Binary genetic genetic algorithm trading technology
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Correspondence should be addressed to Chien-Feng Huang. This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The advancement of information technology in financial applications nowadays have led to fast market-driven events that prompt flash decision-making and actions issued by computer algorithms.
This new breed of technology involves the implementation of high-speed trading strategies which generate significant portion of activity in the financial markets and present researchers with a wealth of information not available in traditional low-speed trading environments.
In this study, we aim at developing feasible computational intelligence methodologies, particularly genetic algorithms GAto shed light on high-speed trading research using price data of stocks on the microscopic level. Our empirical results show that the proposed GA-based system is able to improve the accuracy of the prediction significantly for price movement, and we expect this GA-based binary genetic genetic algorithm trading technology to advance the current state of research for high-speed trading and other relevant financial applications.
The advances of information technology and big data research in finance have led to an ever increasing pace to market-driven events and information that prompt decision-making and actions by computerized high-speed trading strategies. Speed has become more important to traders in financial markets because faster trading may bring about more profit opportunities, which appears to drive an arms race among traders to utilize high-speed trading technology for an edge over others.
This new breed of trading technology and platform involves the implementation of low-latency, high-speed trading strategies and has now resulted in remarkable portion of activities in the financial markets [ 1 ]. The time series data in high-speed trading ranges from the granular data of stock transactions at regular intervals of several seconds to the price data irregularly spaced with quotes arriving randomly at intervals of a fraction of seconds, which is mostly referred to as high-frequency trading HFT.
More recently, binary genetic genetic algorithm trading technology technologies of HFT have also been binary genetic genetic algorithm trading technology into the financial markets worldwide, including Asia [ 3 ]. In high-speed trading research, the process of price formation in market microstructure generally produces large amount of data in relatively short periods of time.
The sheer volume of trading data generated in such environments provides plenty of resources for modeling and decision-making in big data research for financial applications. Recent microstructure research and advances in econometric modeling have facilitated an understanding of the characteristics of high-speed data [ 4 ].
In Taiwan, the Taiwan Stock Exchange TWSE is the major platform for stock trading in which transactions of various stocks typically occur binary genetic genetic algorithm trading technology regular intervals of five seconds. In this study, we thus aim to develop novel methodology to shed light on the research in the context of high-speed trading. In the past binary genetic genetic algorithm trading technology, there have been a number of computational intelligence CI approaches studied for financial applications due to its significant impact on the human society, ranging from fuzzy systems, artificial neural networks Binary genetic genetic algorithm trading technologysupport vector machines SVMsand evolutionary algorithms EAs [ 5 ] to hybrid and ensemble models, binary genetic genetic algorithm trading technology with other approaches [ 6 ].
These studies binary genetic genetic algorithm trading technology a wide range of applications, including abnormal noise and fraud detection [ 78 ], arbitrage [ 910 ], bankruptcy detection [ 1112 ], financial forecasting [ 13 — 15 ], and portfolio optimization [ 16 — 18 ].
Although there exist many aforementioned CI methods developed for solving various financial problems, a recent survey by Aguilar-Rivera et al. Among several major financial areas for CI studies, forecasting is a subject that has been extensively investigated. Typically, it consists of the estimation of future values or trends of investment vehicles for relevant decision-making and investment action.
Although perfect prediction is not possible, several GA-based methods have been developed to improve the accuracy of prediction. In these works, the GA techniques have been employed mainly for the optimization task in the proposed models. For instance, Kim and Han [ 20 ] proposed a GA approach to the discretization of continuous variables and the determination of optimal range for the connection weights of the ANNs to predict the stock price index.
They suggested that their approach was able to reduce the numbers of attributes and the performance of forecasting was improved. By comparing against several other models, they showed that their proposed GA-based models outperformed the benchmarks. In addition to the GA-based methods, the class of Genetic Programming GP has been used for similar forecasting tasks, as well. For example, Shao et al. In this work, a guided local search along with hill climbing was also employed to assist EDDIE with the optimization task for the rules and the forecasting time horizons.
