Unusual Use of Parabolic SAR
Parabolic SAR can be improved and successfully used for predicting stock market prices, especially, in trending markets
Traditionally, Parabolic SAR* is considered as a trend following indicator. Probably, few traders would think about using it for prediction. But after testing, I started to believe that it can be successfully used for prediction in conjunction with other indicators, especially, in a trending market. The explanation why it works can be the following. When a trend reverses, the probability of its continuation is more than 50% in average. The software with Neural Network (NN) is able to catch it statistically and show the result in extrapolated curves. The function of SAR in this method is to provide NN with reversal point signals.
The initial hypothesis was the following. Since SAR is able to give a strong signal when a price trend is reversing, this signal can be used for predicting if a new trend is prone to last. To compare predictive ability of SAR with other indicators, it has been implemented into the technical analysis module of Fundamental-Technical Analyzer FTA-2. SAR calculations have been used to collect statistics based on the forecast simulations for major indexes and ETFs during August-October 2011 period. As a result, SAR’s position was mostly in “top ten” indicators’ list.
The research and presented chart are made by Fundamental-Technical Analyzer FTA-2, one of the software modules that enables composing Neural Network forecasts of many indicators with weights accordingly to each indicator’s predictive ability.
Omitting logical rules for acceleration factor and reversal conditions, a recurring core formula for Parabolic SAR is the following:
SAR (current point) = AF * [EP - SAR (previous point)] + SAR (previous point)
where:
AF – Acceleration Factor (normally starts from 0.02 and increases by 0.02 if each next point reaches a new extreme, saturates until 0.2);
EP – Extreme Point (lowest low or highest high).
Conclusions. SAR is especially effective in a trending market. To make it more effective in a sideways market, it is a good idea to use it in conjunction with other indicators. Parabolic SAR can be enriched and successfully used for predicting stock market prices. Combing it with Neural Network allows extracting more statistically stable patterns and, therefore, providing a better accuracy in the forecast. As simulations showed, improved results can be achieved if SAR is transformed into more sensitive indicator by subtracting it from close price (it indicates the degree of SAR and price convergence).
*) SAR stands for Stop-And-Reverse. It has been used by many traders for decades. Its major application is in trading systems to define a trailing stop, i.e., to protect profit when a price trend changes. The term “parabolic” appeared to characterize the indicator parabola shape that is due to using an accelerating factor in the formula.
Elliott Wave: Extracting Extremes and Predicting next One by Neural Network
Article first published as Elliott Wave: Extracting Extremes and Predicting next One by Neural Network on Technorati.
Improved Elliott Wave model can be successfully used as an additional input for making investing decisions in modern market conditions
The Elliott Wave idea is to use in stock market forecast. It is based on a crowd psychology that changes between optimistic and pessimistic trends creating patterns that can be fitted to reoccurred sequences. To use waves for prediction the assumption is made that waves are developing in the sequence of Fibonacci, harmonic, or fractal ratios. So that each wave has a programmed position and characterized by a particular direction and duration with extreme as a reversal point.
In fact, the model has been used by stock market analysts for almost a century. Although it looks very attractive due to its strict formalism and deterministic outcome, its predictive power is weak because of a few reasons. Firstly, the predictive values are dependent on waves that were counted – determining where first and next wave start can be subjective. Secondly, according to Efficient Market Hypothesis, using an exact Elliott Wave model by many traders could lead to the disappearance of the patterns they anticipate. And finally, nowadays one’s trading success based on predictions is rather a chance game in a modern market with its irrational behavior.
The purpose of this research was to explore if Elliott Wave principle can be used these days in stock market forecast. To eliminate the subjectivity in counting waves, Neural Network (NN) was used to analyze and predict waves. Also instead of assuming that waves obey only the sequence of Fibonacci, harmonic, or fractal ratios, a more general approach used – the software processes all extracted waves. Besides, employing NN enables identifying both the price and date of extremes. The first experiment has been done using an artificial data set. The data consist of two sinusoidal functions with different periods. The second group of experiments has been done on real market data.
The main conclusion is that the Elliott Wave idea can be used in predicting stock market. Although it does not generate always accurate and consistent forecasts, its result can be successfully used as an additional input for making a trading or investing decision in modern market conditions.
Charts and calculation results by SMFT-1 (TA-1 sub-system Waves module interface)
It Is Time to Try a New Forecast-simulation Software
A new decision support software system for stock market traders has been release. It is called 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 simulations and statistical analysis show 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 performed 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.
It is known that depending on the character of particular trading shares and current stock 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.
Transforming Moving Average From Lagging To Leading Indicator
Traditionally in technical analysis, chartists consider moving average indicators as lagging ones. In other words, these indicators are able only to confirm long-term trends that already started but not to predict in advance. The difference between simple moving average (SMA) and exponential moving average (EMA) is not so big in that respect although exponential one assigns more weights to the latest points and, therefore, EMA is more sensitive to the latest changes.
A more “predictive” signal can provide a combination of EMA with crossing price curve. Normally, it is used as an affirmation of increase in momentum but, still, the disadvantage of using this combination is that a significant move may have already happened. Therefore, if the trend is not going to last long, traders risk to enter a position too late.
Recent research showed that the predictive abilities of some well-known indicators can be improved by an additional transformation. Briefly, if an indicator is trend-differentially coupled with price – it demonstrates better predictive abilities than a simple indicator. As experiments showed – a similar improvement occurred when EMA indicator has been transformed to a slope of line and differentially-coupled with a price line slope.
