Relevant bibliographies by topics / Candlestik

Academic literature on the topic 'Candlestik'

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Published: 10 March 2023

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Journal articles on the topic "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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Dissertations / Theses on the topic "Candlestik"

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Landi, Alba. "Candlestick Analysis e Sistemi di Decision. Making in Behavioural Financial Computing." Doctoral thesis, Universita degli studi di Salerno, 2016. http://hdl.handle.net/10556/2175.

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2013 - 2014
This work was created and developed to formulating a complementary methodology to Homo Oeconomicus, infact we to a rational economic operator who founded His work on concepts such as optimization, strategy and technical analysis, we propose an operator psychologically involved that is conditioned by emotions and affections such as: fear, stress, anxiety, greed etc. It is undisputed that in the financial markets some euphoria phenomena, mass psychosis, explosions of exaltation and collective hysteria generate high volatility in the system. In order to analyze the emotional aspects that typically characterize the economic operator is established and developed the Behavioural Financeas as a methodological approach which is opposed to the theories that recognize in the technical analysis the most important method for the pricing evaluation market. The intent is to formulate a descriptive model that takes into account the emotional and affective component and the impact that they exercise for a correct interpretation of the currency markets (Forex) and of the decision making process. This work forms part of the methodologies and mathematical and computer technologies in the trading support, proposing a method to make the Candlestick Analysis automatically thanks to an engine or a computational engine specially developed for the pattern recognition and it defines a methodology for the identification and quantification of emotions and affectivity in a trader. The ultimate goal of the work is to provide a possible decision support system in trading activity. [edited by author]
XIII n.s.
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Hu, Wei Long. "Candlestick pattern classification in financial time series." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950658.

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Martinssson, Filip, and Ivan Liljeqvist. "Short-Term Stock Market Prediction Based on Candlestick Pattern Analysis." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209820.

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This study performs a comparative analysis and evaluates the impact of different Relative Strenght Index (RSI) and stop loss configurations on a trading algorithm based on candlesticks patterns. It is tested on both the Swedish OMXS30 market and the UK FTSE100 market. By tweaking the configurations, RSI and stop loss was found to have a substantial impact on the performance of the algorithm. On both OMXS30 and FTSE100 markets the difference between configurations was shown to be significant
Denna studie gör en jämförelse och analyserar olika Relative Strenght Index (RSI) och stop loss-konfigurationers påverkan på en tradingalgoritm som är baserad på candlestick patterns. Algoritmen är testad på svenska OMXS30 och brittiska FTSE100. Genom att testa olika konfigurationer blev slutsatsen att RSI och stop loss hade en stor påverkar på algoritmens resultat. På både OMXS30 och FTSE100 var skillnaden mellan konfigurationerna signifikant.
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Hutton, Simon. "An analysis of Candlestick charting: the predictive power of the three-outside-up and three-outside-down Candlestick patterns in the context of small capitalization stocks in the USA." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/28975.

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This paper examines the predictive power of two Japanese Candlestick patterns for a 49-stock sample of small capitalization stocks drawn from the S&P 600 for the period 1 June 2005 to 15 May 2015. Using the normal approximation to the binomial for statistical testing and a dynamic holding period strategy to test the threeoutside- up and three-outside-down patterns, this study contradicts earlier works that used dynamic holding period strategies for large capitalization stocks and showed moderate levels of statistically significant predictive power. This study finds no statistically significant evidence of the predictive power of the three-outside-up and three-outside- down patterns for the sample and time period considered. Hence, the findings imply that there is no evidence to challenge the Efficient Market Hypothesis.
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Nortje, Alicia. "The butcher, the baker, the candlestick maker: investigating facial recognition for multiple-perpetrator crimes." Doctoral thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29506.

