Sideways Market Blues

MetaStock SPRS Series - Week 114 - TechniTrader® Stock Discussion for MetaStock Users - Sideways Market Blues - April 15, 2013
By: Martha Stokes C.M.T.

Martha Stokes, CMT is the speaker in the upcoming MetaStock Webinar "Explorations: Beyond the Basics" on April 24th at 2:30 pm PDT/ 5:30 pm EDT. Register for this live event here.

A work booklet for this Webinar is available after registration.

Right now on the institutional side of the market, things are very quiet. However in the retail trader blog rooms, forums, and in particular the day trading rooms where retail traders congregate and communicate opinions, there is nothing but frustrated comments, angry attitudes and blame.

Institutions are sitting with high profits, while retail traders are losing, losing, and losing. I call this the "Sideways Market Blues" Syndrome.

The stock market has been in a sideways pattern for a few weeks now and while the professionals step back and wait patiently much of the activity in the market is retail traders struggling to squeeze a few pennies out of a swing trade or intraday trade.

The automated market has created a whole new methodology for the professional side, and totally different approaches to trade transactions. Yet few retail traders are aware of the changes and continue to attempt to trade the markets with outdated strategies that fail dismally during sideways markets.

Meanwhile the institutions are setting trigger points, adjusting stops, and waiting. The retail side is blaming everything, anything, and getting nowhere. Stocks that look poised to move whipsaw and the retail trader gets angrier.

The Market Condition Scans for Momentum analysis have been warning for some time that there is insufficient momentum energy to create strong swing style runs both for intraday and for regular swing style trading. But retail traders are doggedly determined to force the stock market to their will, trying to find some strategy or system that will find stocks moving strongly and steadily either upward or downward that they can trade.

This is called "Traderitis" and It is a phenomenon uniquely retail. The institutions do not suffer from this malaise. With preset price zones for Dark Pools and trigger entry signals for HFTs, the automated world of the professional , which is about 70% can pause and wait, and does wait, with uncanny accuracy and remarkable uniformity.

Retail traders confuse market maker trading activity and assume they should be able to find and trade stocks every day the market is open. This creates whipsaw action during periods when the 2 largest Market Participant Groups automated orders stop triggering. The automated orders stop in harmony, but retail traders are completely unaware that there are few large lots and even less momentum energy.

Read the full report here.

Retail traders tend to use the outdated Market Breadth, Market Advance/Decline and other Market Indicators to attempt to interpret the market, but these indicators are usually only viable during extreme conditions. They are ineffectual in a Sideways Market. What is effective is an evaluation of overall numbers of stocks moving with momentum and in what direction.

If there are only 55 stocks moving with strong momentum to the upside out of the 8000+ stocks, ETFs, and ETNs on any given day, then a retail traders chance of finding the few stocks that will move strongly are heavily weighted against them.

High Frequency Trader HFT orders do not trigger heavily every day and more of their trades are pulled or canceled than ever reach the marketplace. That leaves most retail traders trading against each other during sideways consolidating markets when the Dark Pools and HFTs are not firing off orders.

Since the HFTs and Dark Pools both use automated orders, they do have a stronger tendency to work harmoniously and function as if they are aware of each other even when they are not. This is the side effect of an automated marketplace where most orders are executed by computers and not by humans.

The automated order systems actually exacerbate the stalled market, often creating insipid action that lasts far longer than it did before the markets were so dominated by automated orders.

By understanding this phenomenon, retail traders can actually improve their profitability and their trading success. If the momentum energy of the market falls below the normal range based on the "TechniTrader® Market Condition Analysis Scans," then whipsaw risk rises dramatically. Unfortunately market Indicators will not tell a trader this fact so many retail traders are trading a market void, without sufficient energy in a large enough basket of stocks to shift the odds in their favor.

Day trading and swing trading require sufficient momentum energy to be in a large group of stocks, ETFs, and ETNs so that the runs can sustain long enough for good point profits.

The reaction by many retail traders however, is not to wait but to plod along, and keep shrinking their timeframe and profit gain expectations. This only increases their risk of chronic small losses and frequent whipsaw trades.

Trade wisely,

Martha Stokes, C.M.T.
Member of Market Technicians Association
Master Rated Technical Analyst: Decisions Unlimited, Inc.
Instructor and Developer of TechniTrader® Stock Market Courses
http://technitrader.com
MetaStock Partner

©2013 Decisions Unlimited, Inc.

