Journal articles: 'Candlestik'

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Piasecki, Krzysztof, and Anna Łyczkowska-Hanćkowiak. "Representation of Japanese Candlesticks by Oriented Fuzzy Numbers." Econometrics 8, no. 1 (December 18, 2019): 1. http://dx.doi.org/10.3390/econometrics8010001.

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The Japanese candlesticks’ technique is one of the well-known graphic methods of dynamic analysis of securities. If we apply Japanese candlesticks for the analysis of high-frequency financial data, then we need a numerical representation of any Japanese candlestick. Kacprzak et al. have proposed to represent Japanese candlesticks by ordered fuzzy numbers introduced by Kosiński and his cooperators. For some formal reasons, Kosiński’s theory of ordered fuzzy numbers has been revised. The main goal of our paper is to propose a universal method of representation of Japanese candlesticks by revised ordered fuzzy numbers. The discussion also justifies the need for such revision of a numerical model of the Japanese candlesticks. There are considered the following main kinds of Japanese candlestick: White Candle (White Spinning), Black Candle (Black Spinning), Doji Star, Dragonfly Doji, Gravestone Doji, and Four Price Doji. For example, we apply numerical model of Japanese candlesticks for financial portfolio analysis.

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Orquín-Serrano, Ismael. "Predictive Power of Adaptive Candlestick Patterns in Forex Market. Eurusd Case." Mathematics 8, no. 5 (May 14, 2020): 802. http://dx.doi.org/10.3390/math8050802.

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The Efficient Market Hypothesis (EMH) states that all available information is immediately reflected in the price of any asset or financial instrument, so that it is impossible to predict its future values, making it follow a pure stochastic process. Among all financial markets, FOREX is usually addressed as one of the most efficient. This paper tests the efficiency of the EURUSD pair taking only into consideration the price itself. A novel categorical classification, based on adaptive criteria, of all possible single candlestick patterns is presented. The predictive power of candlestick patterns is evaluated from a statistical inference approach, where the mean of the average returns of the strategies in out-of-sample historical data is taken as sample statistic. No net positive average returns are found in any case after taking into account transaction costs. More complex candlestick patterns are considered feeding supervised learning systems with the information of past bars. No edge is found even in the case of considering the information of up to 24 preceding candlesticks.

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Karmelia, Meilona, Moeljono Widjaja, and Seng Hansun. "Candlestick Pattern Classification Using Feedforward Neural Network." International Journal of Advances in Soft Computing and its Applications 14, no. 2 (July 20, 2022): 80–95. http://dx.doi.org/10.15849/ijasca.220720.06.

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Investment in the capital market can help boost a country’s economic growth. Without a doubt, in investing, a technical analysis of the condition of the stock is needed at that time. One of the technical analyses that can be done is to look at the historical data of stocks. Candlestick charts can summarize historical data that contain price value for Open, High, Low, and Close (OHLC) in the form of a chart. A group of candlesticks will form a pattern that can help investors to see whether the stock is trending up or down. The number of candlestick patterns and the manual determination of candlestick patterns may take time and effort. Feedforward Neural Network (FNN) is one of the algorithms that can help map the input and output of a given dataset. This study aims to implement FNN to classify candlestick patterns found in historical stock data. The test results show that the accuracy for each model scenario does not guarantee whether all patterns can be properly recognized. This is mainly caused by an imbalanced dataset and the classification process cannot be done properly. Testing with the original data has an accuracy of above 85% on each stock, but the average F1-score is below 45%. Further experiments using random under-sampling and Synthetic Minority Oversampling Technique (SMOTE) result in decreased accuracy value, where the lowest is 59% in PT Bukit Asam Tbk share, and an increased average F1-score, but less than 15%. Keywords: Candlestick patterns, feedforward neural network, investment, historical data, OHLC, SMOTE, stocks.

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Kim, Dasol. "Domesticating the Body of the Exotic Other: The Multisensory Use of a Sixteenth-century Brass Candlestick." Das Mittelalter 25, no. 2 (November 10, 2020): 311–37. http://dx.doi.org/10.1515/mial-2020-0040.

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AbstractThrough the medium of a brass candlestick made in a sixteenth-century German foundry, I discuss the Christian European household’s sensory engagement and spatial control of the Muslim body. I argue that the Europeans’ sensory experience of the turbaned candlestick reflects and reinforces their conceptualization of Islamic culture, which is a blend of fear and fascination. The turbaned candlestick allows us to explore issues rarely discussed in the study of metalwork and the European imagery of ‘the East’. The shape and scale of the candlestick suggest that it could have been treated both as a statuette and as a piece of furniture. The inanimate candlestick would have felt livelier when the user touched its body and felt heat, flame, light and smoke from the candlestick. My analysis of the candlestick suggests that it might have been experienced through a comprehensive play of senses.

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Cahyadi, Yoyo. "Analisis Pola Grafik Candlestick pada Pergerakan EUR/USD." Binus Business Review 3, no. 2 (November 30, 2012): 737. http://dx.doi.org/10.21512/bbr.v3i2.1357.

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Candlestick chart is one of the charts which is commonly used in technical analysis. Besides for price overview in the past, this chart has patterns that can be analysed to become guidance about next price movement. There are some patterns with specific name in candlestick chart analysis. This paper discusses candlestick chart patterns in the EUR/USD currency pair within daily time frame. The observation shows that candlestick chart patterns indeed gave more guidance about trend changes. Although the candlestick patternsdid not show everyday, in most cases the patterns gave right guidance.

