Exercise, behavior, and thinking activities all use visual sensory data as their most significant source of information. The more flexible and talented we become, the more we rely on visual intelligence. What general business and decision-makers desire after the analysis is not the data itself, but the value. Therefore, data analyses must be intuitive.
In this way, the visualization of financial data more readily accept: decision-makers can see the story and interpret the data more efficiently. Although visualization analysis can benefit decision-makers, many traditional statistical or machine learning methods for predicting currency movements use quantitative models. These methods do not consider visualization.
We attempt to make good use of the advantages of display and comprehensively enhance the efficiency of intelligence analysis. For example, most traders use charts to analyze and predict currency movement trends, which carry apparent economic benefits. However, in this visualization, the analysis is artificial. We aim to teach machines to achieve the interpretation of visual information like a human brain. We then hope to use the tool to analyze robust financial data visually. The CNN models use in pattern and image recognition problems widely. In these applications, the best possible accuracy has achieved using CNNs.
For example, the CNN models have achieved a accuracy of The CNN models not only give the best performance compared to other detection algorithms but also outperform humans in such cases as classifying objects into fine-grained categories, such as particular breeds of dogs or species of bird. The two main reasons for choosing a CNN model to predict currency movements are as follows:.
The CNN models are good at detecting patterns in images, such as lines. We expect that this property can use to detect trends in trading charts. The CNN models can detect relationships among images that humans cannot find easily. The structure of neural networks can help detect complicated relationships among features. GAF is a novel time-series encoding method proposed by Wang and Oates Wang and Oates , which represents time series data in a polar coordinate system and uses various operations to convert these angles into symmetry matrix.
Each element of the GASF matrix is the cosine of the summation of angles. Our first step to making a GAF matrix is to normalize the given time series data X into values between [0,1]. After normalization, our second step is to represent the normalized time series data in the polar coordinate system. The following two equations show how to get the angles and radius from the rescaled time series data.
Finally, we sum the angles and use the cosine function to make the GASF by the following equation:. The GASF has two essential properties. In other words, normalize data to [0,1] can transform the GASF back into normalized time series data by the diagonal elements.
Second, in contrast to Cartesian coordinates, the polar coordinates preserve absolute temporal relations. 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.
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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. Further, it will adopt in the empirical research on real-world data. The simulation data are including the training data, validation data, and testing data from the Geometric Brownian Motion GBM model. We select eight of the most classic candlestick patterns based on a classic candlestick patterns textbook, The Major Candlesticks Signals, as our training target.
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All of these patterns are reversal patterns, which capture whether the price is going to change. The first four patterns detect the price from downtrend to uptrend, and the last four patterns detect the opposite. We illustrate Morning Star and Evening Star as examples below.
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The Morning Star pattern detects a price changing from a downtrend to an uptrend. The description of this pattern has three stages. First, a downtrend must be confirmed, which means the whole market has an absence of confidence. Second, the depressed atmosphere results in a big black bar. After a calm day, the third bar is a big white bar, which indicates that the investors expect the confidence of the market to reverse. Figure 5 shows the main appearance and rules of Morning Star in detail.
The left-hand side shows the appearance of the Morning Star pattern. The right-hand side shows the critical rules of the Morning Star pattern. The Evening Star pattern detects the price changing from an uptrend to a downtrend. The description of this pattern also has three stages. First, an uptrend must be confirmed, which means the whole market is in a specific situation. Second, good days end with a big white bar. After a calm day, the third bar becomes a big black bar. These indicate that the investors expect the confidence of the market to reverse.
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Figure 6 shows the main appearance and rules of Evening Star in detail, and Fig. The left-hand side shows the appearance of the Evening Star pattern. The right-hand side shows the critical rules of the Evening Star pattern. The definition of our label bases on the rules given in The Major Candlesticks Signals, as shown in Figs. 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.
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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.
Introduction
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.
- Encoding candlesticks as images for pattern classification using convolutional neural networks.
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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.