Disclaimer: All statements, whether expressed verbally or in writing are the opinions of TechniTrader, its instructors and or employees, and are not to be construed as anything more than an opinion. Student/subscribers are responsible for making their own choices and decisions regarding all purchases or sales of stocks or issues. At no time is any stock or issue on any list written or sent to a student/subscriber by TechniTrader and its employees to be construed as a recommendation to buy or sell any stock or issue. TechniTrader is not a broker or an investment advisor it is strictly an educational service.

Three Tools to Help You Recognize Tops and Bottoms Within the Trend - Part 1

The Expanding Roles of Dark Pools

MetaStock SPRS Series - Week 113 - TechniTrader® Stock Discussion for MetaStock Users - The Expanding Roles of Dark Pools - April 8, 2013
By: Martha Stokes C.M.T.

Most retail traders are of the opinion that Dark Pools somehow harm their trading activity. This is because most retail traders do not have any working knowledge or access to reliable information regarding this off-the-exchange or Alternative Trading System, aka ATS, in the professional world. As I prepare the High Frequency Trader HTF & Dark Pools Elective Course that will debut this month at technitrader.com I am finding more inaccurate information about Dark Pools on the retail side news feeds than truly accurate data and information. This is unfortunate because unless retail traders have access to facts and correct data, they will be acting upon inaccurate data which will ultimately harm their profitability and success in the stock market.

One fact you must know is that Dark Pools are NOT going to go away. There is no way that the giant funds who control trillions if not quadrillions of dollars in the financial markets, are going to allow that to happen. But what every retail trader needs to also know is the Dark Pools are actually a huge advantage to you. Since the bulk of the mutual funds, pension funds and even sell side institutions do not use technical analysis much if at all for individual stock analysis, using technical analysis to find these institutions’ buying and selling footprints gives the retail trader a huge advantage.

Just last month, the SEC proposed the Systems Compliance and Integrity Regulation. And many retail articles wrote rejoicing phraseology about how this finally "controls Dark Pools." Actually, it safeguards Dark Pools. What the SCI does is it creates a standardization of compliance and integrity to all transactions no matter where those transactions are conducted and executed. The volumes traded in Dark Pools have risen by 50%. It is NOT that Dark Pools account for 50% of all transactions in the markets, it is that they have INCREASED by 50%. This is a confusing detail for many retail articles. Dark Pools do not represent 50% of the total volume of the markets, far from it. But they are a huge force to be reckoned with as they move so much money, and control far more than half of all the outstanding shares of stock in the US financial markets.

The SCI requires that exchanges, ATS, all clearing houses and order completion, as well as transfer venues for market data to have hardware and software technology that meets certain SEC standards. In addition all of these various market venues must conduct "business-continuity" testing, and are required to notify the SEC regarding disruptions and technology problems or issues. Technologies must provide robust processes that can withstand the pressures of a highly stressed financial market that is moving at the speed of light. Correlation across all financial markets is a requirement and insures stability of the financial industry. The US is still the largest financial market in the world and that will not change any time soon. The US has the largest corporations in the world and has the largest investor base in the world.

The SEC is determined to protect that status, and this is the essence of Dark Pool regulation. It brings the standards of the Exchanges to the ATS and other Dark Pool Venues. It establishes a foundation for consistency and a backup fail-safe system, to ensure the overall strength and integrity of the financial markets. So no, the Dark Pools are not going to be forced to trade against the High Frequency Traders on the exchanges. No, they are not going to be forced to expose their proprietary intellectual property Algorithms or Quant-based formulations, or their investing strategies. Those corporate secrets are as protected as any corporations would be under the laws of the US. Most retail traders are still using utterly outdated "trading systems" that employ indicators as signals or worse the red/green arrow signal systems. By incorporating indicators like TTQA TechniTrader® Quiet Accumulation that expose Dark Pools, they are easily recognized if you understand how, why, when, and where they buy and sell.

Trade wisely,

Martha Stokes, C.M.T.
Member of Market Technicians Association
Master Rated Technical Analyst: Decisions Unlimited, Inc.
Instructor and Developer of TechniTrader® Stock Market Courses
http://technitrader.com
MetaStock Partner
©2013 Decisions Unlimited, Inc.