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Ramadhan, Aditya, Irma Palupi, and Bambang Ari Wahyudi. "Candlestick Patterns Recognition using CNN-LSTM Model to Predict Financial Trading Position in Stock Market." Journal of Computer System and Informatics (JoSYC) 3, no. 4 (September 3, 2022): 339–47. http://dx.doi.org/10.47065/josyc.v3i4.2133.

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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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Rediehs, Laura. "Candlestick Mysteries*." Quaker Studies 18, no. 2 (March 2014): 151–69. http://dx.doi.org/10.3828/quaker.18.2.151.

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Ardiyanti, Ni Putu Winda, Irma Palupi, and Indwiarti Indwiarti. "Trading Strategy on Market Stock by Analyzing Candlestick Pattern using Artificial Neural Network (ANN) Method." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 4 (October 26, 2021): 1273. http://dx.doi.org/10.30865/mib.v5i4.3266.

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Technical analysis plays an important role in a stock market. Traders using technical analysis to find the trading strategy on the market stock. There are some technical indicators tools that can support the technical analysis, such as Moving Average, Stochastic, and others. Candlestick pattern also parts of the tools that used in technical analysis to develop the trading strategy since Candlestick represents the stock behavior. Therefore, understanding the Candlestick pattern and technical indicator tools will be valuable for the traders to predict the trading strategy. This study performs the prediction of trading strategy by analyzing the Candlestick pattern using an Artificial Neural Network (ANN). The technical indicator tools and Candlestick pattern will be generated as the features and label data in the modeling process. The method is applied to four stocks from IDX through their technical indicators for a certain period of time. We find that in the period of 28 days, the model generates the highest accuracy that reached 85.96%. We also used K-Fold Cross-Validation to evaluate the result of model performance that generates

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Xu, Rui, Xiaoming Liu, Hang Wan, Xipeng Pan, and Jian Li. "A Feature Extraction and Classification Method to Forecast the PM2.5 Variation Trend Using Candlestick and Visual Geometry Group Model." Atmosphere 12, no. 5 (April 28, 2021): 570. http://dx.doi.org/10.3390/atmos12050570.

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Currently, the continuous change prediction of PM2.5 concentration is an air pollution research hotspot. Combining physical methods and deep learning models to divide the pollution process of PM2.5 into effective multiple types is necessary to achieve a reliable prediction of the PM2.5 value. Therefore, a candlestick chart sample generator was designed to generate the candlestick chart from the online PM2.5 continuous monitoring data of the Guilin monitoring station site. After these generated candlestick charts were analyzed through the Gaussian diffusion model, it was found that the characteristics of the physical transmission process of PM2.5 pollutants can be reflected. Based on a set three-day period, using the time linear convolution method, 2188 sets of candlestick chart data were obtained from the 2013–2018 PM2.5 concentration data. There existed 16 categories generated by unsupervised classification that met the established classification judgment standards. After the statistical analysis, it was found that the accuracy rate of the change trend of these classifications reached 99.68% during the next period. Using the candlestick chart data as the training dataset, the Visual Geometry Group (VGG) model, an improved convolutional neural network model, was used for the classification. The experimental results showed that the overall accuracy (OA) value of the candlestick chart combination classification was 96.19%, and the Kappa coefficient was 0.960. IN the VGG model, the overall accuracy was improved by 1.93%, on average, compared with the support vector machines (SVM), LeNet, and AlexNet models. According to the experimental results, using the VGG classification method to classify continuous pollution data in the form of candlestick charts can more comprehensively retain the characteristics of the physical pollution process and provide a classification basis for accurately predicting PM2.5 values. At the same time, the statistical feasibility of this method has been proved.

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Ho, Trang-Thi, and Yennun Huang. "Stock Price Movement Prediction Using Sentiment Analysis and CandleStick Chart Representation." Sensors 21, no. 23 (November 29, 2021): 7957. http://dx.doi.org/10.3390/s21237957.

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Determining the price movement of stocks is a challenging problem to solve because of factors such as industry performance, economic variables, investor sentiment, company news, company performance, and social media sentiment. People can predict the price movement of stocks by applying machine learning algorithms on information contained in historical data, stock candlestick-chart data, and social-media data. However, it is hard to predict stock movement based on a single classifier. In this study, we proposed a multichannel collaborative network by incorporating candlestick-chart and social-media data for stock trend predictions. We first extracted the social media sentiment features using the Natural Language Toolkit and sentiment analysis data from Twitter. We then transformed the stock’s historical time series data into a candlestick chart to elucidate patterns in the stock’s movement. Finally, we integrated the stock’s sentiment features and its candlestick chart to predict the stock price movement over 4-, 6-, 8-, and 10-day time periods. Our collaborative network consisted of two branches: the first branch contained a one-dimensional convolutional neural network (CNN) performing sentiment classification. The second branch included a two-dimensional (2D) CNN performing image classifications based on 2D candlestick chart data. We evaluated our model for five high-demand stocks (Apple, Tesla, IBM, Amazon, and Google) and determined that our collaborative network achieved promising results and compared favorably against single-network models using either sentiment data or candlestick charts alone. The proposed method obtained the most favorable performance with 75.38% accuracy for Apple stock. We also found that the stock price prediction achieved more favorable performance over longer periods of time compared with shorter periods of time.

