Market Technician No 90 - March 2021

Market Technician Issue 90 - March 2021

The Journal of the Society of Technical Analysts

THEN AND NOW IN MEMORIAM: PHILIP GRAY STA CHRISTMAS QUIZ

PG.06

PG.09

PG.08

11

25

51

56

RESEARCH

RESEARCH

ANALYST FOCUS

BOOK REVIEW

Cluster-based Feature Selection

Confluence and Correlation = Confidence

What moving averages do you use and why?

Unknown Market Wizards, Jack D Schwager

Xin Man and Ernest P. Chan

Ian Coleman, MSTA

Karen Jones, FSTA

Jeff Boccaccio, MSTA

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Contents FOREWORD Editor's letter Greetings from the Chair Then and Now - Photo Gallery

04 05 06

NEWS STA Christmas Quiz In Memoriam: Philip Gray Introducing our new Compliance Officer

08 09 10

RESEARCH Cluster-based Feature Selection Xin Man and Ernest P. Chan Not a major top for Nasdaq-100 Bruno Estier CFTe Confluence and Correlation = Confidence Ian Coleman MSTA What’s Wrong with Charting?

Clifford Wicken Tendency Forex System: A Backtestable Indicator Yue Wang ANALYST FOCUS Head and shoulders above Ed Blake MSTA What moving averages do you use and why?

Karen Jones FSTA BOOK REVIEW Unknown Market Wizards by Jack D Schwager Jeff Boccaccio MSTA THE STA Benefits of STA Membership STA Calendar 2021 The Education Channel STA Library Bronwen Wood Memorial Prize 2020 Congratulations! Latest STA Diploma MSTAs The STA Executive Committee STA Advertising Rates 2021

11 23 25 32 36

49 51

56

58 59 59 60 61 63 64 65

Disclaimer: The Society is not responsible for any material published in The Market Technician and publication of any material or expression of opinions does not necessarily imply that the Society agrees with them. The Society is not authorised to conduct investment business and does not provide investment advice or recommendations. Articles are published without responsibility on the part of the Society, the editor or authors for loss occasioned by any person acting or refraining from action as a result of any view expressed therein.

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FOREWORD

Editor's Letter

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

Tedious and sad, but the March 2020 edition of the Market Technician came out just before Britain went into lockdown.

Now look where we are, with a terrifying tally of sick and dead, unemployed, bankruptcies and stressed people. I count my blessings as I can work from a comfortable home, my family have not caught the virus, yet I do miss seeing my friends.

So much of life is currently carried out virtually, monthly STA meetings on Zoom - including a very successful Christmas Quiz (pg.08) - and January’s traditional panel discussion hosted in conjunction with the Association Cambiste Internationale, now known as ACI Financial Markets Association.

In terms of the innovative use of modern technology IFTA’s annual meeting organised by Germany’s VTAD, Vereinigung der Technischer Analysten Deutschlands e.V. takes the crown.

Organised via a Facebook group, they had 24 speakers from all over the world, speaking for 24 hours on 24 October 2020. Keeping strictly to time, the audience - which I believe was far larger than that at most real events could watch for free and post written questions for speakers to address if they had the time. A real tour de force. A date for your diaries: IFTA 2021 should be in Philadelphia from 7-9 of October 2021.

The STA Diploma courses, Part 1 and Part 2, are also only online at the moment, with students able to interact with the teacher live during lessons.

I’m told the format suits many, so much so that last year there were not one but two Bronwen Wood Memorial prize-winners. The final exam was invigilated online (an idea for A levels?), with marks above 90% cent needed to have a chance at this prestigious prize.

Well done Abdullah Abasssi and the aptly named Victoria Scholar (pg.00).

A new first for the STA is our compliance officer, Vince Harvey.

Aiming to back up our credentials where we already have a longer track history than many other professional societies and associations - you can read the interview with him in this issue.

Research papers and articles in this Market Technician #90 lean towards the mathematical end of the spectrum - perhaps reflecting current thinking on the subject and the availability of new tools. My gut feeling is that these pieces will resonate with our younger members, inspiring them to do further research of their own. For those of you thinking of submitting a piece, cut-off dates for publication are: 31 December (March issue) and 30 June (September issue).

Our first article by Xin Man and Ernest Chan (pg.11) looks into the way machines can learn, and which features might have the biggest impact on the success of the trading system.

Clifford Wicken’s article (pg.32) also alludes to pools of information and Artificial Intelligence. In his Head and Shoulders article, Ed Blake, (pg.49) whose style is chatty and believable, claims he has a ‘mathematical/ analytical nature’. Ian Coleman’s article (pg.25) focuses on foreign exchange, where he is frank and clear, and Yue Wang (pg.36) has been trading foreign exchange for his own account for 10 years now. Bruno Estier (pg.23) on the Nasdaq index has the insights one would expect from a man who has been at the top of the technical analysis industry for a very

long time, via SAMT (the Swiss market technicians’ society) and IFTA.

Sadly, and very recently, Philip Gray FSTA, (above) who was instrumental in setting up today’s STA, died.

I’ve met him several times, and he was often asked to speak at our summer party monthly meetings (2015 and 2018 I remember) because he was so very good. A big character, charismatic and an all-rounder, his career in finance started in the City in 1972 and ended this year. He told me once, "technical analysis has emerged from the wacko, dirty raincoats to a solid profession underwritten by increasingly sound academic principles. [TA] is a young persons’ game and has adapted to the new reality". We shall miss him. (pg.09).

If the book review (pg.56) of Jack D Schwager’s ‘Unknown Market Wizards’ has inspired you, do remember that the STA library, (pg.60) housed in the Barbican’s main library, has a copy donated to us by the author after his STA webinar interview.

Finally, the photo gallery (pg.06/07) is inspired and delightful. Well done to Patricia Elbaz for digging out the old photos. A little bit like a primary school’s ‘show and tell’ session.

You'll receive your copy of this magazine just after the Spring Equinox (Saturday 20th March this year), both events heralding happier times, new trends and possibilities. You can already see this as plants and trees are flowering or buds emerging following winter's hibernation. We should follow their example.

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FOREWORD

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Greetings from the Chair Despite the unprecedented times of the last 12 months, I am delighted that the STA remained robust and active throughout the period.

Over 2020, we saw the STA change domain name to www.technicalanalysts.com and a revamped and the launch of a modernised website. We also set up a Third Party Committee to build relationships with external organisations such as AlphaMind, CQG, TraderMade and Updata and extended our global reach with a high level international speaker programme which included Jack D Schwager, Dr Ernest Chan and David Keller.

We successfully moved all activities online including the invigilation of the STA exams and continued to white label the Home Study Course to international associations.

Tom Hicks, MSTA

By the time you read this, I hope that the we can start looking forward to a relaxation in restrictions by early summer and that a return to normality will be firmly within our sights for the Autumn. I for one cannot wait.

Best wishes Tom Hicks, MSTA

Chair, Society of Technical Analysts

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FOREWORD

Then and Now Patricia Elbaz, Richard Adcock & Katie Abberton have taken a walk down STA and IFTA memory lane and I am sure you will agree that some of these photos are head and shoulders above the rest!

There does seem to be strong support for my view that apart from minor corrections, we all look the same! We hope you enjoy the photo gallery and look forward to meeting up face-to-face soon.

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NEWS

Christmas Quiz goes off with a bang, and a lot of tricky questions!

The year 2020 has been like no other - not that I can remember - and the festive season was too.

Usually a time when corporate and social entertainment goes into full swing, the pandemic put paid to all that. Well, not exactly.

Keen to engage their working from home employees, and foster a spirit of international cooperation, large firms became creative with HR suggestions from painting classes to magic shows, chocolate tastings courtesy of Hotel Chocolat, to ‘parties’ for thousands via Zoom.

Unsurprisingly, the upmarket vintner Berry Bros & Rudd found itself almost fully booked for wine tastings over Christmas.

According to their events planner Katie Daniel ‘partygoers’ were delivered a corkscrew, glasses and up to three vintage wines only three because ‘‘any more than that on a weekday evening is quite punchy’’.

Not to be outdone, the STA’s December monthly meeting (8th December at 18:30) included a general knowledge quiz run by QQQ Master Lesley-Anne Brewis. She’s been running them for 17 years, following a virtual format only since April, and ‘‘it’s been our busiest year yet!’’ Members had been asked to arrange themselves into teams, coming up with appropriate or witty names. Some were both: Pointless Figures and Anal Technical Analysts. Others neither: Ellie Nellies and Bolly Banders. Other bland: MMS Squad and Edu Stars (to which Clive Lambert, who was on this team, piped up saying: ‘‘see what happens when you let a German (Axel Rudolph) choose the quiz team name’’).

Divided into four sections with about 10 questions in each, the variety of subjects was really something, ranging from what is -275 degrees Celsius and wine and food puzzlers to geography and the Book of Kells.

For those struggling with the technology, there was a Help button so participants could get in touch with the quiz master. Team members could then move into their ‘breakout rooms’ - something I haven’t done as yet - to confer over their answers. The final round introduced penalty points for wrong answers.

Ending promptly at 8pm as advertised, the top three winning teams were too close to call with 35, 36 and 37 points apiece. In ascending order: MMS Squad, Anal Technical Analysts and Pointless Figures. Well done all!

TEAM NAME

SCORE

RANK

Pointless Figures

37

1

Anal Technical Analysts

36

2

MMS Squad

35

3

STA Edu Stars

28

4

Bolly Banders

25

5

Ellie Nellies

23

6

As an aside, I’d add that it was interesting to see the changes that working from home had wrought on some STA members, and also to see them in their native habitat.

Nicole Elliott FSTA

Editor, Society of Technical Analysts

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NEWS

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In Memorium: Philip Gray Philip Gray became Chairman of what was then the Association of Chart and Technical Analysts (ACTA) in 1986 and originated and steered the concept of the change to the Society of Technical Analysts (STA), to be a company limited by guarantee. He was the first STA Chairman. To further enhance the professional nature of the STA, he and the then Education Secretary, Bronwen Wood, initiated the Diploma in Technical Analysis and courses to prepare candidates for the examination.

For these achievements he was created one of the first Fellows of the STA. Philip was a frequent speaker at technical analysis conferences over the years and no one who heard him is likely to forget his delighted cry off ‘and off to the races’ when he found a chart he especially liked! He had international experience in all aspects of investment banking, especially investment management and stock broking. Philip had also been a director of various companies listed on the UK, Zurich, Frankfurt and Johannesburg stock exchanges. In addition, he was at one time the Chairman of the Hong Kong Society of Investment Analysts.

A personal memory from David Charters: Philip was a big man with a big personality, always making people notice his presence in a room. He was a talented fund manager with GT Management and his GT European Fund was a great success. I recall a trip to Frankfurt with him and others when the GT Germany Fund was launched and his impish sense of fun transcended a long and busy day.

We were to be treated to a short river cruise with lunch but en route there was an announcement over the plane's system that the frivolous part of the

Philip's enthusiasm for transforming the old ACTA into what is today a highly professional organization, will serve all of us well for a very long time.

day had been scrapped and that the German Finance Minister would, instead, be analysing in fine detail the state of the economy and the present state of the money supply. Sighs all round! Half an hour later were were boarding a river cruiser for the most magnificent meal.

Philip was subsequently moved to Hong Kong by GT and I visited him a couple of times. He organized the local GT office and made enormously valuable contacts for the company. He was never one to do things by halves.

He was also a great supporter of the Munich Beer Festival and always timed

his visits to Europe to coincide with both that and the annual Cambridge conference, where he was a regular speaker. Life in the bar, at this 3-night residential event, was never dull when he was there.

Philip's enthusiasm for transforming the old ACTA into what is today a highly professional organization, will serve all of us well for a very long time. I last saw Philip at a Fellows' lunch, organized by the STA a few years ago. I was always happy that he was enjoying his retirement in France with his family.

