Market Technician No 95 - September 2023

Editor's Letter

in parallel is climate change, with droughts, floods and some countries becoming ‘too hot to handle’. Consider this in your financial planning.

Despite all of the above, observed volatility in many markets has been very low indeed, with lower turnover and thin markets causing a paucity of data to work with. This has given the lie to the mantra: ‘markets hate volatility’ – AKA fear of the unknown. To help you with this it might be worth reading Robert Carver ’s new book ‘Advanced Futures Trading Strategies’ which we review in this magazine.

Remember: low volume=less turnover=less profits for brokers and intermediaries.

Nicole Elliott, FSTA Technical Analyst, Private Investor, E-journalist for the STA

Since I last penned a letter to you, dear reader, the world has taken some serious steps back towards what we used to call a ‘normal life’. Even I have indulged in ‘revenge travel’, not of the ultra-luxe bucket-list type, but to visit my daughter in South America. Meanwhile, as Ukraine remains front and centre of Western political minds, war fatigue has taken a hold in Sudan, Yemen and other troubled nations. The military/industrial complex seems unaware of the shift in sentiment.

Glancing over the political, economic and financial events of the last six months, the salient feature are steady interest rate hikes by G7 central banks against a backdrop of lower or tumbling wholesale commodity prices. Needless to say, the latter do not feature on the retail customer’s radar because, as with petrol prices, firms jack prices up quickly and reluctantly reduce them later on. Thinking about how things might look on the charts, do pull up monthly, quarterly and annual candlesticks for signs of potential long term (maybe dramatic) reversals.

As predicted in January by analysts at the STA-hosted panel debate, the US dollar index has weakened steadily, admittedly from exceptionally strong levels. At July’s reconvening for an update for the second half of the year, the weaker for longer US dollar theme prevailed again. Panellists also questioned where and when the ‘terminal rate’ for interest rate tightening would occur, keeping an eye on inverted Treasury yield curves in several major countries.

‘Working from home’ and its variants are becoming increasingly embedded, hitting commercial real-estate hard; a recent McKinsey estimate put the global cost at $800 billion. Residential property prices are partially cushioned by new work/life choices, but costs of both mortgages and rents have risen to prohibitive levels for many middle-class families. Despite high job vacancies and low unemployment, salaries have not kept pace with inflation – neither this year nor in the last decade or more – causing what’s now called a ‘cost-of-living’ crisis. The result? Childless cities. Politicians have stuck palliative plasters on each issue as it crops up, but the problem is far bigger. The undertow to demographic trends are lower birth rates and increased longevity. Running

Research papers submitted for this issue have tended to concentrate on well-established methods, with a clear introduction to Elliott Wave theory and deeper analysis of the Commodity Channel Index. Ron William (pg.30) has some interesting observations on behavioural economics and the role of psychology in decision-making.

Towards the back of this issue, you will see the list of the most recent graduates who have passed the STA Diploma Part 2 exam (pg.57) Not only is the Diploma course CISI accredited, but these successful students will now be able to use the letters MSTA (Member of the Society of Technical Analysts) after their names, signalling their transition to fully-fledged technical analysts.

You will note that most are men, with an Eve and a Rosie thrown in for good measure, underlining the massive disparity between the sexes still prevailing in the financial sector. More positively, however, you will also note that they come from all corners of the world. This has been increasingly the case since live interactive lectures, complete with the opportunity to ask questions, were introduced in a virtual mode. The fact that six candidates passed with a distinction is testament to the quality of the lecturers and the way they engage their students in this exciting area of knowledge. The society is looking to extend some tailored courses into UK universities.

Remember, the STA is a non-profit organisation and therefore we rely heavily on volunteers. If you’d be interested in getting involved in any way, please contact Katie Abberton at info@technicalanalysts.com with your ideas, CV and any other material you think might be useful. If you’d like to present at one of our monthly meetings, please send an outline of your proposed subject; likewise for articles submitted for publication in this magazine. More importantly, we love feedback.

STA’s Inaugural ‘Technicals to Trading Systems’ conference, April 2023

Technicals to Trading Systems Conference 2023

Tue, 18 April 2023, 09:00 - 17:30 BST

1 Moorgate Place, London EC2R 6EA

On 18 April, the STA held a conference on the Technicals to Trading Systems.

The conference was hosted and facilitated by Eddie Tofpik and speakers discussed various topics related to trading systems, including overfitting, inflation, long-dated bonds, and building a technical analysis-based quantitative workflow. The conference also featured a panel discussion moderated by Victoria Scholar, Head of Investment at Interactive Investor, with Stephen Hoad, CEO of the Stop Hunter, Alan Dunne, Founder of Archive Capital, and Rustam Lam, CEO of TradeStation International, who discussed various aspects of trading strategies.

Overfitting

Rob Carver, Author and Lecturer at Queen Mary University, kicked off by discussing the issue of overfitting when building a trading system. Carver defined overfitting as the process of fitting a model too closely to a particular set of data, which results in poor performance on new data. He mentioned that

Learn from the best on how to apply technical analysis tools and approaches to trading systems to give you an edge.

overfitting is a common problem in finance because of the high level of noise in the data.

Carver then recommended several strategies to avoid overfitting. One is to use simpler models as they are less likely to overfit. Another is to use out-of-sample data to test the model’s performance. This latter approach ensures that the model is tested on data that it has not seen before and reduces the chances of overfitting.

Carver also emphasised the need to be cautious when adding new variables to a model. While adding more variables can improve performance, it can also lead to overfitting. Therefore, he suggested that one should carefully evaluate the impact of adding new variables and ensure that they do truly add value to the model.

Finally, Carver highlighted the importance of having a robust risk management strategy in place to handle unexpected market movements. He advises that one should have clear exit strategies and be prepared to cut losses when necessary. He also suggested that traders should focus on risk-adjusted returns rather than absolute returns.

Inflation and Long-Dated Bonds

Jessica James, Managing Director and Senior Quantitative Researcher at Commerzbank, discussed several topics related to inflation and long-dated bonds in her talk. She began by discussing the history of inflation, including episodes such as hyperinflation in Germany in the 1920s and the inflationary period of the 1970s in the US. She emphasised that inflation is not a new phenomenon, and that it has occurred in many different historical contexts.

James also talked about the current state of inflation, noting that there are concerns about rising inflation in many countries around the world. She discussed the factors that are contributing to this, including supply chain disruptions and government stimulus measures.

James discussed some of the methods that economists use to try to forecast inflation, including econometric models and surveys of inflation expectations. She emphasised that these methods are not perfect, and that there is a lot of uncertainty involved in forecasting inflation.

James provided a brief history of long-dated bonds, explaining how they have been used by governments to finance long-term projects. She also discussed some of the theoretical concepts behind long-dated bonds, including the relationship between bond prices and interest rates.

Finally, James talked about the trend of governments issuing "super long" bonds, which have maturities of 50 years or more. She discussed some recent events in this area, including the issuance of 100-year bonds by several countries. She also discussed some of the risks associated with super long bonds, including interest rate risk and the possibility of default.

What was fascinating about this talk was that it was based on a paper she wrote last year describing how just small

increases in base rates would have a big impact on these long-dated bond markets – a paper that proved to be almost prophetic in nature as we saw bond market moves last year become the big disruptor in the markets.

Building a TA-Based Quantitative Workflow

Mathew Verdouw, CEO and founder of Optuma, shared insights on building a Technical Analysis (TA)-based Quantitative Workflow. He emphasised the need to embed quantitative processes in the workflow to make it more efficient and effective. Verdouw also emphasised the importance of continuous testing in the workflow. He suggested that it’s essential to test and validate the models continuously, and they should be updated regularly to reflect market changes. Verdouw explained that combining multiple models can be more powerful than relying on a single model. He suggested that it’s crucial to understand the strengths and weaknesses of each model and combine them in a way that provides the best insights. He also suggested that when combining models, one should avoid the pitfall of overfitting the data.

Lastly, Verdouw talked about the importance of increased confidence in knowing when to follow the model. He suggested that having a set of rules in place can help in building confidence. For example, having a rule that states when to enter and exit a trade can help reduce emotional decision-making and increase confidence in following the model. In summary, Verdouw’s tips on building a TA-based Quantitative Workflow include embedding quantitative processes, integrating continuous testing, combining multiple models, and increasing confidence by having a set of rules in place.

Panellists

The panellists were moderated by Victoria Scholar, Head of Investment at Interactive Investor. We listened to Stephen Hoad, CEO the Stop Hunter, Alan Dunne Founder of Achive Capital, and Rustam Lam, CEO of TradeStation International. The panellists discussed various aspects of trading strategies, including their evolution over time, the impact of technology on trading, and the importance of risk management in trading. They also talked about the role of data analytics and machine learning in trading and the need for traders to adapt to changing market conditions. The panellists highlighted the importance of having a clear understanding of the trading strategy’s objectives, as this helps in selecting the appropriate technology and tools. They also discussed the role of human judgment in trading, noting that while technology can help in making faster and more

accurate decisions, it cannot replace human intuition and experience. The panellists also emphasised the importance of risk management in trading and the need to have a welldefined risk management strategy in place. They suggested that traders should focus on risk-adjusted returns rather than absolute returns and should be prepared to cut losses when necessary.

The discussion also covered the role of data analytics and machine learning in trading, with the panellists suggesting that traders should use data to identify patterns and trends in the market and use machine learning algorithms to automate certain aspects of trading. Overall, the panel discussion provided insights into the evolution of trading strategies, the impact of technology on trading, and the importance of risk

system using Python. They also encourage the audience to ask questions and provide feedback, making the session interactive and engaging. Overall, the purpose of the session was to demonstrate that building a trading system using Python is a straightforward process that anyone can learn with the right guidance and resources. They aimed to inspire and encourage traders to explore the possibilities of building their own trading systems using Python.

The purpose of the talk was to demonstrate how simple the workflow was for building a trading system using Python. Trevor explained that Python is a popular programming language among traders because it’s easy to learn and has a wide range of libraries and tools that can be used to build trading systems. Jason then proceeded to demonstrate how to build a trading system step-by-step using Python. He showed how to import data, create technical indicators, and generate trading signals based on those indicators. He also showed how to backtest the trading system using historical data to evaluate its performance. Throughout the demonstration, both Trevor and Jason emphasised the simplicity of the workflow and how easy it is to build a trading

Jeff Boccaccio, founder of Rfactory, and the STA conference organiser, started by discussing the importance of rapid prototyping when building a trading system. He highlighted that it’s crucial to have a practical approach to development and testing of the system in live markets. He built and coded a trading system from scratch and tested it in real-time. He explained that a diversified portfolio of trading systems can help to mitigate risk and improve the overall performance of a trading strategy. Therefore, traders should look to build a portfolio of different systems that are suited to different market conditions. Jeff then proceeded to demonstrate how to build a trading system using Python. He shows how to code the system step-by-step, and how to test it in live markets using historical data. He emphasised the importance of testing the system thoroughly before releasing it to trade on live markets. Throughout the demonstration, Jeff emphasised the importance of monitoring the system and analysing its performance metrics such as win rate, profitability, and drawdown. He suggested that traders should use these metrics to evaluate the system’s performance and make any necessary adjustments. Finally, Jeff ended his talk by humorously questioning what could go wrong with a trading system. He acknowledged that there is always a risk involved in trading and that traders need to be prepared for any potential pitfalls. He encouraged traders to approach trading with a practical and disciplined mindset, and to continuously evaluate and improve their trading systems.

Perry Kaufman (KaufmanSignals.com), one of our keynote speakers, began by stating that success in trading lies in the process and not just in the outcome. He emphasised the importance of understanding what a successful trading system is and how traders can create a system that fits their

Jeff Boccaccio

Trevor Neil

trading style and preferences. He suggested that traders need to first decide on how they want to trade and what type of markets they want to trade in. This involves understanding the different markets and choosing the ones that are most suitable for their trading strategy. Next, Perry discussed the building blocks of a successful trading system. He talked about the importance of having a clear set of rules and guidelines that define when to enter and exit trades, as well as risk management techniques that can help mitigate losses. He also emphasised the importance of continuously monitoring and evaluating the trading system to ensure that it is performing as expected. This involves analysing performance metrics such as win rate, profitability, and drawdown, and making adjustments as needed. Finally, Perry concluded by highlighting the importance of applying this knowledge to actual trading in the market. He encouraged traders to be disciplined and patient, and to stay focused on the process rather than getting caught up in short-term outcomes.

