The AIQ code for Walid Khalil’s sentiment zone oscillator and related system from his article in this issue, “Sentiment Zone Oscillator,” is provided at www.TradersEdgeSystems.com/traderstips.htm. Using the author’s system that is described in the article, I ran a test on the NASDAQ 100 list of stocks using the Portfolio Manager module. The following capitalization settings were used:
Maximum of 10 open positions
Size each position at 10% of mark-to-market total capital
Take no more than three new positions per day
Compute the mark-to-market capital each day
Choose signals based on a relative strength indicator for ranking in descending order for longs.
In Figure 9, I show the equity curve for long-only trading on the NASDAQ 100 list of stocks for the period 12/31/1999 to 3/16/2012. The return averaged 10.3% per year with a maximum drawdown of 56.5% on 3/9/2009. The trend filters that are applied to each stock in the system did not prevent the large drawdown during the two bear markets in the test period. Applying an index-based trend filter might improve the results, but I did not try this due to time constraints.
The code and EDS file can be downloaded from www.TradersEdgeSystems.com/traderstips.htm and is also shown here.
!SENTIMENT ZONE OSCILLATOR !Author: Walid Khailil, TASC May 2012 !Coded by: Richard Denning 3/14/12 !www.TradersEdgeSystems.com
!ABBREVIATIONS: C is [close]. C1 is valresult(C,1). H is [high]. L is [low]. O is [open]. V is [volume]. avgV is expavg(V,50). smaC is simpleavg(C,10).
!INPUTS: szoLen is 14. !SZO PARAMETER trendEMAlen is 60. !LENTH FOR TREND DETERMINATION longLen is 30. !LONG PERIOD SMOOTHING FOR SZO percent is 95. !PERCENTAGE OF RANGE sellLevel is 7. filter is 0.24.
!SZO OSCILLATOR: R is iff(C > C1,1,-1).
rEMA is expavg(R,szoLen). rTEMA is (3*rEMA) - (3*expavg(rEMA,szoLen)) + (expavg(expavg(rEMA,szoLen),szoLen)). SZO is 100*(rTEMA / szoLen). szoHLP is highresult(SZO,longLen). szoLLP is lowresult(SZO,longLen). szoRng is szoHLP - szoLLP. szoPctRng is szoRng * (percent/100). szoOB is szoLLP + szoPctRng. szoOS is szoHLP - szoPctRng. szoMA is simpleavg(szo,longLen). EMAtrend is expavg(C,trendEMAlen).
!SZO TRADING SYSTEM RULES:
!BUYING CONDITION RULES: SZOmaXOzero if szoMA > filter and not valrule(szoMA > filter,1). TrendUp if C > EMAtrend. SZOovrSld if SZO < szoOS. SZOmaUp if SZO > valresult(SZO,1). SZOgtZero if SZO > 0. SZOxoLLP if valrule(SZOovrSld,1) and not SZOovrSld. EMAUp if EMAtrend > valresult(EMAtrend,1).
BuyCond1 if SZOmaXOzero and TrendUP. BuyCond2 if TrendUp and SZOovrSld and szoMA > valresult(szoMA,1). BuyCond3 if SZOgtZero and SZOxoLLP and EMAUp.
Buy if BuyCond1 or BuyCond2 or BuyCond3.
!SELLING CONDITION RULES: SellCond1 if szoMA < -filter and not valrule(szoMA < -filter,1). SellCond2 if szo < sellLevel and not valrule(szo < sellLevel,1) and szoMA < valresult(szoMA,1).
Sell if SellCond1 or SellCond2.
!RELATIVE STRENGTH UDF FOR SELECTING TRADES: STL is 32. !RELATIVE STRENGTH LENGTH Price1 is C. Price2 is C. aL is STL * 0.25. RC3 is (valresult(Price1,3*aL)/valresult(Price2,4*aL)-1)*100. RC2 is (valresult(Price1,2*aL)/valresult(Price2,3*aL)-1)*100. RC1 is (valresult(Price1,1*aL)/valresult(Price2,2*aL)-1)*100. RC0 is (valresult(Price1,0*aL)/valresult(Price2,1*aL)-1)*100. RS_AIQs is 0.4*RC0 + 0.2*RC1 + 0.2*RC2 + 0.2*RC3.