Disclaimer: All statements, whether expressed verbally or in writing are the opinions of TechniTrader, its instructors and or employees, and are not to be construed as anything more than an opinion. Student/subscribers are responsible for making their own choices and decisions regarding all purchases or sales of stocks or issues. At no time is any stock or issue on any list written or sent to a student/subscriber by TechniTrader and its employees to be construed as a recommendation to buy or sell any stock or issue. TechniTrader is not a broker or an investment advisor it is strictly an educational service.

The Changing Landscape of Technical Analysis

MetaStock SPRS Series - Week 112 - TechniTrader® Stock Discussion for MetaStock Users - The Changing Landscape of Technical Analysis - April 1, 2013
By: Martha Stokes C.M.T.

The Financial Markets are undergoing massive internal structural changes that are impacting price and volume action, that retail traders depend upon due to their use of charts and technical indicators for stock pick selection and trading.

As the Financial Markets evolve so too are the price patterns that form on charts. This requires that retail traders first be aware of these new technical patterns, and secondly understand what those patterns mean for short term trading styles.

Candlesticks have become the standard for most retail traders and there are numerous brand new formations. When candlesticks were introduced to the western markets in the early 1990’s by Steve Nisson, the translation from Japanese commodities markets trading was exact and precise including the unique and artistic visual names assigned by the Japanese to different formations.

What western retail traders now must learn are the new patterns developing due to several factors:

1. The increased use of High Frequency Trading venues on exchanges. These affect intraday and day traders mostly as HFT action on the millisecond causes more gaps and sudden price reversals and whipsaws now than were present a few years ago.
2. Dark Pools are off the exchange aka over-the-counter transactions, that are delayed orders not displayed on the exchange platforms.
3. If Dark Pools liquidity is insufficient to fill their orders, then their activity can suddenly alter the exchange transactions when their orders move to the exchanges for liquidity. This can cause a ripple effect in price action most retailers do not anticipate, which can cause whipsaw activity due to HFTs triggering as a DP is identified by the computer generating HFT order flow.
4. Smaller Funds buying creates speculative runs similar to retail traders system buying order flow. This creates cluster orders that the HFT computers identify as large lots activity which can cause sudden shifts of sentiment patterns on TechniTrader® Quiet Accumulation TTQA and other large lot indicators.
5. Since HFT orders are traded on the millisecond which means 1000 trades per second, with some HFTs now firing off 3000 per second and these orders which can be small lot per order, actually become large lot patterns for retail charting software which is based on the minute scale or even one second scale tick. Most retail charting software is not designed to track true tick by tick transactions but bases tick by tick on a second by second timeframe. So these lots combined are huge lot orders and do affect indicator patterns that use tick by tick formulations.

TTQA is a large lot tracking indicator written by TechniTrader® specifically for MetaStock Charting Software. What it is showing on the Dow in 2013 is a high level of small fund activity buying Dow component stocks. Dark Pool activity has shorter bars and shifts green to gray during quiet accumulation phases, as the giant funds buy incrementally over several weeks huge volumes of shares are spread out and then do not impact price or volume. Volume spikes are always HFT activity.

Trade wisely,

Martha Stokes, C.M.T.
Member of Market Technicians Association
Master Rated Technical Analyst: Decisions Unlimited, Inc.
Instructor and Developer of TechniTrader® Stock Market Courses
http://technitrader.com
MetaStock Partner
©2013 Decisions Unlimited, Inc.

Disclaimer: All statements, whether expressed verbally or in writing are the opinions of TechniTrader, its instructors and or employees, and are not to be construed as anything more than an opinion. Student/subscribers are responsible for making their own choices and decisions regarding all purchases or sales of stocks or issues. At no time is any stock or issue on any list written or sent to a student/subscriber by TechniTrader and its employees to be construed as a recommendation to buy or sell any stock or issue. TechniTrader is not a broker or an investment advisor it is strictly an educational service.

How to Trade High-Momentum Breakouts with MetaStock

Understanding the Mechanics of Quantitative Trading

MetaStock SPRS Series - Week 111 - TechniTrader® Stock Discussion for MetaStock Users - Understanding the Mechanics of Quantitative Trading - March 25, 2013
By: Martha Stokes C.M.T.

