Japanese Candlestick Research Articles

The Role of Japanese Candlesticks in Analyzing the Price Movement of the Saudi General Index during the Period (2021-2018)

The Role of Japanese Candlesticks in Analyzing the Price Movement of the Saudi General Index during the Period (2021-2018)

Novel comparative methodology of hybrid support vector machine with meta-heuristic algorithms to develop an integrated candlestick technical analysis model

PurposeThe proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).Design/methodology/approachIn addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent.FindingsResults have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.Research limitations/implicationsIn this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions.Originality/valueIn this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

A study into the relationship between the type of Japanese candlesticks of a nationally significant exchange-traded asset and investor sentiment in the national financial market

Subject. This article examines the relationship between the type of Japanese candlesticks of a nationally significant exchange-traded asset and investor sentiment in the national financial market. Objectives. The article aims to determine the relationship between the type of Japanese candlesticks for a nationally significant asset traded on the national stock exchange and the sentiments of financial market participants. Methods. For the study, we used the methods of induction and deduction, statistical methods for processing the results of observations, and the method of physiognomy in determining the emotions of persons making investment decisions. Results. The article determines the type of Japanese candlesticks for a nationally significant asset traded on the national stock exchange and the sentiments of financial market participants. Conclusions. When taking into account new indicators, the analyst has the means to bring the forecast result closer to real conditions, when, in addition to traditional indicators, combinations of technical analysis tools and psychological indicators in the markets give a new interpretation of the events taking place there. This improves the quality of analytical work and its results. The results of the research described in the article can be applied in the work of a market analyst. The use of new indicators complements and expands the classic analytical tools, improves the quality of market forecasts.

Can Japanese Candlestick Patterns be Profitable on the Component Stocks of the SSE50 Index?

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.

Trend Lines and Japanese Candlesticks Applied to the Forecasting of Wind Speed Data Series

One of the most critical issues for wind energy exploitation is the high variability of the resource, resulting in very difficult forecasting of the power that wind farms can grant. A vast literature has therefore been devoted to wind speed and wind power quantitative forecasting, using different techniques. The widely used statistical and learning models that are based on a continuation in the future of the series’ past behaviour offer a performance that may be much less satisfactory when the values suddenly change their trend. The application to wind speed data of two techniques usually employed for the technical analysis of financial series–namely support and resistances identification and candlestick charts–is investigated here, with the main aim to detect inversion points in the series. They are applied to wind speed data series for two locations in Spain and Italy. The proposed indicators confirm their usefulness in identifying peculiar behaviours in the system and conditions where the trend may be expected to change. This additional information offered to the forecasting algorithms may also be included in innovative approaches, e.g., based on machine learning.

A Novel Approach to Generalized Intuitionistic Fuzzy Sets Based on Interpolative Boolean Algebra

One of the main issues in IFS theory are generalizations of intuitionistic fuzzy set (IFS) definition as well as IFS operations. In this paper, we present the LBIFS-IBA approach by applying operations based on interpolative Boolean algebra (IBA) on generalized IFS. Namely, LBIFS are defined as a special case of Liu’s generalized IFS with the maximal interpretational surface. By extending the interpretational surface, the descriptive power of the approach is enhanced, and therefore the problematic situations when μA+νA>1 can be modeled. In addition, IBA-based algebra secures Boolean properties of the proposed approach. Considerable attention is given to comprehension of uncertainty within LBIFS-IBA, i.e., we propose a novel manner of uncertainty interpretation by treating values from [−1,1] interval. In order to prove its importance, we compare LBIFS-IBA with several well-known IFS generalizations, showing that only our approach offers meaningful uncertainty interpretation is all selected cases. Additionally, we illustrate the practical benefits of LBIFS-IBA by applying it to an example of modeling Japanese candlesticks for price charting and paying special attention to uncertainty interpretation.

Forecasting US Stock Market Returns: A Japanese Candlestick Approach

A Japanese candlestick chart consists of not only the closing price but also the high, low and opening price information. Using the Japanese candlestick, this paper investigates the forecasting power of the shadow in Japanese candlestick chart. Empirical studies performed with the US stock market show that 1) there is a significant Halloween effect in the shadow; 2) shadow is valuable for predicting the stock market returns in both statistical and economic sense; 3) the predictability reported by the shadow can not be explained by either the CAPM model or the Fama-French three-factor model. This paper confirms that predictability of the stock market can be improved if more price information is used.

Application of Cloud Model in Qualitative Forecasting for Stock Market Trends.

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.

