Developing a rule change trading system for the futures market using rough set analysis

Many technical indicators have been selected as input variables in order to develop an automated trading system that determines buying and selling trading decision using optimal trading rules within the futures market. However, optimal technical trading rules alone may not be sufficient for real-world application given the endlessly changing futures market. In this study, a rule change trading system (RCTS) that consists of numerous trading rules generated using rough set analysis is developed in order to cover diverse market conditions. To change the trading rules, a rule change mechanism based on previous trading results is proposed. Simultaneously, a genetic algorithm is employed with the objective function of maximizing the payoff ratio to determine the thresholds of market timing for both buying and selling in the futures market. An empirical study of the proposed system was conducted in the Korea Composite Stock Price Index 200 (KOSPI 200) futures market. The proposed trading system yields profitable results as compared to both the buy-and-hold strategy, and a system not utilizing a genetic algorithm for maximizing the payoff ratio.     

Using rough set to support investment strategies of real-time trading in futures market

Finding proper investment strategies in futures market has been a hot issue to everyone involved in major financial markets around the world. However, it is a very difficult problem because of intrinsic unpredictability of the market. What makes things more complicated is the advent of real-time trading due to recent striking advancement of electronic communication technology. The real-time data imposes many difficult tasks to futures market analyst since it provides too much information to be analyzed for an instant. Thus it is inevitable for an analyst to resort to a rule-based trading system for making profits, which is usually done by the help of diverse technical indicators. In this study, we propose using rough set to develop an efficient real-time rule-based trading system (RRTS). In fact, we propose a procedure for building RRTS which is based on rough set analysis of technical indicators. We examine its profitability through an empirical study.     

Intelligent stock trading system with price trend prediction and reversal recognition using dual-module neural networks

This article presents an intelligent stock trading system that can generate timely stock trading suggestions according to the prediction of short-term trends of price movement using dual-module neural networks(dual net). Retrospective technical indicators extracted from raw price and volume time series data gathered from the market are used as independent variables for neural modeling. Both neural network modules of thedual net learn the correlation between the trends of price movement and the retrospective technical indicators by use of a modified back-propagation learning algorithm. Reinforcing the temporary correlation between the neural weights and the training patterns, dual modules of neural networks are respectively trained on a short-term and a long-term moving-window of training patterns. An adaptive reversal recognition mechanism that can self-tune thresholds for identification of the timing for buying or selling stocks has also been developed in our system. It is shown that the proposeddual net architecture generalizes better than one single-module neural network. According to the features of acceptable rate of returns and consistent quality of trading suggestions shown in the performance evaluation, an intelligent stock trading system with price trend prediction and reversal recognition can be realized using the proposed dual-module neural networks.     

An intelligent hybrid trading system for discovering trading rules for the futures market using rough sets and genetic algorithms

Discovering intelligent technical trading rules from nonlinear and complex stock market data, and then developing decision support trading systems, is an important challenge. The objective of this study is to develop an intelligent hybrid trading system for discovering technical trading rules using rough set analysis and a genetic algorithm (GA). In order to obtain better trading decisions, a novel rule discovery mechanism using a GA approach is proposed for solving optimization problems (i.e., data discretization and reducts) of rough set analysis when discovering technical trading rules for the futures market. Experiments are designed to test the proposed model against comparable approaches (i.e., random, correlation, and GA approaches). In addition, these comprehensive experiments cover most of the current trading system topics, including the use of a sliding window method (with or without validation dataset), the number of trading rules, and the size of training period. To evaluate an intelligent hybrid trading system, experiments were carried out on the historical data of the Korea Composite Stock Price Index 200 (KOSPI 200) futures market. In particular, trading performance is analyzed according to the number of sets of decision rules and the size of the training period for discovering trading rules for the testing period. The results show that the proposed model significantly outperforms the benchmark model in terms of the average return and as a risk-adjusted measure.     

Evaluating machine learning classification for financial trading: An empirical approach

Technical and quantitative analysis in financial trading use mathematical and statistical tools to help investors decide on the optimum moment to initiate and close orders. While these traditional approaches have served their purpose to some extent, new techniques arising from the field of computational intelligence such as machine learning and data mining have emerged to analyse financial information. While the main financial engineering research has focused on complex computational models such as Neural Networks and Support Vector Machines, there are also simpler models that have demonstrated their usefulness in applications other than financial trading, and are worth considering to determine their advantages and inherent limitations when used as trading analysis tools. This paper analyses the role of simple machine learning models to achieve profitable trading through a series of trading simulations in the FOREX market. It assesses the performance of the models and how particular setups of the models produce systematic and consistent predictions for profitable trading. Due to the inherent complexities of financial time series the role of attribute selection, periodic retraining and training set size are discussed in order to obtain a combination of those parameters not only capable of generating positive cumulative returns for each one of the machine learning models but also to demonstrate how simple algorithms traditionally precluded from financial forecasting for trading applications presents similar performances as their more complex counterparts. The paper discusses how a combination of attributes in addition to technical indicators that has been used as inputs of the machine learning-based predictors such as price related features, seasonality features and lagged values used in classical time series analysis are used to enhance the classification capabilities that impacts directly into the final profitability.     