The Human Element in Algorithm Development

I also felt that the presentation by the AlphaMind guys, Steve Goldstein and Mark Randall, where it just felt like a conversation between them and us (the audience), was a great end to the day. They discussed with the audience the importance of considering the human element in algorithm development. While algorithms are often seen as impartial and objective decision-making tools, the reality is that they are developed and implemented by people and can therefore incorporate human biases and limitations. They pointed out that one of the key mistakes in algorithm development is failing to think about how the algorithm will work with people. Algorithms are often developed with a focus on technical functionality and efficiency, without considering how they will interact with users or how they may affect human behaviour. Some systems you just cannot use because it goes too far away from your trading style or just what you are comfortable with.

Additionally, they warned against building algorithms that replicate the same human errors found in discretionary systems. For example, if a human trader has a tendency to make impulsive or emotional decisions, an algorithm developed without considering this tendency may end up making the same mistakes. It’s important to consider the potential biases and limitations of both the algorithm itself and the humans who will interact with it. Another issue they

addressed is the danger of seeking perfection in algorithm development. While it may be tempting to strive for a flawless algorithm, this can lead to overfitting the data and ignoring important aspects of the process. Instead, the focus should be on the desired outcome, but with a balanced approach that considers the entire process and potential trade-offs. Overall, they emphasised the importance of taking a holistic approach to algorithm development, one that considers both the technical and human elements involved. By doing so, we can help ensure that algorithms work well.

Keep it Simple!

In summary I would add that there were some clear themes coming through from several of the speakers such as keeping it simple and not making it too complex and to be careful with the data sets used to back test. I found what Rob Carver does in this respect was pretty interesting and Perry Kaufman put great emphasis on the robustness of any system was another theme. I just loved the way he admitted stealing some ideas from his wife (I personally thought this was hilarious!). Some of the highlights for me were that we had absolutely fantastic speakers on the panel, and their level of expertise just shone through. It was so interesting to hear their thoughts on what is next for systematic trading. And there were some little things like Trevor’s point (and Mathew Verdouw made a similar point) about back testing is where a lot of people start and may not be the best thing to do; and there was a slide at the end Jason’s talk giving links and places to go to learn more about Python that I just thought that was great - I love anything that involves saving me time!

In conclusion, the Technicals to Trading Systems conference provided valuable insights into building effective trading systems, forecasting inflation, and managing risks in trading. The speakers emphasised the importance of simplicity, continuous testing, and risk management in building robust trading systems. The panel discussion highlighted the need for traders to adapt to changing market conditions and the role of technology in trading. Overall, the conference was a valuable learning experience for traders of all levels.

Karen Jones, FSTA Fellow and STA Social Media Content Editor

Mathew Verdouw

International Technical Analyst Day 9/9

The Society of Technical Analysts will be celebrating International Technical Analyst Day again this year on 9 September.

Eddie Tofpik, STA Chair, first proposed this idea to the STA Board at our 2 April 2022 meeting. The idea is to give technical analysts and those using technical and chart analysis at funds, banks, brokers, proprietary trading shops and for themselves, a day to get recognition for the hard work, dedication and diligence that they display through all market conditions and events.

We encourage colleagues, clients and counterparties to say ‘Thank you!’ to their staff technical analysts and TA providers from around the world on this day. As part of our celebration, we are encouraging STA members to send a short note to Katie at info@technicalanalysts.com on who influenced or mentored you in technical analysis and how was this important for your analytical, investment and trading career and process. We will compile the notes and aim to publish them on the day on some of our social media platforms.

STA’s 55th Birthday Party, 14 September

Come celebrate the STA’s 55th anniversary at the historic National Liberal Club on Thursday 14 September. The drinks reception will start at 6.30pm and finish at 9.00pm.

The evening will start with an Awards Ceremony for recent UK MSTAs. The event is a celebration of the STA’s achievements over the last 55 years and an opportunity to not only meet fellow members but also other professionals in the financial markets.

We do hope you can join us! Click below to book your place.

Student Membership now available!

The STA are delighted to announce a new Student membership category.

Students of recognised academic institutions may join the Society for the duration of their course at the discounted rate of £25 a year.

Student members will have to demonstrate their student status. For enquiries, email the office on info@technicalanalysts.com

MEMBERSHIP

The Society of Technical Analysts is a keen supporter of regional meetings in the UK. If you would like to participate in a local meeting, please contact us at info@technicalanalysts.com and the STA will aim to help organise the meetings with you. The STA also works with IFTA in supporting chapters through the process of becoming independent groups over time. We are happy to help out our members and colleagues in any European countries that don’t have a Society on this if you are interested.

Irish Chapter

Alan Dunne has been busy setting the ground for a fall meeting in Dublin. We are being supported by our friends at CMC in this will alert our members when further details are available. If you would like to attend a Dublin meeting, speak or help organise one, please contact us.

STA Chapter Updates STA working with leading universities

The Society of Technical Analysts has an unrelenting goal to offer information, education, examination and accreditation. We have always worked with leading universities and educational providers in order to help reach new audiences and spread the word about what we do.

Scottish Chapter

The STA started the Scottish Chapter meetings again in May with a talk by Gerry Celaya, generously hosted by BlackRock at their Edinburgh office. We will be holding a few more talks this year. If you would like to come along, speak or help organise the talks, please contact us.

We currently run the STA Diploma course at King’s College London, led by Axel Rudolph, FSTA as part of his Head of Education role. Over the coming years, Simon Warren, FSTA, will be working with some leading universities to help set up Technical Analysis modules.

If you would like to learn more about our outreach programmes and find out how to teach your students (or if you are a student and would like us to speak with your teachers and administration staff) please contact us at info@technicalanalysts.com

Axel Rudolph

J.P. Morgan Corporate Challenge

5-6 July 2023, London

The J.P. Morgan Corporate Challenge is the world’s largest corporate running event. Powered by fitness, friendly competition and fun, company teams of all speeds and abilities complete a 5.6 kilometre race while spending quality time together outside of work.

Elliott Waves and Time Cycles: A Unique Combination for Forecasting the Dow Jones Industrial Average

Elliott Waves: a proven methodology

The stock market always acts as a leading indicator of the economy. As such, being able to predict the market allows us to forecast where our economy is headed. Inter-market analysis provides a very good view at the macro level for analysing equity markets, but things become very complex when these inter-market parameters start behaving in an unusual and unpredictable fashion.

Post Covid19, there were co-ordinated efforts by central banks to fight the slowdown globally by cutting interest rates and extending a liberal monetary policy. Subsequently, many countries have found themselves facing inflationary pressure over the past year or so. This has led to central banks, including the US Federal Reserve, raising interest rates at the fastest pace in decades. Such rapid changes in macro-economic factors can turn investment decision making into a challenge.

Ashish Kyal, CMT, MBA

Ashish Kyal is a trader and mentor, and also the founder of Wavesstrategy.com and Ashish Kyal Trading Gurukul

He is the author of 'Effective Trading in Financial Markets using Technical Analysis' (October 2020). Ashish is a Chartered Market Technician (CMT) and a member of CMT - US. He holds an MBA and a Bachelor of Engineering from Mumbai University

Things are going to become more and more complex as technology effectively makes the world smaller and smaller. The Elliott Wave principle provides an unparalleled tool for predicting where the different world markets are headed. This methodology incorporates the patterns of life and time, cultural and social behaviour. It has stood the test of time and has proven its validity in the last century.

One of the basic premises of TA is that history repeats itself and markets are fractal in nature. By this it means that the patterns and price behaviour we witness on the shortest time frame charts can be witnessed even on the longest ones. This is the reason why the concepts of TA, and in turn Elliott Waves, can be applied across the time frames (see Trivedi and Kyal, 2021).

When using Elliott Waves, the smallest of the waves combine together to form a higher degree wave pattern and these in turn combine together to make a much higher degree wave construction.

This is the reason why the forecasting ability of wave theory is very strong, and it can be carried out right from the shortest of time frames to those stretching years ahead.

“The Elliott Wave principle provides an unparalleled tool for predicting where the different world markets are headed.”

The Elliott Wave principle

Impulse and Corrective Patterns

It is best to understand the patterns of Elliott Wave theory by categorising it between Impulse and Corrective Patterns.

A few Elliott Wave patterns with rules are explained below that have references to subsequent research and application on charts. Do note these are only a few out of many patterns that can be utilised to understand the Elliott Wave charts on the Dow Jones Industrial Average (DJIA).

Impulse Patterns

An Impulse Pattern is comprised of five waves and are labelled as numbers. Prechter and Frost (2005) categorised the major movements in form of wave 1, wave 3 and wave 5 as Impulse Waves. Impulse Waves divide into five sub-waves and travel in the direction of one higher degree wave. Wave 1, wave 3 and wave 5 moves in the direction of the trend whilst wave 2 and wave 4 move counter to it.

The Rules of Impulse Patterns

Following are the three rules that have to be followed for a valid Normal Impulse Pattern:

1. Wave 2 will not retrace more than 100% of wave 1. This means that wave 2 cannot move beyond the starting point of wave 1.

2. Wave 3 cannot be the shortest among the impulse waves and it is very commonly the longest. This means that wave 3 cannot be shorter in terms of price with respect to both wave 1 and wave 5. It can be shorter than one of the waves but not both waves at the same time.

3. Wave 4 cannot enter into the territory of wave 1. This means that in an impulse up move wave 4 cannot move below the highest point of wave 1 and in case of an impulse down move wave 4 cannot overlap with the lowest point of wave 1.

Ending Diagonal Impulse

This is a variation to an impulse pattern and is also known as diagonal triangle pattern. It takes the form of a wedge pattern with the two trendlines moving either upside or downside. In this pattern:

• Wave 2 will not retrace the complete of wave 1.

• Wave 3 will not be the shortest among the impulse waves.

The third rule has an exception where wave 4 mostly overlaps with the price territory of wave 1.

The Ending Diagonal that appears in wave 5 or wave C is an important pattern. Sub-waves of ending diagonal pattern are all threes or corrective in structure (3-3-3-3-3). Therefore, wave 1, wave 3 and wave 5 are all threes within ending diagonal.

Flat Corrective Patterns

A flat correction is comprised of 3-3-5 sub-waves. This means that wave A is corrective, and followed by corrective wave B and then impulse wave C. Wave C can form a Normal Impulse or an Ending Diagonal Impulse.

Flat Corrections are further categorised into Regular Flat Patterns, Irregular Flat Patterns and Running Flat Corrections:

Running Flat correction

In an upward sloping running flat correction, wave B ends well above the beginning of wave A and wave C fails to travel towards the end of wave A. The move on the upside is strong and so it results in a pattern of price correction and is tilted in the direction of the bigger degree trend. In the case of bull correction, post completion of the running flat pattern there is a very strong rise that covers the maximum distance in the minimum time.

Applying the methodology

We now investigate the DJIA using Elliott Wave methodology, analysing the market from Cycle degree to Minor degree.

After forming a low in March 2009, the DJIA showed a rise in the form of an Impulsive Pattern.

Note:

• This rise is marked Impulsive and there are clear subdivisions within wave (3), which itself is the largest and subdivided.

• Post the completion of wave (3), there was a sideways correction in the form of wave (4) and then a final push on the upside in the form of wave (5) before the crash of February–March 2020.

• Post completion of wave (5), the sharp crash on the downside was just the first leg of the corrective Flat Pattern, labelled wave (A). The top of wave (5) also completed a higher degree wave (1).

• The corrective pattern labelled as waves (A), (B) and (C) is a running Flat correction. Post completion of wave (1), wave (2) formed a running flat corrective pattern that started in February 2020 and completed in October 2022.

• Wave (C) of wave 2 forms an ending Diagonal Impulse Pattern.

From a trading perspective, the Ending Diagonal Pattern provides a very good trading opportunity.

Figure 1: Weekly chart of the DJIA

Figure 2 shows a daily chart of the DJIA with a much closer look into the Ending Diagonal Pattern and the start of another Impulse Move up.

Following on from Figure 1, Figure 2 shows a clear fall in 5 waves (numbered in blue circles) in the form of an Ending Diagonal pattern. The rules of an Ending Diagonal Impulse are followed here, with an overlap of wave iv with that of wave i, which is a variation of the Normal Impulse rules. Wave v on the downside completes one bigger degree wave (c), and this in turn also completes one higher degree wave ii, as shown in Figure 1.

• The DJIA formed a major low on 13 October 2022 at the 28660 level. The rise from that is sharp and broke above the ii-iv trendline. This marked the beginning of another Impulse Wave on the upside of primary degree wave (iii), which is subdivided into 5 waves.