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Wicaksono, Soetam Rizky, Rudy Setiawan, and Purnomo. "Candlestick Pattern Research Analysis, Future and Beyond: A Systematic Literature Review Using PRISMA." Journal of Computer Science and Technology Studies 4, no. 2 (December 11, 2022): 157–64. http://dx.doi.org/10.32996/jcsts.2022.4.2.19.

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Online stock market circumstances allow traders to examine in real time or periodically with free or paid criteria and indicators. Candlestick charts and historical data help traders predict stock values. These forecasting methods rely on traders' experience. Such unscientific judgements lack empirical facts and mathematically established theories, which are rarely published in recognized scientific journals. Initial research revealed a gap between candlestick research and practice, creating a novel idea without scientific backing. Given the different study possibilities, the literature review must address the following questions: what's the trend in candlestick indicator research over the past five years, and what's ahead for candlestick stock price predictions? This study used PRISMA to conduct its literature review. Ten articles were duplicated in three indexes. Last, the article content is compared to the research questions. Only 20 Scopus (S) papers have more than 10 citations, and 2 don't have full paper access, so only 11 match the conditions. 100 publications were obtained from Google Scholar (GS), then re-filtered to obtain 19 with more than 10 citations and 6 without full paper access, for a total of 11 articles. 100 articles from Semantic Scholar (SS) met the first requirements. Duplicate articles in each database were rechecked to produce 24 valid articles for future research. Economic and IT publications employ candlestick patterns in the study. SLR screening and literature research yielded expert systems, historical research, ichimoku, local studies, and technological analysis. Expert system group dominates research, but no technique dominates implementation. Future research can be new. Candlestick patterns have only been tested on local stock markets in one country; therefore, economic crises, commercial acts, or conflicts may lead the method to fail.

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Leonardo, Johan, and Rilla Gantino. "ANALISA TEKNIKAL PEMBUATAN TRADING PLAN KEPUTUSAN INVESTASI PADA 3 SAHAM PERBANKAN BUMN YANG TERDAFTAR PADA INDEKS LQ45 TAHUN 2014 - 2019." Jurnal Riset Akuntansi 13, no. 2 (October 22, 2021): 174–85. http://dx.doi.org/10.34010/jra.v13i2.4651.

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Tujuan dari penelitian ini adalah untuk memperoleh bukti empiris tentang pengaruh candlestick, level support dan level resistance, trendline, dan Moving Average Convergence Divergence (MACD) terhadap pengambilan keputusan investasi. Sampel dalam penelitian ini adalah perusahaan perbankan yang terdaftar di Bursa Efek Indonesia selama periode 2014â€“2019. Sumber data penelitian ini berasal dari sotfware pro trader. Penelitian ini menggunakan pendekatan kualitatif dengan metode analisis deskriptif. Hasil dalam penelitian ini menemukan bahwa candlestick, level support dan level resistance, trendline, dan Moving Average Convergence Divergence (MACD) berpengaruh terhadap pengambilan keputusan investasi. Candlestick berpengaruh terhadap keputusan investasi, level support dan level resistance berpengaruh terhadap pengambilan keputusan investasi, trendline berpengaruh terhadap pengambilan keputusan investasi, dan Moving Average Convergence Divergence (MACD) berpengaruh terhadap pengambilan keputusan investasi.

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Wang, Jun, Xiaohan Li, Huading Jia, Tao Peng, and Jinghua Tan. "Predicting Stock Market Volatility from Candlestick Charts: A Multiple Attention Mechanism Graph Neural Network Approach." Mathematical Problems in Engineering 2022 (September 13, 2022): 1–16. http://dx.doi.org/10.1155/2022/4743643.

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As an important part of financial market, stock market price volatility analysis has been the focus of academic and industry attention. Candlestick chart, as the most widely used indicator for evaluating stock market price volatility, has been intensively studied and explored. With the continuous development of computer technology, the stock market analysis method based on candlestick chart is gradually changed from manual to intelligent algorithm. However, how to effectively use stock market graphical indicators to analyze stock market price fluctuations has been pending solution, and deep learning algorithms based on structured data such as deep neural networks (DNN) and recurrent neural networks (RNNs) always have the problems of making it difficult to capture the laws and low generalization ability for stock market graphical indicators data processing. Therefore, this paper proposes a quantification method of stock market candlestick chart based on Hough variation, using the graph structure embedding method to represent candlestick chart features and multiple attention graph neural network for stock market price fluctuation prediction. The experimental results show that the proposed method can interpret the candlestick chart features more accurately and has superiority performance over state-of-the-art deep learning methods, including SVM, CNN, LSTM, and CNN-LSTM. Relative to these algorithms, the proposed method achieves an average performance improvement of 20.51% in terms of accuracy and further achieves at least 26.98% improvement in strategy returns in quantitative investment experiments.

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Tharavanij, Piyapas, Vasan Siraprapasiri, and Kittichai Rajchamaha. "Profitability of Candlestick Charting Patterns in the Stock Exchange of Thailand." SAGE Open 7, no. 4 (October 2017): 215824401773679. http://dx.doi.org/10.1177/2158244017736799.