Evolving trading strategies using directional changes

The majority of forecasting methods use a physical time scale for studying price fluctuations of financial markets, making the flow of physical time discontinuous. Therefore, using a physical time scale may expose companies to risks, due to ignorance of some significant activities. In this paper, an alternative and original approach is explored to capture important activities in the market. The main idea is to use an event-based time scale based on a new way of summarising data, called Directional Changes. Combined with a genetic algorithm, the proposed approach aims to find a trading strategy that maximises profitability in foreign exchange markets. In order to evaluate its efficiency and robustness, we run rigorous experiments on 255 datasets from six different currency pairs, consisting of intra-day data from the foreign exchange spot market. The results from these experiments indicate that our proposed approach is able to generate new and profitable trading strategies, significantly outperforming other traditional types of trading strategies, such as technical analysis and buy and hold.     

A Forex trading expert system based on a new approach to the rule-base evidential reasoning

Currently FOREX (foreign exchange market) is the largest financial market over the world. Usually the Forex market analysis is based on the Forex time series prediction. Nevertheless, trading expert systems based on such predictions do not usually provide satisfactory results. On the other hand, stock trading expert systems called also “mechanical trading systems”, which are based on the technical analysis, are very popular and may provide good profits. Therefore, in this paper we propose a Forex trading expert system based on some new technical analysis indicators and a new approach to the rule-base evidential reasoning (RBER) (the synthesis of fuzzy logic and the Dempster–Shafer theory of evidence). We have found that the traditional fuzzy logic rules lose an important information, when dealing with the intersecting fuzzy classes, e.g., such as Low and Medium and we have shown that this property may lead to the controversial results in practice. In the framework of the proposed in the current paper new approach, an information of the values of all membership functions representing the intersecting (competing) fuzzy classes is preserved and used in the fuzzy logic rules. The advantages of the proposed approach are demonstrated using the developed expert system optimized and tested on the real data from the Forex market for the four currency pairs and the time frames 15 m, 30 m, 1 h and 4 h.     

Financial volatility trading using a self-organising neural-fuzzy semantic network and option straddle-based approach

Financial volatility refers to the intensity of the fluctuations in the expected return on an investment or the pricing of a financial asset due to market uncertainties. Hence, volatility modeling and forecasting is imperative to financial market investors, as such projections allow the investors to adjust their trading strategies in anticipation of the impending financial market movements. Following this, financial volatility trading is the capitalization of the uncertainties of the financial markets to realize investment profits in times of rising, falling and side-way market conditions. In this paper, an intelligent straddle trading system (framework) that consists of a volatility projection module (VPM) and a trade decision module (TDM) is proposed for financial volatility trading via the buying and selling of option straddles to help a human trader capitalizes on the underlying uncertainties of the Hong Kong stock market. Three different measures, namely: (1) the historical volatility (HV), (2) implied volatility (IV) and (3) model-based volatility (MV) of the Hang Seng Index (HSI) are employed to quantify the implicit volatility of the Hong Kong stock market. The TDM of the proposed straddle trading system combines the respective volatility measures with the well-established moving-averages convergence/divergence (MACD) principle to recommend trading actions to a human trader dealing in HSI straddles. However, the inherent limitation of the MACD trading rule is that it generates time-delayed trading signals due to the use of moving averages, which are essentially lagging trend indicators. This drawback is intuitively addressed in the proposed straddle trading system by applying the VPM to compute future projections of the volatility measures of the HSI prior to the activation of the TDM. The VPM is realized by a self-organising neural-fuzzy semantic network named the evolving fuzzy semantic memory (eFSM) model. As compared to existing statistical and computational intelligence based modeling techniques currently employed for financial volatility modeling and forecasting, eFSM possesses several desirable attributes such as: (1) an evolvable knowledge base to continuously address the non-stationary characteristics of the Hong Kong stock market; (2) highly formalized human-like information computations; and (3) a transparent structure that can be interpreted via a set of linguistic IF–THEN semantic fuzzy rules. These qualities provide added credence to the computed HSI volatility projections. The volatility modeling and forecasting performances of the eFSM, when benchmarked to several established modeling techniques, as well as the observed trading returns of the proposed straddle trading system, are encouraging.     