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In the United States, 20% of all violent crime is committed by multiple perpetrators. Despite the prevalence of multiple-perpetrator crimes, most published eyewitness research uses a single-perpetrator paradigm: that is, witnesses view a crime committed by a single perpetrator whom they must recognise later. Multiple-perpetrator crimes, however, present with several problems. Police procedure for administering multiple-suspect parades is poorly defined. Furthermore, eyewitnesses must make multiple identifications, and are tasked with a unique memory problem of perpetrator-role assignment. I studied these problems in the following ways: (a) a survey among South African detectives (N = 75) to investigate how multiple suspect parades are administered in practice; (b) two face recognition experiments where the number of face-attribute pairs was manipulated at encoding to investigate the effect of set size on both item recognition (for attributes and faces), and associative memory performance (i.e., matching identity to role; N = 70, and N = 67); (c) an eyewitness experiment where participants studied a simulated crime committed by up to 10 perpetrators whom they had to recognise later (N = 200); and (d) a set of simulations testing a revised version of the Interactive Activation and Competition network proposed by Burton et al. (1990) as a computational account of the memory difficulties experienced by eyewitnesses to multiple-perpetrator crimes. Overall, the results suggest that associative memory is particularly vulnerable to the negative effects of set size, and that role-players in law and psychology should consider the implications of these difficulties in court and the laboratory.
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Medved, Paul D. ""A candlestick lifted high" John Calvin and the expansion of the Kingdom of God /." Theological Research Exchange Network (TREN), 2007. http://www.tren.com/search.cfm?p006-1536.

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Maia, Abra?o Vieira. "Timing no mercado de a??es no brasil com padr?es candlesticks e indicadores associados." Universidade Estadual de Feira de Santana, 2018. http://tede2.uefs.br:8080/handle/tede/683.

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Submitted by Verena Pereira (verenagoncalves@uefs.br) on 2018-07-20T23:30:41Z No. of bitstreams: 1 Disserta??oMestradoVers?oFinalCD.pdf: 2552948 bytes, checksum: 021c9ef4954fba969e5860759364257e (MD5)
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The biggest challenge for stock market traders is to identify the timing to enter and exit in a trade. This research uses a trading system based on bullish and bearish candlesticks patterns to identify buy and sell signals in the brazilian?s stock market with exit through the chandelier method. The trading system was tested during the period 2005 and 2010 for application in two strategies between 2011 and 2016. The statistical significance and robustness of the strategies were evaluated through skewness, kurtosis, Monte Carlo, z-score, t-test and walk-forward. They revealed some prediction in bullish candlesticks standards
O maior desafio para os investidores no mercado de a??es ? identificar o momento para entrar e sair de uma negocia??o. Esta pesquisa utilizou um sistema de negocia??o baseado em padr?es candlesticks de alta e de baixa para gerar sinal de compra/venda no mercado brasileiro de a??es e vender/comprar por meio do m?todo chandelier exit. O sistema de negocia??o foi testado para simular negocia??es no per?odo entre 2005 e 2010 e para aplica??o em duas estrat?gias no per?odo entre 2011 e 2016. A signific?ncia estat?stica e robustez das estrat?gias foram avaliadas por meio daskewness, kurtosis, Monte Carlo, z-score, t-test e walk-forward. Eles revelaram algum grau de predi??o nos padr?es de candlesticks de alta
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Sy, Madeline. "A Riddle in Nine Syllables: The Maternal Body in Sylvia Plath's Maternity Poems." Scholarship @ Claremont, 2018. http://scholarship.claremont.edu/scripps_theses/1103.

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This thesis endeavors to intervene in the manner Plath’s maternity poems have been discussed by examining the psychological negotiations of identity that occurred while the speaker’s in Plath’s poems are pregnant with child. The method of this thesis is also a departure from the historicized criticism and interpretations of Plath’s poems that often conflate the experience of the speaker with the details of Plath’s life. This analysis will focus on the poems “Metaphors,” “You’re” and “Nick and the Candlestick” which feature subtle imagery that not only illustrate the speaker’s preoccupation with her own pregnancy but also constructs a metaphorical representation of the maternal body as the locus for the mother’s negotiation of identity. The different forms that the maternal body is represented through, from inanimate objects to a cavernous opening, allow the speaker to fully explore a broad gamut of emotions related to motherhood. The enlargement and reduction of the maternal body, the use of relational language and local instances of transformation are all motifs and conventions that the speakers in Plath’s poems use to navigate the shifting terrain of individual identity during and after maternity. In examining the more abstract poems related to maternity that depict the maternal body through metaphor, this article endeavors to explore the disparate sensations and experiences conveyed in Plath’s poetry.
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Vašíček, Marek. "Testování úspěšnosti základních svícových formací v technické analýze." Master's thesis, Vysoká škola ekonomická v Praze, 2017. http://www.nusl.cz/ntk/nusl-360477.