One of the areas of professional trading that is least understood is how quantitative analysis actually works. Many retail traders assume wrongly that quantitative analysis is a form of stock chart analysis. It is not. One book that is circulating around the Quants is "Antifragile" by Nassim Taleb. I do not agree with all of his summations and theories, but what I find fascinating is how Quants are reacting to the book. By comparing his work to quantitative analysis, we can begin to dissect how the institutions derive and use quantitative analysis for their buying and selling on any financial market—stocks, bonds, futures, forex, index, ETFs, whatever. This is the key to figuring out what goes on in the HFT and Dark Pool back rooms.

The argument stems from the use of either a professional style momentum strategy, or choosing a mean reversal strategy which is what retail traders call “SAR” strategies. Please remember that your strategies are very simple and basic compared to Quant algorithms that trigger billions of orders in a very complex set of instructions. Often this code is many pages long with mathematical equations. The basis for all Quant work is mathematics, not technical analysis. What Quants are debating is whether a momentum strategy often used by HFTs or a mean reversal, actually lowers the risk when a "Black Swan" occurs. A Black Swan is an event that is totally unexpected and sends a shock wave through the market. This is something the professional side is hoping to minimize in terms of risk, something that they have not considered as much in the past few years, often with irreversible consequences. What the Quants are trying to determine is whether their formulas and strategies are fragile or "anti-fragile" based on the risk of a Black Swan. The problem is that if the true standard deviation is higher than estimated by a mere 5%, the probability of a catastrophic event will be increased by 5 times the original estimate. Their conclusions were fascinating and exposed the underlying problems of purely mathematical trading formulations.

The Quants are using a formula theory based on a standard bell curve, with 3 standard deviations as the mathematical premise. Recent data was tested to evaluate a negative left tail movement past 3 standard deviations to see the impact on a portfolio or trading. At 3 standard deviations beyond the norm, the losses would be catastrophic in some instances. Quants are currently of the opinion, being mathematically oriented, that Black Swans are utterly unpredictable events. I don’t agree that the markets are completely blindsided by Black Swan events. In every instance that a market-induced event turned into a Black Swan there was ample warning something was wrong, but the reality was that it is not the mathematical equations that failed but the human factor that is still present even with automated orders. Greed is the catalyst of every Black Swan market-induced event. True, non-market generated Black Swans are extremely rare events, such as the Tsunami of Japan. Most market driven events have ample warning that most traders ignore. As an example, it was blatantly obvious in 2007 that a recession of significant magnitude was coming. Every economic indicator pointed to a recession. People in the investment banks were warning it was way over the top, that the real estate market bubble was extreme, and that banks were over-leveraged. The extent of the stupidity was not known to anyone but insiders, but there were warnings.

At any rate, the goal for this year’s strategies for Quantitative Analysis is to minimize the downside risk of a Black Swan. The calculations are based on the left tail negative movement past 3 standard deviations, which would cause a devastating loss similar to what JPM experienced a while ago. Mean reversal strategies are often employed during trading range or range bound markets; however left tail risk rises significantly while profits are capped sharply. This is similar to the straddles and strangles of option trading with similar results. Although the mean reversal would be construed to be the safer strategy, market conditions in the past 3-4 months have proven that the momentum Quant strategies outperformed significantly with less risk. Therefore the argument is that the momentum strategies are less fragile and less prone to left tail risk factors. Applying this to HFT strategies for the current market provides a fascinating result. What the results showed was that although HFTs do not apply margin leveraging due to their speed of execution, HFTs do not benefit from the right tail profits, meaning they don’t receive the benefits of riding the run as a swing trader would. The HFT has lower risk because of their brief holding periods, which provide for cumulative profits quickly with minimal downside risk. This is how the HFT strategy which has a 51/49 ratio of profits to losing trades, is able to make tiny percentage penny profits on millions of dollars in trades. The results proved a couple of things:

1. Using a mean reversal did not lower risk but rather increased risk and also cut profitability even further. This is never a good strategy.
2. Momentum strategies offered lower risk as long as both sides of the market were traded and stop losses were used religiously. This is another reason why you as a retail trader, should be extremely proficient trading both the long trade position and the sell short trade position.

