Addaptron Software Release New Stock Market Software SMT1

Addaptron Software announced a software release, SMT1 (Stock Market Tools, release 1), a new advanced software system for End-Of-Day (EOD) traders. One of new advantages is all-in-one output forecast signal. This signal (number) is the result of processing data by Artificial Intelligence (AI) Forecast Module. The set of data consists of technical indicators, waves prediction, pattern filter, and cycles extrapolation. Based on Machine learning results, AI decides how to interpret all relevant data and express the conclusion in a single number.

SMT1 is intended for EOD traders with intermediate or advanced knowledge in the Stock Market and computer software. The software consists of four major functionalities: Forecast, Backtest, Simulation, and Tracking. SMT1 is provided with User’s Manual which helps to understand the general structure of the software, connections between functional modules, and how effectively utilize all features.

The software uses EOD historical prices data as input. SMT1 includes a free Downloader that allows downloading EOD historical quotes files of selected symbols (some of most traded leveraged ETFs) from Addaptron Software server for free. Optionally, users can use own input data files. User’s Manual explains how to use own input files.

The main concept of the software is to work (i.e., predict, simulate and optimize trading performance) with the group of well-traded leveraged ETFs to maximize overall return. Each ETF has inverse counterpart and represents different industries that allows finding a potential winner every day. Although the software is suited to a specific niche, users can try to use own group of symbols.

Stock Market traders use different types of sell signal to exit position. Since exit signal cannot be reliable enough, some traders use stop loss and profit target to exit position. Addaptron Software has done numerous computer simulations to learn if adding more exit conditions can improve trading return. The research discovered that a better trading return in the long run can be achieved by using as many as four conditions for exit. This multi-trigger exit concept has been implemented in SMT1 as a new 4-Way Exit Method. This is another SMT1 advantage.

The software also includes an extra feature to record buy-sell transactions, analyze a current position, recommend the action, and measure trading performance. Since AI is able to optimize many settings parameters, the number of user-defined parameters is minimized so that users can save time.

Downloading and installing SMT1 is a very easy process and explained step-by-step on download page . All retail traders are eligible for free fully-functional version during initial 30-day period.

The example of SMT1 user interface: tab-page Simulation (back testing): 

SMFT-2 is a New Stock Market Prediction Software

SMFT-2 (Stock Market Forecast Tools) is a new integrated system by Addaptron Software. It is the next generation of the software that intended to replace older SMFT-1 version. As a result of a few recent development projects, the new version is based on new advanced methods, provides more accurate prediction, and is easier to use.

Prediction modules is built with back-test calculation to estimate the accuracy of forecast within the recent performance periods. Additionally, the back-testing computations is important if more than one method is used. It allows estimating the weight of each method in a composed result; the weights that are proportional to the ability of the methods to predict the price.

SMFT-2 currently includes five major modules:

TA Predictor - prediction for day or week period based on Technical Analysis, pattern recognition and Neural Networks (generates composite result). Back-analysis models optimization and batch calculation for comparative analysis included.

Waves - Elliott Wave model: back-test optimization, up to 10 waves forecast.

Cycles - prediction based on cycle analysis.

Week day - search for maximum performance using price behavior depending on week day. It allows discovering the best entry/exit days of week; batch calculation included.

Month day - search for maximum performance using price behavior depending on month day. It allows discovering the best entry/exit days of month; batch calculation included.

The implemented methods are statistically proven and widely used. All modules share the same EOD (end-of-day) input data. The software is provided with a free Downloader that allows downloading EOD historical quotes files from the Internet for free. A fully-functional software SMFT-2 is free during initial 30-day period. The software and associated documentation are delivered via download links over the Internet. For technical requirements, installation instruction, and download link, visit SMFT-2 download page.

About Trading Decision Support System TraDeSS-1

Trading Decision Support System TraDeSS-1 is for institutional traders and investors who deal with ETFs, commodities, and big-volume equities. It is a comprehensive and effective software to help finding the best trading opportunities, maximizing profitability using several predictive models with back-testing features, and optimizing algorithms by running simulations.

TraDeSS-1 is equipped with an advanced forecasting state-of-the-art system. The predicting can be done using nine forecast methods of different nature. Only one method, a combination of a few ones, or all together can be used. Each method is provided with back-test calculation to estimate the accuracy of forecast within the recent performance period. The back-testing computations play an important role if more than one method is selected. It allows assigning a weight to each method in a composed result; the weights are proportional to the ability of the methods to predict the price.