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This diploma thesis deals with the testing of the success of individual candlestick patterns of technical analysis. In the first part the theoretical basis of technical analysis and candlestick patterns will be presented. The second part will define basic candlestick patterns and their program definition. Backtesting on historical data will verify the success of individual candlestick patterns on EURUSD currency pair. In the third part, a trading system will be built based on the results of the testing of the candlestick patterns. An optimal setting of the trading system will be proposed. The aim of the thesis is to test success rate of candlestick patterns and find out if the candlestick pattern trading system is able to generate profits.
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Giaquinto, Cláudia Daniela Melo. "Metodologia para quantificação e acompanhamento de indicadores-chave de desempenho operacional." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/172050.

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Indicadores-chave de desempenho (KPIs) exercem um papel de extrema importância na indústria de processos, auxiliando na tomada de decisão. No entanto, para serem representativos precisam ser calculados de forma confiável. O presente trabalho propôs uma metodologia para o cálculo destes KPIs com base em técnicas de detecção do estado estacionário, remoção de ruído, propagação de erros e análise de sensibilidade. Estes KPIs foram apresentados, de acordo com o que consta na literatura, em uma nova ferramenta gráfica de acompanhamento proposta pelos autores, denominada StatSSCandlePlot. O StatSSCandlePlot apresenta os KPIs no padrão candlestick, que é bastante utilizado no mercado de ações, incluindo informações adicionais. O grande diferencial do StatSSCandlePlot é que os indicadores e suas respectivas propriedades exibidas são calculadas a partir de técnicas que englobam o tratamento de dados e análises estatísticas. A metodologia proposta foi aplicada em um estudo de caso de um chuveiro contendo dois princípios de aquecimento, gás e energia elétrica. Para este estudo, foi criado o Índice de Qualidade do Banho (IQB), que é um indicador dependente da temperatura e da vazão de saída, cujos dados foram avaliados em três cenários distintos, o primeiro quando o sistema é submetido a distúrbios na vazão, no segundo ocorre uma queda na temperatura da água fria e no último, o IQB foi avaliado quando o sistema foi submetido a distúrbios na vazão sob uma nova estratégia de controle da planta. A partir do StatSSCandlePlot, foi possível identificar as tendências do indicador nos diferentes cenários, a parcela de cada janela no estado estacionário, os valores a serem considerados do indicador e, de forma complementar, identificar a variável que mais influenciou na variação do indicador, através da análise de sensibilidade.
Key performance indicators (KPIs) play an extremely important role in the process industry, aiding in decision-making. However, to be representative they need to be calculated reliably. The present work proposed a methodology for the calculation of these KPIs based on steady state detection, noise removal, error propagation and sensitivity analysis techniques. These KPIs were presented, as far as it is known, in a new graphical KPIs monitoring tool proposed by the authors, called StatSSCandlePlot. StatSSCandlePlot introduces KPIs in the candlestick standard, which is widely used in the stock market, including additional information. The major difference of StatSSCandlePlot is that the indicators and their respective displayed properties are calculated from techniques that encompass data processing and statistical analysis. The proposed methodology was applied in a case study of a shower containing two principles of heating, gas and electric energy. For this study the Bath Quality Index (BQI) was created, which is a temperature and output flow dependent indicator, whose data were evaluated in three different scenarios, the first one when the system was submitted to flow disturbances, in the second one, a decrease in the temperature of the cold water and in the last one, the IQB was evaluated when the system was submitted to disturbances in the flow under a new strategy of control of the plant. From the StatSSCandlePlot, it was possible to identify the trends of the indicator in the different scenarios, the portion of each window in the steady state, the values to be considered in the indicator and, in a complementary way, to identify the variable that most influenced the variation of the indicator, through the sensitivity analysis.
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Books on the topic "Candlestik"

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Weigand, Edith S. Candlestick witness. Denver, Co: Zhera Publications, 1994.

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Atlas, Ted. Candlestick Park. Charleston, S.C: Arcadia Pub., 2010.

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Candlestick Park. Charleston, S.C: Arcadia Publishing, 2010.

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1955-, Scheppe Wolfgang, ed. Candlestick Point. Göttingen, Germany: Steidl, 2011.

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Candlestick charts: An introduction to using candlestick charts. Petersfield: Harriman House, 2009.

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Candlestick charting demystified. New York, NY: McGraw-Hill, 2013.

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The Candlestick Course. Hoboken, NJ, USA: Wiley, 2003.

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Peters, Nick. Miracle at Candlestick! Atlanta, Ga: Longstreet Press, 1993.

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Wilson, Wendell E. Miners' candlestick patents. 2nd ed. Tucson, Ariz: Mineralogical Record, 1985.

