Candlestick chart based trading system using ensemble learning for financial assets

SANTUR, Yunus

doi:10.14744/sigma.2022.00039

Cited by 3 publications

(3 citation statements)

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“…In recent years, there has been an increase in the number of approaches based on machine learning for popular financial assets such as the Bist100 index, BTC, gram gold, dollar, and brent oil [21][22][23][24][25][26][27][28][29].…”

Section: Experimental Studies For Bist100 Index Btc Gold Dollar and B...mentioning

confidence: 99%

A New Algorithmic Trading Approach Based on Ensemble Learning and Candlestick Pattern Recognition in Financial Assets

AYCEL

SANTUR

2022

Turkish Journal of Science and Technology

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Financial assets considered as time series are chaotic in nature. The main goal of investors is to take a position at the right time and in the right direction by making predictions about the future on this chaotic series. These time series consist of the opening, low, high and closing price of a certain period. The approaches used to make predictions about trend direction and strength using moving averages and indicators based on them have noise and lag problems as they are obtained statistically. Candlestick charts, on the other hand, reflect the price-based psychology of bear and bull investors, as well as facilitating the interpretation of price movements by consolidating the said opening, closing, lowest and highest prices in a single image. It is known that it was applied to Japanese rice markets for the first time in history and there are 100+ candle patterns. In this study, an extensible architecture software framework using factory pattern and object-oriented approach is proposed for defining candlestick patterns and developing intelligent learning algorithms based on them. The proposed software framework can also be used in the development of new robotic approaches in terms of being applicable to all kinds of financial assets in every period.

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Section: Experimental Studies For Bist100 Index Btc Gold Dollar and B...mentioning

confidence: 99%

A New Algorithmic Trading Approach Based on Ensemble Learning and Candlestick Pattern Recognition in Financial Assets

AYCEL

SANTUR

2022

Turkish Journal of Science and Technology

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“…The candlestick pattern is one instrument used in technical analysis. In financial time series, Thomas N. Bulkowski identified 103 candlestick patterns that potentially forecast the course of price trends [3]. However, the absence of a thorough definition of a pattern might lead to classification discrepancies, necessitating market investors use of pattern recognition abilities when making judgments based on visual data [4].…”

Section: Introductionmentioning

confidence: 99%

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

Ramadhan

Palupi

Wahyudi

2022

JoSYC

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Investors need analytical tools to predict the price and to determine trading positions. Candlestick pattern is one of the analytical tools that predict price trends. However, the patterns are difficult to recognize, and some studies show doubts regarding the robustness of the recognizing system. In this study, we tested the predictive ability of candlestick patterns to determine trading positions. We use Gramian Angular Field (GAF) to encode candlestick patterns as images to recognize 3-hour and 5-hour of 6 candlestick patterns with Convolutional Neural Network (CNN), coupled with the Long short-term memory (LSTM) model to predict the close price. The trading position consists of buying and selling position with a hold period of several hours. Our results show CNN successfully detected 3-hour and 5-hour GAF candlestick patterns with an accuracy of 90% and 93%. LSTM can predict the close price trend with 155.458 RMSE scores and 0.9754% MAPE with 10-hour look back. With a hold duration of three hours and CNN-LSTM as an additional model, the test data's 85 candlestick patterns are recognized with 82.7% accuracy, compared to 60% accuracy of profitable trading positions when CNN candlestick pattern recognition is used alone. Compared to employing CNN candlestick pattern identification alone, the CNN-LSTM model combination can improve the prediction power of candlestick patterns and offer more lucrative trading positions.

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“…For this purpose, text processing and interpretation approaches can be used. In this case, the most general approach that can be used is to make sense of the related footnotes by reading them automatically and classifying the emotions [7].…”

Section: Introductionmentioning

confidence: 99%

A web scraping-based approach for fundamental analysis platform in financial assets

SANTUR

Ulaş

Karabatak

2022

Journal of New Results in Science

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There are two main benefits of using fundamental analysis for investors and portfolio managers. First, investing in a company with good ratios has lower risks. The second reason is that it is possible to evaluate share prices with internal valuation methods based on ratios. These price valuations can be more meaningful when combined with technical analysis data. Many data terminals provide processes such as fundamental analysis data and price valuation on a paid and licensed basis. However, the balance sheet data of publicly traded markets are publicly available and can be obtained and interpreted by web scraping methods. This study presents an approach in which basic analysis and price evaluation are made with balance sheets and ratios using open-source tools and web scraping.

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Candlestick chart based trading system using ensemble learning for financial assets

Cited by 3 publications

References 0 publications

A New Algorithmic Trading Approach Based on Ensemble Learning and Candlestick Pattern Recognition in Financial Assets

A New Algorithmic Trading Approach Based on Ensemble Learning and Candlestick Pattern Recognition in Financial Assets

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

A web scraping-based approach for fundamental analysis platform in financial assets

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