Stock Market Forecast Tools SMFT-1 has been updated. One of the major changes is a new module in Technical Analyzer TA-1 sub-system. The new module analyzes waves and predicts the next extreme (high or low price) similar to Elliott Wave theory of recurrent stock market price structures. Fundamentally, the Elliott Wave model is based on a crowd psychology that follows between optimistic and pessimistic trends creating patterns that can be fitted to natural sequences.
However, instead of assuming that waves obey only the sequence of Fibonacci, harmonic, or fractal ratios, a more general approach has been developed – the software takes into consideration all extracted waves. Due to employing Neural Network (NN) the module enables identifying both the price and date of extremes. The software has a flexibility in tuning and optimizing computing parameters by users. It allows choosing the number of counted waves in each case for NN training in a range within 9..24. Also one can set the amount of iterations for NN learning from 50 to 5,000.
TA-1 Waves module interface
Addaptron Software released the first beta version of a new decision support software system for stock market traders – Trading Forecast Weekly TFW-1. It provides weekly EOD forecasts for 20 selected ETFs by cloud computing. TFW-1 allows choosing best trading opportunities by comparative analysis, assessing different strategies using back-test simulation, finding the best configuration of algorithms among 64 possible combinations and optimizing triggering parameters, as well as, monitoring results by stand-alone computing.
The statistical analysis of simulations shows that systems based on predicted entry-exit signals generate a better profit than random-entry trading systems. TFW-1 enables forecasts to increase a trading profit. The calculations are done on server-side by a robust artificial intelligence system. Since sometimes predictions can fail, to preserve capital in a volatile market, the software enables simulating different risk management approaches. TFW-1 simulation allows testing generated predictions combined with buy-sell signals.
Depending on the character of particular trading shares and current market condition, some ideas can work better than others. To optimize strategy for each particular case, the software enables testing a given idea and finding automatically the best set of algorithms. It is especially important for exit points to minimize losses and ultimately to maximize an overall profit. Some algorithms have customizable parameters that can also be optimized. All optimizations can be done either automatically by scanning 64 possible logical combinations and adjusting numerical parameters or manually.
TFW-1 allows monitoring the simulated or actual completed transactions, reflecting total trading activity, and evaluating the success of trading in overall by stand-alone computing. It enables working with many separate data files that is convenient in case of managing multiple assets and keeping the archives of older activities. You are welcome to download and use for free (during the first 30-day period) a fully-functional version of TFW-1.
Some indicators can provide a better prediction than others so that it seems logically to use the best selected ones to build a composite forecast. On the other hand, even the best indicators can fail. The questions is how to get a consistent good accuracy in predicting – by using only a few best indicators or many good ones. The answer is not obvious and, therefore, a factual comparative analysis would be needed to shed some light on this issue. This short report is based on limited statistical researches; it is an attempt to reach a certain conclusion.
About Expert Method. Apparently, the more good forecasts are taken into consideration, the more precise can be an approximation to actual value. There is Expert Method. This method can be explained by following. As example, an experimentalist shows a pen and asks a group of about 40 people to write down their estimate of the length. Then he collects notes and calculates the average number – normally it is almost 100% accurate. Why it works? Because everyone makes errors in different directions so that averaging gives a precise result.
The Details of Experiment. To find an optimal number of top performing indicators, two tests have been done – using artificial data and real market data. Artificial data allow performing forward testing with more consistent statistics. Although back-testing has been done on out-of-sample sets, it did not have the same forward-testing success every time. Forward testing showed that in average few indicators might produce less accurate prediction than many.
The researches and presented chart are made by Technical Analyzer TA-1 (the software is able to compose Neural Network forecasts of many indicators with weights accordingly to each indicator’s predictive ability).
Conclusion. The main conclusion is that relying on a couple of best indicators might yields less consistent success over a long run than using many best and good ones. However, too many is another extreme and not good. The second conclusion is that the list of best indicators is not static – it evolves depending on many factors, including market conditions and, probably, on the number of traders employing particular indicators to make their buy-sell decisions. Thirdly, if the best current top of indicators is known, here is a magic number – it is around 30. And finally, better results are possible if indicators are combined accordingly to their latest back-testing ranking.
To address the challenges of the modern stock market, Addaptron Software has made improvements to its line of software products. Now a few decision support tools have been included in a single system – Stock Market Forecast Tools SMFT-1. This integrated system allows traders to use several computer programs for affordable monthly subscription fee. New users are granted to use the fully-functional system for free during the first 30 days.
