Candlestick Charts Research Articles

Effectiveness of Using Candlestick Charts to Forecast Ethereum Price Direction: A Machine Learning Approach

Cryptocurrency is a form of decentralized digital currency. Ethereum is the second-largest cryptocurrency by market capitalization and the largest altcoin. Cryptocurrencies including Ethereum are highly volatile. Hence, shortterm directional forecasts in the cryptocurrency market have become a widely discussing topic. Candlestick charts are useful visualizations of the open, high, low and close prices which can identify patterns and gauge the near-term direction of prices. This research explores the effectiveness of forecasting hourly Ethereum closing price direction based on candlestick charts within a short time horizon. The proposed forecasting algorithm incorporates clustering methods such as fuzzy K-means, K-means and partition around medoids clustering to cluster candlestick chart properties namely upper shadow length, body length and lower shadow length. Classification methods such as random forest, support vector machine and K-nearest neighbour were used to forecast closing price direction using 16 different predictor variable sets including open, high, low and close prices, candlestick chart price direction, USL, BL and LSL. The accuracy for all considered cases was around 50%. Clustering improved the accuracy slightly and including the CPD with the predictor variable sets under consideration can increase the accuracy slightly. However, this approach is performing better in predicting the Down cases to the total number of actual Down cases because there is a higher sensitivity of 81.20% based on the SVM with Open, High, Low and Close at t in the clustering ignored method.

Japanese candlestick charts for diabetes.

Continuous glucose monitoring (CGM) is a popular technology among the diabetic population, especially in patients with type 1 diabetes and those with type 2 diabetes treated with insulin. The American Diabetes Association recommends standardization of CGM reports with visual cues, such as the ambulatory glucose profile. Nevertheless, interpreting this report requires training and time for CGM to be cost-efficient. In this work it has been proposed to incorporate Japanese candlestick charts in glucose monitoring. These graphs are used in price analysis in financial markets and are easier to view. Each candle provides extra information to make prudent decisions since it reports the opening, maximum, minimum and closing glucose levels of the chosen time frame, usually the daily one. The Japanese candlestick chart is an interesting tool to be considered in glucose control. This graphic representation allows identification of glucose trends easily through the colors of the candles and maximum and minimum glucose values.

Stock Price Prediction Using Candlestick Patterns and Sparrow Search Algorithm

Accurately forecasting the trajectory of stock prices holds crucial significance for investors in mitigating investment risks and making informed decisions. Candlestick charts visually depict price information and the trends in stocks, harboring valuable insights for predicting stock price movements. Therefore, the challenge lies in efficiently harnessing candlestick patterns to forecast stock prices. Furthermore, the selection of hyperparameters in network models has a profound impact on the forecasting outcomes. Building upon this foundation, we propose a stock price prediction model SSA-CPBiGRU that integrates candlestick patterns and a sparrow search algorithm (SSA). The incorporation of candlestick patterns endows the input data with structural characteristics and time series relationships. Moreover, the hyperparameters of the CPBiGRU model are optimized using an SSA. Subsequently, the optimized hyperparameters are employed within the network model to conduct predictions. We selected six stocks from different industries in the Chinese stock market for experimentation. The experimental results demonstrate that the model proposed in this paper can effectively enhance the prediction accuracy and has universal applicability. In comparison to the LSTM model, the proposed model produces an average of 31.13%, 24.92%, and 30.42% less test loss in terms of MAPE, RMSE and MAE, respectively. Moreover, it achieves an average improvement of 2.05% in R2.