The AIQ code for Walid Khalil’s sentiment zone oscillator and related system from his article in this issue, “Sentiment Zone Oscillator,” is provided at www.TradersEdgeSystems.com/traderstips.htm. Using the author’s system that is described in the article, I ran a test on the NASDAQ 100 list of stocks using the Portfolio Manager module. The following capitalization settings were used:
Maximum of 10 open positions
Size each position at 10% of mark-to-market total capital
Take no more than three new positions per day
Compute the mark-to-market capital each day
Choose signals based on a relative strength indicator for ranking in descending order for longs.
In Figure 9, I show the equity curve for long-only trading on the NASDAQ 100 list of stocks for the period 12/31/1999 to 3/16/2012. The return averaged 10.3% per year with a maximum drawdown of 56.5% on 3/9/2009. The trend filters that are applied to each stock in the system did not prevent the large drawdown during the two bear markets in the test period. Applying an index-based trend filter might improve the results, but I did not try this due to time constraints.
The code and EDS file can be downloaded from www.TradersEdgeSystems.com/traderstips.htm and is also shown here.
!SENTIMENT ZONE OSCILLATOR !Author: Walid Khailil, TASC May 2012 !Coded by: Richard Denning 3/14/12 !www.TradersEdgeSystems.com
!ABBREVIATIONS: C is [close]. C1 is valresult(C,1). H is [high]. L is [low]. O is [open]. V is [volume]. avgV is expavg(V,50). smaC is simpleavg(C,10).
!INPUTS: szoLen is 14. !SZO PARAMETER trendEMAlen is 60. !LENTH FOR TREND DETERMINATION longLen is 30. !LONG PERIOD SMOOTHING FOR SZO percent is 95. !PERCENTAGE OF RANGE sellLevel is 7. filter is 0.24.
!SZO OSCILLATOR: R is iff(C > C1,1,-1).
rEMA is expavg(R,szoLen). rTEMA is (3*rEMA) - (3*expavg(rEMA,szoLen)) + (expavg(expavg(rEMA,szoLen),szoLen)). SZO is 100*(rTEMA / szoLen). szoHLP is highresult(SZO,longLen). szoLLP is lowresult(SZO,longLen). szoRng is szoHLP - szoLLP. szoPctRng is szoRng * (percent/100). szoOB is szoLLP + szoPctRng. szoOS is szoHLP - szoPctRng. szoMA is simpleavg(szo,longLen). EMAtrend is expavg(C,trendEMAlen).
!SZO TRADING SYSTEM RULES:
!BUYING CONDITION RULES: SZOmaXOzero if szoMA > filter and not valrule(szoMA > filter,1). TrendUp if C > EMAtrend. SZOovrSld if SZO < szoOS. SZOmaUp if SZO > valresult(SZO,1). SZOgtZero if SZO > 0. SZOxoLLP if valrule(SZOovrSld,1) and not SZOovrSld. EMAUp if EMAtrend > valresult(EMAtrend,1).
BuyCond1 if SZOmaXOzero and TrendUP. BuyCond2 if TrendUp and SZOovrSld and szoMA > valresult(szoMA,1). BuyCond3 if SZOgtZero and SZOxoLLP and EMAUp.
Buy if BuyCond1 or BuyCond2 or BuyCond3.
!SELLING CONDITION RULES: SellCond1 if szoMA < -filter and not valrule(szoMA < -filter,1). SellCond2 if szo < sellLevel and not valrule(szo < sellLevel,1) and szoMA < valresult(szoMA,1).
Sell if SellCond1 or SellCond2.
!RELATIVE STRENGTH UDF FOR SELECTING TRADES: STL is 32. !RELATIVE STRENGTH LENGTH Price1 is C. Price2 is C. aL is STL * 0.25. RC3 is (valresult(Price1,3*aL)/valresult(Price2,4*aL)-1)*100. RC2 is (valresult(Price1,2*aL)/valresult(Price2,3*aL)-1)*100. RC1 is (valresult(Price1,1*aL)/valresult(Price2,2*aL)-1)*100. RC0 is (valresult(Price1,0*aL)/valresult(Price2,1*aL)-1)*100. RS_AIQs is 0.4*RC0 + 0.2*RC1 + 0.2*RC2 + 0.2*RC3.