The comparative analysis of simulations shows that systems based on predicted entry-exit signals generate a better profit in around 70% cases than random-entry trading systems. As well as, it should be noted that a multi-model forecast provides a significant improvement over the best individual forecast. It can be explained by the existence of many different independent factors contributing to the error in each forecast which is normally distributed around an actual value.

Since sometimes predictions can fail, to preserve a principal amount in a volatile market, the software enables simulating different risk management approaches. Depending on the character of particular trading assets and the current market conditions some ideas can work better than others. To optimize the strategy in a particular case, the software enables testing different algorithm configurations and finding automatically the best ones. It is especially important for exit points to minimize losses (and ultimately maximize an overall profit). All optimizations can be done automatically by scanning 64 possible logical combinations and adjusting numerical parameters.

TraDeSS-1 has a functionality that allows estimating a hypothetical maximum possible profit in case of 100% accurate forecast. Although an actual forecast cannot be so accurate, this feature combined with comparative analysis enables discovering the best trading opportunities among different types of financial instruments. Calculating maximum theoretical return allows finding optimal buy and sell signals. Also it helps estimating a reasonable amount of initial investment at given transaction fee. Users can choose to re-invest each time a new or the same amount and see the difference in results.

The software has forward testing and assets management features. It allows monitoring the simulated or actual completed transactions, reflecting total trading activity, and evaluating the success of trading in overall. It enables working with many separate data files that is convenient in case of managing multiple assets and keeping the archives of older activities.

TraDeSS-1 has also a few independent tools, such as, technical indicators predictor, cycle analysis forecast, Neural Network (NN) forecast, fundamental 3-month rating model for equities, etc. The detailed description is presented in User's Manual (accessible from menu Help after downloading and installing the software). The software is available via the registration (no payment data collected and no obligation to buy).

A fully-functional software during initial 30-day period is free. To continue using the software after 30 days, the subscription is required (subscription link is available from the software interface). Technical support and updates are included. Annual lease and perpetual license are available. Paid on-site training (how to use the software) can be provided.

SP-500 Index Cycle Analysis Forecast for Spring and Summer of 2012

There is a simple predictive model that is built on an assumption that the stock market has a semi-cyclical nature. Many technical analysts use cycle analysis in their comprehensive research. The cycles may not be stable all the time but the probability of repeating patterns can be big enough to get a consistent trading profit.

Cycle analysis can be made using charts. However, the distinctive cycles in pattern can be masked by more powerful factors (fundamental data, bad/good news, global events, etc.) that over-drive the market time-from-time. Therefore, a special software can be very useful for extracting hidden cycles.

The chart below shows S&P-500 index forecast for April-May and summer months of 2012. The calculation has been performed using Stock Market Predictor SMAP-3. According to this forecast the stock market might continue its uptrend until the end of May, then have some correction in June and top in July.

Predicting the Next Move of the Market by Elliott Wave

The Elliot Wave model is based on a crowd psychology that changes between optimistic and pessimistic states creating patterns that can be fitted to natural sequences. These days, the Elliott Wave principle can be improved and used in stock market forecast. Combining this method with Neural Network (NN) helps to eliminate the subjectivity in counting waves.

Instead of assuming that waves obey only the sequence of Fibonacci, harmonic, or fractal ratios, a more general approach can be used to process all extracted waves. Besides, employing NN enables identifying both the price and date of extremes. Although the Elliott Wave 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.

The chart and the portion of output result below can give a clue for a possible next move of S&P-500 for the next week:

Symbol: ^GSPC
Total waves number: 126
Cases number: 108
Target: 1,382.35$ at 2012-02-21 (change 3.10% from 2012-02-14)
     No        Date                $           %   up/down  period
    115  2012-01-02         1,284.62        2.88    100.00     3
    116  2012-01-04         1,265.26       -1.51      0.00     2
    117  2012-01-11         1,296.82        2.49    100.00     5
    118  2012-01-12         1,277.58       -1.48      0.00     1
    119  2012-01-20         1,322.28        3.50    100.00     5
    120  2012-01-24         1,306.06       -1.23      0.00     1
    121  2012-01-26         1,333.47        2.10    100.00     2
    122  2012-01-30         1,300.49       -2.47      0.00     2
    123  2012-02-09         1,354.32        4.14    100.00     8
    124  2012-02-10         1,337.35       -1.25      0.00     1
    125  2012-02-13         1,353.35        1.20    100.00     1
    126  2012-02-14         1,340.83       -0.93      0.00     1