The proposed method was then tested against several financial time series and it was reported that the method was able to improve the previous version of the EDDIE for financial forecasting. Recently, the methods of estimation of distribution algorithms EDAs have been studied in the area of evolutionary computation for several research problems. For financial prediction, for instance, Peralta Donate and Cortez [ 23 ] developed a NN-based forecasting approach in which a univariate marginal distribution algorithm UMDA was proposed for the optimization task.
In this method, the best half of the current population is selected to form a portion of the new population whereas the remaining individuals are generated by the probability distribution computed by binary genetic genetic algorithm trading technology method. Using the time series data of the Dow Jones Industrial Average, the authors compared their method with the ARIMA models, random forest, echo state networks, and SVMs and showed that their method was able to attain lower mean-squared error than others.
In addition to the Binary genetic genetic algorithm trading technology studies discussed thus far, various types of GA-based methodologies have been developed for financial research and applications, and an extended survey is provided in [ 19 ].
However, since the advanced IT technology for fast trading platforms has been made available to public just recently, high-speed trading is still a relatively new subject to CI researchers. In particular, to the best of our knowledge, the existing major CI research has provided forecasting techniques based on the information extracted from regular, macroscopic prices, for example, daily price of a stock.
In contrast, in the context of high-speed trading, the microscopic price structures are more important because the formation of the actual transaction price typically resulted from different auction prices on the microscopic level.
Therefore, these microstructures shall provide more information than macrostructures for price forecasting. By this rationale, in this study, we thus aim to develop a CI-based methodology to tackle the forecasting task for high-speed trading.
Since the review by Aguilar-Rivera et al. As our experimental results show later, using the microscopic price data from the call auction market, the methodology we proposed is indeed more effective than conventional approaches for forecasting in the context of high-speed binary genetic genetic algorithm trading technology. To sum up, the overall proposed methodology in this study is to offer binary genetic genetic algorithm trading technology models for the real-world high-speed trading applications.
Our objective is to advance the current state of the research for the class of CI-based search algorithms particularly tailored for forecasting in the high-speed trading environment, in order to further our understanding of the complex binary genetic genetic algorithm trading technology in stock market and the applicability of the CI-based algorithms to such problems.
Currently, in the call auction market of Taiwan, the transaction prices of a stock, both the best five bid and ask prices, and their sizes are available to all market participants. In this work, we propose to use these publically available microscopic data to construct intelligent models for price forecasting.
Before delving into the details of the methods studied, we provide the financial background for the call auction market first. In the Taiwan Stock Exchange TWSEthe execution prices of stock trading during regular trading sessions are determined by the periodic call auction principles http: Orders are collected over a specified period of time the current period is five binary genetic genetic algorithm trading technology per auctionwhich will be matched at the end of that period using the following rules: In this auction system, right after the end of each matching period, a set of information is binary genetic genetic algorithm trading technology to the public, including the execution price and volume and the prices and volumes of both the five highest unexecuted bid quotes and the five lowest unexecuted ask quotes.
The unexecuted orders, together with the new, subsequent orders from the investors will then enter into the system to participate in the next call auction.
For illustration, Table 1 shows an example of bid and ask quotes prior binary genetic genetic algorithm trading technology matching. For this example, using the call binary genetic genetic algorithm trading technology rule for the price binary genetic genetic algorithm trading technology enables the largest volume of orders to be executed, the system then determines the execution price to be The results for the remaining binary genetic genetic algorithm trading technology bid and ask quotes are shown in Table 2.
In the call auction market, the disclosed bid quotes contain the five highest prices and corresponding volumes for sale of the stock, and the disclosed ask quotes contain the five lowest prices and corresponding volumes for buying it. Intuitively, these bid and ask orders indicate the extent of demand and supply for a stock, respectively, which may be used to forecast the price movement in the future because the price tends to go up or down if the demand is more or less than the supply.
In this paper, using the disclosed information, we thus intend to propose an intelligent GA-based system for the forecasting task of stock price. In this section, we provide descriptions for several methodologies employed for this study: As discussed in the previous section, at the end of each call auction, the disclosed information includes the execution price and volume and the five best unexecuted prices and volumes of binary genetic genetic algorithm trading technology and ask orders.