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This article investigates the profitability of candlestick patterns. The holding periods are 1, 3, 5, and 10 days. Two exit strategies are studied. One is the Marshall–Young–Rose (MYR) exit strategy and the other is the Caginalp–Laurent (CL) exit strategy. The MYR applies a prespecified date to exit the market. In contrast, the CL sets an exit price equal to an average holding period closing price, assuming that investors liquidate their positions evenly within this period. The daily data include open, high, low, and close prices of component stocks of the SET50 index (the 50 largest capitalization stocks in the Stock Exchange of Thailand [SET]) for a 10-year period from July 3, 2006, to June 30, 2016. This study tests the predictive power of bullish and bearish candlestick reversal patterns both without technical filtering and with technical filtering (Stochastics [%D], Relative Strength Index [RSI], Money Flow Index [MFI]) by applying the skewness adjusted t test and the binomial test. The statistical analysis finds little use of both bullish and bearish candlestick reversal patterns since the mean returns of most patterns are not statistically different from zero. Even the ones with statistically significant returns do have high risks in terms of standard deviations. The binomial test results also indicate that candlestick patterns cannot reliably predict market directions. In addition, this article finds that filtering by %D, RSI, or MFI generally does not increase profitability nor prediction accuracy of candlestick patterns.

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Flynn, Richard. "Critic over the candlestick." Lion and the Unicorn 21, no. 3 (1997): 453–56. http://dx.doi.org/10.1353/uni.1997.0016.

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Walkiewicz, M. R., P. J. Fox, and R. E. Scholten. "Candlestick rubidium beam source." Review of Scientific Instruments 71, no. 9 (September 2000): 3342–44. http://dx.doi.org/10.1063/1.1288261.

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Deng, Shangkun, Zhihao Su, Yanmei Ren, Haoran Yu, Yingke Zhu, and Chenyang Wei. "Can Japanese Candlestick Patterns be Profitable on the Component Stocks of the SSE50 Index?" SAGE Open 12, no. 3 (July 2022): 215824402211178. http://dx.doi.org/10.1177/21582440221117803.

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In this study, we investigate the profitability of 10 well-known Japanese candlestick charting patterns using daily-based data on the component stocks of the Chinese SSE50 index, which involves a lengthy sample period from January 2000 to December 2018. The main contribution of this paper is that we conduct the first predictive power examination of Japanese candlestick patterns on the Chinese SSE50 stocks while taking into account trend and overbought/oversold conditions, and their profitability over different holding periods. Experimental results indicate that several bullish candlestick patterns such as Long White and Bullish Gap can produce a significant positive average return over certain holding periods. In addition, empirical results show that none of the bearish candlestick patterns we examined offers predictive power. However, without considering trend and overbought/oversold conditions, we find that the bearish pattern Gravestone Doji over a 10-day holding period has superior profitability if it is applied as a contrary trading signal. The robustness of our results is confirmed based upon a bootstrap analysis and an out-of-sample test. The findings of this study are beneficial for the market traders engaged in transaction of the SSE50 component stocks.

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Hsu, Yu-Chia. "Using Machine Learning and Candlestick Patterns to Predict the Outcomes of American Football Games." Applied Sciences 10, no. 13 (June 29, 2020): 4484. http://dx.doi.org/10.3390/app10134484.

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Match outcome prediction is a challenging problem that has led to the recent rise in machine learning being adopted and receiving significant interest from researchers in data science and sports. This study explores predictability in match outcomes using machine learning and candlestick charts, which have been used for stock market technical analysis. We compile candlestick charts based on betting market data and consider the character of the candlestick charts as features in our predictive model rather than the performance indicators used in the technical and tactical analysis in most studies. The predictions are investigated as two types of problems, namely, the classification of wins and losses and the regression of the winning/losing margin. Both are examined using various methods of machine learning, such as ensemble learning, support vector machines and neural networks. The effectiveness of our proposed approach is evaluated with a dataset of 13261 instances over 32 seasons in the National Football League. The results reveal that the random subspace method for regression achieves the best accuracy rate of 68.4%. The candlestick charts of betting market data can enable promising results of match outcome prediction based on pattern recognition by machine learning, without limitations regarding the specific knowledge required for various kinds of sports.

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Stasiak, Michał Dominik. "Candlestick—The Main Mistake of Economy Research in High Frequency Markets." International Journal of Financial Studies 8, no. 4 (October 10, 2020): 59. http://dx.doi.org/10.3390/ijfs8040059.

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One of the key problems of researching the high-frequency financial markets is the proper data format. Application of the candlestick representation (or its derivatives such as daily prices, etc.), which is vastly used in economic research, can lead to faulty research results. Yet, this fact is consistently ignored in most economic studies. The following article gives examples of possible consequences of using candlestick representation in modelling and statistical analysis of the financial markets. Emphasis should be placed on the problem of research results being detached from the investing practice, which makes most of the results inapplicable from the investor’s point of view. The article also presents the concept of a binary-temporal representation, which is an alternative to the candlestick representation. Using binary-temporal representation allows for more precise and credible research and for the results to be applied in investment practice.

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Kulinich, A. A. "SPECIFIC OF THE GLOUCERSTER CANDLESTICK''S EKPHRASIS IN THE NOVEL “THE CHILDREN''S BOOK” BY A.S. BYATT." Juvenis scientia, no. 8 (August 30, 2018): 22–24. http://dx.doi.org/10.32415/jscientia.2018.08.05.