The image below shows EMA divergence indicator forecast by Technical Analyzer TA-1. This interface represents one of three major modules of TA-1. The module provides with more than 50 popular technical indicators. Except chart analysis, indicators can be used as input for Neural Network to build 10-day price forecast. There is an option to compose forecast from all indicators – each indicator’s forecast is added with the weight proportionally to the current ability of the indicator to predict prices.
Model To Predict Stock Market Using Butterfly Effect
As it was reported in the research “The Technical Indicator to Watch Rapid Sell-off“, one of the ways to predict a steep downtrend could be to monitor the speed and acceleration of prices (high-order derivatives). As statistics showed, a typical picture is that specific stock market short-term changes are coupled to big long-term negative changes in the future. Evidently, the same idea can be applied to long-term positive changes.
Considering the impact of short-term changes on longer periods can look revolutionary since normally analysts use a longer period to predict a shorter one. For example, to predict one-day price change, chartists consider up to 12 previous candlesticks in patterns. If we assume that markets are partly obeyed general natural laws, a similarity when a small seed is able to grow into a huge tree might look less surprising.
There is a hypothesis that many dynamic processes including financial markets can be described by Chaos theory. One of the theory’s ideas is “Butterfly Effect” (BE). According to this effect, the slightest disturbance of input parameters can cause a huge change in the outcome (a high sensitivity to initial conditions – link to video).
There is another explanation why BE might be applied to financial markets. Big market participants (BMP) have powerful resources to get important information before it becomes known to others. They also deal with a much bigger volume of funds and, therefore, are able to affect the market by changing supply-demand equilibrium. Also BMP have resources to react quickly and due a huge volume of money inflow or outflow, the market equilibrium can be changed very fast.
One of simple conclusions is that fast market movements might signal a high probability of changing long-term expectations. Except fast moves, BMP may move prices with different degrees of acceleration. So that in most cases, BMP’s short-term actions should have special pattern signatures and, therefore, some typical short pattern signatures can literally predefine following huge long-term changes.
New Trading Decision Support Systems Group on LinkedIn
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!
The Stock Market Is Likely Taking Summer Break
S&P-500 Index Drop from May 2 to June 8, 2011
Major stock market indexes slowly went down during the last several weeks, from May 2 to June 8, 2011. Since this decline has already broken a few important technical chart levels, many technical analysts believe that the trend might continue with sideways moves for a few weeks ahead.
Except seasonal reason, this May stock market decline was also caused by fundamental factors. The US corporate profits in the first quarter dropped for the first time in more than two years. The US unemployment rate in May increased from 9.0% to 9.1%. It looks likely that investors overreacted to strong corporate earnings at the beginning of this year. Economists believe that the government stimulus money could have artificially inflated the market. Evidently, without additional money injection the US economy would undergo a transition to a self-sustaining recovery.
From the fundamental point of view it is not clear how precisely the ending of stimulus is reflected in the current market evaluation. So that when the bond-buying program ends at the end of June, the US stock market might reach a more accurate equilibrium. Other surprises might be brought by the second quarter reporting season.
The Stock Market Is Entering A Slow Summer Season
Not so long ago, S&P warned the US government of consequences if a massive federal deficit is not fixed; as it was estimated, there is a 33% chance it would lower existing triple-A rating. The US trade deficit worsened. More automobiles and other goods were sold abroad but oil imports increased. So that high oil prices is one of the reasons that the trade deficit rose 6% in March from February. The unemployment monthly rate slightly grew, from 8.8% to 9%. The productivity growth slowed in the 1st quarter.
However, long-term fundamentals look strong. During the last several quarters in a row, the US biggest corporations had stronger revenues and better profits. The recent slight correction in commodities prices will help the economy growth. The current market weakness can be partly explained by typical for this time of year assets re-location – many big investors are making summer seasonal adjustments to their portfolios. They are switching to cash and bonds trying to anticipate a statistically known summer market slowdown.
Technology Sector Might Do Well During the Next Three Months
The chart below shows the comparative analysis of expected sectors’ performance for the next three months – April, May, and June of 2011. The 11 sectors (no data for Conglomerates) have been composed of selected more than 500 US and Canadian stocks. Apparently, Technology and Financial sectors look the best:
The chart has been calculated using Investment Analyzer InvAn-4. The calculation is based on a rank of stocks. Sector ranks distribution allows making comparative analysis of sectors’ ratings (sectors are formed from the stocks recorded in InvAn-4 internal database). The highest ranked stocks are expected to be the most probable best performers within the next three-month period. Stocks are ranked on the basis of the composite rating which is a combination of fundamental, technical, and timing ratings. Such combination allows realistic modeling the quality of a company and its stock in the market.
The Best Technical Indicators of 2010
Is it possible to find the best technical indicators that are the best for all cases? The best way to find the answer is to collect and analyze data and make a comparative analysis for many years. In fact, statistics shows that depending on different time-frame, market conditions, industry specifics, type of stock or ETF, and other factors, some indicators might be best but other worst, and vice versa. The major conclusion – it is better to select the best indicators for a particular case.
In general, the question can be answered if an average is calculated (although it might be not so helpful). Specifically, concerning average forecasting 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
- Directional Movement System
Another problem is that there are not only many technical indicators but also many different interpretations of their signals. Some traders use particular favorite indicators and insist that their interpretations are right. A computer program could decide using back-testing which indicator should be trusted more and another less for particular market conditions and a specific stock. It could compose the forecast with weights accordingly to predictive ability of each technical indicator. The example of such program is Investment Analyzer (10-day forecast using Neural Network).
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