Most changes have been made to former Investment Analyzers InvAn-3 and InvAn-4. Now the system that inherited their redeveloped parts is called Technical Analyzer TA-1. Among major differences, its technical indicators forecast has been improved with flexibility to adjust weights in composed result. Also a new module has been added to predict trends using correlation analysis with forward shifting.
Different chart patterns including candlesticks have been known in technical analysis for long. Now many of them considered as typical or classical ones. These patterns can signal a particular bullish or bear trend. Naturally, the signals are not successful all the time but give at least the most probable scenario of price movement. On the other hand, many other patterns have not been described yet. However, whether these patterns are known or unknown, many of them can be repeatable in the future and, therefore, they all can be used for prediction in the stock market.
To facilitate the job to memorize many different patterns and analyze a huge amount of charts, some chartists use special software tools – pattern recognition systems. These tools normally perform statistical analysis and filtration assuming that the patterns are generated by a probabilistic system and can be persistent in the future. The statistical researches of charts also show that indeed many formed patterns have typical structures of price developments.
Technical Analyzer TA-1 (TA) by Addaptron Software has a feature to predict a future trend of stock, ETF, or index prices using pattern similarity filters. The prediction period can be chosen within a range 1..60 trading days; the period of historical data that used for matching is recommended in 4..16 times longer than prediction period. TA searches for the best matches by scanning all historical data from the internal database. The typical number of scanned patterns is around 200,000 (if the number of symbols in the database is around 500).
TA ranks all possible matches on the basis of maximum correlation and minimum deviation within given historical period. It performs pattern matching using open, high, low, and close prices and volume data. When scanning is completed, depending on degree of similarity, TA ranks all possible matches within given historical period and then combine them into a single structure. TA composes forecast using several best matched patterns (top ranked). Since the statistical regularities of the patterns help to create more stable picture, TA allows adding up many top-rated patterns. The composite result is built as a weighted average with weights that are proportional to patterns’ ranks.
To try free version of TA, visit Addaptron Software download page to download and install it.
Evidently, it is impossible to find technical analysis indicators that are always have the best predictive abilities in all cases. According to statistical researches, depending on time-frame, market conditions, industry, type of stock or ETF, and other factors, onetime some indicators might be best but other worst, and vice versa. In general, in terms of comparative analysis, utmost only average numbers can be considered. As example, according to average predictions success based on the statistics during 2010, the five of top winning indicators are: Relative Strength Index, Money Flow Index, Twiggs Money Flow, On Balance Volume, and Directional Movement System.
Therefore, it is better to select somehow the best indicators for a particular case. However, it can be a time-consuming process. One of the solutions is to allow a computer program to decide which indicator should be trusted more and another less for particular market conditions and a specific shares using back-testing. Such computer program could compose the forecast with weights accordingly to predictive ability of each technical indicator. The example of such program is Technical Analyzer TA-1 (TA). It uses technical analysis module which is based on Neural Network.
The recent researches showed that predictive abilities of some classical indicators can be improved by an additional transformation. In short, if an indicator is trend-differentially coupled with price – it demonstrates better predictive abilities than a pure indicator. This idea has been used to improve a new release of TA software. The list of existing indicators in TA Technical Analysis module has been empowered by two new divergence-modified indicators – Relative Strength Index (RSI) and Moving Average Convergence/Divergence (MACD). These two indicators have been transformed to a slope of line and differentially-coupled with a price line slope. Indicator and price transformation to line slopes has been performed using Least Squares Linear Regression within a sliding 10-day period (moving window).
Free version of TA is available on Addaptron Software download page
Technical analysts use many different indicators. Not all indicators are equally good. Some of them have better predictive abilities than others at given conditions. Statistical results of the research for stocks and indexes during the last several months have showed the way to improve some regular indicators. In short, if an indicator is trend-differentially coupled with price – it demonstrates better predictive abilities than a pure indicator.
Among studied indicators are Relative Strength Index (RSI) and Moving Average Convergence/Divergence (MACD) indicators. They have been transformed to a slope of line and differentially-coupled with a price line slope. Indicators and price transformation to line slopes has been performed using Least Squares Linear Regression within a sliding 10-day period (moving window). A comparative analysis of indicators’ predictive abilities showed that these two coupled ones are better than around 90% of all other (57) the most popular indicators.
The chart below shows an example of such forecast:
The research results presented here have been calculated using a feature of Technical Analyzer TA-1 (TA). Except analyzing chart with indicators and historical data, TA enables to perform a 10-day forecast using Artificial Neural Network. The calculations can be done on the basis of one selected technical indicator or all available. If all indicators used, TA decides how much weight should be assigned for each indicator’s forecast in a composite result by using back-testing for particular market conditions and a specific stock. Each weight is proportional to a predictive ability of a corresponding technical indicator.
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