The downtrend and uptrend define from regression. If the slope is higher or lower enough, the trend is confirmed. The definition of slope in our implementation is as follows, Fig. If the current slope is over the 70th percentile of the group, then it will be defined as a positive or negative trend. We must note that the other pattern rules are slightly different between the simulation and the real data.
The rules from the simulation data are similar to the book. Nevertheless, the number of samples is insufficient in real-world data because of the strictness of the rules. Hence, we relax the rules to obtain sufficient data slightly. For example, the Bullish Engulfing pattern requires the opening price of the last bar to be lower than the closing price of the previous bar. If this rule is too strict, we relax the condition such that the opening price of the last bar only needs to be less than or equal to half of the real body of the previous bar.
After this step, the shape of the data matrices will be 10,10,4. In the second step, we train this 3-d matrices data with the CNN model. According to the previous section, the candlestick patterns cannot judge from a single value such as closing or opening price. Therefore, we need to combine opening, high, low, and closing prices OHLC and make the data features more reasonable.
In order to close to humans have seen, we consider using the upper shadow, lower shadow, and real-body, which are more intuitive features for humans. Figures 9 and 10 are based on different features respectively of the Morning Star and Bearish Engulfing patterns through. Examples are the Morning Star patterns. Examples are the Bearish Engulfing patterns. Figures 9 and 10 show the visualization of the GASF matrix in two kinds transformation rules.
Figure 10 shows more capable of extracting distinctive features observed than Fig. Because the differences between the opening, high, low, and closing prices OHLC are generally small, resulting in high similarity among these four GASF matrices. Hence, we process the data into the features of the second transformation rule CULR. When we use this transformation rule, the four features are not similar and pop out the significant 2-D features in the GASF matrix.
From another perspective, this is a more intuitive approach that aligns with the observations of traders. Therefore, we design our experiments using. The GAF-CNN model works well with the simple neural architecture, two convolutional layers with 16 kernels, and one fully-connected layer with denses. The max-pooling layer, which uses general picture classification, calculates the maximum value for each patch of the feature map usually.
In other words, it may bring benefits about calculating cost-saving, but truncate the characteristics of the time series, which means discard information of data. Therefore, we design an experiment using a max-pooling layer or not in simulation data. Figure 11 illustrates where to use the max-pooling or not. Previous research on the candlestick with deep learning is about trading strategy but lack of pattern classification.
It is hard to find the result from other studies to compare the GAF-CNN model, so we chose the Long Short-Term Memory model LSTM for reliable comparison since it is a standard method to accomplish the time series classification or regression tasks in the current year. Our goal is to achieve or surpass the performance of the LSTM model. The architecture used in this study include two hidden layer size of LSTM layer and follow by a dense layer Smirnov and Nguifo Each experiment searches times to find out the best model and predict testing data.
Figures 13 and 14 respectively show the confusion matrix of GAF-CNN model without max-pooling layer and with the different feature sets:. The accuracy is The result of using 2 closing, upper shadow, lower shadow, and real-body CULR can achieve If we focus on the result from class 1 to class 8, then the performance is Figures 15 and 16 show the confusion matrix of LSTM model with two feature sets respectively. The accuracy of using 1 opening, high, low, closing prices OHLC is To explore more about the model training process, a comparison of the first 50 epochs under different conditions would help to realize the rate of convergence.
Figure 17 and 18 depict the difference of both feature sets and using max-pooling or not respectively. The loss and accuracy are from the first 50 epochs between using the max-pooling layer and without using the max-pooling layer. Therefore, we used two times as much data in training set for class 0, which is the noisy data for the other classes.
The purpose of this is to help the model clearly distinguish the patterns and increase the robustness. Based on the results of the simulation data, we chose to use closing, upper shadow, lower shadow, and real-body CULR as our feature set, and to exclude the pooling layers in our model. Figure 19 shows the confusion matrix of the real-world framework.
First of all, Fig. In Fig. This result is intuitive that this feature set is more close to trader way, observing the characteristics of the candlestick. Secondly, the model also converges faster when using 2 without max-pooling layer than 1 with the max-pooling layer in Fig. The result can explain that the dependency on time series data contains many essential features. The complete time-series information will be truncated after the processing of the max-pooling layer, making it harder for the convolutional model to capture more detail features.
It achieves Besides, our results show that class 0, which is the other class, has reduced precision and recall. The class does not affect the usability of the framework because, although class 0 does not perform well, as long as the accuracy of the other classes is high enough, the cost of misclassification is small. The result in Fig. Therefore, our experimental results show that the GAF and the CNN framework are well-suited for candlestick pattern recognition for both simulation and real-world trading data.
Candlestick pattern recognition is an indicator that traders often judge with news, fundamentals, and technical indicators. However, even today, most traders decide by using their vision and experience. Although many people have directly drawn up rules to find patterns, the process is too cumbersome and hard to judge without the provision of soft scores.
To better align with how traders identify patterns, we chose to use the two-dimensional CNN model. In the simulation framework, we use eight candlestick patterns to test how the max-pooling layer and feature sets impact our model. The results indicate the following:. We think that the time series are truncated and lead to the loss of practical information. Using the feature set of closing price, upper shadow, lower shadow, and real-body CULR is better than using the simple feature set of opening, high, low, and closing prices OHLC.