• Within this pattern, it seems that wave 3 has begun moving on the upside after completing wave 2 at the lows 31805 on 24 March 2023.

• Wave 2 has retraced 50% as per the Fibonacci ratio of wave 1.

• Wave 3 is usually the extended wave and will measure 1.618 times wave 1. So as long as the wave 2 low at 31805 is intact, upside targets can be near the 41540 level, the lifetime high of the index.

• Post this, the wave 4 corrective pattern will retrace a portion of wave 3. Do note that the target of 41540 is based on the assumption that wave 1 is not extended, and that wave 3 is going to be an extended leg.

• A conservative target would be that wave 3 is 0.618 times of wave 1, in the case of wave 1 being the most extended, or longest, of the impulse waves. This gives a conservative target of 35520.

• A break below 31805 will indicate that wave 2 is not yet over and still ongoing.

• A breach below 28660, the low of wave 1, will be a signal to assume an alternate Elliott Wave possibility, with a more bearish outcome likely.

• So, 31805 and 28660 become important risk-management levels for traders and investors.

Traders should be looking for buying opportunities in the index as per the Elliott Wave pattern and the concept of higher highs and higher lows. With the probable targets given, it is important for traders to ensure continuous evaluation in comparison to actual price movement.

Figure 2: Daily chart of the DJIA

A combination of Elliott Waves and Time Cycles can provide high conviction setups when both these independent TA elements are in synchronisation.

While price and volume are studied majorly with reference to time, time as an independent variable of study can also yield valuable insights for trading decisions (see Trivedi and Kyal, 2021). The periodic fluctuations impacting a price series are referred to as time cycles. They are important because cycle periodicity can change, meaning a trader has to keep tweaking the cycle length from time to time to keep it up to the mark with the changing market dynamics. At the same time, a trade not timed correctly can result into a non-desirous outcome and it is therefore important to combine the price forecasting tools along with time cycles to understand the major turning junctures. There are recurring cycles in financial markets and, by identifying the cyclicality, one can forecast the possible tops or bottoms and accordingly take trading or investing positions.

To identify a cycle period, observational analysis is a simpler way. There are different methods that can be used to isolate the cycles. By a visual inspection, one can try to identify a rhythmic movement in prices and whether troughs are formed at periodic intervals. Try to identify two or three important lows that seem equidistant and then see if the next lows are also around this time period. Also note that it is not necessary for prices to form a low exactly on the cycle period day, week or month. Time is more dynamic when compared with prices and a Time Cycle gives approximations of possible lows.

In Figure 3, lows are formed around the red vertical lines. The difference between the red vertical lines is 33 monthly candles. The period of the Time Cycle is 33 months, starting in this case at 2004. The following are important takeaways:

• The principle of variation states that the cycle can deviate from its mean periodicity and therefore there are cycle of varying length around that mean value can be seen.

• The DJIA is forming lows anywhere between 30–36 months, giving an average of a 33-month Time Cycle.

• Upward thrust is seen post the cycle lows that took prices higher for many months ahead on all of the occasions except during the period post September 2014 where prices did not result in an immediate upward thrust from the cycle period.

• Post Covid19, a low was formed near March 2020 which was also near to this 33-month Time Cycle period.

• Interestingly, recent lows formed on DJIA in October 2022 is also near to this 33-month Time period and we are already

Figure 3: Dow Jones Industrial Average Monthly chart – 33-Month Time Cycle

seeing the upward thrust in prices.

• A break below 28660 levels will suggest that prices have topped out for months ahead as the low of the Time Cycle is broken. This gives a similar risk management level for investors to the one suggested by the Elliott Wave pattern.

This time observation is in synchronisation to that of price forecasting as per Elliott Waves, as mentioned earlier.

To conclude, Elliott Waves with Time Cycles form a unique combination for applying advanced technical analysis concepts. It is important to understand the concepts properly before trading or investing on this basis. If applied prudently with proper understanding, it is possible to take investment or trading decisions right from the smallest time frame for intraday trading to long-term charts for investment decisions. These studies can also be combined with fundamental technique for timing the entry or exit more prudently. Also do note the forecasting and expected outcome can change with newer inflow of price data.

References

Smita Roy Trivedi and Ashish H Kyal (2021). Effective Trading in Financial Markets Using Technical Analysis, Routledge. R. Prechter Jr and A.J. Frost (2005) Eliott Wave Principle - Key to Market Behavior, New Classics Library.

Using Commodity Channel Index Divergence for Price Forecasting

Introduction

Over the last year and a half, I have been using the Commodity Channel Index (CCI) on the SPX and observing a few instances of fairly noticeable price corrections following varying periods of divergence between price and this unbound oscillator. I took the opportunity of writing this article to gather more data on this particular aspect and draw some conclusions on how one may interpret the data for trading purposes, that is, by creating the foundations for a structured approach.

What is the CCI indicator?

In this 2004 article David Penn discusses the original purpose of CCI’s creator, Donald Lambert, in forecasting commodity prices within a wider cycle analysis; invented in 1980 in the wake of booming 1970s commodities, the indicator was in response to what Lambert saw as a need for a more standardised way of approaching indicator design.

As he put it:

Francesco Sani

Francesco Sani is a private trader based in Scotland, with an interest in technical analysis, forex and SPX. He has been a guest writer on DailyFX and ForexCrunch , and gained his CMT level 1 in 2020, finally joining the STA in 2022.

“Rather than develop separate rules to determine each commodity’s fluctuations, some standardisation technique had to be found”.

An advantage of the CCI is that it is unbound: unlike oscillators such as RSI, it has theoretically no upper or lower limit, which can be very useful in determining the severity of overbought and oversold conditions when new relatively extreme +/values are present within the intended price analysis window. The formula is shown in Figure 1.

It differs from the RSI, for example, in that mean deviation is present in the calculation. This could be why the CCI, as we shall see, appears more responsive than the RSI to pinpointing genuine overbought and oversold signals. The RSI usually lags by several days in diverging from price compared with the CCI, given the same time-period for both (see here for more: Investopedia).

Figure 1: The formula of the Commodity Channel Index

Apropos settings, I used HLC3 (High, Low, Close divided by 3) and a 21-period; while the default is 14 days, I find that 21 is more valuable as it more closely fits a single trading month: May 2023 is a good example (see Figure 2) as the first day of the month was a Monday and we can see that, having crossed out in red all non-trading days (two UK bank holidays, King Charles II’s coronation holiday, and weekends), we were left with 20 trading days, thus showing why the 21-period setting is a good fit for the average trading month.

traders wanting to ‘buy the dip’ or ‘sell the rip’, and for investors wanting to anticipate signs that a so-called bull market may show exhaustion. There were some examples of positive divergence found while preparing this article: they will not be discussed here but I have included that data separately in Appendix Three

The initial stage, marking instances of negative divergence (henceforth: ND) on the chart, yielded thirty-two examples covering a twenty-year period (2003-2023): in Appendix One you will be able to see all such examples, in reverse chronological order (most recent first). Here, I will show the first one and explain what information is shown, and the rationale behind it (see Figure 3).

Example 1

The top part shows the SPX closing daily price (blue line) marked by three vertical lines: each line is labelled with the closing price for that day (in red). The three price points correspond to columns B, C, and D1 in the Appendix Two table, and they represent the following:

1) price at the start of ND;

2) price at the end of ND;

3) price at the nearest peak/trough, post-ND.

Instead of using the default +/- 100 markers, I used for this article what I would normally do, that is +/- 200 markers: why? If we only look at the Appendix Two table, columns I1 and I2, we can see how for 10 out of the 32 samples (=31.25%) the CCI values at B (the start of each ND period) were ABOVE +200; a further 5 were above 185(.00), thus 15.62%, meaning that (31.25 + 15.62=) 46.87% of the sampled ND instances – almost half – displayed starting CCI values of at least +185, or higher.

Furthermore, 14 samples displayed post-ND values BELOW -200 (column I2), which is nearly 50%. Having observed the CCI in practice for a long time, I can confirm that the 200 markers are much a more useful lens through which one may filter out the noise and focus on those situations where a more reliable probability of genuine price reversals may be signalled (with or without divergence, just generally): for example, from June 2022 to now (June 2023) there were five instances where CCI came to within 14 points above/below +200 (end of July, November, February, April and May), three of which have been listed in this article as displaying divergence from price; the moves from price when CCI bounces off the +/-100 regions are never in my experience as significant, therefore for all of these reasons I always set the CCI ‘overbought/oversold’ levels at double the default.

The chosen instrument for the study was the S&P500 spot (SPX) daily chart: unlike commodities or foreign exchange, indices and stocks are tendentially one-directional (bullish) with downtrends or significant corrections being more exceptional and infrequent than in other asset classes. Given this premise, the study favoured prioritising negative divergence instances between CCI and SPX: investigating this phenomenon could be useful both for the day or swing

Figure 2: We were left with just 20 trading days

Figure 3: An example of negative divergence

Also marked, in red, is the duration in calendar days for the ND, with dates. The choice of calendar, rather than trading days, was dictated by practical reasons. Using trading days would have meant having to research and include all US holidays/ non-trading days listed in the NYSE calendar on top of weekends, potentially complicating matters somewhat. Where calculated targets fell on a weekend or non-trading day (according to the chart), the next actual trading day was used, and this was notated in the table using a single asterisk.

At the bottom of the chart example, the CCI is also marked with an orange line for the same period as that on the SPX price line. Choosing each ND period was based simply on the rule that as soon as price and CCI started moving in tandem, that would signal the return to convergence between the two. Occasionally, as in Example 25, two closely-spaced ND periods have been treated as one, although convergence occurred within that period: this did not create a skew as there were other instances of ND periods of almost equivalent length.

Column D2 on the Appendix Two table shows the nearest peak/trough after D1 – what we could call a secondary peak/trough –and this was done in order to see what happened to price after the ND ended and price had an initial reaction (D1). The choice of both D1 and D2 points (across all samples) was based on what seemed a significant enough price move, and also one accompanied by a significant shift in the CCI graph.

While I did not make a retrospective table of all distances between D1 and D2 across the samples, I focused instead on the overall time distance (in days) between B (the ND start date) and the farthest chosen price point going forward; the summary from the full data in Appendix Six is as follows:

Global range (all ND samples, points B to farthest out):

• 32 to 187 days;

• Mean: 95.93 days;

• Median: 82.5 days.

While the thirty-two samples all focused on ND, the labels for data points D1 and D2 included the words “peak/trough” together, because in some instances the divergence failed and there was no significant trough that could be identified within a reasonable time-span following the end of divergence: there were seven such instances, and in these cases none of the data points measured yielded a negative price move. Table 1 shows a list of them as a quick summary, taken from data in Appendix Two:

It is easy to see, in retrospect, why some of these NDs failed to bring any significant correction. Let us take the first example, which occurred in 2017: despite four ND occurrences listed for that year, three of them failed to bring more than a 48-point price drop across the price points measured, and the one listed above brought none. From 3/1/17 to 3/1/18 the SPX rose by 20.5%, which explains in part why no amount of ND between CCI and price could quite buck the trend.

The second example (marked no.20) from 2014 tells a similar story: although there were two sampled NDs for that year, one of these had no negative price impact across the sampled period and the other only pushed price down by a maximum of 40 points. The entire year, measured from the peak of 31/12/13 to 31/12/14 was up about 13.4%, which was pretty good and explains the issues in terms of effective ND impact; however, as noted in the entry for column D2 at example 20 in the Appendix Two table, price DID make a significant move lower to 1850 on 15/10, just nine calendar days after the last price point measure (column E2): with the exception of dips below it in late January and mid-April, this price had essentially not been seen since the start of that year, therefore it seems that the 58-day ND listed above did eventually signal that price would peak less than two months later, with a subsequent correction down of just over 8% in a matter of 27 calendar days (falling from 2014 on 18/9 to 1850 on 15/10).

Table 1: None of the data points measured yielded a negative price move

I should add that my sample size (32 instances sampled from two decades of SPX price) is significant enough for the purpose of this article, however for building a workable model there would have to be further data points to test the profit factor of CCI divergence (negative and/or positive), including other time-frames and other instruments within this or other asset classes (bonds, commodities, forex, etc.). The author used manual collection of data straight from the chart, slowly making connections between the different statistics to arrive at a detailed enough hypothesis for how a CCI-based divergence model might be applied in forward testing. I should add that the choice of using a line (closing prices) chart was dictated purely by the desire to emphasise price-to-CCI divergence with as much visual impact as possible, something that a candlestick chart may have partially hampered.

