Cnn stock market future forex indicator

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cnn stock market future forex indicator

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However, forecasting stock prices is difficult. Cowles [ 1 ] showed that no skills existed to predict the stock market. Fama [ 2 ] proposed efficient market theory, which states that a stock price already reflects all new information related to the stock and implies that no one can beat the market because stock prices are already set fairly.

Contrary to this theory, many attempts have been made to predict stock prices to obtain profits using various techniques [ 3 , 4 , 5 , 6 ] since determining the market timing for buying or selling a stock at a certain price is an important part of a trading strategy [ 7 ].

Taking an econometric approach, Keim et al. French et al. Jeantheau [ 10 ] suggested that under stationary conditions, the autoregressive conditional heteroskedastic model could be applied to predict stock prices. Ariyo et al. They found that this model had the potential to predict short-term future stock prices on the New York Stock Exchange and the Nigeria Stock Exchange. When using an econometric method, it is advantageous to have explanatory power because this power derives the results given the theoretical background.

However, the assumptions used in econometric models do not necessarily hold in the real world. Artificial neural networks ANNs , by contrast, are not limited by these assumptions and can detect nonlinear relationships in the characteristics of data. Thus, many scholars have been studying the prediction of stock prices based on these models. Kimoto et al.

Kim et al. The results indicated that this approach outperformed conventional neural networks. Tsang et al. Yudong et al. Wang et al. The denoising process was executed on the input data to remove noise. This model achieved more accuracy than did a conventional back propagation neural network based on statistical tests such as mean absolute error, root mean-square error, and mean absolute percentage error.

For time series data, such as text, signals, stock prices, and so on, a long short-term memory LSTM is superior for learning temporal patterns in deep neural networks DNNs. A LSTM overcomes a vanishing gradient problem in a recurrent neural network RNN to learn long-term dependencies in time series data using memory cells and gates.

Attempts have been made to forecast stock prices using this network. Chen et al. They showed that the prediction accuracy improved as the number of inputs increased. Nelson et al. Experimental results showed that their proposed LSTM was more accurate than other machine learning models, such as random forest, multilayer perceptron, and pseudo-random models. Bao et al. First, they used a wavelet transformation to reduce high dimensionality stock data to low dimensionality signal data.

Second, these data were reproduced using a stacked autoencoder. Finally, they used an LSTM to predict stock prices. Financial time series data can be used not only as numeric data but also as image data that is transformed in predicting stock prices.

Technical analysis uses chart images to predict stock prices [ 18 , 19 , 20 , 21 ]. This method involves finding patterns in chart images using technical indicators such as moving averages, Bollinger bands, stochastic oscillators, and so on [ 6 , 22 ]. DNNs, especially convolutional neural networks CNNs , can learn or extract these features themselves.

CNNs have been outstanding for deep learning techniques in the computer vision field, object detection, segmentation, and so on. Through the ImageNet challenge, one of the biggest challenges in the computer vision field, many models, such as AlexNet [ 23 ] Krizhevsky et al. Recent attempts have tried to apply stock chart images to CNNs. They showed that the proposed model was more accurate than a candlestick chart without the data visualization methods applied.

Hu et al. First, they used the convolutional autoencoder method as a tool to extract nonlinear features of the candlestick chart. Second, they clustered the features in a hidden weight layer in the autoencoder. Finally, they constructed a portfolio based on the Sharpe ratio from each cluster. Studies have looked at improving the accuracy of predicting target values by fusing data from different data types or other resources rather than learning from one representation of the data.

Ba et al. They used Caltech-USCD bird and flower data and Wikipedia articles corresponding to the image data, and they extracted features of encyclopedia articles to complement the image data features to predict unseen image classes. The empirical results of this study indicated that their proposed model had a better performance than that of a single model.

Ma et al. The RNN was used to learn the features of the label dependency of the image data. This model performed better than did state-of-the-art models, such as K-nearest neighbor search models, softmax prediction models, metric learning models, and so on.

Donahue et al. Attempts have been made to construct feature fusion-based forecasting models for financial time series. Guo et al. The proposed model, which fused three feature selection techniques, had a better performance than those of other models that combined two feature selection techniques. However, this study used only one representation for the financial time series data. An optimal architecture constructed from different representations will learn duplicate features but will also learn the different characteristics of each architecture, which can improve the prediction accuracy.