The model achieved an average accuracy of Although the 0 class is prone to misclassification, the model is still available for practical work as long as the main pattern resolutions and recall are high enough. The model obtained In real-world data, class 0 has more false positives than other types, but the main kind of recall is a certain extent. It can be considered a more conservative model. Now we only use the eight main candlestick patterns. Thus, the entire architecture in finance candlestick, and the extensibility of the models is enormous.
In this study, we find that the Convolutional Neural Network model can detect financial time series data effectively, and our research workflow is as follows:. The data in this stage is still a 10 by 4 matrix, where 4 represents the features. Encode time series data by Gramian Angular Summation Field. The data will become 10 by 10 by 4 in this stage. Each framework of training, validation, and testing is with the Convolutional Neural Network model.
The first step is each experiment test in the simulation framework, then apply the result of feature sets and neural architectures to the real-world framework. In all experiments, the convolution model use only two convolutional layers with 16 kernels and one fully-connected layer with denses.
All these processes illustrate in Fig. Aziz, R, Verma C, Srivastava N Artificial neural network classification of high dimensional data with novel optimization approach of dimension reduction. Ann Data Sci — Article Google Scholar. Google Scholar. Bulkowski, TN Encyclopedia of candlestick charts, Vol. Wiley, Hoboken. Book Google Scholar. Dhar, V, Chou D A comparison of nonlinear methods for predicting earnings surprises and returns.
Fukushima, K, Miyake S Neocognitron: A selforganizing neural network model for a mechanism of visual pattern recognition In: Competition and cooperation in neural nets, — Gamboa, JCB Deep learning for time-series analysis. Invest Manag Financ Innov — Hall, SC Predicting financial distress. J Financ Serv Professionals He, Z Optimal executive compensation when firm size follows geometric brownian motion. Rev Financ Stud — Inf Sci — Krizhevsky, A, Sutskever I, Hinton GE Imagenet classification with deep convolutional neural networks In: Advances in neural information processing systems, — LeCun, y, Bengio Y, et al.
Handb Brain Theory Neural Netw LeCun, Y, et al. Accessed 30 Apr Li, T, Kou G, Peng Y Improving malicious urls detection via feature engineering: Linear and nonlinear space transformation methods. Inf Syst J Bank Finance — Nison, S Japanese candlestick charting techniques: a contemporary guide to the ancient investment techniques of the Far East. Penguin, Westminster. Adv Analytics Learn Temporal Data Fuzzy Sets Syst — J Int Money Finance — Download references.
You can also search for this author in PubMed Google Scholar. Yun-Cheng Tsai conceived of the presented idea. Jun-Hao Chen developed the theory and performed the computations. All authors discussed the results and contributed to the final manuscript. Both authors read and approved the final manuscript. Correspondence to Yun-Cheng Tsai. Jun-Hao Chen and Yun-Cheng Tsai declare that we have no significant competing financial, professional or personal interests that might have influenced the performance or presentation of the work described in this manuscript.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Reprints and Permissions. Chen, JH. Encoding candlesticks as images for pattern classification using convolutional neural networks. Financ Innov 6, 26 Download citation. Received : 30 April Accepted : 20 May Published : 04 June Anyone you share the following link with will be able to read this content:.
Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all SpringerOpen articles Search. Download PDF. Abstract Candlestick charts display the high, low, opening, and closing prices in a specific period. Introduction Financial market forecasts are critical research topics in commercial finance and information engineering. The primary diagonal of the Gramian Angular Field matrix is the particular case.
Preliminary Candlestick Japanese start using technical analysis to trade rice in the 17th century Wagner and Matheny Full size image. The convolutional operation. The pooling operation. The classic LeNet model. Methodology This section begins with the overall experiment design, then illustrates the method of label creation, GAF-CNN model, feature selection, and neural architecture searching, respectively.
Experiment design Considering real-world data lacking and complexity, it starts with simulation data to ensure GAF-CNN model work and progress feature selection and neural architecture search. Illustration of label creation We select eight of the most classic candlestick patterns based on a classic candlestick patterns textbook, The Major Candlesticks Signals, as our training target.
The morning star and evening star patterns recognize in real-world data. The flowchart of our slope definition. The max-pooling use-case. Results Baseline Previous research on the candlestick with deep learning is about trading strategy but lack of pattern classification. The result is the difference between feature sets and neural architectures. The loss and accuracy are from the first 50 epochs within two feature sets.
The confusion matrix is from the real data framework. The workflow of the entire experiment. Discussion Simulation results First of all, Fig. Empirical results The result in Fig. Conclusions Candlestick pattern recognition is an indicator that traders often judge with news, fundamentals, and technical indicators.
The results indicate the following: 1. Workflows In this study, we find that the Convolutional Neural Network model can detect financial time series data effectively, and our research workflow is as follows: 1. Eight candlestick labels reference from The Major Candlestick Signals.
