• The Journal of Information Systems
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• v.12 no.2
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• pp.1-20
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• 2003

This paper deals with the application of the genetic algorithm to the technical trading rule of the stock market. MACD(Moving Average Convergence & Divergence) and the Stochastic techniques are widely used technical trading rules in the financial markets. But, it is necessary to determine the parameters of these trading rules in order to use the trading rules. We use the genetic algorithm to obtain the appropriate values of the parameters. We use the daily KOSPI data of eight years during January 1995 and October 2002 as the experimental data. We divide the total experimental period into learning period and testing period. The genetic algorithm determines the values of parameters for the trading rules during the teaming period and we test the performance of the algorithm during the testing period with the determined parameters. Also, we compare the return of the genetic algorithm with the returns of buy-hold strategy and risk-free asset. From the experiment, we can see that the genetic algorithm outperforms the other strategies. Thus, we can conclude that genetic algorithm can be used successfully to the technical trading rule.

• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.94-103
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• 2021

This study aims to propose technical trading rules for Bitcoin futures and empirically analyze investment performance. Investment strategies include standard trading rules such as VMA, TRB, FR, MACD, RSI, BB, using Bitcoin futures daily data from December 18, 2017 to March 31, 2021. The trend-following rules showed higher investment performance than the comparative strategy B&H. Compared to KOSPI200 index futures, Bitcoin futures investment performance was higher. In particular, the investment performance has increased significantly in Sortino Ratio, which reflects downside risk. This study can find academic significance in that it is the first attempt to systematically analyze the investment performance of standard technical trading rules of Bitcoin futures. In future research, it is necessary to improve investment performance through the use of deep learning models or machine learning models to predict the price of Bitcoin futures.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.210-218
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• 2023

The author presents a simple data-driven intraday technical indicator trading approach based on Genetic Programming (GP) for return forecasting in the Bitcoin market. We use five trend-following technical indicators as input to GP for developing trading rules. Using data on daily Bitcoin historical prices from January 2017 to February 2020, our principal results show that the combination of technical analysis indicators and Artificial Intelligence (AI) techniques, primarily GP, is a potential forecasting tool for Bitcoin prices, even outperforming the buy-and-hold strategy. Sensitivity analysis is employed to adjust the number and values of variables, activation functions, and fitness functions of the GP-based system to verify our approach's robustness.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.4
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• pp.202-211
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• 2014

Pairs trading is a type of arbitrage investment strategy that buys an underpriced security and simultaneously sells an overpriced security. Since the 1980s, investors have recognized pairs trading as a promising arbitrage strategy that pursues absolute returns rather than relative profits. Thus, individual and institutional traders, as well as hedge fund traders in the financial markets, have an interest in developing a pairs trading strategy. This study proposes pairs trading rules (PTRs) created from a price ratio between securities (i.e., stock index futures) using rough set analysis. The price ratio involves calculating the closing price of one security and dividing it by the closing price of another security and generating Buy or Sell signals according to whether the ratio is increasing or decreasing. In this empirical study, we generate PTRs through rough set analysis applied to various technical indicators derived from the price ratio between KOSPI 200 and S&P 500 index futures. The proposed trading rules for pairs trading indicate high profits in the futures market.

• Management & Information Systems Review
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• v.37 no.1
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• pp.41-53
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• 2018

This study validates the trading rules based market anomalies and technical analysis in the Korean stock market. For the analysis, we built decile portfolios on the basis of corporate characteristics factors that clearly demonstrate specific patterns of stock returns including the firm size, book-to-market equity, and accruals. This portfolio was used to develop a portfolio based on the moving average trading strategy which was used for popular technical analysis tools, and then that was evaluated using the Sharpe ratio. We also created a zero-cost portfolio to identify the profitability and success rate of the moving average trading strategy. We lastly sought to ensure a more robust evaluation by calculating the Sortino ratio of the portfolio based on the moving average trading strategy with various lags. Key findings from this validation are as follows. First, a smaller firm size, a higher book-to-market equity, and lower accruals led to larger average returns. Second, the risk-adjusted performance of the moving average trading strategy was the highest in terms of the firm size, followed by book-to-market equity and accruals. Third, the returns of the zero-cost portfolios all had a positive value, with its overall success rate hovering over 68.8%, demonstrating the successfulness of the moving average trading strategy. Fourth, various evaluations revealed the economic usefulness of our trading strategy that used market anomalies and technical analysis.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.271-278
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• 2017

There has been an influx of traders and researchers eager to gain a better understanding of the market due to the rapid growth of the FOREX market. Traders with varying degree of experience are also often inundated with information, analysis methods as well as trading rules when making a trading decision on buying/selling a currency exchange pair. Thus, this paper reviews the current computational tools and analysis methods used within the FOREX trading community and proposes the development of a web-based trading platform with e-learning features to support beginners. Novice traders could also benefit from the use of the proposed e-learning trading platform as it helps them gain valuable knowledge and navigate the FOREX market in real-time. Even experienced traders would find it useful as the platform could be used for actual trading and acts as a reference point to understand the reasoning behind the certain technical analysis implementation that are still unclear to them.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.4
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• pp.123-129
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• 2013

