[논문]유전알고리즘 활용한 실시간 패턴 트레이딩 시스템 프레임워크

이석준; 정석재

doi:10.11627/jkise.2013.36.4.123

[국내논문] 유전알고리즘 활용한 실시간 패턴 트레이딩 시스템 프레임워크
Conceptual Framework for Pattern-Based Real-Time Trading System using Genetic Algorithm 원문보기

Journal of Korean Society of Industrial and Systems Engineering = 한국산업경영시스템학회지, v.36 no.4, 2013년, pp.123 - 129

Abstract ▼ AI-Helper

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.

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문제 정의

This study demonstrates the usefulness of the rough set and DTW for constructing PRTS in the Korean stock futures market. In particular, the PRTS using the rough set and the DTW was useful in various market situations.

제안 방법

This study proposes a procedure for constructing a pattern-based real-time trading system (PRTS) that is based on rough set analysis [16] of technical indicators and dynamic time warping (DTW) algorithm, originally introduced by Bellman [17]. The DTW algorithm is one of many techniques that can be used to measure similarity between two time series (or two patterns) particularly when the two are not aligned properly on time axis [17].
The trading rules of pattern groups that correspond to the time sequence with the smallest distance will initially be used for a new time sequence in the testing period. In this study, all of the holdout trading intervals (i.e., length of time sequence) for 10 starting points and 6 patterns are fixed at 20 days, which represents the monthly effect in the futures market.
At each trail over a user-defined period, each of reducts is tested by simulating their trading performance over historical data. The fitness evaluation of every string is calculated at each iteration using criteria : the return rates discussed in section 2.
For the practical selection of patterns, the estimated Sharpe ratio was used to evaluate the performance of the pattern base as a rule portfolio. The Sharpe ratio is defined as a ratio of the expected difference between the return rates of a given portfolio and the risk-free asset over the standard deviation of the difference [20].

대상 데이터

Real-time data is the stock futures market data which consist of the open, high, low, close prices, and trading volume. This study used nine oscillators that were created from the data. The definitions and formulas of the oscillators are listed in [Table 1].

이론/모형

It is an important process because core information is extracted in the concrete rule from the transformed data. Each reduct that corresponds to six patterns is selected by the GA method in this study. Notice that RRTS used manual reducer that is trial and error.

성능/효과

LAP, LDP, and FTP at first starting point), then the rule portfolio exhibits the best performance. Despite an average return rate of 16.03% in the third combination of pattern groups, the investment using rough set analysis can be profitable compared with the average 5% of the open market interest rate and the return overcomes the performance of RRTS.

후속연구

Because real-time data or trading causes the market to be more volatile and unpredictable than before, the PRTS provides a desirable, robust solution compared with various situations. In the meantime, we envision that this study will encourage further studies of the PRTS because it incorporates basic tools within the rough set and DTW. A more elaborate procedure for the PRTS might be developed with the help of other information reduction techniques such as manual reducer, dynamic reducts, and approximate hitting set approaches.

참고문헌 (20)

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