In terms of model-building, I looked at the available data in different ways, trying to answer some of the following questions:

1) Does the rule used in columns E1 and E2 (Appendix Two table), which looks at price beyond the ND divergence period (E1 adds the ND period from the end point, and E2 adds it to D1 which means it has further reach into the future), need to be refined? Personal observations of ND x 2.5 and ND x 3 price points, that is price at double the distance of a ND period (calculated from its inception), or double and half, or triple, seems to sometimes coincide with some degree of accuracy with significant price moves, especially (for example) in not strongly trending price or in mature trending price scenarios: ranges can be created, e.g. price forecasts where the targets are between x2 and x4 of the ND period, or variations of this. An idea to be tested would also be whether the length of the targets would be inversely proportional to the ND period, thus maintaining a constant ratio between all forecasts that ensures that outliers (e.g. very short or very long ND periods) did not overly skew projections and undermine the model;

2) Does the length of the ND period influence the subsequent price moves? Chart 3 in Appendix Four tries to address this issue:

Negative divergence duration (Orange) an highest negative price change (Blue). CHRONOLOGICALLY.

Having collected all highest negative price changes post-ND for each instance and overlaid them on the ND durations, I noticed that sometimes there was a strong positive correlation, meaning that a higher ND duration would lead to a strong drop in price, as can be seen in example 17 (Figure 4), where a 50-day ND duration led to price dropping 125 points within 19 calendar days from the end of ND.

Example 20 is also interesting, again because it goes above 50 ND days (NB: at the bottom of Appendix Two’s table you may see that across the thirty-two samples, the ND duration mean is 35.75 and the median 31, thus making 50 something on the larger end of the range):

Figure 4: Sometimes there was a strong positive correlation

What is remarkable here is that, while price actually rose post-ND at the closest peak (D1, 1907), and rose further at the next chosen price point (D2, 2014), within about a month it dropped 164 points to a level, as already mentioned further up in this article, that had not been seen since close to the start of that year.

Another version of the above chart from Appendix Four, namely Chart 2 (reproduced as Figure 5), confirms how sometimes there is positive correlation between price moves in negative territory and ND duration (the lines move in opposite directions because increases in negative numbers point down whereas those in positive numbers (i.e. number of ND days) point up), but other times it seems that shorter ND periods can also engender decent price moves or that ND duration and price moves are inversely correlated.

Figure 5: Positive correlation between price moves in negative territory and ND duration

Ratio between hightest negative and highest positive price change (Blue); negative divergence duration (Orange).

3) The third question is how CCI change from ND inception (B) to the first significant peak/trough (D1) influences price moves post-ND, in other words: how much does an increase in relative change in CCI influence price moves? To answer this, I overlaid B-to-D1 price change points with I3 data (CCI change from B to D1) after ordering the two by increasing CCI change (most positive to most negative); the result is Chart 1 from Appendix Four, which I reproduce here as Figure 6.

Price change, B to D1 (Blue) and CCI change, B to D1 (Orange), SPX, 2003 - 2023 daily chart (32 points). Data ordered by CCI change value (most positive to most negative). NOT CHRONOLOGICALLY.

Figure 6: Correlation or causation?

Immediately, the chart brings to the eye a relationship between progressively negative (= sub-zero) price changes and also pro

gressively negative CCI changes: by the time CCI changes reach the -400 mark, SPX price changes are subsequently almost entirely negative. Whether this be correlation rather than causation remains an open question.

Conclusion

Having seen the power of price-to-CCI divergence at work over past-price data, the next question is the following: how can we use this sample to infer future price dynamics and forecasting?

While much of the time preparing this article was spent on preparing observational data from which to draw conclusions, the author was keen to demonstrate how a basis for a model, discretionary or automated, could be laid down; to this end, two examples were prepared on price areas not previously examined, in order to maintain impartiality on what could be found if a model were to be applied for price discovery.

Figure 7 shows the first of the two examples:

Here we see how, by applying the formula:

[NDd X med(Sgd/NDgd )] – NDd ]

where d = duration, S = Samples’, g = global, and med = median value, we obtain a timeline for an expected target that, significantly, lands almost exactly on a major cycle lowover 1000 pips below the cycle high at the start of the divergence.

Figure 7: EUR/USD weekly chart

Assuming that the end of divergence took place today instead (that is, a 27-day period), we could calculate our price target as follows:

1. take the mean (-75(.37) points) of all observed highest negative price movements in the study (range: 0 to -245), subtract it from current price (4395); the result is 4320. I chose the mean in this case as it is greater than the median (-51);

2. take the ND period (27 days) and multiply it by the median ratio of all the ratios between B-to-D2 durations and ND durations, which is 2.64, thus (27 x 2.64=) 71.28; then, subtract the ND period (27) and it will yield (71.28 – 27=) 44.28, meaning that the price time target would be Sat. 12/8, or rather Mon. 14/8 (the nearest subsequent trading day).

[NB: both charts are from Appendix Five].

Tomorrow there is important data hitting the markets, that is PCE for the US: this could have significant impact on price given that the Federal Reserve’s restrictive policy is now very data-dependent and the ‘PCE deflator’ is one of the measures of inflation that the bank watches very closely: a hotter-than expected print could trigger a sell-off as markets would expect further hiking (hiking for longer) from the Fed, potentially the reverse outcome also being true in case of a cooler PCE figure. Given this, and the Non-Farm Payrolls the following Friday, it could be that the figure projected in the above example may need to be significantly revised, also given that we do not know as yet whether the current ND period is over or whether it may continue, in which case we would have to see price-indicator convergence before adjusting the forecast price/date target.

As a closing thought, I wish to add that the range from all ratios in Appendix Six could potentially be used in calculating future price targets, therefore the one in the above SPX could use any multiplier from the median that we used (2.64) to maximum (4.6). If the latter, then the result would be:

27 x 4.6 = 124.2 – 27 = 97.2

which means our target would not be until the first week in October.

Figure 8: SPX daily chart

Figure 8 shows the SPX current chart (at the time of writing, 29/6/23) showing a ND period from 2 to 29 June: given that the ND median duration (shown in the Averages section below the table at Appendix Two) is 31 days, this would take us to next Monday (3 July).

Thus, a range could be envisaged, from the middle of August to the first week in October, during which we would expect price to correct lower by at least the median (from the sum of all highest negative price changes in the sample set) and up to the mean of median + maximum, which in our case means 4395 – [(-51 + -245) : 2] = 4395 – [296 : 2] = 4395 – 188 = 4207 thus a price target could be anywhere between 4320 and 4207.

Using values from an indicator such as the ATR (Average True Range), calibrated in the same way (21 days in our case), could potentially assess the realised volatility at the time of setting targets post-ND, thus trying to see how many points one may realistically be able to expect within the time target in question. Ratios of ATR values and of sampled price moves’ median/ mean values could be used to calculate a model that incorporated quantifiable points/pips expectations based on recent prices, thus enabling the observer to modulate price correction forecasts through the prism of a model more responsive to recent (actualised) volatility.

Resources used:

Mean and Mode calculator: https://www.calculatorsoup.com/calculators/statistics/mean-median-mode.php

Definition and formula for the CCI: https://www.investopedia.com/terms/c/commoditychannelindex.asp

History of the CCI http://traders.com/documentation/feedbk_docs/2004/10/Charting/charting.html

How important is one’s mindset in the game of alpha-driven performance?

This was not a ‘top-of-mind’ question when starting in the industry nearly 30 years ago. I was busy studying during my teenage years, while actively exploring career paths, keen to gain some practical work experience. Although even back then, I had developed a passion for markets, behavioural patterns, ultimately crowd psychology, coupled with the phenomena of cycles. Key themes that would be learnt more in depth from veteran industry colleagues and mentors such as STA Fellows David Fuller and Robin Griffiths (Figure 1). STA members can read more on their pioneering work in the book publications ‘Crowd Money’ and ‘Mapping the Markets’ (Figure 2 & 3) below.

Ron William, CFTe

Ron William is a cross-asset strategist, educator and performance coach, with over 20 years of track experience, founder of RWA , an award-winning global macro-tactical research and advisory firm, to a wide range of financial institutions & professionals, producing differentiated alpha, insightful idea generation and unique market timing.

Ron also applies a “ market & mind” approach at IntensiChi , uusing the latest techniques in behavioural-risk models and neuroscience sourced from expert groups. He further supplements with mentoring / coaching, trained by the International Coaching Federation (ICF), and teaches a regulatory approved masterclass in Applied Behavioural Science, with financial institutions, CFA Societies and as part of a Master’s University Degree.

Driven by high-integrity education, Ron serves as part of the education committee of the International Federation of Technical Analysts (IFTA), Development Director at the Foundation of the Study of Cycles (FSC), Head of SAMT’s Geneva Chapter, and an honorary member of ESTA. He is also a visiting lecturer at universities, active guest speaker for the CFA, CAIA and CISI, and senior teacher at colleges offering an accredited diploma in trading and investing.

Robin Griffiths, Ron William & David Fuller, London, 2016

But it was earlier on when the learning curve sharpened, upon trading my own portfolio, ahead of debuting as an FX strategist, on the Emerging Market desk of an independent research firm. It was during this time when the life changing question on the importance of mindset dawned. It later inspired diversifying my professional experience in both markets and mind, across the fields of trading psychology, behavioural finance, neuroscience and performance coaching.

Many will likely recall the infamous trading experiment conducted in the 1980s based on the classic nature-nurture question: “Are successful investors born with the skill, or can they be trained?” - popularised in the classic trading book ‘The Way of the Turtle’ by Curtis Faith. The so-called ‘turtle experiment’ demonstrated that a proven strategy can be modelled and learnt, but only to those willing to make the required changes, to be a disciplined and consistent performer.

This is what I uncovered in an exclusive interview with leading Trading Psychologist Dr. Van Tharp (Figure 4 & 5 ), while attending his ‘Peak Performance’ flagship training, at their US-based headquarters of the Van Tharp Institute. Part of the backstory connection to the ‘turtle experiment’ - is that Dr. Tharp actually interviewed as one of the early Turtle Trader nominations and had first-hand experience of their selection process. STA members can review additional coverage with Dr. Tharp during past event archive at the STA London in October 12, 2018 (blog & video), IFTA webinar & media interview

An Interview with Dr. Van Tharp, founder of the Van Tharp Institute

published by Wiley & Sons, in addition to four acclaimed books published by McGraw Hill: Super Trader, Trade Your Way to Financial Freedom, the New York Times Bestseller, Safe Strategies for Financial Freedom, and Financial Freedom Through Electronic Day Trading.

Tharp is the only trading coach featured in Jack Schwager’s bestselling book, The Market Wizard’s: Interviews with Great Traders He has been featured in Forbes, Barron’s Market Week, Technical Analysis of Stocks and Commodities, Investors Business Daily and Futures and Options World, and Trader’s Journal just to name a few.

Dr. Tharp has collected over 5,000 successful trading profiles by studying and researching individual traders and investors, including many of the top traders and investors in the world. From these studies he developed a model for successful trading and investing in which other people can adopt and learn.

He has developed a five-volume Peak Performance Home Study Course, teaching the results of this ten-year study.

He also developed the Investment Psychology Inventory Profile to help people better understand their strengths and challenges in relation to trading or investing.

He also has developed a course on How to Develop a Winning Trading System That Fits You, and written and published The Definitive Guide to Position SizingTM

He published the Market Mastery newsletter for over 10 years, and now publishes a weekly e-newsletter, Tharp’s Thoughts. Dr. Tharp wanted to get the vital information that traders needed to as many people as possible; therefore, he decided to offer his newsletter at no charge. Before that subscriptions to his newsletters were as much as $249 per year.

RON WILLIAM (RW): Thank you Dr. Tharp for this follow up interview opportunity, on behalf of the Swiss Association of Market Technicians (SAMT), affiliated with the International Federation of Technical Analysts (IFTA).

We also want to recognize many leading financial market professionals around the world that are interested in your life’s work on trading psychology and transformation. I must also congratulate you on a truly amazing Peak 101

workshop, which I had the pleasure of just attending here at your Van Tharp Institute (VTI) headquarters in Cary, North Carolina, USA, during April 2017.

My opening question is about your life story. Dr. Tharp, what inspired you on the path of trading psychology and transformation?

Dr. Tharp: Well it’s interesting, because I just learned that Mark Douglas, author of Trading in the Zone, passed away last year. He and I both started at the same time as preeminent people within the world of psychology and trading. Mark was inspired by the Seth Material and I was inspired by A Course in Miracles. I started working through A Course in Miracles in 1982, and by the time I finished, this business was pretty much full-time operation for me. Even then, when I didn’t know that much about transformation and it didn’t happen that quickly, it still felt like my mission was transformation through a trading metaphor.