Chart and calculation results by SMFT-1 (a version of FTA-2 sub-system Waves module interface)

Automated Recognition of Candlestick Patterns

The idea of using the chart with candlesticks (or candles) for predicting market prices is very old. Two centuries ago, Japanese rice trader found that the candlesticks pattern chart could be used as a tool to predict future prices in a free market with a natural demand-supply balance. The method was improved later by others and today it is successfully used by many traders and investors in the stock market.

A candlestick is presented using high, low, opening, and closing prices during a certain trading period, for example, trading day. A regular candlestick figure consists of Real Body, Upper Shadow, and Lower Shadow. The Real Body size is proportional to the difference between opening and closing prices. Real Body can be two types - white (green) for uptrend and black (red) for downtrend. Upper Shadow size is proportional to the difference between either high price and closing price in case of uptrend or high price and opening price in case of downtrend. Similarly, Lower Shadow size is proportional to the difference between either low price and opening price in case of uptrend or low price and closing price in case of downtrend.



The number of candlesticks that is normally used for predicting can range within 1..12. Evidently, the number of different combinations of several candlesticks in a row can be big. Some believe that there are only 12 major candlestick patterns, others consider this number is 70 or even more. Anyway, in case of chart analysis, it is necessary to remember at least major patterns and process many charts in order to make forecast successful.

Apparently, statistical methods combined with computer power can be a good solution to make the candlestick patterns recognition work less time-consuming and more effective. For example, Neural Network (NN) can help to automate a candlestick patterns recognition task. NN should be properly trained in order to be able to recognize and predict further movements. One of the obvious problems of implementing a candlestick pattern NN predicting system is a formalization of inputs, i.e., the way how to express each candlestick shape and relative position of all candlesticks in numerical values.

Preparing Data for Neural Network. The idea is simple - look at several candlesticks, recognize pattern, and predict the next candlestick. But how to convert a candlestick shape in numerical values? For simplicity, let's consider one major characteristic of each candlestick. In case of using six candlesticks to predict the performance within the next seventh one (actually the value of Real Body can be considered as an equivalent of performance), the data row for training neural network would be presented by the following:



In reality we need to use more input parameters including shadows, relative position of each candlestick, etc. so that it can be, for example, 60 inputs for each row:



The candlesticks pattern can be formalized in different ways. Which one is the best? It may depend on the type of NN that is used and statistical characteristic of input data. Evidently, only practical testing (out-of-sample test) can indicate which formalization can give the best result for most cases. Logically, all numbers that describe candlestick shape should be expressed in relative units. For example, Real Body size can be converted using the following formula:

RB = 100% * (C - O) / O

where RB - Real Body relative size, C - closing price, O - opening price.

Two more numbers can express the Upper an Lower Shadows relatively to Real Body. The following figure shows distances that used for calculation relative Upper and Lower Shadows:


US = 100% * c / a
LS = 100% * c / b

where US, LS - relative Upper and Lower Shadows correspondingly. US and LS can have values within 0..100%; minimum value equals 0 if Real Body size equals 0, and maximum value equals 100 if Shadow size equals 0.

Therefore, we can use these three numbers for formalizing one candlestick. The number of candlesticks that can be used for historical period can be up to 12. So that the number of inputs for NN can be equal 3 * 12 = 36.

Two More Inputs for Candlestick Pattern Recognition. As it was discussed above, we can use three major numbers to describe the pattern of one candlestick - relative size of Real Body, relative size of Upper Shadow and Lower Shadow. Also we can use 12 candlesticks with these three parameters for each as inputs. However, it would be insufficient to use only these parameters since each candlestick can have different position and their relative position traditionally is used for the analysis and prediction.

The simplest position parameter would be a percentage deviation from an average of all candlesticks position. It could be just closing prices of each period of candlestick. The formula for calculation:


CP_i = 100% * (C_i / C_aver - 1)

where CP_i - relative position of i-candlestick; C_i - closing price of i-candlestick; C_aver - average of all closing prices (all 12 candlesticks).