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The article deals with the Gloucerster candlestick's ekphrasis in the novel «The Children's Book» by Antonia Byatt, a famous English writer of the 20th century. Pictorial ekphrasises of the candlestick are analysed in the text through their functions. Ekphrasises of different artefacts have been analysed by modern scholars, the author's works included, but no detailed analysis of the phenomen under study (the Gloucerster candlestick) has been an object of literary research yet, which shows novelty and actual importance of the analysed issue.

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Kiky, Andreas, and Ika Yanuarti. "Candlestick Accuracy and Investor Gain." International Review of Business Research Papers 13, no. 1 (March 1, 2017): 66–77. http://dx.doi.org/10.21102/irbrp.2017.03.131.05.

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Dwayne Brenna. "Candlestick Park, San Francisco 1984." NINE: A Journal of Baseball History and Culture 18, no. 2 (2010): 158. http://dx.doi.org/10.1353/nin.0.0087.

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Pan, Wei, Jide Li, and Xiaoqiang Li. "Portfolio Learning Based on Deep Learning." Future Internet 12, no. 11 (November 18, 2020): 202. http://dx.doi.org/10.3390/fi12110202.

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Traditional portfolio theory divides stocks into different categories using indicators such as industry, market value, and liquidity, and then selects representative stocks according to them. In this paper, we propose a novel portfolio learning approach based on deep learning and apply it to China’s stock market. Specifically, this method is based on the similarity of deep features extracted from candlestick charts. First, we obtained whole stock information from Tushare, a professional financial data interface. These raw time series data are then plotted into candlestick charts to make an image dataset for studying the stock market. Next, the method extracts high-dimensional features from candlestick charts through an autoencoder. After that, K-means is used to cluster these high-dimensional features. Finally, we choose one stock from each category according to the Sharpe ratio and a low-risk, high-return portfolio is obtained. Extensive experiments are conducted on stocks in the Chinese stock market for evaluation. The results demonstrate that the proposed portfolio outperforms the market’s leading funds and the Shanghai Stock Exchange Composite Index (SSE Index) in a number of metrics.

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Lin, Yaohu, Shancun Liu, Haijun Yang, Harris Wu, and Bingbing Jiang. "Improving stock trading decisions based on pattern recognition using machine learning technology." PLOS ONE 16, no. 8 (August 6, 2021): e0255558. http://dx.doi.org/10.1371/journal.pone.0255558.

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PRML, a novel candlestick pattern recognition model using machine learning methods, is proposed to improve stock trading decisions. Four popular machine learning methods and 11 different features types are applied to all possible combinations of daily patterns to start the pattern recognition schedule. Different time windows from one to ten days are used to detect the prediction effect at different periods. An investment strategy is constructed according to the identified candlestick patterns and suitable time window. We deploy PRML for the forecast of all Chinese market stocks from Jan 1, 2000 until Oct 30, 2020. Among them, the data from Jan 1, 2000 to Dec 31, 2014 is used as the training data set, and the data set from Jan 1, 2015 to Oct 30, 2020 is used to verify the forecasting effect. Empirical results show that the two-day candlestick patterns after filtering have the best prediction effect when forecasting one day ahead; these patterns obtain an average annual return, an annual Sharpe ratio, and an information ratio as high as 36.73%, 0.81, and 2.37, respectively. After screening, three-day candlestick patterns also present a beneficial effect when forecasting one day ahead in that these patterns show stable characteristics. Two other popular machine learning methods, multilayer perceptron network and long short-term memory neural networks, are applied to the pattern recognition framework to evaluate the dependency of the prediction model. A transaction cost of 0.2% is considered on the two-day patterns predicting one day ahead, thus confirming the profitability. Empirical results show that applying different machine learning methods to two-day and three-day patterns for one-day-ahead forecasts can be profitable.

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Nugroho, Fauyhi Eko. "TRADING OTOMATIS PERDAGANGAN FOREX MENGGUNAKAN METODE MARTINGALE DAN CANDLESTICK SEBAGAI ACUAN TRANSAKSI DI EXNESS." Simetris : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer 7, no. 1 (April 1, 2016): 153. http://dx.doi.org/10.24176/simet.v7i1.499.

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Perdagangan forex merupakan suatu perdagangan mata uang asing dimana seorang trader mendapatkan keuntungan dari perbedaan nilai mata uang satu dengan mata uang asing lainnya yang dapat berubah-ubah secara berkala. Salah satu faktor analisa dalam keputusan jual beli dalam perdagangan forex adalah menggunakan analisa teknikal. Analisa teknikal menggunakan data dan metode statistik berdasarkan data histori harga untuk mendukung keputusan jual atau beli. Trader sering mengalami loss karena emosi dan psikologis. Faktor emosi dan psikologis yang dimaksud diantaranya adalah keserakahan, kelelahan, kurang konsentrasi dan lainnya. Salah satu solusi untuk mengatasi masalah tersebut adalah dengan menggunakan expert advisor . Expert advisor adalah aplikasi yang digunakan trader untuk melakukan trading secara otomatis dan mampu melakukan trading tanpa seorang trader harus memantau pergerakan harga selama 24 jam karena telah diberikan logic dengan menggunakan bahasa pemrograman mql 4. Expert advisor yang dibuat menggunakan metode martingale dan candlestick sebagai acuan transaksinya dalam pembukaan posisi jual atau beli. dari hasil penelitian pengujian Expert advisor dapat disimpulkan penggunaan metode martingale dan candlestick sebagai acuan transaksinya sebagai dasar logika expert advisor membantu dalam pengambilan keputusan trading. Kata kunci: expert advisor, forex, martingale, candlestick, mq 4.