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NEWS

Introducing our new Compliance Officer: Meet Vince Harvey, Compliance Cubed Ltd Interview with Richard Adcock MSTA STA Company Secretary side of the regulators.

Richard: Delighted to meet you Vince and to have you join the STA Ethics and Compliance Committee as our Compliance Officer. Tell us a little about yourself.

Vince: I’m married to Brigitte and we have three sons, three daughters in law and five grandchildren. I have worked in financial services pretty much since I graduated from university and in that time have worked for a range of businesses, moving into a compliance role in 2000. Compliance Cubed was set up in 2013 and keeps me very busy, fortunately. I am looking forward to contributing to the discussions in the Ethics and Compliance Committee and hope that I am able to add some value.

Richard: What sort of clients do you advise?

Vince: Throughout my career, the word diversification has been central to many conversations. Having been made redundant four times previously and found new employers, when it happened the fifth time, I finally got the message that I should do something for myself. While many of my clients are investment firms, I also work with some insurance brokers, payment-service businesses, employee-benefits advisers, credit brokers and mortgage advisers. This provides a varied workload and some protection for the business if one sector hits a difficult patch.

Part of my work has been to help new firms obtain FCA authorisation but an increasing proportion of my time is now spent advising existing authorised businesses on how to stay on the right

Richard: What do you think are the biggest challenges facing organisations when it comes to navigating the regulatory requirements?

Vince: The issue I hear most often is about moving goal posts - it is difficult to run a business as well as monitor regulators’ websites to keep on track. The other is the sheer volume and ‘legalese’ - I mustn’t complain though because, if understanding the rules was easy, firms wouldn’t need compliance consultants.

Richard: Will there be an end to regulatory ‘creep’?

Vince: Unfortunately, not for the foreseeable future! The UK has brought into UK legislation all of the previous EU regulations, but we shall see in 2021 and beyond whether our Government tries to maintain equivalence or allow financial services regulations to diverge. It’s possible, given the volume of financial services activity with other European countries, that we will attempt to follow EU developments - without a seat at the table in determining what shape those developments take. Alternatively, we could look to strike out with our own set of rules and seek to compete globally. Either way, our rules will continue to change.

Richard: As a new ‘normal’ begins to emerge, what can companies do to ensure they are Covid proof?

Vince: Having a clear business plan is a good start - too many businesses are ticking along doing the same things they have done for years. Many people have said to me that virtual meetings were thought to be likely to be normal in around five years - what Covid has done is accelerate their acceptance.

The ability to communicate and collaborate as a team has been challenged but businesses have

found ways to make it work. With a clear business plan that is effectively communicated to staff, new ways to build teams will emerge and new technologies will become available.

The FCA, for example has, been talking about innovation as the key to reaching a wider audience - it has calculated that in the UK there are around 7.5m people with investible assets of £10,000 or more that are sitting on deposit. Individually, those clients aren’t attractive to mainstream advice firms but - with growing acceptance of virtual interactions and online services - this is a market that could become more attractive.

Resilience is another area which the FCA has been emphasising in its communications. Having enough cash to weather storms is one thing, but making sure that systems are robust and that the right people are engaged requires thought on what the new ‘normal’ could look like.

Richard: What impact do you think leaving Europe will have on the regulatory landscape?

Vince: As indicated in an earlier answer, the impact depends on the political lead provided; our regulators will have to implement the will of Parliament.

As I write, the terms of our relationship after the transition are still to be agreed and I have little confidence that it will be a ‘good’ deal. The EU cannot appear to be generous to the UK as that would cause political issues with other nations. My guess is that, in financial services, we will adopt equivalence (maybe not officially) and so our rules will evolve as though we were still in Europe.

Richard: Thank you, Vince, for your time. We look forward to utilising your valuable professional expertise.

For more information on Vince Harvey visit Compliance Cubed Ltd

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NEWS

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Cluster-based Feature Selection Abstract Feature importance in machine learning shows how much information a feature contributes when building a supervised learning model, in order that we can exclude uninformative features from the predictive model (feature selection). It also improves human interpretation of the resulting model. Recently, Man & Chan (2021) compared the stability of features selected by different methods such as MDA, SHAP, or LIME when they are subjected to the computational randomness of the selection algorithms. In this article, we study whether the cluster-based MDA (cMDA) method proposed by López de Prado (2020) improves predictive performance, feature stability and model interpretability. We applied cMDA to two synthetic datasets, a clinical public dataset and two financial datasets. In all cases, the stability and interpretability of the cMDA-selected features are superior to MDA-selected features.

Xin Man Xin Man works as a quantitative research consultant for PredictNow.ai. and QTS Capital Management, LLC. She holds a masters degree in Financial Mathematics from McMaster University, Canada.

Ernest P. Chan Dr Ernest Chan is the Managing Member of QTS Capital Management, LLC., a commodity-pool operator and trading advisor specialising in crisis alpha and machine learning. He also runs PredictNow.ai, A financial machinelearning start-up.

1. Introduction Financial investors are often reluctant to trust machine-learning algorithms because of their “black-box” nature: there is no transparency and no justification of how they arrive at their predictions. Feature selection is a technique that attempts to improve the transparency and interpretability of machine learning models by ranking the importance of the features used. However, feature selection algorithms often suffer from a stability problem, as discussed by Man & Chan (2021). As we change the random seed in training a machine learning model, the top selected features may also change, reducing interpretability. In this paper, we investigate a clusterbased technique pioneered by López de Prado (2020) to see whether it can improve stability and interpretability of the important features.

Clustering is an effective unsupervised method for grouping features so that the those in the same group are more like each other than those in other groups. A good clustering algorithm can minimise the between-clusters similarity and maximise the within-cluster similarity. Popular clustering approaches include K-means and hierarchical clustering. The K-means algorithm requires the user to fix the number K of clusters. It is intended for situations in which all features are numerical and Euclidean distance is chosen as the metric. By contrast, hierarchical algorithms do not require K to be predetermined and can also adapt to categorical features and non-Euclidean metrics. Clusters at each level of the hierarchy are created by merging clusters at a lower level and we end up with a single cluster containing all features at the highest level. More details regarding clustering algorithms can be seen in Hastie, Tobshirani and Friedman (2009) and López de Prado (2020).

With a clustering algorithm, the full feature space is split into multiple non-overlapping clusters. Using the rank-based importance score, we calculate an importance score for each cluster. Those clusters with scores higher than a chosen threshold can be selected for training a machine learning model. Clustering features with ‘similar’ information is a straightforward way to isolate irrelevant features. Features that do not belong to any important clusters can be dropped. The chance of losing useful information can also be reduced as any features belonging to a significantly informative cluster will not be discarded.

Clustering improves interpretability as a cluster offers a higher level of abstraction.

For example, in finance, we may find that volatilities computed using three-, five-

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and seven-day lookback periods are in the same cluster. That clearly identifies the cluster as “historical volatility”. In addition, clustering may improve the stability of the important features, since their relative importance within a cluster won’t cause some of them to be dropped, and it is less likely that the importance rank of a whole cluster of features will change drastically if we use a different random seed. As discussed in Man & Chan (2021), stability of features improves interpretability.

The rest of this article is organised as follows: • Section 2 introduces the cMDA algorithm and its use of hierarchical clustering to compute importance score at the cluster level; • Section 3 compares the predictive performance of MDA vs cMDA using two synthetic datasets; • Section 4 compares the predictive performance of MDA vs cMDA on two popular datasets, including a financial dataset that uses technical and fundamental indicators to predict the S&P 500 stock index excess returns.

• Finally, the algorithm is applied to our proprietary trading strategy returns dataset to see if it can identify interpretable clusters and improve the strategy’s performance. We find high stability and interpretability of the selected clusters in these financial applications, which should make machine learning employing this technique appealing to investors.

2. cMDA using hierarchical clustering Cluster-based feature selection consists of two steps: clustering features and ranking clusters. To begin clustering features, we define a distance matrix from the pair-wise correlations of the features Di,j)= √(½(1-pi,j). As discussed in López de Prado (2020), the ideal distance matrix should be based on one of the information-theoretic metrics, but the correlation matrix is still the one most commonly used in finance. The selection of distance matrix won’t affect the subsequent procedures, though it may affect the predictive performance.

Next, a clustering method should be used to split the feature set into smaller sets according to the distance matrix. K-means and hierarchical algorithms are popular clustering methods. The K-means clustering algorithm fixes the number K of clusters and the observations are assigned to each cluster based on the distance to the centre point. By contrast, hierarchical clustering works in a ‘bottom-up’ manner. Starting from the bottom, every single feature is taken as a cluster. As we ascend to the next level, the two closest clusters are merged. At the end of the process, all the features will be included in a single cluster. We then cut the hierarchical tree at the proper level to create an optimal set of clusters. The outputs of hierarchical clustering have more structure and are more informative than the unstructured set of flat clusters returned by the K-means algorithm.

In the following analysis, we use the hierarchical algorithm as the clustering method: The number of clusters is determined by finding the number (from 2 to the number of samples minus 1) that maximises the “clustering quality” q. The clustering quality is related to the silhouette coefficient (Rousseeuw, 1987) which represents how similar a sample is to samples in its own cluster compared with those in other clusters. For the data sample i, its silhouette coefficient bi-ai is defined as Si , where ai is the average distance between i and all other samples in the same cluster, and bi is the max {ai-bi average distance between i and all the samples in the nearest cluster of which i is not a member. Then for a given partition, the E[S] measure of clustering quality q is defined as q= Std[S] , where E[S] and Std[S] are the mean and variance of silhouette coefficients for all samples in the training data.

After finding the optimal number of clusters based on maximising q and assigning the features to each cluster, the feature importance algorithm is performed on the clusters rather than individual features. This means that during MDA feature selection, all the features in a cluster are permuted at the same time, as described in López de Prado (2020). Since this article focuses on how the clustering method can add value to model performance rather than comparison across different feature importance algorithms, we omit presenting the implementation of clustered LIME and SHAP and only discuss clustered MDA. If a feature is isolated by a cluster, MDA and clustered MDA are the same. The feature importance is measured by the rank-based score proposed by Man & Chan (2021). As the importance score of a cluster is determined by the mean of the importance scores of the features contained in it, a large cluster won’t necessarily be more important than a smaller cluster with fewer features.

3. Predictive Performance on Synthetic Data To test how the proposed method responds to synthetic data, we construct a dataset composed of both informative and noisy

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features. As defined by López de Prado (2018), we have:

1) informative features that are used to determine the label; and 2) noisy features that bear no information on determining the labels and are drawn from standard normal distributions.

According to the descriptions in scikit-learn.org, the informative features are drawn independently from a standard normal distribution. However, to introduce clusters into our data, we first randomly draw multiple ‘centroids’ and generate informative features around them from normal distributions centred around these centroids. We provide a detailed description of the algorithm for creating these clustered features, and how they map to the labels, in Appendix 2.

The dataset has 1,000 samples and 40 features comprised of 20 informative and 20 noisy features. These 20 informative features form three clusters with six or seven features in each cluster.

The selected features are analysed in Table 1. From Panel A, cMDA tends to keep all the informative features but it also includes a small number of noisy features. In contrast, MDA chooses far fewer features but filters out all the noisy features. The downside is that it also drops a lot of informative features. We may figuratively say that cMDA has a higher recall but lower precision than MDA. Denote ‘I_m_n’ as the m-th informative feature which is assigned to n-th synthetic cluster. For example, ‘I_20_2’ means the 20th informative feature which belongs to the 2nd synthetic cluster. ‘N_m’ represents the m-th noisy feature. Panel B shows that all the features in the ‘0’ synthetic classification cluster are put into the most important selected cluster. The informative features in ‘1’ and ‘2’ synthetic clusters are not recovered by the algorithm since each of them has two features in the same selected cluster and the rest of their features are grouped into another selected cluster. Panel C shows the synthetic regression data selects all the informative features of their original clusters to form the top two most important selected clusters, but each cluster also includes one or two noisy features.