Bullish and Bearish Engulfing Japanese Candlestick patterns: A statistical analysis on the S&P 500 index

Technical analysts believe that by studying past price action, it is possible to forecast future prices of specific securities. Technical analysts rely on the use of charts, which graphically show the past fluctuations of prices. This study focuses on the chart style known as Japanese candlesticks, which relies on four pieces of information for every session: Open, High, Low and Close prices. It is believed that some candle patterns possess predictive capabilities that can alert investors of imminent price tops, bottoms, or price trend continuations. For this study, we performed a statistical analysis, using historical prices of the S&P 500 index, of the effectiveness of Bullish Engulfing and Bearish Engulfing patterns, which are believed to forecast bottoms and tops respectively. Results indicate that the Bearish Engulfing provide strong short-term forecasting power when using the Open and High criteria but not the Close criterion. Likewise, the Bullish Engulfing offered strong short-term forecasting power when using the Open and Low criteria but not the Close criterion.

Representation of Japanese Candlesticks by Oriented Fuzzy Numbers

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.

A fuzzy decision system for money investment in stock markets based on fuzzy candlesticks pattern recognition

This article proposes a novel fuzzy recommendation system for stock market investors. This intelligent decision tool uses fuzzy Japanese candlesticks and includes the effect of currency devaluation on the forecasting. To do so, first the next market session is obtained by a new designed fuzzy forecasting trading system. Then, it is compared to the one obtained by a non-parametric system based on the k-nearest neighbor technique. Finally, an amount of money to be invested is considered using a new capital management fuzzy strategy. The results have been compared to an analogous fuzzy trading system that has the same all-or-nothing investment strategy with risk control, but where this capitalization is not included. Both intelligent decision systems have been applied to two very different stock markets, the American Nasdaq100 and the Spanish Ibex35 markets, using the Buy and Hold investment strategy as benchmark. Results prove that the proposed fuzzy system with capitalization is profitable and presents high stability, and could be a good support system for investors.

The role of pairing the RSI and the Japanese candlestick in making sure to choose the right time to sell and purchasing the stock Applied in a selected sample of companies listed in the Iraqi Stock Exchange

The issue of determining the appropriate timing for the decisions of buying and selling shares is one of the most important topics and the concern of all investors in the stock market, whether they are natural or Morality persons . This interest has been generated by many of those interested in technical analysis to invent techniques, methods and indicators for the purpose of analyzing the performance of the stock market. Maximize the chances of profit and reduce the chances of loss, and investors suffer from the problem of choosing the right time to conduct the sale or purchase of shares of different sectors and companies, and contribute technical analysis techniques in tracking the movement of the prices of those shares to indicate their direction If it is in the case of continuous rise or in the case of continuous decline, if the trend of the movement of shares in the case of continuous rise, this rise will not continue to the end must come a period of time in which prices fluctuate and change direction downward and vice versa in the case of continuous decline, Technical analysis techniques that move through the different graphics and shapes we can use and make use of in the timing of buying and selling shares. There are a lot of technical analysis techniques including Japanese candlesticks, RSI and many more, but important when we want to use We need to use at least two analytical techniques in order to avoid uncertainty in determining the real market trends and thus making the right investment decision, whether it is the sale of shares and the analysis of the mechanism of supply and demand.

Developing A Forex Expert Advisor Based on Japanese Candlestick Patterns and Technical Trading Strategies

Developing A Forex Expert Advisor Based on Japanese Candlestick Patterns and Technical Trading Strategies

Formalization of japanese candlestick patterns taking into account features of foreign exchange markets

Работа посвящена исследованию методов технического анализа финансовых рынков. Глобализация и развитие информационных технологий привели к увеличению оборота валютного рынка. Современные вычислительные средства позволяют решать любые проблемы, связанные с анализом больших объемов данных. Поэтому актуален технический аспект при построении торговой стратегии. Технический анализ - это торговая дисциплина, используемая для оценки ценных бумаг и определения торговых возможностей путем анализа статистических данных, полученных от торговой деятельности, таких как движение цены и объема. Важной частью технического анализа являются паттерны японских свечей. Паттерны японских свечей - это технический инструмент, который собирает данные за несколько временных рамок в отдельные ценовые бары. В статье рассмотрены основные паттерны японских свечей, относящиеся к моделям разворота тенденции. Выполнена формализация рассмотренных паттернов – предложены формульные выражения для дальнейшей автоматизации процесса поиска паттернов. Реализованы полученные модели в виде индикатора для торговой платформы cTrader с использованием объектно-ориентированного языка программирования C #.. Показаны результаты исследований работы разработанного индикатора. В статье рассмотрены основные паттерны японских свечей, относящиеся к моделям разворота тенденции, выполнена формализация рассмотренных паттернов, а также реализация полученных моделей в виде индикатора для торговой платформы cTrader . Показаны результаты работы разработанного индикатора.

A Statistical Analysis Of The Predictive Power Of Japanese Candlesticks

Japanese Candlesticks is a technique for plotting past price action of a specific underlying such as a stock, index or commodity using open, high, low and close prices. These candlesticks create patterns believed to forecast future price movement. Although the candles’ popularity has increased rapidly over the last decade, there is still little statistical evidence about their effectiveness over a large number of occurrences. In this work, we analyze the predictive power of the Shooting Star and Hammer patterns using over six decades of historical data of the S&P 500 index. In our studies, we found out that historically these patterns have offered little forecasting reliability when using closing prices but were highly reliable when using high price for the Shooting Star and low price for the Hammer.