Comparison of Stock “Trading” Decision Support Systems Based on Object Recognition Algorithms on Candlestick Charts

Comparison of Stock “Trading” Decision Support Systems Based on Object Recognition Algorithms on Candlestick Charts

Automated cryptocurrency trading approach using ensemble deep reinforcement learning: Learn to understand candlesticks

Despite their high risk, cryptocurrencies have gained popularity as viable trading options. Cryptocurrencies are digital assets that experience significant fluctuations in a market operating 24 h a day. Recently, considerable attention has been paid to developing trading bots using machine-learning-based artificial intelligence. Previous studies have employed machine learning techniques to predict financial market trends or make trading decisions, primarily using numerical data extracted from candlesticks. However, these data often overlook the temporal and spatial information of candlesticks, leading to a limited understanding of their significance. In this study, we utilize multi-resolution candlestick images containing temporal and spatial information. Our rationale for using visual information from candlestick charts is to replicate the decision-making processes of human trading experts. To achieve this, we employ deep reinforcement learning algorithms to generate trading signals based on a state vector that includes embedded candlestick-chart images. The trading signal is generated using a multi-agent weighted voting ensemble approach. We test the proposed approach on two BTC/USDT datasets under both bullish and bearish market scenarios. Additionally, we use an attention-based technique to identify significant areas in the candlestick images targeted by the proposed approach. Our findings demonstrate that models using candlestick images 'as-is', outperform those using raw numeric data and other baseline models.

Machine learning techniques for stock price prediction and graphic signal recognition

Stock market analysis is extremely important for investors because knowing the future trend and grasping the changing characteristics of stock prices will decrease the risk of investing capital for profit. Thereupon, the prediction of stock prices and identifying the graphic signals of candlestick charts, which are two crucial tasks in stock price analysis, attract much attention from investors owing to the returns and risks that coexist in financial markets. To introduce a reliable approach for addressing these challenges, this paper proposes the modeling strategies based on machine learning (ML) techniques. A vector autoregression (VAR)-based rolling prediction model is proposed for forecasting stock prices, and a Gaussian feed-forward neural networks (GFNN)-based graphic signal identification method is introduced to recognize different types of stock price signals. The experimental results demonstrate better performance comparing with the state-of-the-art methods, and it can be successfully applied in real-world stock exchange strategies.

Tourism Business Sustainability Analysis in the Post-Pandemic Era: A Systematic Literature Review

Tourism is one of the areas that has undergone a recovery during the post-pandemic period. However, numerous factors must be considered before this industry can recover from the epidemic that has led tourism to decline globally. This is a crucial question for tourist stakeholders in post-pandemic times. Post-pandemic tourism faced several obstacles in practically all nations, and local governments handled them in different ways. Many experts began writing about tourism's sustainability during and after the pandemic. Based on this, a literature evaluation is required that carefully filters and examines the post-pandemic sustainability of tourism. The results of the review can answer the research questions: whether the right and feasible policies or strategies are implemented in the post-pandemic period for tourism industry players; and what technology is most suitable for tourism sustainability in the post-pandemic period. To answer these questions, a literature review on the topic of tourist sustainability in the post-pandemic period was conducted. The PRISMA approach was used in this study's literature review to attain this purpose. The initial selection was made using Scopus, Proquest, Emerald and Semantic Scholar databases resulting in a total of 386 articles since 2020 based on articles that have been applied filtering and screening processes; they can be categorized into four article topics, namely: Case Study, Conceptual, Strategy and Technology. The findings of the article review that PRISMA can be utilized. For the first question about policies and strategies, synergy and cooperation between components within the penta helix are required before defining tourism-related policies. To ensure tourist sustainability after the crisis phase, it is vital to generate optimism and excitement among tourism industry participants and local residents. Concerning the second research subject, the filtering study lacks research using methods other than those in the trend, such as MCDM or candlestick charts. Future studies can utilise past research. So the distance between research results and actual market conditions can be overcome; if the gap is crossed, the study's conclusions can be employed empirically in stock trading, not just on paper.

Shortlisting machine learning-based stock trading recommendations using candlestick pattern recognition

Quantitative stock trading systems apply automated, data-driven models to invest in the stock markets. In the last two decades, the machine learning community has deeply explored the use of per-stock machine learning models to forecast next-day stocks’ directions and generate profitable trading signals. On the basis of the experience of discretionary traders, some promising attempts to enhance classifier performance by integrating the knowledge extracted from Japanese candlestick charts have been made. However, machine learning-based trading systems tend to generate an excessive number of false signals and do not necessarily consider the information provided by candlestick patterns in the appropriate manner. To alleviate these negative effects, this paper proposes to decouple the machine learning and pattern recognition steps so that the trading system can generate a reduced number of double-checked trading recommendations. Specifically, it proposes to selectively filter out the machine learning-based trading recommendations that are deemed as potentially unreliable according to the recognized graphical patterns. To this aim, it explores various alternatives to combine pattern recognition strategies with different machine learning models, including various shallow and deep supervised models and autoregressive techniques. The experiments, carried out on different market exchanges and under different conditions, demonstrate the effectiveness of the proposed approach in terms of return of investment and maximum drawdown of the trading system.

