Stock trading system to assist decision-making is an emerging research area and has great commercial potentials. Successful trading operations should occur near the reversal points of price trends. Traditional technical analysis, which usually appears as various trading rules, does aim to look for peaks and bottoms of trends and is widely used in stock market. Unfortunately, it is not convenient to directly apply technical analysis since it depends on person’s experience to select appropriate rules for individual share. In this paper, we enhance conventional technical analysis with Genetic Algorithms by learning trading rules from history for individual stock and then combine different rules together with Echo State Network to provide trading suggestions. Numerous experiments on S&P 500 components demonstrate that whether in bull or bear market, our system significantly outperforms buy-and-hold strategy. Especially in bear market where S&P 500 index declines a lot, our system still profits.
Data mining in stock market has been a hot topic for a long time due to its potential profits. Unfortunately, stock market is a complex and dynamic system with noisy, non-stationary and chaotic data series (Peters, 1994). Stock movement is affected by complicated factors, which can be divided into two groups: one is determinant, such as gradual power change between buying and selling side; the other is random factors, such as emergent affairs or daily operation variations (Bao & Yang, 2008). Therefore, data mining in stock market is very difficult and challenging. Recently, advances in artificial intelligence have led to a number of interesting new approaches to stock data mining, based on non-linear and non-stationary models. Among them, soft computing techniques, such as fuzzy logic, neural networks and probabilistic reasoning draw most attention because of their ability to handle uncertainty and noise in stock market (Vanstone and Tan, 2003 and Vanstone and Tan, 2005). Applications range from time series prediction, classification to rule induction.
Although past studies have attained remarkable achievement in stock data mining, especially price prediction, they seldom directly guide trading. Future price forecast is not enough to suggest ideal trading operation to get profit as much as possible. An ideal trading operation should occur at the peak or bottom of price trend, that is, a good investor will sell stocks near the top of the trend and buy them close to the bottom. Thus, it is important to predict not only the future price but also when the price trend will hit the peak or bottom. In real market, technical analysis is widely used to assist decision-making. Its central idea is to look for peaks, bottoms, trends and indicators to estimate the possibility of current trend reversal and then make buy/sell decisions based on technical indicators which are some statistics derived from recent historical data (Bao & Yang, 2008). However, traditional technical analysis suffers from some shortcomings. First, it is difficult to directly apply technical analysis on individual stocks, especially for green hand. Technical analysis usually appears in a form as a trading rule. Take the popular “Golden Cross” and “Dead Cross” for example, if the sigh of (long-term moving average) − (short-term moving average) changes from positive to negative, it is called “Golden Cross” which indicates to buy stocks; if the sign of (long-term moving average) − (short-term moving average) changes from negative to positive, it is called “Dead Cross” which suggests to sell stocks. In the above description, it is hard to decide the time spans for both long-term and short-term moving average (MA) because each stock should have its own appropriate time spans. Investors usually choose those parameters according to their experience. Second, there are various technical analysis approaches, such as moving average approach, relative strength indicator (RSI) approach and stochastic indicator approach. Not all of them are effective for every stock. How to choose proper technical analysis methods for individual stock is also difficult for ordinary investors.
In this paper, we propose an intelligent stock trading system based on enhanced technical analysis and neural network. Genetic Algorithm (GA) is utilized to improve traditional technical analysis by learning appropriate parameters for each trading rule. Then, the improved trading rules behave as experts together to give trading suggestions with a novel neural network-Echo State Network (ESN). The experiments demonstrate that whether in bull or bear market, our system will gain more income than buy-and-hold strategy. Particularly, it can still earn in bear market.
The rest of the paper is organized as follows: Section 2 describes the application of GA to improve traditional technical analysis; Section 3 introduces ESN and our system; Section 4 shows the experiments and results. Finally, we make a conclusion and suggest for further research.
In this paper, we propose a novel stock trading system based on ESN, which combines a variety of technical analysis approaches enhanced by GA. Simulated experiments in the whole market present that no matter in bull or bear markets, our trading system will obtain more income than buy-and-hold strategy. Especially in bear market in which S&P 500 index drops a lot, our system still profits.
Although our system can facilitate investors in trading decision, how to select appropriate parameters of our model for individual stock is worth further exploration. Parameters, such as the radius of ESN’s reservoir and trading threshold, may influence the profits of our system. The results of the above simulated experiments are attained by manually selecting proper parameters. Unfortunately, it is very difficult to determine parameters. Stock market is a very complicated dynamic system. The parameters which are suitable for training set cannot be guaranteed to be proper for testing set.
Further work also includes introducing more enhanced technical analysis approaches and augmenting the system with other soft computing techniques.
