The following are the three ways in which we preprocess our images:. Crop the images without the information of the x-axis and y-axis. This is because we want our input data to be as clean as possible. Use the RGB color space to capture the information of moving average lines. Different colors will be given to each moving average line, so the moving average lines will be represented in the different channels.
Invert the color space to highlight only the lines in the image. The background will become black, which means the value of each background pixel is zero. We used moving average lines to simulate our inputs and increase similarity to the trading charts.
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We also tried to set the different y-axes in the same scale. The image of the moving average lines is shown in Figure 9. There are still many different permutations and combinations of the price and moving average lines. The inverted one is shown in Figure In workflow 1, we tried three architectures. The default time region is 20; in each region, we used every 5-day period to create the image data and used the next day to label the input images.
Each architecture used the framework of the moving windows and predicted times. The three architectures we tried are as follows:. We used the first two architectures architectures 1 and 2 because we expected that a simple model could solve our problem; however, the results were not good. Therefore, we next used a deeper structure with architecture 3; we added more convolution layers and filters in the first two layers to help the model extract more detailed information.
We hoped that a more complex architecture would help solve this problem. Unfortunately, neither the simple nor the complex architecture worked well. The complex one did not improve the performance of classification. The experimental results of each architecture are shown below. For architecture 1, we carried out three experiments. The results of the three experiments are described in Figures 11 — 13 , respectively. There is no significant improvement between parameters; the model often predicts the action to be doing nothing. The parameters used in the second experiment are the same as those used in the first experiment; only the architecture of the model is different.
The performance of the second architecture is also poor, with the model once again giving the prediction of taking no action often. One result is shown in Figure We made some changes to the architectures because we obtained poor performance with the architectures and experiments above: we added two more convolution layers and an additional pooling layer to make the model deeper and more complex.
With the new, more complex architecture, we designed three kinds of experiments. The parameters of each experiment are almost the same; the only difference between the three experiments is the number of kernels. This is because we expected more filters would capture more features of the image.
In experiments 1—3, the number of kernels is designated as 5, 10, and The results of each experiment are described in Figures 15 — From the results of the three architectures, we can clearly see that none of the experiments yielded good performance.
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Additionally, each model is unstable due to overfitting. This is because the number of input images is too small to train the convolution model; if the time region is 20 and if we use every 5-day period to create an image, we only have 16 images of training data. The convolution model can fit the given 16 images training data well but cannot recognize images with many differences to the training data.
The only way to obtain more real-world training data is to extend the time region; in finance, however, older information does not help predict future data.
Additional data would only increase the occurrence of noise, meaning we cannot simply extend the time region to collect more training data; an alternative approach is required. Before addressing the real-world data, we wanted to fit the model with the simulated data. This is because the simulated data can give us sufficient data with little noise. In addition, simulated data accurately represent a subset of the real-world data and therefore may be easier to fit.
If we can fit the small world well, the convolution model can learn strategies from it. The three experiments, trained with the simulated data, are introduced in detail as follows. In experiment 1, we used every day period to create an image and the following 5 days as the holding days; that is, we may use days 1—20 as the input image and day 25 to label day The images of the three different classes are shown in Figures 18 — We can clearly see that each class cannot be easily distinguished by humans; this also makes it difficult for the convolution model to recognize the pattern of each class.
In the training process of this case, which is shown in Figure 21 , the loss of the training data and the validation data was not decreasing. The overfitting problem also occurred after the th epoch. This time, the accuracy of the simple convolution model is better than the moving average one. Figures 22 and 23 show the confusion matrix of the training and testing data. Inspired by experiment 1, we tried to use the moving average as our strategy.
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This makes it easier for the convolution model to detect the difference between the strategies. The training process is shown in Figure The problem of overfitting does not occur, which can be explained by the loss of the training data and the validation data. The confusion matrix of the training data and the testing data are shown in Figures 28 and From the result, we can see that the accuracy of each class is not significantly impacted by the overfitting problem.
The accuracy of the testing data is only slightly lower than the training data.
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The experimental results of the images scaled to the maximum and minimum of the prices and moving average are as follows. Figures 30 — 32 show the images classified by the moving average strategy. We used every day period to create an image and the following 5 days as the holding days.
The three kinds of labeled images are shown in Figures 33 — In this case, the images are also distinguished by our strategy. We expected that the accuracy of the classification will be good; the results proved this. We also examine the visualization after the convolution layer. The outputs after the first two convolution layers with the demo image are shown in Figures 37 and 38 ; we can clearly see that the kernels in the first two layers can capture the shape of the lines.
In this image, which is the buy action, the convolution model can clearly capture the pattern of the increasing trend. In workflow 1, neither the simple nor the complex CNN architecture produced the expected performance. The main cause of this is the lack of data for each convolution model.
To expand more training data to fit CNN models parameters, we may attempt to use older historical data. However, the older historical data would only introduce additional noise and further mislead the convolution model. Therefore, we narrowed the scope of our research to fit in the simulated world, which is generated by applying the GBM calibrated from the real-world data. In workflow 2, the main difference between the first two experiments experiments 1 and 2 and the last two experiments experiments 3 and 4 is the strategies employed. In the first two experiments, the trend of the different labels was not obvious, whereas, in the last two experiments, the trend was clearly seen by the human eye.
Therefore, the convolution model showcases better performance for the last two strategies, especially for the buy and sell actions. We conclude that if the strategy is clear enough to make the images obviously distinguishable, then the CNN model can predict the prices of a financial asset; the default AlexNet model is also considered good enough for prediction. There are additional factors that we intend to research in future, for example, combining the convolution model with the other architectures like the LSTM.
The architecture of the time-series model may help the convolution model to capture more information from the pixel images. Binkowski, G. Marti and P. Donnat, Autoregressive convolutional neural networks for asynchronous time series, Search in Google Scholar. Borovykh, S. Bohte and C. Oosterlee, Conditional time series forecasting with convolutional neural networks, Browne, Optimal investment policies for a firm with a random risk process: exponential utility and minimizing the probability of ruin, Math. Di Persio and O.
Honchar, Artificial neural networks approach to the forecast of stock market price movements, Int. Eun and S. Shim, International transmission of stock market movements, J. Fukushima and S. Miyake, Neocognitron: a self-organizing neural network model for a mechanism of visual pattern recognition, in: S.
Amari and M. Arbib, Eds. Mittelman, Time-series modeling with undecimated fully convolutional neural networks, Wang, B. Raj and E. Xing, On the origin of deep learning, Objective The Journal of Intelligent Systems provides readers with a compilation of stimulating and up-to-date articles within the field of intelligent systems. The focus of the journal is on high quality research that addresses paradigms, development, applications and implications in the field of intelligent systems.
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These zones show a possible trend reversal by bars earlier than the standard Hull moving average. Additionally, there are arrows to enter a position and the second is the same MA for another timeframe, which can be selected in This is a quick script that combines two standard indicators, the Awesome Oscillator and MACD histogram, to highlight the beginnings of periods of fast price movement divergence between the two.
Of course both I have been asked by many people for more leading indicators so this one is for you all! Buy when the indicator line is green and sell when it is red. Let me know if there are other indicators you would like to see me Introduction Adaptive technical indicators are importants in a non stationary market, the ability to adapt to a situation can boost the efficiency of your strategy. A lot of methods have been proposed to make technical indicators "smarters" , from the use of variable smoothing constant for exponential smoothing to artificial intelligence.
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