According to the matching mechanism for the bid and ask orders, the new execution price at the next call shall be determined by the unexecuted orders at the current call and the other continuous influx of new orders entering into the system before the next call. Since the new coming orders are not disclosed to the public, market participants can only utilize the execution price and volume and the five best unexecuted bid and ask quotes at the current call for price prediction in the future.
In order to predict the price at the next call, Hu and Chan [ 24 ] proposed the following rule to infer the imbalance in the order book. The rationale for this rule is that when the execution transaction price is equal to the bid, there are certain buying orders left unfulfilled; so these remaining demand orders may push up the price in the future.
Conversely, if the execution transaction price is equal to the ask, there are some selling orders left unfulfilled and these supply orders then tend to push down the price. Therefore, Rule 1 may be used to predict the movement direction of the transaction price at the next call.
In addition to this method, we propose to study other versions of more sophisticated models for the call auction market. We describe the regression-based methods in the next subsection. A Linear Regression Models. Linear regression models have been studied in several financial applications, including the task of stock selection [ 2526 ] and the studies for the impact of order imbalance in the call auction market [ 2427 ]. Since the major component of this study is concerning the forecasting task under the call auction mechanism, which is similar to the ones in [ 2427 ] where the authors mainly used linear regression methods for their studies, in this work, we thus also propose to employ the linear regression models as follows.
Consider a given set with training instances at time. Each training instance serves as the input to generate a corresponding outputfor. Using and to denote the regression coefficients and the error terms, respectively, a linear regression model often takes the form where if is the input dimension. In contrast to the simple rule-based forecasting method in the previous subsection, a more sophisticated model can be constructed through the linear regression model above.
In a nutshell, the transaction price of the next call may hinge on the disclosed, best five ask from the selling side and best five bid from the buying side quotes which represent the supply and demand pressure of the stock, respectively. In this call auction market, here we designate each input variable to be the binary genetic genetic algorithm trading technology of the bid or ask price and corresponding volume in order to model the degree of binary genetic genetic algorithm trading technology buying or selling strength at each particular price level.
Thus, these ten variables may be used as the inputs to the regression model and the price of the stock at the next call may serve as the output of the model. Therefore, once the regression model is constructed, it can be used to predict the future price of the stock as long as the values of the input variables are provided. B Logistic Regression Models.
The linear regression method may be used to model continuous variables such binary genetic genetic algorithm trading technology the future stock price discussed above. However, other versions of regression models may be useful. For instance, if the goal is to predict future price to go up or down, then a binary output of the model may be more appropriate. In this case, a binary logistic model is studied here binary genetic genetic algorithm trading technology an alternative to linear regression, which is used to estimate the probability of the binary response variable price going up or down based on the same set of input variables in the last subsection.
The logistic regression model we use in this study works as follows. Here, we consider again, in the previous subsection, the given set with training instances at time. The logistic regression employs a standard logistic functionwhich can be defined as Note that is interpreted as the probability of the dependent variable equal to one of the binary outcomes.
In this model, the 10 variables of best five ask and five bid quotes and the binary response variable price up or down at the next call are used to compute binary genetic genetic algorithm trading technology logistic regression model. Once the regression model is constructed, it can be used to predict the direction of price change in the future as long as the values of the input variables are provided.
We will describe how to use the linear and logistic regression models for forecasting in Section 2. Genetic algorithms [ 28 ] have been used as computational models of natural evolutionary systems and as a class of adaptive algorithms for solving optimization problems. GA operate on an evolving population of artificial agents.
Each agent is comprised of a genotype often a binary string encoding a solution to some problem and a phenotype the solution itself. GA regularly start with a population of randomly generated agents within which solution candidates are embedded. In each iteration, a new generation is created by applying crossover and mutation to promising candidates selected according to probabilities biased in favor of the relatively fit agents.
As a result, evolution occurs by iterated stochastic variation of genotypes and selection of the best phenotypes in an environment according to how well the respective solution solves a problem. Successive generations are created in the same manner until a well-defined termination binary genetic genetic algorithm trading technology is met.
The core of this class of algorithms lies in the production of new genetic structures along the course of evolution, thereby providing innovations to solutions for the problem at hand.
The steps of a simple GA are shown in the following.