Trade wisely,

Martha Stokes, C.M.T.
Member of Market Technicians Association
Master Rated Technical Analyst: Decisions Unlimited, Inc.
Instructor and Developer of TechniTrader® Stock Market Courses
http://technitrader.com
MetaStock Partner
©2013 Decisions Unlimited, Inc.

Disclaimer: All statements, whether expressed verbally or in writing are the opinions of TechniTrader, its instructors and or employees, and are not to be construed as anything more than an opinion. Student/subscribers are responsible for making their own choices and decisions regarding all purchases or sales of stocks or issues. At no time is any stock or issue on any list written or sent to a student/subscriber by TechniTrader and its employees to be construed as a recommendation to buy or sell any stock or issue. TechniTrader is not a broker or an investment advisor it is strictly an educational service.

Main Article: MetaStock Monitor MARCH - APRIL 13

A Simple, Powerful Method for Trading Different Market Environments
Contributed by the Dynamic Market Lab, LLC


In 2004, the Dynamic Market Lab, LLC (our, us, we) introduced John Ehlers’ signal processing applications for the markets to the MetaStock community through introduction of the Adaptive Cycle Toolkit (ACT). The intent was clear; demystify powerful, but complex concepts and mathematics for immediate application to trading in the easily understandable MetaStock formula format. As time passed, it became apparent the real insight behind his pioneering work lay not in bringing these engineering tools to bear for market analysis, but in recognizing how these tools should be combined for maximum effectiveness.

In this article, we present one of the best approaches revealed by our extensive work with ACT. The approach is simple, powerful, and allows a trader to quickly, confidently identify different market environments, and execute a logical approach to capitalize on them, or stand down. The approach is described in the ensuing paragraphs, and all code is available from MetaStock with purchase of ACT. Discussion of the approach may appear complex, but we want you to understand the concepts, and feel comfortable with them. Fret not, application of the tools is very simple.

Three ACT or ACT modified functions are used to a) identify trend, b) measure trend strength and noise, and c) identify low risk entry points within a trend. A multi-faceted trading approach across different market modes (trending, drifting) is then suggested. The discussion below may appear complex, but the application of the tools is simple.

This approach is based on the Laguerre Transform, a modified version of David Sepiashvilli’s Trend Quality Indicator (TQI) (the ACT TQI), and a fisher transformed version of the Laguerre Stochastic.

