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Cover of Designing Stock Market Trading Systems Paperback by Stock trading simulation for Microsoft Takagi Sugeno Fuzzy inference system learned using most popular soft computing techniques for stock market no thus no network will be able to predict the output and this simple system has provided insight into the design of a successful ANN-based financial trading system, In this paper, a novel decision support system using a computational efficient functional link Trading in stock market is one of the popular channels of financial investment.
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Users may not always know their interests beforehand and they might change Management, Database Designer and Developer,. Teaching In this paper, a novel decision support system using a computational efficient functional link Trading in stock market is one of the popular channels of financial investment. So trading decision taken based on particular technical indicators may not The overall performance of the model compared to other soft computing This has led soft computing techniques to have unmatched growth in the fields Design and development of specific methodologies for social media analytics in the Applications of sentiment analysis and recommender systems: stock market clarity in presentation or fall outside the scope of the special issue will not be Designing Stock Market Trading Systems: WIth and Without Soft Computing Whilst reviewing this book I was struck by the observation that it is only recently that books on systems and systems development and testing have become available to retail traders.
According to the forecast of stock price trends, investors trade stocks. Unexpectedly, the performance of some traditional ML algorithms is not much worse than are significant to choose the best algorithm for stock trading in different markets. The LSTM can add and delete information for neurons through the gating unit.
To determine selectively whether information passes or not, it consists of a Sigmoid neural network layer and a pair multiplication operation. Each element output by the Sigmoid layer is a real number between [0, 1], representing the weight through which the corresponding information passes. In the LSTM neural network, there is also a layer containing tanh activation function which shown in Eq.
It is used for updating the state of neurons. Equation 3 shows the calculation method of forgetting probability. The input gate determines how much new information is added to the neuron state. The output gate is used to control how many current neural unites state are filtered and how many controlling units state are filtered which are shown in Eqs.
To enhance the expressive power of the model, the loop body at each moment can be repeated many times. As shown in Fig. During the process of constructing the task model, the dropout method was used. Dropout refers to the temporary discarding of the neural network unit from the network according to a certain probability during the training of the deep learning network, which is a means to prevent over-fitting.
The neural network model without dropout is shown in Fig. The LSTM-based deep recurrent neural network model with dropout layer was used as the contrast model to verify the feasibility and applicability of the proposed associated neural network model. Since the daily opening price, the lowest price and the highest price of the stock are associated to one another, and the opening price, the lowest price and the highest price are respectively predicted by different networks generally, the associations between one another are separated. Therefore, based on the deep recurrent neural network, a structural model of multi-value associated neural network associated net based on LSTM is designed, it is shown in Fig.
The specific data processing flow of the multi-value associated neural network model is shown in Fig. Data through the input layer to all three branches simultaneously. These three branches predict the opening price, the lowest price and the highest price respectively. Therefor the model recombines the output of the left branch opening price and the output of the LSTM network of the second branch as the input parameter data of the predicted lowest price, and the highest price is subject to the opening price of the day, the impact of the lowest price, so the output of the left branch opening price and the output of the intermediate branch lowest price and the output of the LSTM network of the third branch form the highest of the new data forecast price.
In the model training phase, the total loss L total is used as the evaluation function, and the goal is to get the minimal value. The calculation method of the total loss is shown in Eq. Regression method is used to predict a specific value, which is not a pre-defined category, but an arbitrary real number. Regression problem generally has only one output, and the output is the predicting value.
The loss function used in regression problems commonly is the mean square error MSE Eq. It is the expectation of the square of the difference between the estimated parameter and the actual parameter. MSE can evaluate the degree of change of the function. The smaller the value of MSE, the better the accuracy of the prediction model describing the experimental data.
Therefore, in the training phase, MSE is used as the criterion to measure the quality of a network model. Deep learning often requires a lot of time and computational resources to train. It is needed to find an optimization algorithm that requires less resources and has faster convergence speed. The Adam optimization algorithm is an extension of the stochastic gradient descent algorithm and has great advantages in solving the non-convex optimization problem.
During the training phase, the Adam optimization algorithm is used in the model, and L total is used as the evaluation function. Multiple values associated with neural network model algorithm framework as shown in Fig. Then the Adam algorithm is used to optimize the total loss. When the number of iterations did not reach the set number of iterations in the model, the training will continue to reduce the total loss, otherwise training will stop. There is a parameter of step size in the input of the LSTM neural network that means how many historical data to remember as a reference for predicting the current price.
In order to use a relatively good step size in the experiment of the multi-value associated model, a comparison experiment is performed with sample data, at the step size of 5, 10, 20 and 30, and with the iteration number of The loss variation graphs are shown in Figs. According to the loss variation graph at the step size of 5, 10, 20 and 30, it is found that the loss at the step size of 10 and 20 decreases the fastest and finally reaches a steady state.
By comparing the average loss as shown in Table 1 , it is found that the average loss at step size of five is the lowest. The average loss at the step size of 20 differs from the loss at the step size by 0. Considering the loss variation graph and the average loss comprehensively, 20 is chosen as the step size in the model. The experimental data in this paper are the actual historical data downloaded from the Internet. Three data sets were used in the experiments, one is Shanghai composite index and the others are two stocks of PetroChina stock code on Shanghai stock exchange and ZTE stock code on Shenzhen stock exchange.
Shanghai composite index has historical data; PetroChina has historical data and ZTE has historical data. Each data set is divided into a training set and a test set in chronological order at the ratio of Each data set has seven technical parameters. It is used these technical parameters as basic input attributes, and the OP, LP and HP of the next day as output values of the model.
The identifiers of technical parameters related to stock are shown in Table 2. Due to the different measurement unit of different stock index data, for avoiding the impact of different measurement unit, all the attribute data are normalized to fall within a same range. In this paper, the min—max normalization method is used.
The normalization function is shown in Eq. Through the normalization operation, the data is scaled to [0, 1], which not only speeds up the gradient descent to find the optimal solution, but also improves the accuracy. As shown in Table 3 , it is found that the mean square error of the three models gradually decreased with the increase of training times. From the dimension analysis of the same training times, with the increase of the training times, the LSTM average mean square error is lower than the other two models, but in the test phase, the LSTM has the worst prediction effect and the lowest average accuracy.
Because LSTM has been over-fitting as the number of training increases. Because our model is more complex and requires a larger number of iterations. In order to verify this conjecture, several experiments is conducted on Associated Net. The opening price, the highest price and the lowest price of the next day were trained and predicted by the Associated Net model. As shown in Table 4 , and the experimental results proved our conjecture. The root of this problem is that the associated network model is composed of multiple deep-recurrent neural networks.
The model is complex, the number of neurons is large, and multiple output losses are combined, so the loss of the model decreases slowly. According to the analysis experiment, the model loss chart of each model for iterations is drawn, as shown in Figs. The output of the Associated Net is the total loss and his three sub-losses opening price loss, lowest price loss, highest price loss.
From the analysis for the loss chart, it is found that the loss of each model is gradually decreasing. The LSTM model has multiple fluctuations during the training process.
Introduction
Moreover, the individual sub-loss of the associated network model is also gradually decreased. As shown in Table 4 , although the total loss of Associated Net is higher than that of the other two models, its sub-loss is very low, and by increasing the number of iterations, the total loss of Associated Net is gradually reduced. In order to verify the universality of the model, the historical data of two stocks of PetroChina and ZTE are used to verify the universality of the model.
The experimental results are shown in Figs. Combined Fig. Chapter Case Studies Dublin, Irel and. Short-link Link Embed.
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