Richard Muller, a longtime TradingExpert Pro client, former Reuters TV Equity Analyst and now senior instructor at The Trading Prism has made available the recording of his Tuesday May 1, 2012 webinar.
In this 80 minute session, Richard covered the big picture market review of the S&P 500, Nasdaq, and the Dow Jones Index. This was followed by Sector rotation review: A detail look at the sectors to spot the ones where there is strength, and weakness. Richard then identified 10 Individual stock and option trading ideas. Finally he reviewed AAPL and possible plays going forward.
Richard Muller, a longtime TradingExpert Pro client, former Reuters TV Equity Analyst and now senior instructor at The Trading Prism is running a FREE webinar Tuesday May 1, 2012 at 2pm eastern.
AGENDA
• Big picture market review of the S&P 500, Nasdaq, and the Dow Jones Index. What the expert ratings are telling us of where the market might be heading next • Sector rotation review: A detail look at the sectors to spot the ones where there is strength, and weakness • Individual stock trading strategies discussed: A look at potential trade ideas in stocks or options to add to your watch list
Richard’s insight and experience is unparalleled. Don’t miss out, get access to this live event by simply registering for Richard’s FREE Prism Post using the link below. We look forward to seeing you on Tuesday May 1st.
Richard Muller was the former Global Equities Analyst, Thomson Reuters, is an Active Trader and Senior Instructor at The Trading Prism.
Richard’s investment trading career started out in emerging markets in 1995, up to 2000, where he did extensive equity fundamental analysis, as well as macro market analysis while based in South Africa. Over the last 10 years, while based in the UK, he has built up extensive global equity research and macro market analysis as both a buy side equity analyst, as well as a global equities proprietary trader.
Previous positions included sell side equity research analyst with JP Morgan Chase and HSBC. Buy side analyst with Reabourne, part of Close Brothers, and as proprietary trader with Van Der Moolen Securities Ltd. Richard also held a position as CEO of ETI Investment, an investment management firm.
Recently Richard Muller was global equities analyst with Thomson Reuters, where he delivered investment ideas on the Reuters Insider financial TV channel. Richard qualified as a Chartered Management Accountant, and holds a Masters of Science degree in investments, MSc ISIB.
In this hour long recorded webinar, Richard Muller, senior instructor at The Trading Prism, former Reuters TV equity analyst and long time AIQ TradingExpert Pro user covers an indepth analysis of the current markets sector rotation and potential stock and option candidates that Richard is adding to his watch list. A bonus 20 minute segment covers an indepth technical analysis of AAPL and the remarkable rebound today. Richard, incidentally was, holding AAPL calls overnight!
This link opens the Adobe flash recording in a new browser window.
Many traders use indicators to determine when to enter and exit trades. Most charting software includes dozens of different indicators that can be displayed on the charts. Popular indicators such as the Stochastic, and MACD, are frequently discussed when traders get together. I have listened to a number of these discussions, the interesting thing is that people typically explain why they use a particular indicator by citing an number of examples of when it has worked for them. When they do, another trader will typically say something like, ‘well it did not work for me, so I use the XYZ indicator which is much more reliable’. When I ask the second trader why his XYZ indicator is more reliable, the explanation usually involves a few more examples of good trades.
Examples do not prove anything. It is possible to flip a coin and have it come up heads five times in a row. Few traders would observe this and then think that when you flip a coin it always comes up heads. Yet for some reason people will read an article about an indicator that shows four or five examples of good trades it produced, and then they will go and risk their money trading the technique. They typically trade the new technique until it produces several losses in a row, and then they start looking for another article, that describes a ‘better’ technique, and the process repeats itself in an endless search for a better trading system.