New efficient hybrid candlestick technical analysis model for stock market timing on the basis of the Support Vector Machine and Heuristic Algorithms of Imperialist Competition and Genetic

In this paper, two hybrid models are used for timing of the stock markets on the basis of the technical analysis of Japanese Candlestick by Support Vector Machine (SVM) and Heuristic Algorithms of Imperialist Competition and Genetic. In the first model, SVM and Imperialist Competition Algorithm (ICA) are developed for stock market timing in which ICA is used to optimize the SVM parameters. In the second model, SVM is used with Genetic Algorithm (GA) where GA is used for feature selection in addition to SVM parameters optimization. Here the two approaches, Raw-based and Signal-based are devised on the basis of the literature to generate the input data of the model. For a comparison, the Hit Rate is considered as the percentage of correct predictions for periods of 1–6 day. The results show that SVM-ICA performance is better than SVM-GA and most importantly the feed-forward static neural network of the literature as the standard one.

A Nonlinear Autoregressive Model with Exogenous Variables Neural Network for Stock Market Timing: The Candlestick Technical Analysis

In this paper, the nonlinear autoregressive model with exogenous variables as a new neural network is used for timing of the stock markets on the basis of the technical analysis of Japanese Candlestick. In this model, the “nonlinear autoregressive model with exogenous variables” is an analyzer. For a more reliable comparison, here (like the literature) two approaches of Raw-based and Signal-based are devised to generate the input data of the model. The correct predictions percentages for periods of 1- 6 days with the total number of buy and sell signals are considered. The result proves that to some extent the approaches have similar performances while apparently, they are superior to a feed-forward static neural network. The created network is evaluated by the measure of Mean of Squared Error and the proposed model accuracy is calculated to be extremely high.

Correlation estimation using components of Japanese candlesticks

Using the wick’s difference from the classical Japanese candlestick representation of daily open, high, low, close prices brings efficiency when estimating the correlation in a bivariate Brownian motion. An interpretation of the correlation estimator given in [Rogers, L.C.G. and Zhou, F., Estimating correlation from high, low, opening and closing prices. Ann. Appl. Probab., 2008, 18(2), 813–823] in the light of wicks’ difference allows us to suggest modifications, which lead to an increased efficiency and robustness over the baseline model. An empirical study of four major financial markets confirms the advantages of the modified estimator.

The predictive power of Japanese candlestick charting in Chinese stock market

Abstract This paper studies the predictive power of 4 popular pairs of two-day bullish and bearish Japanese candlestick patterns in Chinese stock market. Based on Morris’ study, we give the quantitative details of definition of long candlestick, which is important in two-day candlestick pattern recognition but ignored by several previous researches, and we further give the quantitative definitions of these four pairs of two-day candlestick patterns. To test the predictive power of candlestick patterns on short-term price movement, we propose the definition of daily average return to alleviate the impact of correlation among stocks’ overlap-time returns in statistical tests. To show the robustness of our result, two methods of trend definition are used for both the medium-market-value and large-market-value sample sets. We use Step-SPA test to correct for data snooping bias. Statistical results show that the predictive power differs from pattern to pattern, three of the eight patterns provide both short-term and relatively long-term prediction, another one pair only provide significant forecasting power within very short-term period, while the rest three patterns present contradictory results for different market value groups. For all the four pairs, the predictive power drops as predicting time increases, and forecasting power is stronger for stocks with medium market value than those with large market value.

Wrapper ANFIS-ICA method to do stock market timing and feature selection on the basis of Japanese Candlestick

Predicting stock prices is an important objective in the financial world. This paper presents a novel forecasting model for stock markets on the basis of the wrapper ANFIS (Adaptive Neural Fuzzy Inference System)-ICA (Imperialist Competitive Algorithm) and technical analysis of Japanese Candlestick. Two approaches of Raw-based and Signal-based are devised to extract the model's input variables with 15 and 24 features, respectively. The correct predictions percentages for periods of 1–6 days with the total number of buy and sell signals are considered as output variables. In proposed model, the ANFIS prediction results are used as a cost function of wrapper model and ICA is used to select the most appropriate features. This novel combination of feature selection not only takes advantage of ICA optimization swiftness, but also the ANFIS prediction accuracy. The emitted buy and sell signals of the model revealed that Signal databases approach gets better results with 87% prediction accuracy and the wrapper features selection obtains 12% improvement in predictive performance regarding to the base study. In addition, since the wrapper-based feature selection models are considerably more time-consuming, our presented wrapper ANFIS-ICA algorithm's results have superiority in time decreasing as well as increasing prediction accuracy as compared to other algorithms such as wrapper Genetic algorithm (GA).