Except above introduced parameter, it could be reasonable to add one more parameter to distinguish negative and positive candlesticks (black and white) since it makes a significant difference to investors' psychology. So that each black candlestick would have 0 value, white - 1. To summarize, 5 parameters multiplied by 12 (the total number of candlesticks) give 60 inputs.

Numerous tests show many possibilities of improving NN candles patterns recognition abilities. For example, output result can be composed from selected optimized calculations based on different historical periods. As well as, there are many other different ways to formalize the shapes and relative positions of candlesticks.

Optimal Solution. There is an automated tool FTA-2 (free use of fully-functional version for one month). It has module which enables using Neural Network to recognize typical candles patterns and predict future prices. This module predicts only one next candle but the candles pattern prediction can be successfully used for different widths of candle, i.e., the number of trading days in one candle. The module is enhanced to calculate result that is composed from different historical periods that allows making the forecast more accurate. Also it can perform comparative forecast analysis for many symbols.

Useful resources:

• Candlestick basics - major signals
  
• Neural Network basics - introduction
  
• The software which enables using Neural Network to recognize typical candles patterns and predict future prices - about FTA-2
  

The Stock Market Remains Weak Despite Positive Reports

In general, the stock market demonstrates weakness despite better-than-expected US economic reports and news. The US unemployment rate dropped to 8.6% in November from 9% in October. It is a lowest level in more than 2.5 years, since March 2009. October US retail sales were 7.2% higher than the same month a year ago. Furthermore, the retail sales have been raising for 5 straight months.

Improvement in consumer spending was one of the reasons why the economy grew at the best of the year annual rate of 2.5% in the 3^rd quarter. US auto sales increased in 14% in November. Also during the 4-day Thanksgiving weekend consumer spending reached a record 16% in total sales from a year ago. Although some of these numbers might be reevaluated and revised in the future, in overall, they indicate improvements.

The Stock Market Outlook Would Be More Positive

After a few years of struggle to find an alternative to over-leveraged financial models, the transition to a new era of a deleveraged economy has begun. Recently, the necessity of this transition is confirmed by rising European and Global financial uncertainties. Evidently, these are negative factors that still keep investing risk at higher than normal level. On the other hand, there are positive signs of recovery.

The third quarter US GDP almost doubled to 2.5%. The unemployment rate has improved from 9.1% to 9.0% in September. These two numbers cause some debates concerning how they are calculated and if they are able actually to reflect the reality of economics, however, other numbers also look good. The most important numbers that push the shares prices are corporate earnings. They are relatively healthy. Therefore, the possibility of a double recession now seem less likely than before.

Furthermore, S&P-500 is trading below historical price-earning ratio. Many stocks look cheap and attractive. Since most investors are still scared by the market volatility of the recent years, they keep a lot of cash and potentially able to lift the demand and prices. If the future outlook of the stock market stays strong, further uptrend can be just a matter of time.

Better Expectations Drive the Stock Market

A sluggish economical recovery, weak job market, financial crisis in Europe, and the fear of global recession kept the major stock market indexes in a choppy pattern during 3^rd quarter. However, October started with some good news and better-than-expected statistics. These were able to lift S&P-500 from 1075 to 1225 (almost 14%) for two weeks. Among good economic news: September was one of the best months of the year for the US automobile industry and retails. Also according to the latest GDP forecasts, the 3^rd quarter can be the best quarter in a year.

The slow economy, high unemployment, Euro-zone crisis, and the threat of global recession might not find a quick solution but as old news all these have been priced already in a market equilibrium. The question is what will be the next. As a rule, the future expectation is the thing that drives the stock market, not the past performance. Statistically, November and December are bullish months of a year. In addition, this time the annual cycle may be propelled by positive economic projections. Therefore, if no more bad news wakes up the pessimism again, 2011 has a chance to end on a positive note.

October: Is It Time for Bull?

Lately, the economists re-evaluated their previous estimates and significantly lowered the predictions for the US economic growth this year and in 2012. This fundamental equilibrium reset, as one-time event, coincidentally has combined with a cyclical September-October low market season. A high volatility, remaining macroeconomic risks, and crowd fears persist. A lot of confusions still dominate the stock market. What is next to expect?