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Table 1: Selected Features on Synthetic Datasets Panel A: Number of informative features selected by cMDA and MDA Synthetic Classification

Synthetic Regression

cMDA

All 20 informative features, 1 noisy feature

All 20 informative features, 5 noisy features

MDA

Only 11 informative features, 0 noisy feature

Only 9 informative features, 0 noisy feature

Panel B: Selected clusters in classification data

Panel C: Selected clusters in regression data

Cluster Importance Score

Features

Cluster Importance Score

Features

0.328

‘I_2_2’ ‘I_3_1’ ‘I_4_0’ ‘I_6_2’ ‘I_8_0’ ‘I_9_0’ ‘I_12_0’ ‘I_14_0’ ‘I_16_0’ ‘I_17_0’ ‘I_19_1’

0.256

‘I_0_1’ ‘I_3_1’ ‘I_11_1’ ‘I_13_1’ ‘I_15_1’ ‘I_18_1’ ‘I_19_1’ ‘N_10’ ‘N_16’

0.212

‘I_1_2’ ‘I_2_2’ ‘I_5_2’ ‘I_6_2’ ‘I_7_2’ ‘I_10_2’ ‘N_2’

0.116

‘I_4_0’ ‘I_8_0’ ‘I_9_0’ ‘I_12_0’ ‘I_14_0’ ‘I_16_0’ ‘I_17_0’ ‘N_6’ ‘N_12’

0.164

‘I_0_1’ ‘I_1_2’ ‘I_5_2’ ‘I_7_2’ ‘I_10_2’ ‘I_11_1’ ‘I_13_1’ ‘I_15_1’ ‘I_18_1’ ‘N_2’

We can see that cMDA does a good job of grouping together related informative features, at least for the top cluster. This clustering improves human interpretability, reduces the substitution effect and can potentially improve predictive accuracy.

For most of the datasets in this paper, the data is split into training sets, validation sets and testing sets in the ratio 60:20:20 (some datasets are differently split and this is noted in the text). The model is trained and features are clustered in the training set. The clusters are ranked in the validation set and then the features in the top clusters with above-average importance scores are selected. This would be just the top cluster in both the synthetic classification and regression examples. Using the selected features, the prediction performance is evaluated on the testing set. In Table 2, we compare the out-of-sample results based on the full feature set versus the selected feature subset. The cMDA approach outperforms the full set in both datasets. cMDA also outperforms MDA in the classification dataset but underperforms it in the regression dataset.

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Table 2: Prediction Performance Comparison on Synthetic Datasets Synthetic Classification

Synthetic Regression

F1

AUC

Acc

MSE

MAE

R2

cMDA

0.975

0.998

0.973

545460.60

585.39

0.9626

MDA

0.960

0.996

0.957

436676.44

510.08

0.9700

Full

0.975

0.995

0.973

718870.06

662.79

0.9607

Given that the predictive performances of cMDA and MDA are close, cMDA should be favoured given the increase in interpretability and, as we shall see later, the stability of the selected features.

4. Cluster Interpretability and Stability First, we take the Breast Cancer dataset ¹ as an example. This dataset is a binary classification dataset with target variables showing whether the cancer is malignant or benign, and 30 features which are characteristics of each of 569 medical images. The clustering algorithm groups those 30 features into eight clusters. The cluster importance scores, and the features within them, are listed in Table 3. Since these clusters have clearly human-interpretable themes, we also apply a descriptive “Topic” to them.

Table 3: Feature Clustering for Breast Cancer Dataset Topic

Cluster Importance Scores

Features

Geometry summary

0.360

‘mean radius’ ‘mean perimeter’ ‘mean area’ ‘mean compactness’ ‘mean concavity’ ‘mean concave points’ ‘radius error’ ‘perimeter error’ ‘area error’ ‘worst radius’ ‘worst perimeter’ ‘worst area’ ‘worst compactness’ ‘worst concavity’ ‘worst concave points’

Texture summary

0.174

‘mean texture’ ‘worst texture’

Geometry error

0.112

‘compactness error’ ‘concavity error’ ‘concave points error’ ‘fractal dimension error’

¹ The data is taken from https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic).

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Smoothness error

0.092

‘smoothness error’

Symmetry error

0.062

‘symmetry error’

Texture error

0.056

‘texture error’

Symmetry summary

0.055

‘mean symmetry’ ‘worst symmetry’

Fractal dimension

0.049

‘mean fractal dimension’ ‘worst fractal dimension’

Smoothness summary

0.042

‘mean smoothness’ ‘worst smoothness’

As the scores of clusters with topics ‘Geometry summary’ and ‘Texture summary’ are greater than the average of the 17 features, these two clusters are selected. While individual feature importance results give ‘worst concave points’, ‘worst perimeter’, ‘worst radius’, ‘mean concavity’, ‘area error’ and ‘worst texture’ as the most important features, we can easily see here that geometry of the tumour is the most important cluster, while texture is the second most important.

The rank-based ‘instability’ of the cluster j is defined as its variance: Vj = Var(r1j, ... , rnj).

If we apply this only to the top k clusters, the ‘instability index’ is defined as: I = √

V(1)+ ... +V(k) k

'

where V(k) is the variance of the kth-most important cluster. According to Figure 1, the instability index increases with k and the most important cluster (Geometry summary) is ranked in 1st place for all 100 runs and the second most important cluster (Texture summary) is ranked in 2nd place for 99 runs. Notably, the features selected from these two clusters are almost always positioned in the top.

Figure 1: Instability Analysis for Breast Cancer Dataset

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After selecting the features from the top two clusters, we train a new random forest on the combined training and validation set and use that to make predictions on the testing set. We can see that cMDA has the best out-of-sample performance on AUC but that F1 and Acc underperform non-clustering methods.

Table 4: Prediction Performance Comparison on Breast Cancer Dataset F1

AUC

Acc

cMDA

0.953

0.990

0.953

MDA

0.981

0.982

0.974

Full

0.954

0.980

0.939

Next, we conduct the analysis on predicting S&P 500 excess returns using economic factors, as discussed in Man and Chan (2021). The data ranges from January 1945 to December 2019. Excess return is defined as the monthly SPX index return minus the risk-free rate. The features are a set of fundamental and technical factors that include dividend price ratio (d/p), dividend yield (d/y), earning price ratio (e/p), dividend payout ratio (d/e), stock variance (svar), book to market (b/m), net equity expansion (ntis), T-Bill rate (tbl), long term yield (lty), long term return (ltr), term spread (tms), default yield spread (dfy), default return spread (dfr) and inflation (infl). Fractional differentiation (López de Prado, 2018) is applied to all these features prior to the machine learning process. The clustering algorithm groups these features into two clusters as shown in Table 5.

Table 5: Feature Clustering for S&P Dataset Topic

Cluster Scores

Features

Fundamental

0.63

d/p, d/y, e/p, d/e, svar, ntis, ltr, tms

Technical

0.37

b/m, tbl. Lty, dfy, dfr, infl

As these clusters are highly human-interpretable, we again apply descriptive topics to them. The ‘Fundamental’ cluster contains 8 features and has higher importance score. The ‘Interest rate’ cluster contains 6 features. This cluster can also be called the ‘unimportant’ cluster, since we only have two clusters, and is not selected to train the final random forest model.

Figure 2 shows these two clusters are very stable. The instability index remains zero when involving either one or two clusters.

‘Fundamental’ and ‘Technical’ clusters are constantly ranked in the first and second places for all 100 runs.

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Figure 2: Instability Analysis for S&P Dataset

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We split the data into training, validation and testing sets with the periods January 1945-December 2005, January 2006-December 2015 and January 2016-December 2019, respectively. The out-of-sample prediction performance on the testing set is summarised as follows:

Table 6: Predictive Performance Comparison on S&P Dataset F1

AUC

Acc

cMDA

0.558

0.561

0.537

MDA

0.551

0.514

0.533

Full

0.432

0.461

0.483

The metrics F1 score, AUC score and Accuracy obtained with the testing set are shown in Table 6. We can see that cMDA outperforms MDA in out-of-sample prediction on all metrics for this dataset.

Application to Trading Strategy Meta-Labelling In this section, we apply clustering-based feature selection to a dataset with the labels equal to the sign of actual historical returns of our proprietary Tail Reaper trading strategy. We want to see if this algorithm can select stable features and improve the trading performance. This application of financial machine learning is termed “meta-labelling” (López de Prado, 2018). ² See www.qtscm.com/accounts for more details.

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The data is from January 2013 to June 2020 with 160 features. We split the data into training/validation/testing sets over periods 2013-2017, 2018-2019 and 2020. cMDA groups the 160 features are into 44 clusters. Among them, eight clusters containing 81 features with above-average importance scores are selected to train a new random forest model. Since the features are proprietary, we do not display the clusters that identify them. Suffice to say that the top two clusters are highly human-interpretable, while the lower ranked clusters are mixed bags of disparate features.

From Figure 3, the instability index increases with number of clusters and the most and second most important clusters are steadily ranked in 1st and 2nd places respectively for all 100 runs. The third most important cluster is not as stable as the first two.

Given that the third cluster is a mixed bag of features of uninterpretable theme, this isn’t a surprising result.

Figure 3: Instability Analysis for Trading data

Table 7 shows the comparisons of ‘cMDA’ with 81 features in selected clusters, original ‘MDA’ with 20 selected features and ‘Full’ features set with total 160 features. As the top two clusters selected by ‘cMDA’ are intuitively interpretable, we also show the results of ‘cMDA(top 2)’ which contains 41 features from the top two clusters. The out-of-sample (test set) performance of a predictive model based on cMDA significantly outperforms all others through F1, AUC and accuracy.

Table 7: Prediction Performance Comparison F1

AUC

Acc

cMDA

0.658

0.672

0.614

cMDA (top 2)

0.595

0.640

0.571

MDA

0.602

0.537

0.529

Full

0.481

0.416

0.414

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Conclusions Ranking a cluster is more stable than ranking a feature and such stability enhances the model interpretability. It is also easier to interpret the clusters by examining the common characteristics of the features contained within each cluster. For example, for the S&P 500 excess returns dataset, we can identify the top cluster as fundamental indicators, while the second-ranked cluster as mainly technical indicators.

The clustering algorithm also improves the predictive performance over non-clustered MDA feature selection on the S&P 500 excess returns dataset and the proprietary Tail Reaper strategy returns dataset, though not on the synthetic datasets. Their predictive performances on the Breast Cancer dataset are similar.

In this article, the clustering algorithm is driven by a correlation-based metric. As the distance matrix just need to satisfy nonnegativity, identity, symmetric and sub-additivity, we may be able to improve the model performance by choosing other infotheoretic metrics which also satisfy these conditions. We also chose hierarchical clustering instead of K-means. We will discuss our reason for doing so in Appendix 1. Further work can also investigate whether clustering can improve the SHAP and LIME feature-selection methods that we compared in Man and Chan (2021).

ACKNOWLEDGEMENTS We thank Radu Ciobanu, Sayooj Balakrishnan, and Roger Hunter for many useful suggestions and technical assistance.

References • • • •

• •

Hastie, T., R. Tobshirani and J. Friedman (2009) The elements of statistical learning. 2nd Edition, Spring.

López de Prado, M. (2018) Advances in financial machine learning. John Wiley & Sons.

López de Prado, M. (2020) Machine learning for asset managers. Cambridge University Press.

Man, X. and E.P. Chan (2021) The best way to select features: Comparing MDA, LIME and SHAP. Journal of Financial Data Science, Winter 2021. DOI: https://doi.org/10.3905/jfds.2020.1.047 Rousseeuw, P. (1987) Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Computational and Applied Mathematics 20: 53-65.

Xiong, H., J. Wu and J. Chen (2008) K-means clustering versus validation measures: a data distribution perspective. IEEE Transactions on Systems, Man, and Cybernetics, Part B 39(2): 318-331.