RW: How clear was it this would be your mission in life?

Dr. Tharp: It was more fuzzy then but I knew then that it was my bliss and that I really got a lot out of helping people. Now it’s really obvious. In those days, it was more about simply enjoying that I was helping people transform. In contrast, I didn’t like doing research or working for the government and that type of thing. Going in this direction was very much part of my destiny.

RW: What can you tell us about your background in Psychology and Modelling techniques?

Dr. Tharp I always wanted to develop models. Psychology is where I started, but I didn’t get my modelling techniques out of psychology. I learned to develop models from studying Neuro Linguistic Programming (NLP). The idea behind NLP modelling is that you find a number of people who do things well and find out what they do in common. Once you have the common tasks, then you need to find the three ingredients of each task which are beliefs, mental states, and strategies.

I modelled the trading process, the process of developing a trading system that fits you, and how to use position sizing to meet your objectives. There are probably now around 115 Tharp Think concepts™, which do not make a model exactly but they are more of a set of beliefs which came from numerous sources. These concepts really help people transform and perform well as a trader. The infinite wealth model™ is another one. We are working on market types which demonstrates that it’s really insane to design a system that is expected to work well across different

According to Dr. Tharp it was a failed experiment given they had “rejected something like 90% of [traders], and from the remainder selected a few Turtles.” Moreover, the results were dispersed, as “the Turtles had huge differences in their results causing some of them to say that even with the thorough selection process, the experiment didn’t really work.” When I probed further and asked why, he replied that “not everyone can become a great trader, in large part because they are not willing to change who they believe themselves to be.” It was in that subtle and yet profound observation that I realised

“not everyone can become a great trader, in large part because they are not willing to change who they believe themselves to be.”

DR. VAN THARP

Figure 4: An interview with Dr. Tharp

Ron William, CMT, MSTA

Ron William & Dr. Van Tharp at the VTI workshop in London, October 2017

Figure 5: Ron William & Dr. Van Tharp at VTI workshop, 2017

The Roadmap to Transformation

I became fascinated by the turtle story and researched further the concept of change and transformation work, sourcing a broad spectrum of experts, while also learning from my own self-experience, compounded with the training and coaching of other professionals. It led to the realisation that greater selfawareness, better management and optimisation of our naturenurture attributes can further impact, as part of a framework that I developed called ‘The Roadmap to Transformation’ (Figure 6).

This was first shared with a live audience of 400 trading professionals in Zurich, Switzerland at the flagship IG Trading Day, then IFTA’s conference in Egypt, during 2019. It later featured as part of a trading psychology interview series, that I would discuss with IGTV’s veteran journalist Jeremy Naylor and former colleague at Bloomberg, the global business and financial information service.

Nature

Behaviour = Biology

Survival Instincts

Peak Performance

Mind - Body Ecology

Awareness

Behavioural & Neuroscience

Change

Cognitive bias & Temperaments

Resilience & Coherence

Transformation

Nurture

Behaviour = Environment

Beliefs & Values

Trading

Risk Money

Figure 6: The Roadmap to Transformation

Ron William & Jeremy Naylor, IGTV, London 2019

While the laws of nature are fixed, such as survival instincts, cognitive bias and temperaments, they can still be better managed or optimised, using self-assessment training, applied best practices and performance coaching, to help incorporate desirable changes. It’s also important to realise the power of nurture, in terms of environmental influences, specifically through beliefs and values that impact principles of trading, risk and money. The peak performance journey starts with greater self-awareness, change strategies, which can lead to a viable and sustainable transformation of results. Resilience and coherence are important qualities to ensure long-term results. Timing is typically case-specific but can begin to show after a minimum timeframe of 6-12 months.

Ultimately the blend of greater self-awareness, behavioural optimisation and peak performance helps to enhance the ‘behavioural alpha’ (BA) in a portfolio. This can be typically analysed in a tangible metric of BA filters, categorised in either of two different groups, emotional or cognitive bias (Figure 7 ).

“Emotional biases arise spontaneously as a result of attitudes and feelings that cause decisions to deviate from rational decisions of traditional finance”. It is primarily driven by the instinctual cycle of greed and fear. A notable example of a fearbased emotional bias is ‘loss-aversion’, which typically leads to under allocating, or profit-taking too soon. Behavioural Finance expert and Nobel prize winner, Daniel Kahneman, developed extensive research on this area, as part of his ‘prospect theory’ - highlighting the asymmetric relationship between risk and reward, rooted in how the pain of loss is twice as great as the pleasure of gain.

Industry impact?

Sustainable industry impact is best achieved firm-wide, across key teams, at investment policy level and asset allocation decisions, led by a growth mindset culture, topdown approach. This is what I currently do with a selection of firms, with the support of regulatory guidance. The demand for more adaptive thinking, resilience, and stress response management (SRM), has shot up among financial professionals, in recent years, post COVID and the market shock of 2022. Moreover, both industry peers and I continue to see further demand, against a backdrop of compounding macro headwinds, ongoing shifts in market regime and elevated volatility. The instability of performance returns has led to many seeking alpha differentiators, risk optimisation and enhanced market & mind strategies.

A vital practice is to correctly align three key pillars;

1. Mindset, or personality temperament, 2. Strategy (e.g., passive vs. active). 3. Market regime (e.g., macro, trend, volatility). Focusing on the mindset or personality temperament, this can be mapped with standardised psychometric assessment tools and latest neuroscience techniques. For instance, a study found that the successful archetype for certain traders or investors is the NTJ type ((N) intuitive, (T)thinking, (J)judging) in the Myers & Briggs' 16 personality types test. This features big picture, strategic thinkers, that can see outside the box, objectively identify key patterns and cause-effect relationships, using a disciplined and systematic approach. Readers can select this survey link to review their own results.

Alternatively, “cognitive biases are basic statistical, information processing or memory errors that cause decisions to deviate from rationality”. Moreover, this can be subdivided into two micro groups of belief perseverance or information processing, with the former being psychologically driven and the latter, data centric. One popular example of belief perseverance is confirmation bias, when seeking selective information to support one’s own opinion or to interpret the facts in a way that suits our own world view.

While a common example of information processing, is ‘outcome bias’ as part of our bottom-line driven industry, when a decision is based on the monetary outcome of previous events, without regard to qualitative context or process.

Other portfolio related biases include ‘thesis drift,’ akin to the temptation to change the thesis to fit our emotions, or ‘concentration risk’ - the tendency to place excessive weight on specific positions. Behavioural biases can also be overcome by identification and mitigation strategies, using psychometric scores, assessment surveys, combined with proven best practices. An overview of top biases is featured for educational reference in (Figure 7 ). based on left and rightbrain associations.

“While the laws of nature are fixed... they can still be better managed or optimised, using self-assessment training, applied best practices and performance coaching, to help incorporate desirable changes.”

RON WILLIAM

“A vital practice is to correctly align three key pillars;

1. Mindset, or personality temperament,

2. Strategy & 3. Market regime.”

RON WILLIAM

Daniel Kahneman

Figure 7: Top Behavioural Biases

Figure 8: Scenario Planning

Source: Future Station 2000. futurestation.ro

Ron William, CFTe, NLP, Founder & Performance Coach

Scenario planning

Strong demand is also emerging for training in ‘Scenario-planning’, an area that I specialise in and teach at the Van Tharp Institute, which is based on the art of strategic thinking, scenario testing and decision science. Several clients find it especially useful for simulation work on different market scenarios, which can include macro-economic surprises, geopolitical tensions, or thematic event risk such as the latest 2023 banking crisis. Originally popularised by the military, to learn how to fight in the ‘fog of war’, it is more about preparation, then prediction of outcomes, which can often be described as VUCA (volatile, uncertain, complex, and uncertain) - see related media link for more details. The key benefit is in reshaping our mental model of thinking into a big picture, strategic, lateral mindset that can prepare for multiple futures, often unknown vs. solely forecasting, which often has a narrow margin of error and can lead to tunnel vision, with risk of being blind sighted by certain risk-reward scenarios ( Figure 8).

Qualitative, high-touch and inner self-awareness methods also remain key. One example is investment journaling which is a popular and effective technique to improve behavioural analysis and integrate them into portfolios or best practices. Key areas to focus on include analysis of your market & mind work, accountability lessons and forward guidance. Mindset techniques are also powerful for clarity of mind, adaptive thinking, and resilience. Techniques that work well with some professionals include mental rehearsal, visualisation and mindfulness or coherence training.

Finally, brain training, based on the principle of neuroplasticity, aims to incorporate changes at a cerebral level to achieve desired changes in such response patterns. This is sparked by redirected neuropathways that lead to a chain reaction of our thoughts, emotions, behaviour, then actions. Research demonstrates the brain is like a muscle and can literally be changed by a variety of mental strategies and exercises. It's a classic example of how the “software changes the hardware.”

Traders and investors can better navigate volatile market conditions and behavioural biases following the ‘A* strategy’ developed as part of my work.

ASSESS

Market & mind assessment of vital signs for performance, risk, strategy, and biases.

Acceptance is key to real change. Accept what you cannot change, such as the market condition, but change what you cannot accept, like your strategy playbook.

Enhance your edge, be creative, innovate, and sharpen the learning curve.

Take action! Apply a systematic process with clearly defined rules and follow with discipline.

New best practice only works once we let go of past non-useful behaviour.

Develop routine mental strategies to ensure a flow and sustainable impact.

Happy Trading! May you all rise onwards and upwards!

Welcome any questions and key insights on social media via LinkedIn or email: rwilliam@intensichi.com

Listen to the STA’s podcast interview with Ron William.

References:

1. E. Traecy, Crowd Money, Harriman House (2013)

2. R. Griffiths & D. Owen, Mapping the Markets, The Economist, (2006)

3. C. Faith, The Way of The Turtle, Wiley, (2007)

4. Dr. Van Tharp Interview, (2019)

5. Dr. V. Tharp, Trading Beyond the Matrix, Wiley, (2013)

6. D. Kahneman, Thinking Fast or Slow, Penguin (2012)

Derivative Sub-cyles, Lucas Numbers & the 9/56 Year Grid

A final version of David’s February 2022 article

Introduction

The 9/56 year cycle consists of a grid with intervals of 56 years on the vertical (called sequences) and multiples of nine years on the horizontal (called sub-cycles). Various financial phenomena cluster with high significance in these patterns.

• Major US and Western European financial panics 1760 - 1940 (McMinn, 1986, 1993, 2021).

• Beginning and ending of US bear markets 1886-2021 as measured by the Dow Jones Industrial Average (DJIA) (McMinn, 2022).

• Major DJIA annual one day falls 1886-2021 (McMinn, 2023).

Clearly, the 9/56 year cycle plays a key role in US trading activity, a finding that offers vital clues for further research.

The 9/56 year, 18/56 year, 36/56 year and 54/56 year grids gave rise to numerous sub-cycles on the diagonals of these patterns, with intervals of 2, 11, 20, 29, 38, 47 and 65 years. Because these patterns were based on the original 9/56 year grid, they have been described as ‘derivative sub-cycles’. This paper will explore these concepts in detail.

David McMinn

David McMinn completed a Bachelor of Science degree at the University of Melbourne in 1971 (Geology major) and subsequently became a Minerals Economist in ANZ Banking Group Ltd . Since leaving this position in 1982, he has conducted private research on cycles arising in seismic and financial trends, publishing numerous papers on cycle theory, especially in relation to the 9/56-year cycle.

No firm definition exists of what constituted a ‘major’ crisis in economic history. Even so, Kindleberger (1996, Appendix B) gave a compilation of what he considered were ‘major’ financial crises from 1760 to 1990. This author was selected given his pre-eminence in economic history and because he presented the most comprehensive list of historic financial crises. He provided an external reference independent of the assessment.

In the text, years presented in BOLD experienced major crises, as listed by Kindleberger (1996).

The year of best fit was taken as the year commencing March 1 in the tables, as it offered the greatest significance. The 56 year sequences in the 9/56 year grid have been numbered from 01 to 56, with 1761, 1817, 1873, 1929, 1985 being designated Sequence 01, 1762, 1818, 1874, 1930, 1986 as Sequence 02 and so forth (McMinn, 1993). A Chi Square test has been applied where appropriate in the text.