Europe troubles might continue to affect the global markets. The unemployment rate and housing market might not improve quickly. However, the US corporate reported earnings continue to exceed the profitability estimates in most cases. A lot of stocks are now cheaper than during the 2008-2009 market calamity. The future expectation is the thing that drives the market, not the past performance. That is why many investors and traders hope for rallies.

Crashes Always Come Unexpectedly

What did exactly cause the recent sell-off ? There are different opinions among experts - debt-ceiling standoff, US credit rating downgrade, disappointing economic growth statistics, Europe troubles, or all combined together. As usually, it happened suddenly and quickly. There might be at least two ideas why recent declines happen faster and faster. The first one is that investors reaction to uncertainty and tolerance for risk have changed; it takes less and less to scare investors.

The second reason is a fast electronic trading. It allows programmed computers to sell under particular circumstances in milliseconds that develops a temporal disconnection between the actual factors and reasonable market prices - the market overreacts and makes irrational movements before reaching an after-crash equilibrium. As a matter of fact, the latest huge swings with changing directions every day have never been seen before in S&P-500 history.

In new era of globalization, mutual dependencies, and markets interconnection, the system crises turned out to be a new threat to our global well-being. Nonetheless, it seems the investors' expectation now has changed - the US and global economic growth is likely to be less healthy in the near future than previously estimated.

Some resources: Trading Strategies for a Stock Market Crash,
How to Escape from a Bear

The Stock Market Is Ready To Advance To New Highs

The US unemployment rate increased during the last three months and reached 9.2%. Even so, in a long-term perspective, a high unemployment is on its new "normal" level and a small increase looks rather as a noise, not a strong signal. Also normally, unemployment rate is considered as a lagging indicator and does not indicate what will happen in the future. Often, unemployment numbers continue to increase even after the economy is recovering because businesses are reluctant to hire new employees.

After careful consideration some analysts have made a conclusion that ending the bond-buying program can actually have a positive effect on the market. Although US housing is not changing, it is in almost the same bad shape for two years without strong factors that may prevent it from positive moves. The factory orders increased in May. Oil price is down that helps the economy to gather a recovery momentum. Credit availability and business investments are improving.

From quarter to quarter, corporate earnings are fluctuating and it might be a sign that the economic recovery is in a early developing stage. Many chronic problems did not disappear yet but their influence is already reflected in the indexes. Everyday bad news have almost no effect on a strengthening market. July opens second-quarter earnings season. In terms of valuation, the stocks are trading now at attractive levels and if corporate balance sheets still remain healthy, almost nothing left to prevent the stock market from advancing to new highs.

How To Use Chart Patterns To Predict Trend

There are many classical well-know chart patterns that found a long time ago and now considered as "typical" ones. Also there are many other patterns that can signal a particular, bullish or bear trend, but not described yet. Known or unknown patterns that are persistent can be repeatable in the future; therefore, they can be used to predict the future price movement.

To ease the job to memorize all patterns and analyze a huge amount of charts, many chartists use different software tools - pattern recognition systems. These tools normally perform statistical classification of patterns assuming that the patterns are generated by a probabilistic system (the stock market is a semi-probabilistic system).

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. The prediction period can be chosen within a range 1..60 trading days; the period of historical data that used for matching recommended in 4..16 times longer than prediction period. TA searches for the best matches by scanning all historical data from the internal database.

TA ranks all possible matches on the basis of maximum correlation and minimum deviation within given historical period. TA performs pattern matching using open, high, low, and close prices and volume data. When scanning is completed, depending on degree of similarity, it ranks all possible matches within given historical period and then combine them. 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 proportionally pattern ranks.

To try free fully-functional version of TA, visit Addaptron Software download page to download and install it.

Two More Indicators Implemented in Technical Analyzer

Collected and analyzed data allow comparing different technical analysis indicators. However, according to statistical researches, the problem is that depending on 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. In general, the question can be answered only for some average analysis. 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.

Technical analysts know that it is better to select the best indicators for a particular case. Evidently, 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).

The recent researches showed that predictive abilities of some classical indicators can be improved by additional transformations. 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 the next 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. Indicators and price transformation to line slopes has been performed using Least Squares Linear Regression within a sliding 10-day period (moving window).