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Bulkowski, Thomas N. Encyclopedia of Candlestick Charts. New York: John Wiley & Sons, Ltd., 2008.

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Book chapters on the topic "Candlestik"

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Shimizu, Yosuke. "Reading Candlestick Charts." In Breakthroughs in Technical Analysis, 51–82. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119204749.ch4.

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Mann, Andrew D., and Denise Gorse. "Deep Candlestick Mining." In Neural Information Processing, 913–21. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70096-0_93.

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Nelli, Fabio. "Candlestick Charts with jqPlot." In Create Web Charts With jqPlot, 167–72. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0862-5_7.

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Nelli, Fabio. "Candlestick Charts with D3." In Create Web Charts with D3, 151–60. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0865-6_6.

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Nelli, Fabio. "Candlestick Charts with jqPlot." In Beginning JavaScript Charts, 267–72. Berkeley, CA: Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-6290-9_12.

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Nelli, Fabio. "Candlestick Charts with D3." In Beginning JavaScript Charts, 503–12. Berkeley, CA: Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-6290-9_23.

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Marszałek, Adam, and Tadeusz Burczyński. "Ordered Fuzzy Candlesticks." In Theory and Applications of Ordered Fuzzy Numbers, 183–94. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59614-3_10.

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Birmingham, David. "Butchers, Bakers and Candlestick-Makers." In Switzerland: A Village History, 119–42. London: Palgrave Macmillan UK, 2000. http://dx.doi.org/10.1057/9780230287273_7.

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"candlestick." In The Fairchild Books Dictionary of Interior Design. Fairchild Books, 2021. http://dx.doi.org/10.5040/9781501365171.695.

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"Candlestick." In Encyclopedic Dictionary of Archaeology, 225. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-58292-0_30107.

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Conference papers on the topic "Candlestik"

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Guo, Siou-Jhih, Fu-Chun Hsu, and Chih-Chieh Hung. "Deep Candlestick Predictor: A Framework toward Forecasting the Price Movement from Candlestick Charts." In 2018 9th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP). IEEE, 2018. http://dx.doi.org/10.1109/paap.2018.00044.

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Belford, Peter. "Candlestick stock analysis with genetic algorithms." In the 8th annual conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1143997.1144295.

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Zen-Yu Quan. "Stock prediction by searching similar candlestick charts." In 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW 2013). IEEE, 2013. http://dx.doi.org/10.1109/icdew.2013.6547474.

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Ho, Kin-Hon, Tse-Tin Chan, Haoyuan Pan, and Chin Li. "Do Candlestick Patterns Work in Cryptocurrency Trading?" In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671826.

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Chen, Jun-Hao, Cheng-Han Wu, Yun-Chneg Tsai, and Samuel Yen-Chi Chen. "Explainable Digital Currency Candlestick Pattern AI Learner." In 2022 14th International Conference on Knowledge and Smart Technology (KST). IEEE, 2022. http://dx.doi.org/10.1109/kst53302.2022.9727231.

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Xu, Chenghan. "Image-based Candlestick Pattern Classification with Machine Learning." In ICMLT 2021: 2021 6th International Conference on Machine Learning Technologies. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3468891.3468896.

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Patil, Yogita, and Manish Joshi. "Cluster Driven Candlestick Method for Stock Market Prediction." In 2020 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2020. http://dx.doi.org/10.1109/icscan49426.2020.9262356.

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Lee, Chiung-Hon Leon. "Modeling Personalized Fuzzy Candlestick Patterns for Investment Decision Making." In 2009 Asia-Pacific Conference on Information Processing, APCIP. IEEE, 2009. http://dx.doi.org/10.1109/apcip.2009.207.

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Huili Li, Wing W. Y. Ng, John W. T. Lee, Binbin Sun, and Daniel S. Yeung. "Quantitative study on candlestick pattern for Shenzhen Stock Market." In 2008 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2008. http://dx.doi.org/10.1109/icsmc.2008.4811250.

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Pei, Yongqiang, Jiawei Wang, and Xueqing Tang. "Stock Reversal Pattern Mining Based on Fuzzy Candlestick Lines." In 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). IEEE, 2020. http://dx.doi.org/10.1109/icaica50127.2020.9182613.

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Reports on the topic "Candlestik"

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Cooper, Gene R. Moments on a Coning M864 by a Liquid Payload: The Candlestick Problem and Porous Media. Fort Belvoir, VA: Defense Technical Information Center, July 2006. http://dx.doi.org/10.21236/ada453380.

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