Financial trading decisions based on deep fuzzy self-organizing map

The volatility features of financial data would considerably change in different periods, that is one of the main factors affecting the applications of machine learning in quantitative trading. Therefore, to effectively distinguish fluctuation patterns of financial markets can provide meaningful information for the trading decision. In this article, a novel intelligent trading system based on deep fuzzy self-organizing map (DFSOM) companied with GRU networks is proposed, where DFSOM is utilized for the clustering of financial data to acquire multiple fluctuation patterns in an unsupervised way. Firstly, in order to capture the trend features and evade the effect of high noises in financial data, the images of extended candlestick charts instead of raw data are processed and the obtained features are applied for the following unsupervised learning, where candlestick charts are produced with both price and volume information. Secondly, by using the candlestick features, a two-layer deep fuzzy self-organizing map is constructed to carry out the clustering, where two-layer models carry out the clustering in multiple time scales to improve the processing of time-dependent information. Thirdly, GRU networks are used to implement the prediction task, based on which an intelligent trading model is constructed. The feasibility and effectiveness of the proposed method are verified by using various real financial datasets.

Candlestick Pattern Research Analysis, Future and Beyond: A Systematic Literature Review Using PRISMA

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.

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

Financial assets considered as time series are chaotic in nature. The main goal of investors is to take a position at the right time and in the right direction by making predictions about the future of this chaotic series. These time series consist of the opening, low, high, and closing prices of a certain period. The approaches used to make predictions about trend direction and strength using moving averages and indicators based on them have noise and lag problems as they are obtained statistically. Candlestick charts, on the other hand, reflect the price-based psychology of bear and bull investors, and facilitate the interpretation of price movements by consolidating the said opening, closing, lowest and highest prices in a single image. It is known that it was applied to Japanese rice markets for the first time in history and there are more than 100 candle patterns. In this study, an extensible architecture software framework using factory patterns and an object-oriented approach is proposed for defining candlestick patterns and developing intelligent learning algorithms based on them. In the studies carried out for financial assets, the profit factor, which shows the portfolio gain of the strategy, is used. It is desirable that this number of wins be greater than 1. When the proposed approach is tested for 5 major financial assets, this value was obtained as greater than 1 for all assets. The proposed software framework can also be used in the development of new robotic approaches in terms of being applicable to all kinds of financial assets in every period.

Forecasting in FOREX the spot price interval of tomorrow with the same information of today. An analysis of the seven majors using a linear regression model based on interval arithmetic

Traders and investors in FOREX use past values to forecast future values where the use of soft computing algorithms is essential. In this path, new linear regression models for interval-valued data (IVD) adapted to interval-valued time series (ITS), or candlestick charts, such as the alpha-[B] model, can help. This is an interval linear regression model based on interval arithmetic with dependent and independent interval-valued variables (IVV) and two parameters, a real number α and an interval [B]. It has been developed for two generic IVV but an open issue is to adapt this model to an autoregressive dynamic (ARD) framework for two consecutive periods. This paper addresses this issue with daily IVD of the seven majors (AUD/USD, EUR/USD, GBP/USD, NZD/USD, USD/CAD, USD/CHF, USD/JPY) in 2010–2021 and some accuracy metrics. An analysis of the alpha estimates is included when using a sliding window of ten until 240 days. Consequently, several rankings for the majors are shown. As a new key research result in FOREX, the alpha-[B]-ARD model is strongly recommended for trading the USD/CHF, USD/CAD, and EUR/USD. Hence, the use of this model provides a key competitive advantage for banks, multinational companies, and organizations. Further research issues are suggested, including extensions of this methodology to stock markets and econophysics.