From this motivation, in this study, we propose a feature fusion model that integrates a CNN and LSTM to fuse features of different representations from financial time series data to improve accuracy in predicting stock prices. This model learns the patterns of chart images and reflects the temporal characteristics contained in the financial time series data.

To construct a CNN that is optimized for stock chart images, we use residual learning and bottleneck architecture to extract hidden patterns in the stock chart images [ 26 ]. Next, we design the optimal LSTM model using temporal information on close prices and trading volumes. When we train the feature fusion LSTM-CNN model, we use joint training to reflect each model training procedure simultaneously to improve the efficiency of the proposed model.

Using this data, we create different representations to fit our models. We create four stock chart images to check which image is the most appropriate for predicting stock prices. Then, using early fusion, we create three fusion chart images that combine stock chart images with volume information to incorporate more information. The remainder of the paper consists of four sections.

Section 2 explains the materials and methods included in our suggested model. Section 3 details our experimental procedure. The experimental results and discussion are described in Section 4. Finally, Section 5 provides our conclusions of this study.

We collect trade high, low, open, and close price and volume data, which cover 97, data points running from October 14, to October 16, , from the Thomson Reuter Database. Using this financial time series data, we create different representations to match the input types of each model to extract features from the CNN and LSTM.

Fig 1 shows how we set the training, validation, and testing dataset. We use the 68, data points from October 14, , to June 20, , as training data; 10, data points from June 21, , to July 31, as validation data; and 19, data points until October 16, , as testing data. In Fig 2 , we set the window length to 30 minutes, rolling window to 1 minute, and predict term to 5 minutes.

It means we will predict the stock price after five minutes by looking at the data for the previous 30 minutes based on a minute-by-minute current time point. Using the financial time series data, we create four stock chart images as inputs for the CNN, as shown in Fig 3. All of the stock chart images use RGB colors. Fig 3a is a candlestick chart that is comprised of high, low, open, and close prices. Candlestick charts have often been used to identify patterns [ 34 — 36 ].

Fig 3b is a line chart that is comprised of high and low prices. Siripurapu [ 37 ] tried to predict stock prices using a line chart, but the experiment failed because of the lack of information in the chart image. To create the model in this experiment, we incorporate a middle price by averaging the high and low prices, and we then fill the colors between the prices to provide more information to the CNN.

We call this chart a filled line chart f-line chart , which is shown in Fig 3c. In addition to stock price data, trading volume data plays an important role in predicting stock prices [ 5 , 38 ]. Based on this notion, we construct bar charts of the trading volume data to determine whether this data, reconstructed as an image, serves as a key feature to predict stock prices.

Fig 3d is a bar chart of trading volume data. To input the data into the CNN, we resize and crop these images to be x pixels. In the case of supervised learning, each piece of data should have a corresponding label.

However, similar data, such as video data, multi-label image data, and so on, may correspond to one label. By extracting the features of these data and expressing them as one data type, the amount of information can be increased, making learning more efficient. Li et al. They showed that this method provided significant results when using image fusion.

Snoek et al. Early fusion methods are used to combine components of video data, such as visual features, auditory features, and textual features, before executing a trading algorithm. In contrast, late fusion methods combine these input features after executing trading algorithms separately. In this study, we will fuse stock charts with the bar chart shown in Fig 3 using the early fusion method since the bar chart is used as an important factor in sharing the label with the stock charts to predict stock prices.

We call the resulting image a fusion chart image. We create three fusion chart images that combine the stock price charts i. Fig 4 shows three fusion chart images using the early fusion method. Fig 4a is a combination of a candlestick chart and a bar chart that we call a candlebar chart. Fig 4b is a combination of a line chart and bar chart that we call a linebar chart. Fig 4c is a combination of an f-line chart and bar chart that we call an f-linebar chart. The sizes of these images are the same as those of the stock chart images, as described in section 2.

The data used in the LSTM should be time series data to extract the sequential features of the data. In this study, we choose adjusted close price and trading volume data as inputs to the LSTM. Fig 5 shows the preprocess of stock time series data. As making an input data, we take adjusted close price and volume data based on window length, its dimension could be 30x2. We transformed this dataset into logarithmic returns to reduce noise using Eq 1.

Dimensions of the data is 29x2. As making an output data, since our goal is to predict stock prices, we take adjusted close price only. Based on the last sequence of the input data, we take this value and adjusted close price which takes the predict length into account. For example, in Fig 5 , we have sequences of adjusted close price and volume data.