Charles P. Kindleberger (1910 - 2003)

The 9/56 Year Cycle

A 9/56 year grid can accommodate many of the major crises in financial history (see Table 1). Of the 30 major financial crises listed by Kindleberger (1996, Appendix B) for the 1760-1940 era, some 16 appeared in this layout (significant p < .001).

Table 1: 9/56 year grids & financial crises 1760–2020.

Year beginning 1 March

Kindleberger’s major US and Western European crisis years highlighted in RED Sources: McMinn (1986, 1993, 2021).

Derivative Sub-cycles

Numerous series can be generated based on the diagonals of the 9/56 year grid. These patterns share the same 56-year sequences on the vertical, but have different intervals on the horizontal.

Nine year sub-cycles in Table 1 may produce important 47 year and 65 year sub-cycles on the diagonals – see Table 2 .

Table 2: 9/56 year grid gave the important 47 year and 65 year sub-cycles on the diagonals.

In Table 2, the years *asterisked indicate a key 47 year sub-cycle:

Sq 12 1716 English crisis (Jan - Mar 1717). + 47

Sq 03 1763 Amsterdam panic (Sep). + 47

Sq 50 1810 English great panic (Jan 1811). + 47

Sq 41 1857 US and European panics (Aug – Nov).

The years denoted by ^ indicate another 47 year sub-cycle:

Sq 12 1772 English panic (Jun).

+ 47

Sq 03 1819 US panic (May).

+ 47

Sq 50 1866 English Black Friday (11 May).

+ 47

Sq 41 1913 1913-1914 US and European war panics.

+ 47

Sq 50 1960 No impact.

+ 47

Sq 23 2007 US subprime crisis. World crisis.

The years denoted with # gave a 65-year sub-cycle:

Sq 32 1792 British panic (Feb 1793). US panic (22 Mar).

+ 65

Sq 41 1857 US and European panics (Aug - Nov). + 65

Sq 50 1922 German crisis (9 Jan 1923). Default on war reparations. + 65

Sq 03 1987 US Black Monday (19 Oct).

In Table 3, a 38 year subcycle has been *asterisked.

Sq 21 1781 US deflation. Ending of the Revolutionary War.

Sq 03 1819 US panic (Apr - May).

Sq 41 1857 US banking panic (13 Oct). European crises (autumn).

1895 No impact.

1933 US banking panic (Mar).

Table 3: 18/56 year grid gave 38- and 74-year sub-cycles on the diagonals.

Also, years denoted with # yielded a 74 year sub-cycle.

1671 English crisis. Stop of the exchequer (2 Jan 1672).

Sq 41 1745 English Black Friday (6 Dec).

Sq 03 1819 US and English crises.

Sq 21 1893 US Black Wednesday (26 Jul).

In Table 4, the years *asterisked in Table 4 yielded the most important 20 year sub-cycle in economic history as follows:

Sq 01

1873

Austrian Black Friday (9 May).

US Black Friday (19 Sep).

Sq 21 1893 US Black Wednesday (26 Jul).

Sq 41 1913 1913-14 US and European war crises.

Sq 05 1933 US banking crisis (6-9 Mar).

1953 No impact.

1973 US$ crisis.

Arab oil embargo imposed (17 Oct). UK secondary bank crisis (Nov - Dec).

Also, a 92 year sub-cycle in financial history was marked with ^ and commenced in 1653.

Sq 05 1653 English crisis.

Spanish national bankruptcy.

Sq 41 1745 English Black Friday (6 Dec).

Sq 21 1837 US panic (10 May).

Sq 01 1929 US Black Monday (28 Oct).

2021 World covid crisis.

Russian stock market collapse (24 Feb 2022).

Table 4: 36/56 grid gave 20 year and 92 year sub-cycles on the diagonals.

Table 5: Another 36/56 year grid gave a 20 year sub-cycle on the diagonal.

Note: Years ending in 0 are denoted by an *asterisk.

The Table 5 grid yielded another important 20 year sub-cycle.

Sq 48 1920 1920-21 US and UK crises.

Sq 12 1940 US panic (May). German invasion of France. UK panic (Jun). French capitulation.

Sq 32 1960 No impact.

Sq 52 1980 US crises (Mar).

2000 After Greenspan Bubble.

2020 Corona virus panic (Mar).

Table 6: 54/56 year grid gave rise to two year and 58 year sub-cycles on the diagonals.

In Table 6, years *asterisked yielded an important two year sub-cycle.

Sq 48 1927 German Black Friday (13 May).

Sq 12 1929 US Black Monday (28 Oct).

Sq 32 1931 World crisis. Great Depression.

Sq 52 1933 US bank holiday imposed (6 Mar).

1935 No impact.

1937 Stock market panic. 1937-38 US depression.

Also, a key 58-year sub-cycle has been denoted by ^.

Sq 48 1815 US and UK crises.

Sq 12 1873 Austrian Black Friday (9 May). US Black Friday (19 Sep).

Sq 32 1931 World crisis. Great Depression.

Sq 52 1989 Japanese Bubble collapse (Jan 1990).

The 9/56-year panic cycle is complex giving rise to many derivative sub-cycles involving intervals of 2, 20, 38, 47, 65, 74 and 92 years among others.

Eclipse Cycles & The 9/56-Year Grid

What activates the 9/56-year cycle? Several Moon Sun cycles align very closely at 9.0 and 56.0 solar years based on lunisolar cycles in integral and half integral numbers (McMinn, 2021). Because of these alignments, any events clustering in a 9/56-year grid will have the lunar ascending node (LAN) in two segments sited approximately 180 degrees opposite on the ecliptic (1st and 2nd harmonics) with no exceptions. For events falling in the same 56-year sequence, LAN will be sited on a narrow sector of the ecliptic (1st harmonic) with no exceptions. For events occurring at around the same time of year, the mean position of Apogee will be sited in three ecliptic segments 120º apart (3rd harmonic) with no exceptions. There is also a near perfect 6th harmonic between the ecliptic position of the Sun and the angle between the LAN and Apogee (McMinn, 2016). How weak lunisolar tidal harmonics actually functioned in relation to financial crises and the 9/56-year cycle remained unknown.

NB: The lunar nodes are sited in the heavens where the plane of the Earth’s orbit around the Sun (the ecliptic) is intersected by the plane of the Moon’s orbit around the Earth. Where the Moon crosses the ecliptic from south to north gives rise to the lunar ascending node (LAN) and from north to south gives the lunar descending node. Apogee is sited in the lunar orbit, where the Moon is greatest distance from the Earth. The lunar nodes and apogee are key determinants of terrestrial tides.

Fibonacci-Lucas Numbers

Fibonacci - Lucas numbers can be directly linked to various Moon Sun eclipse cycles. Series A in Table 7 gives the additive series commencing 35 and 6 lunar months, in which each number is the sum of the previous two. Lucas numbers (in terms of solar years) align with the following eclipse cycles – Tritos (11 years), Saros (18 years), Inex (29 years), 47 year cycle, Short Callippic (76 years) and the 123 year cycle (McMinn, 2013). For cycles less than 11 years and over 123 years, the link with Lucas numbers peters out, as solar years align less precisely at integral numbers.

NB: Fibonacci numbers are the series commencing 0, 1, 1,

2, 3, 5, 8, 13, 21, 34, 55... in which each number is the sum of the previous two. Likewise, Lucas numbers commence 2, 1, 3, 4, 7, 11, 18, 29,47, 76, 123... and is the sister series to the Fibonacci numbers.

Based on the formula by Van den Bergh (1955), the interval between two solar (or lunar) eclipses can be established using the formula:

T = a.Inex + b.Saros

where T is the interval between successive eclipses in numbers of lunar months. a and b are integral numbers (zero, negative or positive).

The Saros equals 18 solar years and the Inex 29 solar years, both of which are Lucas numbers. Based on van den Bergh’s formula, the series commencing 35 and 6 lunar months is composed in multiples of the Inex and Saros in patterns of Fibonacci numbers (see Series A, Table 7). The Saros or Inex number may be positive or negative for eclipse cycles below 135 lunar months. For eclipse cycles of 223 lunar months or more, the Saros and Inex numbers are always positive.

Series B and C in Table 7 were composed in multiples of the Saros and Inex that also yielded Fibonacci numbers based on the van den Bergh formula. The three series were variations upon a common theme.

(a) Based on the formula T = a.Inex + b.Saros by van den Bergh (1955).

(b) One Callippic equals 76.0 solar years (940 lunar months) or four Metonic cycles of 19.0 solar years each. The Short Callippic falls in eclipse cycles and is equal to the Callippic minus one lunar month (939 lunar months).

(c) van Gent (2017). listed a 246 year eclipse cycle (unnamed) of 3040 lunar months, which divided by two gave the 123 year cycle (1520 lunar months).

(d) van Gent listed a 112 year eclipse cycle. Divide this by two gave the 56 year cycle of 692.5 lunar months, which was a key component of the 9/56 year grid.

Source of Eclipse Data: van Gent (2022).

Table 7: Additive eclipse cycles & Fibonacci-Lucas numbers.

The 9/56 year grid may be reconstructed to give various derivative patterns. Each of these can be associated with an eclipse cycle based on half integral and integral numbers of lunar months, as well as integral numbers of solar years. Some of the cycles align closely with Lucas numbers – seven years (Tzolkinex), 11 years (Tritos), 29 years (Inex) and 47 years (unnamed). Additionally, 18 years (Saros) and 76 years (Short Callippic) divided by two gave the half Lucas numbers nine and 38 year subcycles respectively (see Table 8). These all appear in the additive eclipse Series A in Table 7. The 20 year cycle (Half 40 year cycle), 56 year cycle (Half 112 year cycle) and 65 year cycle (Unidos) show up in the additive eclipse cycles Series B in Table 7

Table 8: The 9/56 year grid, moon-sun eclipse cycles & Lucas numbers.

* Lucas number.

# Half Lucas number.

Source of Eclipse Data: van Gent (2022)

Discussion & Conclusions

The 9/56 year cycle in Table 1 was intriguing because it may account for the various derivative sub-cycles based on the diagonals of the various 9/56 year grids. All these sub-cycles, in turn, can be linked directly to lunisolar cycles. By implication, the complexity of market cycles can be reduced to a few basic principles based on Moon Sun tidal harmonics. If validated, it would be a major advance in cycle theory and firmly support the Moon Sun Hypothesis. This views financial markets as being mathematically structured in time and moving in tune with lunisolar tidal effects. It falls well outside the prevailing paradigms in economics and the sciences. Even so, one should always trust the evidence rather than follow the dogma.

Fibonacci - Lucas numbers appeared strongly in Moon Sun cycles (McMinn, 2013), a finding which provided theoretical support for using these factors in financial research. The intimate link between Fibonacci – Lucas numbers and Saros – Inex cycles may have something to do with terrestrial latitude and longitude. In the Saros, a similar solar eclipse is repeated about 120o further west in terrestrial longitude every 18.03 solar years. For the Inex, a similar solar eclipse is repeated at the same terrestrial longitude, but in the opposite latitude (ie: north to south or south to north) every 28.95 solar years. Latitude and longitude may be important variables to be assessed in these cycles, but this has yet to be proven.

Eclipse cycles determined terrestrial tidal harmonics and were based on the changing angles between the Moon and Sun in the heavens. Lunar and solar eclipses may look impressive from Earth, but no direct link could be established between the timing of eclipses and the timing of financial distress. This option was explored extensively but with no success. There was a strong Moon Sun theme in the financial activity, but how these two luminaries influenced trading sentiment was unknown.

Many derivative cycles may be generated from the 9/56 year layout. However, such complexity may be reduced to a few basic principles based on lunisolar cycles. This boosts the potential for making accurate financial forecasts years in advance – the Holy Grail of technical analysis. However, any further advance in cycle theory will require major research breakthroughs.

Resources used:

van den Bergh, G. (1955) Periodicity and Variation of Solar (and Lunar) Eclipses, Volume 1, H.D. Tjeenk Willink.

van Gent, R. H. (2022) A Catalogue of Eclipse Cycles Solar and Lunar eclipse predictions from antqiuty to the present. June.

Kindleberger, C. P. (1996). Manias, Panics & Crashes. John Wiley & Sons. Third edition. 263 p.

McMinn, D. (1986) The 56 Year Cycles & Financial Crises. 15th Conference of Economists. The Economics Society of Australia. Monash University, Melbourne. 18 p. August 25-29.

McMinn, D. (1993) The 56 Year Cycle & Financial Crises. The Australian Technical Analysts Association Newsletter. p 21-24. September.