Predicting Stock Market Volatility from Candlestick Charts: A Multiple Attention Mechanism Graph Neural Network Approach

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.

Candlestick Pattern Classification Using Feedforward Neural Network

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.

Quantitative study of candlestick pattern & identifying candlestick patterns using deep learning for the Indian stock market

The stock market is an integral aspect of any country’s economic infrastructure. Analyzing and attempting to play the markets to maximize profits is an endeavor a large fraction of the population aspires to. Candlestick patterns are the backbone of Technical Analysis, used for trading in the stock market. There are a number of candlestick patterns in the market, each with its own benefits and downsides. Due to this, the task that befalls the hands of analysts is deciding which patterns provide the most effective gauge of the current market situation. Due to the large level of noise and widely recognized semi-strong form of market efficiency, analyzing and forecasting the stock market is infamously difficult. For traders that use Technical Analysis to trade, it's critical to be able to recognize candlestick patterns quickly. We will be attempting to determine their respective effectiveness with respect to the Indian Stock Market via exploratory analysis conducted on real-world market data. Also, we'll use candlestick charts to train neural networks and subsequently find patterns. Deep Learning will be used to recognize candlestick patterns in large-cap Indian equities.

Stock Trading Strategies Based on Deep Reinforcement Learning

The purpose of stock market investment is to obtain more profits. In recent years, an increasing number of researchers have tried to implement stock trading based on machine learning. Facing the complex stock market, how to obtain effective information from multisource data and implement dynamic trading strategies is difficult. To solve these problems, this study proposes a new deep reinforcement learning model to implement stock trading, analyzes the stock market through stock data, technical indicators and candlestick charts, and learns dynamic trading strategies. Fusing the features of different data sources extracted by the deep neural network as the state of the stock market, the agent in reinforcement learning makes trading decisions on this basis. Experiments on the Chinese stock market dataset and the S&P 500 stock market dataset show that our trading strategy can obtain higher profits compared with other trading strategies.

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.

Stock Price Movement Prediction Using Sentiment Analysis and CandleStick Chart Representation.

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.

Quality-aware energy efficient scheduling model for fog computing comprised IoT network

The present phase of the IoT (Internet of Things) networks has no limits to perform loaded data transmissions and computational requirements, which is made possible by Fog Computing technology. However, excessive energy resources and considerable loss of quality in loaded data transmission remains a significant challenge to the Fog Computing comprised IoT networks. This manuscript endeavors to portray a novel scheduling algorithm that optimizes edge node and fog node scheduling processes. The proposed approach is a supervised learning approach, titled "Quality-aware Energy Efficient Scheduling Model for Fog computing comprised IoT networks. The statistical measures moving averages and Heiken-Ashi patterns (Heiken-Ashi has been used in conjunction with candlestick charts to predict the present scheduling opportunities easily) have been used as a base for the proposed scheduling model. The experimental study signifies the performance of the proposed model and is scaled against the contemporary models.

Sistem Analisis Rekomendasi Saham Pada Indeks LQ45 Menggunakan Indikator Moving Average Convergence Divergence (MACD) dan Relative Strength Index (RSI)

The LQ45 stock index is a stock exchange that is in great demand by investors in Indonesia. Therefore, the movement of the stock price is needed by investors to see the investment business prospects. However, stock price movements in a certain period are very volatile. In this case, investors need a monitoring system to assist their investment decisions. One of the most popular analyzes of stock price movements is technical analysis. In this study, the system is a website that can be accessed by the public. It provides technical analysis indicators of Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI), and Candlestick charts. The system uses the AnyChart JS component as a modern JavaScript library chart. The waterfall method is used to assist develop this system. The system consists of two sources API, namely Rakuten Rapid (APIdojo.net) and finnhub.io with a query provider that can connect the Yahoo Finance API. The results of stock recommendations in this system come from determining the largest value for several recommendations generated by the finnhub.io based on basic technical and aggregate analysis (MACD, RSI, and Moving Average). Thus, this recommendation is only a suggestion and cannot be used as an absolute reference.