Appendix 1: Reasons to choose hierarchical clustering K-means uses the Euclidean distance metric and results in nearly identical cluster sizes , with a limited number of clusters. By contrast, hierarchical clustering can generate many more clusters and other forms of distance metrics can be used. For example, Jaccard similarity, which measures the distance between two binary categorical variables, is not a Euclidean metric and cannot be applied to K-means clustering, but it can be used for hierarchical clustering. In the example datasets we studied, some clusters do contain more features than others. The number of features in each cluster should adapt to the nature of the features, and a tendency to produce clusters of equal size is not desirable.

Appendix 2: The algorithm for generating synthetic clusters Suppose we want to generate a dataset of n samples with K synthetic clusters, m informative features, p noisy features, we can follow the procedures below: 1. Sample k centroids independently from Uniform(-10,10); 2. The number of informative features per cluster c₁, c₂, …, c K is [m/K] or [m/K]+1; 3. For the ith cluster, independently generate ci features by sampling n times per feature from a univariate normal distribution with mean equal to the value of the ith centroid and with standard deviation as 0.5. In other words, we draw n× ci random numbers from N(ci, 0.5) to populate all features within the ith cluster for all the n samples; 4. For a classification model, randomly assign the label 0 or 1 with probability 0.5 to each of the sample. Create a random matrix Mm×m by sampling from Uniform(-1 ,1).

Form a product of M and the informative feature matrix of each class label X m×(n/2) to create MX m×(n/2). Stack the matrix of two classes to get MX m×n. In other words, we map two different linear combinations of informative features to the two class labels, where the coefficients of the linear combinations are random but fixed over the samples with the same label; 5. For a regression model, create a random matrix Mm×m by sampling from Uniform(-1 ,1). Form a product of M and the entire informative feature matrix X m×n to create MX m×n. Create a random vector βm×1 by sampling from Uniform(0, 100), and then set the label for sample n as yn×1 = (MX m×n)T βm×1. In other words, we map a linear combination of informative features to the continuous labels, where the coefficients of the linear combinations are random but fixed over all samples; 6. Add the p×n noisy features matrix by sampling from a standard normal distribution.

³ Due to the ‘uniform effect’ proposed and discussed in Xiong, Wu and Chen (2008), K-mean tends to generate clusters with relatively uniform sizes.

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Not a major top for Nasdaq-100 Introduction As the US equity market is undergoing a correction in the last three weeks and as last month we mentioned that October to April is the seasonally bullish period, a review of the leading sector of the US market, the Nasdaq100 is necessary to evaluate the strength of its uptrend.

Still in Uptrend

Bruno Estier CFTe

The Relative Strength (RS) of the Nasdaq100 versus the S&P500 (dotted green line on the upper panel) has been stalling below its July top taking the form of a rising triangle and in Oct 2020 at levels well above the low made in early September though having flattened. But it shows an uptrend In Oct as the chart does not display a lower low. Therefore it is likely that the Technology sector mainly represented within the Nasdaq100 is still a leading sector for the US equity market.

Bruno Estier is a Global Market Advisor and Technical Analyst coach in Geneva, Switzerland for professional Traders and Portfolio Managers.

Past President of the Swiss Association of Market Technicians (SAMT) for 12 years, he served also as Chairman and as Secretary on the board of directors of IFTA. Bruno founded the French Society of Technical Analysts (AFATE) in 1990. He holds the Diploma from the STA and the professional certifications from IFTA. You can find his work at Bruno Estier Strategic Technicals, www.estier.net/ bruno bruno.estier@bluewin.ch and he here shares his US equities outlook, written in November 2020 for Wealthgram.

The bullishness is not limited to a few large Technology stocks as we note that the Relative Strength of Small Caps versus S&P500 (black line on upper panel) has been bottoming and rose mid-September to October 2020, which is a classic bullish sign of widening Breadth. Thus, the pullback of the Nasdaq100 in Nov is seen as a pause in the Bull market than the beginning of a Bear market. This pullback relieves an overbought situation, which was highlighted by its rise since May 2020 between the first & second Bollinger band and by a spike in late August and a retest in October of the second upper Band. toward the Moving average 40 week (9931). The fear can be noted on the VXN (orange dotted line on the upper panel) reaching the previous spike high of September near 41.30 %.

However , volatility above 40% in the VXN is rare and often signals a nearby low on the underlying Equity index! So overall, in Nov 2020 it may well be time to be contrarian and not to panic along the classic price momentum indicators, like STOCHASTICS or MACD on the lower panel which are crossing down. Of course, the Nasdaq100 needs to display a move up to avoid breaking below the previous low of 10,677, ideally holding above 10,900 the rising former resistance trend line dating from October 2018. Such a rebound in price will validate the ranging pattern between 12,430 and 10,700, which medium term will open the door for higher prices toward 14,000. So, trend-followers beware!

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This chart of the NDX100 is represented on a log scale from September 2018 onwards in weekly candles with Ichimoku clouds. The green dotted line on the upper panel shows the Relative Strength (RS) of the Nasdaq 100 versus the S&P 500, which is ranging flat. The RS of Small caps versus the S&P 500 (the black solid line) has been rebounding since midSeptember. The volatility of the Nasdaq100 VXN (the orange dotted line) closed October 2020 near its previous high, expressing extreme bearishness short term.

On the lower panel, the MACD crossed down in mid-September 2020, though is still positive, which means the medium-term uptrend remains intact and is just pausing. The recent crossing down of STO near 50% line is a classic occurrence during a range and is not systematically a Sell signal.

The Nasdaq 100 is testing the MA 20-week for a second time, may rebound on the former rising resistance trend line

(dotted blue line) from October 2018. Only a weekly close below 10,677 would call the risk for a further decline toward the rising Moving 40-week average (9,931).

Source of data: www.stockcharts.com.

This information is being furnished to you solely for your information and as example of theoretical technical analysis and coaching and does not constitute a recommendation to purchase any security. Neither this document nor its contents, nor any copy of it may be altered in any way. This document is not directed to, or intended for distribution to or use by, any person or entity of any jurisdiction where such distribution, publication or use would be unlawful.

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Confluence and Correlation = Confidence Introduction CONFLUENCE

CORRELATION

CONFIDENCE

an act or process of merging

the process of establishing a relationship or connection between two or more things.

the feeling or belief that one can have faith in or rely on someone or something.

We all need confidence to be able to place a trade and, more importantly, to let it run its course. We have planned the trade, now we need to trade the plan. This is my approach to currency trading.

Ian Coleman MSTA Ian Coleman is an independent technical analyst. Ian started his financial career at the age of 18 working as a Junior Swiss Broker at Godsell Astley and Pearce (London).

The highlight of his career was PIA-First winning The Best Research House for FX in 2015. Ian continues to study and gains great enjoyment teaching others what he has learnt during his 30+ years in the financial markets. Ian holds the Society of Technical Analyst diploma.

A bit of history I have been active in the financial markets for over 30 years. I started as a junior broker on the Swiss Desk at Godsell Astley and Pearce at the age of 18 and worked my way through the ranks to head up the Forward Swiss desk at RP Martins at 26.

With a wife and young son, the lifestyle became too much, and I moved away from institutional broking soon after. I then immersed myself in technical analysis and it has been my passion, if not obsession, ever since.

I am fanatical about Fibonacci and strongly believe that markets do move around the sequence of numbers and percentages. Many years ago, I developed an MT4 program that would highlight the 62 EMA from various timeframes on one chart.

This was as close to 61.8 as I could get, the Golden Ratio being 0.618 and 1.618. I would then use these lines for support and resistance. The lines would move higher or lower as the price in that product fluctuated. This simple system has now been greatly expanded.

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CONFLUENCE

an act or process of merging Cypher or Symmetrical patterns The book ‘Trade What You See’ by Larry Pesavento and Leslie Jouflas started me on the path of Cypher or Symmetrical patterns.

I thank them both for their insight. We are looking at Pattern Recognition using the Fibonacci extension and retracement tools.

These patterns include: The Crab; The Butterfly; The Bat; The Gartley (222); The Three Drives Pattern; And, finally, the AB=CD pattern.

I am going to concentrate on the first four.

Fibonacci In Elliott Wave theory we concentrate on the main Fibonacci levels. These are: 38.2%, 50%, 61.8%, 161.8%, 261.8% and 423.6% (423.6% being for commodities and commodity-based currencies). In Cypher patterns we look to: 38.2%, 50%, 78.6%, 88.6%, 127.2%, 161.8%, 2.24%, 261.8% and 361.8%. We are looking for areas of confluence, the act of merging or where two or more Fibonacci levels meet.

The formation of a Cypher pattern There are four legs to the formation(s). These are the XA leg, AB, BC and CD. The final result is the creation of two triangle patterns.

The completed picture looks like a Bat extending its wings. This is where one of the formations gets its name.

The build • XA: the XA leg can be of any length. It is from this wave that we start to take our calculations.

• AB: this is our first retracement and sets the tone for the pattern.

• BC: BC moves in the same direction as XA but cannot breach level A. • CD: the CD leg is our finishing leg and completes the whole formation.

The completed pattern offers a bearish or bullish BIAS.

The Gartley (222) • XA: first leg of any length • AB: retraces 0.618% of the XA leg • BC: moves towards A but does not breach that level (common pullbacks are between 38.2 and 88.6% of leg AB) • CD: moves to 1.27% or 1.618% of leg AB AND 0.786% of XA (confluence)

Figure 1: Bearish Gartley GBPUSD eight hours

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The Crab • • • •

XA: first leg of any length AB: retraces either 0.382% or 0.618% of the XA leg BC: moves towards A but does not breach that level (common pullbacks are between 38.2 and 88.6% of leg AB) CD: moves to either 2.24% or 3.618% of leg AB AND 1.618% of XA (confluence)

Figure 2: Bearish Crab EURCHF one hour

The Butterfly • • • •

XA: first leg of any length AB: retraces 0.786% of the XA leg BC: moves towards A but does not breach that level (common pullbacks are between 38.2 and 88.6% of leg AB) CD: moves to either 1.618% or 2.618% of leg AB AND either 1.27% or 1.618% of XA (confluence)

Figure 3: Bearish Butterfly GBPUSD one hour

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The Bat • • • •

XA: first leg of any length AB: retraces either 0.382% or 50% of the XA leg BC: moves towards A but does not breach that level (common pullbacks are between 38.2 and 88.6% of leg AB) CD: moves to either 1.618% or 2.618% of leg AB AND 88.6% of XA (confluence)

Figure 4: Bearish Bat GBPJPY daily

What we can note is that the Gartley and the Bat are inside the XA leg. I call these Inside Cyphers. The Crab and the Butterfly are extended from XA. I call these Outside Cyphers. The patterns have very specific levels for the first pullback leg (AB) and the finishing leg (CD). The Gartley can only have a retracement of 61.8%, the Bat 38.2 or 50%, the Butterfly 78.6%. This defined pullback level then highlights what pattern we are possibly building. It should also be noted that the textbook rules are not to trade the formation until it is fully completed.

Fibonacci confluence areas If the confluence zone is not too wide, then I have found that to be more than ample. To outline, I am looking for a confluence area of Fibonacci levels, with the extension of the AB leg (CD) being an exhaustion level of 78.6%, 88.6%, 1.27%, 161.8%, 2.24%, 261.8% (linked to Elliott Wave) and 361.8%.

I also find that the Outside Cyphers are more beneficial on a Reward against Risk basis as the markets tend to be overbought or oversold and the reversal much stronger. I want to have power in the analysis with multiple reasons to set up my trade setup. In this case, it is the confluence area. NOTE: a cypher pattern never extends past 161.8% of leg XA. Multiple Timeframes Knowing the bias in multiple timeframes is extremely important. I do not want to be selling USDJPY from a one-hour bearish Gartley pattern only to find support from a daily bullish Bat formation. We must take note of the higher times and work lower into our intraday charts. What we want to see is a bullish formation on, say the four-hour chart, to line up with a 61.8% pullback support in the weekly chart. We are then using a confluence of Fibonacci levels to add to the trade set up bias and adding more power to the trade: EURAUD (8hr 4hr), 88.6%.