We will apply these output data equally to the output of the chart image because the data representations are made from different but identical input data. RMSE is a good measure for revealing relatively large forecast errors [ 41 ], RMAE is useful for revealing the systematic bias of the model, and MAPE is a measure of the accuracy of predictions in statistics.

These equations are as follows:. The performance of the network can be improved by deepening the network. This method has complicated feature representation capacity by using a complex function that increases the non-linearity in extracting features, improving the performance of the network.

However, due to the network deepening, not only can overfitting occur because of the vanishing gradient problem but also a degradation problem, through which the training loss increases despite the deepening of the network, may arise [ 26 ]. Therefore, using a method to prevent overfitting and the degradation problem while keeping the network deep becomes important. ResNet has overcome this problem by using residual learning and bottleneck methods [ 26 ].

We will use these methods to create a CNN that is optimized for stock chart images. Fig 6a shows a common method to extract features of input X by passing through existing weight layers. A degradation problem may occur even if the network is deeply piled up. To solve this problem, He et al.

In the case of a shortcut connection, the input X is mapped to the feature F X through the activation function without going through the weight layer. Setting the residual to zero makes the optimization easier. This method can solve the problem of degradation due to the deepening of the network [ 26 ]. Fig 7b represents a bottleneck structure that is designed with three weight layers. The feature of this structure is that it decreases time complexity by reducing the computed parameters and increases the number of filters about four times, which can extract many features, by reconstructing 3x3 64 filters as 1x1 64 filters, 3x3 64 filters, and 1x1 filters, which can be compared to Fig 7a.

Thus, this structure can be given a substantial amount of information. A variety of networks use residual learning and bottleneck architecture [ 42 — 44 ]. In Fig 8 , conv1 is a convolutional layer; res-conv1, res-conv2, and res-conv3 are constructed using the residual learning and bottleneck methods; and the fully connected layers are abbreviated as fc1, fc2, and fc3. We modify the ResNet model to match our stock chart images. In the input stage, we resize and crop the stock chart images so that they are x pixels, and we then fine-tune the numbers of convolutional and fully connected layers as well as hyperparameters, such as the dimension of convolutional layers, the number of neurons in fully connected layers, the dropout ratio, and so on, using trial and error.

The SC-CNN model is not quite as deep as the ResNet model, which has fifty layers, because stock chart images are low-dimensional data. However, in order to extract the nonlinear characteristics of the chart images, we try to maintain the depth by using these methods. From this procedure, we construct four convolution layers and three fully connected layers.

Table 1 shows the architectures of the SC-CNN model optimized for stock chart images as a result of trial and error. The experimental procedure will be described in section 3. In addition to image data, various other data types are used in applying deep learning technology; in particular, financial data are often time-series data.

RNNs were initially used to learn the sequential patterns of time series data. However, in the case of RNNs, the problem of the vanishing gradient, which occurs as the network deepens, has not been solved. The network that solved this problem was LSTM. Hochreiter and Schmidhuber [ 45 ] used gate process and memory blocks to solve the vanishing gradient problems in the RNN context.

Fig 9 shows the memory block of an LSTM, and Eqs 6 to 11 show the calculations for each gate and cell state,. For the input and output gates, the weights corresponding to each gate are calculated, and the sigmoid function is taken as the activation function. The sigmoid function takes a value between zero and one. If the output value is one, the corresponding value should be kept, but if it zero, the corresponding value should be completely discarded. For the remaining gate, the input modulate gate, tanh is used to determine how much new information should be reflected in the cell state.

In order to extract the sequential features of the stock time-series data, we design the optimal LSTM model. As mentioned in section 2. The data are generated as a stacked data type so that the data can be inputted simultaneously. The structure of the model consists of two LSTM layers and three fully connected layers determined by trial and error. Fully connected layers are configured to improve the nonlinear prediction power by constructing three layers and to fuse this model with the features extracted from the chart data in our feature fusion LSTM-CNN model.

In predicting stock prices, the fusion of the different features comprising the extracted stock chart images and stock time series data from the same data, can improve the training model. Fig 11 represents the architecture of our proposed feature fusion LSTM-CNN model, whose construction is carried out through a total of three steps. We use the same architecture as in Table 1 , which is based on our experiments with the stock chart images shown in Fig 3 using the SC-CNN model. In training our proposed model, we applied the joint-training method, wherein we can achieve upper-bound performances of our proposed method [ 46 , 47 ].