Trading should be data driven, not based on emotion, wishful thinking, or hot tips from TV hosts. To be data driven one needs to test and analyze trading tools and find out what really works, and when each tool should be used. Traders must understand which tool to use for a specific task, and have a clear understanding of how the tool works, and what can and cannot be done with it. Traders need to extensively test several trading systems, and based on the results of that testing they develop a toolbox of different trading techniques that have shown effective results. They are not trading based on hunches they are using data driven techniques to defelop effective trading tools and understand the specific market environments in which they work. Data driven traders first check the current market conditions, and then pull out the tools that have shown interesting test results in those market environments. The testing process helps traders understand how stocks usually behave after forming a specific pattern. Understanding what a stock is most likely to do forms the beginning of a trading strategy. Trading without this information is taking unknown risks.
with Richard Muller, TradingExpert Pro user . Richard Muller senior instructor at The Trading Prism covers the current US market situation with trading signals from the AIQ Tradingexpert Pro software. He follows this with a sector rotation breakdown of the sectors leading and lagging the current market trend. Plus a look into money flow and other stock filters for possible option plays.
In this 40 minute recorded event from the City of London, Steve Hill, President of AIQ Systems and senior instructor at The Trading Prism, discusses the aspects of trading psychology that differentiate a successful trader from a failed trader. Successful traders don’t overcome fear and greed, they manage fear and greed to their advantage.
New traders are often focused on what happens after they sell, or what happened to positions they did not take, or what happened if they would have ignored the the trading systems rules. This sort of focus develops bad habits that eventually cause problems. I want tools that are repeatable, I want tools that work more often than they fail just like this paycheck stub generator software, and then I just focus on using those tools in specific market environments where they have worked well. There is no technique that leads to profits on every trade. I have seen the adds for systems that work ’98% of the time’, but the guy selling them is the one making the money. Trading is a statistical business, and traders find tools that have a good percentage of success and use them in appropriate market conditions.
Traders make their money by testing and analyzing tools and techniques in order to learn what actually works, and what just sounds good but does not lead to profits. A few examples can be very misleading, traders test their potential trading tools over many different trades in different types of market conditions. Trading an untested system is taking unknown risks and often just churns the account. Extensive testing has led to several practical trading techniques based on the Bollinger Bands. I have found that in most cases I am ahead in the long run to avoid trading when the market is extended above the upper Band. As indicated by the testing data shared in ‘How to Take Money From the Markets’, it is generally best to not initiate new positions when the market is extended above the upper Band. You can also see from just glancing at a two year chart of the NASDAQ that when the market becomes extended above the upper Band it most often pulls back, or trades sideways, for a few days in order to get back below the band.
Trading should be data driven, not based on emotion, wishful thinking, or hot tips from TV hosts. To be data driven one needs to test and analyze trading tools and find out what really works, and when each tool should be used. Traders must understand which tool to use for a specific task, and have a clear understanding of how the tool works, and what can and cannot be done with it. I have extensively tested several trading systems, the results of this testing on specific trading trading tools are outlined in ‘How to Take Money from the Markets’, and Money-Making Candlestick Patterns. The testing process helps us understand how stocks usually behave after forming a specific pattern such as being outside the Bollinger Bands, showing strong distribution or accumulation, or pulling back or retracing during a trend. Understanding what a stock is most likely to do forms the beginning of a trading strategy. Trading without this information is taking unknown risks. Taking unknown risks can lead to disaster…
Trading is about risk management. In order to understand what the risks are traders need to understand exactly how and when their trading patterns work. Testing different tools, and understanding how each trading tool performs in different market conditions, is one way to get a clearer picture of what is an effective trading tool and how to use it.
Identifying and developing trading systems with an edge is a lot of work. Making the trades is the easy part. Too many traders skip the analysis part and end up losing money because they have not put in the effort to develop trading tools that provide an edge. Trading some technique without having carefully tested and analyzed it generally leads to disappointment. The traders job is not to focus on making trades, but instead to focus on what types of patterns should be traded; and in which types of market conditions each trading tool works best.
Trading should be data driven, not based on emotion, wishful thinking, or hot tips from TV hosts. To be data driven one needs to test and analyze trading tools and find out what really works, and when each tool should be used. Traders must understand which tool to use for a specific task, and have a clear understanding of how the tool works, and what can and cannot be done with it. I have extensively tested several trading systems, the results of this testing on specific trading trading tools are outlined in ‘How to Take Money from the Markets’, and Money-Making Candlestick Patterns. The testing process helps us understand how stocks usually behave after forming a specific pattern such as being outside the Bollinger Bands, showing strong distribution or accumulation, or pulling back or retracing during a trend. Understanding what a stock is most likely to do forms the beginning of a trading strategy. Trading without this information is taking unknown risks.
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