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Farhan, Andi, Tjetjep Djuwarsa, and Radia Purbayati. "Analisis Teknikal Pergerakan Saham PT Bank Jago Tbk dengan Menggunakan Indikator Candlestick dan Moving Average Convergence Divergence." Indonesian Journal of Economics and Management 2, no. 3 (July 24, 2022): 517–25. http://dx.doi.org/10.35313/ijem.v2i3.3783.

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This study aims to determine the movement of shares of PT Bank Jago Tbk for the period January 1 – May 20 2022 using candlestick indicators and Moving Average Convergence Divergence (MACD), this study focuses on the economic aspect. The method used is descriptive method. The type of data used is qualitative data. The source of data used is secondary data obtained from charts on tradingview.com tools. Referring to the results of the analysis that has been carried out, the movement of PT Bank Jago Tbk's shares shows a decline (downtrend) using both candlestick and MACD indicators. This indicates that market participants should take a sell action.

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Tomà, P. "The “candlestick sign” on cerebral ultrasound." Pediatric Radiology 21, no. 4 (May 1991): 319. http://dx.doi.org/10.1007/bf02018640.

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Tsao, Chung Chen, Kei Lin Kuo, I. Chien Hsu, and G. T. Chern. "Analysis of Core-Candlestick Drill in Drilling Composite Materials." Key Engineering Materials 419-420 (October 2009): 337–40. http://dx.doi.org/10.4028/www.scientific.net/kem.419-420.337.

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Unlike ductile metals cutting mechanism, the interfaces between fiber and matrix as a transitional layer experience mismatched deformation in machining process. In general, the most frequent operation performed on composite materials is drilling with a twist drill to generate a hole owing to their versatility and low production cost. However, delamination is one of the most common defects in drilling laminated fiber-reinforced composites and can cause a significant reduction in the load-carrying capacity of a structure. At the periphery, using such special drills as saw drill, candlestick drill and core drill, reducible to causing delamination damage than the twist drill. Experimental results indicated that the diameter ratio and feed rate have statistical and physical significance on the thrust force obtained with a core-candlestick drill.

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Hassen, Oday A., Saad M. Darwish, Nur A. Abu, and Zaheera Z. Abidin. "Application of Cloud Model in Qualitative Forecasting for Stock Market Trends." Entropy 22, no. 9 (September 6, 2020): 991. http://dx.doi.org/10.3390/e22090991.

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Forecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. The use of technical analysis for financial forecasting has been successfully employed by many researchers. The existing qualitative based methods developed based on fuzzy reasoning techniques cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. Extended fuzzy sets (e.g., fuzzy probabilistic set) study the fuzziness of the membership grade to a concept. The cloud model, based on probability measure space, automatically produces random membership grades of a concept through a cloud generator. In this paper, a cloud model-based approach was proposed to confirm accurate stock based on Japanese candlestick. By incorporating probability statistics and fuzzy set theories, the cloud model can aid the required transformation between the qualitative concepts and quantitative data. The degree of certainty associated with candlestick patterns can be calculated through repeated assessments by employing the normal cloud model. The hybrid weighting method comprising the fuzzy time series, and Heikin–Ashi candlestick was employed for determining the weights of the indicators in the multi-criteria decision-making process. Fuzzy membership functions are constructed by the cloud model to deal effectively with uncertainty and vagueness of the stock historical data with the aim to predict the next open, high, low, and close prices for the stock. The experimental results prove the feasibility and high forecasting accuracy of the proposed model.

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Brim, Andrew, and Nicholas S. Flann. "Deep reinforcement learning stock market trading, utilizing a CNN with candlestick images." PLOS ONE 17, no. 2 (February 18, 2022): e0263181. http://dx.doi.org/10.1371/journal.pone.0263181.

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Billions of dollars are traded automatically in the stock market every day, including algorithms that use neural networks, but there are still questions regarding how neural networks trade. The black box nature of a neural network gives pause to entrusting it with valuable trading funds. A more recent technique for the study of neural networks, feature map visualizations, yields insight into how a neural network generates an output. Utilizing a Convolutional Neural Network (CNN) with candlestick images as input and feature map visualizations gives a unique opportunity to determine what in the input images is causing the neural network to output a certain action. In this study, a CNN is utilized within a Double Deep Q-Network (DDQN) to outperform the S&P 500 Index returns, and also analyze how the system trades. The DDQN is trained and tested on the 30 largest stocks in the S&P 500. Following training the CNN is used to generate feature map visualizations to determine where the neural network is placing its attention on the candlestick images. Results show that the DDQN is able to yield higher returns than the S&P 500 Index between January 2, 2020 and June 30, 2020. Results also show that the CNN is able to switch its attention from all the candles in a candlestick image to the more recent candles in the image, based on an event such as the coronavirus stock market crash of 2020.

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Vaughan, Laura. "Butcher, baker, candlestick-maker and… healthcare centre?" Journal of Urban Design 27, no. 1 (January 2, 2022): 69–72. http://dx.doi.org/10.1080/13574809.2022.1994772.