Try free fully-functional software during 30 days; it is available on Addaptron Software download page

June 2011: SP-500 Might Not Touch 1250, At Least For Now

One of the popular ideas now is that until S&P-500 index touch 1250 number, it is too early to buy or sell. However, on the assumption that currently many market participants are looking at charts and use technical analysis to make their buy-sell decisions, the index might not move down too much, at least for now.

If almost everyone is considering that it is not good time yet to sell (or go short) and waiting for this magic number, this move might not happen. The reason is that there will be no sellers but mostly holders that are waiting. It can be a typical situation when an expectation affects the market. The index might stall for a while and then move up. Also a statistical cycle analysis method based on action-reaction idea indicate that such reverse will happen after June 15-17 and before reaching 1250 value:

Chart has been calculated using cycles predictor

Are Technical Indicators Useful?

Yes and No. Evidently, there are periods when technical analysis works and the periods when it does not. The reasons can be many – fundamental changes, revised expectations, unexpected news, etc. During the periods when a majority of market participants make their decisions based on past market performance, as a rule, there would be some technical indicators that relatively work well.

Are there best ones? During some periods, some indicators might be the best winners, others – the worst losers. But all things are subject to change. Besides, indicators and markets are often a two-way system, i.e., markets can be affected when a huge number of investors use the same indicator(s). For example, the indicator predicts flat 2 days and then an uptrend for 3 days. If everybody follows, the forecast fails because price would be driven up first 2 days by buying volume and then 3 days flat or down-trending due to taking profit.

Other technical methods. In many cases, due to a semi-stochastic nature of the market, probabilistic statistical methods are able to predict well. Some of the systems that employed such methods is Neural Network (NN). NN often suffers from “over-fitting”. It is bad because over-fitting gives an exact but sometimes wrong result more often than an approximate but correct one. Cycle analysis, as another statistical method, has other advantages and disadvantages.

Market Crowd Factor: Why Trend Might Not Be Your Friend Sometimes

“An advertisement said, 'Send me $1 and I will let you know how it was easy for me to become a millionaire.'
A man who wants to be a millionaire sent $1 in an envelop.
After a while he received a reply: 'I became a millionaire by asking to send me $1'
The next day, the advertisement said, 'Send me $1 and I will let you know how it was easy for me to become a billionaire.'”
- Author unknown

An advanced and robust principle, idea, or system might help to win in the stock market. But what if it is used by almost all market participants? Would everyone be a winner? Read to find out about cases when stock investing can be successful or risky.

Although a successful stock market investing or trading is complicated, in general, it consists of three major parts: analyzing data and possible future, making decision, and executing this decision. The same applies in case of automated trading - just these parts incorporated in the system and work in the same way. If we abstract from non-controllable external factors, we could clearly see that the quality of our decision and consequently the outcome depend on input data and their processing to make an optimal decision. What input data are the best and how to convert them into a meaningful information and finally into a right action?

Quality vs. quantity. First of all, apparently, too many data, especially, that are non-relevant to the searching result, might make the solution too noisy and with error ranges that exceed the accuracy of a certain solution. That is why most market participants use a limited but sufficient enough the set of input data in order to transform it further into a necessary information with a minimal uncertainty. The second important task is a processing with the highest quality of extracting worthy information. A good processing enables enriching the information while trying to avoid losing any value in the translation process.

Sometimes trend might be your friend. Is it possible to find and use the best universal principles and systems in stock trading? The second question - is it possible to make money using methods, systems, or ideas that are employed by many? It seems collective actions can be beneficial. For instance, if there is a uptrend - many would join the movement and add more pushing power up. Although a crowd behavior could be useful, sometimes, it can lead to unexpected results.

What if everyone does the same. Typical example - many thought that February could be a good month to invest because institutional funds' managers could increase their portfolios in March. Many converted cash into stocks in February pushing market up. Then when buying power exhausted and funds were unable to support this trend - the stock market collapsed. Another examples, several years ago October was a low month but since too many talked about this annual cycle, it is not the case anymore - everybody buys in October and pushes the market higher.