Trend following algorithms in automated derivatives market trading

Trend following (TF) is trading philosophy by which buying/selling decisions are made solely according to the observed market trend. For many years, many manifestations of TF such as a software program called Turtle Trader, for example, emerged in the industry. Surprisingly little has been studied in academic research about its algorithms and applications. Unlike financial forecasting, TF does not predict any market movement; instead it identifies a trend at early time of the day, and trades automatically afterwards by a pre-defined strategy regardless of the moving market directions during run time. Trend following trading has been popular among speculators. However it remains as a trading method where human judgment is applied in setting the rules (aka the strategy) manually. Subsequently the TF strategy is executed in pure objective operational manner. Finding the correct strategy at the beginning is crucial in TF. This usually involves human intervention in first identifying a trend, and configuring when to place an order and close it out, when certain conditions are met. In this paper, we evaluated and compared a collection of TF algorithms that can be programmed in a computer system for automated trading. In particular, a new version of TF called trend recalling model is presented. It works by partially matching the current market trend with one of the proven successful patterns from the past. Our experiments based on real stock market data show that this method has an edge over the other trend following methods in profitability. The results show that TF however is still limited by market fluctuation (volatility), and the ability to identify trend signal.     

大数据分析的应用案例——投资模型的稳健性

交易模型的稳健性,指的是该模型的利润率曲线的波动性较小,没有大起大落。针对一个基于支持向量回归（SVR）技术的算法交易模型的稳健性问题,提出了使用若干导出指标训练统一的交易模型的策略,以及投资组合多样化的方法。首先,介绍基于支持向量回归技术的算法交易模型;然后,基于常用指标,构造了若干导出指标,用于股票价格的短期预测。这些指标,刻画了近期价格运动的典型模式、超买/超卖市场状态,以及背离市场状态。对这些指标进行了规范化,用于训练交易模型,使得模型可以泛化到不同的股票;最后,设计了投资组合多样化方法。在投资组合里,各个股票之间的相关性,有时会导致较大的投资损失;因为具有较强相关关系的股票,其价格朝相同方向变化。如果交易模型预测的价格走势不正确,引起止损操作,那么这些具有较强相关关系的股票,将引发雪崩式的止损,于是导致损失加剧。把股票根据相似性聚类到不同类别,通过从不同聚类类别中选择若干股票来构成多样化的投资组合,其中,股票的相似性,通过交易模型在不同股票上近期的利润曲线的相似度进行计算。在900只股票10年的价格大数据上进行了实验,实验结果显示,交易模型能够获得超过定期存款的超额利润率,年化利润率为8.06%。交易模型的最大回撤由13.23%降为5.32%,夏普指数由81.23%提高到88.79%,交易模型的利润率曲线波动性降低,说明交易模型的稳健性获得了提高。     

A multiple fuzzy inference systems framework for daily stock trading with application to NASDAQ stock exchange

The aim of this study is to develop an expert system for predicting daily trading decisions in a typical financial market environment. The developed system thus employs a Multiple FISs framework consisting of three dedicated FISs for stock trading decisions, Buy, Hold and Sell respectively. As input to the Multiple FISs framework, the system takes the fundamental information of the respective companies and the historical prices of the stocks which are processed to give the technical information. The framework suggests the investor to Buy, Sell or Hold on a daily basis for a portfolio of stock taken into consideration. Experimenting the framework on selected stocks of NASDAQ stock exchange shows that including the fundamental data of the stocks as input along with the technical data significantly improves the profit return than that of the system taking only technical information as input data. Characterised as a stock market indicator, the framework performs better than some of the most popularly used technical indicators such as Moving Average Convergence/Divergence (MACD), Relative Strength Index (RSI), Stochastic Oscillator (SO) and Chaikin Oscillator (CO). The developed framework also gives better profit return compared to an existing model with similar objective.     