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Gdakowicz, Anna. "Japanese candlestick charting residential real estate market." Zeszyty Naukowe Uniwersytetu Szczecińskiego Finanse, Rynki Finansowe, Ubezpieczenia 2015, no. 75 (June 30, 2015): 149–58. http://dx.doi.org/10.18276/frfu.2015.75-12.

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Hau, Lene Vestergaard, J. A. Golovchenko, and Michael M. Burns. "A new atomic beam source: The ‘‘candlestick’’." Review of Scientific Instruments 65, no. 12 (December 1994): 3746–50. http://dx.doi.org/10.1063/1.1145246.

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Zilberkweit, Ian. "FROM BANKER TO BAKER VIA CANDLESTICK MAKER." Business Strategy Review 25, no. 4 (November 24, 2014): 62–65. http://dx.doi.org/10.1111/j.1467-8616.2014.01125.x.

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Hunt, Cheryl. "Candlestick and faces: Aspects of lifelong learning." Studies in the Education of Adults 31, no. 2 (October 1999): 197–209. http://dx.doi.org/10.1080/02660830.1999.11661412.

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Deftereos, Savas P., Marianna Skordala, Ioanna Spanopoulou, and Panos Prassopoulos. "The “Candlestick Sign” on Head Ultrasound Imaging." Journal of Pediatrics 188 (September 2017): 303–303. http://dx.doi.org/10.1016/j.jpeds.2017.04.038.

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Brownsword, R., E. E. H. Pitt, and J. Wilkin. "A Technical Note on the ‘Gloucester Candlestick’." Journal of the British Archaeological Association 138, no. 1 (January 1985): 168–70. http://dx.doi.org/10.1179/jba.1985.138.1.168.

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38

Moyle, Peter B. "Fish imagery in art 53: dolphin candlestick." Environmental Biology of Fishes 38, no. 4 (December 1993): 320. http://dx.doi.org/10.1007/bf00007525.

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Octasylva, Annuridya. "ANALISIS TEKNIKAL SAHAM KONTRUKSI." Jurnal IPTEK 6, no. 2 (August 31, 2022): 23–32. http://dx.doi.org/10.31543/jii.v6i2.211.

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Indeks LQ45 terdiri dari 45 saham-saham aktif di BEI. Sektor saham konstruksi adalah salah satu sektor vital bagi negara. Analisis saham terbagi menjadi dua, yaitu analisis teknikal dan analisis fundamental. Analisis teknikal sendiri adalah analisis yang mengasumsikan bahwa harga saham telah merefleksikan semua informasi tentang perusahaan tersebut. Indikator utama dalam analis teknikal adalah grafik, salah satunya adalah grafik candlestick. Agar akurat dalam menentukan keputusan investasi saham maka digunakan pendekatan rasio fibonacci dan stochastic oscillator. Hasil dari penelitian ini adalah: (1) Pergerakan harga saham perusahaan konstruksi BUMN yang berada pada LQ45 Periode 2018 dengan menggunakan grafik candlestick secara umum mengalami trend menurun. (2) Setiap saham dari konstruksi BUMN pada LQ45 periode 2018 memiliki level support dan resistance yang berbeda-beda. (3) Dengan menggunakan indikator stochastic oscillator semua saham konstruksi BUMN pada LQ45 periode 2018 berada pada normal zone.

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Jain, Rachit, Puru Bhardwaj, and Priyanshu Soni. "Can the Market of Cryptocurrency Be Followed with the Technical Analysis?" International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2425–45. http://dx.doi.org/10.22214/ijraset.2022.41760.

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Abstract: This paper focuses on the role of technical analysis in cryptocurrency and whether cryptocurrency can be followed with technical analysis or not. During this study, we have used the most established technical tools and indicators like the Candlestick pattern, volumes, trends, the Moving Average, Moving Average Convergence Divergence, the Relative Strength Index, Bollinger Bands, Fibonacci Retracement, etc. By analyzing Bitcoin charts, the results indicate that the indicators can be used to generate a significantly positive return. It is found that cryptocurrency can be followed with technical analysis like equity market and forex market. We have used charts that explain how Bitcoin can be traded through technical analysis which is the most hyped-up topic for today’s generation. Keywords: Technical Analysis, Cryptocurrency, bearish, bullish, traders, candlestick pattens, technical indicators, securities, Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI), Bollinger Bands and, trends.

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Harimbawa, Dipta, Moh Hasanudin, Bagas Putra Pradana, and Afiat Sadida. "Design and Development of Information Systems Supporting Stock Investors "Batch of Automatic Stock Analysis System"." Ilomata International Journal of Management 3, no. 2 (April 30, 2022): 222–41. http://dx.doi.org/10.52728/ijjm.v3i2.455.

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Many information systems make it easier for stock investors to make decisions to buy shares. But no information system provides an easy-to-understand display and exclusive features to make it easier for investors to make decisions and find information about stock investments, especially for novice investors. Therefore, this study aims to design and build a web-based information system that can assist stock investors in making decisions and seeking information about stock investments. The design of this information system is equipped with a stock overview, moving average analysis, analysis of Minervini trends templates, analysis of candlestick patterns, stock screeners, and stock lists. Stocks that can be analyzed using technical analysis and candlestick patterns are only stocks belonging to the LQ45, KOMPAS100, IDX80, IDX30, JII70, Investor33, Pefindo25 constituents. The results of this study are stock investors can get stock recommendations in real-time and information about stock investments quickly.