The loss functions of the models are defined as follows in Eqs 12 to 15 :. These parameters indicate the degree of reflection of each model loss. We take not only the fusion of image and temporal features, which come from concatenating each feature, but also each separate attribute such as image and temporal features. It is composed of three steps. This process involves learning the graphical features of the chart image.

In this process, the temporal feature is learned from the stock time series. We proceed with the experiment as follows. First, we construct the optimized stock chart images for the SC-CNN model shown in Fig 3 , and we then check which stock chart image is the most appropriate for predicting stock prices. Second, we use the fusion chart images shown in Fig 4 to check if these images have better performances than those of stock chart images that are not fused using the SC-CNN model.

The result of this experiment shows that adding information to the chart image is meaningful in predicting stock prices. Finally, we describe the fusion of different representation features of the same data by constructing the feature fusion LSTM-CNN model that we are proposing in this study. Unlike the image data used in the ImageNet challenge, our chart image data come from low-dimensional datasets and do not require as deep a CNN.

If we use the ResNet architecture without modification, overfitting is more likely to occur. Reflecting this problem, we use residual learning and the bottleneck method to capture the nonlinear features of the data by increasing the depth of the network. Furthermore, we construct three fully connected layers to improve the nonlinear predictive power. The dimensions of the convolutional layers and the outputs of the fully connected layers are determined by trial and error, and these values are shown in Table 1.

In addition, we add dropout effects to the fully connected layers of 0. In Fig 3 , the input data are stock chart images, and we fine-tune the hyperparameters to identify the features of the chart images. We set the learning rate, weight decay, and iteration to 0. From this experiment, we can construct an SC-CNN model that is optimized for stock chart images and find the optimal stock chart images for predicting stock prices. Using this model, we can check whether fusion chart images are better than stock chart images for predicting stock prices.

Using the early fusion method, fusion chart images are created by fusing bar charts with other stock chart images, as shown in Fig 4. The architecture of the SC-CNN model and the hyperparameters, such as the learning ratio, weight decay, number of iterations, and so on, are the same as in past experiments since they are already optimized for stock chart images. Furthermore, these settings increase the reliability of our experiment without changing the parameters. From this experiment, we can check whether fusion chart images are better than stock chart images.

In the previous two experiments, chart image data are used as input data. In addition to the features that can be extracted from the chart images in estimating stock prices, since stock prices are a time series, this sequential information can help to predict stock prices if the network is constructed using fusion with the features extracted from the images.

In this experiment, we use stock time series data, specifically, close price and trading volume data. In forecasting stock prices, the close price has been used in the literature as an input [ 4 , 6 , 11 , 15 , 49 ] and trading volumes are also an important factor in predicting stock prices [ 5 , 38 ]. We use the log returns of the close price and trading volume data instead of the raw data to remove noise.

We also stack the data in a 29x2 form to simultaneously input these two values. Using these input data, we construct two stacked LSTM layers and three fully connected layers. The outputs of the LSTM layers and fully connected layers are decided by trial and error. Hyperparameters such as batch size, learning rate, and weight decay are set equal to their values in the above experiments.

From this experiment, we can check which features are better for predicting stock prices. Based on this result, when we fuse different features from each model, we can determine the extent to which we will reflect each feature in the fully connected layer stage. Using these two different representations made from the same data, we can extract the corresponding features of each model.

By fusing these features, if we train the feature fusion LSTM-CNN model, the difference between the prediction and target values can be reduced. Fig 11 shows our proposed model. The training procedure is composed of three stages. In the input phase, the chart image and time series data, which share the same time interval, enter the SC-CNN model and the ST-LSTM model, which are made up of the same data in the input phase, respectively.

This architecture is the same as that described in section 2. The last stage is to fuse the features of the different representations from each model.

Cnn stock market future forex indicator forex time zone converter download

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Forex signal 30 free download Using the early fusion method, fusion chart images are created by fusing bar charts with other stock chart images, as shown in Fig 4. Lecture notes in computer science including subseries lecture notes in artificial intelligence and lecture notes in bioinformaticsLNCS, — Hence for predicting the direction of Nifty and bank nifty from this single model time distributed layer can rapid7 ipo used in the future as well without much of a change in the model architecture. Candlestick charts have often been used to identify patterns [ 34 — 36 ]. Model architecture. Along with the companies, market enthusiasts have also extensively researched the area of using machines to predict stock markets. Hong Kong's monetary policy moves in lockstep with the Fed, as its currency is pegged to the US dollar in a tight range.
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