One more danger of crowd. Some classical books warn investors - when everyone is very bullish in the stock market - this is time when the market can crush because there is no one left who can invest more money to support the uptrend. It was in case of a hi-tech bubble in 2000. What factors influence our investing or trading decision? The opinion of most market participants? But their opinions link to their actions - that is why a trend might be built on sands and it can let you down anytime.

The benefit of using a new or rare approach. Let's imagine the situation - a group of people read the same book, for example, "How to Find Mushrooms" and all go to one forest. Evidently, then there would be more chances to find mushrooms in the places that are not recommended in this book. The conclusion - stock trading can be successful also in case of using something new or something that is used by limited number of traders.

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The Chain of Unfortunate Events Struck the Stock Market

The first hit on a weak global economy recovery was the re-appearing of European debt problems. Then as it turned out after revised estimate of the fourth quarter, the US economy grew 2.8% instead of 2.9% during 2010. Next, the uprisings in the Middle East induced oil prices to surge. A soaring oil price caused the fear of inflation. The market reaction to oil price overshadowed the good news of US improving labor market.

China normally has a trade surplus with the world but this time it reported a significant trade deficit for February. Higher prices for oil and other commodities increased its imports and decreased exports, raising concerns about Chinese inflation and growth. All above was enough to scare the bull but even more was set to come.

The natural disaster in Japan added a big portion of negativity to the stock market. Now investors are so concerned by a fragile global economy. Disrupting global supply and the estimates of an infrastructure damage show that the losses are disturbing. Evidently, this disaster is going to bring huge economical consequences to a global economy.

Worries might continue to dominate the market for a while. However, no matter what happens, fear always exceeds a possible realistic impact of any event. Hopefully, Japan's catastrophe will be the last one in the series of bad events of this year. To a natural law of harmonic balance, April, as a corporate reporting month, might bring some good news to the stock market. By the way, US job market is gradually healing - the unemployment rate is the lowest since April 2009.

Typical 10 Phases of Stock Market Disturbance

Within bear or bull market there are always fluctuations in stocks prices. It can be said about indexes, ETFs, and most other investing instruments. As example, let's consider an equilibrium market state that is based on a realistic evaluation. Assume it is a starting point. Then at some moment a good news released with the expectation that is above a realistic evaluation. The first reaction would be a price up-move (stage 1).



As prices are tend to rise, many would follow a simple strategy to join a growth movement that additionally enforced by greed (stage 2). Since there are always some participants in the market that might got this news with a delay or are too big to make the decision and perform transactions fast, the curve price might continue rising but with a slight less slope (stage 3). Normally, news can be accompanied by other overly optimistic opinions. Also there is always a room for some errors and miscalculations. These factors can be materialized in a short spike of prices (stage 4).

At some point, when a buying power exhausted and there are no other factors to sustain the growth, a reversal happens. All fast trading systems and dynamic participants of the market including short-sellers push the market down rapidly (stage 5). When the correction technically becomes more obvious, many start selling; the movement becomes stronger additionally enforced by fear and leads prices below the equilibrium line (stage 6).

Since fear is more strong drive than greed, normally the value of downtrend gradient is bigger than uptrend one. Two phases that are similar to ones existing in the uptrend curve part, delay (7) and miscalculation (8), follow until a bounce back (9). The after-bounce curve part can have a decaying-fluctuation pattern (stage 10). This pattern finally approaches the market evaluation to the equilibrium line.


Practically, very often, all described above consequent 10 phases might not be observed clearly due to several reasons. One of them is a fact that a single isolated news happens very seldom. Another typical reason is that all factors that drive the market might not be available in the form of publicly available information all the time.

© Alex Shmatov. Published with permission of the copyright owner. Further reproduction prohibited without permission.

2010 Stock Market Strength Might Propagate Into 2011

S&P-500 index has grown 12.78% during 2010 and it is ending on a positive note. The US corporate profits continue improving and companies have a lot of cash that provide opportunities for business optimization, better dividends, and make stocks more attractive. Also during the last several months the stock market became more predictable from the technical analysis point of view.

Many experts believe that the corporate earnings will grow further in 2011. Evidently, the prices of most shares will move in a natural cyclical manner with dynamical responds to unexpected news, as it was before. In average, major indexes are expected to perform around the same as in 2010. Anything can happen but, as always, extremes have less probability than averages.

Wishing You

Hopefully, 2011 will be a better year with many new opportunities for stock investors and traders!