Combining technical trading rules using particle swarm optimization

Technical trading rules have been utilized in the stock market to make profit for more than a century. However, only using a single trading rule may not be sufficient to predict the stock price trend accurately. Although some complex trading strategies combining various classes of trading rules have been proposed in the literature, they often pick only one rule for each class, which may lose valuable information from other rules in the same class. In this paper, a complex stock trading strategy, namely performance-based reward strategy (PRS), is proposed. PRS combines the two most popular classes of technical trading rules – moving average (MA) and trading range break-out (TRB). For both MA and TRB, PRS includes various combinations of the rule parameters to produce a universe of 140 component trading rules in all. Each component rule is assigned a starting weight, and a reward/penalty mechanism based on rules’ recent profit is proposed to update their weights over time. To determine the best parameter values of PRS, we employ an improved time variant particle swarm optimization (TVPSO) algorithm with the objective of maximizing the annual net profit generated by PRS. The experiments show that PRS outperforms all of the component rules in the testing period. To assess the significance of our trading results, we apply bootstrapping methodology to test three popular null models of stock return: the random walk, the AR(1) and the GARCH(1, 1). The results show that PRS is not consistent with these null models and has good predictive ability.     

The role of algorithmic trading systems on stock market efficiency

The rapid development of information technology has changed the dynamics of financial markets. The main purpose of this study is laid on examining the role of IT based stock trading on financial market efficiency. This research specifically focused on algorithmic trading. Algorithmic trading enables investors to trade stocks through a computer program without the need for human interventions. Based on an empirical analysis of the Korean stock market, this study discovered the positive impact of algorithmic trading on stock market efficiency at three-fold. First, the study results indicate that algorithmic trading contributes to the reduction in asymmetric volatility, which causes inefficiency of information in a stock market. Second, an algorithmic trading also increases the operation efficiency of a stock market. Arbitrage trading contributes on the equilibrium between the spot market and futures market as well as on the price discovery. Third, algorithmic trading provides liquidity for market participants contributing to friction free transactions. The research results indicate that stock exchanges based on electronic communications networks (ECNs) without human intervention could augment a financial market quality by increasing trading share volumes and market efficiency so that it can eventually contribute to the welfare of market investors.     

A link mining algorithm for earnings forecast and trading

The objective of this paper is to present and discuss a link mining algorithm called CorpInterlock and its application to the financial domain. This algorithm selects the largest strongly connected component of a social network and ranks its vertices using several indicators of distance and centrality. These indicators are merged with other relevant indicators in order to forecast new variables using a boosting algorithm. We applied the algorithm CorpInterlock to integrate the metrics of an extended corporate interlock (social network of directors and financial analysts) with corporate fundamental variables and analysts’ predictions (consensus). CorpInterlock used these metrics to forecast the trend of the cumulative abnormal return and earnings surprise of S&P 500 companies. The rationality behind this approach is that the corporate interlock has a direct effect on future earnings and returns because these variables affect directors and managers’ compensation. The financial analysts engage in what the agency theory calls the “earnings game”: Managers want to meet the financial forecasts of the analysts and analysts want to increase their compensation or business of the company that they follow. Following the CorpInterlock algorithm, we calculated a group of well-known social network metrics and integrated with economic variables using Logitboost. We used the results of the CorpInterlock algorithm to evaluate several trading strategies. We observed an improvement of the Sharpe ratio (risk-adjustment return) when we used “long only” trading strategies with the extended corporate interlock instead of the basic corporate interlock before the regulation Fair Disclosure (FD) was adopted (1998–2001). There was no major difference among the trading strategies after 2001. Additionally, the CorpInterlock algorithm implemented with Logitboost showed a significantly lower test error than when the CorpInterlock algorithm was implemented with logistic regression. We conclude that the CorpInterlock algorithm showed to be an effective forecasting algorithm and supported profitable trading strategies. A preliminary version of this paper was presented at the Link Analysis: Dynamics and Statics of Large Networks Workshop on the International Conference on Knowledge Discovery and Data Mining (KDD) 2006.     

A Takagi–Sugeno fuzzy model combined with a support vector regression for stock trading forecasting

The turning points prediction scheme for future time series analysis based on past and present information is widely employed in the field of financial applications. In this research, a novel approach to identify turning points of the trading signal using a fuzzy rule-based model is presented. The Takagi–Sugeno fuzzy rule-based model (the TS model) can accurately identify daily stock trading from sets of technical indicators according to the trading signals learned by a support vector regression (SVR) technique. In addition, when new trading points are created, the structure and parameters of the TS model are constantly inherited and updated. To verify the effectiveness of the proposed TS fuzzy rule-based modeling approach, we have acquired the stock trading data in the US stock market. The TS fuzzy approach with dynamic threshold control is compared with a conventional linear regression model and artificial neural networks. Our result indicates that the TS fuzzy model not only yields more profit than other approaches but also enables stable dynamic identification of the complexities of the stock forecasting system.     

基于金融技术指标的用电数据分析

针对已有用电数据分析缺乏有效描述趋势性特征的不足,适应性地将金融领域中十字过滤线(VHF)、异同移动平均线(MACD)等技术指标迁移至用电数据分析中,提出了基于金融技术指标的异动检测算法和负荷预测算法.所提异动检测算法通过统计各指标的统计情况划定阈值,并采用阈值检测捕捉用户异常用电行为.所提负荷预测算法通过提取14项与...     

Trend following deep Q-Learning strategy for stock trading

Computers and algorithms are widely used to help in stock market decision making. A few questions with regards to the profitability of algorithms for stock trading are can computers be trained to beat the markets? Can an algorithm take decisions for optimal profits? And so forth. In this research work, our objective is to answer some of these questions. We propose an algorithm using deep Q-Reinforcement Learning techniques to make trading decisions. Trading in stock markets involves potential risk because the price is affected by various uncertain events ranging from political influences to economic constraints. Models that trade using predictions may not always be profitable mainly due to the influence of various unknown factors in predicting the future stock price. Trend Following is a trading idea in which, trading decisions, like buying and selling, are taken purely according to the observed market trend. A stock trend can be up, down, or sideways. Trend Following does not predict the stock price but follows the reversals in the trend direction. A trend reversal can be used to trigger a buy or a sell of a certain stock. In this research paper, we describe a deep Q-Reinforcement Learning agent able to learn the Trend Following trading by getting rewarded for its trading decisions. Our results are based on experiments performed on the actual stock market data of the American and the Indian stock markets. The results indicate that the proposed model outperforms forecasting-based methods in terms of profitability. We also limit risk by confirming trading actions with the trend before actual trading.     

A genetic network programming with learning approach for enhanced stock trading model

In this paper, an enhancement of stock trading model using Genetic Network Programming (GNP) with Sarsa Learning is described. There are three important points in this paper: First, we use GNP with Sarsa Learning as the basic algorithm while both Technical Indices and Candlestick Charts are introduced for efficient stock trading decision-making. In order to create more efficient judgment functions to judge the current stock price appropriately, Importance Index (IMX) has been proposed to tell GNP the timing of buying and selling stocks. Second, to improve the performance of the proposed GNP-Sarsa algorithm, we proposed a new method that can learn the appropriate function describing the relation between the value of each technical index and the value of the IMX. This is an important point that devotes to the enhancement of the GNP-Sarsa algorithm. The third point is that in order to create more efficient judgment functions, sub-nodes are introduced in each node to select appropriate stock price information depending on the situations and to determine appropriate actions (buying/selling). To confirm the effectiveness of the proposed method, we carried out the simulation and compared the results of GNP-Sarsa with other methods like GNP with Actor Critic, GNP with Candlestick Chart, GA and Buy&Hold method. The results shows that the stock trading model using GNP-Sarsa outperforms all the other methods.     

Mining associative classification rules with stock trading data – A GA-based method

Associative classifiers are a classification system based on associative classification rules. Although associative classification is more accurate than a traditional classification approach, it cannot handle numerical data and its relationships. Therefore, an ongoing research problem is how to build associative classifiers from numerical data. In this work, we focus on stock trading data with many numerical technical indicators, and the classification problem is finding sell and buy signals from the technical indicators. This study proposes a GA-based algorithm used to build an associative classifier that can discover trading rules from these numerical indicators. The experiment results show that the proposed approach is an effective classification technique with high prediction accuracy and is highly competitive when compared with the data distribution method.     

A gradient boosting decision tree approach for insider trading identification: An empirical model evaluation of China stock market

Insider trading is a kind of criminal behavior in stock market by using nonpublic information. In recent years, it has become the major illegal activity in China’s stock market. In this study, a combination approach of GBDT (Gradient Boosting Decision Tree) and DE (Differential Evolution) is proposed to identify insider trading activities by using data of relevant indicators. First, insider trading samples occurred from year 2007 to 2017 and corresponding non-insider trading samples are collected. Next, the proposed method is trained by the GBDT, and initial parameters of the GBDT are optimized by the DE. Finally, out-of-samples are classified by the trained GBDT–DE model and its performances are evaluated. The experiment results show that our proposed method performed the best for insider trading identification under time window length of ninety days, indicating the relevant indicators under 90-days time window length are relatively more useful. Additionally, under all three time window lengths, relative importance result shows that several indicators are consistently crucial for insider trading identification. Furthermore, the proposed approach significantly outperforms other benchmark methods, demonstrating that it could be applied as an intelligent system to improve identification accuracy and efficiency for insider trading regulation in China stock market.