Let us not ignore the 88.6% pullback. If this lines up with Cypher pattern in a lower timeframe, then we can project that lower time frame setup into a higher timeframe target. This offers a close to entry stop setup and an aggressive potential target (example: selling GBPUSD one-hour targeting GBPUSD weekly).

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Figure 5: GBPUSD one-hour sell setup (Butterfly)

Figure 6: Potential target GBPUSD weekly

CORRELATION

the process of establishing a relationship or connection between two or more things.

There can be varying factors when we look at correlation. You might see a Bearish Outside Candle on the four-hour GBPUSD chart. This, combined with a bullish Outside Candle on EURGBP, would lead you to believe that you are seeing GBP weakness.

Two correlating facts leading to one conclusion. Going back to Fibonacci levels, but sticking with our currency examples, a 261.8% extension on GBPUSD at overbought extremes may lead us to believe that the pair may turn lower (view 1). How about the selloff stalling in EURGBP at the weekly Fibonacci support of 61.8% (view 2)? Both scenarios would give a GBP weakening bias offering correlation. What if we add another cross into the mix? GBPAUD is close to a 423.6% extension. And so on...

Major Currencies If we take the major currencies traded around the world, we have eight in total. They are: USD (US Dollar), EUR (The Euro), GBP (The British Pound), CHF (The Swiss Franc), JPY (The Japanese Yen), CAD (The Canadian

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Dollar), AUD (The Australian Dollar), NZD (The New Zealand Dollar) That means that for every single currency, we have a total of seven counter currencies to trade against. That is 27 currency crosses in total, and 28 if we include the standalone USD Index (DXY). Do we need all GBP pairs to be pointing to a weakening pound? It is true that ‘the more the better’. What if we only have five?

Example: GBPAUD and GBPCAD still look bullish (positive). This then goes against our weakening GBP view. However, this may be because commodities are looking very weak and CAD and AUD are even weaker than GBP. We would stay away from these crosses in this example and look to counter currencies that are forecast to be strong.

Prime Set Up We want to be trading a weak single currency against a strong single currency to get the best moves. The foreign exchange market is in fact a large matrix with all currency pairs competing against each other. How can we keep track of this? I use a simple spreadsheet.

Figure 7: Currency Correlation

Inputs • BUY = Buy at the market • SELL = Sell at the market • BUY D = Buy dips • SELL R = Sell rallies I am using technical analysis and Fibonacci extension or retracement tools to analyse each currency pair or cross. This then offers a bias for the BASE currency and the COUNTER currency. As you can see by the Matrix, many of the single currencies in this example are MIXED. However, we have a clear bias in the USD (4th Column). This highlights seven BUY dip calls. We also have an opposite bias on the NZD with a clear SELL rallies bias.

Trade Setup We then need to look to NZDUSD to see if we can highlight a sell trade using our bias from the Matrix spreadsheet (selling NZD and buying USD). This should also be a cypher pattern. Is this case we have a clear Bearish Butterfly:

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Figure 8: A Clear Bearish Butterfly

Conclusion The cypher patterns are our setups and offer our currency bias for the Matrix spreadsheet. Most of the time the currency cross will be in the middle of forming the pattern. In this scenario we highlight that we are looking at Selling Rallies or Buying Dips. This then offers us our BIAS for each single currency. We want a BASE currency and a COUNTER currency to have an opposite bias. We then look to that currency cross for a cypher pattern to give us our trade setup.

• Strong against Weak = BUY BASE / SELL COUNTER • Weak against Strong = SELL BASE / BUY COUNTER Confluence and Correlation gives me Confidence.

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What’s Wrong with Charting?

Introduction A definition of charting to be found on Google is “the use of charts of fundamental data to predict future trends and to guide investment strategies”. Professor J K Galbraith commented that Chartism is a “hocus-pocus of lines and areas on a chart” (see The Great Crash 1929, John Kenneth Galbraith, First published 1954) and he might have singled out moving averages. However, Professor Galbraith would surely have agreed that market participants leave their traces on share price charts – which must be the basis of charting. Yet present day investors are still confronted with this contradiction - why?

Clifford Wicken MIET Clifford S Wicken, now retired, is an electrical engineer by profession having worked for the Post Office on the design and development of its automatic letter handling machines. He is a longstanding private investor with a strong inclination to technical analysis. His circumstances have meant he has had to work entirely alone without contact with others in the investment field.

This paper uses maths to penetrate noise and thus uncover significant trends.

However, frequent tedious calculations are avoided by employing the very considerable innate computing power of the human brain. In the context of charting, noise is brought about by those looking for a quick gain, the ill-informed and dabblers generally. In the main, chartists seek to distil the longer-term trend from raw share prices by using moving averages to smooth out the noise.

Typically a 200-day moving average is used to smooth out intermediate and shortterm trends. The fundamental problem with the 200-day moving average is that it introduces a killer delay - it is no help to learn weeks after the event that the market has turned. As a palliative, 200- and 50-day moving averages are used in combination. Another feature of the 200-day moving average, which works against its use in deducing the current main trend, is that it often includes irrelevant data from the previous trend.

What is needed is a distillation of the current main trend based exclusively on data from the lifetime of that trend. It is not possible to identify a new long-term trend at its beginning because at first there will be nothing to distinguish it from noise, but with time any significant new trend will gradually become apparent. The lines and areas on a chart to which Professor Galbraith was referring were artificial constructions, whereas - to be really useful - trend-lines need to be linked to the objectives, strategies and constraints of those whose actions produce them.

Artificial intelligence, with its self-learning ability, comes to mind as a means that could be used in this capacity, but the author does not have access to this technology.

However, with practice the human brain becomes adept at teasing out a significant trend from the ever-present noise. This ability seems plausible when one considers that a cricketer’s brain can identify the trajectory of a ball and calculate the spot to which he should run to catch it.

“to be really useful - trend lines need to be linked to the to the objectives, strategies and constraints of those whose actions produce them”.

Clifford Wicken, MIET

Newton’s Law of Temperature Change These points are illustrated using the share price chart of Black Rock World Mining, Figure 1. If one has straight lines in mind this chart could easily be dismissed as the V shaped recovery situation (coloured red) but might there be another curve which actually explains what is going on?

To the trained eye, the shape of the curve going forward from March 2020 begins to look uncannily like that of Newton’s Temperature Rise curve (coloured red in Figure 2), which states that when an object is placed in warmer surroundings its temperature rises at a rate proportional to the difference between its temperature and the surroundings. It would be interesting to find out by calculation how good the

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Figure 1: Black Rock World Mining

Figure 2: Black Rock World Mining

match really is and then attempt an explanation.

Substituting price for temperature in Newton’s formula:

practiced in spotting and sketching this curve hiding amongst the noise.

p = P×(1 – e-(t/T))

Having spotted the possibility of Newton’s curve at play, it can be sketched onto the graph of price. Should it be desired to plot the curve using the formula, one needs to estimate only the maximum price and the time-constant, both of which will be suggested by the sketch. The point at which a horizontal line at 63.2% of the maximum intersects the sketched curve fixes the time-constant - which with the maximum price is all that is needed to plot the curve. As a check it is useful to know that a straight line drawn from the origin and having the gradient of the price curve at the origin reaches the maximum in the time constant. In Figure 5 logs10 are used to plot the graph, which somewhat distorts its shape, and the gradient line from the origin to the price maximum is no longer a straight line - which to a hands-on chartist would be of only theoretical interest. Newton’s law of warming has a counterpart in the law of cooling and this too turns up on price charts, as in Figure 6.

Where: t = time (measured in sessions) p = price at session t P = maximum price T = time-constant e = 2.7182 There certainly seems to be a relationship between the raw price curve and the one obtained by calculation. Other examples are given in Figures 3 and 4.

Figure 2 is used to draw attention to some useful properties of this formula. Most importantly, the rate of price rise is proportional to the difference between the price at any one time and the maximum price, causing the curve to gradually flatten out. If the price continued to rise at its initial rate it would reach the maximum price in a period of time known as the time-constant, and in the duration of the time-constant the price always rises to 0.632 of the maximum. The price approaches but never reaches the maximum. These features are useful in sketching the curve which is all that is necessary when putting it to use - it is not necessary to spend endless hours in tedious calculation, as after a while one becomes

Interpretation of the Price Curve based on Newton’s Law of Temperature Rise Lacking knowledge of fund management, only a tentative explanation can be offered for the appearance of Newton’s curve in the context of share prices. Black Rock World Mining is a large investment trust, £887m, with a dividend of 5.4%.

It would be attractive to pension funds and other financial institutions that need a regular income. It would also have

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Figure 3: Alliance Trust

Figure 4: Foreign & Colonial Inv. Trust

a following amongst long term private investors and at times there would be speculators of many shades including dabblers.

Exponential Rise and Fall Two other curves that are of interest are the exponentially rising share price and its counterpart the exponentially falling price. The general formula for an exponential rise is y = ax, ‘exponent’ being just another word for index. In the context of this paper the formula becomes:

p = aᵗ Where: p = price

a = constant

t = time (measured in sessions)

Managers of large funds employ highly qualified staff to search out and value potential constituents for their funds. If there is a share that fits in with the aims of the fund and the current price is significantly below that at which they have valued it, then a decision might be made to add it to the fund. Unlike small investors, such managers are unable to satisfy their need with a single purchase, so they will begin a purchasing programme, over the course of which the price will rise towards what they consider to be its fundamental worth (maximum price in Figure 2). As the price rises, the incentive to buy diminishes, hence the shape of the curve. At some point the price will flatten out and start moving noisily sideways until an event occurs that puts an end to the trajectory just described. This noise is provided by the speculators and dabblers.

In February 2020, the share price fell precipitously to well below its generally accepted fundamental value. The market turned up towards the end of March and at this point Newton’s law took over. By September, the price at which the share had been valued had been reached and the price started moving more or less sideways. A trend such as the one just described might be terminated at any time by a change in the fortune of the company or some major event beyond its control (e.g. a change of government or an international upheaval).

Figure 7 is another example taken from Black Rock World Mining, to which has been added the share price calculated using this formula.

A curve of this shape in the share price of a fund might, for example, have been brought about by the announcement of spectacular progress by a technology start-up in which the fund has a sizeable holding. The price begins to rise and gather momentum. The rise being soundly based draws in more and more investors. Even after the price is fully valued it continues to rise - simply because it is rising. Eventually this herd effect is ended because speculative money is running out and profit taking is setting in. This curve has its counterpart in a collapsing share price.

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In Practice For much of the time share prices just meander about a level trend or follow one that is gently rising or falling, punctuated from time-to-time by snippets of the curves discussed in this paper. When a gently declining long-term trend is spotted, a sale can be timed for when noise carries the price above the trend line.

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Figure 5: Black Rock World Mining (Log 10)

Sometimes a trend is so obscured by noise that its very existence seems fanciful until it is compared with the charts of other funds in the same category.

The private investor’s repertoire should include an emergency stop, at say 12% below the trend line, to deal with a sudden bear market collapse to which trend investing is too slow to respond. Private investors are better able to deal with this situation than financial institutions who are not able to sell large holdings quickly.

It could be argued that by the time one of the curves that this article has focused on becomes recognisable it is too late to be useful. Perhaps, but the end of one trend is often the earliest intimation that a new one will be taking over.

Figure 6: Black Rock World Mining

Figure 7: Black Rock World Mining

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Tendency Forex System: A Backtestable Indicator Abstract It’s necessary to translate some core logic into automated trading systems. On the one hand, the effectiveness of the algorithms can be tested; on the other hand, our working efficiency can be improved. Indicators with backtesting functions are important auxiliary tools for manual trading. In 2016, I developed the Tendency Forex System in the eSignal charting system with JavaScript. This article gives a brief introduction.

What is the Tendency Forex System?