References Aziz, R, Verma C, Srivastava N Artificial neural network classification of high dimensional data with novel optimization approach of dimension reduction. Article Google Scholar Fukushima, K, Miyake S Neocognitron: A selforganizing neural network model for a mechanism of visual pattern recognition In: Competition and cooperation in neural nets, — Google Scholar He, Z Optimal executive compensation when firm size follows geometric brownian motion.
Google Scholar LeCun, Y, et al. Article Google Scholar Nison, S Japanese candlestick charting techniques: a contemporary guide to the ancient investment techniques of the Far East. Acknowledgements Thanks to Prof. Jane Yung-Jen Hsu for constructive discussion and great support. View author publications. Ethics declarations Competing interests Jun-Hao Chen and Yun-Cheng Tsai declare that we have no significant competing financial, professional or personal interests that might have influenced the performance or presentation of the work described in this manuscript.
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The short shade fr om one or the other side indicates greater chances of the movement in definite direction. In such circumstances, even a small growth in volume of trade may cause a strong price movement; more often there is a trend to reverse. Let us remind the main candlestick trading systems:. Any pattern makes sense only wh ere it reaches the strongest level.
If reversal pattern succeeds then it will be followed by continuous definite movement. All trading patterns made up of candlesticks would lose their significance if during current movement trend or correction in price movement this pattern applied more than once. This is especially true for Doji candlestick patterns.
The most reliable Japanese candlestick signals appear on Daily timeframe. Following timeframe decrease, the reliability of the signals lowers. Candlestick forex trading strategy uses this candlestick pattern as reversal signal or the correction start. Trading asset: any currency pair. Trading period: the European and the US sessions. Timeframe: D1 or H1. Candlestick trading strategy for signal to buy: The formation of candlestick «engulfing» pattern is required on the low of the downward trend.
The signal is confirmed: it can be Doji candlestick pattern or one more Engulfing pattern in the same direction. Low of the first Engulfing pattern must not be renewed, moreover - the more remote the price, the stronger a trading signal. At the moment of the next candlestick opening we will open a long position. Stop Loss will be fixed below a Low confirmation signal. Candlestick strategy forex for signal to sell: The formation of candlestick «engulfing» pattern is required on the high of the upward trend.
The signal is confirmed: Doji candlestick pattern or one more Engulfing pattern in the same direction. High of the first Engulfing pattern must not be renewed. We will open a short position at the moment of the next candlestick formation. Stop Loss will be set above the High confirmation signal. The trading strategy uses candlestick patterns with high reliability level and sliding average for the determination of the current trend.
EMA 9 is advised for the popular currency pair trading on M15 timeframe. Trading period: the European and the US trading session. For the long position buy , the existence of the «free» bullish candle above EMA 9 is required. The Stop Loss is fixed in max level of the «free candle». For a short position sell a «free bearish candle» should be fixed below the moving average. The entry at the opening of the next candle depends on the market or should be made by a pending Sell Stop order.
Candlestick charts highlight the open and the close of different time periods more distinctly than other charts, like the bar chart or line chart. Candlestick formations and price patterns are used by traders as entry and exit points in the market. Forex candlesticks individually form candle formations, like the hanging man, hammer, shooting star, and more. Forex candlestick charts also form various price patterns like triangles , wedges, and head and shoulders patterns.
While these patterns and candle formations are prevalent throughout forex charts they also work with other markets, like equities stocks and cryptocurrencies. Trading forex using candle formations:. The hanging man candle , is a candlestick formation that reveals a sharp increase in selling pressure at the height of an uptrend. It is characterized by a long lower wick, a short upper wick, a small body and a close below the open.
It is a bearish signal that the market is going to continue in a downward trend. Learning to recognize the hanging man candle and other candle formations is a good way to learn some of the entry and exit signals that are prominent when using candlestick charts. This means that each candle depicts the open price, closing price, high and low of a single week. The hanging man candle below circled is a bearish signal.
A shooting star candle formation, like the hang man, is a bearish reversal candle that consists of a wick that is at least half of the candle length. The long wick shows that the sellers are outweighing the buyers. A shooting star would be an example of a short entry into the market, or a long exit. Traders could take advantage of the shooting star candle by executing a short trade after the shooting star candle has closed.
Traders could then place a stop loss above the shooting star candle and target a previous support level or a price that ensures a positive risk-reward ratio. A positive risk-reward ratio has been shown to be a trait of successful traders. The hammer candle formation is essentially the shootings stars opposite. It is a bullish reversal candle that signals that the bulls are starting to outweigh the bears.
It is characterized by its long wick and small body. A hammer would be used by traders as a long entry into the market or a short exit. The image below is an example of how a forex trader would use the hammer candle formation to enter a long trade, while placing a stop-loss below the hammer candle and a take profit at a high enough level to ensure a positive risk-reward ratio. Supplement your understanding of forex candlesticks with one of our free forex trading guides.
Our experts have also put together a range of trading forecasts which cover major currencies, oil , gold and even equities. DailyFX provides forex news and technical analysis on the trends that influence the global currency markets. Leveraged trading in foreign currency or off-exchange products on margin carries significant risk and may not be suitable for all investors. We advise you to carefully consider whether trading is appropriate for you based on your personal circumstances.
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