McMinn, D. (2013) Fibonacci - Lucas Numbers, Moon-Sun Cylcles & Financial Timing. Market Technician, Journal of the Society of Technical Analysts. Issue 75. p 9-13. October.

McMinn, D. (2021) 9/56 Year Cycle & Financial Panics. Cycles Magazine. Vol 50, No 4. p 31-51. July.

McMinn, D. (2022) 9/56 Year Cycle & DJIA Bear Markets. Market Technician, Journal of the Society of Technical Analysts. Issue 92. p 29-35. September.

McMinn, D. (2023) DJIA annual one day falls, lunar phase & the 9/56 year cycle. Market Technician, Journal of the Society of Technical Analysts. Issue 93. p 29-35. March.

Patricia Elbaz puts 5 questions to David Watts, Trading System Consultant, professional Engineer and Fellow of the STA.

Patricia Elbaz, MSTA Technical Analyst & Lecturer

David, the best part of the 2019 IFTA Cairo Conference, aside from an excellent program, was the opportunity to chat to you, exchange views in a beautiful setting and have some great advice for my son who was studying Electrical Engineering at the time!

1. It’s a pleasure to interview you for the STA Journal. Can you tell us when you first took an interest in Technical Analysis?

DW: Oh way back when I guess I joined the society around 1992, when we had the meetings at Flemings. There were a few graduated friends of mine in the City. I guess I then took LSE Registered Representative exams around 1990, and was going into No 1 Cannon Street as I recall, working alongside a friend Ian Bell, who was producing a market letter for the trading floor. So the STA seemed the next step.

2. In your experience which indicators would you recommend using when starting to learn about Technical Analysis?

DW: So when I first looked at charts, the evident downtrends and uptrends forming straight lines and channels got me

David Watts

thinking, that charts and hence markets are following a geometric archetype. So not so sure it’s is an indicator as its geometry! I guess the simplest form of that geometry is what we call a ‘trendline’. So after reading the tape, that’s where I would start.

Technical Indicators help define where you are on the map, and of course price and time are primary. But I like a puzzle and with computers we can set things up to reveal divergences and make things more readily apparent. In programming I loved to play with digital filters, which really are just a special case moving average. Then way back my favourite one was probably “ARIMA”– an Autoregressive Integrated Moving Average.

3. How would you say Technical Analysis methods and usage have changed since you started in the industry?

DW: Well I can recall hand drawing charts and taking a lot of time poring over them. Then with handheld calculators we could quickly calculate moving averages and other indicators, but then the era of computers changed everything. As I recalled the other day what took say six hours could then be completed in a few minutes. Today with the power of AI developing so fast and billions going into its development, it’s clear that we are again on a new threshold in computer and sentiment analysis.

4. Do you think that AI will have a positive impact on trading and more specifically on the use of Technical Analysis in the future?

DW: As always there is both opportunity and adversity. Self learning systems have the ability to pull in vast amounts of data and produce highly accurate models. The trouble has always been knowing when that model is no longer working. But AI will get smarter and self correct – so watch out! Then something of course is lost in expertise that’s hard to quantify, as we become more reliant upon the algorithms. I guess as before there is a lot of knowledge that no longer resides in an individual person. In some way you can see that factories can produce excellent goods, but an artisan craftsman still has a place if he can produce a quality and individualised product.

It is only a matter of time before these systems are highly effective self correct and no doubt like chess can beat the best experts around today.

5. Finally, having been with you on the STA Board I remember how much work went into the book ‘Striking Gold’, your brainchild! Can you tell us more about your labour of love, did you face much hurdle, any major resistance (!) whilst putting it together and who would benefit from reading it?

DW: Well, I knew that we had a huge resource of papers written for the journal thanks to the numerous devoted editors over the years. Then many leading lights in the world of Technical Analysis had produced an astounding amount of material. So it was clear that this material would make an excellent book. Then it was pointed out that the society was soon to reach its 50 year since its founding, so the aim was to have the book ready for the 50th year. So the work began pre-covid, which changed everything - so that 50th year was

lost! However thanks to Katie Abberton and the STA office nearly all the issues were obtained, they were scanned into pdf format and obtained electronically.

With the chapters, I tried to make the book diverse, with something on each area of TA. Then we needed introductions to each chapter, so thankfully so many fellows contributed, including you on Gann!

The publishers wanted to edit out many of the comments in the introductions, such that it would have become more public relations than TA. Then Covid hit, which I had, the first time pre – vaccine which was very nasty, from colleagues I might add, who went skiing in that Austrian ski resort, which first spread Covid throughout Europe. Anyway, finally it was coming together, in discussion; the board made the decision to self publish the book, as otherwise it was check mate. Finally the title suggestion came from Karen Jones, so it was a real group effort overall. Then Andrew our graphic designer did an excellent job and with friends who had publishing experience chipping in, I had the framework edition. So an edit and it could be published. So then Katie again sourced the printer and Hooray, the first edition was finally out. We have some printed versions of the book available. Any UK members wishing for a copy please email the STA office on info@technicalanalysts.com

Thank you so much David for continuing to share your wisdom with us. As I always say ‘David is the essential piece of the puzzle!’ You have a way of connecting the dots at meetings and facilitating everything. It is part of your sharp Engineering mind and we are so grateful that you share your knowledge and enthusiasm with us.

We look forward to seeing you at future STA events, which are listed on www.technicalanalysts.com

Patricia Elbaz puts 5 questions to Gautam Shah, Founder & Chief Strategist at Goldilocks Premium Research.

Patricia Elbaz, MSTA Technical Analyst & Lecturer

Gautam we are delighted to interview you for the STA journal and to share your knowledge with our many readers.

1. First of all Congratulations on all your awards - we can’t keep up! Before you tell us all about that, can you tell us how you were introduced to Technical Analysis?

GS: As a student, finishing college, my father exposed me to the various fields in finance (i.e fundamental analysis, derivatives, accounting, technical analysis etc). I realised that I was able to grasp the concepts of technical analysis quickly and enjoyed studying. This made me take up the art of chart reading more seriously. The various degrees followed (MSTA with Distinction, CFTe and CMT) and then an opportunity to work for one of India’s largest financial services firm as Head Technical Analyst for 15 years. After that, in 2019, I founded Goldilocks Premium Research

Gautam Shah

PE: Well done; at the Global Technical Analyst Awards 2020. Goldilocks Premium Research was declared in three categories: Technical Analyst of the Year, Best Specialist Research and Best Emerging Market Research.

2. Tell us more about your many awards.

GS: Yes, we were the only Indian technical research firm to be awarded thrice at the Technical Analysts Awards in multiple categories. It was a dream come true to be competing with the best in the business worldwide! We do put a great amount of emphasis on the quality of research and the awards have been a testimony of our input.

3. How would you say Technical Analysis methods and usage have changed since you started in the industry?

GS: The markets have evolved. Trends are far more complex and much faster. The advent of social media, AI and flow of information seem to have disrupted technical analysis in some sense. So in today’s world you need to blend the old school methods with the new age tools. The approach has to be holistic to derive the best results and to be on top of trends. However, the big advantage is that the rewards come quicker if the reading is correct.

4. In your experience do you think that AI will have a positive impact on trading in the future?

GS: AI will definitely make technical analysis a lot more complex and even challenging. However, the human mind and eyes can’t be beaten because it is the best judge of greed and fear. AI will keep all technicians on their toes all the time because there is “now” no room for error. One will have to work with a lot of finesse and have a deeper understanding of the various tools of technical analysis.

5. To conclude, Gautam, what words of wisdom would you give students or young graduates starting out in trading and learning the many technical indicators?

GS: Stay disciplined. Stick to quality and have strict risk management. Go deeper into all the tools that technical analysis provides. Do a lot of back testing to understand today’s trends better. The journey from textbooks to the chart on your screen will be full of adventures. Don’t forget to enjoy the process and remember - “the market is always right”.

Thank you so much Gautum for your contribution to the journal and for all your research in technical analysis. We hope to see you at future STA events if you are visiting!

For all listed events, click on www.technicalanalysts.com

“AI will definitely make technical analysis a lot more complex and even challenging. However, the human mind and eyes can’t be beaten because it is the best judge of greed and fear.”

GAUTAM SHAH.

Advanced

Book Review

Gerry Celaya, MSTA STA Head of Marketing and Membership

2023,

This is the fourth book written by Robert Carver and it delivers on what it promises, 30 fully tested strategies for multiple trading styles and time frames.

For those who don’t know the author, he is now an independent trader and author, and is a visiting lecturer at Queen Mary, University of London. He spent over a decade working at Barclays trading exotic derivative products, and with AHL as a portfolio manager. A lot of this experience comes through in his writing, as he guides the reader through some basic futures trading information, and in a clear step-by-step manner, leads you through his futures trading strategies. I found his thinking when he talks about his strategies very interesting, along with the historical results for the different strategies and blended strategies that he provides. The strategies build on themselves for the most part, and he adds different parameters to filter in or out different ideas in order to improve the returns, control risk or both. This is not a heavy math oriented text book, but some previous financial market knowledge and experience would probably be helpful. I would think that any futures desk, prop trading firm, hedge fund and retail traders who are willing to put in the discipline of trading in a systematic manner (futures or other markets) would benefit from reading this book. Not so much as to agree with all of the strategies or conclusions, but because his process of developing, testing and using the strategies is really informative.

Traditional technical analysts and chartists won’t feel a lot of love in this book. Carver has little time for Fibonacci ratios, for example, and he makes that clear in a few comments. If, as a technical analyst, I had any quibbles with his book is that he explains the use of exponential moving averages instead of simple moving averages in his strategies as wishing to avoid the effect of a potentially large number swing dropping out of the simple moving average data base. He uses moving averages in order to identify trends with a combination of short and medium-term averages. I am

Harriman House Robert Carver

betraying my age here, but when I was first programming indicators we used exponential moving averages for data storage reasons. Once you had the exponential moving average figure, you only needed yesterday’s EMA number and todays close to calculate the next EMA figure. This took care of potential database problems, if you had to store 250 or more days of closes for a simple moving average, for example. The front loading effect of EMA vs SMA (current data has more weight in the EMA) that Carver observes is, in my view, one of the other reasons that systematic traders like them as they can react faster to current price changes.

Robert Carver insists that the first part of the book (Basic Directional Strategies) be read before reading the other parts of the book. This makes sense as he describes a lot of the process involved in trading futures (S&P 500 for the most part) and his construction of the data sets used to back test his strategies. If you skip this part, the rest of the book would be a struggle. Don’t look for detailed ‘buy the contract if a close xxx percent above the close of xxx days, with a take profit at some percent higher and a stop loss at some percent lower’ sort of strategy in this book. Instead, the development of the strategies is more subtle than that, and he illustrates the importance of considering trading a portfolio of different futures using his strategies. His strategies incorporate ideas on identifying which futures to trade, trend and volatility measures, risk control and money management.

system as well, as this is such a tricky thing to get right in the investment world. It can be a very subjective issue, and Carver is very honest in detailing the strategies results. As a standalone strategy the results are negative, but it may be useful to combine the value strategy with other systems (the assumption that correlations hold up is important).

Even very experienced futures traders (institutional or retail) are likely to find the discussion of carry, how to roll positions and some of the relative spread strategies useful. The last part of the book goes into these tactics in some detail (parts of this are covered in part one and through other strategies). The process of how Carver selects his contracts, execution of his trades and risk management is informative.

I found the book interesting as he uses real market data, transformed in a clear manner to create a data set that can be used for testing strategies. A lot of my quantitative trading experience has involved developing models or working with traders who use randomly generated datasets and use different parts of this data set to develop and test ideas.

Robert Carver dives into the actual futures data and shows the results through clear graphs and tables through his interesting, and very informative, book.

Another key difference from other trading styles is that Carver is always in the market. His strategies may add to positions or reduce them, as circumstances change, and this process is part of some of his investment strategies.

His discussion of skew was very interesting to me, as this is often overlooked in other research or trading books. The results are interesting, as is his warning that while trading skew may be tempting, the potential for very large losses is worth considering. I applaud his discussion of trading a value

“I found the book interesting as he uses real market data, transformed in a clear manner to create a data set that can be used for testing strategies.”

GERRY CELAYA.

Robert Carver

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• Chance to hear talks by international practitioners

• Access to research from market analysts around the globe

• CPD (Continuous Professional Development).