Yue Wang Yue Wang was previously a hepatobiliary surgeon with a Master Degree who, 15 years ago, started to learn about foreign exchange and has been a full time trader since 2010.

The Tendency Forex System is an automated, back-testable, trend-following system.

It works on 240-minute timeframe and uses indicators on daily charts as filters. No repaint, no Grid, no Martingale. It can directly generate buy/sell arrows at market.

Stop-loss level is 5 pips beyond the highest high and the lowest low of the recent range. The profit target will also be flagged on the chart. If there are signs of a potential short-term trend reversal, the system will generate an exit signal regardless of profit or loss.

For USDJPY, EURUSD and DXY, the trading signals can be directly used for live trading. For AUDUSD, NZDUSD, EURJPY, AUDJPY, NZDJPY, CADJPY, the equity curve in the backtesting is not satisfied, but the profit target hitting ratio is nearly 60%. It is a reliable “back testable indicator” for manual traders looking to buy dips or sell rallies toward the profit target.

Figure 1: Tendency Forex System on USDJPY 240 minute chart

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Figure 2: Tendency Forex System on DXY 240 minute chart

Backtesting Condition: • Historical Data Feed: eSignal charting system • Period: From Jan. 2010 to Oct. 2020 • Initial Virtual Balance: $100k • Contract Size: Fixed 1 standard lot per trade

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Figure 3: Backtesting of USDJPY - Equity curve (Close to Close)

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Figure 4: Backtesting of USDJPY - Strategy Analysis

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Figure 5: Backtesting of USDJPY - Trade Analysis 1

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Figure 6: Backtesting of USDJPY - Trade Analysis 2

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Figure 7: Backtesting of USDJPY - Periodical Analysis Annual Trading Summary

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Figure 8: Backtesting of USDJPY - Equity Run-Up & Drawdown (%)

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1. Timeframe On 240 min chart, there are six candles per trading day, which are relatively balanced and can reflect the overall situation in Asia, Europe and New York session. The 120- and 180-minute charts are more susceptible to short-term volatility and as such cannot fully represent short-term trends.

2. Currency pairs According to the survey from BIS in 2016, EURUSD and USDJPY accounted for 23% and 17.7% of the Daily Trading Volume, respectively. The perfect liquidity is conducive to the development of short-term trends.

3. Filters • 3.1 For DXY and EURUSD, they can be used as Filters for each other.

• 3.2 I have another trading system that is specifically used to calculate the potential next target level; the hitting ratio is higher than 70%. It is used by myself only and has not been named yet. (I will refer to it merely as “the Alpha System” in this paper.) Its logic is totally different from the Tendency Forex System. If the direction of the target of the two systems is the same, this generally means a high conviction trading opportunity. If not, it’s clear that I will have to be cautious.

Figure 9: The Alpha System on USDJPY 240-minute chart

4. Optimisation We compared the optimisation results of the Tendency Forex System; SPSS 24.0 was used for statistical analysis. The studies’ parameters were displayed as Mean ± SD (Standard Deviation) for continuous variables. The comparison between the two groups was performed by t test. The comparison between multiple groups was performed by variance analysis and Dunnett’s t test. A P value <0.05 was considered statistically significant for all analysis. The statistical charts were drawn by GraphPad Prism 8.0.

4.1 Optimisation was done year by year: • The Tendency Forex System was optimised with the historical data from 2010 to 2015 to get the “optimised setting for 2016”; we then calculated the total net profit in 2016 with the “optimised setting 2016” • The Tendency Forex System was optimised with the historical data from 2010 to 2016 to get the “optimised setting for 2017”; we then calculated the total net profit in 2017 with the “optimised setting 2017”

And so on.

4.2 Optimisation results:

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4.2.1 After optimisation, the overall return was increased by at least 10% compared to the results of the default setting. It shows the optimisation method is very effective. The details can be seen in Table 1.

Table 1: Annualised Return by Default setting and Optimised setting Annualised Return (USD) 2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Default Setting

6086

8295

3231

16695

6915

8747

4835

8327

11785

1941

8514

Optimisation 2010-2015

5705

4825

7491

24523

6920

5865

Optimisation 2010-2016

6900

4061

7687

20317

-1070

8155

27952

Optimisation 2010-2017

6900

4061

7687

20317

-1070

8155

27952

7554

Optimisation 2010-2018

8381

3201

6256

20905

8677

11990

22826

5893

9093

Optimisation 2010-2019

8381

3201

6256

20905

8677

11990

22826

5893

9093

-343

However, in the variance analysis on the Average Annualised Return, there was no statistical significance among multiple groups (F=0.218, P=0.953>0.05). The details can be seen in Table 2.

Table 2: Variance Analysis of the Average Annualised Return Group

n

Average Annualised Return (USD)

F

P

Default Setting

11

7761.00±4057.46

0.218

0.953

Optimisation 2010-2015

6

9221.50±7554.98

Optimisation 2010-2016

7

10571.71±10021.70

Optimisation 2010-2017

8

10194.50±9339.44

Optimisation 2010-2018

9

10802.44±6744.40

Optimisation 2010-2019

10

9687.90±7270.14

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Figure 10: Variance Analysis of the Average Annualised Return

In theory, pairwise comparisons are only necessary if there is s statistical difference in variance analysis. However, for rigorous purpose, a Dunnett’s t test was done with one group (Default setting) as a control group and we then compared all the other groups to it. All the Significance was higher than 0.05, further confirmed there was no statistical significance among multiple groups. The details can be seen in Table 3.

Table 3: Dunnett’s t test on the Average Annualised Return Dunnett t (2-sided)ᵃ (I) Group 2

(J) Group2

Mean Difference (I-J)

Std. Error

95% Confidence Interval Lower Bound

Upper Bound

Optimisation 2010-2015

Default Setting

1460.50000

3784.93631

.996

11412.5003

Optimisation 2010-2016

Default Setting

2810.71429

3605.76130

.913

12291.5971

Optimisation 2010-2017

Default Setting

2433.50000

3465.30566

.941

11545.0729

Optimisation 2010-2018

Default Setting

3041.44444

3351.99594

.852

11855.0840

Optimisation 2010-2019

Default Setting

1926.90000

3258.51247

.971

10494.7369

4.2.2 The annualised net profit of 2016, 2017, 2018, 2019, and 2020 was calculated with the optimised settings. Compared with the default setting, although the annualised return in 2016 was significantly higher, the overall profit was reduced by nearly 30%.

The details can be seen in Table 4.

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Table 4: The annualised net profit of optimised and default setting The annualised net profit (USD) 2016

2017

2018

2019

2020

Total

Optimised year by year

10384

7554

8336

-343

-641

25290

Default Setting

4835

8327

11785

1941

8514

35402

However, in the t test of the Average Annualised Return, there was no statistically significant correlation between the two groups (t= 0.706, p=0.500 > 0.05). The details can be seen in Table 5.

Table 2: t test of the Average Annualised Return of Default setting and Optimised setting Group

n

Average Annualised Return (USD)

t

p

Optimised year by year

5

5058.00±5171.82

0.706

0.500

Default Setting

5

7080.40±3781.46

Figure 11: t test of the Average Annualised Return of Default setting and Optimised setting

4.3 Discussion The optimisation of a trading system results from the efforts of programmers over a long time. However, in the Tendency Forex System, all the indicators are essentially using default settings. To my knowledge, over optimisation is a major issue with most Algos, which can lead them to show excellent performance in the backtesting but then fail to work well in live forward trading.

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Over the past 15 years, although I’m a manual trader whose work is based on Elliott Wave Theory and trend-following logic, I have created several automated trading systems. None has worked well except the Tendency Forex System. I think too much data mining may be the problem. It more-or-less gets some results - for example the 50-period Moving Average may be more sensitive for symbol A than symbol B - but when you move from backtesting to a live forward test, this kind of system generally will not work. Then I started to use the default setting of several indicators looking for universal adaptation of more symbols. According to the statistical analysis, I think there is no value in optimising the Tendency Forex System further.

5. Risk Control A valid trading system is only part of the success; the risk management is even more important.

• The trading signals of DXY, EURUSD, USDJPY can be directly used for live trading with fixed lot size or fixed risk percentage.

DXY and EURUSD can only select one of them to trade. In the backtesting of USDJPY from 2010, the Max Strategy Drawdown is 5.57% and the Max close to close drawdown is 4.72%. Based on the parameters, you can determine the fixed lot size or risk percentage according to your own risk appetite.

• 5.2 AUDUSD, NZDUSD, EURJPY, AUDJPY, NZDJPY, CADJPY: Fixed daily or weekly risk is preferred for them. For example, once we set the daily fixed risk is 1.5%, no matter how many trading signals there are, the total daily risk should be controlled no higher than 1.5%.

• 5.3 Additional position: as we discussed in 3.2, when the target of the Tendency Forex System and the Alpha System have the same direction, we may have a potential high conviction trading opportunity. In this case, I’m inclined to add another position with a fixed lot size or fixed risk percentage.

6. Emotion Management Although I have acquired some expertise after hard study and work, how to follow my own rules is still one of the important challenges I face. The key logic of the Tendency Forex System is to chase the short-term trend, but in the consolidation phase some “buy high / sell low” opportunities are still inevitable. In Figure 6, we see that during the 10 years’ of backtesting, the maximum consecutive loss streak was made up of nine trades. Fortunately, I have not encountered a similar situation since; however, as the trading volume increases according to my account balance, sometimes I still feel some psychological pressure.

The only solution is to keep saying “Keep the faith!” to myself.

Conclusion 1. The Tendency Forex System is a relatively stable trading strategy. For retail investors, it can directly provide entry, exit, stop loss and target levels; for professional traders, it can be used as a “backtestable indicator” for reference.

2. There is no value in optimising the Tendency Forex system any further.

Limitations

1.

2.

At the current stage, due to the type of the exported backtesting file, we could not merge the backtesting reports of DXY, EURUSD and USDJPY , so it is hard to evaluate the overall backtesting result of multiple currency pairs (such as the total Strategy Drawdown and Close to Close Drawdown).

It is necessary to do further research for the “Trend Index”, which could be used to distinguish between trending markets and consolidation markets, and hence further reduce false signals.

Acknowledgments I would like to express my thanks to the following: My parents, who taught me to understand and develop the objective and rigorous attitude from a young age. My dear wife Flora ZH, for your silent dedication and for your support throughout my volatile trading career! To Celia Yang, Peter Kostros, Chris Weston and Sean Lee for long term understanding and encouragement. To the STA admin for supporting me when I joined as well as to Nicole Elliott, Katie Abberton and the STA Journal Committee for your great trust and support during the writing and revision of this article. All the best for 2021! I believe that there is still a lot to learn.

I welcome all feedback or market discussions by email: yuewang2000@outlook.com.

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Head and shoulders above: Ed Blake, MSTA I was honoured when the STA asked me to contribute to the Head and Shoulders article, but what on earth would I write about?

In the past, I’ve been asked why I was drawn to technical analysis, what my current position entails and what are my favourite tools/techniques? However, for this article I thought I would give a brief run down on how I ended up being a technical analyst.

Ed Blake Ed Blake is Chief Fixed Income Technical Analysts, IGM, Informa Financial Intelligence

My first run in with technical analysis was in 1990 - at that time, I actually thought I’d invented it! My father had bought me some shares in the newly privatised London Electricity. Every day, I would check the previous day’s close in the newspaper, plot it on a line chart, add trendlines and make simplistic projections. I quickly worked out that I was making almost as much money from the shares as I was from pocket money - all without having to do chores for my parents. At around the same time, my school was really ramping up the pressure to choose a career. I’d grown out of wanting to be a fireman or train driver but didn’t really have a clue what I wanted to be. Then it struck me; I’d done comparatively well out of owning shares, so why not do it professionally and become a yuppie. That would get me one step closer to my true dream at that time - of owning a Porsche. There you have it - my main motivation was completely superficial and materialistic!