The aim of this study is to design an intelligent pattern-based real-time trading system (PRTS) using rough set analysis of technical indicators, dynamic time warping (DTW), and genetic algorithm in stock futures market. Rough set is well known as a data-mining tool for extracting trading rules from huge data sets such as real-time data sets, and a technical indicator is used for the construction of the data sets. To measure similarity of patterns, DTW is used over a given period. Through an empirical study, we identify the ideal performances that were profitable in various market conditions.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.257-264
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• 2003

In the context of a dynamic trading environment, the ultimate goal of the financial forecasting system is to optimize a specific trading objective. This paper proposes a two-phase (extraction and filtering) stock trading system that aims at maximizing the rates of returns. Extraction of stocks is performed by searching specific time-series patterns described by a combination of values of technical indicators. In the filtering phase, several rules are applied to the extracted sets of stocks to select stocks to be actually traded. The filtering rules are automatically induced from past data. From a large database of daily stock prices, the values of technical indicators are calculated. They are used to make the extraction patterns, and the distributions of the discretization intervals of the values are calculated for both positive and negative data sets. We assumed that the values in the intervals of distinctive distribution may contribute to the prediction of future trend of stocks, so the rules for filtering stocks are automatically induced from the data in those intervals. We show the rates of returns when using our trading system outperform the market average. These results mean rule induction method using distributional differences is useful.

• Management & Information Systems Review
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• v.31 no.3
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• pp.21-46
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• 2012

This paper attempts to offer an effective strategy of hedge fund based on trade probability control in the futures market. By using various technical indicators, we create an association rule and transforms it into a trading rule to be used as an investment strategy. Association rules are made by the combination of various technical indicators and the range of individual indicator value. Adjustments of trade probabilities are performed by depending on the rule combinations and it can be utilized to establish an effective investment strategy onto the risk management. In order to demonstrate the superiority of the investment strategy proposed, we analyzed a profitability using the futures index based on KOSPI200. Experiments results show that our proposed strategy could effectively manage and response the dynamics investment risks.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.59-71
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• 2014

System trading is becoming more popular among Korean traders recently. System traders use automatic order systems based on the system generated buy and sell signals. These signals are generated from the predetermined entry and exit rules that were coded by system traders. Most researches on system trading have focused on designing profitable entry and exit rules using technical indicators. However, market conditions, strategy characteristics, and money management also have influences on the profitability of the system trading. Unexpected price deviations from the predetermined trading rules can incur large losses to system traders. Therefore, most professional traders use strategy portfolios rather than only one strategy. Building a good strategy portfolio is important because trading performance depends on strategy portfolios. Despite of the importance of designing strategy portfolio, rule of thumb methods have been used to select trading strategies. In this study, we propose a SVM-based strategy portfolio management system. SVM were introduced by Vapnik and is known to be effective for data mining area. It can build good portfolios within a very short period of time. Since SVM minimizes structural risks, it is best suitable for the futures trading market in which prices do not move exactly the same as the past. Our system trading strategies include moving-average cross system, MACD cross system, trend-following system, buy dips and sell rallies system, DMI system, Keltner channel system, Bollinger Bands system, and Fibonacci system. These strategies are well known and frequently being used by many professional traders. We program these strategies for generating automated system signals for entry and exit. We propose SVM-based strategies selection system and portfolio construction and order routing system. Strategies selection system is a portfolio training system. It generates training data and makes SVM model using optimal portfolio. We make $m{\times}n$ data matrix by dividing KOSPI 200 index futures data with a same period. Optimal strategy portfolio is derived from analyzing each strategy performance. SVM model is generated based on this data and optimal strategy portfolio. We use 80% of the data for training and the remaining 20% is used for testing the strategy. For training, we select two strategies which show the highest profit in the next day. Selection method 1 selects two strategies and method 2 selects maximum two strategies which show profit more than 0.1 point. We use one-against-all method which has fast processing time. We analyse the daily data of KOSPI 200 index futures contracts from January 1990 to November 2011. Price change rates for 50 days are used as SVM input data. The training period is from January 1990 to March 2007 and the test period is from March 2007 to November 2011. We suggest three benchmark strategies portfolio. BM1 holds two contracts of KOSPI 200 index futures for testing period. BM2 is constructed as two strategies which show the largest cumulative profit during 30 days before testing starts. BM3 has two strategies which show best profits during testing period. Trading cost include brokerage commission cost and slippage cost. The proposed strategy portfolio management system shows profit more than double of the benchmark portfolios. BM1 shows 103.44 point profit, BM2 shows 488.61 point profit, and BM3 shows 502.41 point profit after deducting trading cost. The best benchmark is the portfolio of the two best profit strategies during the test period. The proposed system 1 shows 706.22 point profit and proposed system 2 shows 768.95 point profit after deducting trading cost. The equity curves for the entire period show stable pattern. With higher profit, this suggests a good trading direction for system traders. We can make more stable and more profitable portfolios if we add money management module to the system.