The new group Trading Decision Support Systems is intended to be a resource for individual/institutional traders/investors and software developers in stock market area to share ideas, initiate and participate discussions, benefit from the collective intelligence, and to expand network. It will be primarily focused on such topics as:
- Trading EOD and intraday different asset classes: trading tips, strategies, why, how, and results.
- Trading systems: algorithms, methods, technologies, human factor, and statistics.
- Software tools to support traders decisions: forecast methods, simulations, back-testing, and optimization.
- Technical Analysis: indicators and chart patterns.
- Fundamental Analysis: financial ratios and predictive models.
- News: analysis and formalization by converting to measurable variables to automate systems with contributing news factor.
- Numerical methods, data processing, artificial intelligence, and modeling in stock market areas.
Many things remain unchangeable in a trading world – supply-demand price balance, greed-fear driven mistakes, as well as, ability to think, make right decisions, and find the best solutions. When once winning approaches, strategies, or methods failed, many traders are prone to analyze the reasons why it happened. Then they create new approaches and develop new successful systems. If systems are automated, it is easy and fast to test them, collect and analyze back-testing and live statistics, and then make necessary improvements. That is why it is important to implement the best ideas in software applications that can be also used by others.
The computational technologies are changing. Systems empowered by Artificial Intelligence have self-learning abilities that enable them to adapt to market changes. One of the purposes of this group is to bring together the developers of decision support software and traders-users for mutual benefits: the developers get more ideas about their products’ improvements and make a better progress in developing software for traders, the users arise issues relating to their needs and wants. Hopefully everyone will find something useful participating in this group.
You are welcome to join this newly created networking group. Be the first to start a relevant discussion, promote your product or service. Please join Trading Decision Support Systems group on LinkedIn!
Stock market investors and traders are making their decisions trying to analyze all relevant factors. Indeed, a good decision should be based on a sufficient number of criteria taking into consideration many company’s fundamentals, its stock characteristics, and external factors. During this process the market participants dealing with selecting sufficient volume of data and converting quantitative and qualitative parameters into valuable information. How to deal with a huge amount of data? How to choose the right data to analyze? What is the best way to process all info inputs?
One of the parts in decision-making chain that is often overlooked is a proper interpretation of data. There are different risks of converting data into valuable information. One of them occurs when some of the factors are exceedingly overvalued and others are neglectfully undervalued and, therefore, this might significantly diminish the quality of output signals for making decision.
One of the solutions is to use a computer program Investment Analyzer InvAn-3/4. It processes automatically all input data converting them into one number, i.e., composite rating. For better analysis, important company-stock characteristics are divided into homogeneous groups as components for further calculation of the composite rating. These components are: fundamental, technical, and timing ratings (FTTR). Finally, the program combines FTTR that allows measuring the quality of a company-stock by a single number.
The composite rating is a result of harmonic averaging of FTTR that initially transformed by Quality Auto-Function (HAQF method) with different weights. Applying HAQF to FTTR allows modeling stock and its company quality very realistically. It enables to reflect accurately the quality of company-stock as a system. In other words, this composition algorithm of the components ensures the following three principles:
- System reliability depends on reliability of its weakest part(s)
- Too good some system’s component(s) cannot mask the rest of components
- The composite result is formed by all components accordingly to each component’s importance
The synthesis of ratios and parameters is performed on the basis of HAQF method that allows modeling company-stock state and dynamics very close to reality. Quality Function (QF) transforms these ratios and parameters (factors) with different critical and sufficient values. The main purpose of Quality Function is to model system quality by normalizing with two characteristic parameters – sufficient and critical. When a certain parameter has its value less than critical, the function approaches to zero. If some parameter exceeds the sufficient value, the function ceases increasing. The following example of non-symmetrical Quality Function (critical value is 30, sufficient value is 250) is to help to understand QF:
The purpose of this research was to compare two statistical methods: one that based on Cycle Analysis, another – on a simple Neural Network. Price and volume data were used to train this particular Neural Network. These statistical forecasts were built using historical data of S&P-500 index for six months (from June 2009 to January 2010).
The charts below shows how actual 5-day performance (yellow line) differ from predicted performances by these two methods. The top half is the comparison of Neural Network prediction, bottom half – Cycle Analysis. Green bars mean buy signals, red – sell*.
Three major conclusions for this particular historical period:
- Cycle Analysis prediction gives signals too early, Neural Network prediction – too late.
- In average, the prediction by Cycle Analysis showed slightly better accuracy than the one by Neural Network.
- It seems logical to combine these two methods to improve the accuracy.
* ) The calculations have been performed by an integrated experimental system that combined two applications: Cycle Analysis predictor SMAP-3 and Neural Network predictor NNSTP-2
This blog is changing its profile starting from June 2011. Weekly S&P-500 index forecast will be unavailable anymore. The subjects of further blog posts will be focused mostly on stock market technical analysis and EOD trading algorithms issues.
Admin
The chart shows S&P-500 index forecast for the period from May 31 to June 3, 2011. The possible ^GSPC behavior is an uptrend. The prediction has been calculated using Neural Network Stock Trend Predictor NNSTP-2.
The chart shows a probable behavior of S&P-500 index for the next week, May 23 – 27, 2011. The forecast has been calculated by pattern recognition system. It predicts some fluctuations with a slight uptrend.
The two charts below show a possible behavior of S&P-500 index for the next week, May 16-20, 2011. The first chart is neural network forecast, the second one is pattern similarity forecast. Both methods predict a slight uptrend.
The chart presents a potential behavior of S&P-500 index for the next week, May 9 – 13, 2011. The forecast has been created by pattern recognition system. The method predicts some fluctuations with eventual uptrend.
The chart below shows a possible behavior of S&P-500 index for the next week, May 2 – 6, 2011. The chart is pattern similarity forecast. The method predicts an uptrend until Thursday.
The two charts below show a possible behavior of S&P-500 index for the next week, April 25 – 29, 2011. The first chart is pattern similarity forecast, the second chart is neural network forecast. Both methods predict some continuing slight uptrend but with eventual reversal to a downtrend.