Still intent on a career in the City, I believed the easiest way to achieve this would be to study economics at Durham University. The only problem was that I’d never studied economics before, so whilst everyone else was getting smashed in their first term, I was busy getting up to speed with the A-level syllabus. With hindsight, I could have studied anything at university, but my economics degree gave me a solid foundation and did ultimately help me get my first job. It was in my second year at Durham that I bumped into my old friend Technical Analysis. This lecture brought a mix of emotions - disappointment at not having invented it back in 1990 and optimism that people could indeed make a career out of it. I remember to this day the lecturer (clearly a random walker) baulking at the idea of people being paid high salaries to predict the unpredictable. I on the other hand saw it as a viable career. Those Porsche keys just got a step closer.

Durham University

Within a month of graduating, I had started work as a research analyst at Rudolf Wolff commodity brokers - a ring-dealing member of the LME. My role as an LME base metals analyst was to refine my fundamental skills developed at Durham and

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also to start learning about Technical Analysis. In my first week I was given a copy of Murphy’s bible - Technical Analysis of the Financial Markets - and was set regular homework/tests on reversal patterns and the like. I was also sent on a number of courses, including Investment Research in Cambridge and the City of London Business School. Within a couple of years, my role had evolved, and I found myself being forced to make a decision between pursuing fundamental analysis and technical analysis - the latter was always going to be the winner. My mathematical/analytical nature lends itself to technical analysis, and I loved the way that technical analysis could always explain price action even when the fundamentals didn’t align.

becoming virtually untradable in the short term. The thing I love about my job is that as some markets lock into sideways ranges or become overly volatile, there is always something else which is trending nicely and lends itself perfectly to technical analysis. You’ve just got to put in the hours and crunch those charts.

STA Diploma Part 2 Online Course

At around the same time, Rudolf Wolff was in the midst of a merger, and I was concerned about losing my job, so I jumped ship to MCM which later became Informa Global Markets, “IGM”, an independent research firm catering to institutional clients.

I have been here for over 20 years now. I started covering emerging markets, moved onto FX and then Rates. In my current role, I oversee the technical analysis coming out of London and cover anything with a chart. IGM’s technical analysis product features 50 instruments (FX, Rates, Commodities and Equity Indices) which my colleagues and I follow closely. In addition, I produce special reports on anything else that looks interesting and offers scope for significant profitable moves. I like the fact that no two days are the same despite covering many of the same instruments day in day out.

And 2020 was especially busy - a perfect storm for markets with Covid, Brexit and the US elections all playing their part. The market volatility has been amazing, with some instruments

Some 10 years ago, I was invited to become an examiner for the STA diploma II paper - a chance to give something back. I remember it being a surreal experience being asked to mark the same paper that I had sat myself only a few years before. I had been recommended by others in the profession and felt honoured and privileged to be asked. Over the years, I’ve marked hundreds of papers from across the globe and often wonder how those budding techies are getting on.

So what is it that I love about technical analysis?

I think you have to be a particular type of person to do TA - attention to detail, mathematical bias and an analytical approach to everything - which fits perfectly with my personality. I love mathematics (which gives my wife endless ammunition for leg pulling) and I have an analytical approach to everything I do. The fact that TA has kept cropping up throughout my life confirms in my head that I was destined to be a techie.

What advice would I give someone trying to break into TA? I’d say you need a methodical analytical approach - that might not

lend itself to everyone. If that is you, though, then stick with it and look at as many charts as possible to gain experience and to hone your skills. I’m still learning my craft despite this being my 24th year of being a techie. Markets are always evolving and techniques which may have worked for years may quickly stop working as effectively and that is where you earn your keep.

My main motivation is to get things right - not just direction, but trades.

Anyone can pick a direction, but we all know it’s very easy to lose money even when you’ve got the direction nailed down.

Now, going full circle, I know there will be some out there wondering if I ever did get that Porsche. I am embarrassed to say that there is still a space on the key rack for the Porsche fob and, in the mean time, I will just have to make do with our people carrier... I suppose that’s what happens with you have four kids. If only Porsche did seven-seaters!

“So what is it that I love about technical analysis? I think you have to be a particular type of person to do TA - attention to detail, mathematical bias and an analytical approach to everything - which fits perfectly with my personality.”

Ed Blake, MSTA

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What moving averages do you use and why?

For some people charts are a source of fascination and a guide for trade allocation - they provide a great source of information for entry and exit points. After all it is much easier on the pocket (and heart rate) to buy the dip in a bull move or sell the rally in a bear market. One of the issues I am frequently asked about regarding technical analysis is on the use of moving averages (or MAs) and, in particular, what are the optimal MAs to use:

Price ‘mean reverts’ to the 55- and 200-period MA My reply is always the same: I never optimise MAs – that is just not the way that I use them. Markets spend a great deal of time not actually trending and anybody using a crossover signal for entry and exit levels into a market will be whipsawed a lot! So I choose not to use them in this way.

Karen Jones FSTA Karen Jones is a Managing Director and Head of FICC Technical Analysis Research at Commerzbank Corporates and Markets. A Fellow of the Society, Karen serves on the Executive Committee as Director and Treasurer.

I primarily use a 55- and 200-period simple MA. I have noticed that, over time, markets have a tendency to mean revert (or gravitate) to their long-term MAs. Time and time again I see markets gravitate towards their 55- and 200-period MAs, and price action in relation to these MAs can be extremely informative. They can be used to confirm or question existing trends or used as entry points and even target zones.

One of the best illustrations of this that I have seen over recent years of the interaction of price in relation to the 55- and 200-week MA is on the EUR/USD weekly chart.

Figure 1 contains the bear trend between 1995 and 2001, overlaid with a 55- and 200-week MA.

So let’s take a closer look at the chart: Let’s take a closer look at how the price behaved around these MAs and what information that gave us. The blue line is the 55-week MA and the brown line is the 200-week MA. It illustrates very clearly that the market gravitated towards these longer-term MAs, how rejection of price at that MA re-affirmed the trend and even provided entry points. The MAs themselves could have even been used as a stop/ loss.

At point (a), the price starts to break down through the 55-week MA and immediately sells off to the 200-week MA (the 200-week MA acted like a magnate for price, the break below the 55-week MA acted as the trigger or a sell signal). It stages a rebound from here and the market rallies to and tests the 55-week MA several times over the second half of 1996 (point b); however, the 55-week MA holds steadfast (it acted as resistance and again provided selling opportunities). This suggests a new bear trend is taking hold. The subsequent break below the 200-week MA at point (c) confirmed the bear trend was indeed underway.

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Figure 1: EUR/USD weekly chart 1995-2001 Corrective rebounds hold below the 200 week moving average in a bear trend.

b

a

Touches of the 55 week moving average provide selling opportunities in a bear trend.

e d c

f

g

Touches of the 55-week MA provide selling opportunities in a bear trend Subsequent corrective rallies of the bear trend are thwarted by the 55-week MA (point d), thus reinforcing the fact that the bear trend is entrenched at this point. Then in April 1998, the price starts to trade through its 55-week MA - this was a warning sign that a deeper corrective rebound was about to be seen. In August 1998, EUR/USD saw a huge rally higher, halting at the 200-week MA (point e), the high was 1.2315, and the 200-week MA was located at 1.2311.

The 200-week MA at point (e) provoked failure - this told us that the move higher had been nothing but a correction, the bear trend was alive and well and the market was heading lower. The subsequent break below the 55-week MA confirmed the bear trend and subsequent rallies all kept well below the 55-week MA until December 2000, when the price breached it. As seen previously, this warned that a corrective rebound was in the offing.

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Figure 2: EUR/USD 2001-2007 Touches of 55 week moving average provided buying opportunities.

Break above 200 week moving average confirmed bull tend is now underway.

k j

i

h

Gyrations around the 55-week MA acted as a reversal signal In fact, the break above the 55-week MA was not warning us of a correction - it was warning us of REVERSAL. Look how the price oscillated around its 55-week MA during 2001- 2002. This was an indication that the bear trend was losing momentum and the market became side-lined.

The break above the 200-week MA acted as confirmation of the trend change Then in April 2002 the market began to break higher aggressively. It cleared its 200-week MA in July 2002 and this was confirmation of a trend change and the resumption of a new bull trend (point h). Notice that the market subsequently pulled back to test the 200-week MA support repeatedly throughout the summer of 2002. The market then headed higher and held above its 55-week MA. • • •

•

In a bull trend, touches of 55-week MA provided buying opportunities; Tests of the 55-week MA (at points i and j) provided major buying opportunities; When the price breached this MA support in May 2005, the market sold off towards its 200-week MA (point k) and recovered.

Conclusion: we were still in a bull trend; Financial shocks throughout 2009-2010 made EUR/USD much more volatile and the market traded through these MAs. Were they still useful?

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Figure 3: EUR/USD weekly chart 2006-2010 i The 55 week ma held the massive spike higher at the end of 2008.

The break below the 55-week MA in August 2008 confirmed a period of weakness; the subsequent break, sharp selloff and rebound held below this MA (l). However, since August 2009, we have seen the market oscillate around the 55- and 200-week MAs - so what did this mean?

We viewed the market as attempting to ‘normalise’ during a period of massive financial shocks. In other words, we viewed this as the market ‘mean-reverting’ to its 55- and 200-week MAs. In very simple terms, while we are below these long-term MAs we are neutral to bearish. A weekly close through them, however, has the potential to ‘pivot’ the market into more positive territory.

At what stage are we at now? What can the MAs tell us? Since July 2020 we have seen a substantial rally take hold, following the break above the 55- and 200-week MAs. We have also seen a minor breach of the 2008-20 downtrend. This was not sustained and the market subsequently oscillated around here (e.g. in October 2020 this downtrend was 1.1745).

It is interesting to note that - even during the Covid-19 crises, a US presidential election and Brexit - we can still make use of this analysis. Will we maintain a neutral to positive bias while above the 200-week MA at 1.1395 and our long-term target? The 200-month MA has proved a very effective barometer for more 30 years (see Figure 4). This suggests that these long-term MAs very much remain relevant.

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Conclusion and Summary • Markets have a tendency to ‘mean revert’, or gravitate towards their long-term MAs; • Despite huge volatility seen over the past few years the 55- and 200-period MAs have proved very effective.

• Tests/breaks of the 55- and 200-period MAs can be useful entry and exit points in your overall trading strategy.

Figure 4: EUR/USD Monthly chart

All charts courtesy of CQG Inc.

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BOOK REVIEW

Book Review: Unknown Market Wizards,

by Jack D Schwager Book review by Jeff Boccaccio MSTA

When most traders and investors are asked about their favourite trading books, one of the Market Wizards books is normally at the top of the list.

There is good reason for this. Unknown Market Wizards is the latest in the series, where Jack Schwager sits down with top-performing traders for what seems to us readers like an informal chat. Like anyone who has mastered their craft, Jack makes this appear easy when it is anything but. His ability to uncover what makes a trader tick through casual conversation, dig deeper when you’re willing him to and then distil the results into bite-sized pieces is what sets these books apart.

Unknown Market Wizards: Jack D. Schager

The ‘Unknown’ nature of this new group of Market Wizards is based on them being independent or solo traders who primarily trade for themselves and for the most part have kept to themselves. This is in contrast with most traders in the other Market Wizards books who are generally professionals working at large trading firms or hedge funds, and are often trading someone else’s money.

“The ‘Unknown’ nature of this new group of Market Wizards is based on them being independent or solo traders who primarily trade for themselves and for the most part have kept to themselves.”

This subtle difference means the anecdotes and lessons are even more relatable to any reader as there is an even lower barrier to entry. Now, more than ever, before one can get involved even by trading with a small amount of money and Jack has unearthed a number of individuals who were able to start small and not just outperform but do so with long-term risk adjusted returns that would seem unattainable to most larger firms. These 11 traders have run the four-minute mile - they’ve demonstrated to the rest of us what is possible- and that alone is just as important as the lessons learned along the way.