STA Calendar 2023/24

STA Diploma Part 2 exam (online)

Tuesday 5 September

STA Monthly Meeting (Sep 2023)

Tuesday 12 September 6.30pm Via Live Webinar

Ashish Kyal

STA Annual Celebration

Celebrating 55 years

Thursday 14 September 6.30pm National Liberal Club

IFTA’s 36th Annual Conference

Thurs/Fri/Sat 5-7 October 2023

Jakarta, Indonesia

Hosted by AATI, Indonesia

STA Monthly Meeting (Oct 2023)

Tuesday 10 October 6.30pm

One Moorgate Place Speaker to be confirmed

STA Diploma Part 1 Course (online)

Wednesday 11 October

STA Monthly Meeting (Nov 2023)

Tuesday 14 November 6.30pm Via Live Webinar Speaker to be confirmed

STA Diploma Part 1 Exam (online)

Monday 4 December

AGM and Christmas Party

Tuesday 12 December 6.30pm One Moorgate Place

Joint STA/ACI/The Broker Club Market Outlook Panel

Tuesday 9 January 6.30pm One Moorgate Place

STA Diploma Part 2 Course (online)

Wednesday 10 January

STA Monthly Meeting (Feb 2024)

Tuesday 13 February 6.30pm Via Live Webinar Speaker to be confirmed

STA Diploma Part 1 Exam (online)

Monday 4 March

STA Monthly Meeting (Mar 2024)

Tuesday 12 March 6.30pm One Moorgate Place Speaker to be confirmed

STA Monthly Meeting (Apr 2024)

Tuesday 9 April 6.30pm Via Live Webinar Speaker to be confirmed

STA Diploma Part 2 Exam (online)

Thursday 25 April

The Education Channel

Visit Meetings on the STA website for information on monthly meetings and videos.

Month Speaker Description

June 2023

May 2023

April 2023

March 2023

February 2023

November 2022

October 2022

September 2022

Andreas Clenow

Matt Cowart

Nick Radge

Stephen Hoad

Dr Ken Long

Larry Williams

Jeff Boccaccio

Steve O’Hare

Quantitative Point and Figure Trading

Volume Prediction Methods

Trading Multiple Strategies: Diversify and Smooth the Equity Curve

Gaining an Edge in Systematic and Algorithmic Trading using Renko Charts

Fireside chat with Jeff Boccaccio

In conversation with Jeff Boccaccio

Simplifying (and Automating) Crypto in 3 Indicators

Alternative data sets

STA Library (Online and Physical)

STA UK members are eligible to join the Barbican library as standard adult library members.

The STA are delighted that STA UK members are able to access the City of London Barbican Library in-person or digitally via their Libby App. Library members can choose from a great selection of eBooks, eAudiobooks, eMagazines, eComics and music videos which can be downloaded onto many devices including tablets, mobile phone and many devices compatible with the Libby App.

As before, UK STA members wishing to become a member of the City of London libraries do this by going into the Barbican Library or another one of their libraries to join with proof of home address and ID, or you can apply for temporary online membership which will currently give access to Overdrive and Libby. If your library card has expired, then you will need to get it reinstated before you will be able to access Overdrive / Libby.

Once you have a valid membership number you can access the collection by downloading the Libby / Overdrive app to your device, search for City of London Libraries and then input your library membership number.

Over the coming months the STA and the Barbican Library are working together to add a selection of technical analysis. Meantime, UK members an explore the collection of books, and magazines

Special Journal Offer!

We have put together a great offer for you. Book onto any of our courses, including the Home Study Course, before 31 October 2023 and save £50.

Simply click HERE and enter code JNLPROMO in the coupon box to redeem your discount.

STA Diploma Part 1 Course

6x1 evening a week classes

1 evening exam preparation session

2 hour exam

Qualification accredited by CISI and IFTA

STA education: get qualified in technical analysis: Booking is well underway for the CISI and IFTA accredited online STA Diploma Part 1 and Diploma Part 2 courses. The two courses have been designed to cater for newcomers and experienced professionals who are looking to challenge themselves. They will learn to develop the methodology, tools and confidence to make better informed trading and investment decisions in any asset class, anywhere in the world.

The course takes place from October to December each year. Delivered online via live Zoom, lectures are held once a week, from 6.00pm to 7.30pm London time.

The 2023 course will start on Wednesday 11 October 2023 It costs £1,395 if booked by 30 September; £1,595 thereafter.

This course is designed for those with little or no previous experience and individuals looking to initiate themselves in the practice of technical analysis. The course will give you an introduction to technical analysis and provide you with the tools to progress to the Diploma Part 2 Course. The Diploma Part 1 schedule enables you to maximise your learning while complementing your work and home life. The course is accredited for Continuing Professional Development (CPD) by the Chartered Institute for Securities and Investment (CISI).

Programme at a Glance

• Introduction to technical analysis and comparison to fundamental analysis

• Construction and interpretation of Line, Bar, Point and Figure and Candlestick charts; introduction to HeikinAshi, Three-Line Break, Renko and Kagi charts

• Support and resistance, theory, identification, utilisation, breakouts

• Trend and return lines, where and how to draw them

• Fibonacci numbers and retracements

• Reversal and continuation patterns, target projection from patterns

• Moving averages, different types and how to interpret them

• Momentum, indicators/oscillators, relative strength, sentiment measures; definition, interpretation and use

• Dow Theory, introduction to Elliott Wave Theory - how to use technical analysis strategically

Lectures are delivered via live Zoom webinar and are fully interactive with students being able to ask questions as they would in a classroom. Any students unable to watch live will be able to catch up with a recording post event and email the STA office if any questions. They may also post questions on the STA Student Forum which will be answered by course lecturers. Students are able to gain access to lecture recordings for the duration of the course.

Dates for 2023 course are:

• Wednesday 11 October

• Wednesday 18 October

• Wednesday 25 October

• Wednesday 1 November

• Wednesday 8 November

• Wednesday 15 November

• Wednesday 22 November

The Part 1 exam will be held on Monday 4 December during the daytime.

“Very interesting first part of course. It gives the keys to understand the basics of technical analysis and to be able to talk about it with someone more experienced.”

Nicolas Dupin, EDF Trading, Student on the 2022 Diploma Part 1 Course

STA Diploma Part 2 Course

12x1 evening a week classes

Exam preparation video & guide booklet

Three-hour exam

Qualification accredited by CISI and IFTA

The course starts in January of each year and consists of 12 Wednesday online evening lectures (from 6.00pm7.30pm London time. The STA Diploma Part 2 exam is held in the April.

The 2023 STA Diploma Part 2 Course will commence on Wednesday 10 January 2023 and costs £2,199 if booked by 30 December 2023; £3,199 thereafter.

The Part 2 Course provides you with advanced professional knowledge, understanding and skills to use technical analysis as a vital investment tool or to pursue a career in technical analysis within the investment community. Basic technical analysis knowledge is a prerequisite for attending this course.

During the 12-week programme you will learn from leading experts and develop both theory and practical experience in the major techniques, analytical tools and indicators to enable you to select the most advantageous portfolios, trades, hedges and much more for your clients, your employers or your own trading systems.

The Diploma Part 2 Course provides you with a deeper understanding of technical analysis, added confidence and the capabilities to further develop your career. The course is accredited for Continuing Professional Development (CPD) by the Chartered Institute for Securities and Investment (CISI).

Programme at a Glance

• The practical application of support, resistance and price objectives by market professionals - how they build on the essential basics and add advanced techniques e.g. Fibonacci projections; working in different time frames

• Construction and advanced applications of Candlestick and Point and Figure charts, including Point and Figure moving averages and indicators.

• Advanced moving average, momentum indicator and oscillator techniques; use of market breadth and sentiment measures.

• The practical application by market professionals of Dow, Elliott Wave and Gann Theory; Ichimoku Charts; Market

Profile®; Behavioural Finance; Risk Management - and much, much more.

Lectures are delivered via live Zoom webinar and are fully interactive with students being able to ask questions as they would in a classroom. Any students unable to watch live will be able to catch up with a recording post event and email the STA office if any questions. They may also post questions on the STA Student Forum which will be answered by course lecturers. Students are able to gain access to lecture recordings for the duration of the course.

Dates for the 2024 course will be as follows:

• Lecture 1 (Wednesday 10 Jan)

• Lecture 2 (Wednesday 17 Jan)

• Lecture 3 (Wednesday 24 Jan)

• Lecture 4 (Wednesday 31 Jan)

• Lecture 5 (Wednesday 7 Feb)

• Lecture 6 (Wednesday 14 Feb)

• Lecture 7 (Wednesday 21 Feb)

• Lecture 8 (Wednesday 28 Feb)

• Lecture 9 (Wednesday 6 March)

• Lecture 10 (Wednesday 13 March)

• Lecture 11 (Wednesday 20 March)

• Lecture 12 (Wednesday 27 March)

The Diploma Part 2 Exam will take place on Thursday 25 April (daytime).

“Good course, with good content. A helpful team should you need assistance.”

Rosie Fox, ADM, Student on the 2023 Diploma Part 2 Course

Balance professional development and your personal life with the STA Home Study Course©

Why purchase the Home Study Course?

The world-class e-learning Home Study Course (HSC) © is written by leading industry practitioners, making it one of the best online products available on the technical analysis market. Whether this is your first introduction to technical analysis, you want to refresh your existing knowledge, or you wish to become a qualified technical analyst, the STA offers a tailored Home Study Course as part of our portfolio of world respected courses preparing students for our internationally accredited STA Diploma qualification.

You can learn from the comfort of your home at times that best suit you. Although website based, it is fully downloadable and may be used online or offline via PC, Mac, iPad or Android machines.

What will it cover?

• The syllabi for both STA Diploma Part 1 & Part 2 examinations

• 15 in-depth subject teaching units

• Exercises to self-test progress

• Exam preparation module & video

• Advice on report writing.

...find out more here

Since the HSC is International Federation of Technical Analysts (IFTA) syllabus compliant it can also be used to prepare candidates for both the IFTA CFTe I and II examinations.

Who is the course for?

The course is intended for individuals who want to use technical analysis in a professional manner or who want to become a qualified technical analyst and advance their career. Enrol and start studying now!

For more details click below or contact the STA office on +44 (0) 207 125 0038 or info@technicalanalysts.com

When would you like to start?

Learn at your own pace rather than in a classroomthe HSC course is designed for those who need a truly part-time study option with maximum flexibility!

Congratulations to the latest STA Diploma MSTAs

Distinction

Antreas Aletraris

Joel Burke

Muhammad Hanis Bin Zulkiflee

Nils Kujath

Ramzi Abou Abdallah

Theodoros Theodorou

Pass

Adrian Dacruz

Ahmad Fauzan Amrulla

Anastasis Xynaris

Andrew Woods

Badr Almeeman

Barney Fountain

Charlie Ablett

Christopher Colley

Edward Wilson

Eve Danbury

George Steel

Harry Springthorpe

Hugo Bromell

James Dorsey

Jordi Den Hartog

Josh Thomas

Josh Livett

Michael Roby

Nicol Rainy-Brown

Nicolas Dupin

Nik Mohd Radzi

Nik Wan

Peter Bryant

Petros Steriotis

Pyae Phyo Hein

Rosie Fox

Sean Lunn

Walid Koudmani

Wei Terd Phang

Zac Ellis

STA Executive Committee

Please keep the articles coming in!

The success of the Journal depends on its authors, and we would like to thank all those who have supported us with their high standard of work. The aim is to make the Journal a valuable showcase for members’ research - as well as to inform and entertain readers.

Keep up to date with the conversation by joining us on:

Eddie Tofpik MSTA, ACI-UK, ACSI Chair

David Watts BSc (Hons) CEng MICE MIWEM MSTA Systems and Website Specialist

Richard Adcock MSTA Vice Chairman & Co Secretary

Mark Tennyson d’Eyncourt FSTA Programmes

Jeff Boccaccio MSTA Director

Karen Jones BSc FSTA Treasurer

Executive Commitee on STA website

BA MA MSTA Head of Marketing

STA Advertising Rates 2023/24

The Society of Technical Analysts Journal The Market Technician is a bi-annual publication, published in pdf format only. The STA will accept advertisements in this publication if the advertising does not interfere with its objectives.

The appearance of advertising in the Market Technician is neither a guarantee nor an endorsement by the STA.

Circulation

The Market Technician has a circulation of approximately 1,500. Readership includes technical analysts, traders, brokers, dealers, fund managers, portfolio managers, market analysts, other investment professionals and private investors.

Advertising policy

Advertising is subject to approval by the STA Journal Committee. All advertisements must be non-discriminatory and comply with all applicable laws and regulations. The STA reserves the right to decline, withdraw and/or edit at their discretion.