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Hung, Chih-Chieh, and Ying-Ju Chen. "DPP: Deep predictor for price movement from candlestick charts." PLOS ONE 16, no. 6 (June 21, 2021): e0252404. http://dx.doi.org/10.1371/journal.pone.0252404.

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Forecasting the stock market prices is complicated and challenging since the price movement is affected by many factors such as releasing market news about earnings and profits, international and domestic economic situation, political events, monetary policy, major abrupt affairs, etc. In this work, a novel framework: deep predictor for price movement (DPP) using candlestick charts in the stock historical data is proposed. This framework comprises three steps: 1. decomposing a given candlestick chart into sub-charts; 2. using CNN-autoencoder to acquire the best representation of sub-charts; 3. applying RNN to predict the price movements from a collection of sub-chart representations. An extensive study is operated to assess the performance of the DPP based models using the trading data of Taiwan Stock Exchange Capitalization Weighted Stock Index and a stock market index, Nikkei 225, for the Tokyo Stock Exchange. Three baseline models based on IEM, Prophet, and LSTM approaches are compared with the DPP based models.

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Fock, J. Henning, Christian Klein, and Bernhard Zwergel. "Performance of Candlestick Analysis on Intraday Futures Data." Journal of Derivatives 13, no. 1 (August 31, 2005): 28–40. http://dx.doi.org/10.3905/jod.2005.580514.

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BİÇİCİ, H. Kamil. "Patterned Lamp and Candlestick Gravestones in İznik Museum." Journal of Turkish Studies Volume 7 Issue 3, no. 7 (2012): 637–61. http://dx.doi.org/10.7827/turkishstudies.3476.

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Xie, Haibin, Kuikui Fan, and Shouyang Wang. "The role of Japanese Candlestick in DVAR model." Journal of Systems Science and Complexity 28, no. 5 (November 28, 2014): 1177–93. http://dx.doi.org/10.1007/s11424-014-2201-2.

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Xu, Rui, Wenjie Wu, Yanpeng Cai, Hang Wan, Jian Li, Qin Zhu, and Shiming Shen. "Feature Extraction and Prediction of Water Quality Based on Candlestick Theory and Deep Learning Methods." Water 15, no. 5 (February 22, 2023): 845. http://dx.doi.org/10.3390/w15050845.

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In environmental hydrodynamics, a research topic that has gained popularity is the transmission and diffusion of water pollutants. Various types of change processes in hydrological and water quality are directly related to meteorological changes. If these changing characteristics are classified effectively, this will be conducive to the application of deep learning theory in water pollution simulation. When periodically monitoring water quality, data were represented with a candlestick chart, and different classification features were displayed. The water quality data from the research area from 2012 to 2019 generated 24 classification results in line with the physics laws. Therefore, a deep learning water pollution prediction method was proposed to classify the changing process of pollution to improve the prediction accuracy of water quality, based on candlestick theory, visual geometry group, and gate recurrent unit (CT-VGG-GRU). In this method, after the periodic changes of water quality were represented by candlestick graphically, the features were extracted by the VGG network based on its advantages in graphic feature extraction. Then, this feature and other scenario parameters were fused as the input of the time series network model, and the pollutant concentration sequence at the predicted station constituted the output of the model. Finally, a hybrid model combining graphical and time series features was formed, and this model used continuous time series data from multiple stations on the Lijiang River watershed to train and validate the model. Experimental results indicated that, compared with other comparison models, such as the back propagation neural network (BPNN), support vector regression (SVR), GRU, and VGG-GRU, the proposed model had the highest prediction accuracy, especially for the prediction of extreme values. Additionally, the change trend of water pollution was closer to the real situation, which indicated that the process change information of water pollution could be fully extracted by the CT-VGG-GRU model based on candlestick theory. For the water quality indicators DO, CODMn, and NH3-N, the mean absolute errors (MAE) were 0.284, 0.113, and 0.014, the root mean square errors (RMSE) were 0.315, 0.122, and 0.016, and the symmetric mean absolute percentage errors (SMAPE) were 0.022, 0.108, and 0.127, respectively. The established CT-VGG-GRU model achieved superior computational performance. Using the proposed model, the classification information of the river pollution process could be obtained effectively and the time series information could also be retained, which made the application of the deep learning model to the transmission and diffusion process of river water pollution more explanatory. The proposed model can provide a new method for water quality prediction.

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Cohen, Gil. "BEST CANDLESTICKS PATTERN TO TRADE STOCKS." International Journal of Economics and Financial Issues 10, no. 2 (March 14, 2020): 256–61. http://dx.doi.org/10.32479/ijefi.9298.

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Thompson, Neville. "BIRMINGHAM BRASS CANDLESTICKS. Jean M. Burks." Art Documentation: Journal of the Art Libraries Society of North America 6, no. 4 (December 1987): 184. http://dx.doi.org/10.1086/adx.6.4.27947845.

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Mouncey, Peter. "Book Review: The Butcher, the Baker, the Candlestick Maker." International Journal of Market Research 59, no. 4 (July 2017): 537–39. http://dx.doi.org/10.2501/ijmr-2017-038.

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Thammakesorn, Siriporn, and Ohm Sornil. "Generating Trading Strategies Based on Candlestick Chart Pattern Characteristics." Journal of Physics: Conference Series 1195 (April 2019): 012008. http://dx.doi.org/10.1088/1742-6596/1195/1/012008.

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Journal articles on the topic 'Candlestik'

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