Jack D. Schager Photo by Nathalie Schueller

One potential criticism I’ve heard relates to this: has Jack just managed to find the statistical outliers, those lucky few traders who happen to find themselves in the far right tail of the distribution? I find this difficult to

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BOOK REVIEW

believe for a number of reasons. For example, Jack’s criteria for trader selection was at least 10 years of performance, verified by his team from monthly statements. It’s tough to remain consistently lucky for that long. Luck does crop up for some of the individual trade anecdotes, like when Jeffrey Neumann managed to exit at the top of a pump-and-dump stock rally using a dial-up internet connection whilst on Safari in Kenya after receiving a text message from a friend.

But these are balanced with unlucky trades as well, such as when Amrit Sall’s computer crashed just after he placed a trade during an ECB press conference resulting in a 24% loss.

Good luck and bad luck can affect the outcome of individual trades, but these average out over time - it is the consistency of the trader’s approach, process and mindset that leads to long term outperformance and these traders have all found their own way to do just that.

This is a common theme from all the Market Wizards. They find a method that works for them and the techniques they use are quite varied. Peter Brandt uses a purely technical approach whilst others started out using technicals but switched to a more fundamental slant (though it seems the technicals tend to live on for timing). The exception is Chris Camillo, who doesn’t really use either and instead uses his own social media data mining he calls ‘social arbitrage’ to determine underlying trends and popular stocks using key phrases. Only one of the traders used a systematic approach with the rest all using varying levels of discretion for their decision making.

It was nice to discover that three of the traders are based in our own backyard in London. They all know each other and spent time together at a proprietary trading firm so perhaps unsurprisingly their styles have many similarities. Their roots are in quite short-term intraday trading that, contrary to the usual advice, revolves around trading both scheduled and unscheduled news events. News moves the markets and presents asymmetric opportunities for the prepared and nimble trader and these traders have specialised in the art whilst each evolving in their own way.

Jack’s casual yet professional style, along with his in-depth knowledge of the subject matter, allows him to communicate with each trader at their own level whilst making the reader feel as though they are there in the room. Most of the interviews took place on the traders’ home turf and many of them openly shared charts of trade examples that Jack described well. If I was allowed a single indulgence, I would have liked to have seen some of these charts reproduced in the book but that may just be the technical analyst in me.

Jack skilfully extracts a number of key lessons that came up as common themes during his conversations. This alone is worth the cost of admission and should be required reading for any novice trader and a regular refresher for even the most experienced.

The lessons found in all of the Market Wizards books are

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timeless but the markets, world events and trading situations are not. The early books in the series are based more on the markets of the 80s, which may be anecdotally familiar, but the recency of the markets discussed in Unknown Market Wizards means that many readers will have not just lived but traded through the events discussed in the interviews. For myself, this makes the conversations far more relevant and seeing how other traders navigated the same markets I was trading gives me a motivational kick in the proverbial to up my game going forward. A wisdom filled, entertaining and motivational read - what more could one ask for?

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THE STA

Benefits of STA membership

The STA holds 11 monthly meetings in the City of London, including a summer and Christmas party where canapés and refreshments are served.

As a service to our members, many of whom are unable to attend all our monthly meetings, we have been making videos of meeting presentations for several years.

Key benefits • Chance to hear talks by leading practitioners.

• Networking.

• CPD (Continuous Professional Development).

Key benefits • Never miss the latest meeting.

• Browse our extensive video archive of previous meetings.

The STA has been running educational courses on technical analysis for 25 years.

Student members have access to an education forum which is available in the member’s area of the website.

Key benefits • Courses are taught by leading authorities in their field such as authors, highly regarded professionals and Fellows.

• The STA also offers a Home Study Course for self-study.

The STA ”Market Technician” journal is published online twice a year.

Key benefits Members receive the latest issue of the “Market Technician” via e-mail.

They are also able to access an archive of past editions in the member’s area of the website. Technical analysts from all over the world contribute to the STA journal.

Key benefits Members can ask questions on technical analysis in the Technical Analysis Forum which a course lecturer, author or Fellow will answer.

The STA has an extensive library of classic technical analysis texts. There are over 1000 books in the collection. It is held at the Barbican Library with a smaller selection available at the City Library, a reference library in London. As a member you can now browse which titles are available on-line.

Key benefits Members are encouraged to suggest new titles for the STA book collection and, where possible, these are acquired for the library. The complete listing of books held can be downloaded in Excel format from within the member’s area.

The Society of Technical Analysts and the Chartered Institute for Securities & Investment (CISI) have formed a partnership to work together on areas of mutual interest for our respective memberships.

Key benefits CISI examination exemptions for STA Diploma Part 1 and 2 holders. MSTAs with three+ years’ experience can become full members (MCSI).

Endorsed by the Chartered Institute for Securities & Investment (CISI), members of the STA are entitled to receive continuing professional development points (CPD for their attendance on the taught course lectures.

Key benefits • Remain compliant.

• Be informed of all new industry developments.

STA members benefit from significant discounts on technical analysis books, magazines and software.

Key benefits STA members currently enjoy discounts from: • Your Trading Edge.

• The Technical Analyst Magazine.

• MT Predictor.

• CQG. • Tradermade and the Global Investor bookshop.

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THE STA

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STA Calendar 2021

Tuesday 9 March 2021

More information about the STA events can be found here

Tuesday 13 April 2021

6.30pm Via Live Webinar. Niels Kaastrup-Larsen in conversation with Alistair Philip

6.30pm Via Live Webinar Tom Bundgaard, Kairos Commodities

Tuesday 11 May 2021

Tuesday 8 June 2021

6.30pm Via Live Webinar. Speaker to be confirmed

6.30pm Via Live Webinar. Speaker to be confirmed

Thursday 22 April 2021 STA Diploma Part 2 Exam (online)

Monday 5 July 2021 Monday 1 March STA Diploma Part 1 Exam (online)

Meetings currently being held via webinar due to COVID restrictions until further notice.

Tuesday 13 July 2021 6.30pm Via Live Webinar. Joint STA/ACI Panel Debate

STA Education: the LSE courses, and the Diploma in Technical Analysis The Education Channel: Monthly meetings videos are available to members here www.technicalanalysts.com/meetings Year

2021

2020

Month

Speaker

Description

February

Clive Lambert

Support and Resistance - Hidden Gaps and Other Methods

January

2021 Outlook

Panel debate with ACI UK

November

Jack Schwager

In conversation with Steven Goldstein

October

Charlie Morris

Bitcoin: digital gold or a speculative growth asset

September

Dr Ernest Chan

Tail hedging the age of machine learning

July

David Keller

The five modes of mindful investors

June

Andrew Pancholi

The most critical economic and financial time period of our generatio

May

Zaheer Anwari

Using the UK and US indices to determine bullish and bearish markets

April

Sankar Sharma

Crush it with clouds

March

David Linton

The future of technical analysis

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THE STA

STA Library STA members are eligible to join the library as standard adult library members. They need to attend in person to the library to join - bringing with them proof of name (STA membership card, bank card, staff pass etc) and proof of address (driving licence, recent bank statement, utility bill etc).

The library address is Barbican Library, Silk Street, London, EC2Y 8DS. google maps For full details on address and opening times, visit www.technicalanalysts.com/library

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THE STA

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Bronwen Wood Memorial Prize 2020 We are delighted that two equally outstanding candidates - Abdullah Abbasi and Victoria Scholar (both students on the 2020/1 Diploma Part 1 and 2 courses) - won the 2020 Bronwen Wood Memorial Prize.

This award is made for the best STA Diploma Part 2 Examination paper written each year if a score of 90% or above has been achieved. It can be made to more than one candidate if more than one outstanding paper is received (as in this case).

Bronwen Wood was one of the founder Board members of the modern STA and died suddenly in late-December 2002. Bronwen was instrumental in developing both the STA Diploma Examination and the courses leading to the examination. She wrote and marked all the papers in the early years. Even when she went to work in Abu Dhabi from 1993 to 1999, Bronwen retained a lively interest in how the educational side of the STA was progressing.

Abdullah, a final year BSc Economics and Finance student at University College Dublin with a strong interest in the financial markets, said: “The STA course was an excellent learning experience, and I am delighted to be named joint winner of the 2020 Bronwen Wood Memorial Prize”.

We look forward to presenting this Award to Abdullah and Victoria in person at a special Awards ceremony in 2021 along with all other successful MSTA candidates in the UK. In the meantime, well done to them and also all our new MSTAs. The 2020 cohort has been outstanding with over 80% passing the exam.

For more info on the Award and our examinations please visit www.technicalanalysts.com/education/examinations

Victoria works at IG Group as a TV presenter, producer and market analyst. She previously worked in financial journalism at CNBC and Bloomberg, having started her career on the equity trading floor at Nomura. She said: “Having spent a lot of time focused on the fundamentals, the diploma taught me a whole new way of looking at the markets through technical analysis. I really enjoyed each week’s lectures and getting to know the expert technical analysts who teach the course as well. The exams have been extremely helpful for my career and I am thrilled to be the joint winner of this year’s Bronwen Wood Memorial Prize.”

“The STA course was an excellent learning experience, and I am delighted to be named joint winner of the 2020 Bronwen Wood Memorial Prize”.

Abdullah Addasi

NEWS RESEARCH

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

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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.

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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 here 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 classroom - the HSC course is designed for those who need a truly parttime study option with maximum flexibility!

Buy now: £1,195.00

Congratulations to the latest STA Diploma MSTAs Distinction George Starr Hassan Abdullah Jeevainderan Sagathevan

Pass Aakriti Jhunjhunwala Aaron H Selby Andreas Thrasyvoulou Asanka Weeratunge Conrad Janse van Rensburg Giorgos Nathanael Hsiao De YEH Jitender Pal Singh John Tuohy Kleanthis Avraamides Kyriakos Kadis Michael Michaelides

Min Than Htut Dr Muhammad Arif Bin Mokhtar Muhammad Daniel Bin Muhammad Nor Effendy Pooya William Khamooshi Robert Szucs Sam Richard Burns Sofia Pirea Stelios Photiou Suhaili Saari Tim Hopson William Carlisle

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THE STA

STA Executive Committee

Richard Adcock MSTA Vice Chairman & Co Secretary

Jeff Boccaccio MSTA Director

Patricia Elbaz BA (Comb hons) MSTA Director

Mark Tennyson d’Eyncourt FSTA Programmes

Tom Hicks MEng MSTA MSCI Chairman of the STA

Karen Jones BSc FSTA Treasurer

Clive Lambert MSTA MCSI Vice Chairman

Eddie Tofpik MSTA, ACI-UK, ACSI Head of Marketing

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

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:

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STA Advertising Rates 2021 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. Position

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Circulation The Market Technician has a circulation of approximately 1500. Readership includes technical analysts, traders, brokers, dealers, fund managers, portfolio managers, market analysts, other investment professionals, and private investors.

Contact Contact Katie Abberton, Society of Technical Analysts on info@technicalanalysts.com or +44 (0) 207 125 0038 for more information.

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.

The Society is not responsible for any material published in The Market Technician and publication of any material or expression of opinions does not necessarily imply that the Society agrees with them. The Society is not authorised to conduct investment business and does not provide investment advice or recommendations.

Articles are published without responsibility on the part of the Society, the editor or authors for loss occasioned by any person acting or refraining from action as a result of any view expressed therein.

Society of Technical Analysts Dean House Vernham Dean Andover Hampshire SP11 0JZ tel: +44 (0) 20 7125 0038 info@technicalanalysts.com www.technicalanalysts.com

The Society of Technical Analysts (STA) is recognised worldwide as one of the largest and most widely respected not-forprofit organisations which trains and accredits members of the investment community, from industry professionals to private individuals, interested in the study of technical analysis. We have been setting the standards in technical analysis for nearly 50 years and have been teaching at several UK universities such as LSE, King’s College, Queen Mary etc. for nearly 25 years.

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