), | 118 |
| abstract_inverted_index.Motivated | 81 |
| abstract_inverted_index.Network), | 128 |
| abstract_inverted_index.Optimized | 90 |
| abstract_inverted_index.Utilizing | 29 |
| abstract_inverted_index.achieving | 197 |
| abstract_inverted_index.confirmed | 173 |
| abstract_inverted_index.developed | 168 |
| abstract_inverted_index.exceeding | 201 |
| abstract_inverted_index.fine-tune | 139 |
| abstract_inverted_index.including | 111 |
| abstract_inverted_index.optimized | 132, 143 |
| abstract_inverted_index.patterns. | 206 |
| abstract_inverted_index.promising | 189 |
| abstract_inverted_index.Chiroptera | 135 |
| abstract_inverted_index.Prediction | 36 |
| abstract_inverted_index.Techniques | 93 |
| abstract_inverted_index.individual | 193 |
| abstract_inverted_index.introduces | 87 |
| abstract_inverted_index.movements. | 28 |
| abstract_inverted_index.objective, | 84 |
| abstract_inverted_index.portfolios | 194 |
| abstract_inverted_index.predicting | 3 |
| abstract_inverted_index.prediction | 199 |
| abstract_inverted_index.stability, | 73 |
| abstract_inverted_index.(Artificial | 126 |
| abstract_inverted_index.Candlestick | 10 |
| abstract_inverted_index.Engineering | 97 |
| abstract_inverted_index.SOEMLT-FEA. | 104 |
| abstract_inverted_index.algorithmic | 44 |
| abstract_inverted_index.classifiers | 144, 158 |
| abstract_inverted_index.engineering | 170 |
| abstract_inverted_index.information | 49 |
| abstract_inverted_index.parameters. | 141 |
| abstract_inverted_index.predictions | 24, 77 |
| abstract_inverted_index.reliability | 71 |
| abstract_inverted_index.candlesticks | 20 |
| abstract_inverted_index.demonstrates | 188 |
| abstract_inverted_index.experiential | 176 |
| abstract_inverted_index.interpreting | 14 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5101984838 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I85461943 |
| citation_normalized_percentile.value | 0.70796125 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |