الگوریتم های معاملات در بازارهای مشتقات خودکار

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.

Trend following (TF) (Fong & Tai, 2009) is a reactive trading method in response to the real-time market situation; it does neither price forecasting nor predicting any market movement. Once a trend is identified, it activates the trading rules and adheres rigidly to the rules until the next prominent trend is identified. Trend following does not guarantee profit every time, but nonetheless in a long term period it may probably profit by obtaining more gains than loses. The nature of TF makes it as an ideal ingredient in implementing a decision-making component in automated trading software where human intervention is not required in automatic buying and selling. Automated trading here is referred to an ‘auto-pilot’ mode by which the software system decides when to buy or sell a stock (in a form of option, future contract, warrant, etc.) from the derivative market. Though the philosophy of trend following is simple – identify a current market trend and trade strictly according to the pre-defined rules (sometimes known as strategies), how the rules or strategies should be formulated has become an important research problem that deserves attention from researchers. Rules such as when to buy and sell or when to open and close a position, as signaled from the market trend have a direct impact into the profitability of automated market trading. TF algorithms in this context are termed as automated trading methods that are guided by the current market trend (signals from the trend) and specified by the rules (reactions to the trend). For an example of a most basic TF algorithm, buying and selling are cued by the conditions when the market trend which is represented by its moving average rises over an up-threshold, and falls below a down-threshold respectively. The values of the thresholds are predefined as a part of the trading rules. To the best of the authors’ knowledge, surprisingly, TF algorithms have not been investigated in the computer science community. In contrast a lot of research papers on stock market forecast and prediction by soft computing can be found in literature. Fig. 1 classifies clearly that trading algorithms can be powered by two different domains of techniques, namely Predictive and Reactive. Predictive types of forecasting and trading in a stock market have a long history in academic research, which generally covers non-linear prediction by artificial neural works, decision trees and other regression models. These tools usually aim at predicting the future market movement ahead by analyzing over the historical data. TF algorithms however, belong to the latter category, which conduct trading decision solely by reacting to the current market trend. There are several variants of TF algorithms, depending on how the rules and the predefined thresholds are specified. Without the need of training up a decision model, and subsequently updating it, like those under the predictive type, TF is readily implemented as a rule-based system in automated trading software. Full-size image (18 K) Fig. 1. Classification of trading algorithms. Figure options The objective of this paper is to present a performance comparison of the five TF algorithms which have been recently proposed by the authors. The contribution of this paper is twofold: Theoretically insights are gained in terms of performance strength, weakness and characteristics of each TF algorithm under test, so that prospect of future research in improving from the existing TF algorithms is warranted. Secondly the TF algorithms presented in this paper serve as useful designs of the decision-making core for implementing an automated stock market trading system. The structure of this paper is as follow: Section 2 describes four possible implementations of TF algorithms, highlighting especially their shortcomings and possible improvement. Section 3 presents in details the latest addition to the family of TF algorithms called trend recalling algorithm. A simulation prototype of automated trading system is developed, for the purpose of evaluating their performances comparatively. The experiments carried out by the simulator are reported in Section 4. Section 5 concludes.

Trend following has been known as a rational stock trading technique that just rides on the market trends with some preset rules for deciding when to buy or sell. TF has been widely used in industries, but none of it was studied academically in computer science communities. In this paper, we pioneer in formulating TF into algorithms and evaluating their performance. First, we describe statistic and dynamic TF algorithms. Next, to improve the performance, we introduce fuzzy logic into our trade system, which forms our third and fourth versions of trading algorithms. The properties of these trading algorithms are generally built upon the experiences of previous trading algorithms, such as the membership definition and fuzzy sets generation. All these trading algorithms are later verified on Hang Sang Index futures market in a simulated environment, and the result is encouraging. These trading algorithms show an outstanding performance in the wild bullish and bearish markets between the years of 2007–2009. However, during the year of 2010, they seem to be under performing, as they are no longer generating great profits. This is due to the large flip-flop changes of the market. We observe that frequent market trend fluctuations in large extents deter trend following algorithms and cause the degradation in performance. To alleviate this problem, we extend the original TF algorithm by adding a market trend recalling function in our last trading algorithm. Trading strategy that used to make profit from the past is recalled for serving as a reference for the current trading. The trading strategy is recalled by matching the current market trend with the most similar past market trend which is known to produce good profit. Matching market trend patterns is a complex task since patterns can be quite different in details. The concept of financial cycle was also taken into account of in the trend recalling algorithm so that the algorithm is adaptable to market behavioral changes. This trading algorithm is also verified on Hang Sang Index futures contracts in simulated environment. Our simulation results show that the improved TF model with trend recalling is able to generate profit from stock market at more than three times of ROI. In summary, contributions of this research are summarized as follows: • Developed an automated trading system prototype, which provides a cornerstone for future development, and experimental platform for evaluating trading algorithms and programmed. • Provided an alternative view and comparative of two kinds of trading algorithms (predictive model vs. reactive model). • Proposed five innovative trading algorithms based on trend following concepts. As for the future work, we are planning to test the algorithms on other stock market data. We are also planning to further improve the calculation of P and Q in dynamic TF algorithms by applying time series segmentation methods.