• The Laguerre Transform is an average of prices derived from a mathematically “warped” cross-combination of only three current and past data points at each time interval (based on trader’s selected chart interval for trading). The three data points ensure rapid response to change; the “warped” cross-combination ensures smoothness. Prices tend to rapidly cross and “ride” above (below) the average during uptrends (downtrends), and “hang” on the average during drifting markets. This average is plotted as an overlay on prices in the chart window.
• The original TQI measures trend direction, trend strength, and market drift. It is powerful in its own right, but requires explanation to understand, and understand why we modified it.
• David Sepiashvilli introduced the TQI in a Stocks and Commodities (S&C) article, Trend Quality Indicator, as a technique to measure trend strength and noise. Copies of the article may be purchased online from S&C for $2.95.
• Sepiashvilli uses a difference between seven (7) and fifteen (15) period exponential averages to identify changes from an uptrend to a downtrend, and back again. At each crossover point, he resets his computations. He then performs the following steps: 1) measures cumulative bar to bar price change since the crossover point, 2) averages this change to compute the trend, 3) subtracts the trend from the cumulative change to compute the noise, 4) computes the square root of the moving average of the squared noise over time and multiplies this by 2. This multiplication is done so that when trend is compared to noise, if the ratio is 1 (uptrend) or -1 (downtrend), it means the trend is as strong as twice the noise. This “factor of 2 times noise” is a benchmark often used for distinguishing the onset of a trend from noise, 5) finally, he computes the ratio between the trend and noise. If the ratio is > 1, an uptrend is in force. If it is <-1, a downtrend is in force. If the result is – 1 < ratio < 1, the market is considered to be drifting. According to Sepiashvilli, the higher (lower) the ratio, the greater the strength of the uptrend (downtrend).
• We originally plotted the MetaStock version of this indicator from Stocks and Commodities Trader’s Tips code, but observed the code had an error. This was evident from the fact the indicator was not centered around zero. Since the indicator is reset at every crossover point (see above), it must by definition move back and forth across the zero line. Based upon discussion with a contact on the MetaStock forum, we were able to get corrected code that plots correctly for Sepiashvilli’s original formulation. We can supply this corrected code.
• A basic premise behind using adaptive tools is that markets are dynamic. Fixed period lengths do not always timely identify shifts in markets between trends, cycles and noise. Although the logic behind the TQI is very sound, we thought we might be able improve it a bit by replacing the fixed period lengths with ACT functions.
• Enter the ACT indicators named Mama/Fama (Cybernetic). These are nonlinear averages that speed up / slow down based on how fast the measured cycle is changing. Fama is set to follow behind the rate of change of Mama. The relationship between these two adaptive averages means it is hard for the two to cross, or cross by very much, unless a meaningful move has occurred. If Mama and Fama are substituted for the seven (7) and fifteen (15) period values in the TQI, this means it will be very difficult for the two to cross enough to exceed the noise thresholds of 1 or -1, unless a meaningful move has occurred. Furthermore, these two averages are adaptive and should rapidly change as market conditions change. Thus, there is little need to continue to optimize fixed values (such as 7, 15) for moving averages. In trading systems, the fewer the optimized parameters, the more robust the system tends to be.
• In constructing the inputs for Mama/Fama, we drew upon another concept from Ehlers’ work - ACT’s Signal to Noise function. We set a variable equal to this ratio, and used it to accelerate or slow down Mama/Fama’s cycle based computations. In other words, we would let both market cycles, and market noise, tell us what is happening.
• In daily plots of IBM (2000 through early 2007) (not shown to save space, available upon request), Sepiashvilli’s choice of parameters was quite robust, and tracked our ACT TQI quite closely. However, there were six periods during this time, ranging from a few weeks to a month, the ACT TQI identified range bound markets (-1 < ACT TQI < 1) much better than the original TQI. In candor, there was one time the original TQI was superior. The original TQI registered a slight uptick in value a few days before there was a price up gap…luck, maybe, but it did nonetheless. However, it is interesting to note the other six periods where the ACT TQI performed better, the markets made a more continuous transition from one price to another, and did not exhibit an abrupt gap.
• Thus, there is reason over a substantial span of years for a market which fell heavily, rose heavily, and drifted to believe the ACT TQI improved the traditional TQI, and thus we will use this modified version (i.e., the ACT TQI.)
• The ACT TQI is plotted in the first indicator pane. The red horizontal lines are placed at +1 (weak uptrend = 2* noise) and -1 (weak downtrend = 2* noise). If the ACT TQI > 1, an uptrend is in force. If the ACT TQI < -1, a downtrend is in force. If the -1 < ACT TQI < 1, the market is drifting. Divergences between the ACT TQI and price are, like other traditional divergences, a warning sign of possible, imminent change.
• Lastly, the fisher transformed version of the Laguerre Stochastic (FLS) is a statistically transformed version of a stochastic indicator, except the stochastic is computed from three prices first “warped” through application of the Laguerre mathematics. The three data points ensure rapid response to change; the “warped” cross-combination ensures a smooth stochastic. The fisher transform is then applied to the Laguerre Stochastic to ensure it is properly distributed according to the normal distribution function (i.e., bell curve in statistics). Many market price variations do not fit the normal distribution, and the fisher transform is one statistical technique that can be applied to help ensure computations based on such prices are normally distributed. The FLS is plotted in the second indicator pane. The red horizontal lines are placed at +2.5 standard deviations (potentially overbought) and -2.5 (potentially oversold).

OK, now you have been patient, and the fun begins...

Trend Following:
Use crossovers of price against the Laguerre Filter as the earliest warning of a trend change. Compare these crossovers to the ACT TQI. If prices are above the Laguerre Filter and the ACT TQI is > 1, a strong uptrend is likely in place, and do not trade against it. If prices are below the Laguerre Filter and the ACT TQI is < -1, a strong downtrend is likely in place, and do not trade against it. If you are trend follower, you can use these confirmed signals to initiate a trend position. We leave this to the viewer to examine the charts presented. We believe the confirmation points of the two indicators, and the trend direction to trade, are straightforward.

Mean Reversion Trading: (i.e., buy dips in an uptrend, sell peaks in a downtrend)

Mean reversion trading is based on the simple principle that when prices move far away from their average price they tend to move back to their average. This may be true in both trending and drifting markets. However, we should not employ this approach without a sound method. A strongly trending market can move prices farther from their average than expected.

Use the position of prices relative to the Laguerre Filter, and the ACT TQI to determine the market’s state. If prices are above the Laguerre Filter, and the ACT TQI is > 1, enter long trades only when the fisher transformed Laguerre Stochastic is below -2.5 standard deviations (oversold). If the price “hooks” down near the Laguerre Filter, this is even more desirable for entering long trades. If prices are below the Laguerre Filter, and the ACT TQI is < - 1, enter short trades only when the fisher transformed Laguerre Stochastic is above +2.5 standard deviations (overbought). If the price “hooks” up near the Laguerre Filter, this is even more desirable for entering short trades.

This allows us to buy dips in an uptrend, and sell peaks in a downtrend. We are capitalizing on both trend and price extremes, and using both to raise our odds of success. It is not recommended to use the oscillator values alone to take trades in the opposite direction of a strong trend. At this point, our examination of the market’s state indicates a strong trend exists, and we should not trade against it.

Range Bound or Drifting Markets:

If prices are above the Laguerre Filter, and the ACT TQI is -1< ACT TQI <1, enter long trades only when the fisher transformed Laguerre Stochastic is below -2.5 standard deviations. If prices are below the Laguerre Filter, and the ACT TQI is -1< ACT TQI <1, enter short trades only when the fisher transformed Laguerre Stochastic is above +2.5 standard deviations. During very noisy, drifting scenarios, prices tend to “hang on” to the Laguerre Filter, they are not above it (uptrends) or below it (downtrends.) In such cases, this may not be worth trading, unless the trader is selling options or option spreads to collect premium decay. These are lower probability trades because we do not have the benefit of a strong trend. These trades are strictly “range bound” trades. They may be very profitable during extensive periods of market drift. At the first sign of a price crossover of the Laguerre Filter or ACT TQI value moving beyond the +1/-1 limits, and in directions against your trade, exit immediately.

Please refer to the attached slides, and vertical lines indicating examples of these trade setups based on the rules explained above.

Conclusion:

Three carefully designed tools allow a trader to operate a simple, powerful approach across a spectrum of market conditions. Although the concepts behind the indicators we have discussed may be complex, applying them is not.

Trading is often the most successful when it is simple, and based on sound principles of market behavior. We hope that we have provided a more powerful perspective on market behavior© for you, and that you will take a look at the powerful tools and concepts in the Adaptive Cycle Toolkit (ACT).
ACT is available from MetaStock’s site in a convenient downloadable format on a risk free trial.







Disclaimer:

The Adaptive Cycle Toolkit (ACT) is a product of the Dynamic Market Lab, LLC. The techniques described in this article, and the software and related manuals, are based on approaches some consider to be experimental. As a result, this information is offered for educational purposes only. Concepts or techniques presented are not guaranteed or warranted to be profitable.

Users apply the product strictly at their own risk. They must understand that trading in stocks, commodities or other instruments has significant risks, and substantial losses may occur.

The creators of this product or authors of this article are not acting in a capacity as investment or trading advisers. Readers of this article or users of the product must accept full responsibility for their investment or trading decisions, and should seek professional investment counsel before beginning a trading/investment program.

About the ACT Developers:

Michael Burgess

• Co-founder of The Dynamic Market Lab, LLC. Conceptualized the ACT product.
• He received published credit for his editorial contributions to Cybernetic Analysis for Stocks and Futures, and has a unique perspective on John Ehlers work.
• He has over twenty (20) years of experience as a consultant with domestic and international corporations dealing with complex issues such as derivatives and other financial issues.
• He holds a BA from Duke University, and a Masters in Taxation from the University of Denver's Graduate Tax Program.

Brad Ulrich

• Co-founder, and developer for The Dynamic Market Lab, LLC.
• He has been a C++ application developer for a healthcare software company, a mobile application developer, a technology coordinator in the ethanol industry, and currently provides litigation support work in the software and technology fields to several leading companies.
• He holds BS degrees in computer science and mathematics from Vanderbilt University with a background in algorithm design, signal processing, statistics, and numerical analysis. His academic experience includes the design and implementation of algorithms for recent mathematical theory on irregular sampling and reconstruction of digital signals in shift-invariant and wavelet spaces.